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manysat: a parallel sat solver in this paper, manysat a new portfolio-based parallel sat solver is thoroughly described. the design of manysat benefits from the main weaknesses of modern sat solvers: their sensitivity to parameter tuning and their lack of robustness. manysat uses a portfolio of complementary sequential algorithms obtained through careful variations of the standard dpll algorithm. additionally, each sequential algorithm shares clauses to improve the overall performance of the whole system. this contrasts with most of the parallel sat solvers generally designed using the divide-and-conquer paradigm. experiments on many industrial sat instances, and the first rank obtained by manysat in the parallel track of the 2008 sat-race clearly show the potential of our design philosophy.
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extending sat solvers to cryptographic problems cryptography ensures the confidentiality and authenticity of information but often relies on unproven assumptions. sat solvers are a powerful tool to test the hardness of certain problems and have successfully been used to test hardness assumptions. this paper extends a sat solver to efficiently work on cryptographic problems. the paper further illustrates how sat solvers process cryptographic functions using automatically generated visualizations, introduces techniques for simplifying the solving process by modifying cipher representations, and demonstrates the feasibility of the approach by solving three stream ciphers. ::: ::: to optimize a sat solver for cryptographic problems, we extended the solver's input language to support the xor operation that is common in cryptography. to better understand the inner workings of the adapted solver and to identify bottlenecks, we visualize its execution. finally, to improve the solving time significantly, we remove these bottlenecks by altering the function representation and by pre-parsing the resulting system of equations. ::: ::: the main contribution of this paper is a new approach to solving cryptographic problems by adapting both the problem description and the solver synchronously instead of tweaking just one of them. using these techniques, we were able to solve a well-researched stream cipher 26 times faster than was previously possible.
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68,481
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gesture based 3d man-machine interaction using a single camera this paper describes a new gesture based input interface system that allows users to control both 2d and 3d applications using simple hand gestures. using a single camera attached to the computer, the system tracks the user's hand in three dimensions and computes up to four parameters in real-time (60 hz). the system recognizes three gestures that can be interpreted as discrete commands to applications. this system is an off-shoot of an earlier system called gesture vr that requires multiple cameras. since the new system uses a single video source it can run readily on a standard home computer equipped with an inexpensive camera and is, therefore, accessible to most users. the system can be used with applications that require 2d and 2d interactions. examples discussed in this paper include 3d virtual fly-throughs, graphical scene composers and video games.
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visual gesture interfaces for virtual environments virtual environments provide a whole new way of viewing and manipulating 3d data. current technology moves the images out of desktop monitors and into the space immediately surrounding the user. users can literally put their hands on the virtual objects. unfortunately techniques for interacting with such environments have yet to mature. gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. a natural, more intuitive method of interaction would be to allow the user to grasp objects with their hands and manipulate them as if they were real objects. we are investigating the use of computer vision in implementing a natural interface based on hand gestures. a framework for a gesture recognition system is introduced along with results of experiments in colour segmentation, feature extraction and template matching for finger and hand tracking and hand pose recognition. progress in the implementation of a gesture interface for navigation and object manipulation in virtual environments is discussed.
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testing noninterference, quickly information-flow control mechanisms are difficult to design and labor intensive to prove correct. to reduce the time wasted on proof attempts doomed to fail due to broken definitions, we advocate modern random testing techniques for finding counterexamples during the design process. we show how to use quickcheck, a property-based random-testing tool, to guide the design of a simple information-flow abstract machine. we find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important. we propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. we also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. taken together, our best methods enable us to quickly and automatically generate simple counterexamples for all these bugs.
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a theory of information-flow labels the security literature offers a multitude of calculi, languages, and systems for information-flow control, each with some set of labels encoding security policies that can be attached to data and computations. the exact form of these labels varies widely, with different systems offering many different combinations of features addressing issues such as confidentiality, integrity, and policy ownership. this variation makes it difficult to compare the expressive power of different information-flow frameworks. to enable such comparisons, we introduce label algebras, an abstract interface for information-flow labels equipped with a notion of authority, and study several notions of embedding between them. the simplest is a straightforward notion of injection between label algebras, but this lacks a clear computational motivation and rejects some reasonable encodings between label models. we obtain a more refined account by defining a space of encodings parameterized by an interpretation of labels and authorities, thus giving a semantic flavor to the definition of encoding. we study the theory of semantic encodings and consider two specific instances, one based on the possible observations of boolean values and one based on the behavior of programs in a small lambda-calculus parameterized over an arbitrary label algebra. we use this framework to define and compare a number of concrete label algebras, including realizations of the familiar taint, endorsement, readers, and distrust models, as well as label algebras based on several existing programming languages and operating systems.
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exploring students learning behavior with an interactive etextbook in computer science courses we present empirical findings from using an interactive electronic textbook (etextbook) system named opendsa to teach sophomore- and junior-level computer science courses. the web-based etextbook infrastructure allows us to collect large amounts of data that can provide detailed information about students' study behavior. in particular we were interested in seeing if the students will attempt to manipulate the electronic resources so as to receive credit without deeply going through the materials. we found that a majority of students do not read the text. on the other hand, we found evidence that students voluntarily complete additional exercises (after obtaining credit for completion) as a study aid prior to exams. we determined that visualization use was fairly high (even when credit for their completion was not offered). skipping to the end of slideshows was more common when credit for their completion was offered, but also occurred when it was not. we measured the level of use of mobile devices for learning by cs students. almost all students did not associate their mobile devices with studying. the only time they accessed opendsa from a mobile device was for a quick look up, and never for in depth study.
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design, development, and learning in e-textbooks: what we learned and where we are going in the last decade, the use of e-textbooks has received attention in research and practice. however, the expanded use of e-textbooks was not easily achieved because of the missing standards in learning content and functionalities, and barriers in utilizing e-textbooks, such as screen reading and intellectual property protection. this paper provides insights on the design, development, and learning with e-textbooks by reviewing studies, project reports, and cases on its use. results reveal the increased promotion and implementation of e-textbook development in several countries. criticisms on different e-textbook types began during the early stages of open multimedia learning resources and digitized textbooks, and continued until the integration of information and communication technologies, authoring tools, and learning platforms. the study examined advantages of e-textbooks and different factors that influenced e-textbook applications. the study also reviewed the literature on learning through e-textbooks in terms of acceptance and perception of users, and the comparison of the learning effectiveness of this format with printed textbooks. moreover, learning in e-textbooks is not fully realized, and requires increased in-depth studies. this paper suggests investigating the pedagogical design of e-textbooks and further evaluation of e-textbook functions to support learning.
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multicast routing and wavelength assignment in multihop optical networks this paper addresses multicast routing in circuit-switched multihop optical networks employing wavelength-division multiplexing. we consider a model in which multicast communication requests are made and released dynamically over time. a multicast connection is realized by constructing a multicast tree which distributes the message from the source node to all destination nodes such that the wavelengths used on each link and the receivers and transmitters used at each node are not used by existing circuits. we show that the problem of routing and wavelength assignment in this model is, in general, np-complete. however, we also show that for any given multicast tree, the wavelength assignment problem can be solved in linear time.
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route optimization of multicast sessions in sparse light-splitting optical networks in this paper, we investigate the multicast routing problem in sparse splitting networks (mr-ssn). the mr-ssn problem is to find a route from the source node of a session to all destinations of the session such that the total number of fibers used in establishing the session is minimized while the multicast capable nodes are evenly distributed throughout the network. we present a heuristic based on tabu search that requires only one transmitter for the source node and one wavelength for a multicast session in this paper. we test our heuristic on a wide range of network topologies and random sessions and conclude that the difference between our solution and ilp optimal solution in terms of the number of fibers used for establishing a multicast session is within 10% nearly all the time and within 5% in about half of the time.
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explicit search result diversification through sub-queries queries submitted to a retrieval system are often ambiguous. in such a situation, a sensible strategy is to diversify the ranking of results to be retrieved, in the hope that users will find at least one of these results to be relevant to their information need. in this paper, we introduce xquad, a novel framework for search result diversification that builds such a diversified ranking by explicitly accounting for the relationship between documents retrieved for the original query and the possible aspects underlying this query, in the form of sub-queries. we evaluate the effectiveness of xquad using a standard trec collection. the results show that our framework markedly outperforms state-of-the-art diversification approaches under a simulated best-case scenario. moreover, we show that its effectiveness can be further improved by estimating the relative importance of each identified sub-query. finally, we show that our framework can still outperform the simulated best-case scenario of the state-of-the-art diversification approaches using sub-queries automatically derived from the baseline document ranking itself.
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image clustering based on a shared nearest neighbors approach for tagged collections browsing and finding pictures in large-scale and heterogeneous collections is an important issue, most particularly for online photo sharing applications. since such services are experiencing rapid growth of their databases, the tag-based indexing strategy and the results displayed in a traditional matrix representation may not be optimal for browsing and querying image collections. naturally, unsupervised data clustering appeared as a good solution by presenting a summarized view of an image set instead of an exhaustive but useless list of its element. we present a new method for extracting meaningful and representative clusters based on a shared nearest neighbors (snn) approach that treats both content-based features and textual descriptions (tags). we describe, discuss and evaluate the snn method for image clustering and present some experimental results using the flickr collections showing that our approach extracts representative information of an image set.
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63167150
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opcode sequences as representation of executables for data-mining-based unknown malware detection malware can be defined as any type of malicious code that has the potential to harm a computer or network. the volume of malware is growing faster every year and poses a serious global security threat. consequently, malware detection has become a critical topic in computer security. currently, signature-based detection is the most widespread method used in commercial antivirus. in spite of the broad use of this method, it can detect malware only after the malicious executable has already caused damage and provided the malware is adequately documented. therefore, the signature-based method consistently fails to detect new malware. in this paper, we propose a new method to detect unknown malware families. this model is based on the frequency of the appearance of opcode sequences. furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. in addition, we provide empirical validation that this new method is capable of detecting unknown malware.
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learning to detect and classify malicious executables in the wild we describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. we gathered 1,971 benign and 1,651 malicious executables and encoded each as a training example using n-grams of byte codes as features. such processing resulted in more than 255 million distinct n-grams. after selecting the most relevant n-grams for prediction, we evaluated a variety of inductive methods, including naive bayes, decision trees, support vector machines, and boosting. ultimately, boosted decision trees outperformed other methods with an area under the roc curve of 0.996. results suggest that our methodology will scale to larger collections of executables. we also evaluated how well the methods classified executables based on the function of their payload, such as opening a backdoor and mass-mailing. areas under the roc curve for detecting payload function were in the neighborhood of 0.9, which were smaller than those for the detection task. however, we attribute this drop in performance to fewer training examples and to the challenge of obtaining properly labeled examples, rather than to a failing of the methodology or to some inherent difficulty of the classification task. finally, we applied detectors to 291 malicious executables discovered after we gathered our original collection, and boosted decision trees achieved a true-positive rate of 0.98 for a desired false-positive rate of 0.05. this result is particularly important, for it suggests that our methodology could be used as the basis for an operational system for detecting previously undiscovered malicious executables.
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joint 3d-reconstruction and background separation in multiple views using graph cuts this paper deals with simultaneous depth map estimation and background separation in a multi-view setting with several fixed calibrated cameras, two problems which have previously been addressed separately. we demonstrate that their strong interdependency can be exploited elegantly by minimizing a discrete energy functional, which evaluates both properties at the same time. our algorithm is derived from the powerful "multi-camera scene reconstruction via graph cuts" algorithm presented by kolmogorov and zabih (2002). experiments with both real-world as well as synthetic scenes demonstrate that the presented combined approach yields even more correct depth estimates. in particular, the additional information gained by taking background into account increases considerably the algorithm's robustness against noise.
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robust graph-cut scene segmentation and reconstruction for free-viewpoint video of complex dynamic scenes current state-of-the-art image-based scene reconstruction techniques are capable of generating high-fidelity 3d models when used under controlled capture conditions. however, they are often inadequate when used in more challenging outdoor environments with moving cameras. in this case, algorithms must be able to cope with relatively large calibration and segmentation errors as well as input images separated by a wide-baseline and possibly captured at different resolutions. in this paper, we propose a technique which, under these challenging conditions, is able to efficiently compute a high-quality scene representation via graph-cut optimisation of an energy function combining multiple image cues with strong priors. robustness is achieved by jointly optimising scene segmentation and multiple view reconstruction in a view-dependent manner with respect to each input camera. joint optimisation prevents propagation of errors from segmentation to reconstruction as is often the case with sequential approaches. view-dependent processing increases tolerance to errors in on-the-fly calibration compared to global approaches. we evaluate our technique in the case of challenging outdoor sports scenes captured with manually operated broadcast cameras and demonstrate its suitability for high-quality free-viewpoint video.
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198,235
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69161924
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warped register file: a power efficient register file for gpgpus general purpose graphics processing units (gpgpus) have the ability to execute hundreds of concurrent threads. to support massive parallelism gpgpus provide a very large register file, even larger than a cache, to hold the state of each thread. as technology scales, the leakage power consumption of the sram cells is getting worse making the register file static power consumption a major concern. as the supply voltage scaling slows, dynamic power consumption of a register file is not reducing. these concerns are particularly acute in gpgpus due to their large register file size. this paper presents two techniques to reduce the gpgpu register file power consumption. by exploiting the unique software execution model of gpgpus, we propose a tri-modal register access control unit to reduce the leakage power. this unit first turns off any unallocated register, and places all allocated registers into drowsy state immediately after each access. the average inter-access distance to a register is 789 cycles in gpgpus. hence, aggressively moving a register into drowsy state immediately after each access results in 90% reduction in leakage power with negligible performance impact. to reduce dynamic power this paper proposes an active mask aware activity gating unit that avoids charging bit lines and wordlines of registers associated with all inactive threads within a warp. due to insufficient parallelism and branch divergence warps have many inactive threads. hence, registers associated with inactive threads can be identified precisely using the active mask. by combining the two techniques we show that the power consumption of the register file can be reduced by 69% on average.
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energy-efficient time-division multiplexed hybrid-switched noc for heterogeneous multicore systems nocs are an integral part of modern multicore processors, they must continuously support high-throughput low-latency on-chip data communication under a stringent energy budget when system size scales up. heterogeneous multicore systems further push the limit of noc design by integrating cores with diverse performance requirements onto the same die. traditional packet-switched nocs, which have the flexibility of connecting diverse computation and storage devices, are facing great challenges to meet the performance requirements within the energy budget due to latency and energy consumption associated with buffering and routing at each router. in this paper, we take advantage of the diversity in performance requirements of on-chip heterogeneous computing devices by designing, implementing, and evaluating a hybrid-switched network that allows the packet-switched and circuit-switched messages to share the same communication fabric by partitioning the network through time-division multiplexing (tdm). in the proposed hybrid-switched network, circuit-switched paths are established along frequently communicating nodes. our experiments show that utilizing these paths can improve system performance by reducing communication latency and alleviating network congestion. furthermore, better energy efficiency is achieved by reducing buffering in routers and in turn enabling aggressive power gating.
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reconciling statechart semantics statecharts are a visual technique for modelling reactive behaviour. over the years, a plethora of statechart semantics have been proposed. the three most widely used are the fixpoint, statemate, and uml semantics. these three semantics differ considerably from each other. in general, they interpret the same statechart differently, which impedes the communication of statechart designs among both designers and tools. in this paper, we identify a set of constraints on statecharts that ensure that the fixpoint, statemate and uml semantics coincide, if observations are restricted to linear, stuttering-closed, separable properties. moreover, we show that for a subset of these constraints, a slight variation of the statemate semantics coincides for linear stuttering-closed properties with the uml semantics.
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big-step semantics with the popularity of model-driven methodologies, and the abundance of modelling languages, a major question for a requirements engineer is: which language is suitable for modelling a system under study? we address this question from a semantic point-of-view for big-step modelling languages (bsmls). bsmls are a popular class of behavioural modelling languages in which a model can respond to an environmental input by executing multiple, possibly concurrent, transitions. we deconstruct the semantics of a large class of bsmls into high-level, orthogonal semantic aspects and discuss the relative advantages and disadvantages of the semantic options for each of these aspects to allow a requirements engineer to compare and choose the right bsml. we accompany our presentation with many modelling examples that illustrate the differences between a set of relevant semantic options.
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297,991
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201657586
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duty cycle adaptive adjustment based device to device (d2d) communication scheme for wsns device to device (d2d) communication is a key candidate for 5g. its purpose is to enable direct communication between user devices that are close to each other, thereby reducing the load on the base station. wireless sensor networks (wsns) have received a lot of attention as the basis for d2d. the opportunistic routing (or) scheme is proposed to deal with data transmission problems in loss wsns. in this paper, a duty cycle adaptive adjustment-based bopportunistic routing (dcaaor) scheme is proposed to speed up reliable data transmission. according to the wake-up rule of nodes, we propose three different dcaaor schemes, respectively. in modified opportunistic routing (mor) scheme, the nodes are random awake/sleep. in active slot uniform distribution (asud) scheme, the active slot of relay nodes are evenly distributed by adaptive adjustment and the active slot group (asg) scheme divides the active slots of the relay nodes into groups. the active slots of each group are the same, and the active slots of different groups are evenly distributed. after a lot of theoretical analysis, the asud and asg scheme proposed in this paper is superior to the simple modified mor scheme in terms of energy consumption and delay. when the duty length $\tau =20$ , the energy consumption of the nodes in the mor scheme is reduced by 38.75% compared with the or scheme, and the other two schemes are reduced by 41.83%.
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minimizing convergecast time and energy consumption in green internet of things real-time surveillance systems with green wireless sensor networks (wsns) are vital for maintaining high energy efficiency in many situations. this paper considers a scenario utilizing green wsns to monitor the situation of internet of things (iot), which constitute one of the most crucial sources of electricity consumption in information and communications technologies (ict). more specifically, we focus on optimizing the cluster structure to minimize the delay and energy consumption for aggregation convergecast in green wsns. we first find the optimal value of the network cluster radius for minimizing the delay through theoretical analysis. we then propose a novel cluster network architecture in which clusters that are far from the sink are small, allowing inter-cluster data aggregation to be processed earlier, and clusters that are near the sink are relatively large to allow more time for intra-cluster data aggregation. hence, the sensor nodes can be scheduled in consecutive time slots to reduce the number of state transitions, consequently achieving the goal of minimizing both delay and energy consumption. simulation results indicate that the proposed algorithm outperforms previously reported solutions in terms of both schedule length and lifetime, thereby demonstrating its effectiveness.
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a very modal model of a modern, major, general type system we present a model of recursive and impredicatively quantified types with mutable references. we interpret in this model all of the type constructors needed for typed intermediate languages and typed assembly languages used for object-oriented and functional languages. we establish in this purely semantic fashion a soundness proof of the typing systems underlying these tils and tals---ensuring that every well-typed program is safe. the technique is generic, and applies to any small-step semantics including λ-calculus, labeled transition systems, and von neumann machines. it is also simple, and reduces mainly to defining a kripke semantics of the godel-lob logic of provability. we have mechanically verified in coq the soundness of our type system as applied to a von neumann machine.
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a semantic model for graphical user interfaces we give a denotational model for graphical user interface (gui) programming using the cartesian closed category of ultrametric spaces. the ultrametric structure enforces causality restrictions on reactive systems and allows well-founded recursive definitions by a generalization of guardedness. we capture the arbitrariness of user input (e.g., a user gets to decide the stream of clicks she sends to a program) by making use of the fact that the closed subsets of an ultrametric space themselves form an ultrametric space, allowing us to interpret nondeterminism with a "powerspace" monad. algebras for the powerspace monad yield a model of intuitionistic linear logic, which we exploit in the definition of a mixed linear/non-linear domain-specific language for writing gui programs. the non-linear part of the language is used for writing reactive stream-processing functions whilst the linear sublanguage naturally captures the generativity and usage constraints on the various linear objects in guis, such as the elements of a dom or scene graph. we have implemented this dsl as an extension to ocaml, and give examples demonstrating that programs in this style can be short and readable.
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internet of things (iot): a vision, architectural elements, and future directions ubiquitous sensing enabled by wireless sensor network (wsn) technologies cuts across many areas of modern day living. this offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. the proliferation of these devices in a communicating-actuating network creates the internet of things (iot), wherein sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (cop). fueled by the recent adaptation of a variety of enabling wireless technologies such as rfid tags and embedded sensor and actuator nodes, the iot has stepped out of its infancy and is the next revolutionary technology in transforming the internet into a fully integrated future internet. as we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. this paper presents a cloud centric vision for worldwide implementation of internet of things. the key enabling technologies and application domains that are likely to drive iot research in the near future are discussed. a cloud implementation using aneka, which is based on interaction of private and public clouds is presented. we conclude our iot vision by expanding on the need for convergence of wsn, the internet and distributed computing directed at technological research community.
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chapter ten free and open source geospatial tools for environmental modelling and management abstract geospatial (geographical) software systems (gis) are used for creating, viewing, managing, analysing and utilising geospatial data. geospatial data can include socioeconomic, environmental, geophysical, and technical data about the earth and societal infrastructure and it is pivotal in environmental modelling and management (emm). desktop, web-based, and embedded geospatial systems have become an essential part of emm, providing pre- or post-processing of geospatial data, analysis and visualisation of results or a graphical user interface (gui). many local, regional, national, and international efforts are underway to create geospatial data infrastructures and tools for viewing and using geospatial data. when environmental attribute data is linked to these infrastructures, powerful tools for environmental management are instantly created. the growing culture of free/libre and open source software (foss) provides an alternative approach to software development for the field of gis (foss4g). to provide an overview of foss4g for emm, we analyse platforms, software stacks, and emm workflows. in the foss world the barriers to interoperability are low and thus the software stack tends to be thicker than in the proprietary platform. the foss4g world thrives on the evolution of software stacks and platforms. we provide examples of software stacks built from current foss4g that support emm workflows and highlight the advantages of foss4g solutions including opportunities to redistribute resulting modelling tools freely to end-users and to support general goals of openness and transparency with respect to modelling tools.
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two basic algorithms in concept analysis we describe two algorithms for closure systems. the purpose of the first is to produce all closed sets of a given closure operator. the second constructs a minimal family of implications for the ”logic” of a closure system. these algorithms then are applied to problems in concept analysis: determining all concepts of a given context and describing the dependencies between attributes. the problem of finding all concepts is equivalent, e.g., to finding all maximal complete bipartite subgraphs of a bipartite graph.
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an efficient algorithm for increasing the granularity levels of attributes in formal concept analysis necessary and sufficient conditions for identifying different types of concepts.an efficient and unified method of concept classification.a preprocessing routine to help create new concepts and fix the covering relation.an efficient algorithm for increasing the granularity levels of attributes in fca. in the basic setting of formal concept analysis, a many-valued attribute needs to be replaced with several one-valued attributes. these one-valued attributes can be interpreted as a certain level of granularity of the corresponding many-valued attribute. in this paper, we explore theoretical relationships between concepts before and after increasing the granularity level of one attribute, based on which we introduce an efficient method of concept classification. moreover, a new preprocessing routine is proposed to help generate new concepts and restore lattice order relation. these two procedures can considerably reduce the comparisons between sets, compared to the original zoom-in algorithm. by employing these two procedures, we introduce an efficient algorithm, referred to as unfold, to increase the granularity levels of attributes. the algorithm can perform a zoom-in operation on a concept lattice associated with a coarser data granularity to obtain a new one that consists of finer formal concepts without building the new lattice from scratch. we describe the algorithm and present an experimental evaluation of its performance and comparison with another zoom-in algorithm. empirical analyses demonstrate that our algorithm is superior when applied to various types of datasets.
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automated programmable control and parameterization of compiler optimizations we present a framework which effectively combines programmable control by developers, advanced optimization by compilers, and flexible parameterization of optimizations to achieve portable high performance. we have extended rose, a c/c++/fortran source-to-source optimizing compiler, to automatically analyze scientific applications and discover optimization opportunities. instead of directly generating optimized code, our optimizer produces parameterized scripts in poet, an interpreted program transformation language, so that developers can freely modify the optimization decisions by the compiler and add their own domain-specific optimizations if necessary. the auto-generated poet scripts support extra optimizations beyond those available in the rose optimizer. additionally, all the optimizations are parameterized at an extremely fine granularity, so the scripts can be ported together with their input code and automatically tuned for different architectures. our results show that this approach is highly effective, and the code optimized by the auto-generated poet scripts can significantly outperform those optimized using the rose optimizer alone.
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poet: parameterized optimizations for empirical tuning the excessive complexity of both machine architectures and applications have made it difficult for compilers to statically model and predict application behavior. this observation motivates the recent interest in performance tuning using empirical techniques. we present a new embedded scripting language, poet (parameterized optimization for empirical tuning), for parameterizing complex code transformations so that they can be empirically tuned. the poet language aims to significantly improve the generality, flexibility, and efficiency of existing empirical tuning systems. we have used the language to parameterize and to empirically tune three loop optimizations - interchange, blocking, and unrolling - for two linear algebra kernels. we show experimentally that the time required to tune these optimizations using poet, which does not require any program analysis, is significantly shorter than that when using a full compiler-based source-code optimizer which performs sophisticated program analysis and optimizations.
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analysis of a nonpreemptive priority queue with spp arrivals of high class abstract this paper considers a nonpreemptive priority queueing system with two priority classes of customers, where high priority customers arrive to the system in accordance with a switched poisson process ( spp ) and low priority customers in accordance with a poisson process. using the supplementary variable technique, we derive the joint probability generating function of the stationary queue length distributions and the laplace-stieltjes transforms of the stationary waiting time distributions of high and low priority customers. we also present some numerical results in order to show the computational feasibility of the analytical results.
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discrete-time queueing models with priorities this phd-dissertation contains analyses of several discrete-time two-class priority queueing systems. we analyze non-preemptive, preemptive resume as well as preemptive repeat priority queues. the analyses are heavily based on probability generating functions that allow us to calculate moments and tail probabilities of the system contents and packet delays of both classes. ::: ::: the results are applicable in heterogeneous telecommunication networks, when delay-sensitive traffic gets transmission priority over best-effort traffic. our results predict the influence of priority scheduling on the qos (quality-of-service) of the different types of traffic.
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using the wisdom of the crowds for keyword generation in the sponsored search model, search engines are paid by businesses that are interested in displaying ads for their site alongside the search results. businesses bid for keywords, and their ad is displayed when the keyword is queried to the search engine. an important problem in this process is 'keyword generation': given a business that is interested in launching a campaign, suggest keywords that are related to that campaign. we address this problem by making use of the query logs of the search engine. we identify queries related to a campaign by exploiting the associations between queries and urls as they are captured by the user's clicks. these queries form good keyword suggestions since they capture the "wisdom of the crowd" as to what is related to a site. we formulate the problem as a semi-supervised learning problem, and propose algorithms within the markov random field model. we perform experiments with real query logs, and we demonstrate that our algorithms scale to large query logs and produce meaningful results.
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query suggestion using hitting time generating alternative queries, also known as query suggestion, has long been proved useful to help a user explore and express his information need. in many scenarios, such suggestions can be generated from a large scale graph of queries and other accessory information, such as the clickthrough. however, how to generate suggestions while ensuring their semantic consistency with the original query remains a challenging problem. in this work, we propose a novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph. without involvement of twisted heuristics or heavy tuning of parameters, this method clearly captures the semantic consistency between the suggested query and the original query. empirical experiments on a large scale query log of a commercial search engine and a scientific literature collection show that hitting time is effective to generate semantically consistent query suggestions. the proposed algorithm and its variations can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.
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appearance-based person reidentification in camera networks: problem overview and current approaches recent advances in visual tracking methods allow following a given object or individual in presence of significant clutter or partial occlusions in a single or a set of overlapping camera views. the question of when person detections in different views or at different time instants can be linked to the same individual is of fundamental importance to the video analysis in large-scale network of cameras. this is the person reidentification problem. the paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. methods that effectively address the challenges associated with changes in illumination, pose, and clothing appearance variation are discussed. more specifically, the development of a set of models that capture the overall appearance of an individual and can effectively be used for information retrieval are reviewed. some of them provide a holistic description of a person, and some others require an intermediate step where specific body parts need to be identified. some are designed to extract appearance features over time, and some others can operate reliably also on single images. the paper discusses algorithms for speeding up the computation of signatures. in particular it describes very fast procedures for computing co-occurrence matrices by leveraging a generalization of the integral representation of images. the algorithms are deployed and tested in a camera network comprising of three cameras with non-overlapping field of views, where a multi-camera multi-target tracker links the tracks in different cameras by reidentifying the same people appearing in different views.
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people reidentification in surveillance and forensics: a survey the field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. this is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. people reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. however, current identification techniques being utilized present many difficulties and shortcomings. for instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. despite the many advances in this field, reidentification is still an open problem. this survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. this begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2d and 3d model-based approaches. the survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.
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transparent implementation of conservative algorithms in parallel simulation languages parallel discrete event simulation offers significant speedup over the traditional sequential event list algorithm. a number of conservative and optimistic algorithms have been proposed and studied for parallel simulation. we examine the problem of transparent execution of a simulation model using conservative algorithms, and present experimental results on the performance of these transparent implementations. the conservative algorithms implemented and compared include the null message algorithm, the conditional-event algorithm, and a new algorithm which is a combination of these. we describe how dynamic topology can be supported by conservative algorithms. language constructs to express lookahead are discussed. finally, performance measurements on a variety of benchmarks are presented, along with a study of the relationship between model characteristics like lookahead, communication topology and the performance of conservative algorithms.
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distributed simulation: a case study in design and verification of distributed programs the problem of system simulation is typically solved in a sequential manner due to the wide and intensive sharing of variables by all parts of the system. we propose a distributed solution where processes communicate only through messages with their neighbors; there are no shared variables and there is no central process for message routing or process scheduling. deadlock is avoided in this system despite the absence of global control. each process in the solution requires only a limited amount of memory. the correctness of a distributed system is proven by proving the correctness of each of its component processes and then using inductive arguments. the proposed solution has been empirically found to be efficient in preliminary studies. the paper presents formal, detailed proofs of correctness.
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incorporating partitioning and parallel plans into the scope optimizer massive data analysis on large clusters presents new opportunities and challenges for query optimization. data partitioning is crucial to performance in this environment. however, data repartitioning is a very expensive operation so minimizing the number of such operations can yield very significant performance improvements. a query optimizer for this environment must therefore be able to reason about data partitioning including its interaction with sorting and grouping. scope is a sql-like scripting language used at microsoft for massive data analysis. a transformation-based optimizer is responsible for converting scripts into efficient execution plans for the cosmos distributed computing platform. in this paper, we describe how reasoning about data partitioning is incorporated into the scope optimizer. we show how relational operators affect partitioning, sorting and grouping properties and describe how the optimizer reasons about and exploits such properties to avoid unnecessary operations. in most optimizers, consideration of parallel plans is an afterthought done in a postprocessing step. reasoning about partitioning enables the scope optimizer to fully integrate consideration of parallel, serial and mixed plans into the cost-based optimization. the benefits are illustrated by showing the variety of plans enabled by our approach.
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hyracks: a flexible and extensible foundation for data-intensive computing hyracks is a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. hyracks allows users to express a computation as a dag of data operators and connectors. operators operate on partitions of input data and produce partitions of output data, while connectors repartition operators' outputs to make the newly produced partitions available at the consuming operators. we describe the hyracks end user model, for authors of dataflow jobs, and the extension model for users who wish to augment hyracks' built-in library with new operator and/or connector types. we also describe our initial hyracks implementation. since hyracks is in roughly the same space as the open source hadoop platform, we compare hyracks with hadoop experimentally for several different kinds of use cases. the initial results demonstrate that hyracks has significant promise as a next-generation platform for data-intensive applications.
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tree based test case generation and cost calculation strategy for uniform parametric pairwise testing problem statement: although it is very important to test any system extensively it is usually too expensive to do so owing to the cost and the resources that are involved in it. software testing is a very important phase of software development to ensure that the developed system is reliable. some systematic approach for testing is essential to test any system and make it acceptable. combinatorial software interaction testing is one which tests all possible software interactions. this interaction could be at various levels such as two way interaction (pairwise) or three or four or five or six way interactions. combinatorial interaction testing had been used in several fields. it was reported in literature that pairwise combinatorial interaction testing had identified most of the software faults. approach: in this study we proposed a new strategy for test suite generation, a tree generation strategy for pairwise combinatorial software testing, with parameters of equal values. the algorithm considered one parameter at a time systematically to generate the tree until all the parameters were considered. this strategy used a cost calculation technique iteratively for each of the leaf nodes to generate the test suite until all the combinations were covered. results: the experimental data showed that we had achieved about 88% (or more in some cases) of reduction in the number of test cases needed for a complete pairwise combinatorial software interaction testing. conclusion: thus, the strategy proposed had achieved a significant reduction in minimizing the number of test cases that was generated.
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a parallel tree based strategy for test data generation and cost calculation for pairwise combinatorial interaction testing software testing is a very important phase of the software development cycle which ensures that the system developed is reliable and acceptable. optimizing the test suite size of software eliminates the unnecessary cost and resources that are involved in testing. sometimes it is not possible to exhaustively test any system due to huge number of test cases. in order to test any system and make it acceptable, combinatorial software interaction testing has been used in several fields. investigations have concluded that most of the software faults could be identified by pairwise combinatorial interaction testing. researchers have applied parallel algorithms to various combinatorial optimisation problems and have succeeded in significant time reduction in solving the problems. large and/or computationally expensive optimization problems sometimes require parallel or high-performance computing systems to achieve reasonable running times.
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579130
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beyond isolation: research opportunities in declarative data-driven coordination there are many database applications that require users to coordinate and communicate. friends want to coordinate travel plans, students want to jointly enroll in the same set of courses, and busy professionals want to coordinate their schedules. these tasks are difficult to program using existing abstractions provided by database systems because in addition to the traditional acid properties provided by the system they all require some type of coordination between users. this is fundamentally incompatible with isolation in the classical acid properties. in this position paper, we argue that it is time for the database community to look beyond isolation towards principled and elegant abstractions that allow for communication and coordination between some notion of (suitably generalized) transactions. this new area of declarative data-driven coordination (d3c) is motivated by many novel applications and is full of challenging research problems. we survey existing abstractions in database systems and explain why they are insufficient for d3c, and we outline a plethora of exciting research problems.
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hilda: a high-level language for data-drivenweb applications we propose hilda, a high-level language for developing data-driven web applications. the primary benefits of hilda over existing development platforms are: (a) it uses a unified data model for all layers of the application, (b) it is declarative, (c) it models both application queries and updates, (d) it supports structured programming for web sites, and (e) it enables conflict detection for concurrent updates. we also describe the implementation of a simple proof-ofconcept hilda compiler, which translates a hilda application program into java servlet code.
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cooperative mobile robotics: antecedents and directions there has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. as yet, few applications of collective robotics have been reported, and supporting theory is still in its formative stages. in this paper, the authors give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. the authors describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.
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communication in reactive multiagent robotic systems multiple cooperating robots are able to complete many tasks more quickly and reliably than one robot alone. communication between the robots can multiply their capabilities and effectiveness, but to what extent? in this research, the importance of communication in robotic societies is investigated through experiments on both simulated and real robots. performance was measured for three different types of communication for three different tasks. the levels of communication are progressively more complex and potentially more expensive to implement. for some tasks, communication can significantly improve performance, but for others inter-agent communication is apparently unnecessary. in cases where communication helps, the lowest level of communication is almost as effective as the more complex type. the bulk of these results are derived from thousands of simulations run with randomly generated initial conditions. the simulation results help determine appropriate parameters for the reactive control system which was ported for tests on denning mobile robots.
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style adaptive bayesian tracking using explicit manifold learning characteristics of the 2d contour shape deformation in human motion contain rich information and can be useful for human identification, gender classification, 3d pose reconstruction and so on. in this paper we introduce a new approach for contour tracking for human motion using an explicit modeling of the motion manifold and learning a decomposable generative model. we use nonlinear dimensionality reduction to embed the motion manifold in a low dimensional configuration space utilizing the constraints imposed by the human motion. given such embedding, we learn an explicit representation of the manifold, which reduces the problem to a one-dimensional tracking problem and also facilitates linear dynamics on the manifold. we also utilize a generative model through learning a nonlinear mapping between the embedding space and the visual input space, which facilitates capturing global deformation characteristics. the contour tracking problem is formulated as states estimation in the decomposed generative model parameter within a bayesian tracking framework. the result is a robust, adaptive gait tracking with shape style estimation.
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gait tracking and recognition using person-dependent dynamic shape model the characteristics of the 2d shape deformation in human motion contain rich information for human identification and pose estimation. in this paper, we introduce a framework for simultaneous gait tracking and recognition using person-dependent global shape deformation model. person-dependent global shape deformations are modeled using a nonlinear generative model with kinematic manifold embedding and kernel mapping. the kinematic manifold is used as a common representation of body pose dynamics in different people in a low dimensional space. shape style as well as geometric transformation and body pose are estimated within a bayesian framework using the generative model of global shape deformation. experimental results show person-dependent synthesis of global shape deformation, gait recognition from extracted silhouettes using style parameters, and simultaneous gait tracking and recognition from image edges.
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integrating organizational requirements and object oriented modeling in recent years we have observed a growing influence of the object-oriented paradigm. unfortunately, the current dominant object oriented modeling technique, i.e. the unified modeling language, uml, is ill equipped for modeling early requirements which are typically informal and often focus on stakeholder objectives. instead, uml is suitable for later phases of requirement capture which usually focus on completeness, consistency, and automated verification of functional requirements for the new system. we present a set of guidelines for the integration of early and late requirements specifications. for early (organizational) modeling we rely on the i* framework, whereas for late (functional) requirements specification, we rely on a precise subset of uml. a small example is used to illustrate how the requirements process iterates between early and late requirements.
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an mda approach for goal-oriented requirement analysis in web engineering web designers usually ignore how to model real user expectations and goals, mainly due to the large and heterogeneous audience of the web. this fact leads to websites which are difficult to comprehend by visitors and complex to maintain by designers. in order to ameliorate this scenario, an approach for using the i* modeling framework in web engineering has been developed in this paper. furthermore, due to the fact that most of the existing web engineering approaches do not consider how to derive conceptual models of the web application from requirements analysis we also propose the use of mda (model driven architecture) in web engineering for: (i) the definition of the requirements of a web application in a computational independent model (cim), (ii) the description of platform independent models (pims), and (iii) the definition of a set of qvt (query/view/transformation) transformations for the derivation of pims from requirements specification (cim), thus to enable the automatic generation of web applications. finally, we include a sample of our approach in order to show its applicability and we describe a prototype tool as a proof of concept of our research.
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the workload onparallel supercomputers: modeling the characteristics of rigid jobs the analysis of workloads is important for understanding how systems are used. in addition, workload models are needed as input for the evaluation of new system designs, and for the comparison of system designs. this is especially important in costly large-scale parallel systems. luckily, workload data are available in the form of accounting logs. using such logs from three different sites, we analyze and model the job-level workloads with an emphasis on those aspects that are universal to all sites. as many distributions turn out to span a large range, we typically first apply a logarithmic transformation to the data, and then fit it to a novel hyper-gamma distribution or one of its special cases. this is a generalization of distributions proposed previously, and leads to good goodness-of-fit scores. the parameters for the distribution are found using the iterative em algorithm. the results of the analysis have been codified in a modeling program that creates a synthetic workload based on the results of the analysis.
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self-configuring network traffic generation the ability to generate repeatable, realistic network traffic is critical in both simulation and testbed environments. traffic generation capabilities to date have been limited to either simple sequenced packet streams typically aimed at throughput testing, or to application-specific tools focused on, for example, recreating representative http requests. in this paper we describe harpoon, a new application-independent tool for generating representative packet traffic at the ip flow level. harpoon generates tcp and udp packet flows that have the same byte, packet, temporal and spatial characteristics as measured at routers in live environments. harpoon is distinguished from other tools that generate statistically representative traffic in that it can self-configure by automatically extracting parameters from standard netflow logs or packet traces. we provide details on harpoon's architecture and implementation, and validate its capabilities in controlled laboratory experiments using configurations derived from flow and packet traces gathered in live environments. we then demonstrate harpoon's capabilities in a router benchmarking experiment that compares harpoon with commonly used throughput test methods. our results show that the router subsystem load generated by harpoon is significantly different, suggesting that this kind of test can provide important insights into how routers might behave under actual operating conditions.
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284,207
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12294869
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network-aware query processing for stream-based applications this paper investigates the benefits of network awareness when processing queries in widely-distributed environments such as the internet. we present algorithms that leverage knowledge of network characteristics (e.g., topology, bandwidth, etc.) when deciding on the network locations where the query operators are executed. using a detailed emulation study based on realistic network models, we analyse and experimentally evaluate the proposed approaches for distributed stream processing. our results quantify the significant benefits of the network-aware approaches and reveal the fundamental trade-off between bandwidth efficiency and result latency that arises in networked query processing.
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challenges and experience in prototyping a multi-modal stream analytic and monitoring application on system s in this paper, we describe the challenges of prototyping a reference application on system s, a distributed stream processing middleware under development at ibm research. with a large number of stream pes (processing elements) implementing various stream analytic algorithms, running on a large-scale, distributed cluster of nodes, and collaboratively digesting several multi-modal source streams with vastly differing rates, prototyping a reference application on system s faces many challenges. specifically, we focus on our experience in prototyping dac (disaster assistance claim monitoring), a reference application dealing with multi-modal stream analytic and monitoring. we describe three critical challenges: (1) how do we generate correlated, multi-modal source streams for dac? (2) how do we design and implement a comprehensive stream application, like dac, from many divergent stream analytic pes? (3) how do we deploy dac in light of source streams with extremely different rates? we report our experience in addressing these challenges, including modeling a disaster claim processing center to generate correlated source streams, constructing the pe flow graph, utilizing programming supports from system s, adopting parallelism, and exploiting resource-adaptive computation.
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208269499
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topic labeled text classification: a weakly supervised approach supervised text classifiers require extensive human expertise and labeling efforts. in this paper, we propose a weakly supervised text classification algorithm based on the labeling of latent dirichlet allocation (lda) topics. our algorithm is based on the generative property of lda. in our algorithm, we ask an annotator to assign one or more class labels to each topic, based on its most probable words. we classify a document based on its posterior topic proportions and the class labels of the topics. we also enhance our approach by incorporating domain knowledge in the form of labeled words. we evaluate our approach on four real world text classification datasets. the results show that our approach is more accurate in comparison to semi-supervised techniques from previous work. a central contribution of this work is an approach that delivers effectiveness comparable to the state-of-the-art supervised techniques in hard-to-classify domains, with very low overheads in terms of manual knowledge engineering.
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dataless text classification: a topic modeling approach with document manifold recently, dataless text classification has attracted increasing attention. it trains a classifier using seed words of categories, rather than labeled documents that are expensive to obtain. however, a small set of seed words may provide very limited and noisy supervision information, because many documents contain no seed words or only irrelevant seed words. in this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label. following this idea, we propose a novel laplacian seed word topic model (lapswtm). in lapswtm, we model each document as a mixture of hidden category topics, each of which corresponds to a distinctive category. also, we assume that neighboring documents tend to have similar category topic distributions. this is achieved by incorporating a manifold regularizer into the log-likelihood function of the model, and then maximizing this regularized objective. experimental results show that our lapswtm significantly outperforms the existing dataless text classification algorithms and is even competitive with supervised algorithms to some extent. more importantly, it performs extremely well when the seed words are scarce.
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probabilistic aggregation strategies in swarm robotic systems in this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. a generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. the latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. two different metrics were used to compare performance of strategies. through systematic experiments, how the aggregation performance, as measured by these two metrics, change 1) with transition probabilities, 2) with number of simulation steps, and 3) with arena size, is studied.
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self-organized aggregation triggers collective decision making in a group of cockroach-like robots self-amplification processes are at the origin of several collective decision phenomena in insect societies. understanding these processes requires linking individual behavioral rules of insects to a choice dynamics at the colony level. in a homogeneous environment, the german cockroach blattella germanica displays self-amplified aggregation behavior. in a heterogeneous environment where several shelters are present, groups of cockroaches collectively select one of them. in this article, we demonstrate that the restriction of the self-amplified aggregation behavior to distinct zones in the environment can explain the emergence of a collective decision at the level of the group. this hypothesis is tested with robotics experiments and dedicated computer simulations. we show that the collective decision is influenced by the available spaces to explore and to aggregate in, by the size of the population involved in the aggregation process and by the probability of encounter zones while the robots explore the environment. we finally discuss these results from both a biological and a robotics point of view.
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scalable and efficient provable data possession storage outsourcing is a rising trend which prompts a number of interesting security issues, many of which have been extensively investigated in the past. however, provable data possession (pdp) is a topic that has only recently appeared in the research literature. the main issue is how to frequently, efficiently and securely verify that a storage server is faithfully storing its client's (potentially very large) outsourced data. the storage server is assumed to be untrusted in terms of both security and reliability. (in other words, it might maliciously or accidentally erase hosted data; it might also relegate it to slow or off-line storage.) the problem is exacerbated by the client being a small computing device with limited resources. prior work has addressed this problem using either public key cryptography or requiring the client to outsource its data in encrypted form. in this paper, we construct a highly efficient and provably secure pdp technique based entirely on symmetric key cryptography, while not requiring any bulk encryption. also, in contrast with its predecessors, our pdp technique allows outsourcing of dynamic data, i.e, it efficiently supports operations, such as block modification, deletion and append.
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a multiple-replica remote data possession checking protocol with public verifiability many cloud storage providers declare that they store multiple replicas of clients’ data in order to prevent data loss. however, currently there is no guarantee that they actually spend storage for multiple replicas. recently a multiple-replica provable data possession (mr-pdp) protocol is proposed, which provides clients with the ability to check whether multiple replicas are really stored at the cloud storage servers. however, in mr-pdp, only private verifiability is achieved. in this paper, we propose a multiple-replica remote data possession checking protocol which has public verifiability. the public verifiability increases the protocol’s flexibility in that a third-party auditor can perform the data checking on behalf of the clients. homomorphic authentication tags based on bls signature are used in the proposed protocol. by security analysis and performance analysis, the proposed protocol is shown to be secure and efficient, which makes it very suitable in cloud storage systems.
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195,696
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relaxing join and selection queries database users can be frustrated by having an empty answer to a query. in this paper, we propose a framework to systematically relax queries involving joins and selections. when considering relaxing a query condition, intuitively one seeks the 'minimal' amount of relaxation that yields an answer. we first characterize the types of answers that we return to relaxed queries. we then propose a lattice based framework in order to aid query relaxation. nodes in the lattice correspond to different ways to relax queries. we characterize the properties of relaxation at each node and present algorithms to compute the corresponding answer. we then discuss how to traverse this lattice in a way that a non-empty query answer is obtained with the minimum amount of query condition relaxation. we implemented this framework and we present our results of a thorough performance evaluation using real and synthetic data. our results indicate the practical utility of our framework.
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vgram: improving performance of approximate queries on string collections using variable-length grams many applications need to solve the following problem of approximate string matching: from a collection of strings, how to find those similar to a given string, or the strings in another (possibly the same) collection of strings? many algorithms are developed using fixed-length grams, which are substrings of a string used as signatures to identify similar strings. in this paper we develop a novel technique, called vgram, to improve the performance of these algorithms. its main idea is to judiciously choose high-quality grams of variable lengths from a collection of strings to support queries on the collection. we give a full specification of this technique, including how to select high-quality grams from the collection, how to generate variable-length grams for a string based on the preselected grams, and what is the relationship between the similarity of the gram sets of two strings and their edit distance. a primary advantage of the technique is that it can be adopted by a plethora of approximate string algorithms without the need to modify them substantially. we present our extensive experiments on real data sets to evaluate the technique, and show the significant performance improvements on three existing algorithms.
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117,817
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type-based analysis of deadlock for a concurrent calculus with interrupts the goal of our research project is to establish a type-based method for verification of certain critical properties (such as deadlock-and race-freedom) of operating system kernels. as operating system kernels make heavy use of threads and interrupts, it is important that the method can properly deal with both of the two features. as a first step towards the goal, we formalize a concurrent calculus equipped with primitives for threads and interrupts handling.we also propose a type system that guarantees deadlock-freedom in the presence of interrupts. to our knowledge, ours is the first type system for deadlock-freedom that can deal with both thread and interrupt primitives.
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type inference for deadlock detection in a multithreaded polymorphic typed assembly language we previously developed a polymorphic type system and a type checker for a multithreaded lock-based polymorphic typed assembly language (mil) that ensures that well-typed programs do not encounter race conditions. this paper extends such work by taking into consideration deadlocks. the extended type system verifies that locks are acquired in the proper order. towards this end we require a language with annotations that specify the locking order. rather than asking the programmer (or the compiler's backend) to specifically annotate each newly introduced lock, we present an algorithm to infer the annotations. the result is a type checker whose input language is non-decorated as before, but that further checks that programs are exempt from deadlocks.
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formal concept analysis approach to cognitive functionalities of bidirectional associative memory abstract pattern association is one among the ways through which human brain stores and recalls information. from the literature, it is evident that cognitive abilities of human brain such as learning, memorizing, recalling and updating of information are performed via concepts and their connections. in this work we have made use of formal concept analysis (fca), a mathematical framework for data and knowledge processing, to represent memories and to perform some of the cognitive functions of human brain. in particular, we model the functionalities of bidirectional associative memories. the proposed model can learn, memorize the learnt information, bi-directionally recall the information that is associated with the presented cue with the help of object-attribute relations that exists in the scenario and update the knowledge when there is a change in the considered scenario. also when a noisy cue is given, the model is capable of recalling the most closely associated pattern by exploiting the concept hierarchy principle of fca. similarly, when a new information is presented on a learnt scenario, the proposed model can update its knowledge by avoiding the need to re-learn scenario. we illustrate the proposed model with a case study and validate with experiments on few real world datasets.
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parallel computing techniques for concept-cognitive learning based on granular computing concept-cognitive learning, as an interdisciplinary study of concept lattice and cognitive learning, has become a hot research direction among the communities of rough set, formal concept analysis and granular computing in recent years. the main objective of concept-cognitive learning is to learn concepts from a give clue with the help of cognitive learning methods. note that this kind of studies can provide concept lattice insight to cognitive learning. in order to deal with more complex data and improve learning efficiency, this paper investigates parallel computing techniques for concept-cognitive learning in terms of large data and multi-source data based on granular computing and information fusion. specifically, for large data, a parallel computing framework is designed to extract global granular concepts by combining local granular concepts. for multi-source data, an effective information fusion strategy is adopted to obtain final concepts by integrating the concepts from all single-source data. finally, we conduct some numerical experiments to evaluate the effectiveness of the proposed parallel computing algorithms.
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291,745
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efficient learning of linear predictors using dimensionality reduction using linear predictors for template tracking enables fast and reliable real-time processing. however, not being able to learn new templates online limits their use in applications where the scene is not known a priori and multiple templates have to be added online, such as slam or sfm. this especially holds for applications running on low-end hardware such as mobile devices. previous approaches either had to learn linear predictors offline [1], or start with a small template and iteratively grow it over time [2]. we propose a fast and simple learning procedure which reduces the necessary training time by up to two orders of magnitude while also slightly improving the tracking robustness with respect to large motions and image noise. this is illustrated in an exhaustive evaluation where we compare our approach with state-of-the-art approaches. additionally, we show the learning and tracking in mobile phone applications which demonstrates the efficiency of the proposed approach.
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online learning of linear predictors for real-time tracking although fast and reliable, real-time template tracking using linear predictors requires a long training time. the lack of the ability to learn new templates online prevents their use in applications that require fast learning. this especially holds for applications where the scene is not known a priori and multiple templates have to be added online. so far, linear predictors had to be either learned offline [1] or in an iterative manner by starting with a small sized template and growing it over time [2]. in this paper, we propose a fast and simple reformulation of the learning procedure that allows learning new linear predictors online.
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150,812
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shape decomposition and skeleton extraction of character patterns this paper proposes an approach to extract skeletons from the character patterns. it first decomposes the pattern into a set of near-convex parts and then extracts skeletons from the parts. in shape decomposition stage, the convex hull information is used to identify the splitting paths. for the skeleton extraction, an operation that ties the adjacent strokes by a knot is developed our control procedure processes a variety of different situations of the adjacent strokes in a systematic way.
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active balloon model based on 3d skeleton extraction by competitive learning we focus on the polygonal representation of a 3d object model which is composed of a lot of points on the object surface. in the 3d animation, it is sometimes necessary to use multiresolution representation of a model to cope with various situations, and it is preferable to represent the models with different resolutions in a certain unified data structure. in order to meet these requirements, we present a new method which generates the approximated model of an original one by using multiple deformable models, called active balloon models (abms). we extract the three-dimensional skeleton of the object. the obtained skeleton comprises nodes and edges, and represents the structure of the object. based on the skeleton, the approximated model is generated by deforming the abms. some experimental works are made to verify the capability of our method.
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168,701
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27068629
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foundational extensible corecursion: a proof assistant perspective this paper presents a formalized framework for defining corecursive functions safely in a total setting, based on corecursion up-to and relational parametricity. the end product is a general corecursor that allows corecursive (and even recursive) calls under "friendly" operations, including constructors. friendly corecursive functions can be registered as such, thereby increasing the corecursor's expressiveness. the metatheory is formalized in the isabelle proof assistant and forms the core of a prototype tool. the corecursor is derived from first principles, without requiring new axioms or extensions of the logic.
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deriving comparators and show functions in isabelle/hol we present an isabelle/hol development that allows for the automatic generation of certain operations for user-defined datatypes. since the operations are defined within the logic, they are applicable for code generation. triggered by the demand to provide readable error messages as well as to access efficient data structures like sorted trees in generated code, we provide show functions that compute the string representation of a given value, comparators that yield linear orders, and hash functions. moreover, large parts of the employed machinery should be reusable for other operations like read functions, etc.
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53,263
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solving factored mdps via non-homogeneous partitioning this paper describes an algorithm for solving large state-space mdps (represented as factored mdps) using search by successive refinement in the space of non-homogeneous partitions. homogeneity is defined in terms of bisimulation and reward equivalence within blocks of a partition. since homogeneous partitions that define equivalent reduced state-space mdps can have a large number of blocks, we relax the requirement of homogeneity. the algorithm constructs approximate aggregate mdps from non-homogeneous partitions, solves the aggregate mdps exactly, and then uses the resulting value functions as part of a heuristic in refining the current best non-homogeneous partition. we outline the theory motivating the use of this heuristic and present empirical results and comparisons.
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between mdps and semi-mdps: a framework for temporal abstraction in reinforcement learning learning, planning, and representing knowledge at multiple levels of temporal ab- straction are key, longstanding challenges for ai. in this paper we consider how these challenges can be addressed within the mathematical framework of reinforce- ment learning and markov decision processes (mdps). we extend the usual notion of action in this framework to include options—closed-loop policies for taking ac- tion over a period of time. examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as mus- cle twitches and joint torques. overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way. in particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methods such as q-learning. formally, a set of options defined over an mdp constitutes a semi-markov decision process (smdp), and the theory of smdps provides the foundation for the theory of options. however, the most interesting issues concern the interplay between the underlying mdp and the smdp and are thus beyond smdp theory. we present results for three such cases: 1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, 2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and 3) we propose a notion of subgoal that can be used to improve the options themselves. all of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. in particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state abstraction, hierarchy, function approximation, or the macro-utility problem.
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239,768
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fast serializable multi-version concurrency control for main-memory database systems multi-version concurrency control (mvcc) is a widely employed concurrency control mechanism, as it allows for execution modes where readers never block writers. however, most systems implement only snapshot isolation (si) instead of full serializability. adding serializability guarantees to existing si implementations tends to be prohibitively expensive. we present a novel mvcc implementation for main-memory database systems that has very little overhead compared to serial execution with single-version concurrency control, even when maintaining serializability guarantees. updating data in-place and storing versions as before-image deltas in undo buffers not only allows us to retain the high scan performance of single-version systems but also forms the basis of our cheap and fine-grained serializability validation mechanism. the novel idea is based on an adaptation of precision locking and verifies that the (extensional) writes of recently committed transactions do not intersect with the (intensional) read predicate space of a committing transaction. we experimentally show that our mvcc model allows very fast processing of transactions with point accesses as well as read-heavy transactions and that there is little need to prefer si over full serializability any longer.
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high-performance concurrency control mechanisms for main-memory databases a database system optimized for in-memory storage can support much higher transaction rates than current systems. however, standard concurrency control methods used today do not scale to the high transaction rates achievable by such systems. in this paper we introduce two efficient concurrency control methods specifically designed for main-memory databases. both use multiversioning to isolate read-only transactions from updates but differ in how atomicity is ensured: one is optimistic and one is pessimistic. to avoid expensive context switching, transactions never block during normal processing but they may have to wait before commit to ensure correct serialization ordering. we also implemented a main-memory optimized version of single-version locking. experimental results show that while single-version locking works well when transactions are short and contention is low performance degrades under more demanding conditions. the multiversion schemes have higher overhead but are much less sensitive to hotspots and the presence of long-running transactions.
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267,382
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6257568
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sensing, tracking, and reasoning with relations suppose we have a set of sensor nodes spread over a geographical area. assume that these nodes are able to perform processing as well as sensing and are additionally capable of communicating with each other by means of a wireless network. though each node is an independent hardware device, they need to coordinate their sensing, computation and communication to acquire relevant information about their environment so as to accomplish some high-level task. the integration of processing makes such nodes more autonomous and the entire system, which we call a sensor net, becomes a novel type of sensing, processing, and communication engine. the sensor net architecture presented in this article starts from a high-level description of the mission or task to be accomplished and then commands individual nodes to sense and communicate in a manner that accomplishes the desired result with attention to minimizing the computational, communication, and sensing resources required. much work remains to be done to refine and implement the relational sensing ideas presented here and validate their performance. we believe, however, that the potential pay-off for the relation-based sensing and tracking we have proposed can be large, both in terms of developing rich theories on the design and complexity of sensing algorithms, as well as in terms of the eventual impact of the deployed sensor systems.
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fault-tolerant compression algorithms for delay-sensitive sensor networks with unreliable links we compare the performance of standard data compression techniques in the presence of communication failures. their performance is inferior to sending data without compression when the packet loss rate of a link is above 10%. we have developed fault-tolerant compression algorithms for sensor networks that are robust against packet loss and achieve low delays in data decoding, thus being particularly suitable for time-critical applications. we show the advantage of our technique by providing results from our extensive experimental evaluation using real sensor datasets.
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133,033
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1946871
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decentralized event correlation for intrusion detection evidence of attacks against a network and its resources is often scattered over several hosts. intrusion detection systems (ids) which attempt to detect such attacks therefore have to collect and correlate information from different sources. we propose a completely decentralized approach to solve the task of event correlation and information fusing of data gathered from multiple points within the network.our system models an intrusion as a pattern of events that can occur at different hosts and consists of collaborating sensors deployed at various locations throughout the protected network installation.we present a specification language to define intrusions as distributed patterns and a mechanism to specify their simple building blocks. the peer-to-peer algorithm to detect these patterns and its prototype implementation, called quicksand, are described. problems and their solutions involved in the management of such a system are discussed.
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information fusion for wireless sensor networks: methods, models, and classifications wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. the way these data are manipulated by the sensor nodes is a fundamental issue. information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. by exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. in this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.
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acceleration techniques for gpu-based volume rendering nowadays, direct volume rendering via 3d textures has positioned itself as an efficient tool for the display and visual analysis of volumetric scalar fields. it is commonly accepted, that for reasonably sized data sets appropriate quality at interactive rates can be achieved by means of this technique. however, despite these benefits one important issue has received little attention throughout the ongoing discussion of texture based volume rendering: the integration of acceleration techniques to reduce per-fragment operations. in this paper, we address the integration of early ray termination and empty-space skipping into texture based volume rendering on graphical processing units (gpu). therefore, we describe volume ray-casting on programmable graphics hardware as an alternative to object-order approaches. we exploit the early z-test to terminate fragment processing once sufficient opacity has been accumulated, and to skip empty space along the rays of sight. we demonstrate performance gains up to a factor of 3 for typical renditions of volumetric data sets on the ati 9700 graphics card.
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a simple and flexible volume rendering framework for graphics-hardware-based raycasting in this work we present a flexible framework for gpu-based volume rendering. the framework is based on a single pass volume raycasting approach and is easily extensible in terms of new shader functionality. we demonstrate the flexibility of our system by means of a number of high-quality standard and nonstandard volume rendering techniques. our implementation shows a promising performance in a number of benchmarks while producing images of higher accuracy than obtained by standard pre-integrated slice-based volume rendering.
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209376317
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cascade object detection with deformable part models we describe a general method for building cascade classifiers from part-based deformable models such as pictorial structures. we focus primarily on the case of star-structured models and show how a simple algorithm based on partial hypothesis pruning can speed up object detection by more than one order of magnitude without sacrificing detection accuracy. in our algorithm, partial hypotheses are pruned with a sequence of thresholds. in analogy to probably approximately correct (pac) learning, we introduce the notion of probably approximately admissible (paa) thresholds. such thresholds provide theoretical guarantees on the performance of the cascade method and can be computed from a small sample of positive examples. finally, we outline a cascade detection algorithm for a general class of models defined by a grammar formalism. this class includes not only tree-structured pictorial structures but also richer models that can represent each part recursively as a mixture of other parts.
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a discriminatively trained, multiscale, deformable part model this paper describes a discriminatively trained, multiscale, deformable part model for object detection. our system achieves a two-fold improvement in average precision over the best performance in the 2006 pascal person detection challenge. it also outperforms the best results in the 2007 challenge in ten out of twenty categories. the system relies heavily on deformable parts. while deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the pascal challenge. our system also relies heavily on new methods for discriminative training. we combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent svm. a latent svm, like a hidden crf, leads to a non-convex training problem. however, a latent svm is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. we believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.
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269,500
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67638
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scalable data partitioning techniques for parallel sliding window processing over data streams this paper proposes new techniques for e ciently parallelizing sliding window processing over data streams on a shared-nothing cluster of commodity hardware. data streams are first partitioned on the fly via a continuous split stage that takes the query semantics into account in a way that respects the natural chunking (windowing) of the stream by the query. the split does not scale well enough when there is high degree of overlap across the windows. to remedy this problem, we propose two alternative partitioning strategies based on batching and pane-based processing, respectively. lastly, we provide a continuous merge stage at the end that combines the results on the fly while meeting qos requirements on ordered delivery. we implemented these techniques as part of the borealis distributed stream processing system, and conducted experiments that show the scalability of our techniques based on the linear road benchmark.
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streamcloud: an elastic and scalable data streaming system many applications in several domains such as telecommunications, network security, large-scale sensor networks, require online processing of continuous data flows. they produce very high loads that requires aggregating the processing capacity of many nodes. current stream processing engines do not scale with the input load due to single-node bottlenecks. additionally, they are based on static configurations that lead to either under or overprovisioning. in this paper, we present streamcloud, a scalable and elastic stream processing engine for processing large data stream volumes. streamcloud uses a novel parallelization technique that splits queries into subqueries that are allocated to independent sets of nodes in a way that minimizes the distribution overhead. its elastic protocols exhibit low intrusiveness, enabling effective adjustment of resources to the incoming load. elasticity is combined with dynamic load balancing to minimize the computational resources used. the paper presents the system design, implementation, and a thorough evaluation of the scalability and elasticity of the fully implemented system.
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27,940
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1723789
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an expressive model for the web infrastructure: definition and application to the browser id sso system the web constitutes a complex infrastructure and, as demonstrated by numerous attacks, rigorous analysis of standards and web applications is indispensable. inspired by successful prior work, in particular the work by akhawe et al. as well as bansal et al., in this work we propose a formal model for the web infrastructure. while unlike prior works, which aim at automatic analysis, our model so far is not directly amenable to automation, it is much more comprehensive and accurate with respect to the standards and specifications. as such, it can serve as a solid basis for the analysis of a broad range of standards and applications. as a case study and another important contribution of our work, we use our model to carry out the first rigorous analysis of the browser id system (a.k.a. mozilla persona), a recently developed complex real-world single sign-on system that employs technologies such as ajax, cross-document messaging, and html5 web storage. our analysis revealed a number of very critical flaws that could not have been captured in prior models. we propose fixes for the flaws, formally state relevant security properties, and prove that the fixed system in a setting with a so-called secondary identity provider satisfies these security properties in our model. the fixes for the most critical flaws have already been adopted by mozilla and our findings have been rewarded by the mozilla security bug bounty program.
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analyzing the browserid sso system with primary identity providers using an expressive model of the web browserid is a complex, real-world single sign-on (sso) system for web applications recently developed by mozilla. it employs new html5 features (such as web messaging and web storage) and cryptographic assertions to provide decentralized login, with the intent to respect users' privacy. it can operate in a primary and a secondary identity provider mode. while in the primary mode browserid runs with arbitrary identity providers (idps), in the secondary mode there is one idp only, namely mozilla's default idp. ::: we recently proposed an expressive general model for the web infrastructure and, based on this web model, analyzed the security of the secondary idp mode of browserid. the analysis revealed several severe vulnerabilities. ::: in this paper, we complement our prior work by analyzing the even more complex primary idp mode of browserid. we do not only study authentication properties as before, but also privacy properties. during our analysis we discovered new and practical attacks that do not apply to the secondary mode: an identity injection attack, which violates a central authentication property of sso systems, and attacks that break an important privacy promise of browserid and which do not seem to be fixable without a major redesign of the system. some of our attacks on privacy make use of a browser side channel that has not gained a lot of attention so far. ::: for the authentication bug, we propose a fix and formally prove in a slight extension of our general web model that the fixed system satisfies all the requirements we consider. this constitutes the most complex formal analysis of a web application based on an expressive model of the web infrastructure so far. ::: as another contribution, we identify and prove important security properties of generic web features in the extended web model to facilitate future analysis efforts of web standards and web applications.
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298,758
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43371968
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ffsc: an energy efficiency communications approach for delay minimizing in internet of things it is desirable for alarm packets to be forwarded to the sink as quickly as possible in wireless sensor networks. in this paper, we initially analyze the theory of the relationships between network configurations and network lifetime as well as transmission delay. then, we propose an approximate optimization approach to minimize the end-to-end delay with a reduced complexity of configuration under the condition that the network lifetime remains greater than the specified target value. a local forwarding approach named fast data collection for nodes far away from the sink and slow data collection for nodes close to the sink (ffsc) is proposed. this approach is energy efficient. moreover, it can further reduce the end-to-end delay. both the comprehensive theoretical analysis and the experimental results indicate that the performance of ffsc is better than the methods proposed by previous studies. relative to the direct forwarding strategy, the ffsc approach can reduce the delay by 7.56%–23.16% and increase the lifetime by more than 25%. it can also increase the energy efficiency as much as 18.99%. relative to the single fixed threshold strategy, the ffsc approach can reduce the delay by 4.16%–9.79% and increase the energy efficiency by 19.28% while still guaranteeing the same lifetime as those previous methods.
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an efficient scheduling model for broadcasting in wireless sensor networks energy efficiency is especially important to the broadcasting operation in wireless sensor networks. it helps to reduce the energy consumption by minimizing the number of relay nodes during the broadcast process in case that the transmission range is identical to all nodes in the network. in this paper, we have introduced an efficient heuristic algorithm emcds to build the minimum connected dominating set with the proposed ordered sequence list. the simulation results show that the proposed emcds algorithm can find smaller cds compared with related works.
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120,388
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15448110
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modeling the data warehouse refreshment process as a workflow application this article is a position paper on the nature of the data warehouse refreshment which is often defined as a view maintenance problem or as a loading process. we will show that the refreshment process is more complex than the view maintenance problem, and different from the loading process. we conceptually define the refreshment process as a workflow whose activities depend on the available products for data extraction, cleaning and integration, and whose coordination events depend on the application domain and on the required quality in terms of data freshness. implementation of this process is clearly distinguished from its conceptual modelling.
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information integration: conceptual modeling and reasoning support information integration is one of the core problems in cooperative information systems. the authors argue that two critical factors for the design and maintenance of applications requiring information integration are conceptual modeling of the domain, and reasoning support over the conceptual representation. in particular they present a general architecture for information integration that explicitly includes a conceptual representation of the application. they illustrate how the architecture can express several integration settings and existing systems. they provide various arguments in favor of the conceptual level in the architecture and of automated reasoning over the conceptual representation. finally, they present a specific proposal of an integration system which realizes the general architecture and is equipped with decidable reasoning procedures.
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222,945
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56211274
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adaptive efficient downlink packet scheduling algorithm in lte-advanced system for a better exploitation of radio resources in the fourth generation networks (4g) long term evolution-advanced (lte-a) and for a better guarantee of service quality requested for users, radio resources management and specifically scheduling, play a key role in reaching the objective. in this paper, we propose a new packet scheduling (ps) algorithm for lte-a downlink transmission that adds a new functionality of an adaptive time domain (td) scheduler to adaptively allocate available resources to gbr (guaranteed bit rate) and ngbr (non gbr) traffic. evaluation of our algorithm and comparison with previous works are also presented. the simulation results have demonstrated the effectiveness of our algorithm in terms of the system throughput as well as the delay and the packet drop rate (pdr) for both gbr and ngbr traffic.
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delay-prioritized scheduling (dps) for real time traffic in 3gpp lte system given that the co-existence of multimedia applications will be a norm in the future wireless systems, their quality of service (qos) requirements need to be guaranteed. this has imposed new challenges in the design of packet scheduling algorithms in these systems. to address those challenges, a new packet scheduling algorithm for real time (rt) traffic in downlink third generation partnership project long term evolution (3gpp lte) system is proposed in this paper. the proposed algorithm utilizes each user's packet delay information and its instantaneous downlink channel conditions when making scheduling decisions. simulation results show that the proposed algorithm outperforms opportunistic scheduling and maximum-largest weighted delay first algorithms by maximizing system throughput and satisfying the qos requirements of the rt traffic.
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288,566
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67451900
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reusability metrics for software components summary form only given. assessing the reusability, adaptability, compose-ability and flexibility of software components is more and more of a necessity due to the growing popularity of component based software development (cbsd). even if there are some metrics defined for the reusability of object-oriented software (oos), they cannot be used for cbsd because these metrics require analysis of source code. the aim of this paper is to study the adaptability and compose-ability of software components, both qualitatively and quantitatively. we propose metrics and a mathematical model for the above-mentioned characteristics of software components. the interface characterization is the starting point of our evaluation. the adaptability of a component is discussed in conjunction with the complexity of its interface. the compose-ability metric defined for database components is extended for general software components. we also propose a metric for the complexity and adaptability of the problem solved by a component, based on its use cases. the number of alternate flows from the use case narrative is considered as a measurement for the complexity of the problem solved by a component. this was our starting point in developing a set of metrics for evaluating components functionality-wise. the main advantage of defining these metrics is the possibility to measure adaptability, reusability and quality of software components, and therefore to identify the most effective reuse strategy.
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complexity metrics for component-oriented software systems component-based software development (cbsd) has become one of the preferred streams for developing large and complex systems by integrating prefabricated software components that not only facilitates the process of software development but is also changing the ways for software professionals to develop software applications. till today, numerous attempts have been made by several organizations, software development teams, developers as well as researchers to improve component-oriented software systems (coss) through improved measurement tools and techniques i.e. through an effective metrics. our paper is a simple attempt to work for the demand of an appropriate and relevant integration metrics for the measurement of complexity of a software component that could be used as one of the approaches for further guidance in component complexity measurement and problem reduction. we represented a component metrics as an instantiation of the integration complexity measurement which can then be evaluated using appropriate metric tools. the work presented in this paper introduces a perception of component-oriented software systems complexity and also defines some new complexity metrics.
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191,414
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12986494
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video rewrite: driving visual speech with audio video rewrite uses existing footage to create automatically new video of a person mouthing words that she did not speak in the original footage. this technique is useful in movie dubbing, for example, where the movie sequence can be modified to sync the actors’ lip motions to the new soundtrack. video rewrite automatically labels the phonemes in the training data and in the new audio track. video rewrite reorders the mouth images in the training footage to match the phoneme sequence of the new audio track. when particular phonemes are unavailable in the training footage, video rewrite selects the closest approximations. the resulting sequence of mouth images is stitched into the background footage. this stitching process automatically corrects for differences in head position and orientation between the mouth images and the background footage. video rewrite uses computer-vision techniques to track points on the speaker’s mouth in the training footage, and morphing techniques to combine these mouth gestures into the final video sequence. the new video combines the dynamics of the original actor’s articulations with the mannerisms and setting dictated by the background footage. video rewrite is the first facial-animation system to automate all the labeling and assembly tasks required to resync existing footage to a new soundtrack.
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automatic viseme clustering for audiovisual speech synthesis. a common approach in visual speech synthesis is the use of visemes as atomic units of speech. in this paper, phonemebased and viseme-based audiovisual speech synthesis techniques are compared in order to explore the balancing between data availability and an improved audiovisual coherence for synthesis optimization. a technique for automatic viseme clustering is described and it is compared to the standardized viseme set described in mpeg-4. both objective and subjective testing indicated that a phoneme-based approach leads to better synthesis results. in addition, the test results improve when more different visemes are defined. this raises some questions on the widely applied viseme-based approach. it appears that a many-to-one phoneme-to-viseme mapping is not capable of describing all subtle details of the visual speech information. in addition, with viseme-based synthesis the perceived synthesis quality is affected by the loss of audiovisual coherence in the synthetic speech.
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174,486
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13841585
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adjacent orientation vector based fingerprint minutiae matching system minutia matching is the most popular approach to fingerprint recognition. we analyzed a novel fingerprint feature named adjacent orientation vector, or aov, for fingerprint matching. in the first stage, aov is used to find possible minutiae pairs. then one minutiae set is rotated and translated. this is followed by a preliminary matching to ensure reliability as well as a fine matching to overcome possible distortion. such method has been deployed to a payroll and security access information system and its workability is encouraging. the information system aims to offer a highly secured and automated identification system for payroll tracking as well as authorized access to working areas.
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adjacent orientation vector based fingerprint minutiae matching system minutia matching is the most popular approach to fingerprint recognition. we analyzed a novel fingerprint feature named adjacent orientation vector, or aov, for fingerprint matching. in the first stage, aov is used to find possible minutiae pairs. then one minutiae set is rotated and translated. this is followed by a preliminary matching to ensure reliability as well as a fine matching to overcome possible distortion. such method has been deployed to a payroll and security access information system and its workability is encouraging. the information system aims to offer a highly secured and automated identification system for payroll tracking as well as authorized access to working areas.
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36,292
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53280899
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evaluation of svm, mlp and gmm classifiers for layout analysis of historical documents this paper presents a comparison between three classifiers based on support vector machines, multi-layer perceptrons and gaussian mixture models respectively to detect physical structure of historical documents. each classifier segments a scaled image of historical document into four classes, i.e., areas of periphery, background, text and decoration. we evaluate them on three data sets of historical documents. depending on data sets, the best classification rates obtained vary from 90.35% to 97.47%.
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dhsegment: a generic deep-learning approach for document segmentation in recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. we argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. in this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. we propose an open-source implementation of a cnn-based pixel-wise predictor coupled with task dependent post-processing blocks. we show that a single cnn-architecture can be used across tasks with competitive results. moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.
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234,546
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126819
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defocus deblurring and superresolution for time-of-flight depth cameras continuous-wave time-of-flight (tof) cameras show great promise as low-cost depth image sensors in mobile applications. however, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. another shortcoming is the limited spatial resolution of currently available tof sensors. in this paper we analyze the image formation model for blurred tof images. by directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of tof cameras. our method outperforms existing methods on both synthetic and real datasets. in the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent tof cameras.
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robust saliency detection via regularized random walks ranking in the field of saliency detection, many graph-based algorithms heavily depend on the accuracy of the pre-processed superpixel segmentation, which leads to significant sacrifice of detail information from the input image. in this paper, we propose a novel bottom-up saliency detection approach that takes advantage of both region-based features and image details. to provide more accurate saliency estimations, we first optimize the image boundary selection by the proposed erroneous boundary removal. by taking the image details and region-based estimations into account, we then propose the regularized random walks ranking to formulate pixel-wised saliency maps from the superpixel-based background and foreground saliency estimations. experiment results on two public datasets indicate the significantly improved accuracy and robustness of the proposed algorithm in comparison with 12 state-of-the-art saliency detection approaches.
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278,244
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9559859
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a scalable location service for geographic ad hoc routing gls is a new distributed location service which tracks mobile node locations. gls combined with geographic forwarding allows the construction of ad hoc mobile networks that scale to a larger number of nodes than possible with previous work. gls is decentralized and runs on the mobile nodes themselves, requiring no fixed infrastructure. each mobile node periodically updates a small set of other nodes (its location servers) with its current location. a node sends its position updates to its location servers without knowing their actual identities, assisted by a predefined ordering of node identifiers and a predefined geographic hierarchy. queries for a mobile node's location also use the predefined identifier ordering and spatial hierarchy to find a location server for that node. experiments using the ns simulator for up to 600 mobile nodes show that the storage and bandwidth requirements of gls grow slowly with the size of the network. furthermore, gls tolerates node failures well: each failure has only a limited effect and query performance degrades gracefully as nodes fail and restart. the query performance of gls is also relatively insensitive to node speeds. simple geographic forwarding combined with gls compares favorably with dynamic source routing (dsr): in larger networks (over 200 nodes) our approach delivers more packets, but consumes fewer network resources.
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location-aided routing (lar) in mobile ad hoc networks a mobile ad hoc network consists of wireless hosts that may move often. movement of hosts results in a change in routes, requiring some mechanism for determining new routes. several routing protocols have already been proposed for ad hoc networks. this report suggests an approach to utilize location information (for instance, obtained using the global positioning system) to improve performance of routing protocols for ad hoc networks.
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217,499
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43334397
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rule-based modeling of biochemical networks a method for the automatic generation of mathematical/computational models that account comprehensively and precisely for the full spectrum of chemical species implied by user-specified activities, potential modifications and interactions of the molecular components of biomolecules is described. a computer-implemented system that includes software was used to generate models. the software has a user interface that allows a user to generate new models and modify existing models.
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efficient, correct simulation of biological processes in the stochastic pi-calculus this paper presents a simulation algorithm for the stochastic π-calculus, designed for the efficient simulation of biological systems with large numbers of molecules. the cost of a simulation depends on the number of species, rather than the number of molecules, resulting in a significant gain in efficiency. the algorithm is proved correct with respect to the calculus, and then used as a basis for implementing the latest version of the spim stochastic simulator. the algorithm is also suitable for generating graphical animations of simulations, in order to visualise system dynamics.
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166,699
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201892152
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optimal home energy management under dynamic electrical and thermal constraints the optimization of energy consumption, with consequent costs reduction, is one of the main challenges in present and future smart grids. of course, this has to occur keeping the living comfort for the end-user unchanged. in this work, an approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed. the proposed algorithm yields an optimal task scheduling under dynamic electrical constraints, while simultaneously ensuring the thermal comfort according to the user needs. on purpose, a suitable thermal model based on heat-pump usage has been considered in the framework. some computer simulations using real data have been performed, and obtained results confirm the efficiency and robustness of the algorithm, also in terms of achievable cost savings.
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hybrid soft computing algorithmic framework for smart home energy management energy management in smart home environments is undoubtedly one of the pressing issues in the smart grid research field. the aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. in this paper an hybrid soft computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. in particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. a genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. from this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. the degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework.
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294,283
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21703984
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constructing a cds-based network backbone for data collection in wireless sensor networks data collection is one of the most important operations in wireless sensor networks. currently, many researches focus on using a connected dominating set to construct a virtual backbone for data collection in wsns. most researchers concentrate on how to construct a minimum connected dominating set because a small virtual backbone incurs less maintenance. unfortunately, computing a minimum size cds is np-hard, and the minimum connected dominating sets may result in unbalanced energy consumption among nodes. in this paper, we investigate the problem of constructing an energy-balanced cds to effectively preserve the energy of nodes in order to extend the network lifetime in data collection. an energy-balanced connected dominating set scheme named dga-ebcds is proposed, and each node in the network can effectively transmit its data to the sink through the virtual backbone. when constructing the virtual backbone in dga-ebcds, we prioritize selecting those nodes with higher energy and larger degree. this method makes the energy consumption among nodes more balanced. furthermore, the routing decision in dga-ebcds considers both the path length and the remaining energy of nodes in the path; it further prolongs the lifetime of nodes in the backbone. our conclusions are verified by extensive simulation results.
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a distributed reliable and energy-efficient topology control algorithm in wireless sensor network topology control is an efficiency method that can enhance the energy efficiency in a wireless sensor network. there are several research discuss the topology control problem only focus on topology construction or topology maintenance. in this paper, we design a distributed reliable and energy- efficient topology control algorithm both take into consider with topology construction and maintenance. in the topology construction phase, we build a reliable topology to reduce the packet retransmission. we use intermittently links and natural of wireless network. anyone can receive the packet, when it in someone's coverage. therefore, we combined these features to build a high network reachable probability. it can enhance the network efficiency. in addition to, we propose the dynamic topology maintenance method, which can balance the energy consumption by multi-level energy threshold. our proposed method can obtain the high network reachable probability and to extend the network lifetime. experiments indicate the superiority of the proposed algorithm in terms of average energy consumption and network lifetime.
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30,498
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52021524
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multi-task learning of social psychology assessments and nonverbal features for automatic leadership identification in social psychology, the leadership investigation is performed using questionnaires which are either i) self-administered or ii) applied to group participants to evaluate other members or iii) filled by external observers. while each of these sources is informative, using them individually might not be as effective as using them jointly. this paper is the first attempt which addresses the automatic identification of leaders in small-group meetings, by learning effective models using nonverbal audio-visual features and the results of social psychology questionnaires that reflect assessments regarding leadership. learning is based on multi-task learning which is performed without using ground-truth data (gt), but using the results of questionnaires (having substantial agreement with gt), administered to external observers and the participants of the meetings, as tasks. the results show that joint learning results in better performance as compared to single task learning and other baselines.
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multi-domain and multi-task prediction of extraversion and leadership from meeting videos automatic prediction of personalities from meeting videos is a classical machine learning problem. psychologists define personality traits as uncorrelated long-term characteristics of human beings. however, human annotations of personality traits introduce cultural and cognitive bias. in this study, we present methods to automatically predict emergent leadership and personality traits in the group meeting videos of the emergent leadership corpus. prediction of extraversion has attracted the attention of psychologists as it is able to explain a wide range of behaviors, predict performance, and assess risk. prediction of emergent leadership, on the other hand, is of great importance for the business community. therefore, we focus on the prediction of extraversion and leadership since these traits are also strongly manifested in a meeting scenario through the extracted features. we use feature analysis and multi-task learning methods in conjunction with the non-verbal features and crowd-sourced annotations from the video blog (vlog) corpus to perform a multi-domain and multi-task prediction of personality traits. our results indicate that multi-task learning methods using 10 personality annotations as tasks and with a transfer from two different datasets from different domains improve the overall recognition performance. preventing negative transfer by using a forward task selection scheme yields the best recognition results with 74.5% accuracy in leadership and 81.3% accuracy in extraversion traits. these results demonstrate the presence of annotation bias as well as the benefit of transferring information from weakly similar domains.
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128,059
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1610133
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dense planar slam using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3d slam systems. we present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by rgb-d sensors or dense multi-view stereo reconstruction). our method offers the every-pixel descriptive power of the latest dense slam approaches, but takes advantage directly of the planarity of many parts of real-world scenes via a data-driven process to directly regularize planar regions and represent their accurate extent efficiently using an occupancy approach with on-line compression. large areas can be mapped efficiently and with useful semantic planar structure which enables intuitive and useful ar applications such as using any wall or other planar surface in a scene to display a user's content.
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continuous humanoid locomotion over uneven terrain using stereo fusion for humanoid robots to fulfill their mobility potential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. in this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. the perception system continuously integrates stereo imagery to build a consistent 3d model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. we illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from lidar. to our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot — and additionally to do so in continuous motion. a particular integration challenge was ensuring that these two computationally intensive systems operate with minimal latency (below 1 second) to allow re-planning while walking. the results of extensive experimentation and quantitative analysis are also presented. our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations.
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on modeling of coevolution of strategies and structure in autonomous overlay networks currently, on one hand, there exist much work about network formation and/or growth models, and on the other hand, cooperative strategy evolutions are extensively investigated in biological, economic, and social systems. generally, overlay networks are heterogeneous, dynamic, and distributed environments managed by multiple administrative authorities, shared by users with different and competing interests, or even autonomously provided by independent and rational users. thus, the structure of a whole overlay network and the peers' rational strategies are ever coevolving. however, there are very few approaches that theoretically investigate the coevolution between network structure and individual rational behaviors. the main motivation of our article lies in that: unlike existing work which empirically illustrates the interaction between rational strategies and network structure (through simulations), based on egt (evolutionary game theory), we not only infer a condition that could favor the cooperative strategy over defect strategy, but also theoretically characterizes the structural properties of the formed network. specifically, our contributions are twofold. first, we strictly derive the critical benefit-to-cost ratio (b/c) that would facilitate the evolution of cooperation. the critical ratio depends on the network structure (the number of peers in system and the average degree of each peer), and the evolutionary rule (the strategy and linking mutation probabilities). then, according to the evolutionary rules, we formally derive the structural properties of the formed network in full cooperative state. especially, the degree distribution is compatible with the power-law, and the exponent is (4-3v)/(1-3v), where v is peer's linking mutation probability. furthermore, we show that, without being harmful to cooperation evolution, a slight change of the evolutionary rule will evolve the network into a small-world structure (high global efficiency and average clustering coefficient), with the same power-law degree distribution as in the original evolution model.
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adaptive routing with stale information we investigate adaptive routing policies for large networks in which agents reroute traffic based on old information. it is a well known and practically relevant problem that old information can lead to undesirable oscillation effects resulting in poor performance. we investigate how adaptive routing policies should be designed such that these effects can be avoided.the network is represented by a general graph with latency functions on the edges. traffic is managed by a large number of agents each of which is responsible for a negligible amount of traffic. initially the agents' routing paths are chosen in an arbitrary fashion. from time to time each agent revises her routing strategy by sampling another path and switching with positive probability to this path if it promises smaller latencies. as the information on which the agent bases her decision might be stale, however, this does not necessarily lead to an improvement. the points of time at which agents revise their strategy are generated by a poisson distribution. stale information is modelled in form of a bulletin board that is updated periodically and lists the latencies on all edges.we analyze such a distributed routing process in the so-called fluid limit, that is, we use differential equations describing the fractions of traffic on different paths over time. in our model, we can show the following effects. simple routing policies that always switch to the better alternative lead to oscillation, regardless at which frequency the bulletin board is updated. oscillation effects can be avoided, however, when using smooth adaption policies that do not always switch to better alternatives but only with a probability depending on the advantage in the latency. in fact, such policies have dynamics that converge to a fixed point corresponding to a nash equilibrium for the underlying routing game, provided the update periods are not too large.in addition, we also analyze the speed of convergence towards approximate equilibria of two specific variants of smooth adaptive routing policies, eg., for a replication policy adopted from evolutionary game theory.
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query by image and video content: the qbic system research on ways to extend and improve query methods for image databases is widespread. we have developed the qbic (query by image content) system to explore content-based retrieval methods. qbic allows queries on large image and video databases based on example images, user-constructed sketches and drawings, selected color and texture patterns, camera and object motion, and other graphical information. two key properties of qbic are (1) its use of image and video content-computable properties of color, texture, shape and motion of images, videos and their objects-in the queries, and (2) its graphical query language, in which queries are posed by drawing, selecting and other graphical means. this article describes the qbic system and demonstrates its query capabilities. qbic technology is part of several ibm products. >
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cires: a system for content-based retrieval in digital image libraries this paper presents cires, a new online system for a content-based retrieval in digital image libraries. content-based image retrieval systems have traditionally used color and texture analyses. these analyses have not always achieved adequate level of performance and user satisfaction. the growing need for robust image retrieval systems has led to a need for additional retrieval methodologies. cires addresses this issue by using image structure in addition to color and texture. the efficacy of using structure in combination with color and texture is demonstrated.
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46,454
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35652025
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a critical analysis of incremental iterative data flow analysis algorithms a model of data flow analysis and fixed point iteration solution procedures is presented. the faulty incremental iterative algorithm is introduced. examples of the imprecision of restarting iteration from the intraprocedural and interprocedural domains are given. some incremental techniques which calculate precise data flow information are summarized. >
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an efficient hybrid algorithm for incremental data flow analysis our exhaustive and incremental hybrid data flow analysis algorithms, based on iteration and elimination techniques, are designed for incremental update of a wide variety of monotone data flow problems in response to source program changes. unlike previous incremental iterative methods, this incremental algorithm efficiently computes precise and correct solutions. we give theoretical results on the imprecision of restarting iteration for incremental update by fixed point iteration which provided motivation for our algorithm design. described intuitively, the main algorithm idea is to factor the data flow solution on strong connected components of the flow graph into local and external parts, solving for the local parts by iteration and propagating these effects on the condensation of the flow graph to obtain the entire data flow solution. the incremental hybrid algorithm re-performs those algorithm steps affected by the program changes.
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finding a path subject to many additive qos constraints a fundamental problem in quality-of-service (qos) routing is to find a path between a source-destination node pair that satisfies two or more end-to-end qos constraints. we model this problem using a graph with <i>n</i> vertices and <i>m</i> edges with <i>k</i> additive qos parameters associated with each edge, for any constant <i>k</i> ≥ 2. this problem is known to be np-hard. fully polynomial time approximation schemes (fptas) for the case of <i>k</i> = 2 have been reported in the literature. we concentrate on the general case and make the following contributions. 1) we present a very simple <i>o</i>(<i>km</i> + <i>n</i>log<i>n</i>) time <i>k</i> -approximation algorithm that can be used in hop-by-hop routing protocols. 2) we present an fptas for one optimization version of the qos routing problem with a time complexity of <i>o</i>(<i>m</i>(<i>n</i>/ε)<sup><i>k</i>-1</sup>).3) we present an fptas for another optimization version of the qos routing problem with a time complexity of <i>o</i>(<i>n</i> log <i>n</i> + <i>m</i>(<i>h</i>/ε)<sup><i>k</i>-1</sup>) when there exists an <i>h</i>-hop path satisfying all qos constraints. when <i>k</i> is reduced to 2, our results compare favorably with existing algorithms. the results of this paper hold for both directed and undirected graphs. for ease of presentation, undirected graph is used.
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multiconstrained qos routing: greedy is good a fundamental problem in quality-of-service (qos) routing is to find a path connecting a source node to a destination node that satisfies k ges 2 additive qos constraints. this multi-constrained path problem (mcp) is known to be np-complete. in a recent paper, xue et at. showed that the shortest path with respect to a single auxiliary edge weight (obtained by combining the k edge weights into a single metric) is a if-approximation to mcp, in the sense that the largest ratio of path weight over its corresponding constraint is within a factor of k from minimum. in this paper, we present a simple greedy algorithm and prove that this greedy algorithm is also a if-approximation algorithm to mcp. extensive computational results show that this greedy algorithm is superior to the previously best known if-approximation algorithm in terms of the quality of the path computed. our algorithm is as simple as dijkstra's shortest path algorithm, and is therefore suitable for implementation in internet protocols.
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auction-based resource allocation for cooperative communications distributed and efficient resource allocation is critical for fully realizing the benefits of cooperative communications in large scale communication networks. this paper proposes two auction mechanisms, the snr auction and the power auction, that determine relay selection and relay power allocation in a distributed fashion. a single-relay network is considered first, and the existence and uniqueness of the nash equilibrium (i.e., the auction's outcome) are proved. it is shown that the power auction achieves the efficient allocation by maximizing the total rate increase, and the snr auction is flexible in trading off fairness and efficiency. for both auctions, the distributed best response bid updates globally converge to the unique nash equilibrium in a completely asynchronous manner. the analysis is then generalized to networks with multiple relays, and the existence of the nash equilibrium is shown under appropriate conditions. simulation results verify the effectiveness and robustness of the proposed algorithms.
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theoretical analysis of selective relaying, cooperative multi-hop networks with fairness constraints we consider the problem of selective relaying in multi-hop networks. at each slot, a relay and a node along the optimal non-cooperative path are opportunistically selected to transmit to the next-hop node in a cooperative manner. being a promising scheme for fair resource allocation, the proportional fair scheduling (pfs) algorithm provides excellent balance between throughput and fairness via multi-user diversity and game-theoretic equilibrium. to maximize the overall utility along a cooperative multi-hop path, we apply the proportional fair (pf) criterion in selecting nodes and relays for cooperative transmission. furthermore, we analyze and provide an analytical expression for end-to-end throughput of an opportunistic relaying, cooperative multi-hop path with proportional fairness constraints over a rayleigh flat-fading channel. to our knowledge, it is the first time that a closed-form expression is obtained for the throughput of a proportional fair relaying, cooperative multi-hop path. this research is an extension of previous theoretical work on pf for cellular networks.
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utility-function-driven resource allocation in autonomic systems we study autonomic resource allocation among multiple applications based on optimizing the sum of utility for each application. we compare two methodologies for estimating the utility of resources: a queuing-theoretic performance model and model-free reinforcement learning. we evaluate them empirically in a distributed prototype data center and highlight tradeoffs between the two methods
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resource allocation for autonomic data centers using analytic performance models large data centers host several application environments (aes) that are subject to workloads whose intensity varies widely and unpredictably. therefore, the servers of the data center may need to be dynamically redeployed among the various aes in order to optimize some global utility function. previous approaches to solving this problem suffer from scalability limitations and cannot easily address the fact that there may be multiple classes of workloads executing on the same ae. this paper presents a solution that addresses these limitations. this solution is based on the use of analytic queuing network models combined with combinatorial search techniques. the paper demonstrates the effectiveness of the approach through simulation experiments. both online and batch workloads are considered
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a graphical query language supporting recursion we define a language g for querying data represented as a labeled graph g . by considering g as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. we do not propose g as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. the user is aided in this formulation by means of a graphical interface. the provision of regular expressions in g allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free horn clauses. however, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.
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graphdb: modeling and querying graphs in databases a fuse striker affords a visual or other indication of an operated condition of an associated fuse and is mounted within the fuse and comprises a housing structure coaxially arranged within the fuse with an end of the housing in contact with an end of the fuse, closure structure for the opposite end of the striker housing and comprising mating half sections with a longitudinal groove formed in at least one complementary surface through which an elongated high resistance ignition element extends and which is in shunt with the fusible elements of the fuse so as to contact with pyrotechnic material interposed between the closure means and a cup like piston element having an outwardly flared lip portion whereby ignition of the pyrotechnic element drives the piston toward the opposite end of the housing so as to thrust a striker pin mounted therein through the fuse cap so as to afford a visual indication of the operated condition of the fuse, the pyrotechnic material being ignited upon rupture of the fusible elements by the resulting flow of current through the shunt connected ignition element.
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explicitly representing expected cost: an alternative to roc representation abstract this paper proposes an alternative to roc representation, in which the expected cost of a classi er is represented explicitly. this expected cost representation maintains many of the advantages of roc representation, but is easier to understand. it allows the experimenter to immediately see the range of costs and class frequencies where a particular classi er is the best and quantitatively how much better it is than other classi ers. this paper demonstrates there is a point/line duality between the two representations. a point in roc space representing a classi er becomes a line segment spanning the full range of costs and class frequencies. this duality produces equivalent operations in the two spaces, allowing most techniques used in roc analysis to be readily reproduced in the cost space.
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what roc curves can’t do (and cost curves can this paper shows that roc curves, as a method of visualizing classifier performance, are inadequate for the needs of artificial intelligence researchers in several significant respects, and demonstrates that a different way of visualizing performance – the cost curves introduced by drummond and holte at kdd’2000 – overcomes these deficiencies.
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sensecam image localisation using hierarchical surf trees the sensecam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. we propose a method for automatically determining the wearer's location using an annotated image database, described using surf interest point descriptors. we show that surf out-performs sift in matching sensecam images and that matching can be done efficiently using hierarchical trees of surf descriptors. additionally, by re-ranking the top images using bi-directional surf matches, location matching performance is improved further.
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sift, surf and seasons: long-term outdoor localization using local features local feature matching has become a commonly used method to compare images. for mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. in this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). two different types of image feature algorithms, sift and the more recent surf, have been used to compare the images. we show that two variants of surf, called u-surf and surf-128, outperform the other algorithms in terms of accuracy and speed.
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291
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2204231
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competitive co-evolution of predator and prey sensory-motor systems a recent trend in evolutionary robotics research is to maximize self-organization in the design of robotic systems in order to reduce the human designer bias. this article presents simulation experiments that extend nolfi and floreano's work on competitive co-evolution of neural robot controllers in a predator-prey scenario and integrate it with ideas from work on the 'coevolution' of robot morphology and control systems. the aim of the twenty-one experiments summarized here has been to systematically investigate the tradeoffs and interdependencies between morphological parameters and behavioral strategies through a series of predator-prey experiments in which increasingly many aspects are subject to self-organization through competitive co-evolution. the results illustrate that competitive co-evolution has great potential as a method for the automatic design of robotic systems.
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co-evolving predator and prey robots in this article i briefly discuss the role that artificial (robotic) models can play in the study of competing co-evolutionary dynamics, the main results obtained in the research works addressing the evolution of predator and prey robots, and the implications of these studies for robotics. in particular i discuss the factors that cause the convergence toward a cyclical dynamic and the factors that enable prolonged innovation phases eventually leading to open-ended processes.
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282,256
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208884101
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bilateral space video segmentation in this work, we propose a novel approach to video segmentation that operates in bilateral space. we design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. we compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.
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kernel-induced label propagation by mapping for semi-supervised classification kernel methods have been successfully applied to the areas of pattern recognition and data mining. in this paper, we mainly discuss the issue of propagating labels in kernel space. a kernel-induced label propagation (kernel-lp) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space. the essence of kernel-lp is to perform joint label propagation and adaptive weight learning in a transformed kernel space. that is, our kernel-lp changes the task of label propagation from the commonly-used euclidean space in most existing work to kernel space. the motivation of our kernel-lp to propagate labels and learn the adaptive weights jointly by the assumption of an inner product space of inputs, i.e., the original linearly inseparable inputs may be mapped to be separable in kernel space. kernel-lp is based on existing positive and negative lp model, i.e., the effects of negative label information are integrated to improve the label prediction power. also, kernel-lp performs adaptive weight construction over the same kernel space, so it can avoid the tricky process of choosing the optimal neighborhood size suffered in traditional criteria. two novel and efficient out-of-sample approaches for our kernel-lp to involve new test data are also presented, i.e., (1) direct kernel mapping and (2) kernel mapping-induced label reconstruction, both of which purely depend on the kernel matrix between training set and testing set. owing to the kernel trick, our algorithms will be applicable to handle the high-dimensional real data. extensive results on real datasets demonstrate the effectiveness of our approach.
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image segmentation evaluation: a survey of unsupervised methods image segmentation is an important processing step in many image, video and computer vision applications. extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. to date, the most common method for evaluating the effectiveness of a segmentation method is subjective evaluation, in which a human visually compares the image segmentation results for separate segmentation algorithms, which is a tedious process and inherently limits the depth of evaluation to a relatively small number of segmentation comparisons over a predetermined set of images. another common evaluation alternative is supervised evaluation, in which a segmented image is compared against a manually-segmented or pre-processed reference image. evaluation methods that require user assistance, such as subjective evaluation and supervised evaluation, are infeasible in many vision applications, so unsupervised methods are necessary. unsupervised evaluation enables the objective comparison of both different segmentation methods and different parameterizations of a single method, without requiring human visual comparisons or comparison with a manually-segmented or pre-processed reference image. additionally, unsupervised methods generate results for individual images and images whose characteristics may not be known until evaluation time. unsupervised methods are crucial to real-time segmentation evaluation, and can furthermore enable self-tuning of algorithm parameters based on evaluation results. in this paper, we examine the unsupervised objective evaluation methods that have been proposed in the literature. an extensive evaluation of these methods are presented. the advantages and shortcomings of the underlying design mechanisms in these methods are discussed and analyzed through analytical evaluation and empirical evaluation. finally, possible future directions for research in unsupervised evaluation are proposed.
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survey over image thresholding techniques and quantitative performance evaluation we conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. the thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. the comparison is based on the combined performance measures. we identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 spie and is&t. (doi: 10.1117/1.1631316)
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neural speech recognizer: acoustic-to-word lstm model for large vocabulary speech recognition we present results that show it is possible to build a competitive, greatly simplified, large vocabulary continuous speech recognition system with whole words as acoustic units. we model the output vocabulary of about 100,000 words directly using deep bi-directional lstm rnns with ctc loss. the model is trained on 125,000 hours of semi-supervised acoustic training data, which enables us to alleviate the data sparsity problem for word models. we show that the ctc word models work very well as an end-to-end all-neural speech recognition model without the use of traditional context-dependent sub-word phone units that require a pronunciation lexicon, and without any language model removing the need to decode. we demonstrate that the ctc word models perform better than a strong, more complex, state-of-the-art baseline with sub-word units.
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deep speech 2: end-to-end speech recognition in english and mandarin we show that an end-to-end deep learning approach can be used to recognize either english or mandarin chinese speech-two vastly different languages. because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. key to our approach is our application of hpc techniques, enabling experiments that previously took weeks to now run in days. this allows us to iterate more quickly to identify superior architectures and algorithms. as a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. finally, using a technique called batch dispatch with gpus in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.
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determinants of application service provider (asp) adoption as an innovation the trend toward external procurement of software and services has led to the emergence of application service providers (asps). while prior research has investigated the use of asps as a form of outsourcing, this study considers asp from multiple perspectives, both as a genre of outsourcing and as an it innovation. employing tornatzky and fleischer's (1990) theoretical framework, this study identifies the most important determinants of asp adoption. a model that explains asp diffusion and infusion is specified and tested using pls. some of the most important factors of asp adoption identified in the study include: top management orientation, integration capability, internal is expertise and competitive pressure. other factors such as relationship management, service quality, relative advantage and cost were found to be less important. with the phenomenon of asp at a very early stage, this study provides an opportunity to understand the immediate and practical implications of its adoption.
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a meta-analysis of research on information technology implementation in small business the small business sector is one of the fastest growing sectors of the economy. the firms in this sector are becoming increasingly dependent on information systems (is) for their operations. traditional research in is has primarily focused on large corporations. the problems, opportunities, and management issues encountered by small business in the is area are unique, and research is too limited to provide useful guidelines. this study compares the research literature on is implementation and research on is in small business, examines the commonality and differences, and identifies research gaps. an overall research framework is developed to review the research in the two areas and determine areas of opportunity. as a follow-up of this analysis, a research model is developed to explore the factors influencing the adoption of computer-mediated communication technologies in small business. the model incorporates some of the innovation factors that are identified as potential gaps in the earlier analysis. th...
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corpus-guided sentence generation of natural images we propose a sentence generation strategy that describes images by predicting the most likely nouns, verbs, scenes and prepositions that make up the core sentence structure. the input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. as predicting actions from still images directly is unreliable, we use a language model trained from the english gigaword corpus to obtain their estimates; together with probabilities of co-located nouns, scenes and prepositions. we use these estimates as parameters on a hmm that models the sentence generation process, with hidden nodes as sentence components and image detections as the emissions. experimental results show that our strategy of combining vision and language produces readable and descriptive sentences compared to naive strategies that use vision alone.
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frame- and segment-level features and candidate pool evaluation for video caption generation we present our submission to the microsoft video to language challenge of generating short captions describing videos in the challenge dataset. our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. we propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. using these diverse features we train models specializing in two separate input sub-domains. we then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. we argue that this approach is better suited for the current video captioning task, compared to using a single model, due to the diversity in the dataset. efficacy of our method is proven by the fact that it was rated best in msr video to language challenge, as per human evaluation. additionally, we were ranked second in the automatic evaluation metrics based table.
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the moment: a mobile tool for people with depression or bipolar disorder the moment is a mobile application for people with depression or bipolar disorder to monitor their emotional ups and downs, reveal their emotional patterns, and eventually find a peaceful way to live with their emotions, rather than fighting with them. the system consists of two main components: a smartphone application for the users to track their feelings and memories about events, and a sensor recording their physiological responses. the data will then be visualized in several ways and can be shared with trusted individuals or mental health professionals. the goal is to make the user more aware of her mood swings and the precursors to them; to reveal patterns of the swings; to provide a record for healthcare providers; to build a library of personalized interventions for future use; and to create an effective network of social supports.
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sleepstellar: a safety kit and digital storyteller for sleepwalkers sleepwalking affects 2-4% of adults and can be a potentially dangerous condition leading to severe safety incidents. we interviewed sleepwalkers and sleep disorder experts; and investigated sleepwalking forums to understand their needs and characteristics. research shows that sleepwalkers not only face safety issues, but also social embarrassment leading to conflicts in their self-image and self-awareness. we present sleepstellar that includes a safety kit to protect sleepwalkers and a platform to encourage digital storytelling for overcoming embarrassment issues. the safety kit provides customized rfid stickers to stick to the potentially harmful places (like staircases, kitchen stove) and connecting it with a sleep tracking wearable that alarms when the patient is close to an rfid sticker. the accompanying mobile app tracks sleepwalking pattern and creates stellar-like beautiful visualizations to be viewed by sleepwalkers and share anonymously with the public along with their individual stories to encourage public engagement, thus enhancing a sleepwalker's self-image and overcome embarrassment issues.
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a semantic overlay network for p2p schema-based data integration today data sources are pervasive and their number is growing tremendously. current tools are not prepared to exploit this unprecedented amount of information and to cope with this highly heterogeneous, autonomous and dynamic environment. in this paper, we propose a novel semantic overlay network architecture, paris, aimed at addressing these issues. in paris, the combination of decentralized semantic data integration with gossip-based (unstructured) overlay topology management and (structured) distributed hash tables provides the required level of flexibility, adaptability and scalability, and still allows to perform rich queries on a number of autonomous data sources. we describe the logical model that supports the architecture and show how its original topology is constructed. we present the usage of the system in detail, in particular, the algorithms used to let new peers join the network and to execute queries on top of it and show simulation results that assess the scalability and robustness of the architecture.
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icluster: a self-organizing overlay network for p2p information retrieval we present icluster, a self-organizing peer-to-peer overlay network for supporting full-fledged information retrieval in a dynamic environment. icluster works by organizing peers sharing common interests into clusters and by exploiting clustering information at query time for achieving low network traffic and high recall. we define the criteria for peer similarity and peer selection, and we present the protocols for organizing the peers into clusters and for searching within the clustered organization of peers. icluster is evaluated on a realistic peer-to-peer environment using real-world data and queries. the results demonstrate significant performance improvements (in terms of clustering efficiency, communication load and retrieval accuracy) over a state-of-the-art peer-to-peer clustering method. compared to exhaustive search by flooding, icluster exchanged a small loss in retrieval accuracy for much less message flow.
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58,741
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209444394
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borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning deep neural networks require a large amount of labeled training data during supervised learning. however, collecting and labeling so much data might be infeasible in many cases. in this paper, we introduce a deep transfer learning scheme, called selective joint fine-tuning, for improving the performance of deep learning tasks with insufficient training data. in this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. however, the source learning task does not use all existing training data. our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. experiments demonstrate that our deep transfer learning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. such tasks include caltech 256, mit indoor 67, and fine-grained classification problems (oxford flowers 102 and stanford dogs 120). in comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model. codes and models are available at https://github.com/zyyszj/selective-joint-fine-tuning.
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domain adaptive transfer learning with specialist models transfer learning is a widely used method to build high performing computer vision models. in this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. we find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. these findings are important given the continued increase in dataset sizes. we further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning. our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice.
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244,293
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209516424
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fake face detection methods: can they be generalized? with advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. these fake faces can be used to conduct personation attacks on the targeted subjects. availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. in this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. to this extent, we have collected a new database of more than 53,000 images, from 150 videos, originating from multiple sources of digitally generated fakes including computer graphics image (cgi) generation and many tampering based approaches. in addition, we have also included images (with more than 3,200) from the predominantly used swap-face application that is commonly available on smartphones. extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.
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forensictransfer: weakly-supervised domain adaptation for forgery detection distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. naive classification approaches based on convolutional neural networks (cnns) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. however, on examples from unseen manipulation approaches, their performance drops significantly. to address this limitation in transferability, we introduce forensic-transfer (ft). we devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training. to this end, we learn a forensic embedding based on a novel autoencoder-based architecture that can be used to distinguish between real and fake imagery. the learned embedding acts as a form of anomaly detector; namely, an image manipulated from an unseen method will be detected as fake provided it maps sufficiently far away from the cluster of real images. comparing to prior works, ft shows significant improvements in transferability, which we demonstrate in a series of experiments on cutting-edge benchmarks. for instance, on unseen examples, we achieve up to 85% in terms of accuracy, and with only a handful of seen examples, our performance already reaches around 95%.
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238,157
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203697300
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a centralized scheduling algorithm for ieee 802.15.4e tsch based industrial low power wireless networks time-slotted channel hopping (tsch) is a part of an emerging ieee 802.15.4e standard to enable deterministic low-power mesh networking. it promises to pave the way to the future internet of (important) things by offering high reliability and low latency for wireless industrial applications. nonetheless, the standard only provides a framework but it does not mandate a specific scheduling mechanism. in this paper, we propose a centralized adaptive multi-hop scheduling method (amus) based on the latest tsch mac. amus first enables sequential multi-hop scheduling to provide low latency guarantee for time-critical applications. secondly, the novel tentative cell allocation method allocates additional resources to vulnerable links such that possible mac retransmissions can be accommodated within the same slotframe, hence significantly reducing the delay caused by interference or collisions. last but not least, the battery power of the node can be further conserved by adopting the proposed end-of-q notification mechanism. preliminary simulation results have confirmed that amus outperforms other popular scheduling algorithms in the literature.
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6tisch centralized scheduling: when sdn meet iot the deterministic networking paradigm, which is prevalent in operational technology (ot), is now getting traction at the ietf and the ieee, to enable the convergence of ot with information technology (it), and the industrial internet vision, whereby the automation world can leverage it technology to optimize ot processes. new working groups (wgs) are now emerging at the ietf to develop new routing and resource allocation schemes for ipv6/mpls-based deterministic layer-3 networks. in this work, we present the challenges that the detnet and the 6tisch efforts will be facing to enable that convergence, particularly how detnet can apply software defined networking (sdn) centralized methods to provide global optimizations, and how 6tisch can reuse and extend the detnet work for lowpower wireless sensor networks (wsns).
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124,098
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4861808
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scheduling data collection with dynamic traffic patterns in wireless sensor networks the network traffic pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. this paper develops a novel tdma schedule that is capable of efficiently collecting sensor data for any network traffic pattern and is thus well suited to continuous data collection with dynamic traffic patterns. following this schedule, the energy consumed by sensor nodes for any traffic pattern is very close to the minimum required by their workloads given in the traffic pattern. the schedule also allows the base station to conclude data collection as early as possible according to the traffic load, thereby reducing the latency of data collection. experimental results using real-world data traces show that, compared with existing schedules that are targeted on a fixed traffic pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic traffic patterns.
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a power-efficient distributed tdma scheduling algorithm with distance-measurement for wireless sensor networks this paper describes a power-efficient distributed tdma slot scheduling algorithm which the slot allocation priority is controlled by distance measurement information in details. in our former proposed scheme, l-drand+, an extension of lamport's bakery algorithm for prioritized slot allocation based on the distance measurement information between nodes and a packet-based transmission power control had been applied. in this paper, we propose its enhanced scheme with a weighted rule control and state machines refinements of l-drand+, named l-drand++. this aims at the achievement of media access control methods which can construct a local wireless network practically by limiting the scope, and eliminate the redundant power consumption in the network. the proposed scheme can be shown as a possible replacement of drand algorithm for z-mac scheme in a distance-measurement-oriented manner. in addition, to evaluate the ordered node sequence determined by the algorithm, node sequence metric is proposed. by using the metric, we can evaluate protocol behaviors according to the environmental situation around the node.
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42,531
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392192
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manifold alignment without correspondence manifold alignment has been found to be useful in many areas of machine learning and data mining. in this paper we introduce a novel manifold alignment approach, which differs from "semi-supervised alignment" and "procrustes alignment" in that it does not require predetermining correspondences. our approach learns a projection that maps data instances (from two different spaces) to a lower dimensional space simultaneously matching the local geometry and preserving the neighborhood relationship within each set. this approach also builds connections between spaces defined by different features and makes direct knowledge transfer possible. the performance of our algorithm is demonstrated and validated in a series of carefully designed experiments in information retrieval and bioinformatics.
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semisupervised alignment of manifolds in this paper, we study a family of semisupervised learning algorithms for “aligning” different data sets that are characterized by the same underlying manifold. the optimizations of these algorithms are based on graphs that provide a discretized approximation to the manifold. partial alignments of the data sets—obtained from prior knowledge of their manifold structure or from pairwise correspondences of subsets of labeled examples— are completed by integrating supervised signals with unsupervised frameworks for manifold learning. as an illustration of this semisupervised setting, we show how to learn mappings between different data sets of images that are parameterized by the same underlying modes of variability (e.g., pose and viewing angle). the curse of dimensionality in these problems is overcome by exploiting the low dimensional structure of image manifolds.
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204,821
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3270730
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locadio: inferring motion and location from wi-fi signal strengths context is a critical ingredient of ubiquitous computing. while it is possible to use specialized sensors and beacons to measure certain aspects of a user's context, we are interested in what we can infer from using the existing 802.11 wireless network infrastructure that already exists in many places. the context parameters we infer are the location of a client (with a median error of 1.5 meters) and an indicator of whether or not the client is in motion (with a classification accuracy of 87%). our system, called locadio, uses wi-fi signal strengths from existing access points measured on the client to infer both pieces of context. for motion, we measure the variance of the signal strength of the strongest access point as input to a simple two-state hidden markov model (hmm) for smoothing transitions between the inferred states of "still" and "moving". for location, we exploit the fact that wi-fi signal strengths vary with location, and we use another hmm on a graph of location nodes whose transition probabilities are a function of the building's floor plan, expected pedestrian speeds, and our still/moving inference. our probabilistic approach to inferring context gives a convenient way of balancing noisy measured data such as signal strengths against our a priori assumptions about a user's behavior.
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a task-efficient sink node based on embedded multi-core soc for internet of things abstract with the increase of collected information, the computing performance of single-core sink node for internet of things (iots) cannot satisfy with the demand of large data processing any more. therefore, the sink node which based on embedded multi-core soc for iots and maximizing its computing performance has brought into focus in recent years. in this paper, we design a multi-core task-efficient sink node (tesn) based on heterogeneous architecture and the weighted-least connection (wlc) task schedule strategy has been proposed to improve its efficiency. there are two types of cores in the sink node, master core and slave cores. the master core deals with tasks allocation and the seven slave cores deal with data processing. all of the cores are communicating with each other through mailbox. by considering each core’s real-time processing information and computing performance, the proposed wlc can balance each core’s load and reduce network congestion. the x i l i n x v 5 platform is used to evaluate the performance of wlc and round-robin (rr) algorithms for multi-core sink node. the experiment results show that the wlc strategy improves the processing speed obviously, achieves load balance and avoids large scale congestion of sink node in the sensor networks of iots.
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215,710
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3757715
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reactive search for traffic grooming in wdm networks in this paper the reactive local search (rls) heuristic is proposed for the problem of minimizing the number of expensive add-drop multiplexers in a sonet or sdh optical network ring, while respecting the constraints given by the overall number of fibers and the number of wavelengths that can carry separate information on a fiber. rls complements local search with a history-sensitive feedback loop that tunes the prohibition parameter to the local characteristics of a specific instance. ::: ::: simulation results are reported to highlight the improvement in adm cost when rls is compared with greedy algorithms used in the recent literature.
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algorithm for traffic grooming in optical networks to minimize the number of transceivers we study the problem of traffic grooming to reduce the number of transceivers in optical networks. we show that this problem is equivalent to a certain traffic maximization problem. we give an intuitive interpretation of this equivalence and use this interpretation to derive a greedy algorithm for transceiver minimization. we discuss implementation issues and present computational results comparing the heuristic solutions with the optimal solutions for several small example networks. for larger networks, the heuristic solutions are compared with known bounds on the optimal solution obtained using integer programming tools.
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138,207
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211519562
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a concurrent, generational garbage collector for a multithreaded implementation of ml this paper presents the design and implementation of a “quasi real-time” garbage collector for concurrent caml light, an implementation of ml with threads. this two-generation system combines a fast, asynchronous copying collector on the young generation with a non-disruptive concurrent marking collector on the old generation. this design crucially relies on the ml compile-time distinction between mutable and immutable objects.
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hierarchical memory management for parallel programs an important feature of functional programs is that they are parallel by default. implementing an efficient parallel functional language, however, is a major challenge, in part because the high rate of allocation and freeing associated with functional programs requires an efficient and scalable memory manager. in this paper, we present a technique for parallel memory management for strict functional languages with nested parallelism. at the highest level of abstraction, the approach consists of a technique to organize memory as a hierarchy of heaps, and an algorithm for performing automatic memory reclamation by taking advantage of a disentanglement property of parallel functional programs. more specifically, the idea is to assign to each parallel task its own heap in memory and organize the heaps in a hierarchy/tree that mirrors the hierarchy of tasks. we present a nested-parallel calculus that specifies hierarchical heaps and prove in this calculus a disentanglement property, which prohibits a task from accessing objects allocated by another task that might execute in parallel. leveraging the disentanglement property, we present a garbage collection technique that can operate on any subtree in the memory hierarchy concurrently as other tasks (and/or other collections) proceed in parallel. we prove the safety of this collector by formalizing it in the context of our parallel calculus. in addition, we describe how the proposed techniques can be implemented on modern shared-memory machines and present a prototype implementation as an extension to mlton, a high-performance compiler for the standard ml language. finally, we evaluate the performance of this implementation on a number of parallel benchmarks.
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42,430
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67867554
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an integer linear programming approach to the single and bi-objective next release problem contextthe next release problem involves determining the set of requirements to implement in the next release of a software project. when the problem was first formulated in 2001, integer linear programming, an exact method, was found to be impractical because of large execution times. since then, the problem has mainly been addressed by employing metaheuristic techniques. objectivein this paper, we investigate if the single-objective and bi-objective next release problem can be solved exactly and how to better approximate the results when exact resolution is costly. methodswe revisit integer linear programming for the single-objective version of the problem. in addition, we integrate it within the epsilon-constraint method to address the bi-objective problem. we also investigate how the pareto front of the bi-objective problem can be approximated through an anytime deterministic integer linear programming-based algorithm when results are required within strict runtime constraints. comparisons are carried out against nsga-ii. experiments are performed on a combination of synthetic and real-world datasets. findingswe show that a modern integer linear programming solver is now a viable method for this problem. large single objective instances and small bi-objective instances can be solved exactly very quickly. on large bi-objective instances, execution times can be significant when calculating the complete pareto front. however, good approximations can be found effectively. conclusionthis study suggests that (1) approximation algorithms can be discarded in favor of the exact method for the single-objective instances and small bi-objective instances, (2) the integer linear programming-based approximate algorithm outperforms the nsga-ii genetic approach on large bi-objective instances, and (3) the run times for both methods are low enough to be used in real-world situations.
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software release planning: an evolutionary and iterative approach abstract to achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. in adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. the technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. the technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. this paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called evolve aimed at the continuous planning of incremental software development. the optimization method used is iterative and essentially based on a genetic algorithm. a set of the most promising candidate solutions is generated to support the final decision. the paper evaluates the proposed approach using a sample project.
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