waashk/dblp huggingface dataset:
a time optimal algorithm for evaluating tree pattern queries. the tree pattern matching, which is used to find all occurrences of a tree pattern in an xml database, is a core operation for xml query processing. in this paper, we study this issue and discuss a new algorithm for processing unordered tree pattern queries, which works bottom-up. its main idea is a new labeling method for tree pattern queries. both the time and space complexities of the algorithm are bounded by o(|d|&middot;|q|), where q stands for a tree pattern and d is a largest data stream associated with a node q of q, which contains the database nodes that match the node predicate at q. in addition, the algorithm can be adapted to an indexing environment with xb-trees being used.
gray networking: a step towards next generation computer networks. modern networks are very complex. it is highly desirable to reduce management complexity in next generation network design. researchers have been seeking inspiration in natural observations to help better manage the ever increasing complexity of modern networks. bio-inspired and cognitive networks have shown tremendous promise towards better adapting networks to local stimuli intelligently, and to some extent without human intervention. in this paper, we discuss why the human brain is an excellent model for designing next generation smart networks. insights gained into macro-behavior of the human brain and its structural organization in the last decade are discussed. we identify features that can be adapted for network modeling. we then propose a network design model based on our understanding of the mind, how cognition is achieved, how memory is formed, etc. we end this paper with a real life network design problem we address using the proposed general model.
focused retrieval with proximity scoring. we present in this paper a scoring method for information retrieval based on the proximity of the query terms in the documents. the idea of the method first is to assign to each position in the document a fuzzy proximity value depending on its closeness to the surrounding keywords. these proximity values can then be summed on any range of text -- including any passage or any element -- and after normalization this sum is used as the relevance score for the extent. some experiments on the wikipedia collection used in the inex 2008 evaluation campaign are presented and discussed.
infrastructureless storage in dynamic environments. this paper studies the use of highly dynamic networks as infrastructures for persistent storage of data that offer services at specific geographical zones in a decentralized and distributed way. we propose a new algorithm, based on repulsion techniques, to self-organize the nodes that store and serve the information. in this work, we focus on the evaluation of our algorithm when faced to different simulated failures in order to measure its robustness and compare it with an existing approach.
where people and cars meet: social interactions to improve information sharing in large scale vehicular networks. efficient delivery of information in vehicular networks is crucial for the creation of useful and usable applications that need to cope with nomadic large-scale environments. context-awareness is often key to improve efficiency of a vehicle network since it allows to make informed decisions on the data routing, data locality and data necessity for different moving objects. in this paper we show how the social network of vehicle residents, as part of the overall context, allows us to improve the information sharing in the vehicular network significantly. we demonstrate this by deploying a social ubiquitous-help-system (uhs) on top of a vehicular network. we analyze how uhs operates in a vehicular network using a network simulation of realistic large scale vehicular movement data and show that the social interactions increases the efficiency, relevance and quality of information in data delivery.
a fast multiplier-less edge detection accelerator for fpgas. real time video is used in a wide variety of applications, ranging from video surveillance to medical imaging. these operations require significant amounts of processing power, especially when high resolution frames are used. a large percentage of processing time is used in edge detection kernels. thus, accelerating these kernels is of vital importance in achieving satisfactory frame rates for real time performance, even in high resolutions. this paper proposes a hardware coprocessor to the xilinx microblaze processor which accelerates edge detection significantly, while keeping the hardware requirements low, by using no multipliers at all. using a xilinx spartan 3e fpga, we have reported a frame rate of 157 frames per second in 4cif format, which corresponds to a 4x speedup over the software only solution. the speedup was achieved with only 1131 slices and 5 block rams hardware occupation, which makes the solution very attractable.
a fast approximation strategy for summarizing a set of streaming time series. summarizing a set of streaming time series is an important issue that reliably allows information to be monitored and stored in domains such as finance [12], networks [2, 1], etc. to date, most of existing algorithms have focused on this problem by summarizing the time series separately [12, 4]. moreover, the same amount of memory has been allocated to each time series. yet, memory management is an important subject in the data stream field, but a framework allocating equal amount of memory to each sequence is not appropriate. we introduce an effective and efficient method which succeeds to respond to both challenges: (1) a memory optimized framework along with (2) a fast novel sequence merging method. experiments with real data show that this method is effective and efficient.
t2d: a peer to peer trust management system based on disposition to trust. while the trust paradigm is essential to broadly extend the communication between the environment's actors, the evaluation of trust becomes a challenge when confronted with initializing the trust relationship and validating the transitive propriety of trust. whether between users or between organizations, existing solutions work to create for peer to peer networks, flexible and decentralized security mechanisms with trust approach. however, we have noticed that the trust management systems do not make the most of the subjectivity, more specifically, the notion of disposition to trust although this aspect of subjectivity has a strong influence on how to assess direct and a transitive trust. for this reason in our study, we tackle this problem by introducing a new distributed trust model called t2d (trust to distrust) which is designed to incorporate the following contributions: (i) a behavior model which represents the disposition to trust; (ii) initialization of trust relationship (direct and transitive) according to the defined behavior model.
optimized java card transaction mechanism based on object locality. transaction mechanism is important for reliable update of data on multi-application cards like java card. the improvement of transaction performance is critical because of limited resources on smart cards. this paper presents an optimized transaction mechanism based on high object locality on java card. at first, the analysis of object access and storage locality in applet transactions is given and then the transaction memory scheme in ram and eeprom is designed based on new value logging. secondly, we design the caching and search method of logging items based on access locality so as to improve the speed of the transaction logging. at last, we optimize the commit process based on storage locality in order to reduce the number of eeprom writing. the test results show that this optimized mechanism expands the transaction capacity and improves the execution speed of java card applets.
a lightweight framework for testing database applications. testing is one of the most expensive tasks in today's software development cycle and it is very important to devise techniques that speed up the whole testing process. in a recent paper [9], it has been shown that tests on database applications can be speeded up by using proper test execution strategies and test optimization algorithms. this papers proposes a new test execution strategy called safe-optimistic and a new test optimization algorithm called slice*. safe-optimistic is better than the strategy proposed in [9] in which it reduces testing time without jeopardizing test quality. slice* has a better performance than the test optimization algorithms in [9] in all cases and it keeps on performing well even if the subject application and the test suite evolve.
query-oriented clustering: a multi-objective approach. document clustering techniques have been widely applied in information retrieval to reorganize results furnished as a response to user's queries. following the cluster hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of relevant documents are likely to be gathered in a single cluster. usually, systems organizing search results as a set of clusters consider this tendency as a very advantageous phenomenon, since it allows to filter the results provided by the initial search. adopting a different point of view, we rather consider the cluster hypothesis as a hindrance to the information access since it prevents the emergence of the various aspects of the query. the risk induced is to restrict the perception of the subject to an unique point of view. therefore, we propose to rather distribute the relevant documents over clusters by orienting the organization of the clusters according to the user's topic. the aim is to attract the clusters around the latter in order to highlight the thematic differences between documents which are strongly connected to the query. rather than modifying the inter-documents similarity computation as it is the case in several studies, we propose to directly act on the organization of the clusters by using a multi-objective evolutionary clustering algorithm which, besides the classical internal cohesion, also optimizes the query proximity of the clusters. first experimental results highlight the great benefit which may be gained by our way of query consideration.
transpeer: adaptive distributed transaction monitoring for web2.0 applications. in emerging web2.0 applications such as virtual worlds or social networking websites, the number of users is very important (tens of thousands), hence the amount of data to manage is huge and dependability is a crucial issue. the large scale prevents from using centralized approaches or locking/two-phase-commit approach. moreover, web2.0 applications are mostly interactive, which means that the response time must always be less than few seconds. to face these problems, we present a novel solution, transpeer, that allows applications to scale-up without the need to buy expensive resources at a data center. to this end, databases are replicated over a p2p system in order to achieve high availability and fast transaction processing thanks to parallelism. a distributed shared dictionary, implemented on top of a dht, contains metadata used for routing transactions efficiently. both metadata and data are accessed in an optimistic way: there is no locking on metadata and transactions are executed on nodes in a tentative way. we demonstrate the feasibility of our approaches through experimentation.
combining global and local information for enhanced deep classification. compared to traditional text classification with a flat category set or a small hierarchy of categories, classifying web pages to a large-scale hierarchy such as open directory project (odp) and yahoo! directory is challenging. while a recently proposed "deep" classification method makes the problem tractable, it still suffers from low classification performance. a major problem is the lack of training data, which is unavoidable with such a huge hierarchy. training pages associated with the category nodes are short, and their distributions are skewed. to alleviate the problem, we propose a new training data selection strategy and a na&iuml;ve bayes combination model, which utilize both local and global information. we conducted a series of experiments with the odp hierarchy containing more than 100,000 categories to show that the proposed method of using both local and global information indeed helps avoiding the training data sparseness problem, outperforming the state-of-art method.
semantic content distribution with aggregated profiles. content-based networks have similar characteristics with traditional publish/subscribe system. these networks are more dynamic, scalable and disseminate the data based on user interests. end users subscribe to network according to their interest profiles, publishers publish contents to the network and network delivers them asynchronously according to user subscriptions. expressive subscription methodology of user interests and compact interest representation is essential to build scalable content-based network. this paper presents an approach for distributing rdf documents in information dissemination network called semantic content-based network (scbn). this architecture supports information dissemination with complex information representation through graphs. this paper has the following two key contributions. in first part, we present a semantic based user profile description and methodology for user interest subscription to semantic content-based network. second part describes a distributed profile aggregation framework for generating generic and compact profiles in the network.
tracking random finite objects using 3d-lidar in marine environments. this paper presents a random finite set theoretic formulation for multi-object tracking as perceived by a 3d-lidar in a dynamic environment. it is mainly concerned with the joint detection and estimation of the unknown and time varying number of objects present in the environment and the dynamic state of these objects, given a set of measurements. this problem is particularly challenging in cluttered dynamic environments such as in urban settings or marine environments, because, given a measurement set, there is absolutely no knowledge of which object generated which measurement, and the detected measurements are indistinguishable from false alarms. the proposed approach to multi-object tracking is based on the rigorous theory of finite set statistics (fisst). the optimal bayesian multi-object tracking is not yet practical due to its computational complexity. however, a practical alternative to the optimal filter is the probability hypothesis density (phd) filter, that propagates the first order statistical moment of the full multi-object posterior distribution. in contrast to classical approaches, this random finite set framework does not require any explicit data associations. in this paper, a gaussian mixture approximation of the phd filter is applied to track variable number of objects from 3d-lidar measurements by estimating both the number of objects and their respective locations in each scan. experimental results obtained in marine environments demonstrate the efficacy and tracking performance of the proposed approach.
feature selection for ordinal regression. ordinal regression (also known as ordinal classification) is a supervised learning task that consists of automatically determining the implied rating of a data item on a fixed, discrete rating scale. this problem is receiving increasing attention from the sentiment analysis and opinion mining community, due to the importance of automatically rating increasing amounts of product review data in digital form. as in other supervised learning tasks such as (binary or multiclass) classification, feature selection is needed in order to improve efficiency and to avoid overfitting. however, while feature selection has been extensively studied for other classification tasks, is has not for ordinal regression. in this paper we present four novel feature selection metrics that we have specifically devised for ordinal regression, and test them on two datasets of product review data.
xtemplate 3.0: adding semantics to hypermedia compositions and providing document structure reuse. hypermedia composite templates define generic structures of nodes and links that can be reused in different document compositions. the xtemplate language is an xml-based solution for defining composite templates for hypermedia documents in order to embed semantics into a composition that does not have it in prior. the use of templates intend to facilitate the authoring of interactive applications in digital tv systems, as long as iptv systems. xtemplate 3.0 extends the previous xtemplate versions, incorporating new features to the language and increasing its expressiveness. as an application of xtemplate, this work extends ncl (nested context language) with xtemplate, adding semantics to ncl contexts and providing document structure reuse.
semiotic inspection method in the context of educational simulation games. software process simulation is increasingly being used as an approach for analyzing complex business, for supporting management planning, for helping with software process training and learning and for supporting the software process improvement. in addition to providing to users a simulation tool that supports all these aspects, it is also important to consider some other requirements during the tool's design, such as efficient and effective communication of the designer's message to the user. in this way, we show how semiotic concepts can be used in the analysis and generation of knowledge through the application of the semiotic inspection method (sim), a semiotic engineering evaluation method. in this paper we present a scientific application of sim to a software engineering simulation game focusing the analysis on feedback aspects and issues. the results go beyond the system analyzed and point to considerations regarding simulation games used in educational contexts.
a formal approach to reuse successful traceability practices in spl projects. software product line (spl) engineering has to deal with interrelated, complex models such as feature and architecture models, hence traceability is fundamental to keep them consistent. commonly, a traceability schema must be started from scratch from project to project. to avoid that, useful traceability practices to solve day to day problems should be modeled explicitly and kept as part of the traceability knowledge gained, and then organizations can reduce time and effort in implementing traceability in new projects. this paper presents an approach for formalizing and reusing traceability practices in spl engineering. using this formalization approach a traceability metamodel is defined, incorporating the particular traceability practices performed in spl engineering. customized traceability methodologies for spl projects will be systematically and formally generated from this metamodel. these resulting methodologies will have already incorporated the traceability knowledge proven as successful in previous projects, facilitating the reuse of such practices. in this paper, we focus specifically on the product derivation process, to show the advantages of this formalization approach to reuse traceability knowledge.
towards semantic-based adaptation decisions for context-aware mobile computing. this paper studies the problem of adaptation decisions for context-aware mobile computing. in most context-aware systems to date, context-aware adaptation decisions are made by developers during the design-time or compile-time. such approaches inevitably place an immense load on developers, especially in an extremely dynamic environment like mobile computing, to anticipate, formulate, and maintain adaptation rules. in this paper, we explore automated context-aware adaptation decisions at run-time to get around this problem. the resulting middleware system, campus, utilizes ontologies to capture the underlying semantics of involved entities, and perform dl and fol reasoning on these ontologies to automatically derive adaptation decisions at run-time. the campus implementation has been evaluated with a number of case studies. the results are significant in that they show that campus can greatly reduce the efforts required to developed context-aware mobile application without significant degradation in its performance.
an adaptive information extraction system based on wrapper induction with pos tagging. information extraction (ie) performs two important tasks: identifying certain pieces of information from documents and storing them for future use. this work proposes an adaptive ie system based on boosted wrapper induction (bwi), a supervised wrapper induction algorithm. however, some authors have shown that boosting techniques face difficulties during the processing of natural language texts. this fact became the rationale for coupling parts-of-speech tagging with the bwi algorithm in our proposed system. in order to evaluate its performance, several experiments were carried out on three standard corpora. the results obtained suggest that the union of pos tagging and bwi offers a small gain of 3--5% of performance over the original bwi algorithm for unstructured texts. these results position our system among the very best similar ie systems endowed with pos tagging, according to a comparison presented and discussed in the article.
detecting narrow passages in configuration spaces via spectra of probabilistic roadmaps. in this paper, we explore the connection between the spectral properties of a configuration space with those of the underlying probabilistic road map. we explore this relationship in a simple motion planning example which leads to a new method of characterizing narrow passages using the so called graph laplacian of the prm.
reactive parallel processing for synchronous dataflow. the control flow of common processors does not match the specific needs of reactive systems. key issues for these systems are preemption and concurrency, combined with timing predictability. to model reactive systems, synchronous programming languages are well-suited, which can be either synthesized to hardware or compiled to c and run on a normal processor. both of these approaches have significant drawbacks: the generation of hardware is inflexible, the timing analysis of the generated c code is complicated. we propose a special parallel processor, designed to execute programs written in the synchronous dataflow language lustre, or its graphical variant scade. this approach achieves an efficient but still predictable execution. we introduce the processor as well as compiler from lustre and scade. to validate our approach, we compare a prototype of the processor, running on an fpga, with a microblaze processor that executes c code generated from lustre programs.
using logic-based reduction for adversarial component recovery. a current means to protect intellectual property embedded in both circuits and software involves creating a functionally equivalent variant with subjective qualities related to difficulty of reverse engineering. in this paper, we consider the problem of protection in a smaller, generalized class of programs based on boolean logic primitives. we consider boolean logic reduction as one means to quantify hardness of undoing structural transformations designed to impede reverse engineering. we detail our experiences in using both commercial synthesis tools and organic red-team tools that simplify transformations using known basic logic patterns. using simple component recovery on candidate circuits, we show how specific variation methods impact adversarial analysis and posit relationships between specific transformations and corresponding difficulty of reversal.