ppxscal/embedding_network huggingface dataset:
A Methodology to Characterize Critical Section Bottlenecks in DSM Multiprocessors
Authors: Benjamín Sahelices, Pablo Ibáñez, Víctor Viñals, J. M. Llabería
Venue: Proceedings of the 15th International Euro-Par Conference on Parallel Processing (2009)

Understanding and optimizing the synchronization operations of parallel programs in distributed shared memory multiprocessors (dsm), is one of the most important factors leading to significant reductions in execution time.This paper introduces a new methodology for tuning performance of parallel programs. We focus on the critical sections used to assure exclusive access to critical resources and data structures, proposing a specific dynamic characterization of every critical section in order to a) measure the lock contention, b) measure the degree of data sharing in consecutive executions, and c) break down the execution time, reflecting the different overheads that can appear. All the required measurements are taken using a multiprocessor simulator with a detailed timing model of the processor and memory system.We propose also a static classification of critical sections that takes into account how locks are associated with their protected data. The dynamic characterization and the static classification are correlated to identify key critical sections and infer code optimization opportunities (e.g. data layout), which when applied can lead to significant reductions in execution time (up to 33 % in the SPLASH-2 scientific benchmark suite). By using the simulator we can also evaluate whether the performance of the applied code optimizations is sensitive to common hardware optimizations or not.
Improving Search in P2P by Location Identification
Authors: Lei Xiao, Liangzhong Shen, ZhongYi Hu, Fei Zhou
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01 (2009)

P2P networks is an important part of the next generation of Internet, so how to search the resources in the P2P networks efficiently is the key problem of the realization of the P2P network. While studying the search technology of different mechanisms, this paper proposes a method to improve the search by the identification of the location, which processes the search request in advance according to the positional linked list and achieve the aim of reducing the average distance and the average requesting number of P2P network information search.
Preliminary results of the effects of interactive computer labs
Authors: Frederick Butler, Melanie Butler
Venue: Journal of Computing Sciences in Colleges (2009)

This paper details a research study on computer laboratory assignments designed for Liberal Arts Math. Preliminary results are mixed: students performed better on problem-solving assessments when given an opportunity to complete the assignments in a monitored setting, but performance was weaker on some course assessments. Statistical analyses are included.
Task-based dialog management using an agenda
Authors: Wei Xu, Alexander I. Rudnicky
Venue: Proceedings of the ANLP-NAACL 2000 Workshop on Conversational Systems (2000)

Dialog management addresses two specific problems: (1) providing a coherent overall structure to interaction that extends beyond the single turn, (2) correctly managing mixed-initiative interaction. We propose a dialog management architecture based on the following elements: handlers that manage interaction focussed on tightly coupled sets of information, a product that reflects mutually agreed-upon information and an agenda that orders the topics relevant to task completion.
UNDERTOW: multi-level segmentation of real-valued time series
Authors: Tom Armstrong, Tim Oates
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

The discovery of meaningful change points, finding segments, in both categorical and real-value data time series is a well-studied problem. Prior segmentation algorithms and tasks operate under overly restrictive assumptions (e.g., a priori knowledge of the number of segments, trivial inputs) and in singular domains (e.g., finding common regions in images, speaker change detection). We introduce a domain-independent algorithm, UNDERTOW, which discovers segment boundaries in real-valued time series and constructs hierarchies of segments to form macro segments.
Comparison of Cluster Ensembles Methods Based on Hierarchical Clustering
Authors: Kai Li, Lan Wang, Lifeng Hao
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Cluster ensembles method is considered as a robust and accurate alternative to single clustering runs. It mainly consists of both generation of individual member and fusion methods. In this paper, we study the cluster ensembles where individual members are obtained based on k-means clustering algorithm and fusion method of hierarchical clustering is used. Three consensus functions, which are single linkage, complete linkage and average linkage, respectively, is studied and discussed in hierarchical clustering fusion. For evaluating performance of cluster ensembles, adjusted rand index is considered. Experimental results show that performance of cluster ensembles with the average linkage is superior to one with single linkage and complete linkage. Moreover, we also study the relationship between accuracy and ensemble size of the three methods.
Semantic Web Usage Mining: Using Semantics to Understand User Intentions
Authors: Till Plumbaum, Tino Stelter, Alexander Korth
Venue: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH (2009)

In this paper, we present a novel approach to track user interaction on a web page based on JavaScript-events combined with the Semantic Web standard Microformats to obtain more fine-grained and meaningful user information. Today's user tracking solutions are mostly page-based and lose valuable information about user interactions. To get an in-depth understanding of user's interests and intentions from observing him while interacting on a website, interaction data needs to be tracked on an event rather than on a page basis enhanced with semantic knowledge to understand the user intention. Our goal is to create an easy-to-integrate user tracker that is capable of collecting tracking information of configurable depth and feeding a highly sophisticated user model needed to provide personalized services such as recommendation and search.
Augmenting interactive tables with mice & keyboards
Authors: Björn Hartmann, Meredith Ringel Morris, Hrvoje Benko, Andrew D. Wilson
Venue: Proceedings of the 22nd annual ACM symposium on User interface software and technology (2009)

This note examines the role traditional input devices can play in surface computing. Mice and keyboards can enhance tabletop technologies since they support high fidelity input, facilitate interaction with distant objects, and serve as a proxy for user identity and position. Interactive tabletops, in turn, can enhance the functionality of traditional input devices: they provide spatial sensing, augment devices with co-located visual content, and support connections among a plurality of devices. We introduce eight interaction techniques for a table with mice and keyboards, and we discuss the design space of such interactions.
Cross-Checking - Enhanced Over-Approximation of the Reachable Global State Space of Component-Based Systems
Authors: Mila Majster-Cederbaum, Christoph Minnameier
Venue: Proceedings of the 3rd International Workshop on Reachability Problems (2009)

State space explosion causes most relevant behavioral questions for component-based systems to be PSPACE-hard. Here, we exploit the structure of component-based systems to obtain a first approximation of the reachable global state space. In order to improve this approximation we introduce a new technique we call cross-checking. The resulting approximation can be used to study global properties of component-based systems, which we demonstrate here for local deadlock-freedom.
Approximating OWL-DL ontologies
Authors: Jeff Z. Pan, Edward Thomas
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

Efficient query answering over ontologies is one of the most useful and important services to support Semantic Web applications. Approximation has been identified as a potential way to reduce the complexity of query answering over OWL DL ontologies. Existing approaches are mainly based on syntactic approximation of ontological axioms and queries. In this paper, we propose to recast the idea of knowledge compilation into approximating OWL DL ontologies with DL-Lite ontologies, against which query answering has only polynomial data complexity. We identify a useful category of queries for which our approach guarantees also completeness. Furthermore, this paper reports on the implementation of our approach in the ONTOSEARCH2 system and preliminary, but encouraging, benchmark results which compare ONTOSEARCH2's response times on a number of queries with those of existing ontology reasoning systems.
Effective DDoS Attacks Detection Using Generalized Entropy Metric
Authors: Ke Li, Wanlei Zhou, Shui Yu, Bo Dai
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

In information theory, entropies make up of the basis for distance and divergence measures among various probability densities. In this paper we propose a novel metric to detect DDoS attacks in networks by using the function of order ¿ of the generalized (Rényi) entropy to distinguish DDoS attacks traffic from legitimate network traffic effectively. Our proposed approach can not only detect DDoS attacks early (it can detect attacks one hop earlier than using the Shannon metric while order ¿=2, and two hops earlier to detect attacks while order ¿=10.) but also reduce both the false positive rate and the false negative rate clearly compared with the traditional Shannon entropy metric approach.
CLINICAL SURFACES --- Activity-Based Computing for Distributed Multi-Display Environments in Hospitals
Authors: Jakob E. Bardram, Jonathan Bunde-Pedersen, Afsaneh Doryab, Steffen Sørensen
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

A multi-display environment (MDE) is made up of co-located and networked personal and public devices that form an integrated workspace enabling co-located group work. Traditionally, MDEs have, however, mainly been designed to support a single "smart room", and have had little sense of the tasks and activities that the MDE is being used for. This paper presents a novel approach to support activity-based computing in distributed MDEs, where displays are physically distributed across a large building. CLINICAL SURFACES was designed for clinical work in hospitals, and enables context-sensitive retrieval and browsing of patient data on public displays. We present the design and implementation of CLINICAL SURFACES, and report from an evaluation of the system at a large hospital. The evaluation shows that using distributed public displays to support activity-based computing inside a hospital is very useful for clinical work, and that the apparent contradiction between maintaining privacy of medical data in a public display environment can be mitigated by the use of CLINICAL SURFACES.
Hybrid Model Based on Artificial Immune System and PCA Neural Networks for Intrusion Detection
Authors: Yu-ping Zhou
Venue: Proceedings of the 2009 Asia-Pacific Conference on Information Processing - Volume 01 (2009)

Intrusion detection systems (IDS) are developing very rapid in recent years. But most traditional IDS can only detect either misuse or anomaly attacks. In this paper, we propose a method combining artificial immune technique and Principal Components Analysis (PCA) neural networks to construct an intrusion detection model capable of both anomaly detection and misuse detection. Initially an artificial immune system detects anomalous network connections. In order to attain more detailed information about an intrusion, PCA is applied for classification and neural networks are used for online computing. The experiments and evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection dataset, which have information on computer network, during normal behavior and intrusive behavior. Results indicate the high detection accuracy for intrusion attacks and low false alarm rate of the reliable system.
N-gram-based statistical machine translation versus syntax augmented machine translation: comparison and system combination
Authors: Maxim Khalilov, José A. R. Fonollosa
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

In this paper we compare and contrast two approaches to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Machine Translation (SMT). SAMT is a hierarchical syntax-driven translation system underlain by a phrase-based model and a target part parse tree. In N-gram-based SMT, the translation process is based on bilingual units related to word-to-word alignment and statistical modeling of the bilingual context following a maximum-entropy framework. We provide a step-by-step comparison of the systems and report results in terms of automatic evaluation metrics and required computational resources for a smaller Arabic-to-English translation task (1.5M tokens in the training corpus). Human error analysis clarifies advantages and disadvantages of the systems under consideration. Finally, we combine the output of both systems to yield significant improvements in translation quality.
Combining clinical assessment scores and in vivo MR spectroscopy neurometabolites in very low birth weight adolescents
Authors: Tone F. Bathen, Gro C. Christensen Løhaugen, Ann-Mari Brubakk, Ingrid S. Gribbestad, David E. Axelson, Jon Skranes
Venue: Artificial Intelligence in Medicine (2009)

Objective: Very low birth weight (VLBW) survivors are at increased risk of neurological impairments that may persist into adolescence and adulthood. The aims of this study were to identify the most important clinical assessments that characterize differences between VLBW and control adolescents, and to look at the relationship between clinical assessments and the metabolites in in vivo MR spectra. Methods: At 14-15 years of age, 54 VLBW survivors and 64 term controls were examined clinically. Several neuropsychological and motor assessments were performed. The magnetic resonance (MR) brain spectra were acquired from volumes localized in the left frontal lobe and contained mainly white matter. Results: Probabilistic neural networks and support vector machines demonstrated that clinical assessments rendered a possibility of the classification of VLBW versus control adolescents. The most important clinical assessments in this classification were visual-motor integration, motor coordination, stroop test, full scale IQ, and grooved pegboard. Through the use of outer product analysis-partial least squares discriminant analysis on a subset of adolescents (n=36), the clinical assessments found to most strongly correlate with the spectral data were the global assessment scale, Wisconsin card sorting test, full scale IQ, grooved pegboard test, and motor coordination test. Clinical assessments that relate to spectral data may be especially dependent on an intact microstructure in frontal white matter.
Impact of Loop Tiling on the Controller Logic of Acceleration Engines
Authors: Hritam Dutta, Jiali Zhai, Frank Hannig, Jürgen Teich
Venue: Proceedings of the 2009 20th IEEE International Conference on Application-specific Systems, Architectures and Processors (2009)

High computational effort in modern signal and image processing applications often demands for special purpose accelerators in a system on chip (SoC). New high level synthesis methodologies enable the automated design of such programmable or non-programmable accelerators. Loop tiling is a widely used transformation in such methodologies for dimensioning of such accelerators in order to match inherent massive parallelism of considered algorithms with available functional units and processor elements. Innately, the applications are data-flow dominant and have almost no control flow, but the application of tiling techniques has the disadvantage of a more complex control and communication flow. In this paper, we present a methodology for the automatic generation of the control engines of such accelerators. The controller orchestrates the data transfer and computation. The effect of tiling on area, latency, and power overhead of the controller is studied in detail. It is shown that the controller has a substantial overhead of up to 50% in for different tiling and throughput parameters. The energy-delay product is also used as a metric for identifying optimal accelerator designs.
The Emotion Mechanism of Artificial Life Fight Behavior
Authors: Guofeng Zhang, Zushu Li
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Applying Emotion Behavior Selection Mechanism to Dom World fight mode, a fight behavior mechanism based on emotions is built to discover how the result of a former fight influences, in emotion pattern, the decision and achievement of a letter fight. On Swarm soft platform of Artificial Life, consulting to the design and data of Dom Word simulation, a simulation of the proposed mechanism has been conducted. The results of the simulation confirm the present mechanism, by centering on the exhibition of self-organization (emergence), a classical characteristic of Artificial Society. Adopting ADI and Netto to evaluate the stimulation results of these two mechanisms, it proved that the present excels Dom World mode. And more, by comparing and analyzing the results of simulations including and excluding feedback emotion, it’s found that feedback mechanism increases the intensity of fight, which supports Darwin’s hypothesis that emotion speeds up the natural selection.
Multi-view and Multi-scale Recognition of Symmetric Patterns
Authors: Dereje Teferi, Josef Bigun
Venue: Proceedings of the 16th Scandinavian Conference on Image Analysis (2009)

This paper suggests the use of symmetric patterns and their corresponding symmetry filters for pattern recognition in computer vision tasks involving multiple views and scales. Symmetry filters enable efficient computation of certain structure features as represented by the generalized structure tensor (GST). The properties of the complex moments to changes in scale and multiple views including in-depth rotation of the patterns and the presence of noise is investigated. Images of symmetric patterns captured using a low resolution low-cost CMOS camera, such as a phone camera or a web-cam, from as far as three meters are precisely localized and their spatial orientation is determined from the argument of the second order complex moment I 20 without further computation.
Extracting relevant snippets for web navigation
Authors: Qing Li, K. Selçuk Candan, Qi Yan
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

Search engines present fix-length passages from documents ranked by relevance against the query. In this paper, we present and compare novel, language-model based methods for extracting variable length document snippets by real-time processing of documents using the query issued by the user. With this extra level of information, the returned snippets are considerably more informative. Unlike previous work on passage retrieval which relies on searching relevant segments for filtering of preoccupied passages, we focus on query-informed segmentation to extract context-aware relevant snippets with variable length. In particular, we show that, when informed through an appropriate relevance language model, curvature analysis and Hidden Markov model (HMM) based content segmentation techniques can facilitate to extract relevant document snippets.
Research and Design of Internet Public Opinion Analysis System
Authors: Quanlong Guan, Saizhi Ye, Guoxiang Yao, Huanming Zhang, Linfeng Wei, Gazi Song, Kejing He
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Internet is becoming a spreading platform for the public opinion. It is important to grasp the internet public opinion in time and understand the trends of their opinion correctly. Text classification plays a fundamental role in a number of information management and retrieval tasks. But web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a system scheme for the analysis of the Internet public opinion (IPO).We apply web-page classification through summarization to extract the most relevant content from the Web pages and then pass them to standard text classification algorithms (NB or SVM). We comprehensive use text classification and text clustering algorithms, which have been shown to be efficient and effective for singly using. Through the result of the experiment,we have proved the superiority of the system system’s architecture in the system design.
Understanding and Applying Cryptography and Data Security, 1st edition
Authors: Adam J. Elbirt
Venue:  (2009)

A How-to Guide for Implementing Algorithms and ProtocolsAddressing real-world implementation issues, Understanding and Applying Cryptography and Data Security emphasizes cryptographic algorithm and protocol implementation in hardware, software, and embedded systems. Derived from the authors teaching notes and research publications, the text is designed for electrical engineering and computer science courses.Provides the Foundation for Constructing Cryptographic ProtocolsThe first several chapters present various types of symmetric-key cryptographic algorithms. These chapters examine basic substitution ciphers, cryptanalysis, the Data Encryption Standard (DES), and the Advanced Encryption Standard (AES). Subsequent chapters on public-key cryptographic algorithms cover the underlying mathematics behind the computation of inverses, the use of fast exponentiation techniques, tradeoffs between public- and symmetric-key algorithms, and the minimum key lengths necessary to maintain acceptable levels of security. The final chapters present the components needed for the creation of cryptographic protocols and investigate different security services and their impact on the construction of cryptographic protocols.Offers Implementation Comparisons By examining tradeoffs between code size, hardware logic resource requirements, memory usage, speed and throughput, power consumption, and more, this textbook provides students with a feel for what they may encounter in actual job situations. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player