ppxscal/embedding_network huggingface dataset:
Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
Venue: Human Language Technology Conference (2009)

Welcome to the workshop on Active Learning for Natural Language Processing! We started organizing this workshop in mid-2008 after strong encouragement in response to some of our own work in the area. As we gathered members of the program committee, the timeliness of the topic resonated with several of them: the growing body of knowledge on active learning and on active learning for NLP in particular makes this topic one worth exploring in a focused workshop rather than in isolated papers in occasional, far-flung conferences. Labeled data is a prerequisite for many popular algorithms in natural language processing and machine learning. While it is possible to obtain large amounts of annotated data for well-studied languages in well-studied domains and well-studied problems, labeled data are rarely available for less common languages, domains, or problems. Unfortunately, obtaining human annotations for linguistic data is labor-intensive and typically the costliest part of the acquisition of an annotated corpus. It has been shown before that active learning can be employed to reduce annotation costs but not at the expense of quality. While diverse work over the past decade has demonstrated the possible advantages of active learning for corpus annotation and NLP applications, active learning is not widely used in many ongoing data annotation tasks. Much of the machine learning literature on the topic has focused on active learning for classification problems with less attention devoted to the kinds of problems encountered in NLP. Related topics such as distributed "human computation", cost-sensitive machine learning, and semi-supervised learning of all kinds are also growing in number as we search for the best ways to overcome the data acquisition bottleneck. We were interested in bringing together researchers to explore the challenges and opportunities of active learning for NLP tasks, language acquisition, and language learning, and we have been rewarded with excellent submissions and a promising program. The workshop received sixteen submissions, eight of which are included in the final program. Two of the accepted papers are short papers which address ongoing work and pertinent issues. We hope that this gathering and these proceedings begin to shed more light on active learning for NLP classification tasks, sequence labeling, parsing, semantics, and other more complex tasks. The papers in the program also begin to address issues involving the application of active learning in real annotation projects.
Feature Clustering for Data Steering in Dynamic Data Driven Application Systems
Authors: Alec Pawling, Greg Madey
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

In this paper, we describe how feature clustering on real-world cell-phone data can be used to locate the impact area of emergency events. We first examine the effect of two emergency events on the call activity in the areas surrounding the events. We then investigate how the time series of the affected areas behave relative to the time series of their respective neighboring areas. Finally, we examine the differences in hierarchical clusterings of the time series of the affected areas and neighboring areas.
Improving software comprehension in regulating safety-critical systems
Authors: S. Purushothaman Iyer
Venue:  (2008)

One primary concern regarding software employed in safety-critical systems is that it should not endanger end users of these systems. Regulatory agencies have been given the task of ensuring that software, when contained in safety-critical systems, conforms to a set of stringent standards. Moreover, these agencies are required to comprehend a piece of software, in the event of its failure, to identify causes of the failure. Software comprehension is, thus, needed to assess software safety and assign blame for a reported software failure. A number of challenges in regulatory practices prevent regulators from achieving a fast software comprehension: (1) There is lack of a good communication channel between regulators and manufacturers, forcing regulators to acquire knowledge of applicant systems from poorly-organized documentation; (2) Traditional program comprehension techniques, such as static slicing, demand that regulators have intimate knowledge of the software. Moreover, when the code is large in size, programming slicing usually yields a slice that is too large to be understood; (3) Given a reported failure, current post-mortem analysis techniques cannot assist regulators to choose wisely on the range of code that contains the software defect causing the reported failure. Therefore, regulators have to perform costly comprehension on the entire system to trace the failure. In this thesis, we present how the accuracy and efficiency of software comprehension can be improved by answering the above-mentioned challenges. First of all, we present a structured, graphical model, called Goal Graph, to improve communication between regulators and vendors. Our case study on applying Goal Graph to the class of generic large-volume infusion pumps demonstrates that the model is flexible and expressive for realistic regulation activities. Secondly, we present a method integrating abstraction and slicing to ameliorate the problem of understanding large slices. The method allows iterative invocations of abstraction and slicing, allows automatic generation of abstraction criteria, allows continuous slicing, and allows a better visualization of abstract executions. Our case study illustrates that effort is reduced in post-mortem analysis when slicing and abstraction are combined. Lastly, we propose a framework to utilize error reports to help regulators localize a failure to a specific portion of code. In this framework, we present solutions to both formalizing error reports and identifying execution sequences that satisfy the error report. Moreover, we devise an algorithm to derive slicing criteria automatically, allowing regulators to use slicing without any knowledge of the system being analyzed. We finally present a case study involving a medical device that illustrates how these automatic techniques can aid program comprehension.
Microanalysis of Mathematical Discourse in a Quasi-Synchronous Chat Environment
Authors: Juan Dee Wee, Chee-Kit Looi
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This paper describes a methodology for microanalysing mathematical discourse in an online collaborative environment. A model is constructed to represent the discourse by linking contributions through uptakes to illustrate the flow of knowledge construction. A framework of analysis of the model is designed to illustrate the moment-to-moment interaction between participants as they collaboratively reason about and manipulate mathematical symbols, concepts and formulas in the form of textual representations. The analysis includes the description of the shift of focus in the discourse leading to the emergence of stages. The key motivation behind this paper is to develop a structure for analysing collaborative learning. More importantly, this methodology uses a holistic approach to understand the process of meaning-making embedded in interactions between chat textual representations.
Linear Programming Based Approximation Algorithms for Feedback Set Problems in Bipartite Tournaments
Authors: Anke Zuylen
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

We consider the feedback vertex set and feedback arc set problems in bipartite tournaments. We improve on recent results by giving a 2-approximation algorithm for the feedback vertex set problem. We show that this result is the best we can attain when using a certain linear program as the lower bound on the optimal value. For the feedback arc set problem in bipartite tournaments, we show that a recent 4-approximation algorithm proposed by Gupta [5,6] is incorrect. We give an alternative 4-approximation algorithm based on an algorithm for feedback arc set in (regular) tournaments in [10,11].
Open Learner Models: Opinions of School Education Professionals
Authors: Alice Kerly, Susan Bull
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

This work surveys the opinions of teachers and other education professionals regarding the potential for Open Learner Models (OLM) in UK schools. We describe the aims of OLM, and of current UK initiatives involving formative assessment and promotion of metacognitive skills. We conclude that UK education professionals appreciate a synergy of these approaches, and that OLM-based systems could be valuable in achieving educational aims in schools.
Robust Detection and Tracking of Multiple Moving Objects with 3D Features by an Uncalibrated Monocular Camera
Authors: Ho Shan Poon, Fei Mai, Yeung Sam Hung, Graziano Chesi
Venue: Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques (2009)

This paper presents an algorithm for detecting multiple moving objects in an uncalibrated image sequence by integrating their 2D and 3D information. The result describes the moving objects in terms of their number, relative position and motion. First, the objects are represented by image feature points, and the major group of point correspondences over two consecutive images is established by Random Sample Consensus (RANSAC). Then, their corresponding 3D points are reconstructed and clustering is performed on them to validate those belonging to the same object. This process is repeated until all objects are detected. This method is reliable on tracking multiple moving objects, even with partial occlusions and similar motions. Experiments on real image sequences are presented to validate the proposed algorithm. Applications of interest are video surveillance, augmented reality, robot navigation and scene recognition.
A 3D scanning system for biomedical purposes
Authors: B. D. Bradley, A. D. C. Chan, M. J. D. Hayes
Venue: International Journal of Advanced Media and Communication (2009)

The use of three-dimensional (3D) scanning systems for acquiring the external shape features of biological objects has recently been gaining popularity in the biomedical field. A simple, low cost, 3D scanning system is presented, which employs the laser light-sectioning technique for data acquisition. A Direct Linear Transformation least squares algorithm is used for camera calibration and Elliptical Fourier Descriptors (EFDs) are used for data smoothing and planar section reconstruction. Results for an experiment demonstrating the validity of the EFD approach are presented. Overall, results presented for three objects scanned with the proposed system demonstrate the validity of the chosen approach. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9 10 May 2008, Ottawa, ON, Canada.
A web based framework for rapid integration of enterprise applications
Authors: Nassim Laga, Emmanuel Bertin, Noel Crespi
Venue: Proceedings of the 2009 international conference on Pervasive services (2009)

In pervasive environment, users access their enterprise applications using heterogeneous devices. However accessing complex applications is time consuming on devices with limited capabilities. Moreover, the communication between these applications is a frequent and valueless action which is currently managed by the end-user himself, by getting data from an application and putting it into another. In this paper we propose a web based framework for application integration. This framework first hides the heterogeneity of accessing devices from the service providers and then facilitates the usage of enterprise applications, by enabling simple communication (e.g. by drag&drop) between independent and heterogeneous services. We rely within this framework on the widget concept, where a widget gives access to a single functionality of an enterprise application. This enables to reuse the widget user interfaces in various contexts.
A Matrix Handbook for Statisticians, 1st edition
Authors: George A. F. Seber
Venue:  (2007)

A comprehensive, must-have handbook of matrix methods with a unique emphasis on statistical applications This timely book, A Matrix Handbook for Statisticians, provides a comprehensive, encyclopedic treatment of matrices as they relate to both statistical concepts and methodologies. Written by an experienced authority on matrices and statistical theory, this handbook is organized by topic rather than mathematical developments and includes numerous references to both the theory behind the methods and the applications of the methods. A uniform approach is applied to each chapter, which contains four parts: a definition followed by a list of results; a short list of references to related topics in the book; one or more references to proofs; and references to applications. The use of extensive cross-referencing to topics within the book and external referencing to proofs allows for definitions to be located easily as well as interrelationships among subject areas to be recognized. A Matrix Handbook for Statisticians addresses the need for matrix theory topics to be presented together in one book and features a collection of topics not found elsewhere under one cover. These topics include: * Complex matrices * A wide range of special matrices and their properties * Special products and operators, such as the Kronecker product * Partitioned and patterned matrices * Matrix analysis and approximation * Matrix optimization * Majorization * Random vectors and matrices * Inequalities, such as probabilistic inequalities Additional topics, such as rank, eigenvalues, determinants, norms, generalized inverses, linear and quadratic equations, differentiation, and Jacobians, are also included. The book assumes a fundamental knowledge of vectors and matrices, maintains a reasonable level of abstraction when appropriate, and provides a comprehensive compendium of linear algebra results with use or potential use in statistics. A Matrix Handbook for Statisticians is an essential, one-of-a-kind book for graduate-level courses in advanced statistical studies including linear and nonlinear models, multivariate analysis, and statistical computing. It also serves as an excellent self-study guide for statistical researchers. 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
Investigation of the accuracy of search engine hit counts
Authors: Ahmet Uyar
Venue: Journal of Information Science (2009)

This study investigates the accuracy of search engine hit counts for search queries. We investigate the accuracy of hit counts for Google, Yahoo and Microsoft Live Search, and the accuracy of single and multiple term queries. In addition, we investigate the consistency of hit count estimates for 15 days. The results show that all three provide estimates for the number of matching documents and the estimation patterns of their counting algorithms differ greatly. The accuracy of hit counts for multiple word queries has not been studied before. The results of our study show that the number of words in queries affects the accuracy of estimations significantly. The percentages of accurate hit count estimations are reduced almost by half when going from single word to two word query tests in all three search engines. With the increase in the number of query words, the error in estimation increases and the number of accurate estimations decreases.
System-on-Chip Test Architectures
Authors: Wang
Venue: Systems On Silicon (2007)

Modern electronics testing has a legacy of more than 40 years. The introduction of new technologies, especially nanometer technologies with 90nm or smaller geometry, has allowed the semiconductor industry to keep pace with the increased performance-capacity demands from consumers. As a result, semiconductor test costs have been growing steadily and typically amount to 40% of today?s overall product cost. This book is a comprehensive guide to new VLSI Testing and Design-for-Testability techniques that will allow students, researchers, DFT practitioners, and VLSI designers to master quickly System-on-Chip Test architectures, for test debug and diagnosis of digital, memory, and analog/mixed-signal designs. KEY FEATURES* Emphasizes VLSI Test principles and Design for Testability architectures, with numerous illustrations/examples.* Most up-to-date coverage available, including Fault Tolerance, Low-Power Testing, Defect and Error Tolerance, Network-on-Chip (NOC) Testing, Software-Based Self-Testing, FPGA Testing, MEMS Testing, and System-In-Package (SIP) Testing, which are not yet available in any testing book.* Covers the entire spectrum of VLSI testing and DFT architectures, from digital and analog, to memory circuits, and fault diagnosis and self-repair from digital to memory circuits.* Discusses future nanotechnology test trends and challenges facing the nanometer design era; promising nanotechnology test techniques, including Quantum-Dots, Cellular Automata, Carbon-Nanotubes, and Hybrid Semiconductor/Nanowire/Molecular Computing.* Practical problems at the end of each chapter for students.
Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
Authors: Xue-Cheng Tai, Chunlin Wu
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

In the recent decades the ROF model (total variation (TV) minimization) has made great successes in image restoration due to its good edge-preserving property. However, the non-differentiability of the minimization problem brings computational difficulties. Different techniques have been proposed to overcome this difficulty. Therein methods regarded to be particularly efficient include dual methods of CGM (Chan, Golub, and Mulet) [7] Chambolle [6] and split Bregman iteration [14], as well as splitting-and-penalty based method [28] [29]. In this paper, we show that most of these methods can be classified under the same framework. The dual methods and split Bregman iteration are just different iterative procedures to solve the same system resulted from a Lagrangian and penalty approach. We only show this relationship for the ROF model. However, it provides a uniform framework to understand these methods for other models. In addition, we provide some examples to illustrate the accuracy and efficiency of the proposed algorithm.
Malyzer: Defeating Anti-detection for Application-Level Malware Analysis
Authors: Lei Liu, Songqing Chen
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

Malware analysis is critical for malware detection and prevention. To defeat malware analysis and detection, today malware commonly adopts various sophisticated anti-detection techniques, such as performing debugger, emulator, and virtual machine fingerprinting, and camouflaging its traffic as normal legitimate traffic. These mechanisms produce more and more stealthy malware that greatly challenges existing malware analysis schemes.In this work, targeting application level stealthy malware, we propose Malyzer, the key of which is to defeat malware anti-detection mechanisms at startup and runtime so that malware behavior during execution can be accurately captured and distinguished. For analysis, Malyzer always starts a copy, referred to as a shadow process, of any suspicious process on the same host by defeating all startup anti-detection mechanisms employed in the process. To defeat internal runtime anti-detection attempts, Malyzer further makes this shadow process mutually invisible to the original suspicious process. To defeat external anti-detection attempts, Malyzer makes as if the shadow process runs on a different machine to the outside. Since ultimately malware will conduct local information harvesting or dispersion, Malyzer constantly monitors the shadow process's behavior and adopts a hybrid scheme for its behavior analysis. In our experiments, Malyzer can accurately detect all malware samples that employ various anti-detection techniques.
Structural coverage criteria for LUSTRE-SCADE programs
Authors: Abdesselam Lakehal, Ioannis Parissis
Venue: Software Testing, Verification Reliability (2009)

LUSTRE is a data-flow synchronous language, on which is based the SCADE tool-suite, widely used for specifying and programming critical reactive applications in the areas of avionics, energy or transport. Therefore, testing LUSTRE programs, that is, generating test data and assessing the achieved test coverage, is a major issue. Usual control-flow-based test coverage criteria (statement coverage, branch coverage, etc.) are not relevant for LUSTRE programs. In this paper, a new hierarchy of adequacy criteria tailored to the LUSTRE language is presented. These criteria are defined on operator networks, which are usual models for LUSTRE programs. The criteria satisfaction measure is automated in LUSTRUCTU, a non-intrusive tool (no instrumentation of the code), based on the symbolic computation of path activation conditions. The applicability and the relevance of the criteria are assessed on a case study. Copyright &copy; 2008 John Wiley & Sons, Ltd. SCADE is a Registered Trademark of ESTEREL TECHNOLOGIES.
Particle swarm optimization based multi-prototype ensembles
Authors: Ammar Mohemmed, Mark Johnston, Mengjie Zhang
Venue: Proceedings of the 11th Annual conference on Genetic and evolutionary computation (2009)

This paper proposes and evaluates a Particle Swarm Optimization (PSO) based ensemble classifier. The members of the ensemble are Nearest Prototype Classifiers generated sequentially using PSO and combined by a majority voting mechanism. Two necessary requirements for good performance of an ensemble are accuracy and diversity of error. Accuracy is achieved by PSO minimizing a fitness function representing the error rate as the members are created. The diversity of error is promoted by using a different initialization of PSO each time to create a new member and by adopting decorrelated training where a penalty term is added to the fitness function to penalize particles that make the same errors as previously generated classifiers. Simulation experiments on different classification problems show that the ensemble has better performance than a single classifier and are effective in generating diverse ensemble members.
First Steps Towards the Automatic Construction of Argument-Diagrams from Real Discussions
Authors: Daan Verbree, Rutger Rienks, Dirk Heylen
Venue: Proceeding of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006 (2006)

This paper presents our efforts to create argument structures from meeting transcripts automatically. We show that unit labels of argument diagrams can be learnt and predicted by a computer with an accuracy of 78,52% and 51,43% on an unbalanced and balanced set respectively. We used a corpus of over 250 argument diagrams that was manually created by applying the Twente Argument Schema. In this paper we also elaborate on this schema and we discuss applications and the role we foresee the diagrams to play.