ppxscal/embedding_network huggingface dataset:
The Legal Concepts and the Layman's TermsBridging the Gap through Ontology-Based Reasoning about Liability
Authors: Ronny van Laarschot, Wouter van Steenbergen, Heiner Stuckenschmidt, Arno R. Lodder, Frank van Harmelen
Venue: Proceeding of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference (2005)

The aim of the BEST-project is to support laymen in judging their legal position through intelligent disclosure of case-law in the area of Dutch tort law. A problem we have to face in this context is the discrepancy between the terminology laymen use to describe their case and the terminology found in legal documents. We address this problem by supporting users to describe their case in common sense terms taken from an ontology. We use logical reasoning to automatically determine law articles that are relevant for determining liability of parties in a case based on this description, thus bridging the gap between the laymen's description and the terminology relevant for certain articles that can be found in legal documents. We introduce the BEST-project and describe the ontology built for supporting case descriptions focussing on its use for automatically determining relevant articles of law.
Plenary lecture III: using robust outlier detection to identify possible flood events
Authors: Azami Zaharim
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

The least squares method has been widely used in time series forecasting and outlier detection. However, the method is not very efficient in identifying outliers because it suffers the masking effect. The aim of this study is to overcome the masking effect by implementing the robust least median squares method in outlier detection. To illustrate, we identified the possible outliers from sixty-one readings of the daily rainfall recorded at Kajang JPS telemetric station. The outliers are then categorized into innovational outlier and additive outlier. Results based on both techniques were compared and it is found that the least median squares method effectively unmasked the effect of outliers as compared to the least squares method.
SAS/STAT(R) 9.1 User's Guide: The FREQ Procedure
Venue:  (2009)

The FREQ procedure produces one-way to n-way frequency and contingency (crosstabulation) tables. For two-way tables, PROC FREQ computes tests and measures of association. For n-way tables, PROC FREQ provides stratified analysis by computing statistics across, as well as within, strata. This chapter was excerpted from the SAS/STAT 9.1 User's Guide.
Cost and benefit analysis of mediated enterprise search
Authors: Mingfang Wu, James A. Thom, Andrew Turpin, Ross Wilkinson
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

The utility of an enterprise search system is determined by three key players: the information retrieval (IR) system (the search engine), the enterprise users, and the service provider who delivers the tailored IR service to its designated enterprise users. Currently, evaluations of enterprise search have been focused largely on the IR system effectiveness and efficiency, only a relatively small amount of effort on the user's involvement, and hardly any effort on the service provider's role. This paper will investigate the role of the service provider. We propose a method that evaluates the cost and benefit for a service provider of using a mediated search engine - in particular, where domain experts intervene on the ranking of the search results from a search engine. We test our cost and benefit evaluation method in a case study and conduct user experiments to demonstrate it. Our study shows that: 1) by making use of domain experts' relevance assessments in search result ranking, the precision and the discount cumulated gain of ranked lists have been improved significantly (144% and 40% respectively); 2) the service provider gains substantial return on investment and higher search success rate by investing in domain experts' relevance assessments; and 3) the cost and benefit evaluation also indicates the type of queries to be selected from a query log for evaluating an enterprise search engine.
An approach to designing an unmanned helicopter autopilot using genetic algorithms and simulated annealing
Authors: Rafael A. Perez, Kimon Valavanis
Venue:  (2008)

This dissertation investigates the application of Genetic Algorithms (GA) and Simulated Annealing (SA) based search techniques to the problem of deriving an auto-pilot that can emulate a human operator or other controller flying a Small unmanned Helicopter (SH). A Helicopter is a type of Vertical Take Off and Landing Vehicle (VTOL). The maneuvers are none aggressive, mild maneuvers, that include u-turns, ascending spirals and other none extreme flight paths. The pilot of the helicopter is a Fuzzy logic Controller (FC) pilot; it is assumed that the pilot executes the maneuvers with skill and precision. The FC pilot is given set-points (points in space) that represent a path/flight maneuver and is expected to follow them as closely as possible. Input/Output data is then collected from the FC pilot executing maneuvers in real time. The collected data include control signals from the FC pilot to the SH and the resulting output signals from the SH that include time, x, y, z coordinates and yaw (the angle of the SH relative to the x, y axis). The Genetic Algorithm/Simulated Annealing based search algorithm attempts to generate a set of mathematical formulas that best map the collected data. The search algorithm presented in this dissertation was implemented in Java and has a JSP (Java Server Pages) graphical user interface. The results obtained show that the search technique developed; termed Genetic Algorithm/Simulated Annealing controller or (GA/SA) controller allows for the derivation of accurate SH control equations. The results include performance quantification of the algorithm in the derivation phase and the testing phase. Graphs are included; they demonstrate the accuracy and path data of the GA/SA controller as compared to the FC pilot and other controllers. The final results showing the formulas found are also included. A technique was also developed during this dissertation to encode the genetic strings that represent the candidate formulas during the search. This technique allowed the combination of strings to yield new formulas that are valid. The results can be used by other investigators to expand the complexity of the formulas generated during the search. The technique has advantages such as the ability to operate in open-loop conditions and is able to fly the SH without the need for set-point data and without the need for GPS or some other location determination technology. The technique may be used as a backup controller that can take over control of a helicopter in case the main controller is unable to function due to a GPS malfunction or another situation where accurate positioning data cannot be obtained.
An Embedded Fingerprints Classification System based on Weightless Neural Networks
Authors: V. Conti, C. Militello, F. Sorbello, S. Vitabile
Venue: Proceeding of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008 (2009)

Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases in Automatic Fingerprint Identification Systems (AFISs). The paper presents a new fast fingerprint classification module implementing on embedded Weightless Neural Network (RAM-based neural network). The proposed WNN architecture uses directional maps to classify fingerprint images in the five NIST classes (Left Loop, Right Loop, Whorl, Arch and Tented Arch) without anyone enhancement phase. Starting from the directional map, the WNN architecture computes the fingerprint classification rate. The proposed architecture is implemented on Celoxica RC2000 board employing a Xilinx Virtex-II 2v6000 FPGA and it is computationally few expensive regards execution time and used hardware resources. To validate the goodness of proposed classificator, three different tests have been executed on two databases: a proprietary and FVC database. The best classification rate obtained is of 85.33% with an execution time of 1.15ms.
Robust named entity detection using an Arabic offline handwriting recognition system
Authors: Krishna Subramanian, Rohit Prasad, Prem Natarajan
Venue: Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data (2009)

Text from Arabic optical handwriting recognition (OHR) systems can provide key indexing information. In particular, the text is rich in named entities (NEs) and detection of such entities is critical for search applications. Traditional approaches for detecting NEs in optical character recognition (OCR) output look for these NEs in the single-best recognition results. Due to the inevitable presence of recognition errors in the single-best output, such approaches usually result in low recall. Given that a lattice is more likely to contain the correct answer, we explore NE detection from word lattices produced by our Arabic handwriting recognition system. Since the improvement in recall is accompanied by a large number of false positives, we use confidence scores based on posterior scores to control precision. We show a 7% improvement in true detects for the same false acceptance rate on using lattices instead of 1-best hypothesis for NE lookup.
Handwritten numeral recognition based on simplified structural classification and fuzzy memberships
Authors: Chichang Jou, Hung-Chang Lee
Venue: Expert Systems with Applications: An International Journal (2009)

Previous handwritten numeral recognition algorithms applied structural classification to extract geometric primitives that characterize each image, and then utilized artificial intelligence methods, like neural network or fuzzy memberships, to classify the images. We propose a handwritten numeral recognition methodology based on simplified structural classification, by using a much smaller set of primitive types, and fuzzy memberships. More specifically, based on three kinds of feature points, we first extract five kinds of primitive segments for each image. A fuzzy membership function is then used to estimate the likelihood of these primitives being close to the two vertical boundaries of the image. Finally, a tree-like classifier based on the extracted feature points, primitives and fuzzy memberships is applied to classify the numerals. With our system, handwritten numerals in NIST Special Database 19 are recognized with correct rate between 87.33% and 88.72%.
Hacking Capitalism: The Free and Open Source Software (FOSS) Movement, 1st edition
Authors: Johan Sderberg
Venue: Routledge Research In Information Technology And Society (2007)

The Free and Open Source Software (FOSS) movement demonstrates how labour can self-organise production, and, as is shown by the free operating system GNU/Linux, even compete with some of the worlds largest firms. The book examines the hopes of such thinkers as Friedrich Schiller, Karl Marx, Herbert Marcuse and Antonio Negri, in the light of the recent achievements of the hacker movement. This book is the first to examine a different kind of political activism that consists in the development of technology from below. 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
Synthesis of predictable networks-on-chip-based interconnect architectures for chip multiprocessors
Authors: Srinivasan Murali, David Atienza, Paolo Meloni, Salvatore Carta, Luca Benini, Giovanni De Micheli, Luigi Raffo
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2007)

Today, chip multiprocessors (CMPs) that accommodate multiple processor cores on the same chip have become a reality. As the communication complexity of such multicore systems is rapidly increasing, designing an interconnect architecture with predictable behavior is essential for proper system operation. In CMPs, general-purpose processor cores are used to run software tasks of different applications and the communication between the cores cannot be precharacterized. Designing an efficient network-on-chip (NoC)-based interconnect with predictable performance is thus a challenging task. In this paper, we address the important design issue of synthesizing the most power efficient NoC interconnect for CMPs, providing guaranteed optimum throughput and predictable performance for any application to be executed on the CMP. In our synthesis approach, we use accurate delay and power models for the network components (switches and links) that are obtained from layouts of the components using industry standard tools. The synthesis approach utilizes the floorplan knowledge of the NoC to detect timing violations on the NoC links early in the design cycle. This leads to a faster design cycle and quicker design convergence across the high-level synthesis approach and the physical implementation of the design. We validate the design flow predictability of our proposed approach by performing a layout of the NoC synthesized for a 25-core CMP. Our approach maintains the regular and predictable structure of the NoC and is applicable in practice to existing NoC architectures.
Improving classification accuracy using automatically extracted training data
Authors: Ariel Fuxman, Anitha Kannan, Andrew B. Goldberg, Rakesh Agrawal, Panayiotis Tsaparas, John Shafer
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Classification is a core task in knowledge discovery and data mining, and there has been substantial research effort in developing sophisticated classification models. In a parallel thread, recent work from the NLP community suggests that for tasks such as natural language disambiguation even a simple algorithm can outperform a sophisticated one, if it is provided with large quantities of high quality training data. In those applications, training data occurs naturally in text corpora, and high quality training data sets running into billions of words have been reportedly used. We explore how we can apply the lessons from the NLP community to KDD tasks. Specifically, we investigate how to identify data sources that can yield training data at low cost and study whether the quantity of the automatically extracted training data can compensate for its lower quality. We carry out this investigation for the specific task of inferring whether a search query has commercial intent. We mine toolbar and click logs to extract queries from sites that are predominantly commercial (e.g., Amazon) and non-commercial (e.g., Wikipedia). We compare the accuracy obtained using such training data against manually labeled training data. Our results show that we can have large accuracy gains using automatically extracted training data at much lower cost.
A GA-Based Approach to ICA Feature Selection: An Efficient Method to Classify Microarray Datasets
Authors: Kun-Hong Liu, Jun Zhang, Bo Li, Ji-Xiang Du
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Although many independent component analysis (ICA) based algorithms were proposed to tackle the classification problem of microarray data, a problem is usually ignored that which and how many independent components can be used to best describe the property of the microarray data. In this paper, we proposed a GA approach for IC feature selection to increase the classification accuracy of two different ICA based models: penalized independent component regression (P-ICR) and ICA based Support Vector Machine (SVM). The corresponding experimental results are listed to show that the IC selection method can further improve the classification accuracy of the ICA based algorithms.
Semantic knowledge modeling in medical laboratory environment for drug usage: CASE study
Authors: Anne Tanttari, Kimmo Salmenjoki, Lorna Uden
Venue: Proceeding of the 2008 conference on Information Modelling and Knowledge Bases XIX (2008)

In this paper we will consider the usage of knowledge modelling and design with conceptual design and analysis in improving the knowledge description of medical data. There exists many XML and information system (IS) based approaches for producing easy to use, reliable, trustworthy and coherent medical systems and services, like the concepts of HL7. In computer applications web, web services and semantic web based technologies and tools are being used in the business and ecommerce scenarios to produce user-focused, (business) process oriented services in future medical IS. Our case is focused on laboratory exams and their knowledge analysis for predicting and analyzing the usage of various drugs by patients. We show with our case the role of semantic knowledge modelling in coordinating various medical services in a real hospital setting, and hope to extend our work in the future towards semantic web based applications that improve and ease the patient treatments in the IS of the future hospitals.
Contract-Based Discovery and Adaptation of Web Services
Authors: Luca Padovani
Venue: Formal Methods for Web Services: 9th International School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM 2009, Bertinoro, Italy, June 1-6, 2009, Advanced Lectures (2009)

A contract describes the observable behavior of a Web service. When looking for Web services providing specific capabilities, the contract can be used as an important search key. This calls for a notion of contract equivalence that goes beyond nominal or structural equivalence.In this paper we define a simple, yet expressive formal language for describing Web service contracts. We provide a natural, set-theoretic semantics of contracts and we use it for defining a family of equivalence relations that can be effectively used for discovering and adapting Web services implementing a specific contract.
Kernel-Based Multi-Imputation for Missing Data
Authors: Shichao Zhang, Yongsong Qin, Xiaofeng Zhu, Jilian Zhang, Chengqi Zhang
Venue: Proceeding of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006 (2006)

A Kernel-Based Nonparametric Multiple imputation method is proposed under MAR (Missing at Random) and MCAR (Missing Completely at Random) missing mechanisms in nonparametric regression settings. We experimentally evaluate our approach, and demonstrate that our imputation performs better than the well-known NORM algorithm.
Internet uses and gratifications: A survey in the Indian context
Authors: Sanjit Kumar Roy
Venue: Computers in Human Behavior (2009)

Uses and gratifications (U&G) theory, aids in the assessment of peoples' motivations for media usage, access and to understand their attitude towards a particular medium. In this paper this theory has been applied to derive the Internet gratification structure of Internet users in the Indian context. This study proceeds in four stages. First, six gratifications for Internet use namely self development, wide exposure, user friendly, relaxation, career opportunities, and global exchange were identified using the exploratory factor analysis. Then the factors were subjected to confirmatory factor analysis. Next with the use of t-tests the study identified the gratifications on which males and females differ significantly. Finally, with the use of discriminant analysis it has been shown that the light and heavy users of Internet differ on some of the gratification factors obtained in the study.
Effects of the Voice Recognition on the Writing of Students with Learning Disabilities
Authors: Hu Lailin
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

This paper uses the experiment method to study whether the dictation composition supporting by the voice recognition can improve writing quality of the student with writing disabilities or not. The investigation shows: the primary students are easy to master the voice recognition system. The voice recognition system responds to the demand of the speed that can satisfy dictating composition. Under the Chinese language environment, this method can promote composition level of the students with writing disabilities.
Atmospheric and Oceanic Computational Science
Authors: Adrian Sandu, Amik St-Cyr, Katherine J. Evans
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

The first workshop on Atmospheric and Oceanic Computational Science brings together computational and domain scientists who develop computational tools for the study of the atmosphere and oceans. These tools are essential for understanding and prediciting weather, air and water pollution, and the evolution of the planet's climate. The dynamics of the atmosphere and of the oceans is driven by a multitude of physical processes and is characterized by a multiple spatial and temporal scales. Moreover, the computations are very large scale: present day models track the time evolution of tens of millions to tens of billions variables. These factors make atmospheric and oceanic simulations a challenging, vibrant research field with a tremendous impact on society at large.Topics covered in this symposium include new methods for spatial and temporal discretization, parallel and high performance computing, advances with existing models, and data assimilation and observation targeting algorithms.
Two new methods for constructing double-ended priority queues from priority queues
Authors: Amr Elmasry, Claus Jensen, Jyrki Katajainen
Venue: Computing (2008)

We introduce two data-structural transformations to construct double-ended priority queues from priority queues. To apply our transformations the priority queues exploited must support the extraction of an unspecified element, in addition to the standard priority-queue operations. With the first transformation we obtain a double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, insert, extract; and the worst-case cost of O(lg n) with at most lg n +&#x00a0;O(1) element comparisons for delete. With the second transformation we get a meldable double-ended priority queue which guarantees the worst-case cost of O(1) for find-min, find-max, insert, extract; the worst-case cost of O(lg n) with at most lg n +&#x00a0;O(lg lg n) element comparisons for delete; and the worst-case cost of O(min {lg m, lg n}) for meld. Here, m and n denote the number of elements stored in the data structures prior to the operation in question.
Achieving goals in decentralized POMDPs
Authors: Christopher Amato, Shlomo Zilberstein
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (2009)

Coordination of multiple agents under uncertainty in the decentralized POMDP model is known to be NEXP-complete, even when the agents have a joint set of goals. Nevertheless, we show that the existence of goals can help develop effective planning algorithms. We examine an approach to model these problems as indefinite-horizon decentralized POMDPs, suitable for many practical problems that terminate after some unspecified number of steps. Our algorithm for solving these problems is optimal under some common assumptions -- that terminal actions exist for each agent and rewards for non-terminal actions are negative. We also propose an infinite-horizon approximation method that allows us to relax these assumptions while maintaining goal conditions. An optimality bound is developed for this sample-based approach and experimental results show that it is able to exploit the goal structure effectively. Compared with the state-of-the-art, our approach can solve larger problems and produce significantly better solutions.