ppxscal/embedding_network huggingface dataset:
An Analysis of Research Themes in the CBR Conference Literature
Authors: Derek Greene, Jill Freyne, Barry Smyth, Pádraig Cunningham
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

After fifteen years of CBR conferences, this paper sets out to examine the themes that have evolved in CBR research as revealed by the implicit and explicit relationships between the conference papers. We have examined a number of metrics for demonstrating connections between papers and between authors and have found that a clustering based on co-citation of papers appears to produce the most meaningful organisation. We have employed an Ensemble Non-negative Matrix Factorisation (NMF) approach that produces a "soft" hierarchical clustering, where papers can belong to more than one cluster. This is useful as papers can naturally relate to more than one research area. We have produced timelines for each of these clusters that highlight influential papers and illustrate the life-cycle of research themes over the last fifteen years. The insights afforded by this analysis are presented in detail. In addition to the analysis of the sub-structure of CBR research, this paper also presents some global statistics on the CBR conference literature.
Exploiting ECC Redundancy to Minimize Crosstalk Impact
Authors: Daniele Rossi, Andre K. Nieuwland, Atul Katoch, Cecilia Metra
Venue: IEEE Design Test (2005)

Editors' note: Signal integrity in high-speed bus designs is put at risk by crosstalk-related bus delays. This article provides a comprehensive study of the usefulness of error-correcting code (ECC) redundancy in reducing such delays. It shows that Dual Rail codes perform better at this task than Hamming codes.—Dimitris Gizopoulos, University of Piraeus; and Yervant Zorian, Virage Logic
eServices for Hospital Equipment
Authors: Merijn Jonge, Wim Linden, Rik Willems
Venue: Proceedings of the 5th international conference on Service-Oriented Computing (2007)

In this paper we explore the idea that by combining different sources of information in a hospital environment, valuable e-services can be developed for reducing cost and improving quality of service.Companies, like Philips Medical Systems, may have a competitive advantage, because they have a large installed base which may provide valuable information already, and because they can change their products to provide additional information.To optimally benefit from this advantage, we created a platform that enables quick development of e-services. The platform enables uniform access to data, combines static with live data, and supports transparent composition of existing, into new services.We discuss the requirements, design, and implementation of the platform, and we show its use in a case study that addresses asset management and utilization services for mobile medical equipment.
Do too many cooks spoil the broth? Using the number of developers to enhance defect prediction models
Authors: Elaine J. Weyuker, Thomas J. Ostrand, Robert M. Bell
Venue: Empirical Software Engineering (2008)

Fault prediction by negative binomial regression models is shown to be effective for four large production software systems from industry. A model developed originally with data from systems with regularly scheduled releases was successfully adapted to a system without releases to identify 20% of that system's files that contained 75% of the faults. A model with a pre-specified set of variables derived from earlier research was applied to three additional systems, and proved capable of identifying averages of 81, 94 and 76% of the faults in those systems. A primary focus of this paper is to investigate the impact on predictive accuracy of using data about the number of developers who access individual code units. For each system, including the cumulative number of developers who had previously modified a file yielded no more than a modest improvement in predictive accuracy. We conclude that while many factors can "spoil the broth" (lead to the release of software with too many defects), the number of developers is not a major influence.
Uniform Interpolation by Resolution in Modal Logic
Authors: Andreas Herzig, Jérôme Mengin
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

The problem of computing a uniform interpolant of a given formula on a sublanguage is known in Artificial Intelligence as variable forgetting. In propositional logic, there are well known methods for performing variable forgetting. Variable forgetting is more involved in modal logics, because one must forget a variable not in one world, but in several worlds. It has been shown that modal logic K has the uniform interpolation property, and a method has recently been proposed for forgetting variables in a modal formula (of mu-calculus) given in disjunctive normal form. However, there are cases where information comes naturally in a more conjunctive form. In this paper, we propose a method, based on an extension of resolution to modal logics, to perform variable forgetting for formulae in conjunctive normal form, in the modal logic K .
Learning and Generalization with the Information Bottleneck
Authors: Ohad Shamir, Sivan Sabato, Naftali Tishby
Venue: Proceedings of the 19th international conference on Algorithmic Learning Theory (2008)

The Information Bottleneck is an information theoretic framework that finds concise representations for an `input' random variable that are as relevant as possible for an `output' random variable. This framework has been used successfully in various supervised and unsupervised applications. However, its learning theoretic properties and justification remained unclear as it differs from standard learning models in several crucial aspects, primarily its explicit reliance on the joint input-output distribution. In practice, an empirical plug-in estimate of the underlying distribution has been used, so far without any finite sample performance guarantees. In this paper we present several formal results that address these difficulties. We prove several finite sample bounds, which show that the information bottleneck can provide concise representations with good generalization, based on smaller sample sizes than needed to estimate the underlying distribution. The bounds are non-uniform and adaptive to the complexity of the specific model chosen. Based on these results, we also present a preliminary analysis on the possibility of analyzing the information bottleneck method as a learning algorithm in the familiar performance-complexity tradeoff framework. In addition, we formally describe the connection between the information bottleneck and minimal sufficient statistics.
QoS-Aware Semantic Service Selection: An Optimization Problem
Authors: José María García, David Ruiz, Antonio Ruiz-Cortés, Jose Antonio Parejo
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

In order to select the best suited service among a set of discovered services, with respect to QOS parameters, a user have to state his or her preferences, so services can be ranked according to these QOS parameters. Current Semantic Web Services ontologies do not support the definition of QOS-aware user preferences, though there are some proposals that extend those ontologies to allow selection based on those preferences. However, their selection algorithms are very coupled with user preferences descriptions, which are defined without semantics or at a different semantic level than service functionality. In this work, we present a service selection framework that transforms user preferences into an optimization problem where the best service is selected. This framework is based on an ontology that conceptualizes these user preferences. Thus, we use a very expressive solution decoupled with the concrete selection technique by using XSL transformations, while describing QOS-aware user preferences at the same semantic level of functional preferences.
Wide-area performance profiling of 10GigE and InfiniBand technologies
Authors: Nageswara S. V. Rao, Weikuan Yu, William R. Wing, Stephen W. Poole, Jeffrey S. Vetter
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

For wide-area high-performance applications, light-paths provide 10Gbps connectivity, and multi-core hosts with PCI-Express can drive such data rates. However, sustaining such end-to-end application throughputs across connections of thousands of miles remains challenging, and the current performance studies of such solutions are very limited. We present an experimental study of two solutions to achieve such throughputs based on: (a) 10Gbps Ethernet with TCP/IP transport protocols, and (b) InfiniBand and its wide-area extensions. For both, we generate performance profiles over 10Gbps connections of lengths up to 8600 miles, and discuss the components, complexity, and limitations of sustaining such throughputs, using different connections and host configurations. Our results indicate that IB solution is better suited for applications with a single large flow, and 10GigE solution is better for those with multiple competing flows.
Patterns for business object model integration in process-driven and service-oriented architectures
Authors: Carsten Hentrich, Uwe Zdun
Venue: Proceedings of the 2006 conference on Pattern languages of programs (2006)

Service-oriented architectures often have the goal to integrate various systems of one or more organizations in a flexible way to be able to quickly react on business changes. Integration based only on services, however, falls short in reaching this goal because the application-specific business object models of multiple external systems (especially legacy systems) need to be integrated into the service-oriented system. When multiple business object models must be integrated into one system, serious data integration issues might arise. Examples of such problems are incompatible data definitions, inconsistent data across the enterprise, data redundancy, and update anomalies. We present patterns that address these issues and describe how to integrate the application-specific business object models of various external systems into a consistent process-driven and service-oriented architecture.
Novel Image Rejection Filter Based on Neural Networks
Authors: Botond Sandor Kirei, Marina Topa, Irina Dornean, Albert Fazakas
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

In this paper a novel image rejection algorithm based on neural networks is proposed. The low-IF receiver architecture and the phenomena of I/Q imbalance (also referred as image interference) are described. The proposed filter is an enhancement of a complex LMS adaptive filter, which separates the desired and image signals, but the recovered signal still suffers from the effects of imbalance parameters. This is corrected by the proposed filter. Simulink simulations were performed in order to prove the functionality of the novel filter. The simulations prove the convergence and stability of the filter. The necessary sample number is given to achieve -60 dB image rejection.
Analyzing Differences in Operational Disease Definitions Using Ontological Modeling
Authors: Linda Peelen, Michel C. Klein, Stefan Schlobach, Nicolette F. Keizer, Niels Peek
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis.
What Have Innsbruck and Leipzig in Common? Extracting Semantics from Wiki Content
Authors: Sören Auer, Jens Lehmann
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

Wikis are established means for the collaborative authoring, versioning and publishing of textual articles. The Wikipedia project, for example, succeeded in creating the by far largest encyclopedia just on the basis of a wiki. Recently, several approaches have been proposed on how to extend wikis to allow the creation of structured and semantically enriched content. However, the means for creating semantically enriched structured content are already available and are, although unconsciously, even used by Wikipedia authors. In this article, we present a method for revealing this structured content by extracting information from template instances. We suggest ways to efficiently query the vast amount of extracted information (e.g. more than 8 million RDF statements for the English Wikipedia version alone), leading to astonishing query answering possibilities (such as for the title question). We analyze the quality of the extracted content, and propose strategies for quality improvements with just minor modifications of the wiki systems being currently used.
Stock Index Prediction Based on Adaptive Training and Pruning Algorithm
Authors: Jinyuan Shen, Huaiyu Fan, Shengjiang Chang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

A tapped delay neural network (TDNN) with an adaptive learning and pruning algorithm is proposed to predict the nonlinear time serial stock indexes. The TDNN is trained by the recursive least square (RLS) in which the learning-rate parameter can be chosen automatically. This results in the network converging fast. Subsequently the architecture of the trained neural network is optimized by utilizing pruning algorithm to reduce the computational complexity and enhance the network's generalization. And then the optimized network is retrained so that it has optimum parameters. At last the test samples are predicted by the ultimate network. The simulation and comparison show that this optimized neuron network model can not only reduce the calculating complexity greatly, but also improve the prediction precision. In our simulation, the computational complexity is reduced to 0.0556 and mean square error of test samples reaches 8.7961×10¿ 5.
The slai methodology: a systematic aspect-oriented identification process for functional and non-functional requirements
Authors: Frank J. Mitropoulos
Venue:  (2008)

Aspect-oriented software development (ASOD) has great potential in reducing software complexity and cost. Aspects have been well defined in the implementation phase of software development, but lack clear understanding in the early phases of software development. Without this early focus on aspects, the true benefits of aspect-oriented programming are lost. This paper proposes a definition of what an aspect is in the requirements phase of software development that focuses on both functional and non-functional requirements. In addition, this paper presents a methodology, the SLAI (Structured Lexicon for Aspectual Identification) Methodology, for the systematic identification of aspects at this stage. This methodology examines all the vocabulary used to define the requirements of the system to ensure that all terms are reused as much as possible, eliminating similar terms for the same concepts. The reuse of terms and verb phrases allows for a systematic process for the identification of aspects at the requirements phase. The SLAI Methodology was validated using a case study of a partial use case requirements documentation including some non-functional requirements. Overall, seven aspects were identified by following the SLAI Methodology, six of which were from the functional requirements. Aspects have traditionally resulted from non-functional requirements, thus, the SLAI Methodology demonstrates that it is possible to identify aspects systematically from all requirements. The SLAI Methodology's systematic process and clear definition of an aspect provide support for complete identification aspects in the early stages of software development and, when integrated with a corresponding aspectual design view, will move ASOD further in the realization of its true benefits.
Ant Based Heuristics for the Capacitated Fixed Charge Location Problem
Authors: Harry Venables, Alfredo Moscardini
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

This paper presents two different $\mathcal{MAX-MIN}$ Ant System ($\mathcal{MM}$AS) based algorithms for the Capacitated Fixed Charge Location Problem (CFCLP) which is a discrete facility location problem that consists of selecting a subset of facilities that must completely supply a set of customers at a minimum cost. The first algorithm is concerned with extending and improving existing work primarily by introducing a previously unconsidered local search scheme based on pheromone intensity. Whilst, the second method makes a transformation of the derived $\mathcal{MM}$AS algorithm into the hyper-cube famework in an attempt to improve efficiency and robustness. Computational results for a series of standard benchmark problems are presented and indicate that the proposed methods are capable of deriving optimal solutions for the CFCLP.
Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI
Authors: Kolawole O. Babalola, Brian Patenaude, Paul Aljabar, Julia Schnabel, David Kennedy, William Crum, Stephen Smith, Tim F. Cootes, Mark Jenkinson, Daniel Rueckert
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

The automation of segmentation of medical images is an active research area. However, there has been criticism of the standard of evaluation of methods. We have comprehensively evaluated four novel methods of automatically segmenting subcortical structures using volumetric, spatial overlap and distance-based measures. Two of the methods are atlas-based --- classifier fusion and labelling (CFL) and expectation-maximisation segmentation using a dynamic brain atlas (EMS), and two model-based --- profile active appearance models (PAM) and Bayesian appearance models (BAM). Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters. Our results showed that all four methods perform on par with recently published methods. CFL performed significantly better than the other three methods according to all three classes of metrics.
On the Construction of Transformation Steps in the Category of Multiagent Systems
Authors: Thomas Soboll
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

Based on the category $\mathbb{MAS}$ of base diagrams of Multiagent Systems (MAS) and morphisms between them, a transformation system for MAS can be established using the well known Double Pushout (DPO) Approach. An important part in the DPO approach is to find a pushout complement for a given situation. This is usualy done by checking the so called "gluing condition". In this contribution a new approach for the pushout complement construction in the category $\mathbb{MAS}$ is introduced. For illustration a simple robot example is presented. 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
Protection of Database Security via Collaborative Inference Detection
Authors: Yu Chen, Wesley W. Chu
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Malicious users can exploit the correlation among data to infer sensitive information from a series of seemingly innocuous data accesses. Thus, we develop an inference violation detection system to protect sensitive data content. Based on data dependency, database schema and semantic knowledge, we con-structed a semantic inference model (SIM) that represents the possible inference channels from any at-tribute to the pre-assigned sensitive attributes. The SIM is then instantiated to a semantic inference graph (SIG) for query-time inference violation detection. For a single user case, when a user poses a query, the detection system will examine his/her past query log and calculate the probability of inferring sensitive information. The query request will be denied if the inference probability exceeds the pre-specified threshold. For multi-user cases, the users may share their query answers to increase the inference prob-ability. Therefore, we develop a model to evaluate collaborative inference based on the query sequences of collaborators and their task-sensitive collaboration levels. Experimental studies reveal that information authoritativeness, communication fidelity and honesty in collaboration are three key factors that affect the level of achievable collaboration. An example is given to illustrate the use of the proposed technique to prevent multiple collaborative users from deriving sensitive information via inference.
Performance of Incomplete Channel Sensing for Frequency Hopping Cognitive Personal Area Networks
Authors: Vojislav B. Misic, Jelena Misic
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

In this paper we propose the use of cognitive radio technology in a wireless personal area network environment. We investigate the performance of cooperative sensing of the activity of primary users by anumber of secondary users equipped with single-channel cognitive radios, and show that even a small number of sensing nodes provides reasonably accurate sensing, subject to certain limitations.
A Compressing Translation from Propositional Resolution to Natural Deduction
Authors: Hasan Amjad
Venue: Proceedings of the 6th international symposium on Frontiers of Combining Systems (2007)

We describe a translation from SAT solver generated propositional resolution refutation proofs to classical natural deduction proofs. The resulting proof can usually be checked quicker than one that simply simulates the original resolution proof. We use this result in interactive theorem provers, to speed up reconstruction of SAT solver generated proofs. The translation is efficient, running in time linear in the length of the original proof, and effective, easily scaling up to large proofs with millions of inferences.
Route-Split Routing with Resiliency to Simultaneous Failure for Mobile Ad Hoc Networks
Authors: Tsuyoshi Mizumoto, Tomoyuki Ohta, Yoshiaki Kakuda
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

As the performance of each node becomes higher, it is expected that the ad hoc network is used for the community network in which a few thousands of mobile nodes exist.In such a network, the number of hops between a source node and a destination node also becomes longer. However, as the route becomes longer, it is difficult to provide the robust and reliable route for mobile ad hoc networks since the multiple route breaks occur at the same time due to the topology change. Therefore, this paper proposes a Route-Split Routing with resiliency to simultaneous failure (RSR). RSR sets up multiple Subroute Management Nodes (SMN's) on the route and each SMN manages the subroute between the SMN and the neighboring SMN. When the multiple route breaks occur at the same time, each subroute is repaired by the SMN. Consequently, RSR can reduce the number of control packets used for the route repair and mitigate the network congestion even in case that the number of nodes in the network becomes very larger.