ppxscal/embedding_network huggingface dataset:
Analysis of a continuum-based beam element in the framework of explicit-FEM
Authors: J. L. Curiel Sosa, A. J. Gil
Venue: Finite Elements in Analysis and Design (2009)

In this paper, a continuum-based (CB) beam element based on the original work of Belytschko [Nonlinear Finite Elements for Continua and Structures, Wiley, New York, 2001] has been modified and analysed in the framework of explicit finite element methods. Two main differences are introduced with respect to the original finite element technology proposed in Belytschko [Nonlinear Finite Elements for Continua and Structures, Wiley, New York, 2001]. Firstly, the velocity gradient tensor is computed in the slave nodes of the finite element rather than in the master nodes, enabling a more effective computational performance in terms of CPU-time as well as convergence. Secondly, the thickness of the element is assumed to be variable, which allows dilation and/or contraction when large deformation processes are involved. A series of numerical examples are presented in order to verify the robustness and capabilities of the algorithm.
Design-time simulation of domain-specific models by incremental pattern matching
Authors: Istvan Rath, David Vago, Daniel Varro
Venue: Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (2008)

In this paper, we present a general purpose discrete event simulation framework for domain-specific visual languages describing system behavior. In our framework, the dynamic semantics of the language is captured by a combination of graph transformation and abstract state machine rules as provided by the model transformation language of VIATRA2, which allows to capture complex model changes at each simulation step. For an efficient execution of the simulation, incremental graph pattern matching is used to avoid the re-computation of enabledness conditions of simulation rules by incrementally keeping track of rule contexts.
Flexible Edge Arrangement Templates for Object Detection
Authors: Yan Li, Yanghai Tsin, Yakup Genc, Takeo Kanade
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

We present a novel feature representation for categorical object detection. Unlike previous approaches that have concentrated on generic interest-point detectors, we construct object-specific features directly from the training images. Our feature is represented by a collection of Flexible Edge Arrangement Templates (FEATs). We propose a two-stage semi-supervised learning approach to feature selection. A subset of frequent templates are first selected from a large template pool. In the second stage, we formulate feature selection as a regression problem and use LASSO method to find the most discriminative templates from the pre-selected ones. FEATs adaptively capture the image structure and naturally accommodate local shape variations. We show that this feature can be complemented by the traditional holistic patch method, thus achieving both efficiency and accuracy. We evaluate our method on three well-known car datasets, showing performance competitive with existing methods.
Robust partitional clustering by outlier and density insensitive seeding
Authors: Mohammad Al Hasan, Vineet Chaoji, Saeed Salem, Mohammed J. Zaki
Venue: Pattern Recognition Letters (2009)

The leading partitional clustering technique, k-means, is one of the most computationally efficient clustering methods. However, it produces a local optimal solution that strongly depends on its initial seeds. Bad initial seeds can also cause the splitting or merging of natural clusters even if the clusters are well separated. In this paper, we propose, ROBIN, a novel method for initial seed selection in k-means types of algorithms. It imposes constraints on the chosen seeds that lead to better clustering when k-means converges. The constraints make the seed selection method insensitive to outliers in the data and also assist it to handle variable density or multi-scale clusters. Furthermore, they (constraints) make the method deterministic, so only one run suffices to obtain good initial seeds, as opposed to traditional random seed selection approaches that need many runs to obtain good seeds that lead to satisfactory clustering. We did a comprehensive evaluation of ROBIN against state-of-the-art seeding methods on a wide range of synthetic and real datasets. We show that ROBIN consistently outperforms existing approaches in terms of the clustering quality.
A logic-based model to support alert correlation in intrusion detection
Authors: Benjamin Morin, Ludovic Mé, Hervé Debar, Mireille Ducassé
Venue: Information Fusion (2009)

Managing and supervising security in large networks has become a challenging task, as new threats and flaws are being discovered on a daily basis. This requires an in depth and up-to-date knowledge of the context in which security-related events occur. Several tools have been proposed to support security operators in this task, each of which focuses on some specific aspects of the monitoring. Many alarm fusion and correlation approaches have also been investigated. However, most of these approaches suffer from two major drawbacks. First, they only take advantage of the information found in alerts, which is not sufficient to achieve the goals of alert correlation, that is to say to reduce the overall amount of alerts, while enhancing their semantics. Second, these techniques have been designed on an ad hoc basis and lack a shared data model that would allow them to reason about events in a cooperative way. In this paper, we propose a federative data model for security systems to query and assert knowledge about security incidents and the context in which they occur. This model constitutes a consistent and formal ground to represent information that is required to reason about complementary evidences, in order to confirm or invalidate alerts raised by intrusion detection systems.
An Application of Self-organizing Data Structures to Compression
Authors: Reza Dorrigiv, Alejandro López-Ortiz, J. Ian Munro
Venue: Proceedings of the 8th International Symposium on Experimental Algorithms (2009)

List update algorithms have been widely used as subroutines in compression schemas, most notably as part of Burrows-Wheeler compression. The Burrows-Wheeler transform (BWT), which is the basis of many state-of-the-art general purpose compressors applies a compression algorithm to a permuted version of the original text. List update algorithms are a common choice for this second stage of BWT-based compression. In this paper we perform an experimental comparison of various list update algorithms both as stand alone compression mechanisms and as a second stage of the BWT-based compression. Our experiments show MTF outperforms other list update algorithms in practice after BWT. This is consistent with the intuition that BWT increases locality of reference and the predicted result from the locality of reference model of Angelopoulos et al. [1]. Lastly, we observe that due to an often neglected difference in the cost models, good list update algorithms may be far from optimal for BWT compression and construct an explicit example of this phenomena. This is a fact that had yet to be supported theoretically in the literature.
Plenary lecture 9: overview of differential reduction for hypergeometric function representation of Feynman diagrams
Authors: Bennie F. L. Ward
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

We present an overview of recent progress in the development of differential reduction algorithms for hypergeometric function representation higher order Feynman diagrams with an eye toward their use in precision theory predictions for LHC/CLIC physics.
Towards a standard protocol for community-driven organizations of knowledge
Authors: Chao Zhou, Christophe Lejeune, Aurélien Bénel
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

This paper deals with the “Web 2.0”, where every user can contribute to the content, “harnessing collective intelligence”. After studying what makes the success of services like Google Base, Del.icio.us and the Open Directory Project, we propose a unifying “REST” protocol for this kind of community-driven organizations of knowledge. The aim is to make the collaboration possible beyond the boundaries of the software and of the resulting communities.
Interoperability challenges for open standards: ODF and OOXML as examples
Authors: Rajiv Shah, Jay Kesan
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

This project assesses the current state of interoperability among different software implementations of the document formats OpenDocument Format (ODF) and Office Open XML (OOXML). Numerous governments are considering mandating either or both formats for their electronic documents. For ODF, the dominant implementation is OpenOffice.org and for OOXML, the dominant implementation is Microsoft Office. This research scores the performance of other alternative implementations for ODF and OOXML. The goal is to assess the current state of interoperability for document formats. The results show that there are no alternative implementations, for either ODF or OOXML, that offer 100% interoperability with the dominant implementations. The results are troubling and cast doubt on the exuberance surrounding open standards. The results suggest an emphasis on improved interoperability testing. Without such testing, governments will be locked-in into the dominant implementations for either standard.
Tuple Spaces as a Flexible Integration Platform for Distributed Learning Environments
Authors: Adam Giemza, Stefan Weinbrenner, Jan Engler, H. Ulrich Hoppe
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

This paper proposes a blackboard architecture based on Tuple Spaces to support flexible integration of existing algorithms with modern learning environments and interfaces. The platform allows for extending existing single user systems to networked environments and for combining heterogeneous implementation languages. The specific implementation supports a generic inter-language communication by adding a Prolog interface to the Java based TSpaces platform. This approach is exemplified by a Java application for symbolic derivation coupled with an intelligent analyser written in Prolog, which uses deductive error recognition and classification to generate feedback to the user.
Scalable Authoring of Diagnostic Case Based Training Systems
Authors: Christian Betz
Venue:  (2007)

Diagnostic Case Based Training Systems (D-CBT) enable learners to learn and practice in a realistic context: In medical education, D-CBT present virtual patients to the learners who are asked to diagnose these patients. D-CBT range from simple, easy-to-author and easy-to-use presentation systems to complex knowledge based systems. This book presents an approach for authors to easily create D-CBT from existing documents using well-known tools: Authors simply annotate and extend the documents to model the knowledge. These annotated documents are automatically transformed into training cases. A scalable knowledge representation is able to capture the content on multiple levels, from simple to highly structured knowledge. This technique of authoring allows easy start and fast development of training cases. Also, authors can iteratively add and structure knowledge to adapt training cases to their learners' needs. The presented system grows in influence: University of Wuerzburg uses it as eLearning platform for all students. The book also discusses the application of the same approach to other domains, especially to knowledge acquisition for the Semantic Web. 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
Computer-Aided Architectural Design Futures 2007: Proceedings of the 12th International CAAD Futures Conference, 1st edition
Authors: Andy Dong, Andy Dong, Vande, Andrew Moere, John S. Gero
Venue:  (2007)

CAAD Futures is a biennial conference that aims to promote the advancement of computer-aided architectural design research and teaching. The conferences are organised under the auspices of the CAAD Futures Foundation. The conference series started in 1985 in Delft and has since traveled to major cities in Europe, Asia, and USA. This volume is the proceedings of the 12th International Conference of CAAD Futures, which took place in Sydney, Australia. The internationally refereed papers in this book present the state of the art in computer-aided architectural design research. The papers in this year's conference theme, Integrating Technologies for Computer-Aided Design, provide the technological foundation for new ways of thinking about using computers in designing and the use of computers in design itself as well as in the education of designers. Information about the 2007 Conference and this book is available at: arch.usyd.edu.au/kcdc/conferences/cf07
An Integrated Approach to Extracting Ontological Structures from Folksonomies
Authors: Huairen Lin, Joseph Davis, Ying Zhou
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Collaborative tagging systems have recently emerged as one of the rapidly growing web 2.0 applications. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find relevant. In turn, the flat and non-hierarchical structure with unsupervised vocabularies leads to low search precision and poor resource navigation and retrieval. This drawback has created the need for ontological structures which provide shared vocabularies and semantic relations for translating and integrating the different sources. In this paper, we propose an integrated approach for extracting ontological structure from folksonomies that exploits the power of low support association rule mining supplemented by an upper ontology such as WordNet.
Mathematical Methods for Knowledge Discovery and Data Mining
Authors: Giovanni Felici, Giovanni Felici, Carlo Vercellis
Venue:  (2007)

The field of data mining has seen a demand in recent years for the development of ideas and results in an integrated structure. Mathematical Methods for Knowledge Discovery & Data Mining focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; and many others. This Premier Reference Source is an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance and insurance, manufacturing, marketing, performance measurement, and telecommunications.
Progress Toward and the Promise of Multidiscipline Engine Simulation
Authors: Russell W. Claus, Thomas Lavelle, Cynthia Naiman, Ambady Suresh, Mark Tuner
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Current engineering systems tools have limitations that restrict the ability to unite conceptual design and detailed analysis. Numerical Propulsion System Simulator (NPSS) is a multidisciplinary systems analysis tool that has been tackling key concurrent design technology barriers for over a decade. This paper will look at some of the successes achieved by NPSS and areas requiring additional development. NPSS is widely used within industry for full engine simulation of gas turbines and (to a lesser extent) rockets. The first version of this tool has focused on providing an object-oriented framework that provides a sound architecture for both low-and high-fidelity systems models. High-fidelity simulations have just recently been achieved on parallel computers. They demonstrate the promise of this new technology and indicate critical areas for future development.
Supporting decisions about the introduction of genetically modified crops
Authors: Marko Bohanec, Martin Žnidaršič
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

Genetically modified (GM) crops have become a real option in modern agriculture. They offer advantages for agricultural production, but they also raise concerns about their ecological and economic impacts. Decisions about GM crops are complex and call for decision support. This paper presents an application of qualitative multi-attribute modeling methodology DEX to the development of model-based decision support systems in two European research projects, ECOGEN and SIGMEA. Three main multi-attribute models were developed for the assessment of GM-crop impacts in terms of (1) economy and ecology, (2) soil quality, and (3) co-existence. All these models were developed in collaboration of domain experts and decision analysts using the software tool DEXi. In the paper, we describe the applied methodology and outline the three models, their implementation and some results of their application. In particular, we discuss our lessons learned and highlight the strong and weak points of this approach.
A Calculus for Mobile Ad-hoc Networks with Static Location Binding
Authors: Jens Chr. Godskesen
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We present a process calculus for mobile ad hoc networks which is a natural continuation of our earlier work on the process calculus CMAN [J.C. Godskesen. A calculus for mobile ad hoc networks. In Proceedings of the 9th International Conference, COORDINATION 2007, volume 4467 of LNCS, pages 132-150, Paphos, Cyprus, June 2007. Springer-Verlag]. Essential to the new calculus is the novel restricted treatment of node mobility imposed by hiding of location names using a static binding operator, and we introduce the more general notion of unidirectional links instead of bidirectional links. We define a natural weak reduction semantics and a reduction congruence and prove our weak contextual bisimulation equivalence to be a sound and complete co-inductive characterization of the reduction congruence. The two changes to the calculus in [J.C. Godskesen. A calculus for mobile ad hoc networks. In Proceedings of the 9th International Conference, COORDINATION 2007, volume 4467 of LNCS, pages 132-150, Paphos, Cyprus, June 2007. Springer-Verlag] yields a much simpler bisimulation semantics, and importantly and in contrast to [J.C. Godskesen. A calculus for mobile ad hoc networks. In Proceedings of the 9th International Conference, COORDINATION 2007, volume 4467 of LNCS, pages 132-150, Paphos, Cyprus, June 2007. Springer-Verlag] we manage to provide a non-contextual weak bisimulation congruence facilitating ease of proofs and being strictly contained in the contextual bisimulation.
On the relation between fuzzy max-Archimedean t-norm relational equations and the covering problem
Authors: Jun-Lin Lin
Venue: Fuzzy Sets and Systems (2009)

The problem of solving a system of fuzzy relational equations with max-Archimedean t-norm composition is studied. It is shown that this problem is closely related to the covering problem, which belongs to the class of NP-hard problems. It is proved that there is a one-to-one correspondence between the minimal solutions of the equations and the irredundant coverings, as previously discovered by Markovskii [On the relation between equations with max-product composition and the covering problem, Fuzzy Sets and Systems, 153 (2005) 261-273] for fuzzy relational equations with max-product composition. Since max-product composition is a special case of max-Archimedean t-norm composition, this work extends Markovskii's work to fuzzy relational equations with max-Archimedean t-norm composition. An extension of Markovskii's algorithm is implemented, yielding a processing time linearly proportional to the square of the number of minimal solutions.
A Semantic Spectrum Analyzer for Realizing Semantic Learning in a Semantic Associative Search Space
Authors: Yasushi Kiyoki, Xing Chen, Hidehiro Ohashi
Venue: Proceeding of the 2006 conference on Information Modelling and Knowledge Bases XVII (2006)

In this paper, we present a learning system with a Semantic Spectrum Analyzer to realize appropriate and sharp semantic vector spaces for semantic associative search. In semantic associative search systems, a learning system is essentially required to obtain semantically related and appropriate information from multimedia databases. We propose a new learning algorithm with a Semantic Spectrum Analyzer for the semantic associative search. A Semantic Spectrum Analyzer is essential for adapting retrieval results according to individual variation and for improving accuracy of the retrieval results. This learning algorithm is applied to adjust retrieval results to keywords and retrieval-candidate data. The Semantic Spectrum Analyzer makes it possible to extract semantically related and appropriate information for adjusting the initial positions of semantic vectors to the positions adapting to the individual query requirements.
ERP systems implementation success factors: IS and non-IS managers' perceptions
Authors: Emad M. Kamhawi, Angappa Gunasekaran
Venue: International Journal of Business Information Systems (2009)

Although previous studies have examined the success of Enterprise Resource Planning (ERP) systems implementations, limited research has considered providing a better understanding of the factors that explain success based on feedback from multiple stakeholders. This research uses a field study to compare between the perceptions of the Information Systems (IS) managers and non-IS managers who work in Bahraini enterprises using ERP systems concerning implementation success and the predicators for such systems. The study results showed that the meaning of success of ERP systems did not seem to differ between IS and non-IS managers. They both adopt user satisfaction and business value metrics to measure success. Moreover, both groups perceived 'resistance' and 'organisational fit' as the prime factors for ERP success. However, their perceptions about the rest of the success factors investigated in this study differed, as IS mangers selected 'ease of use', 'training' and 'technical fit' as success predicators while non-IS managers selected 'competitive pressures', 'strategic fit' and 'business process reengineering' factors. In the end, implications concerning the differences between the perceptions of the two main stakeholders have been put forward.
Multiple Heterogeneous Unmanned Aerial Vehicles, 1st edition
Authors: Anbal Ollero, Ivn Maza
Venue: Springer Tracts In Advanced Robotics (2007)

Aerial robots can be considered as an evolution of the Unmanned Aerial Vehicles (UAVs). This book provides a quite complete overview of issues related to aerial robotics, addressing problems ranging from flight control to terrain perception and mission planning and execution. The major challenges and potentials of heterogeneous UAVs are comprehensively explored. It builds on the results of the European project COMETS, and highlights a number of key research topics in the area of UAV development, including communication, perception, teleoperation and decision making. The monograph emphasizes the current state of technology, the existing problems and potentialities of systems consisting of multiple UAVs which are heterogeneous in view of the different characteristics of the aerial vehicles, the different on-board payloads, and the different on-board information processing capabilities. The book also examines potential applications of UAVs and details a relevant application case: forest fire detection and monitoring.