ppxscal/embedding_network huggingface dataset:
Decomposition Method for Tree Kernels
Authors: Peng Huang, Jie Zhu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

We often meet the tree decomposition task in the tree kernel computing. And tree decomposition tends to vary under different tree mapping constraint. In this paper, we first introduce the general tree decomposition function, and compare the three variants of the function corresponding to different tree mapping. Then we will give a framework to generalize the kernels based on tree-to-tree decomposition with the decomposition function.
Topic 2: Performance Prediction and Evaluation
Authors: Francisco Almeida, Michael Gerndt, Adolfy Hoisie, Martin Schulz
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

In recent years, a range of performance evaluation methodologies and tools has been developed for evaluating, designing, and modeling of parallel and distributed systems. The aim of Topic 2, Performance Prediction and Evaluation, is to bring together system designers and researchers involved with qualitative and quantitative evaluation of large scale parallel machines, Grids, and, especially, multicore architectures suffering from contention for critical resources. Of particular interest is work forming a bridge between theory and practice as well as reporting on success or failure of current approaches.
Stable Learning Algorithm of Global Neural Network for Identification of Dynamic Complex Systems
Authors: Jarosław Drapała, Jerzy Świątek, Krzysztof Brzostowski
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Novel convergence properties of identification algorithm for complex input-output systems, which uses recurrent neural networks, are derived. By the term "complex system" we understand a system containing interconnected sub processes (elementary processes), which can operate separately. Each element of the complex system is modeled by a multi-input, multi-output neural network. A model of the whole system is obtained by composing all neural networks into one global network. Stable learning algorithm of such a neural network is proposed. We derived sufficient condition of stability using the second Lyapunov method and proved that algorithm is stable even if stability conditions for some individual neural networks are not satisfied.
An efficient particle swarm optimization for economic dispatch with valve-point effect
Authors: K. Thanushkodi, S. Muthu Vijaya Pandian, R. S. Dhivyapragash, M. Jothikumar, S. Sriramnivas, K. Vinodh
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

This paper presents a new efficient approach to Economic Dispatch (ED) problems with smooth and non smooth cost functions using a Particle Swarm Optimization (PSO) technique. The practical ED problems have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this paper, an efficient PSO technique is employed so that faster convergence is obtained for the same results published in IEEE Proceedings so far. To show its efficiency and effectiveness, the proposed PSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost Functions considering valve-point loading effects. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Third Intenational Workshop on Quality in Modeling
Authors: Ludwik Kuzniarz, Lars Pareto, Jean Louis Sourrouille, Miroslaw Staron
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

Introduction of Model Driven Development (MDD) raises new challenges in software quality management related to shifting the focus to models from text documents and code. The goal of this workshop was to gather researchers and practitioners interested in the emerging issues of quality in the context of MDD and to provide a forum for discussions of emerging issues related to software quality in MDD. An intended outcome of the workshop was an initial elaboration of a unified quality model for models. The paper reports activities performed in the workshop and the main results achieved throughout the workshop as well as during the follow-up activities. The workshop was divided into two parts: presentation part, where paper contributions were presented and discussed, and working part, where a guided discussion was conducted aimed at elaboration of a common quality model. The working part was preceded by an introductory presentation setting up a frame for the discussion, followed by concise position statements of the participants, and discussion towards formulation of a common quality model. As a follow-up activity, the contributions from the working part were combined into a common quality model published as a technical research report and highlighted in the paper. Future research directions and planned activities are also outlined.
Report on the XML Mining Track at INEX 2005 and INEX 2006
Authors: Ludovic Denoyer, Patrick Gallinari, Anne-Marie Vercoustre
Venue: Comparative Evaluation of XML Information Retrieval Systems: 5th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2006, Dagstuhl Castle, Germany, December 17-20, 2006, Revised and Selected Papers (2007)

This article is a report concerning the two years of the XML Mining track at INEX (2005 and 2006). We focus here on the classification and clustering of XML documents. We detail these two tasks and the corpus used for this challenge and then present a summary of the different methods proposed by the participants. We last compare the results obtained during the two years of the track.
Evaluating the REDEEM Authoring Tool: Can Teachers Create Effective Learning Environments?
Authors: Shaaron Ainsworth, Shirley Grimshaw
Venue: International Journal of Artificial Intelligence in Education (2004)

The REDEEM authoring environment allows teachers to create learning environments from existing computer-based training (CBT) by imposing their pedagogical preferences about how students should best be taught. We conducted two studies where classroom teachers constructed learning environments with REDEEM from pre-existing CBT to explore if this approach is educationally effective. Using a crossover design, the learning outcomes for 14-16 year old students who studied Genetics with these environments (either a REDEEM then CBT course or vice versa) were compared. In the first study, we found that performance of 74 students improved significantly but was not influenced by type of environment. Inspection of process data revealed that students who engaged with REDEEM's features did learn more. In the second study, conducted in a more natural context, a further 15 students completed the courses. REDEEM significantly improved learning compared to CBT. Analysis suggested that REDEEM enhanced performance by supporting additional interactivity but that macro-adaptation did not appear to impact upon learning. Possible interpretations of these results are discussed in the light of the many evaluation issues for authoring tools.
An experimental environment for teaching Java security
Authors: Anna Riccioni, Enrico Denti, Roberto Laschi
Venue: Proceedings of the 6th international symposium on Principles and practice of programming in Java (2008)

In many fields of Computer Engineering education it is crucial that students gain both conceptual understanding and practical skills. To this end, an effective teaching approach relies on a blended-learning strategy that combines face-to-face lessons with students' practice on some suitable educational tool. Such a tool should support students through the whole learning process, and help them to improve their modelling ability along with their programming expertise. In this paper we describe S-vLab, a virtual laboratory for supporting teaching and learning in several applicative domains; in particular, we analyse S-vLab applied to the Information Security field. In this context, one of its main goals is to help students in dealing with the Java Security Platform, supporting different learning styles and allowing multiple formative paths. Using S-vLab students can, in various steps, model a secure system, simulate its behaviour and evaluate its performance, and finally build and test a software prototype by leveraging numerous facilities. These include the provision of extracts of sample Java code, the availability of supporting resources and a continuous and immediate feedback, aimed at pointing out possible errors and suggesting solutions. After describing more in detail the features provided by S-vLab, as well as how the depicted functionalities can help in achieving the settled learning goals, we refer on the results obtained in an experimentation of the virtual lab during a course on Information Security with about 150 students.
An Improved SVM Based on 1-Norm for Selection of Personal Credit Scoring Index System
Authors: Xin Xue, Guoping He
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

The selection of evaluating index system is the key to personal credit scoring, which is a feature selection problem.By improving the typical SVM based on 1-norm, which can select the important and necessary feature of samples, an improved SVM based on 1-norm adapted to the selection of personal credit scoring index system is proposed. Experimental results shows that the new improved method can select evaluating index system with small scale and enhance the generality ability and reduce the arithmetic complexity of the classification machine.
Fuzzy Clustering for the Identification of Hinging Hyperplanes Based Regression Trees
Authors: Tamas Kenesei, Balazs Feil, Janos Abonyi
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

This article deals with the identification of hinging hyperplane models. This type of non-linear black-box models is relatively new, and its identification is not thoroughly examined and discussed so far. They can be an alternative to artificial neural nets but there is a clear need for an effective identification method. This paper presents a new identification technique for that purpose based on a fuzzy clustering technique called Fuzzy c-Regression Clustering. To use this clustering procedure for the identification of hinging hyperplanes there is a need to handle restrictions about the relative location of the hyperplanes: they should intersect each other in the operating regime covered by the data points. The proposed method recursively identifies a hinging hyperplane model that contains two linear submodels by partitioning of the operating region of one local linear model resuling in a binary regression tree. Hence, this paper proposes a new algorithm for the identification of tree structured piecewise linear models, where the branches correspond to linear division of the operating regime based on the intersection of two local linear models. The effectiveness of the proposed model is demonstrated by a dynamic model identification example.
Automatic creation and evaluation of MPEG-7 compliant summary descriptions for generic audiovisual content
Authors: Nuno Matos, Fernando Pereira
Venue: Image Communication (2008)

Today's world is characterized by the increasing availability of audiovisual content, created in many application domains and, thus, with rather different semantic characteristics. Audiovisual content is easily acquired, produced, processed, coded, stored and distributed, progressively making more important the ability for users to selectively consume the large amounts of content available. Automatic audiovisual summarization is a technology playing a major role towards facilitating the user's effective consumption of large amounts of audiovisual data in a reduced amount of time as time is getting more and more precious and scarce. This paper proposes an automatic summarization system for generic audiovisual content based on MPEG-7 compliant summary descriptions. To evaluate the quality of the created summaries, a user evaluation methodology was designed, and applied with promising results, showing that the developed application is able to summarize with success generic audiovisual content, especially high action content. The main novelty of this paper is related to the usage of the MPEG-7 summarization tools in combination with an arousal-based audiovisual summarization model, and to the novel user evaluation methodology and study.
LES approach for high Reynolds number wall-bounded flows with application to turbulent channel flow
Authors: C. Pantano, D. I. Pullin, P. E. Dimotakis, G. Matheou
Venue: Journal of Computational Physics (2008)

We describe a large-eddy simulation approach for turbulent channel flow using the stretched-vortex subgrid-scale model. The inner region of the turbulent boundary layer is not included in the modeling of this attached, wall-bounded flow. Appropriate boundary conditions and closure are derived using a combination of elements from asymptotic expansions, matching, and well-established wall-modeling approaches. The modeling approach for this application combines the stretched-vortex subgrid model with a localized wall-shear-stress treatment that relates the instantaneous wall-parallel velocity to the shear stress via the log-law, as appropriate for this (near-) zero pressure gradient flow. The impermeability boundary condition is built into the method such that only the outer-flow solution is simulated, obviating the need to impose the stiff no-slip condition at the wall. This formulation attempts to minimize numerical and modeling errors introduced by the boundary-condition treatment, while preserving the fundamental elements required to predict low-order statistics of these flows. We present simulation results for turbulent channel flow up to Reynolds number based on the wall-friction velocity of 10^6. These compare favorably with results from large-scale DNS and experimental correlations.
Neighborhood-Based Local Sensitivity
Authors: Paul N. Bennett
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

We introduce a nonparametric model for sensitivity estimation which relies on generating points similar to the prediction point using its k nearest neighbors. Unlike most previous work, the sampled points differ simultaneously in multiple dimensions from the prediction point in a manner dependent on the local density. Our approach is based on an intuitive idea of locality which uses the Voronoi cell around the prediction point, i.e. all points whose nearest neighbor is the prediction point. We demonstrate how an implicit density over this neighborhood can be used in order to compute relative estimates of the local sensitivity. The resulting estimates demonstrate improved performance when used in classifier combination and classifier recalibration as well as being potentially useful in active learning and a variety of other problems.
Bengali and Hindi to English CLIR Evaluation
Authors: Debasis Mandal, Mayank Gupta, Sandipan Dandapat, Pratyush Banerjee, Sudeshna Sarkar
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This paper presents a cross-language retrieval system for the retrieval of English documents in response to queries in Bengali and Hindi, as part of our participation in CLEF 2007 Ad-hoc bilingual track. We followed the dictionary-based Machine Translation approach to generate the equivalent English query out of Indian language topics. Our main challenge was to work with a limited coverage dictionary (of coverage ~ 20%) that was available for Hindi-English, and virtually non-existent dictionary for Bengali-English. So we depended mostly on a phonetic transliteration system to overcome this. The CLEF results point to the need for a rich bilingual lexicon, a translation disambiguator, Named Entity Recognizer and a better transliterator for CLIR involving Indian languages. The best MAP values for Bengali and Hindi CLIR for our experiment were 7.26% and 4.77%, which are 20% and 13% of our best monolingual retrieval, respectively.
Knowledge Acquisition from a Medical Corpus: Use and Return on Experiences
Authors: Lina F. Soualmia, Badisse Dahamna
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The present work aims at refining and expanding user's queries thanks to association rules. We adapted the A-Close algorithm to a medical corpus indexed by MeSH descriptors. The originality of our approach lies in the use of the association rules in the information retrieval process and the exploitation of the structure of the domain knowledge to evaluate the association rules. The results show the usefulness of this query expansion approach. Based on observations, new knowledge is modelled as expert rules.
CGA: Chaotic Genetic Algorithm for Fuzzy Job Scheduling in Grid Environment
Authors: Dan Liu, Yuanda Cao
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

We introduce a Chaotic Genetic Algorithm (CGA) to schedule Grid jobs with uncertainties. We adopt a Fuzzy Set based Execution Time (FSET) model to describe uncertain operation time and flexible deadline of Grid jobs. We incorporate chaos into standard Genetic Algorithm (GA) by logistic function, a simple equation involving chaos. A distinguishing feature of our approach is that the convergence of CGA can be controlled automatically by the three famous characteristics of logistic function: convergent, bifurcating, and chaotic. Following this idea, we propose a chaotic mutation operator based on the feedback of fitness function that ameliorates GA, in terms of convergent speed and stability. We present an entropy based metrics to evaluate the performance of CGA. Experimental results illustrate the efficiency and stability of the resulting algorithm.
Fuzzy Local Binary Patterns for Ultrasound Texture Characterization
Authors: Dimitris K. Iakovidis, Eystratios G. Keramidas, Dimitris Maroulis
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.
Variational Deconvolution of Multi-channel Images with Inequality Constraints
Authors: Martin Welk, James G. Nagy
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

A constrained variational deconvolution approach for multi-channel images is presented. Constraints are enforced through a reparametrisation which allows a differential geometric reinterpretation. This view point is used to show that the deconvolution problem can be formulated as a standard gradient descent problem with an underlying metric that depends on the imposed constraints. Examples are given for bound constrained colour image deblurring, and for diffusion tensor magnetic resonance imaging with positive definiteness constraint. Numerical results illustrate the effectiveness of the methods.
A Framework-Based Environment for Object-Oriented Scientific Codes
Authors: Robert A. Ballance, Anthony J. Giancola, George F. Luger, Timothy J. Ross
Venue: Scientific Programming (1993)

Frameworks are reusable object-oriented designs for domain-specific programs. In our estimation, frameworks are the key to productivity and reuse. However, frameworks require increased support from the programming environment. A framework-based environment must include design aides and project browsers that can mediate between the user and the framework. A framework-based approach also places new requirements on conventional tools such as compilers. This article explores the impact of object-oriented frameworks upon a programming environment, in the context of object-oriented finite element and finite difference codes. The role of tools such as design aides and project browsers is discussed, and the impact of a framework-based approach upon compilers is examined. Examples are drawn from our prototype C++ based environment.
Recursive identification of switched ARX systems
Authors: René Vidal
Venue: Automatica (Journal of IFAC) (2008)

We consider the problem of recursively identifying the parameters of a deterministic discrete-time Switched Auto-Regressive eXogenous (SARX) model, under the assumption that the number of models, the model orders and the mode sequence are unknown. The key to our approach is to view the identification of multiple ARX models as the identification of a single, though more complex, lifted dynamical model built by applying a polynomial embedding to the input/output data. We show that the dynamics of this lifted model do not depend on the value of the discrete state or the switching mechanism, and are linear on the so-called hybrid model parameters. Therefore, one can identify the parameters of the lifted model using a standard recursive identifier applied to the embedded input/output data. The estimated hybrid model parameters are then used to build a polynomial whose derivatives at a regressor give an estimate of the parameters of the ARX model generating that regressor. The estimated ARX model parameters are shown to converge exponentially to their true values under a suitable persistence of excitation condition on a projection of the embedded input/output data. Such a condition is a natural generalization of the well known result for ARX models. Although our algorithm is designed for perfect input/output data, our experiments also evaluate its performance as a function of the level of noise for different choices of the number of models and model orders. We also present an application to temporal video segmentation.
Guest Editor's Introduction: Multimedia Content Modeling and Personalization
Authors: Marios C. Angelides
Venue: IEEE MultiMedia (2003)

There's a wealth of possiblities in the realm of personalized multimedia. Personalizing multimedia content, however, is an extensive process that involves extracting and modeling semantic and structural information about the content as well as metadata; building user profiles either manually or automatically through direct obersvation of user behavior; retrieving and filtering content through the user profiles; and adapting the filtered content to fit usage conditions and user preferences.