ppxscal/embedding_network huggingface dataset:
A Quadratic Kernel for 3-Set Packing
Authors: Faisal N. Abu-Khzam
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

We present a reduction procedure that takes an arbitrary instance of the 3-Set Packing problem and produces an equivalent instance whose number of elements is bounded by a quadratic function of the input parameter. Such parameterized reductions are known as kernelization algorithms, and each reduced instance is called a problem kernel. Our result improves on previously known kernelizations and can be generalized to produce improved kernels for the r-Set Packing problem whenever r is a fixed constant. Improved kernelization for r-Dimensional-Matching can also be inferred.
Estimation of State---Space Spatial Component for Cuboid Track---Before---Detect Motion Capture Systems
Authors: Przemysław Mazurek
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

In the paper spatial component estimation for Track---Before---Detect (TBD) based motion capture systems is presented. Using TBD algorithms is possible to track markers at low Signal---to---Noise Ratio level that is a typical case in motion capture system. In the paper is considered special and the most popular rectangular capture area (cuboid volumen) for three configurations of image processing schemes: full frame processing, single camera optimized and multiple cameras optimized. In the article separate TBD processing for every camera is assumed.
Voice source parameter measurement based on multi-scale analysis of electroglottographic signal
Authors: A. Bouzid, N. Ellouze
Venue: Speech Communication (2009)

This paper deals with glottal parameter measurement from electroglottographic signal (EGG). The proposed approach is based on GCI and GOI determined by the multi-scale analysis of the EGG signal. Wavelet transform of EGG signal is done with a quadratic spline function. Wavelet coefficients calculated on different dyadic scales, show modulus maxima at localized discontinuities of the EGG signal. The detected maxima and minima correspond to the so-called GOIs and GCIs. To improve the GCI and GOI localization precision, the product of wavelet transform coefficients of three successive dyadic scales, called multi-scale product (MP), is operated. This process enhances edges and reduces noise and spurious peaks. Applying the cubic root amplitude on the multi-scale product improves the detection of weak GOI maximum and avoids the GCI misses. Applied on the Keele University database, the method brings about a good detection of GCI and GOI. Based on the GCI and GOI, voicing classification, pitch frequency and open quotient measurements are processed. The proposed voicing classification approach is evaluated with additive noise. For clean signal the performance is of 96.4%, and at SNR level of 5dB, the performance is of 93%. For the fundamental frequency and the open quotient measurement, the comparison of the MP with the DEGG, Howard (3/7), the threshold (35% and 50%), and the DECOM methods show that this new proposed approach is similar to the major methods with an improvement displayed by its lowest deviation.
Sylvester's double sums: The general case
Authors: Carlos DAndrea, Hoon Hong, Teresa Krick, Agnes Szanto
Venue: Journal of Symbolic Computation (2009)

In 1853 Sylvester introduced a family of double-sum expressions for two finite sets of indeterminates and showed that some members of the family are essentially the polynomial subresultants of the monic polynomials associated with these sets. A question naturally arises: What are the other members of the family? This paper provides a complete answer to this question. The technique that we developed to answer the question turns out to be general enough to characterize all members of the family, providing a uniform method.
Stylized lighting for cartoon shader
Authors: Hideki Todo, Ken Anjyo, Takeo Igarashi
Venue: Computer Animation and Virtual Worlds (2009)

In the context of non-photorealistic imaging, such as digital cel animation, lighting is symbolic and stylized to depict the scene's mood and the geometric or physical features of the objects in the scene. Stylized light and shade should therefore be intentionally animated rather than rigorously simulated. However, it is difficult to achieve smooth animation of light and shade that are stylized with a user's intention, because such stylization cannot be achieved using just conventional 3D lighting. To address this problem, we propose a 3D stylized lighting method, focusing on several stylized effects including straight lighting, edge lighting, and detail lighting which are important features in hand-drawn cartoon animation. Our method is an extension of the conventional cartoon shader and introduces a light coordinate system for light shape control with smooth animations of light and shade. We also extend a toon mapping process for detailed feature lighting. Having these algorithms in a real-time cartoon shader, our prototype system allows the interactive creation of stylized lighting animations. We show several animation results obtained by our method to illustrate usefulness and effectiveness of our method. Copyright &copy; 2009 John Wiley & Sons, Ltd. We propose a 3D stylized lighting method, focusing on several stylized effects including straight lighting, edge lighting, and detail lighting which are important features in hand-drawn cartoon animation. To accomplish these, we introduce a light coordinate system for smooth animation of light and shade. We also extend a toon mapping process for detailed feature lighting.
Distributed semantic document retrieval using O-FCN
Authors: Fabio Farina, Silvia Calegari
Venue: Future Generation Computer Systems (2009)

Object-Fuzzy Concept Network (O-FCN) is a recent knowledge representation model to integrate Fuzzy Ontologies in Information Retrieval systems. O-FCNs handle huge data collections and have to face the inherent complexity of semantic manipulation during the retrieval process. Therefore their distribution is an essential requirement to reach good scalability. We present 'Grid2Peer': a distributed architecture for O-FCN-based semantic information retrieval that exploits the self-organization characteristics of both Grid and P2P systems. The most relevant features in Grid2Peer are the adoption of the fuzzy sets to organize the overlay itself, the capability of migrating knowledge towards the location where it is accessed, and granting dynamic load balancing among peers. Numerical simulations are performed in order to analyze these characteristics, evaluating also fuzzy precision and fuzzy recall measures given by the distributed retrieval algorithm for the Grid2Peer architecture.
Emotion recognition using neural networks
Authors: Mehmet S. Unluturk, Kaya Oguz, Coskun Atay
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

Speech and emotion recognition improve the quality of human computer interaction and allow more easy to use interfaces for every level of user in software applications. In this study, we have developed the emotion recognition neural network (ERNN) to classify the voice signals for emotion recognition. The ERNN has 128 input nodes, 20 hidden neurons, and three summing output nodes. A set of 97932 training sets is used to train the ERNN. A new set of 24483 testing sets is utilized to test the ERNN performance. The samples tested for voice recognition are acquired from the movies "Anger Management" and "Pick of Destiny". ERNN achieves an average recognition performance of 100%. This high level of recognition suggests that the ERNN is a promising method for emotion recognition in computer applications.
Automatic sequence of 3D point data for surface fitting using neural networks
Authors: Xueming He, Chenggang Li, Yujin Hu, Rong Zhang, Simon X. Yang, Gauri S. Mittal
Venue: Computers and Industrial Engineering (2009)

In this paper, a neural network-based algorithm is proposed to explore the sequence of the measured point data for surface fitting. In CAD/CAM, the ordered data serves as the input to fit smooth surfaces so that a reverse engineering system can be established for 3D sculptured surface design. The geometry feature recognition capability of back-propagation neural networks is also explored. Scan number and 3D coordinates are used as the inputs of the proposed neural networks to determine the curve which a data point belongs to and the sequence number of the data point on the curve. In the segmentation process, the neural network output is segment number; while the segment number and sequence number on the same curve are the outputs when sequencing those points on the same curve. After evaluating a large number of trials, an optimal model is selected from various neural network architectures for segmentation and sequence. The neural network is successfully trained by the known data and validated the unexposed. The proposed model can easily adapt for new data measured from the same part for a more precise fitting surface. In comparison to Lin et al.'s [Lin, A. C., Lin, S.-Y., & Fang, T.-H. (1998). Automated sequence arrangement of 3D point data for surface fitting in reverse engineering. Computer in Industry, 35, 149-173] method, the presented algorithm neither needs to calculate the angle formed by each point and its two previous points nor causes any chaotic phenomenon of point order.
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
Authors: Dragomir R. Radev, Hongyan Jing, Malgorzata Budzikowska
Venue: NAACL-ANLP 2000 Workshop on Automatic Summarization (2000)

We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.
Cost efficient, adaptive reasoning strategies for pervasive service discovery
Authors: Luke Albert Steller, Shonali Krishnaswamy, Mohamed Methat Gaber
Venue: Proceedings of the 2009 international conference on Pervasive services (2009)

With the emergence of high-end smart phones/PDAs there is an emerging opportunity to enrich mobile/pervasive services with semantic reasoning. This paper presents novel strategies for optimising semantic reasoning for realising semantic applications and services on mobile devices. Our mTableaux algorithm optimises the reasoning process to facilitate service selection. Since even optimised reasoning may be too resource intensive to complete, depending on ontology size and resource availability, we also outline our adaptive reasoning strategy which reduces result accuracy when resources become low. We also evaluate the impact of our strategies on performance and accuracy and show that mTableaux significantly improves performance.
Virtualized recomposition: Cloudy or clear?
Authors: Chris Matthews, Yvonne Coady
Venue: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing (2009)

Virtualization provides a coarse-grained isolation mechanism that results in large systems, with full operating systems and a complete software stack as their foundation. Though much of this foundation is not strictly necessary, the programmatic burden of building systems at a finer-granularity, on a smaller foundation, has previously been shown to be prohibitive. The aim of this work is to revisit this tension, and present an alternative, lightweight and composable approach to virtualization that we call MacroComponents—software components that run in isolation from the rest of the system, but without the full foundations of their more traditionally virtualized counterparts. We argue that this approach will provide a more scalable and sustainable approach for composing robust services in cloud environments, both in terms of dynamic system properties and software engineering qualities.
Combinatorial Algorithms for Structural Variation Detection in High Throughput Sequenced Genomes
Authors: Fereydoun Hormozdiari, Can Alkan, Evan E. Eichler, S. Cenk Sahinalp
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

Recent studies show that, along with single nucleotide polymorphisms and small indels, larger structural variants among human individuals are common. These studies have typically been based high-cost library generation and Sanger sequencing; however, recent introduction of next-generation sequencing (NGS) technologies is changing how research in this area is conducted in a significant way. Highthroughput sequencing technologies such as 454, Illumina, Helicos, and AB SOLiD produce shorter reads than the traditional capillary sequencing, yet they reduce the cost (and/or the redundancy) by a factor of 10 - 100 and perhaps even more. Those NGS technologies with the capability of sequencing paired-ends (or matepairs) of a clone insert (which follows a tight length distribution) have made it feasible to perform detailed and comprehensive genome variation and rearrangement studies. Unfortunately, the few existing algorithms for identifying structural variation among individuals using paired-end reads have not been designed to handle the short read lengths and the errors implied by these platforms. Here, we describe, for the first time, algorithms for identifying various forms of structural variation between a paired-end NGS sequenced genome and a reference genome.
Development of the supplier selection criteria on evaluating wafer supplier: an application of fuzzy Delphi and fuzzy AHP
Authors: Jao-Hong Cheng, Chih-Huei Tang, Chih-Ming Lee
Venue: Proceedings of the 10th WSEAS international conference on Fuzzy systems (2009)

Because of the pressure of globalization in the last two decades, professional services has become an important strategic decision so that supplier selection is a prime concern. In the semiconductor industry, the prior researches worked on analyzing and improving the process, and evaluating the equipment manufacturers. Consequently, this study is to identify critical factors related to the wafer supplier selection. Primary criteria to evaluate supplier selection is acquired by the literatures survey, wafer manufacturers' data and applying fuzzy Delphi method (FDM), and then fuzzy analytic hierarchy process (FAHP) is employed to calculate the weights of these criteria, so as to establish the fuzzy multi-criteria model of wafer supplier selection. The results indicated a greatest weight on the dimension of wafer supplier selection, and seven critical criteria related to wafer supplier selection were: wafer quality, delivery time, service, process capability, price, reputation, and past performance.
Institutionalization does not occur by decree: institutional obstacles in implementing a land administration system in a developing country
Authors: Leiser Silva
Venue: Information Technology for Development (2007)

This article focuses on the implementation of a Spatial Data Infrastructure (SDI) in a developing country. It concentrates on the institutional obstacles of such implementations. Given the heterogeneity of data sources required for SDIs to function, inter-institutional cooperation is a fundamental condition for these infrastructures to operate successfully. It is argued that one of the main obstacles for institutional cooperation is the contradicting meanings assigned to a SDI by users and designers. Those contradicting meanings were identified through an interpretive study conducted in Guatemala. Using hermeneutic analysis, those meanings were synthesized into tour main themes: (a) the contradicting views of Guatemalans and Europeans regarding the rationality of institutions; (b) the link between SDIs and work tasks; (c) institutional jealousy; and (d) the historical resistance to re-examine the institution of land ownership in Guatemala. Each of these themes is examined in the light of critical theory. In so doing, implications for practice and research are discussed. The article concludes by proposing a guideline based on Actor-Network Theory (ANT) that suggests a set of steps required to achieve institutional cooperation.
Digital Museums and Diverse Cultural Knowledges: Moving Past the Traditional Catalog
Authors: Ramesh Srinivasan, Robin Boast, Jonathan Furner, Katherine M. Becvar
Venue: The Information Society (2009)

Web 2.0 technologies have introduced increasingly participatory practices to creating content, and museums are becoming interested in the potentials of “Museum 2.0” for reaching and engaging with new audiences. As technological advances are opening up the ways in which museums share information about the objects in their collections, the means by which museums create, handle, process, and transmit knowledge has become more transparent. For this to be done effectively, however, some underlying contradictions must be resolved between museum practices, which privilege the account of the “expert,” and distributed social technology practices, whose strengths lie in allowing for many, sometimes contradictory, perspectives. This article presents a theoretical position and framework for the adaptation of Web 2.0 technologies within the traditional work of the museum, in ways that support the generation and representation of knowledge in, by, and for diverse communities. We then expand on this theoretical perspective by discussing several case studies of exploratory work in this area, and close the article by presenting a few tactical, bottom-up initiatives that museums and distributed communities can take to facilitate the diffusion of this new conceptual framework. Though the subject of this article is online museums, the issues are relevant to all online collections, in particular portals, online public access catalogs (OPAC), and content management systems.
Modeling of the angle of shearing resistance of soils using soft computing systems
Authors: C. Kayadelen, O. Günaydın, M. Fener, A. Demir, A. Özvan
Venue: Expert Systems with Applications: An International Journal (2009)

Precise determination of the effective angle of shearing resistance (@f') value is a major concern and an essential criterion in the design process of the geotechnical structures, such as foundations, embankments, roads, slopes, excavation and liner systems for the solid waste. The experimental determination of @f' is often very difficult, expensive and requires extreme cautions and labor. Therefore many statistical and numerical modeling techniques have been suggested for the @f' value. However they can only consider no more than one parameter, in a simplified manner and do not provide consistent accurate prediction of the @f' value. This study explores the potential of Genetic Expression Programming, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy (ANFIS) computing paradigm in the prediction of @f' value of soils. The data from consolidated-drained triaxial tests (CID) conducted in this study and the different project in Turkey and literature were used for training and testing of the models. Four basic physical properties of soils that cover the percentage of fine grained (FG), the percentage of coarse grained (CG), liquid limit (LL) and bulk density (BD) were presented to the models as input parameters. The performance of models was comprehensively evaluated some statistical criteria. The results revealed that GEP model is fairly promising approach for the prediction of angle of shearing resistance of soils. The statistical performance evaluations showed that the GEP model significantly outperforms the ANN and ANFIS models in the sense of training performances and prediction accuracies.
Self-modeling in humanoid soccer robots
Authors: Juan Cristóbal Zagal, José Delpiano, Javier Ruiz-del-Solar
Venue: Robotics and Autonomous Systems (2009)

In this paper we discuss the applicability, potential benefits, open problems and expected contributions that an emerging set of self-modeling techniques might bring on the development of humanoid soccer robots. The idea is that robots might continuously generate, validate and adjust physical models of their sensorimotor interaction with the world. These models are exploited for adapting behavior in simulation, enhancing the learning skills of a robot with the regular transference of controllers developed in simulation to reality. Moreover, these simulations can be used to aid the execution of complex sensorimotor tasks, speed up adaptation and enhance task planning. We present experiments on the generation of behaviors for humanoid soccer robots using the Back-to-Reality algorithm. General motivations are presented, alternative algorithms are discussed and, most importantly, directions of research are proposed.
Compressing Pattern Databases with Learning
Authors: Mehdi Samadi, Maryam Siabani, Ariel Felner, Robert Holte
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

A pattern database (PDB) is a heuristic function implemented as a lookup table. It stores the lengths of optimal solutions for instances of subproblems. Most previous PDBs had a distinct entry in the table for each subproblem instance. In this paper we apply learning techniques to compress PDBs by using neural networks and decision trees thereby reducing the amount of memory needed. Experiments on the sliding tile puzzles and the TopSpin puzzle show that our compressed PDBs significantly outperforms both uncompressed PDBs as well as previous compressing methods. Our full compressing system reduced the size of memory needed by a factor of up to 63 at a cost of no more than a factor of 2 in the search effort.
A bilevel optimization approach to machine learning
Authors: Kristin P. Bennett
Venue:  (2008)

A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be selected by the users. While cross validation is a commonly employed and widely accepted method for selecting these parameters, its implementation by a grid-search procedure in the parameter space effectively limits the desirable number of hyper-parameters in a model, due to the combinatorial explosion of grid points in high dimensions. A novel paradigm based on bilevel optimization approach is proposed and gives rise to a unifying framework within which issues such as model selection can be addressed.The machine learning problem is formulated as a bilevel program&mdash;a mathematical program that has constraints which are functions of optimal solutions of another mathematical program called the inner-level program. The bilevel program is transformed to an equivalent mathematical program with equilibrium constraints (MPEC). Two alternative bilevel optimization algorithms are developed to optimize the MPEC and provide a systematic search of the hyper-parameters. In the first approach, the equilibrium constraints of the MPEC are relaxed to form a nonlinear program with linear objective and non-convex quadratic inequality constraints, which is then solved using a general purpose nonlinear programming solver. In the second approach, the equilibrium constraints are treated as penalty terms in the objective, and the resulting non-convex quadratic program with linear constraints is solved using a successive linearization algorithm. The flexibility of the bilevel approach to deal with multiple hyper-parameters, makes it powerful approach to problems such as parameter and feature selection (model selection). In this thesis, three problems are studied: model selection for support vector (SV) classification, model selection for SV regression and missing value-imputation for SV regression. Extensive computational results establish that both algorithmic approaches find solutions that generalize as well or better than conventional approaches and are much more computationally efficient.