ppxscal/embedding_network huggingface dataset:
MPI and Hybrid Programming Models for Petascale Computing
Authors: William D. Gropp
Venue: Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2008)

In 2011, the National Center for Supercomputing Applications at the University of Illinois will begin operation of the Blue Waters petascale computing system. This system, funded by the National Science Foundation, will deliver a sustained performance of one to two petaflops for many applications in science and engineering.Blue Waters will support a variety of programming models, including the "MPI everywhere" model that is the most common among today's MPI applications. In addition, it will support a variety of other programming models. The programming models may be used instead of MPI or they may be used in combination with MPI. Such a combined programming model is often called a hybrid model. The most familiar of the models used in combination with MPI is OpenMP, which is designed for shared-memory systems and is based on the use of multiple threads in each MPI process. This programming model has found mixed success to date, with many experiments showing little benefit while others show promise. The reason for this is related to the use of OpenMP within MPI programs--where OpenMP is used to complement MPI, for example, by providing better support for load-balancing adaptive computations or sharing large data tables, it can provide a significant benefit. Where it is used as an alternative to MPI, OpenMP often has difficulty achieving the performance of MPI (MPI's much-criticized requirement that the user directly manage data motion ensures that the programmer does in fact manage that memory motion, leading to improved performance). This suggests that other programming models can be productively combined with MPI as long as they complement, rather than replace, MPI.
Encoding fuzzy possibilistic diagnostics as a constrained optimization problem
Authors: Antonio Sala
Venue: Information Sciences: an International Journal (2008)

This paper discusses a knowledge-base encoding methodology for diagnostic tasks, transforming such knowledge into constrained optimization problems. The methodology is based on a reinterpretation of the consistent causal reasoning paradigm [D. Dubois, H. Prade, Fuzzy relation equations and causal reasoning, Fuzzy Sets and Systems 45 (2) (1995) 119-134] as an equivalent problem of feasibility subject to equality and inequality constraints (in the binary case). Then, it is extended to the fuzzy case. Preferences under uncertain knowledge are incorporated by transforming the feasibility problem into an optimization one, which may be interpreted in possibilistic terms. The problem is solved by efficient, widely-known, linear and quadratic programming tools, which are able to cope with large-scale problems. Examples illustrating some of the concepts and possibilities of the proposed procedure, as well as a summary comparison with other approaches are also discussed.
Motion Segmentation from Feature Trajectories with Missing Data
Authors: Carme Julià, Angel Sappa, Felipe Lumbreras, Joan Serrat, Antonio López
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

This paper presents a novel approach for motion segmentation from feature trajectories with missing data. It consists of two stages. In the first stage, missing data are filled in by applying a factorization technique to the matrix of trajectories. Since the number of objects in the scene is not given and the rank of this matrix can not be directly computed, a simple technique for matrix rank estimation, based on a frequency spectra representation, is proposed. In the second stage, motion segmentation is obtained by using a clustering approach based on the normalized cuts criterion. Finally, the shape S and motion M of each of the obtained clusters (i.e., single objects) are recovered by applying classical SFM techniques. Experiments with synthetic and real data are provided in order to demonstrate the viability of the proposed approach.
Iterative minimization of H2 control performance criteria
Authors: Alexandre S. Bazanella, Michel Gevers, Ljubiša Mišković, Brian D. O. Anderson
Venue: Automatica (Journal of IFAC) (2008)

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H"2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. A limiting factor in the application of these methods is the lack of useful conditions guaranteeing convergence to the global minimum; several adaptive control algorithms suffer from the same limitation. In this paper the H"2 performance criterion is analyzed in order to characterize and enlarge the set of initial parameter values from which a gradient descent algorithm can converge to its global minimum.
Feature Bispectra and RBF Based FM Signal Recognition
Authors: Yuchun Huang, Zailu Huang, Benxiong Huang, Shuhua Xu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

Automatic communication signal (e.g., FM signal) classification and identification focus on finding the fine feature contained in the almost approximate noisy communication signal comprehensively identifying the the same or different version of transmitters in modern electronic warfare. Direct use of HOS becomes unavailable for on-line application because of its huge computation time and memory space especially in the case of high frequency FM signal. This paper presents a novel view to improve the HOS analysis efficiency by sub-sampling while preserving the noise-contaminated fine feature and eliminating the random Gaussian noise. FM signal-related feature bispectra are also introduced to translate the 2-D feature matching pattern to a 1-D one applicable for an optimal adaptive k-means iterative RBF classifier. Computer simulations show that this novel feature bispectra outperform AIB and SB in terms of computation time and recognition rate for on-line steady FM signal recognition.
FAMA Framework
Authors: Pablo Trinidad, David Benavides, Antonio Ruiz-Cortés, Sergio Segura, Alberto Jimenez
Venue: Proceedings of the 2008 12th International Software Product Line Conference (2008)

FAMA Framework (FAMA FW) is a tool for the automated analysis of variability models (VM). Its main objective is providing an extensible framework where current research on VM automated analysis might be developed and easily integrated into a final product. FAMA FW is built following the SPL paradigm supporting different variability metamodels, reasoners or solvers, analysis questions and reasoner selectors, easing the production of customized VM analysis tools. FAMA FW is written in Java and distributed under LGPL License.
From the Editor in Chief: What's Your PageRank?
Authors: Fred Douglis
Venue: IEEE Internet Computing (2007)

When is advertising good, and when is it bribery. This installment of All Systems Go explores both the good and the bad in online advertising practices.
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression
Authors: Daan Wierstra, Tom Schaul, Jan Peters, Juergen Schmidhuber
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

It has been a long-standing goal in the adaptive control community to reduce the generically difficult, general reinforcement learning (RL) problem to simpler problems solvable by supervised learning. While this approach is today's standard for value function-based methods, fewer approaches are known that apply similar reductions to policy search methods. Recently, it has been shown that immediate RL problems can be solved by reward-weighted regression, and that the resulting algorithm is an expectation maximization (EM) algorithm with strong guarantees. In this paper, we extend this algorithm to the episodic case and show that it can be used in the context of LSTM recurrent neural networks (RNNs). The resulting RNN training algorithm is equivalent to a weighted self-modeling supervised learning technique. We focus on partially observable Markov decision problems (POMDPs) where it is essential that the policy is nonstationary in order to be optimal. We show that this new reward-weighted logistic regression used in conjunction with an RNN architecture can solve standard benchmark POMDPs with ease.
yOWL: An ontology-driven knowledge base for yeast biologists
Authors: Natalia Villanueva-Rosales, Michel Dumontier
Venue: Journal of Biomedical Informatics (2008)

Knowledge management is an ongoing challenge for the biological community such that large, diverse and continuously growing information requires more sophisticated methods to store, integrate and query their knowledge. The semantic web initiative provides a new knowledge engineering framework to represent, share and discover information. In this paper, we describe our efforts towards the development of an ontology-based knowledge base, including aspects from ontology design and population using ''semantic'' data mashup, to automated reasoning and semantic query answering. Based on yeast data obtained from the Saccharomyces Genome Database and UniProt, we discuss the challenges encountered during the building of the knowledge base and how they were overcome.
Use of Circle-Segments as a Data Visualization Technique for Feature Selection in Pattern Classification
Authors: Shir Li Wang, Chen Change Loy, Chee Peng Lim, Weng Kin Lai, Kay Sin Tan
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

One of the issues associated with pattern classification using data-based machine learning systems is the "curse of dimensionality". In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour(kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle-segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.
Efficiently Computing Arbitrarily-Sized Robinson-Foulds Distance Matrices
Authors: Seung-Jin Sul, Grant Brammer, Tiffani L. Williams
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

In this paper, we introduce the HashRF(p,q) algorithm for computing RF matrices of large binary, evolutionary tree collections. The novelty of our algorithm is that it can be used to compute arbitrarily-sized (p ×q) RF matrices without running into physical memory limitations. In this paper, we explore the performance of our HashRF(p,q) approach on 20,000 and 33,306 biological trees of 150 taxa and 567 taxa trees, respectively, collected from a Bayesian analysis. When computing the all-to-all RF matrix, HashRF(p,q) is up to 200 times faster than PAUP* and around 40% faster than HashRF, one of the fastest all-to-all RF algorithms. We show an application of our approach by clustering large RF matrices to improve the resolution rate of consensus trees, a popular approach used by biologists to summarize the results of their phylogenetic analysis. Thus, our HashRF(p,q) algorithm provides scientists with a fast and efficient alternative for understanding the evolutionary relationships among a set of trees.
Vibration Control of Block Forming Machine Based on an Artificial Neural Network
Authors: Qingming Wu, Qiang Zhang, Chi Zong, Gang Cheng
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A two-stage structure model was developed for the vibration control of an actuator platform and a controller based on a three-layer neural network was applied to realize high performance control for the kickstand disturbance of a block forming machine. This paper presents a survey of the basic theory of the back-propagation(BP) neural network architecture including its architectural design, BP algorithm, the root mean square error (RMSE) and optimal model establishment. The situ-test data of the control system were measured by acceleration transducer and the experimental results indicates that the proposed method was effective.
A Survey about the Intent to Use Visual Defect Annotations for Software Models
Authors: Jörg Rech, Axel Spriestersbach
Venue: Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications (2008)

Today, many practitioners have consolidated their experience with software models in collections of design flaws, smells, antipatterns, or guidelines that have a negative impact on quality aspects (such as maintainability). Besides these quality defects, many compilability errors or conformance warnings might occur in a software design. Programming IDEs typically present problems regarding compilability in or near the code (e.g., icons at the line or underlining in the code). Modeling IDEs in MDSD follow a visual paradigm and need a similar mechanism for presenting problems in a clear, consistent, and familiar way. In this paper, we present different visualization concepts for visualizing quality defects and other problems in software models. These concepts use different dimensions such as color, size, or icons to present this information to the user. We used a survey to explore the opinions held by practitioners showing that 89.9% want to be informed about potential defects and prefer icon-, view- and underscore-based concepts to other types of concepts.
Time-Frequency Thresholding: A New Algorithm In Wavelet Package Speech Enhancement
Authors: Gang Wang, Yaohua Xu, Xiaolin Li
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In speech enhancement based on wavelet package, a new concept of time-frequency thresholding and the corresponding algorithm are presented. Based on the masking properties of human auditory system to wavelet denoising, the threshold is modulated to vary with the time-frequency characteristic of the speech's pre-estimate, but not merely the time-variant information as has been adopted in former researches. And via the proposed thresholding, the wavelet package coefficients can be adjusted adaptively and a better denoised speech can be achieved. Experimental results show that compared with the former algorithms the proposed method and algorithm has better performance with less distortion and residual noise.
An e-contracting reference architecture
Authors: Samuil Angelov, Paul Grefen
Venue: Journal of Systems and Software (2008)

Business-to-business e-contracting aims at automating the contracting process between companies. It improves the efficiency of the contracting process and enables the introduction of new business models that can be supported by companies. For the development of an e-contracting system, an architecture is required that describes the system components and the communication channels between them. This paper presents a reference architecture for the development of e-contracting systems. The architecture is designed on the basis of a requirement analysis of e-contracting systems. Established architectural principles are used in its design. The architecture can serve as a foundation in the analysis and design of concrete architectures of e-contracting systems. Furthermore, it can be used as a standardization model that facilitates system integration and communication of ideas. Its value for both software architects and business professionals makes it an important tool in the analysis and implementation of e-contracting systems.
Incremental Learning Method of Simple-PCA
Authors: Tadahiro Oyama, Stephen Karungaru, Satoru Tsuge, Yasue Mitsukura, Minoru Fukumi
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

In this paper, we propose an incremental learning algorithm named Incremental Simple-PCA. This algorithm is added an incremental learning function to the Simple-PCA that is an approximation algorithm of the principal component analysis where an eigenvector can be calculated by a simple repeated calculation. Using the proposed algorithm, it allows to update faster the eigenvector by using incremental data. To verify the effectiveness of this algorithm, we carry out computer simulations on personal authentication that uses face images and wrist motion discrimination using wrist EMG by incremental learning. As a result, we can confirm the effectiveness from the aspects of accuracy and a computing time by comparing the Incremental PCA that gave the incremental learning function to the conventional PCA.
Improved Learning Algorithms of SLFN for Approximating Periodic Function
Authors: Fei Han
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

In this paper, three improved Extreme Learning Machines (ELMs) are proposed to approximating periodic function. According to Fourier series expansion theory, the hidden neurons activation functions in the improved ELM are a class of sine and cosine functions. In addition, the improved ELM analytically determines the output weights of neural networks. In theory, the new algorithm tends to provide the best approximation performance at extremely fast learning speed. The proposed ELMs have better approximation accuracies and faster convergence rate than traditional ELM and gradient-based learning algorithms. Finally, experimental results are given to verify the efficiency and effectiveness of the proposed ELMs.
The segment approach time axis algorithm for proxy bidding application in online auction
Authors: Po-Hsian Huang
Venue: Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering (2008)

As the internet grows rapidly, lots of commercial behaviors break the distant and regional restrictions. The online auction is a popular mode that develops rapidly nowadays. Online auction simplifies the complicated negotiation behavior that solely over the product's price rather than full range of values. For example, the proxy bidding is a function of auction websites which support users to bid and depend on the price of products. However, online auction is considered to be too static and truly reflect human's negotiation behaviors. The purpose of this research is to investigate a better algorithm that can reduce the bidding time of online auction. In this algorithm, multi-attributes of user's requirement and bidding terms were considered simultaneously. This approach provides an austere architecture way for users to make business transaction more flexible, and also enhance the benefit of bidding. This study adopts Segment Approach Time Axis Algorithm (SATAA) to simulate segment time bid in online auction. The results display can effectively to save the costs 94% more than the traditional proxy bidding and increase almost 2 multiple opportunity to win the auction goods. Besides, also the method is proved to fit changeful auction market.
Decision Tree Instability and Active Learning
Authors: Kenneth Dwyer, Robert Holte
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Decision tree learning algorithms produce accurate models that can be interpreted by domain experts. However, these algorithms are known to be unstable --- they can produce drastically different hypotheses from training sets that differ just slightly. This instability undermines the objective of extracting knowledge from the trees. In this paper, we study the instability of the C4.5 decision tree learner in the context of active learning. We introduce a new measure of decision tree stability, and define three aspects of active learning stability. Several existing active learning methods that use C4.5 as a component are compared empirically; it is determined that query-by-bagging yields trees that are more stable and accurate than those produced by competing methods. Also, an alternative splitting criterion, DKM, is found to improve the stability and accuracy of C4.5 in the active learning setting.
Indicators of recursive thinking: an exploration of two approaches to measuring aptitude in recursion
Authors: Bruce R. Vogeli
Venue:  (2008)

Two instruments were developed in this study to measure aptitude in mathematical recursion problem solving, one with the latent trait approach and one with the achievement as aptitude approach. The latent trait approach instrument was designed to assess the cognitive traits, reasoning abilities, and mathematical skills essential to solve problems in mathematical recursion and the achievement as aptitude approach instrument was designed to assess the steps necessary to solve problems in mathematical recursion. The procedures from each phase of the four-phases of test construction were performed and documented for both instruments to evaluate each individually and in comparison with one another. There were two purposes for this study: to provide evidence of the advantages and disadvantages of the latent trait approach and achievement as aptitude approach to aptitude assessment development; and to obtain at least one instrument measuring aptitude in mathematical recursion problem solving with sufficient evidence of validity and reliability to support utility of the instrument. While content validity was established with the latent trait approach instrument, results of the descriptive statistics, item difficulty and item discrimination indices, factor analysis, and calculation of Cronbach's alpha indicated there is insufficient evidence of validity and reliability to support utility of this instrument in its current form. Similarly, results of the achievement as aptitude instrument indicate content validity was established, but evidence of validity and reliability collected from descriptive statistics, factor analysis, and calculation of Cronbach's alpha fail to support utility of this instrument in its current form. The comparison between the results of both instruments developed in this study revealed that the latent trait approach is the stronger of the two. Observation of both sets of item-level means in a 0 to 1 scale depicted higher values for items in the latent trait approach instrument. Cronbach's alpha values were also significantly higher in the latent trait approach instrument than in the achievement as aptitude instrument. Given the higher item-level means and Cronbach's alpha values in the latent trait approach instrument, this instrument is the more effective of the two and could be salvageable with revisions of some of the items.