ppxscal/embedding_network huggingface dataset:
A software development group exercise support environment, EtUDE: the system overview and the system evaluation through applying to classes
Authors: Hiroaki Hashiura, Kotaro Yamashita, Tatsuya Ishikawa, Yuka Isozaki, Seiichi Komiya
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

In the recent years, software development has become larger in scale and more complicated. Furthermore, development with faster delivery and lower cost is required, thus the software development environment is becoming more and more complex. Accordingly, companies seek high potential students in universities who have a practical sense. The information engineering department of Shibaura Institute of Technology provides classes that adopt a more practical approach to software development so that students can obtain knowledge and skills necessary for software development. However, as class hours assigned for learning software engineering are not sufficient, a support system that enables students to practice outside the classroom or school is required. This support system should have work such that it enables each member belonging to the same group to collectively perform tasks just like in the classroom. In order to solve this problem, the authors developed EtUDE [1][2], the group exercise support environment for software development. Although group exercise helps to reduce the burden of instructing compared to individual exercise, it is difficult to offer instructions that meet the individual's needs. However, it is the goal of the exercise-based classes to accomplish the obtainment of knowledge and skills for software development according to each student's level. Therefore, the authors developed the group exercise support environment for software development, EtUDE which features various functions necessary for group exercise support as well as the function that detects learners who do not benefit from the group exercise and need individual instruction. With this, the software development exercise in more practical form will be available, and at the same time, it is made possible to acquire the knowledge and skills necessary for software development according to each student's level. This essay presents the overview of EtUDE system and the outcome of the application of the system.
Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain
Authors: Bogusław Cyganek
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

This paper presents a cascaded system for recognition of the circular road-signs. The system consists of two compound detectors-classifiers. Each operates on the Gaussian scale-space and does template matching in the log-polar domain. The first module is responsible for detection of the potential sign areas at the coarsest level of the pyramid. The second one, in turn, refines the already found places at the finest level. Thanks to this composition, as well as to the efficient matching in the log-polar domain, the system is very robust in terms of recognition of the signs with different scales and rotations, as well as under partial occlusions, poor illumination conditions, and noise.
An Algebraic-Numeric Algorithm for the Model Selection in Kinetic Networks
Authors: Hiroshi Yoshida, Koji Nakagawa, Hirokazu Anai, Katsuhisa Horimoto
Venue: Proceedings of the 10th international workshop on Computer Algebra in Scientific Computing (2007)

We propose a novel algorithm to select a model that is consistent with the time series of observed data. In the first step, the kinetics for describing a biological phenomenon is expressed by a system of differential equations, assuming that the relationships between the variables are linear. Simultaneously, the time series of the data are numerically fitted as a series of exponentials. In the next step, both the system of differential equations with the kinetic parameters and the series of exponentials fitted to the observed data are transformed into the corresponding system of algebraic equations, by the Laplace transformation. Finally, the two systems of algebraic equations are compared by an algebraic approach. The present method estimates the model's consistency with the observed data and the determined kinetic parameters. One of the merits of the present method is that it allows a kinetic model with cyclic relationships between variables that cannot be handled by the usual approaches. The plausibility of the present method is illustrated by the actual relationships between specific leaf area, leaf nitrogen and leaf gas exchange with the corresponding simulated data.
Relational Analysis of Correlation
Authors: Jörg Bauer, Flemming Nielson, Hanne Riis Nielson, Henrik Pilegaard
Venue: Proceedings of the 15th international symposium on Static Analysis (2008)

In service-oriented computing, correlations are used to determine links between service providers and users. A correlation contains values for some variables received in a communication. Subsequent messages will only be received when they match the values of the correlation. Correlations allow for the implementation of sessions, local shared memory, gradually provided input, or input provided in arbitrary order --- thus presenting a challenge to static analysis.In this work, we present a static analysis in relational form of correlations. It is defined in terms of a fragment of the process calculus COWS that itself builds on the Fusion Calculus. The analysis is implemented and practical experiments allow us to automatically establish properties of the flow of information between services.
Algorithm-system scalability of heterogeneous computing
Authors: Yong Chen, Xian-He Sun, Ming Wu
Venue: Journal of Parallel and Distributed Computing (2008)

Scalability is a key factor of the design of distributed systems and parallel algorithms and machines. However, conventional scalabilities are designed for homogeneous parallel processing. There is no suitable and commonly accepted definition of scalability metric for heterogeneous systems. Isospeed scalability is a well-defined metric for homogeneous computing. This study extends the isospeed scalability metric to general heterogeneous computing systems. The proposed isospeed-efficiency model is suitable for both homogeneous and heterogeneous computing. Through theoretical analyses, we derive methodologies of scalability measurement and prediction for heterogeneous systems. Experimental results have verified the analytical results and confirmed that the proposed isospeed-efficiency scalability works well in both homogeneous and heterogeneous environments.
A Robust Audio Fingerprint's Based Identification Method
Authors: Jérôme Lebossé, Luc Brun, Jean-Claude Pailles
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

An Audio fingerprint is a small digest of an audio file computed from its main perceptual properties. Like human fingerprints, audio fingerprints allow to identify an audio file among a set of candidates without retrieving any other characteristics. We propose in this paper a fingerprint extraction algorithm based on a new audio segmentation method. A new scoring function based on q-grams is used to determine if an input signal is a derivated version of a fingerprint stored in the database. A rule based on this scoring function allows to either recover the original input file or to decide that no fingerprint belonging to the database corresponds to the signal. The proposed method is robust against compression and time shifting alterations of audio files.
The Use of a Sophisticated Graphics Interface in Computer- Assisted Instruction
Authors: Albert Stevens, Bruce Roberts, Larry Stead
Venue: IEEE Computer Graphics and Applications (1983)

The more complex the physical system, the greater the likelihood that hands-on operator training can damage it. A computer model like Steamer offers a somewhat safer alternative.
Fuzzy measure of multiple risk factors in the prediction of osteoporotic fractures
Authors: Tuan D. Pham, Miriam Brandl, Nguyen D. Nguyen, Tuan V. Nguyen
Venue: Proceedings of the 9th WSEAS International Conference on Fuzzy Systems (2008)

Progress in the study of osteoporosis can only be made if its symptons can be diagnosed before bone fractures occur. However, osteoporotic fractures still remain unpredicted at large because the conditions leading to such fractures are due to multiple factors which are partially understood and incomplete. While the measurements of bone-mineral density (BMD) are primarily important in the evaluation and prediction of patients at risk of osteoporosis, patients' access to such measurements is not always feasible. This paper utilizes the theory of fuzzy measures to study the interactions of multiple fracture-risk factors. In a particular case study, we identified several subsets of risk factors that both have higher impact factors than those with the inclusion of BMD measurement and can be easily assessed by routine clinical practice. The results have shown a potential application of the fuzzy model for identifying the importance of the interactions of multiple risk factors being not associated with BMD measurements.
Using Information Gain to Filter Information in CLEF CL-SR Track
Authors: M. C. Díaz-Galiano, M. T. Martín-Valdivia, M. A. García-Cumbreras, L. A. Ureña-López
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 describes the first participation of the SINAI team in the CLEF 2007 CL-SR track. The SINAI team has only participated in the English task. The English collection includes segments of audio speech recognition and topics to evaluate the information retrieval systems. This collection contains interviews with survivors of the Holocaust manually segmented. Moreover, each segment includes different fields with extra information. The topics to evaluate the English task are available in Czech, English, French, German, Dutch and Spanish. This year, the team only wants to establish a first contact with the task and the collection. Thus, the collection has been pre-processed using the Information Gain technique in order to filter the fields with most relevant information. The Lemur toolkit has been the Information Retrieval system used in the experiments.
Modeling Preferences in a Distributed Recommender System
Authors: Sylvain Castagnos, Anne Boyer
Venue: Proceedings of the 11th international conference on User Modeling (2007)

A good way to help users finding relevant items on document platforms consists in suggesting content in accordance with their preferences. When implementing such a recommender system, the number of potential users and the confidential nature of some data should be taken into account. This paper introduces a new P2P recommender system which models individual preferences and exploits them through a user-centered filtering algorithm. The latter has been designed to deal with problems of scalability, reactivity, and privacy.
Eye Tracking Impact on Quality-of-Life of ALS Patients
Authors: Andrea Calvo, Adriano Chiò, Emiliano Castellina, Fulvio Corno, Laura Farinetti, Paolo Ghiglione, Valentina Pasian, Alessandro Vignola
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Chronic neurological disorders in their advanced phase are characterized by a progressive loss of mobility (use of upper and lower limbs), speech and social life. Some of these pathologies, such as amyotrophic lateral sclerosis and multiple sclerosis, are paradigmatic of these deficits. High technology communication instruments, such as eye tracking, can be an extremely important possibility to reintroduce these patients in their family and social life, in particular when they suffer severe disability.This paper reports and describes the results of an ongoing experimentation about Eye Tracking impact on the quality of life of amyotrophic lateral sclerosis patients. The aim of the experimentation is to evaluate if and when eye tracking technologies have a positive impact on patients' lives.
Bayesian Network for Future Home Energy Consumption
Authors: Atsushi Takahashi, Shingo Aoki, Hiroshi Tsuji, Shuki Inoue
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

In order to measure purchase preference on future electronic equipments, this paper proposes a method using Bayesian Network that considers causal relationships for questionnaire data. The proposed method is based on three layers network where the causal relationship is sequentially chained by personal attributes, senses of value and future services. Then, there is a problem that the probability value sensitivity becomes fewness when a model becomes complicated even if the evidence for same nodes is assigned. To overcome this problem and to perform probabilistic inference, this paper proposes stepwise assignment for a variety of evidence. As a result, the purchase preference such as "persons who make much of safety prefer products for an inventory management in their house" is found.
Adaptive Optimal Control for Redundantly Actuated Arms
Authors: Djordje Mitrovic, Stefan Klanke, Sethu Vijayakumar
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. Recent developments, such as the iterative Linear Quadratic Gaussian (iLQG) algorithm, have focused on the case of non-linear, but still analytically available, dynamics. For realistic control systems, however, the dynamics may often be unknown, difficult to estimate, or subject to frequent systematic changes. In this paper, we combine the iLQG framework with learning the forward dynamics for a simulated arm with two limbs and six antagonistic muscles, and we demonstrate how our approach can compensate for complex dynamic perturbations in an online fashion.
Developing the Historical Culture Course by Using the Ubiquitous Game-Based Learning Environment
Authors: Jui-Hung Chen, Te-Hua Wang, Wen-Chih Chang, Louis R. Chao
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

With the advance of information technologies, it is much easier to provide and construct various powerful e-learning platforms. Nevertheless, such kinds of e-learning platforms are available worldwide, but the percentage of using these platforms is still unsatisfactory. The main reason lies in the learning content and learning activities couldn't attract learner. Accordingly these e-learning platforms are not able to fit the needs of either learners or instructors. In this paper, we proposed an integrated learning environment called U-GBL (Ubiquitous Game-Based Learning) system. We used the technologies in interactive video, GPS (Global Positioning System), GIS (Geographic Information System) and RFID (Radio Frequency Identification) to construct the interactive game-based learning environment. Learners could utilize PPC (Pocket PC) to have the game-based learning activities anytime and anywhere. And we also demonstrated an example of the historical culture course content to explain the gaming scenario in our learning environment. We hope this U-GBL learning platform could help to enrich the learning motivation and to improve the learning efficiency.
A PCA-Combined Neural Network Software Sensor for SBR Processes
Authors: Liping Fan, Yang Xu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

The high non-linearity, serious time-variability and uncertainty result in a number of very challenging problems in working on the monitoring and control of biological processes. Many important variables are difficult to measure during monitoring and control. Software sensors can give estimation to unmeasured state variables according to the measured information provided by online measuring instruments available in the system. This offers an alternative feasible program for online measurement. A hybrid soft measurement model that combines principal component analysis with artificial neural networks is applied to monitor the sequencing batch reactor (SBR) process. Simulation results show that the most unmeasured variables can be predicted and the method can capture the main trend of the data.
Outlier Detection Based on Voronoi Diagram
Authors: Jilin Qu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Outlier mining is an important branch of data mining and has attracted much attention recently. The density-based method LOF is widely used in application. However, selecting MinPts is non-trivial, and LOF is very sensitive to its parameters MinPts. In this paper, we propose a new outlier detection method based on Voronoi diagram, which we called Voronoi based Outlier Detection (VOD). The proposed method measures the outlier factor automatically by Voronoi neighborhoods without parameter, which provides highly-accurate outlier detection and reduces the time complexity from O(n 2) to O(nlogn). 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
Analysing the Dependence between Variance and Mean Estimated Values: A Theoretical and Experimental Approach
Authors: Adriana Vlad, Bogdan Badea
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

The starting point of this paper is a known theoretical result - for normal populations, mean and variance estimators are statistically independent. By using an original statistical independence test method, we enriched the study by experimentally investigating the mean-variance data pairs in case of non-gaussian laws. We experimentally noticed that, for non-gaussian symmetrical distributions, mean and variance estimators are still uncorrelated, but not independent random variables. We further theoretically proved that, for symmetrical distributions, the two estimators, mean and variance, are uncorrelated. This new result provides the basis for an original skewness test method, i.e. if mean-variance data pairs are correlated, then the probability density function has an asymmetric shape.
Thank you referees, authors and readers of DKE
Authors: Reind P. van de Riet
Venue: Data Knowledge Engineering (2008)

A bit of history about the Data & Knowledge Engineering Journal is given in this article. Furthermore, lists of referees, of their assistants and of special-issue editors are given. Finally, some numbers about distribution of submissions over countries during the last 5 years, numbers of acceptance and rejects also per field of interest, and waiting times are provided.
Avoiding the WCET Overestimation on LRU Instruction Cache
Authors: L. C. Aparicio, J. Segarra, C. Rodríguez, J. L. Villarroel, V. Viñals
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

The WCET computation is one of the main challenges in hard real-time systems, since all further analysis is based on this value. The complexity of this problem leads existing analysis methods to compute WCET bounds instead of the exact WCET. In this work we propose a technique to compute the exact instruction fetch contribution to the WCET (IFC-WCET) in presence of a LRU instruction cache. We prove that an exact computation does not need to analyze the full exponential number of possible execution paths, but only a bounded subset of them. In the benchmark codes we have studied, the IFC-WCET is up to 62% lower than a bound computed with a widely used approach, and the difference between the number of possible execution paths and the ones relevant for the analysis is extremely large.
Mammographic Image Contrast Enhancement through the Use of Moving Contrast Sweep
Authors: Zailani Mohd Nordin, Nor Ashidi Mat Isa, Umi Kalthum Ngah, Kamal Zuhairi Zamli
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Low contrast in mammographic image has always made detection of subtle signs such as the presence of micro calcification within dense tissue a challenge. Therefore, numerous researches have been conducted on contrast enhancement technique for mammographic image. However, most of these methods focus on enhancing specific range of intensity level producing an enhanced version of the original image. This paper proposes a new approach in dealing with contrast enhancement for mammographic images. Instead of producing just a single image, the approach proposed here will generate multiple different images where each of them corresponds to different range of intensity level. These images will then be combined together to form frames of a moving video which would display seamless transition from one intensity level to another. As a result, the radiologist who is performing visual inspection of the image can sweep through the entire range of intensity level of the image which has been contrast enhanced.
Evolution of Multi-class Single Layer Perceptron
Authors: Sarunas Raudys
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

While training single layer perceptron (SLP) in two-class situation, one may obtain seven types of statistical classifiers including minimum empirical error and support vector (SV) classifiers. Unfortunately, both classifiers cannot be obtained automatically in multi-category case. We suggest designing K(K-1)/2 pair-wise SLPs and combine them in a special way. Experiments using K=24 class chromosome and K=10 class yeast infection data illustrate effectiveness of new multi-class network of the single layer perceptrons.
A Discrete Wavelet-Based Approach to 3D Subdivision Surface Simulating within Controllable Error Range
Authors: Weichao Dang, Rongguo Zhang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

The decomposition and reconstruction approach has been proposed for 3D subdivision surface by using two-dimensional discrete wavelet transform. Both high frequency filter and low frequency filter have been applied in order to obtain the simulation of the original surface. The error generated during this process proves to be just high frequency filter. In this way the error range of detail surface can be determined, which makes the error of the simulation controllable and moreover decides whether or not decomposition of subdivided surface proceeds. Fast wavelet transform has been applied to subdivision surface by using Daubechies wavelet. An algorithm has been described for the purpose and simulating results have been analyzed.