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E-learning and social networking services in the Greek School Network

E-learning and social networking services in the Greek Schoo...

IADIS International Conference e-Learning 2011, Part of the IADIS Multi Conference on computer Science and Information Systems 2011, MCCSIS 2011

作者： Paraskevas, Michael Stergatu, Helen Varvarigos, Emmanouel Research Academic Computer Technology Institute N.Kazantzaki str. GR-26504 Patras Greece Dept. of Computer Eng. and Informatics University of Patras GR-26504 Patras Greece

ISBN: (纸本)9789728939380

In this paper, we present the Greek School Network (GSN) and describe some of its advanced electronic services, designed to facilitate learning and collaboration. The main objective of the Greek School Network is to provide a network infrastructure for the interconnection of schools and the introduction of a wide range of telematic services to the educational community on a large scale. GSN is a safe educational network, whose services have received great acceptance and use by the school community. We present in detail the GSN electronic services (Web 2.0) related to elearning and social networking. We also provide statistical data that confirm the high use of the services. © 2011 IADIS.

关键词： E-learning

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Exploring functional connectivity networks in fMRI data using clustering analysis

Exploring functional connectivity networks in fMRI data usin...

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Lecture Notes in computer Science

作者： Liu, Dazhong Zhong, Ning Qin, Yulin International WIC Institute Beijing University of Technology Beijing China School of Mathematics and Computer Science Hebei University Baoding China Dept. of Life Science and Informatics Maebashi Institute of Technology Maebashi Japan

ISBN: (纸本)9783642236044

Some approaches have been proposed for exploring functional brain connectivity networks from functional magnetic resonance imaging (fMRI) data. Based on a popular algorithm K-means and an effective clustering algorithm called Affinity Propagation (AP), a combined clustering method to explore the functional brain connectivity networks is presented. In the proposed method, K-means is used for data reduction and AP is used for clustering. Without setting the seed of ROI in advance, the proposed method is especially appropriate for the analysis of fMRI data collected with a periodic experimental paradigm. The validity of the proposed method is illustrated by experiments on a simulated dataset and a human dataset. Receiver operating characteristic (ROC) analysis was performed on the simulated dataset. Results show that this method can efficiently and robustly detect the actual functional response with typical signal changes in the aspect of noise ratio, phase and amplitude. On the human dataset, the proposed method discovered brain networks which are compatible with the findings of previous studies. © 2011 Springer-Verlag.

关键词： Clustering algorithms

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Implementation of 3D SRAD Algorithm on CUDA

Implementation of 3D SRAD Algorithm on CUDA

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2011 4th International Conference on on Intelligent Networks and Intelligent Systems, ICINIS 2011

作者： Zhu, Hongbing Chen, Ying Wu, Jianguo Gu, Jinguang Eguchi, Kei College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Dept. of Computer Science Faculty of Informatics Hiroshima Kokusai Gakuin University Hiroshima Japan College of Information Science and Technology Wuhan University of Science and Technology Wuhan China Department of Technology Education Shizuoka University Shizuoka Japan

ISBN: (纸本)9780769545431

Speckle reducing anisotropic diffusion (SRAD) for three dimensional (3D) ultrasound images is an anisotropic diffusion approach for smoothing speckled imagery and is based on the conventional anisotropic diffusion and the traditional speckle reducing filter. The 3D SRAD has improved the quality of edge preservation and the smoothness of homogenous regions, but it requires much time to complete complex computation that affects its real-time application. To resolve this problem, one parallel 3D SRAD (3D pSRAD) was proposed on CUDA that is a general purpose parallel computing architecture and has solved many complex computational problems efficiently. In additional, a lot of experiments based on real images were employed to demonstrate the effectiveness and availability for the algorithm. The experimental results revealed a 60X parallel speedup over the original 3D SRAD. © 2011 IEEE.

关键词： Speckle

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Paralleling genetic annealing algorithm on grid

Paralleling genetic annealing algorithm on grid

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2011 4th International Conference on on Intelligent Networks and Intelligent Systems, ICINIS 2011

作者： Zhu, Hongbing Li, Chunli Wu, Jianguo Gu, Jinguang Eguchi, Kei College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Dept. of Computer Science Faculty of Informatics Hiroshima Kokusai Gakuin University Hiroshima Japan College of Information Science and Technology Wuhan University of Science and Technology Wuhan China Department of Technology Education Shizuoka University Shizuoka Japan

ISBN: (纸本)9780769545431

Protein structure prediction (PSP) aims to search min energy for protein, which is a problem of non-deterministic polynomial (NP). A lot of algorithms have been proposed to solve this problem, and the time cost of the algorithms are significantly expensive due to complex computation. Genetic annealing algorithm (GAA) combining with simulated annealing algorithm and genetic algorithm, which is one of the most representative algorithms to be applied into the PSP, also requires large computing power for the complexity of the algorithm itself. To improve the efficiency of the algorithm, we established a grid system, implemented one parallel GAA algorithm in this system, and made some improvements to this algorithm. The results of the experiments shown that the parallel GAA's maximum speed-up rate is 3.81 times the serial algorithm. © 2011 IEEE.

关键词： Genetic algorithms

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Paralleling euclidean particle swarm optimization in CUDA

Paralleling euclidean particle swarm optimization in CUDA

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2011 4th International Conference on on Intelligent Networks and Intelligent Systems, ICINIS 2011

作者： Zhu, Hongbing Guo, Yongmei Gu, Jinguang Wu, Jianguo Eguchi, Kei College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Dept. of Computer Science Faculty of Informatics Hiroshima Kokusai Gakuin University Hiroshima Japan College of Information Science and Technology Wuhan University of Science and Technology Wuhan China Department of Technology Education Shizuoka University Shizuoka Japan

ISBN: (纸本)9780769545431

Euclidean Particle Swarm Optimization (EPSO) is a swarm intelligence algorithm, which has been successful applied to many engineering optimization problems and shown its high search speed in these applications. However, with the increase in the dimension of optimization problems and the number of local optima, the processing speed of the EPSO has become a bottleneck of applications as each particle of them has to calculate separately fitness. In this paper the EPSO has been parallelled in Compute Unified Device Architecture (CUDA) to solve the bottleneck. Five benchmark functions had been employed to examine the performance of the parallelled EPSO (pEPSO), and the experimental results shown that the average processing of calculating fitness had been accelerated to maximum 16.27 times the original algorithm. © 2011 IEEE.

关键词： Graphics processing unit

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A musical mood trajectory estimation method using lyrics and acoustic features

A musical mood trajectory estimation method using lyrics and...

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2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 1st International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, MIRUM'11

作者： Nishikawa, Naoki Itoyama, Katsutoshi Fujihara, Hiromasa Goto, Masataka Ogata, Tetsuya Okuno, Hiroshi G. Dept. of Intelligence Science and Technology Grad. School of Informatics Kyoto University Japan Japan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

ISBN: (纸本)9781450309868

In this paper, we present a new method that represents an overall musical time-varying impression of a song by a pair of mood trajectories estimated from lyrics and audio signals. The mood trajectory of the lyrics is obtained by using the probabilistic latent semantic analysis (PLSA) to estimate topics (representing impressions) from words in the lyrics. The mood trajectory of the audio signals is estimated from acoustic features by using the multiple linear regression analysis. In our experiments, the mood trajectories of 100 songs in ***'s Best of 2010 were estimated. The detailed analysis of the 100 songs confirms that acoustic features provide more accurate mood trajectory and the 21% resulting mood trajectories are matched to realistic musical mood available at ***. © 2011 ACM.

关键词： Linear regression

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Modelling the MOS Device Conductance Using an Extended Tunnelling Model and Subsequent Determination of Interface Traps.

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MRS Online Proceedings Library (OPL) 2011年第1期864卷 E4.25-E4.25页

作者： N. Konofaos Computer Engineering and Informatics Dept. & Research Academic Computer Technology Institute Building B University Campus Patras Greece. email: nkonofao@ceid.upatras.gr

The contribution of the AC conductance on admittance spectroscopy measurements on Metal-Insulator-Semiconductor (MIS) devices allows the calculation of the interface traps density and the relevant time constant. Two equivalent model approaches can be applied in order to explain the experimental results. One model is the statistical model based on the Shockley- Read-Hall (S-R-H) recombination statistics and the other one is a model based on the quantum tunneling effect. Recent evidence suggest that the tunneling model can be equivalent to the statistical one if a continuum of states is also considered in the modeling, creating an extended tunneling model. In the present paper, a further investigation on the extended model is attempted. Admittance spectroscopy data were collected for various Insulator/Si combinations, such as SrTiO3 and BaTiO3 deposited on Si. Both the S-R-H based and the extended tunneling models were used to analyze the data. The results showed that the extended tunneling can model the conductance of the device successfully and can calculate the interface states density and the traps time constant.

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Phase shift accumulation method for timing characterization

Phase shift accumulation method for timing characterization

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2011 IEEE 2nd International Conference on Photonics, ICP 2011

作者： Churayev, Sergey Biryuchinskiy, Sergey Melnikov, Konstantin Paltashev, Timour Dept. of Computer Engineering St.-Pb. University of IT Mechanics and Optics St.Petersburg Russia R and D Dept. Lasers and Information Technologies Co Ltd. Minsk Belarus Dept. of Photonics and Optical Informatics St.-Pb. University of IT Mechanics and Optics St.Petersburg Russia Dept. of Visualization Technology St.-Pb. University of IT Mechanics and Optics St.Petersburg Russia

ISBN: (纸本)9781612842646

This paper explains the creation of new approach of propagation delay measurement in nanostructures during characterization of ASIC standard library cell. Providing more accuracy timing information about library cell (NOR, AND, XOR, etc.) to the design team we can improve a quality of timing analysis inside of ASIC design flow process. Also, this information could be very useful for semiconductor foundry team to make correction in technology process. By comparison of the propagation delay in the CMOS element and result of analog SPICE simulation, we can make assumptions about accuracy and quality of the transistor's parameters. Physical implementation of phase error accumulation method (PHEAM) can be easy integrated at the same chip as close as possible to the device under test (DUT). It was implemented as digital IP core for semiconductor manufacturing process (0.11m, GL130SB). Specialized method helps to observe the propagation time delay in one element of the standard-cell library with up-to picoseconds accuracy and less. Thus, the special useful solutions for VLSI schematic-to-parameters extraction (STPE), basic cell layout verification, design simulation and verification are announced. © 2011 IEEE.

关键词： Signal processing

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Neonatal mortality prediction using real-time medical measurements

Neonatal mortality prediction using real-time medical measur...

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作者： Gilchrist, Jeff Ennett, Colleen M. Frize, Monique Bariciak, Erika Dept. Systems and Computer Engineering Carleton University Ottawa Canada Dept. Controls Communication and Healthcare Informatics Philips Research North America Briarcliff Manor NY United States School of Information Technology and Engineering University of Ottawa Ottawa Canada Faculty of Medicine University of Ottawa Children's Hospital of Eastern Ontario Ottawa Canada

ISBN: (纸本)9781424493388

Current neonatal illness scoring systems are not designed to predict outcomes for individual patients, but rather can provide an overview of a population of patients for objective comparison when reporting outcomes. Having more patient-specific predictions may help physicians make better treatment decisions in a Neonatal Intensive Care Unit (NICU) environment. We developed neonatal mortality prediction models using C5.0 decision tree software that met criteria for clinically useful results (50-60% sensitivity, 90% specificity) for individual patients using data from real-time medical measurement devices. The models were evaluated to identify: (1) the model with the best performance based on minimizing false positives, and (2) the attributes used most often in the best clinically useful models. Performance results showed that the mortality model using summary data during the first 48 hours after NICU admission provided, on average, the highest sensitivity and specificity with the least number of false positives (sensitivity=63%, specificity=94%, positive predictive value=38%), exceeding the performance criteria requested by our clinical partners. The attributes used most often in the best models for predicting mortality with our data were: mean blood pressure, serum pH, immature/total neutrophil ratio, serum sodium, serum glucose, respiratory rate, heart rate, and pO2 blood oxygen level. © 2011 IEEE.

关键词： Decision support systems

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Comparison of Machine Learning Techniques using the WEKA Environment for Prostate Cancer Therapy Plan

Comparison of Machine Learning Techniques using the WEKA Env...

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IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Nikolaos Mallios Elpiniki Papageorgiou Michael Samarinas Dept. of Informatics and Computer Technology Technological Educational Institute of Lamia Lamia Greece FEBU General Hospital of Larissa Greece

The improvement and exploitation of a number of prominent Data Mining techniques in numerous real-world application areas (e.g. Industry, Healthcare and Bioscience) has led to the utilization of such techniques in machine learning environments, in order to extract useful pieces of information of the specified data and support decision making. Throughout this study, a comprehensive techniques' comparison is performed upon a fairly large set of data consisting of real medical incidents of men with the diagnosis of prostate cancer which are receiving medical treatment. 40 patients, suffered previously with prostate cancer and without undergone radiation therapy, were examined for therapy change after already receiving medical treatment. Six parameters were measured for eight subsequent quartiles to assess the patient state and its treatment outcome. Specifically, with the aim of the open source WEKA environment, the given data is tested with a number of machine learning andclassification techniques in order to compare the performance of the chosen algorithms upon the practitioner's decision of a potential therapy change.

关键词： Classification algorithms Medical treatment Machine learning algorithms Machine learning Prostate cancer Blood

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