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Objects of alternative set theory in Set@l programming language 1

15th International conference on Parallel Computing Technologies, PaCT 2019

作者： Levin, Ilya I. Dordopulo, Alexey I. Pisarenko, Ivan V. Melnikov, Andrey K. Southern Federal University Academy for Engineering and Technology Institute of Computer Technologies and Information Security Taganrog Russia Supercomputers and Neurocomputers Research Center Taganrog Russia "InformInvestGroup" CJSC Moscow Russia

ISBN: (数字)9783030256364

ISBN: (纸本)9783030256357

Software porting between high-performance computer systems with different architectures requires a major code revision due to the architectural limitation of available programming languages. To solve the problem, we have proposed an architecture-independent Set@l programming language based on the principles of set-theoretic codeview and aspect-oriented programming. In Set@l, a program consists of a source code, which describes an information graph of a computational problem, and aspects, which adapt an algorithm to the architecture and configuration of a computer system. If an algorithm remains unchanged during its architectural adaptation, calculations and their parallelizing are described within the Cantor-Bolzano set theory. In the case of algorithm modification, some collections are indefinite, and we can not treat them as traditional sets with sharply defined elements. To describe indefinite objects, Set@l applies the alternative set theory developed by P. Vopenka. If collection has indefinite type and structure at some level of abstraction, it belongs to a "class" type. In contrast to a class, the indefiniteness of a semiset is an essential and inalienable attribute. The application of classes, sets and semisets allows to describe various methods of the algorithm implementation and parallelizing as an entire Set@l program. In this paper the Jacobi algorithm for the solution of linear equation systems is considered as an example of the utilization of classes and semisets. © 2019, Springer Nature Switzerland AG.

关键词： Application programs

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Deeplearning Convolutional Neural Network based QoE Assessment Module for 4K UHD Video Streaming 9

Deeplearning Convolutional Neural Network based QoE Assessme...

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9th International conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH)

作者： Ashiquzzaman, Akm Oh, Sung M. M. Lee, Dongsu Jung, Hoehyeong Um, Tai-won Kim, Jinsul Chonnam Natl Univ Sch Elect & Comp Engn Gwangju South Korea Chosun Univ Dept Informat & Commun Engn Gwangju South Korea

ISBN: (纸本)9789897583810

With the rapid development of modern high resolution video streaming services, providing high Quality of Experience (QoE) has become a crucial service for any media streaming platforms. Most often it is necessary of provide the QoE with NR-IQA, which is a daunting task for any present network system for it's huge computational overloads and often inaccurate results. So in this research paper a new type of this NR-IQA was proposed that resolves these issues. In this work we have described a deep-learning based Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. This model processes the RAW RGB pixel images as input, the CNN works in the spatial domain without using any hand-crafted or derived features that are employed by most previous methods. The proposed CNN is utilized to classify all images in a MOS category. This approach achieves state of the art performance on the KoniQ-10k dataset and shows excellent generalization ability in classifying proper images into proper category. Detailed processing on images with data augmentation revealed the high quality estimation and classifying ability of our CNN, which is a novel system by far in these field.

关键词： Deep Learning Convolutional Neural Network computer Networks Video Steaming 4K UHD QoE

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Fical: Focal inter-class angular loss for image classification 34

Fical: Focal inter-class angular loss for image classificati...

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34th IEEE International conference on Visual Communications and Image Processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： Image classification

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Phoenix: A Tool for Automated Data-Driven Synthesis of Repairs for Static Analysis Violations

Phoenix: A Tool for Automated Data-Driven Synthesis of Repai...

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International conference on Software Engineering - Companion, ICSE, Companion

作者： Hiroaki Yoshida Rohan Bavishi Keisuke Hotta Yusuke Nemoto Mukul R. Prasad Shinji Kikuchi Fujitsu Laboratories of America Inc. University of California Berkeley Fujitsu Laboratories Ltd.

ISBN: (数字)9781450371223

ISBN: (纸本)9781728165288

One of the major drawbacks of traditional automatic program repair (APR) techniques is their dependence on a test suite as a repair specification. In practice, it is often hard to obtain specification-quality test suites. This limits the performance and hence the viability of such test-suite-based approaches. On the other hand, static-analysis-based bug finding tools are increasingly being adopted in industry but still facing challenges since the reported violations are viewed as not easily actionable. In previous work, we proposed a novel technique that solves both these challenges through a technique for automatically generating high-quality patches for static analysis violations by learning from previous repair examples. In this paper, we present a tool Phoenix, implementing this technique. We describe the architecture, user interfaces, and salient features of Phoenix, and specific practical use cases of its technology. A video demonstrating Phoenix is available at https://***/***.

关键词： Maintenance engineering Tools computer bugs Static analysis Pipelines Laboratories Java

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A Novel Feature Selection Algorithm Based on Artificial Bee Colony Algorithm and Genetic Algorithm

A Novel Feature Selection Algorithm Based on Artificial Bee ...

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2019 IEEE International conference on Power, Intelligent Computing and Systems, ICPICS 2019

作者： Ge, Junqi Zhang, Xutao Liu, Gongyou Sun, Yu College of Computer Science and Technology Jilin University Changchun130012 China

ISBN: (纸本)9781728137209

The classification of microarray data has positive significance for the judgment of cancer and the determination of clinical programs. However, the high dimensionality and small sample characteristics of the microarray data has brought classification a difficult problem. Aiming at the feature selection problem in microarray data classification, a feature selection algorithm based on artificial bee colony algorithm and genetic algorithm is proposed to solve the dimensionality disaster problem in microarray data classification. Finally, the feature subsets obtained by the algorithm are combined with the SVM(Support Vector Machine)classifier to apply to the six published microarray datasets. The experimental results show that the feature subsets obtained based on the proposed scheme can significantly improve the classification performance. © 2019 IEEE.

关键词： Genetic algorithms

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The Research on Flow Table Optimization Based on Dynamic Timeout Mechanism

The Research on Flow Table Optimization Based on Dynamic Tim...

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2019 IEEE International conference on Power Data Science, ICPDS 2019

作者： Ma, Zhaohui Zhao, Gansen Lin, Chengchuang Luo, Haoyu Zhong, Yuanfu Li, Shuangyin Wu, Qinglan Mo, Zefeng Zhang, Zanbo School of Computer Science South China Normal University Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Statistics and Mathematics Guangdong University of Finance Economics Guangzhou China

ISBN: (纸本)9781728137759

SDN(Software Defined Networking) is a new network architecture, which decouples the control plane from data plane and operates the globle network with elaborate abstraction. The control plane advocates a centralized approach of network control, which provides both flexibility and high efficiency. However, there are lots of problems in this architecture and the bottleneck problem is the network's performance. Existing OpenFlow based flow expiry mechanisms relys on a fixed timeout after which the switch proactively removes the flow entries from its flow table which may causes unnecessary flow table occupation. Therefore, in this paper, a strategy of dynamically adjusting timeout time is proposed and the program is implemented. It can dynamically adjust the timeout time according to the actual situation of equipment and data flow, and can better adapt to the network with different data flows. The experiment shows that the strategy can improve the network's performance and the mechanism designed in this paper is proved to be effective. © 2019 IEEE.

关键词： Software defined networking

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Algorithms and implementation of the control program for the multi-lifting operation

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AIP conference Proceedings 2022年第1期2456卷

作者： D. A. Yampolsky A. D. Lukashuk Ural Federal University named after the first President of Russia B.N.Yeltsin 19 Mira street 620002 Ekaterinburg Russia

high-rise buildings became a commonplace phenomenon of today. Vertical transport used in them is a problem requiring a systematic and ordered approach. It is not a rare case when the vertical direction of transport movement becomes a very complex problem in certain tall structures, especially when different people appear at the same transport doors almost simultaneously. To resolve such situations, it is a running practice to set up a control system for a group of elevators. The system is used to control several coordinated elevators in the building to achieve a more efficient transfer of passengers. Predicting more accurately the movement of elevators is of major importance for planning and controlling the control systems of elevator groups. The article highlights a review of the control algorithms for the multi-lifting operation, where the advantages and disadvantages of non-iterative and iterative control algorithms are considered. It is shown that iterative algorithms are more optimal from the point of view of increasing the performance of elevators in multi-lifting operation. The main principles of the multi-lifting algorithm, based on the redistribution of elevator calls, are given. A computer program is presented that implements an iterative algorithm for multi-lifting control, which can be used to simulate multi-lifting operation in various conditions.

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Overcoming GPU memory capacity limitations in hybrid MPI implementations of CFD 1

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12th International conference on Internet and Distributed Computing Systems, IDCS 2019

作者： Choi, Jake Kim, Yoonhee Yeom, Heon-Young Department of Computer Science Seoul National University Seoul Korea Republic of Department of Computer Science Sookmyung Women’s University Seoul Korea Republic of

ISBN: (数字)9783030349141

ISBN: (纸本)9783030349134

In this paper, we describe a hybrid MPI implementation of a discontinuous Galerkin scheme in Computational Fluid Dynamics which can utilize all the available processing units (CPU cores or GPU devices) on each computational node. We describe the optimization techniques used in our GPU implementation making it up to 74.88x faster than the single core CPU implementation in our machine environment. We also perform experiments on work partitioning between heterogeneous devices to measure the ideal load balance achieving the optimal performance in a single node consisting of heterogeneous processing units. The key problem is that CFD workloads need to allocate large amounts of both host and GPU device memory in order to compute accurate results. There exists an economic burden, not to mention additional communication overheads of simply scaling out by adding more nodes with high-end scientific GPU devices. In a micro-management perspective, workload size in each single node is also limited by its attached GPU memory capacity. To overcome this, we use ZFP, a floating-point compression algorithm to save at least 25% of data usage in our workloads, with less performance degradation than using NVIDIA UM. © Springer Nature Switzerland AG 2019.

关键词： Computational fluid dynamics

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EyeMath: Increasing Accessibility of Mathematics to Visually Impaired Readers 23

EyeMath: Increasing Accessibility of Mathematics to Visually...

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23rd International computer Science and Engineering conference, ICSEC 2019

作者： Dumkasem, Kanlayanee Srisingchai, Padchaya Rattanatamrong, Prapaporn Thammasat University Department of Computer Science Faculty of Science and Technology Pathumthani Thailand

ISBN: (纸本)9781728125442

Mathematics education for visually impaired students is challenging because their learning materials are generally limited to braille books, and audiobooks. In order to increase the chance of learning mathematical content for people with visual impairment, this paper presents the design and development of a cloud-based mobile application called EyeMath, using serverless microservices in Amazon AWS. Users can provide images of page snippets for the application to process and read their content to the users. EyeMath segments an input image into smaller pieces and separates pieces that have only plain text from pieces with mathematical symbols. The mathematical-related pieces are further processed into an Abstract Syntax Tree (AST) and then parsed into Thai sentences. For plain text pieces, EyeMath relies on Tesseract OCR to convert them into text. Finally, results for all pieces are combined together systematically for the device's screen reader program to read aloud. The performance evaluation of the application shows high correctness in reading math content within test images and our usability testing confirms the potential usefulness of the application to visually impaired readers. © 2019 IEEE.

关键词： Optical character recognition

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TSception:A Deep Learning Framework for Emotion Detection Using EEG

TSception:A Deep Learning Framework for Emotion Detection Us...

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International Joint conference on Neural Networks (IJCNN)

作者： Yi Ding Neethu Robinson Qiuhao Zeng Duo Chen Aung Aung Phyo Wai Tih-Shih Lee Cuntai Guan School of Computer Science and Engineering Nanyang Technological University Singapore Neuroscience and Behavioral Disorders Program National University of Singapore Medical School Singapore Singapore Singapore General Hospital Singapore Singapore

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this paper, we propose a deep learning framework, TSception, for emotion detection from electroencephalogram (EEG). TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously. The temporal learner consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of the EEG signal, which learns multiple temporal and frequency representations. The spatial learner takes advantage of the asymmetry property of emotion responses at the frontal brain area to learn the discriminative representations from the left and right hemispheres of the brain. In our study, a system is designed to study the emotional arousal in an immersive virtual reality (VR) environment. EEG data were collected from 18 healthy subjects using this system to evaluate the performance of the proposed deep learning network for the classification of low and high emotional arousal states. The proposed method is compared with SVM, EEGNet, and LSTM. TSception achieves a high classification accuracy of 86.03%, which outperforms the prior methods significantly (p

关键词： Kernel Electroencephalography Convolution Machine learning Feature extraction Electrodes Convolutional neural networks

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