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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3332卷 455-462页

作者： Yang, Chao-Tung Wang, Ko-Tzu Li, Kuan-Ching Lee, Liang-Teh High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung 407 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung 433 Taiwan Dept. of Computer Science and Engineering Tatung University Taipei 104 Taiwan

In this research paper, we integrate and implement High-Availability (HA) and Load-Balancing technologies to clusters of workstations, increasing both the availability and scalability of services and resources in these systems. The cluster of workstations is commonly built and used as web-based VOD servers. Thus, this paper presents the hardware and software configurations of cluster systems working as VOD servers. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Linux

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Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression

Knowledge Distillation with Source-free Unsupervised Domain ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jing Tian Juan Chen Ning Jiang Chen Lin Bai Suqun Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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Situation and location awareness in harsh environment

Situation and location awareness in harsh environment

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Proceedings of the International Convention MIPRO

作者： Miklos Kozlovszky Daniela Zavec Pavlinić Andreja Oder Gábor Fehér Pál Bogdanov MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Budapest Hungary Titera Technical Innovative Technologies Ltd. Slovenia Prevent&Deloza Ltd. Celje Slovenia OMT-LAB Budapest University Hungary

Enhanced environment awareness is a key element of life protection in dangerous situations. Thus the Personal Protection Equipment (PPE) clothing industry is forced in the direction to develop highly innovative “intelligent” products, and equip their products with sensors and actuators combined with smart technologies. Besides basic functions of protection, thermal and ergonomic comfort, these new generation protective garments have additional functions such as monitoring and/or alarming for prevention in case of emergency situation. Such systems are costly because even if the clothing and engineering parts of the development are feasible, their combination is far from trivial. The different system parts have different requirements, and these requirements are sometimes not compatible with each other. Parameters such as cost, bulkiness, accuracy, independency, scalability, robustness are key parameters, which can cause high acceptance or usage prevention rapidly by the end-users. Our research is focusing on the seamless integration of the various subsystems to develop effective and user friendly, intelligent situation aware personal protection systems. Our solution helps to receive accurate information about user performance and activity conditions. The main goal of our work is to adapt the already existing monitoring solution and provide it to people working in harsh environment. The paper describes the results of our development with the description of the main functionalities.

关键词： Data acquisition Sensor fusion Monitoring Accuracy Data integration Data visualization

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LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers

SSRN

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SSRN 2023年

作者： Xu, Jia Tang, Rongrong Lv, Pin Yu, Minghe Yu, Ge Chen, Enhong School of Computer Electronics and Information Guangxi University GuangXi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi530004 China Northeastern University Shenyang110819 China University of Science and Technology of China Hefei230088 China

Knowledge Tracing (KT) is a critical but challenging problem for many educational applications. As an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing KT tasks. However, existing KT methods only consider the positive learning transfer and neglect the negative learning transfer. To this end, in this paper, we propose a novel deep knowledge tracing model, called positive and negative Learning Transfers based Knowledge Tracing (LTKT). To the best of our knowledge, LTKT makes the first attempt to concurrently utilize the positive and negative learning transfer relations among concepts to improve KT results. First, LTKT employs a statistical-based approach to construct a learning transfer graph (LTG). Then, LTKT quantifies the impact of an exercise's practice result on the knowledge state of the concept via a direct learning effect component, after which a learning transfer effect component is carefully designed to quantify the impact of the practice result on the knowledge states of neighboring concepts based on the positive and negative learning transfer relations modeled by LTG. We conduct extensive experiments on public real-world datasets, and the experimental results show that LTKT outperforms all state-of-the-art KT methods and has good interpretability. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPERVISED HIGH-PERFORMANCE SPEECH SEPARATION

TRANSDUCTIVE NONNEGATIVE MATRIX FACTORIZATION FOR SEMI-SUPER...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Naiyang Guan Long Lan Dacheng Tao Zhigang Luo Xuejun Yang Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Centre for Quantum Computation and Intelligent Systems FEIT University of Technology Sydney Sydney NSW 2007 Australia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan 410073 China

ISBN: (纸本)9781479928941

Regarding the non-negativity property of the magnitude spectrogram of speech signals, nonnegative matrix factorization (NMF) has obtained promising performance for speech separation by independently learning a dictionary on the speech signals of each known speaker. However, traditional NM-F fails to represent the mixture signals accurately because the dictionaries for speakers are learned in the absence of mixture signals. In this paper, we propose a new transductive NMF algorithm (TNMF) to jointly learn a dictionary on both speech signals of each speaker and the mixture signals to be separated. Since TNMF learns a more descriptive dictionary by encoding the mixture signals than that learned by NMF, it significantly boosts the separation performance. Experiments results on a popular TIMIT dataset show that the proposed TNMF-based methods outperform traditional NMF-based methods for separating the monophonic mixtures of speech signals of known speakers.

关键词： Speech Dictionaries as Topic Loudspeakers NMF protocol signals mixtures factorization HIGH-PERFORMANCE

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Optical interconnection as an IP macro of a CMOS library

Optical interconnection as an IP macro of a CMOS library

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Hot Interconnects

作者： T. Yoshikawa I. Hatakeyama K. Miyoshi K. Kurata J. Sasaki N. Kami T. Sugimoto M. Fukaishi K. Nakamura K. Tanaka H. Nishi T. Kudoh Real World Computing Partnership Optical interconnection NEC Laboratory Japan Photonic and Wireless Device Research Laboratories NEC Corporation Limited Tsukuba Ibaraki Japan Silicon System Research Laboratories NEC Corporation Limited Sagamihara Japan Production Technology Research Laboratories NEC Corporation Limited Kawasaki Japan Parallel and Distributed Architecture Group Real World Computing Partnership Tsukuba Ibaraki Japan

With the aim of producing a switch LSI with several-hundred-Gb/s-class throughput, we first developed LAN-LSIs with 10-Gb/s-class optical input/output (I/O) buffer. The optical I/O buffer was supplied as a set consisting of an optical-electrical package, soft or hard macros of the optical interface, and library files for a targeted CMOS technology. Using such library files, system LSI designers can implement an optical interface in their chips in a conventional design manner.

关键词： Libraries Optical buffering High speed optical techniques Optical attenuators Optical design Optical interconnections Integrated circuit interconnections Large scale integration CMOS logic circuits Packaging

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Discretization of non-uniform rational B-spline (NURBS) models for meshless isogeometric analysis

arXiv

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arXiv 2023年

作者： Duh, Urban Shankar, Varun Kosec, Gregor Faculty of Mathematics and Physics University of Ljubljana Jadranska 19 Ljubljana1000 Slovenia School of Computing University of Utah Salt Lake CityUT84112 United States "Jožef Stefan" Institute Department E6 Parallel and Distributed Systems Laboratory Jamova cesta 39 Ljubljana1000 Slovenia

We present an algorithm for fast generation of quasi-uniform and variable-spacing nodes on domains whose boundaries are represented as computer-aided design (CAD) models, more specifically non-uniform rational B-splines (NURBS). This new algorithm enables the solution of partial differential equations (PDEs) within the volumes enclosed by these CAD models using (collocation-based) meshless numerical discretizations. Our hierarchical algorithm first generates quasi-uniform node sets directly on the NURBS surfaces representing the domain boundary, then uses the NURBS representation in conjunction with the surface nodes to generate nodes within the volume enclosed by the NURBS surface. We provide evidence for the quality of these node sets by analyzing them in terms of local regularity and separation distances. Finally, we demonstrate that these node sets are well-suited (both in terms of accuracy and numerical stability) for meshless radial basis function generated finite differences (RBF-FD) discretizations of the Poisson, Navier-Cauchy, and heat equations. Our algorithm constitutes an important step in bridging the field of node generation for meshless discretizations with isogeometric *** Codes 65D99, 65N99, 65Y20, 68Q25 Copyright © 2023, The Authors. All rights reserved.

关键词： Computer aided design

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A Fault Classification Prediction Model Based on Loose-Type Wavelet Neural Network for Server Health Management

A Fault Classification Prediction Model Based on Loose-Type ...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Xiaoqi Lu Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China School of Computer Electronics and Information Guangxi University Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

To prevent the serious impact on production efficiency caused by service interruption, data loss, and other issues resulting from server faults, this paper proposes a server fault prediction model named Wavelet Packet Transform Probabilistic Neural Network(WPT-PNN). WPT-PNN loosely mixes wavelet packet transform and probabilistic neural network to achieve quick fault localization and signal denoising effects. The proposed model is validated using server operational data gathered. The experimental results suggest that the WPT-PNN model can effectively manage the challenges of complicated, non-stationary, and noisy signals in the feature extraction stage and extract signal features reliably. In the fault classification prediction stage, our method improves fault prediction accuracy to 81%, and limits the error range within [-2, 4], better matching the requirements for precise and low false-positive fault prediction in servers.

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