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作者： Yang, Chao-Tung Ho, Hsin-Chuan High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 407 Taiwan Department of Computer Science and Information Engineering Tunghai University Lizen High School Taichung Taiwan

The overall popularity of the Internet has helped e-learning become a hot method for learning in recent years. Over the Internet, learners can freely absorb new knowledge without restrictions on time or place. Many companies have adopted e-learning to train their employees. An e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. E-learning has been shown to have impressive potential in e-commerce. At present, most e-learning environment architectures use single computers or servers as their structural foundations. As soon as their work loads increase, their software and hardware must be updated or renewed. This is a big burden on organizations that lack sufficient funds. Thus, in this study we employ a kind of Grid computing technology, called the "Data Grid" to integrate idle computer resources in enterprises into e-learning platforms, thus eliminating the need to purchase costly high-level servers and other equipment.

关键词： Learning systems

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A carbon 2.0 framework based on cloud computing

A carbon 2.0 framework based on cloud computing

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IEEE/International Association for computer and Information science International Conference on computer and Information science

作者： Li, Jiandun Zhang, Wu Peng, Junjie Lei, Zhou Lei, Yu School of Computer Engineering and Science High Performance Computing Center Shanghai University Shanghai China Department of Computer Science and Engineering University of Texas at Arlington Arlington TX United States

ISBN: (纸本)9780769541471

The Copenhagen climate conference 2009 has concluded that we have to change the way how we live. Currently, several carbon management systems are being developed to meet enterprises' requirement. In addition, a number of carbon footprint calculation tools have been developed to fulfill carbon calculation needs from industries, households and individuals. However, under the current circumstance, it is still a challenging issue on how to take advantage of the latest IT technologies to improve carbon calculations with comprehensive functionality. In this paper, we extend carbon 2.0, the concept proposed by previous studies, and introduce a new carbon framework based on cloud computing. This new framework is massively scalable and can configure and deliver carbon-related services dynamically and on demand. Moreover, it is a real low-carbon framework itself! © 2010 IEEE.

关键词： Greenhouse gases

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

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ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Text knowledge representation model based on human concept learning

Text knowledge representation model based on human concept l...

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IEEE International Conference on Cognitive Informatics

作者： Luo, Xiangfeng Cai, Chuanliang Hu, Qingliang School of Computer Engineering and Science Shanghai University Shanghai 200072 China High Performance Computing Center Shanghai University Shanghai 200072 China

ISBN: (纸本)9781424480401

The essential abilities of text knowledge representation, such as automatic construction, carrying abundant semantics and flexible reasoning, should be held due to the rapid growth of web resources and the requirements of the reasoning-based web services. However, current text knowledge representation models either lose many textual semantics or cannot be constructed automatically. To solve the above issues, text knowledge representation model is proposed based on the concept algebra of human concept learning. Then, the degree-2 power series hypothesis is developed and the reasoning ability of text representation is proposed. Finally, the results compared with current knowledge representation models show that our model performs better than other models in representing text knowledge. © 2010 IEEE.

关键词： Knowledge representation

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performance Analysis of Distributed Deep Learning using Horovod for Image Classification 6

Performance Analysis of Distributed Deep Learning using Horo...

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6th International Conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Rakshith, R.M. Lokur, Vineet Hongal, Prateek Janamatti, Vivek Chickerur, Satyadhyan KLE Technological University School of Computer Science and Engineering Hubballi India KLE Technological University Centre for High Performance Computing Hubballi India

ISBN: (纸本)9781665410359

Recent advances in unsupervised feature learning and deep learning methodologies have shown that training large models may significantly improve performance. This research study reviews the topic of training a deep neural network in a distributed manner. The proposed study uses the Horovod to train large models on compute clusters with thousands of computers. It can be viewed that the image classification and segmentation are computationally expensive jobs, with more segmentation. Lower-level tasks may be fine-tuned for enhanced efficiency, but the study demonstrates that the proposed research study can work better and more effectively in categorization by exploiting the distributed deep learning idea. The proposed study demonstrate the application of Horovod on Image classification using CNNs, and our works serve as a benchmark for performance analysis on standard datasets;CIFAR 10 and Cat Vs Dog. © 2022 IEEE.

关键词： Image classification

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performance Analysis of Change Detection Algorithms on Multispectral Imagery 12

Performance Analysis of Change Detection Algorithms on Multi...

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12th International Conference on Computational Intelligence and Communication Networks, CICN 2020

作者： Bidari, Indira Chickerur, Satyadhyan Talikoti, Rekha.M. Kapali, Smita.S. Talawar, Sushmita Sangam, Soumya Kle Technological University School of Computer Science and Engineering Hubballi India Kle Technological University Center for High Performance Computing Hubballi India

ISBN: (纸本)9781728193939

Due to growing human population and technology, huge climate change occurs which impact the environment and lives. Satellite images are profound for monitoring the ground surface. Due to the enormous availability of multispectral images, various applications based on classification, change detection have emerged. Researchers have developed lots of Multispectral Change Detection Methods (MSCD) till date. These methods show essential sub-pixel level details, such as the abundance variation of each underlying material at a given location, or the shift in material distribution within the scene, with time or as a result of significant events such as a natural disaster. The main aim of the proposed work is to present a comparative study of various unsupervised methods for detecting binary changes in multispectral imagery. Through experimental study, we provide a comparative analysis of the algorithms. The various algorithms considered for our study are (1) Principal Component Analysis (PCA), (2) PCA with K-means clustering, (3) Multivariate Alteration Detection (MAD), (4) Iteratively Reweighted Multivariate Alteration Detection (IRMAD). Using real-world multi-temporal multispectral imaging dataset, we assess and compare the performance of all these algorithms and their time efficiency on Central Processing Unit (CPU) and Graphics Processing Unit (GPU). The empirical findings, accompanied by a description of each algorithms pros and cons, are intended to help researchers pick the procedures with the good features for MSCD applications. © 2020 IEEE.

关键词： Change detection

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Hybrid Convolutional Neural Network with Change Detection on Hyperspectral Imagery 1

Hybrid Convolutional Neural Network with Change Detection on...

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1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Bidari, Indira Chickerur, Satyadhyan Thm, Abhishek School of Computer Science and Engineering KLE Technological University Hubballi India Centre for High Performance Computing KLE Technological University Hubballi India

ISBN: (纸本)9781665435420

Change Detection (CD) is one of the major research areas in the field of remote sensing. Hyperspectral Images (HSI's) boosted the change detection technology with their high spectral resolution features. Traditional CD techniques process the low dimensional images not well suited for high dimensional HSI's. Even tiny details can be captured using hyperspectral images, but processing these images is difficult because of their complex high-dimensional data. HSI contains noise and redundancy that affect the spectral features of hyperspectral imagery. We present a hybrid convolution neural network method to address processing complex high dimensional data of hyperspectral images. The proposed network extracts the spectral-spatial information of hyperspectral images by decomposing the change Tensor in three directions. The spectral dimension of the change Tensor is reduced by using I-D Convolution' and then the Tensor is decomposed from two spatial dimensions. A 2-D convolution is applied to extract the spectral and spatial features along different spatial dimensions to improve accuracy. The results on three hyperspectral image datasets illustrate the performance improvement than most state-of-The-Art techniques. © 2021 IEEE.

关键词： Change detection

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HVD-TLS: A novel framework of thread level speculation

HVD-TLS: A novel framework of thread level speculation

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11th IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom-2012

作者： Fan, Xu Li, Shen Zhiying, Wang State Key Lab. of High Performance Computing School of Computer Science and Technology National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769547459

The advent of multicore platform brings a great opportunity to speedup sequential program via thread level parallelism. In this paper we have proposed a novel Thread Level Speculation (TLS) framework HVD-TLS. The framework uses heuristic value prediction to improve the speculation accuracy, reduces the rerolling overhead via value checking based correctness checking mechanism. The framework can partition and schedule task on different processor cores dynamically based on runtime information. We have implemented the HVD-TLS in ANSI C and tested its performance on a 4-core platform. The experiments show that the speedup is 2.2 on the average with a speculation depth equaling to 3. © 2012 IEEE.

关键词： computer architecture

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Building an e-learning platform by Access Grid and Data Grid technologies

Building an e-learning platform by Access Grid and Data Grid...

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Proceedings - 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service, EEE 2004

作者： Ho, Hsin-Chuan Yang, Chao-Tung Chang, Chi-Chung High-Performance Computing Lab. Department of Computer Science Tungahi University Taichung 407 Taiwan National Dali Senior High School Taichung 407 Taiwan

ISBN: (纸本)0769520731

Due to overall popularity of the Internet, e-learning has become a hot method of learning in recent years. Through the Internet, learners can freely absorb new knowledge without the restriction of time and place. Many companies have adopted e-learning to train their employees. The e-learning system can make an enterprise more competitive by increasing the knowledge of its employees. As we know, e-learning has become one of the most potential e-commerce businesses. At present, most e-learning environment architectures are in consideration of a single computer or server as its erecting foundation. As soon as its work load increases, the software and hardware need to be updated or renewed;this is a big burden to a company with insufficient budget. Thus, in this research we employ a kind of Grid computing technology, called "Access Grid", to integrate the idle computer resources in an enterprise into an e-learning platform for substituting the purchase of costly high-level server and equipment. The Access Grid supports group-to-group communication via high-speed networking over the Internet. It provides high quality audio and real-time video interactive interface for e-learning users. All the hardware cost is reasonable so that companies or academies can easily set up their own Access Grid Node. By using Access Grid technologies, enterprises can integrate their training materials into knowledge repository such as "Data Grid" as the e-learning VOD platform so that the knowledge can be shared and published more flexibly and widely.

关键词： Electronic commerce

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Verifying ReLU Neural Networks from a Model Checking Perspective

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Journal of computer science & Technology 2020年第6期35卷 1365-1381页

作者： Wan-Wei Liu Fu Song Tang-Hao-Ran Zhang Ji Wang College of Computer Science National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai 201210China State Key Laboratory for High Performance Computing Changsha 410073China

Neural networks, as an important computing model, have a wide application in artificial intelligence (AI) domain. From the perspective of computer science, such a computing model requires a formal description of its behaviors, particularly the relation between input and output. In addition, such specifications ought to be verified automatically. ReLU (rectified linear unit) neural networks are intensively used in practice. In this paper, we present ReLU Temporal Logic (ReTL), whose semantics is defined with respect to ReLU neural networks, which could specify value-related properties about the network. We show that the model checking algorithm for theΣ2∪Π2 fragment of ReTL, which can express properties such as output reachability, is decidable in EXPSPACE. We have also implemented our algorithm with a prototype tool, and experimental results demonstrate the feasibility of the presented model checking approach.

关键词： model checking rectified linear unit neural(ReLU)network temporal logic

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