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arXiv 2022年

作者： Li, Xiang Zhang, Yazhou Tiwari, Prayag Song, Dawei Hu, Bin Yang, Meihong Zhao, Zhigang Kumar, Neeraj Marttinen, Pekka Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Software Engineering College Zhengzhou University of Light Industry Zhengzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Aalto University Finland School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Engineering Medicine Beijing Institute of Technology Beijing China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed University Punjab Patiala India

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Learning Versatile Convolution Filters for Efficient Visual Recognition

arXiv

引用

arXiv 2021年

作者： Han, Kai Wang, Yunhe Xu, Chang Xu, Chunjing Wu, Enhua Tao, Dacheng State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Noah’s Ark Lab Huawei Technologies FST University of Macau Noah’s Ark Lab Huawei Technologies School of Computer Science the Faculty of Engineering The University of Sydney 6 Cleveland St DarlingtonNSW2008 Australia

—This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, e.g., investigating small, sparse or quantized filters. In contrast, we treat filters from an additive perspective. A series of secondary filters can be derived from a primary filter with the help of binary masks. These secondary filters all inherit in the primary filter without occupying more storage, but once been unfolded in computation they could significantly enhance the capability of the filter by integrating information extracted from different receptive fields. Besides spatial versatile filters, we additionally investigate versatile filters from the channel perspective. Binary masks can be further customized for different primary filters under orthogonal constraints. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters, but require less memory and computation cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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An attention-based approach for single image super resolution

arXiv

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arXiv 2018年

作者： Liu, Yuan Wang, Yuancheng Li, Nan Cheng, Xu Zhang, Yifeng Huang, Yongming Lu, Guojun School of Information Science and Engineering Southeast University Nanjing210096 China Nanjing Institute of Communications Technologies Nanjing211100 China State Key Lab. for Novel Software Technology Nanjing University Nanjing210093 China School of automation Southeast University Nanjing210096 China Faculty of Science and Technology Federation University Australia Melbourne Australia

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR. Copyright © 2018, The Authors. All rights reserved.

关键词： Textures

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Reliable message delivery for mobile agents: push or pull

Reliable message delivery for mobile agents: push or pull

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiannong Cao Xinyu Feng Jian Lu H. Chan S.K. Das Internet Computing and E-Commerce Lab Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China State Key Laboratory for Novel Software Technology Department of Computer Science Nanjing University Nanjing China Center for Research Wireless Mobility and Networking (CrewMan) Department of Computer Science and Engineering University of Technology Arlington TX USA

Two of the fundamental issues in message passing between mobile agents are tracking the migration of the target agent and delivering messages to it. In order to provide reliable message delivery, protocols are needed to overcome message loss caused by asynchronous operations of agent migration and message forwarding. In this paper, two message forwarding approaches, namely push and pull, are explored to design adaptive and reliable message delivery protocols. The pros and cons of these two approaches are evaluated, both qualitatively and quantitatively. The comparative performance evaluation is in terms of network traffic and delay in message processing. We also propose improvements to the pull approach to reduce network traffic and the message delay. We conclude that with different communication and migration patterns and requirements of real-time message processing, specific applications can select different message delivery approaches to achieve the desired level of performance and flexibility.

关键词： Mobile agents Target tracking Mobile communication Protocols Computer science Telecommunication traffic Communication system security Relays Internet laboratories

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Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points

arXiv

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arXiv 2022年

作者： Hou, Fei Wang, Chiyu Wang, Wencheng Qin, Hong Qian, Chen He, Ying State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science Stony Brook University New York United States SenseTime Research Tetras.AI Beijing China School of Computer Science and Engineering S-Lab Nanyang Technological University Singapore

Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://***/houfei0801/ipsr. © 2022, CC BY-NC-SA.

关键词： Iterative methods

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PEPS++: Towards extreme-scale simulations of strongly correlated quantum many-particle models on sunway tainhulight

arXiv

引用

arXiv 2018年

作者： He, Lixin An, Hong Yang, Chao Wang, Fei Chen, Junshi Wang, Chao Liang, Weihao Dong, Shaojun Sun, Qiao Han, Wenting Liu, Wenyuan Han, Yongjian Yao, Wenjun CAS Key Lab of Quantum Information University of Science and Technology of China Hefei China School of Computer Science & Technology University of Science and Technology of China Hefei China CCSE & CAPT School of Mathematical Sciences NELVT Peking University Beijing China Institute of Software & State Key Laboratory of Computer Science CAS Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated J1-J2 model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Entertainment for Education. Digital Techniques and Systems 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Xiaopeng Zhang Shaochun Zhong Zhigeng Pan Kevin Wong Ruwei Yun

ISBN: (数字)9783642145339

ISBN: (纸本)9783642145322

With the technical advancement of digital media and the medium of communication in recent years, there is a widespread interest in digital entertainment. An emerging te- nical research area edutainment, or educational entertainment, has been accepted as education using digital entertainment. Edutainment has been recognized as an eff- tive way of learning using modern digital media tools, like computers, games, mobile phones, televisions, or other virtual reality applications, which emphasizes the use of entertainment with application to the education domain. The Edutainment conference series was established in 2006 and subsequently - ganized as a special event for researchers working in this new interest area of e-learning and digital entertainment. The main purpose of Edutainment conferences is to facilitate the discussion, presentation, and information exchange of the scientific and technological development in the new community. The Edutainment conference series becomes a valuable opportunity for researchers, engineers, and graduate s- dents to communicate at these international annual events. The conference series - cludes plenary invited talks, workshops, tutorials, paper presentation tracks, and panel discussions. The Edutainment conference series was initiated in Hangzhou, China in 2006. Following the success of the first event, the second (Edutainment 2007 in Hong Kong, China), third (Edutainment 2008 in Nanjing, China), and fourth editions (Edutainment 2009 in Banff, Canada) were organized. Edutainment 2010 was held during August 16–18, 2010 in Changchun, China. Two workshops were jointly org- ized together with Edutainment 2010.

关键词： Computers and Education

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

arXiv

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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GPU accelerated fast FEM deformation simulation

GPU accelerated fast FEM deformation simulation

引用

IEEE Asia-Pacific Conference on Circuits and Systems

作者： Youquan Liu Shaohui Jiao Wen Wu Suvranu De State Key Lab of Computer Science Institute of Software Chinese Academy and Sciences Beijing China Faculty of Science and Technology University of Macau Macao China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong China Faculty of Information Technology Macau University of Science and Technology Macao China Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY USA

In this paper we present a general FEM (finite element method) solution that enables fast dynamic deformation simulation on the newly available GPU (graphics processing unit) hardware with compute unified device architecture (CUDA) from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is significantly more flexible with a C language interface. We not only implement FEM deformation computation algorithms with CUDA but also analyze the performance in detail. Our test results indicate that the GPU with CUDA enables about 4 times speedup for FEM deformation computation on an Intel(R) Core 2 Quad 2.0 GHz machine with GeForce 8800 GTX.

关键词： Acceleration Deformable models Computational modeling Finite element methods Graphics Hardware Computer architecture Concurrent computing Performance analysis Algorithm design and analysis

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Router queuing management mechanism with fair bottleneck bandwidth in P2P network

Hunan Daxue Xuebao/Journal of Hunan University Natural Scien...

引用

Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences 2006年第6期33卷 123-126页

作者： Wu, Rong-Hui Li, Zhong-Sheng Yue, Guang-Xue Chen, Li-Ping Mo, Tie-Qiang College of Computer and Communication Hunan Univ Changsha 410082 China College of Information Engineering Jiaxing Univ Jiaxing 314000 China State Key Lab for Novel Software Technology Nanjing Univ Nanjing 210093 China

We proposed a new mechanism called Optimized_RED to improve the performance of queue management of P2P network. Under the RED (Random Early Detection) mechanism for queue management, bandwidth can not be allocated fairly for each flow. The Optimized_RED mechanism applied sliding window, set thresholds dynamically, such as mint, maxt, maxp, according to the network traffics, and adapted queues to network traffics. Numerical simulation showed that the Optimized_RED mechanism for queue management could allocate fairly the bottleneck bandwidth of network for each connection and could smooth the burst flow of bottleneck network.

关键词： Computer networks

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