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作者： null null null null null null null School of Computer and Communication Engineering and Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Information Science and Engineering Fujian University of Technology Fujian China Hunan Valin Lianyuan Iron & Steel Corporation Loudi China Electronics & Information School Yangtze University Jingzhou China Hunan Institute of Scientific and Technical Information Changsha China Technical Quality Department Hunan ZOOMLION Heavy Industry Intelligent Technology Corporation Limited Changsha China

关键词： Object tracking

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TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenjun Li Yiqiang Zhou Xiwei Tang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China School of Computer Science Hunan First Normal University Changsha China

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural Networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index $\left( {r_m^2} \right)$. These results demonstrate the effectiveness of the Transformer’s encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery.

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Analysis of SET pulses propagation probabilities in sequential circuits

Analysis of SET pulses propagation probabilities in sequenti...

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2017年第二届可靠性工程国际会议

作者： Shuo Cai Fei Yu Yiqun Yang School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology

As the feature size of CMOS transistors scales down, single event transient（SET） has been an important consideration in designing logic circuits. Many researches have been done in analyzing the impact of SET. However, it is difficult to consider numerous factors. We present a new approach for analyzing the SET pulses propagation probabilities（SPPs）. It considers all masking effects and uses SET pulses propagation probabilities matrices（SPPMs） to represent the SPPs in current cycle. Based on the matrix union operations, the SPPs in consecutive cycles can be calculated. Experimental results show that our approach is practicable and efficient.

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Trajectory data privacy protection based on differential privacy mechanism

Trajectory data privacy protection based on differential pri...

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2017年第二届可靠性工程国际会议

作者： Ke Gu Lihao Yang Yongzhi Liu Niandong Liao School of Computer and Communication Engineering Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology

In this paper, we propose a trajectory data privacy protection scheme based on differential privacy mechanism. In the proposed scheme, the algorithm first selects the protected points from the user’s trajectory data;secondly, the algorithm forms the polygon according to the protected points and the adjacent and high frequent accessed points that are selected from the accessing point database, then the algorithm calculates the polygon centroids;finally, the noises are added to the polygon centroids by the differential privacy method, and the polygon centroids replace the protected points, and then the algorithm constructs and issues the new trajectory data. The experiments show that the running time of the proposed algorithms is fast, the privacy protection of the scheme is effective and the data usability of the scheme is higher.

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A pruning method based on the dissimilarity of angle among channels and filters

arXiv

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arXiv 2022年

作者： Yao, Jiayi Li, Ping Kang, Xiatao Wang, Yuzhe Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp China

Convolutional Neural Network (CNN) is more and more widely used in various fileds, and its computation and memory-demand are also increasing significantly. In order to make it applicable to limited conditions such as embedded application, network compression comes out. Among them, researchers pay more attention to network pruning. In this paper, we encode the convolution network to obtain the similarity of different encoding nodes, and evaluate the connectivity-power among convolutional kernels on the basis of the similarity. Then impose different level of penalty according to different connectivity-power. Meanwhile, we propose Channel Pruning base on the Dissimilarity of Angle (DACP). Firstly, we train a sparse model by GL penalty, and impose an angle dissimilarity (AD) constraint on the channels and filters of convolutional network to obtain a more sparse structure. Eventually, the effectiveness of our method is demonstrated in the section of experiment. On CIFAR-10, we reduce 66.86% FLOPs on VGG-16 with 93.31% accuracy after pruning, where FLOPs represents the number of floating-point operations per second of the model. Moreover, on ResNet-32, we reduce FLOPs by 58.46%, which makes the accuracy after pruning reach 91.76%. Our code is made public at: https://***/kangxiatao/prune_tf2_master. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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A Pruning Method Based on the Dissimilarity of Angle Among Channels and Filters

A Pruning Method Based on the Dissimilarity of Angle Among C...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiayi Yao Ping Li Xiatao Kang Yuzhe Wang Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp China

关键词： Convolution Encoding Convolutional neural networks Kernel Artificial intelligence

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A Low-Area Overhead Secure Scan Architecture Resisting Scan-Based Attacks for Crypto Chips

A Low-Area Overhead Secure Scan Architecture Resisting Scan-...

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2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017

作者： Weizheng, Wang Jincheng, Wang Shuo, Cai Peng, Liu Tieqiao, Liu College of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China Information Science and Engineering Department Hunan University Changsha410082 China Management School Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9789881404732

Scan design is a widely used design-for-testability (DFT) technique that improves the controllability and observability of integrated circuits (ICs) resulting in the facilitation of the testing. However, it can also be used to access secret information of crypto chips, and thus threaten dramatically the security of the cipher keys. In this paper, we propose a secure scan DFT architecture to thwart scan-based side-channel attacks. This architecture provides the scan chain reset mechanism, and thus can prevent these attacks based on mode switching. Meanwhile, the secret key is isolated from scan chains of an advanced encryption standard (AES) design in the test mode. Therefore, it can also halt the test-mode-only scan attacks. The proposed secure scan DFT technique ensures the security without compromising the testability of original chip. Most important of all, the secure scan test is implemented with extremely low hardware overhead.

关键词： Design for testability

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A Polynomial-Time Algorithm for MCS Partial Search Order on Chordal Graphs

arXiv

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arXiv 2022年

作者： Rong, Guozhen Yang, Yongjie Li, Wenjun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Economic Theory Saarland University Saarbrücken Germany

We study the partial search order problem (PSOP) proposed recently by Scheffler [WG 2022]. Given a graph G together with a partial order on the set of vertices of G, this problem determines if there is an S-ordering that is consistent with the given partial order, where S is a graph search paradigm like BFS, DFS, etc. This problem naturally generalizes the end-vertex problem which has received much attention over the past few years. It also generalizes the so-called F-tree recognition problem which has just been studied in the literature recently. Our main contribution is a polynomial-time dynamic programming algorithm for the PSOP of the maximum cardinality search (MCS) restricted to chordal graphs. This resolves one of the most intriguing open questions left in the work of Scheffler [WG 2022]. To obtain our result, we propose the notion of layer structure and study numerous related structural properties which might be of independent interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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Survey of key technologies on millimeter-wave CMOS integrated circuits

Survey of key technologies on millimeter-wave CMOS integrate...

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2017年第二届可靠性工程国际会议

作者： Fei Yu Lei Gao Lixiang Li Shuo Cai Wei Wang Chunhua Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation & School of Computer and Communication Engineering Changsha University of Science and Technology College of Information Science and Engineering Hunan University

In order to provide guidance for the development of high performance millimeter-wave complementary metal oxide semiconductor(MMW-CMOS) integrated circuits(IC), this paper provides a survey of key technologies on MMW-CMOS IC. Technical background of MMW wireless communications is described. Then the recent development of the critical technologies of the MMW-CMOS IC are introduced in detail and compared. A summarization is given, and the development prospects on MMWCMOS IC are also discussed.

关键词： Survey of key technologies on millimeter-wave CMOS integrated circuits

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CuckooFlow: Achieving Fast Packet Classification for Virtual OpenFlow Switching by Exploiting Network Traffic Locality

CuckooFlow: Achieving Fast Packet Classification for Virtual...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Bing Xiong Zhixiong Hu Yao Luo Jin Wang School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science & Technology Changsha China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The growing trend of network virtualization leads to a widespread deployment of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in OpenFlow-based software defined networks. This paper takes an insight into network traffic locality from the perspective of exact flows and wildcard flows, and propose a fast packet classification scheme CuckooFlow. In particular, we devise an active exact flow cache with cuckoo hashing to predict the respective flow entry for each arrived packet. Subsequently, we formulate its cache hit rate based on packet-in-batch arrivals, and average search length of the packet classification scheme. Finally, our proposed CuckooFlow scheme is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed scheme outperforms traditional ones Open vSwitch and CuckooDistributor in terms of cache hit rates and average search length, and contributes to promote virtual OpenFlow switching performance.

关键词： Telecommunication traffic Switches Acceleration Classification algorithms Cloud computing Virtualization Decision trees

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