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14th Pacific-Rim Conference on Multimedia, PCM 2013

作者： Li, Guorong Zhang, Weigang Pang, Junbiao Huang, Qingming Jiang, Shuqiang Graduate University Beijing China Key Lab of Intell. Info. Process. CAS Inst. of Comput. Tech. Beijing China School of Computer Science and Technology Harbin Inst. of Tech. China Beijing Municipal Key Lab. of Multimedia and Intelligent Software Tec. Beijing Univ. of Tech China

ISBN: (纸本)9783319037301

With the rapid growth of web data, a large amount of web videos are availab.e online. However, how to organize them for facilitating users' experience and government supervision remains a problem yet to be seriously investigated. Topic detection and tracking, which has been a hot research topic for decades, could cluster web videos into different topics according to their semantic content. However, how to online discover topic and track them from web videos and images has not been fully discussed. In this paper, we formulate topic detection and tracking as an online tracking, detection and learning problem. First, by learning from historical data including lab.led data and plenty of unlab.led data using semi-supervised multi-class multi-feature method, we obtain a topic tracker which could also discover novel topics from the new stream data. Second, when new data arrives, an online updating method is developed to make topic tracker adapt to the evolution of the stream data. We conduct experiments on public dataset to evaluate the performance of the proposed method and the results demonstrate its effectiveness for topic detection and tracking. © Springer International Publishing Switzerland 2013.

关键词： Semantics

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous Robotics systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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Tournament selection based artificial bee colony algorithm with elitist strategy

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8916卷 387-396页

作者： Zhang, Meng-Dan Zhan, Zhi-Hui Li, Jing-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University 510275 China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China Key Lab. Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China

Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure. © Springer International Publishing Switzerland 2014.

关键词： Optimization

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HHLPar: Automated Theorem Prover for Parallel Hybrid Communicating Sequential Processes

arXiv

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arXiv 2024年

作者： Jin, Xiangyu Zhan, Bohua Wang, Shuling Zhan, Naijun Key Laboratory of System Software State Key Lab. of Computer Science ISCAS China University of Chinese Academy of Sciences China Huawei Technologies Co. Ltd. China National Key Laboratory of Space Integrated Information System ISCAS China School of Computer Science Peking University China

We present a tool called HHLPar for verifying hybrid systems modelled in Hybrid Communicating Sequential Processes (HCSP). HHLPar is built upon a Hybrid Hoare Logic for HCSP, which is able to reason about continuous-time properties of differential equations, as well as communication and parallel composition of parallel HCSP processes with the help of parameterised trace assertions and their synchronization. The logic was formalised and proved to be sound in Isabelle/HOL, which constitutes a trustworthy foundation for the verification conducted by HHLPar. HHLPar implements the Hybrid Hoare Logic in Python and supports automated verification: On one hand, it provides functions for symbolically decomposing HCSP processes, generating specifications for separate sequential processes and then composing them via synchronization to obtain the final specification for the whole parallel HCSP processes;On the other hand, it is integrated with external solvers for handling differential equations and real arithmetic properties. We have conducted experiments on a simplified cruise control system to validate the performance of the tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

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Optimal point movement for covering circular regions

Optimal point movement for covering circular regions

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23rd International Symposium on Algorithms and Computation, ISAAC 2012

作者： Chen, Danny Z. Tan, Xuehou Wang, Haitao Wu, Gangshan Department of Computer Science and Engineering University of Notre Dame Notre Dame IN 46556 United States Tokai University 4-1-1 Kitakaname Hiratsuka 259-1292 Japan Department of Computer Science Utah State University Logan UT 84322 United States State Key Lab. for Novel Software Technology Nanjing University Hankou Road 22 Nanjing 210093 China

ISBN: (纸本)9783642352607

Given n points in a circular region C in the plane, we study the problem of moving these points to the boundary of C to form a regular n-gon such that the maximum of the Euclidean distances traveled by the points is minimized. These problems find applications in mobile sensor barrier coverage of wireless sensor networks. The problem further has two versions: the decision version and optimization version. In this paper, we present an O(n log2 n) time algorithm for the decision version and an O(n log3 n) time algorithm for the optimization version. The previously best algorithms for these two problem versions take O(n3.5) time and O(n3.5 log n) time, respectively. A by-product of our techniques is an algorithm for dynamically maintaining the maximum matching of a circular convex bipartite graph;our algorithm performs each vertex insertion or deletion on the graph in O(log 3.5 n) time. This result may be interesting in its own right. © Springer-Verlag 2012.

关键词： Wireless sensor networks

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Targeting Requirements Violations of Autonomous Driving systems by Dynamic Evolutionary Search 36

Targeting Requirements Violations of Autonomous Driving Syst...

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36th IEEE/ACM International Conference on Automated software Engineering, ASE 2021

作者： Luo, Yixing Zhang, Xiao-Yi Arcaini, Paolo Jin, Zhi Zhao, Haiyan Ishikawa, Fuyuki Wu, Rongxin Xie, Tao Peking University Ministry of Education Key Lab. of High-Confidence Software Technologies Beijing China Department of Computer Science and Technology School of EECS Peking University Beijing China National Institute of Informatics Tokyo Japan Xiamen University School of Informatics Xiamen China

ISBN: (纸本)9781665403375

Autonomous Driving systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging environmental conditions. Therefore, the ADSs may have to make trade-offs among multiple requirements during the ongoing operation, resulting in one or more requirements violations. For ADS engineers, it is highly important to know which combinations of requirements violations may occur, as different combinations can expose different types of failures. However, there is currently no testing approach that can generate scenarios to expose different combinations of requirements violations. To address this issue, in this paper, we introduce the notion of requirements violation pattern to characterize a specific combination of requirements violations. Based on this notion, we propose a testing approach named EMOOD that can effectively generate test scenarios to expose as many requirements violation patterns as possible. EMOOD uses a prioritization technique to sort all possible patterns to search for, from the most to the least critical ones. Then, EMOOD iteratively includes an evolutionary many-objective optimization algorithm to find different combinations of requirements violations. In each iteration, the targeted pattern is determined by a dynamic prioritization technique to give preferences to those patterns with higher criticality and higher likelihood to occur. We apply EMOOD to an industrial ADS under two common traffic situations. Evaluation results show that EMOOD outperforms three baseline approaches in generating test scenarios by discovering more requirements violation patterns. © 2021 IEEE.

关键词： Iterative methods

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SAR: Semantic analysis for recommendation

arXiv

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arXiv 2017年

作者： Xiao, Han Meng, Lian State Key Lab. of Intelligent Technology and Systems National Lab. for Information Science and Technology Dept. of Computer Science and Technology Tsinghua University Beijing100084 China

Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task. However, most matrix completion methods lack semantic interpretation and usually result in weak-semantic recommendations. To this end, this paper proposes a Semantic Analysis approach for Recommendation systems (SAR), which applies a two-level hierarchical generative process that assigns semantic features and categories for user/item. SAR learns the semantic representations of users/items merely from user ratings on items, which offers a novel path to recommendation by semantic matching with the learned semantic representations. Extensive experiments demonstrate SAR outperforms other state-of-the-art baselines substantially. Copyright © 2017, The Authors. All rights reserved.

关键词： Recommender systems

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Defending P2P networks based on benign worms

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Journal of Computational Information systems 2011年第7期7卷 2532-2539页

作者： Liu, Xin Hu, Zhichao Liu, Guoyou Wang, Zhi Jia, Chunfu College of Information Technical Science Nankai University Tianjin 300071 China State Key Lab. for Novel Software Technology Nanjing University China

Malicious worms can be exploited to destroy the user system and launch DDoS attack against computer networks. We proposed a method of forming secure P2P network using benign worms against malicious worms. The corresponding benign worm with a hitlist is generated to clean the malicious worm and patch peers in the P2P network which is based on the largest distance list. The benign worm propagates along the histlist. The spread of the benign worm is also a distributed patching process. The patch can be disseminated more quickly and the network congestion is much less than centralized patching scheme in P2P networks. In the experiments, after the benign worm accomplished its mission in the P2P network, all the malicious worms on infected peers are cleaned and almost all of the vulnerable peers are patched. Copyright © 2011 Binary Information Press.

关键词： Denial-of-service attack

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Image segmentation with texture clustering based JSEG

Image segmentation with texture clustering based JSEG

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jing Zhang Yong-Wei Gao Sheng-Wei Feng Zhi-Hua Chen Yu-Bo Yuan Department of Computer Science and Engineering East China University of Science and Technology Shanghai State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781467372220

Image segmentation is one of the most crucial techniques in computer vision. In this paper, we propose an improved approach for color image segmentation, Texture clustering based JSEG (T-JSEG). The T-JSEG combines the texture information with the classical JSEG algorithm. We use the Gabor feature clustering to produce texture map and combine it with color map to achieve a new map for J value computing. The color image segmentation result is improved by adding the texture information to the JSEG. Experiments on natural color images of Berkeley Segmentation Database verify that our method is very effective on color image segmentation and more accurate boundaries of regions are found in comparison with the classical JSEG algorithm.

关键词： Color information Image segmentation JSEG Texture structure image segmentation chromatic information texture structure color image texture texture map segmentation Segmentation images natural color textures

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