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2nd International Conference on Precision Mechanical Instruments and Measurement Technology, ICPMIMT 2014

作者： Liao, Chuan Zhu Wang, Wei Jiang, Ming Yan Department of Electronic Engineering of Zhangzhou Institute of Technology 363000 Zhangzhou China Software Development Center Agricultural Bank of China Beijing China School of Information Science and Engineering Shandong University 250100 Jinan China

ISBN: (纸本)9783038351948

With the characteristics of timeliness, rapid spread and easy access, micro-blog has made it possible to timely unearth emergencies and dynamically track the latest development. Considering frequency and timeliness of the words as well as the influence of resources, this paper proposes a sudden topic detection method, especially for micro-blog, and establishes a context-based weight evaluation model. Compared with semantic similarity model, this context-based model is especially more adaptable to micro-blog. Real experiment data from micro-blog proves that the proposed methods could detect sudden incidents effectively with big data processing capacity and low time complexity. © (2014) Trans Tech Publications, Switzerland.

关键词： Big data

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On total unimodularity of edge-edge adjacency matrices

On total unimodularity of edge-edge adjacency matrices

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作者： Matsumoto, Yusuke Kamiyama, Naoyuki Imai, Keiko Tokyo Software Development Laboratory IBM Tokyo Japan Institute of Mathematics for Industry Kyushu University Fukuoka Japan Department of Information and System Engineering Chuo University Tokyo Japan

We consider total unimodularity for edge-edge adjacency matrices that represent adjacency relations between pairs of edges in a graph. These matrices appear in integer programming formulations of the minimum maximal matching problem, the edge dominating set problem, and so on. We investigate the problem of characterizing graphs that have totally unimodular edge-edge adjacency matrices, and give a necessary and sufficient condition for characterization. This condition is the first characterization for total unimodularity of edge-edge adjacency matrices. © 2013 Springer Science+Business Media New York.

关键词： Graph theory

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UAV navigation in high dynamic environments:A deep reinforcement learning approach

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Chinese Journal of Aeronautics 2021年第2期34卷 479-489页

作者： Tong GUO Nan JIANG Biyue LI Xi ZHU Ya WANG Wenbo DU School of Electronic and Information Engineering Beihang UniversityBejing 100083China Key Laboratory of Advanced Technology of Near Space Information System(Beihang University) Ministry of Industry and Information Technology of ChinaBeijing 100083China Research Institute of Frontier Science Beihang UniversityBeijing 100083China College of Software Beihang UniversityBeijing 100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100083China

Unmanned Aerial Vehicle(UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning(DRL) methods have shown promise for addressing the UAV navigation problem,but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving *** this work, we propose an improved DRL-based method to tackle these fundamental *** be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory(LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

关键词： Autonomous vehicles Deep learning Motion planning Navigation Reinforcement learning Unmanned Aerial Vehicle(UAV)

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Diving into Continual Ultra-fine-grained Visual Categorization

Diving into Continual Ultra-fine-grained Visual Categorizati...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Wu, Xiaoyu Gao, Yongsheng Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beijing China Griffith University Institute for Integrated and Intelligent Systems Nathan4111 Australia

ISBN: (纸本)9798350382204

Recent advance in ultra-fine-grained visual categorization (ultra-FGVC) has significantly boosted the capability of deep neural networks for ultra-FGVC tasks. However, building models for continually learning to recognize increasing ultra-fine-grained categories is still under-explored. This limits the application of ultra-FGVC techniques in real-world production. To this, we take the first attempt for continual ultra-FGVC. By evaluating existing continual learning methods on the constructed continual ultra-FGVC benchmark, we observe that the main bottleneck lies in the limited model plasticity for incrementally adapting to new tasks. This can be caused by excessive anti-forgetting constraints as the difficult ultra-FGVC task requires substantial update of parameters, and over-fitting on early tasks given that the ultra-fine-grained categories are with very few training samples. To tackle these problems, we propose a joint self-supervised learning and prompting model. The prompt-based continual learning framework offers proper anti-forgetting operation by fixed pretrained vision transformer and adaptive prompt selection. By jointly optimizing the learnable prompts with an adversarial self-supervised loss, the over-fitting on each continual learning task is mitigated. Extensive experiments demonstrate that the proposed method outperforms existing continual learning methods on the challenging continual ultra-FGVC problem. © 2023 IEEE.

关键词： Deep neural networks

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An efficient and fast polarity optimization approach for mixed polarity Reed-Muller logic circuits

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Frontiers of Computer Science 2017年第4期11卷 728-742页

作者： Zhenxue HE Limin XIAO Fei GU Tongsheng XIA Shubin SU Zhisheng HUO Rong ZHANG Longbing ZHANG Li RUAN Xiang WANG State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China School of Computer Science and Engineering Beihang University Beijing 100191 China School of Electronic and Information Engineering Beihang University Beijing 100191 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100191 China

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.

关键词： genetic algorithm polarity optimization mixed

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A survey on federated learning:a perspective from multi-party computation

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Frontiers of Computer Science 2024年第1期18卷 93-103页

作者： Fengxia LIU Zhiming ZHENG Yexuan SHI Yongxin TONG Yi ZHANG Institute of Artificial Intelligence and Key Laboratory of Mathematics Informatics Behavioral Semantics Beihang UniversityBeijing 100191China State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Pengcheng Laboratory Shenzhen 518055China Zhongguancun Laboratory Beijing 100190China Institute for Mathematical Sciences and Engineering Research Center of Financial Computing and Digital Engineering Renmin University of ChinaBeijing 100872China

Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw *** enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model *** survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training *** also pinpoint future directions to deploy federated learning to a wider range of applications.

关键词： sfederated learning multi-party ycomputation privacy-preserving data mining distributed learning

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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Exploit Detection and Mitigation Technique of Cache Side-Channel Attacks using Artificial Intelligence 2

Exploit Detection and Mitigation Technique of Cache Side-Cha...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sharma, Ishu Dubey, Rajat Ojha, Sharad Shyam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Cybersecurity Expert Allianz Commercial Austin United States Software Development Manager Amazon Austin United States

ISBN: (纸本)9798350340235

As a persistent danger to computer security, cache side-channel attacks take advantage of minute flaws in microarchitectures to access sensitive data without authorization. In-depth analysis of these attacks' numerous forms and related Common Vulnerabilities and Exposures (CVEs) is provided in this study report. The study emphasizes cache side-channel attacks' practical ramifications and how they affect system security. The methods for identifying these threats are examined, with an emphasis on how machine learning and deep learning approaches might be used to strengthen security precautions. The research offers useful insights via pseudo code and data structure figures that highlight the necessity of real-time monitoring and detection tactics in reducing cache side-channel vulnerabilities. Artificial intelligence is used to design proactive countermeasures. The research article calls for proactive security measures to thwart these developing risks in addition to illuminating the complicated landscape of cache side-channel attacks. The contribution to this domain goes beyond theoretical discussion and includes actual application. This paper demonstrates the critical relevance of real-time monitoring and detection tactics in the fight to defend systems from cache side-channel attacks via the presentation of pseudo code and data structure diagrams. The results of this work provide guidance for future research and highlight the crucial value of interdisciplinary cooperation in the continuous quest for safe computing systems. © 2023 IEEE.

关键词： Cache Side Channel Attack Common Vulnerabilitiesand Exposures Hardware Security Microarchitecture Security Vulnerability

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Using EVOWAVE to analyze software evolution 17

Using EVOWAVE to analyze software evolution

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17th International Conference on Enterprise Information Systems, ICEIS 2015

作者： Magnavita, Rodrigo Novais, Renato Mendonça, Manoel Fraunhofer Project Center for Software and Systems Engineering Bahia Brazil Computer Science Department Federal University of Bahia Bahia Brazil Software Development Area SENAI/CIMATEC Bahia Brazil Computer Science Department Federal Institute of Bahia Bahia Brazil

ISBN: (纸本)9789897580963

software evolution produces large amounts of data which software engineers need to understand for their daily activities. The use of software visualization constitutes a promising approach to help them comprehend multiple aspects of the evolving software. However, portraying all the data is not an easy task as there are many dimensions to the data (e.g. time, files, properties) to be considered. This paper presents a new software visualization metaphor inspired by concentric waves, which gives information about the software evolution in different levels of detail. This new metaphor is able to portray large amount of data and may also be used to consider different dimensions of the data. It uses the concepts of the formation of concentric waves to map software evolution data generated during the waves formation life cycle. The metaphor is useful for exploring and identifying certain patterns in the software evolution. To evaluate its applicability, we conducted an exploratory study to show how the visualization can quickly answer different questions asked by software engineers when evolving their software. Copyright © 2015 SCITEPRESS - Science and Technology Publications.

关键词： Life cycle

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Relay navigation strategy study on intelligent drive on urban roads

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The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 79-90页

作者： Gao Hongbo Zhang Xinyu An Lifeng Liu Yuchao Li Deyi State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Information Technology Center Tsinghua University Beijing 100083 China Department of Computer Science and Technology Tsinghua University Beijing 100083 China The 61th Institute of Electronic System Engineering Beijing 100039 China

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.

关键词： intelligent vehicle intelligent drive urban roads relay navigation strategy

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