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arXiv 2021年

作者： Wu, Xuan Han, Jizong Wang, Di Gao, Pengyue Cui, Quanlong Chen, Liang Liang, Yanchun Huang, Han Lee, Heow Pueh Miao, Chunyan Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639798 Singapore State Key Laboratory of Superhard Materials International Center for Computational Method and Software College of Physics Jilin University Changchun130012 China Commercial Quality and Efficiency Department Baidu Inc Beijing100085 China Department of Computer Science Shantou University Shantou515063 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China School of Software Engineering The South China University of Technology Guangzhou510641 China Department of Mechanical Engineering National University of Singapore 117575 Singapore School of Computer Science and Engineering Nanyang Technological University 639798 Singapore College of Software Jilin University Changchun130012 China

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Small-world networks

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A Registration Method for Augmented Reality System Based on Visual SLAM

A Registration Method for Augmented Reality System Based on ...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Zuoming Zhang Man Shu Zixuan Wang Hanwen Wang Xin Wang College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781728140773

In augmented reality, smart devices need to sense their own position in real physical space and complex scene structure to achieve good virtual reality interaction and three-dimensional registration. This paper proposes a technical framework of visual SLAM, which uses VO and BA methods based on feature points to obtain pose, and applies it to the augmented reality system to realize pose estimation, registration, tracking, collision and other effects of intelligent equipment independent of artificial or natural landmarks.

关键词： augmented reality image registration pose estimation robot vision SLAM (robots) augmented reality pose estimation robot vision image registration supersonic low altitude missile Registration

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SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications 4

SNB-PPB: Social-network-based-privacy-preserving Broadcast f...

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4th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2018

作者： Liu, Yanheng Zhu, Haifeng Wang, Jian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789897582936

Internet key Exchange (IKE) can be used in vehicular communications in vehicular ad hoc networks (VANETs), but it requires a stable communication procedure, which is usually difficult to achieve because of the highly dynamic context of a VANET. In this paper, we propose a new method named social-network-based-privacy-preserving broadcast (SNB-PPB) for secure vehicular communications, which can omit the key exchange procedure. It transfers the information of online social networks to offline vehicular communications, dividing messages into different privacy levels and vehicles into different trust levels based on trust relations on social networks. The sender uses attributes on social networks and the message’s privacy level to encrypt messages;only those receivers who satisfy the corresponding trust level can decrypt the broadcast *** the best of our knowledge, this is the first time that social networks have been applied in VANETs to achieve secure vehicular communications and privacy preservation. Our simulation results show that our method enables the sender to decide the amount and percentage of vehicles that can decrypt the received broadcast. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Vehicular ad hoc networks

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Model-based diagnosis of incomplete discrete-event system with rough set theory

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Science China(Information Sciences) 2017年第1期60卷 190-200页

作者： Xuena GENG Dantong OUYANG Yonggang ZHANG Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of EducationJilin University College of Computer Science and Technology Jilin University

Fault diagnosis of discrete-event system(DES) is important in the preventing of harmful events in the system. In an ideal situation, the system to be diagnosed is assumed to be complete; however, this assumption is rather restrictive. In this paper, a novel approach, which uses rough set theory as a knowledge extraction tool to deal with diagnosis problems of an incomplete model, is investigated. DESs are presented as information tables and decision tables. Based on the incomplete model and observations, an algorithm called Optimizing Incomplete Model is proposed in this paper in order to obtain the repaired model. Furthermore, a necessary and sufficient condition for a system to be diagnosable is given. In ensuring the diagnosability of a system, we also propose an algorithm to minimize the observable events and reduce the cost of sensor selection.

关键词： model-based diagnosis diagnosability discrete-event system finite state machine rough set theory

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Copy-Paste Detection Based on a SIFT Marked Graph Feature Vector

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Chinese Journal of Electronics 2017年第2期26卷 345-350页

作者： LYU Yingda SHEN Xuanjing CHEN Haipeng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

To detect copy-paste tampering,an improved SIFT(Scale invariant feature transform)-based algorithm was *** angle is defined and a maximum angle-based marked graph is *** marked graph feature vector is provided to each SIFT key point via discrete polar coordinate *** points are matched to detect the copy-paste tampering *** experimental results show that the proposed algorithm can effectively identify and detect the rotated or scaled copy-paste regions,and in comparison with the methods reported previously,it is resistant to postprocessing,such as blurring,Gaussian white noise and JPEG *** proposed algorithm performs better than the existing algorithm to dealing with scaling transformation.

关键词： Copy-paste tampering Image forensics Marked graph Maximum angle

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Deep Bayesian optimization on attributed graphs

arXiv

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arXiv 2019年

作者： Cui, Jiaxu Yang, Bo Hu, Xia College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science and Engineering Texas A&M University College Station United States

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs;existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

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Computers, Materials & Continua 2018年第12期57卷 549-570页

作者： Lili He Dantong Ouyang Meng Wang Hongtao Bai Qianlong Yang Yaqing Liu Yu Jiang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun130012China School of Information Science&Technology Dalian Maritime UniversityDalian116026China School of Informatics University of EdinburghEdinburghEH89ABUK

In this paper,the clustering analysis is applied to the satellite image segmentation,and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing *** method firstly adopts the fuzzy C-means clustering(FCM)to obtain the satellite cloud image ***,in the cloud image,we dispose the‘high-density connected’pixels in the same cloud clusters and the‘low-density connected’pixels in different cloud ***,we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster ***,using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters,thunderstorm cloud clusters are quickly and accurately *** experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.

关键词： Cloud computing cluster analysis FCM DBSCAN thunderstorm clouds satellite cloud image

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SIoTFog:Byzantine-resilient IoT fog networking

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Frontiers of Information Technology & Electronic engineering 2018年第12期19卷 1546-1557页

作者： Jian-wen XU Kaoru OTA Mian-xiong DONG An-feng LIU Qiang LI Department of Information and Electronic Engineering Muroran Institute of Technology School of Information Science and Engineering Central South University MOE Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University

The current boom in the Internet of Things(IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called "SIoTFog," to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

关键词： Byzantine fault tolerance Fog computing Resource allocation Internet of Things(IoT)

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Group-attention Based Neural Machine Translation

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IOP Conference Series: Materials Science and engineering 2020年第2期782卷

作者： Renyu Hu Hao Xu Yang Xiao Chenjun Wu Haiyang Jia College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Machine translation is a classic problem in natural language process (NLP). Recent years, the encoder and decoder through an attention mechanism has become a trend. Google proposed a new simple network architecture, the Transformer using attention mechanisms only rather than CNN or RNN in 2017. However, it may lose some important information (e.g., grammatical component, etc) when using attention mechanism for whole *** propose a new brand model based on transformer using Group attention layers and group position embedding. It absorbs the features of Group-CNN combines the algorithm in computer vision (CV) and NLP. The model not only pays more attention to the ingredients (e.g., subject, predicate and adverbial, etc), but also enhances the connection of phrase. It outperforms SofA Transformer in using more syntactic information.

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Heuristic black-box adversarial attacks on video recognition models

arXiv

引用

arXiv 2019年

作者： Wei, Zhipeng Chen, Jingjing Wei, Xingxing Jiang, Linxi Chua, Tat-Seng Zhou, Fengfeng Jiang, Yu-Gang Jilin University Fudan University Beihang University National University of Singapore Health Informatics Lab College of Computer Science and Technology Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin130012 China

We study the problem of attacking video recognition models in the black-box setting, where the model information is unknown and the adversary can only make queries to detect the predicted top-1 class and its probability. Compared with the black-box attack on images, attacking videos is more challenging as the computation cost for searching the adversarial perturbations on a video is much higher due to its high dimensionality. To overcome this challenge, we propose a heuristic black-box attack model that generates adversarial perturbations only on the selected frames and regions. More specifically, a heuristic-based algorithm is proposed to measure the importance of each frame in the video towards generating the adversarial examples. Based on the frames’ importance, the proposed algorithm heuristically searches a subset of frames where the generated adversarial example has strong adversarial attack ability while keeps the perturbations lower than the given bound. Besides, to further boost the attack efficiency, we propose to generate the perturbations only on the salient regions of the selected frames. In this way, the generated perturbations are sparse in both temporal and spatial domains. Experimental results of attacking two mainstream video recognition methods on the UCF-101 dataset and the HMDB-51 dataset demonstrate that the proposed heuristic black-box adversarial attack method can significantly reduce the computation cost and lead to more than 28% reduction in query numbers for the untargeted attack on both datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Heuristic methods

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