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IEEE International Conference on Big knowledge (ICBK)

作者： Ze Yang Changyang Tai Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Few clustering methods show good performance on multivariate time series (MTS) data. Traditional methods rely too much on similarity measures and perform poorly on the MTS data with complex structures. This paper proposes an MTS clustering algorithm based on graph embedding called MTSC-GE to improve the performance of MTS clustering. MTSC-GE can map MTS samples to the feature representations in a low-dimensional space and then cluster them. While mining the information of the samples themselves, MTSC-GE builds the whole time series data into a graph, paying attention to the connections between samples from an overall perspective and discovering the local structural feature of MTS data. The proposed MTSC-G E consists of three stages. The first stage builds a graph using the original dataset, where each of the MTS samples is regarded as a node in the graph. The second stage uses the graph embedding technique to obtain a new representation of each node. Finally, MTSC-G E uses the K - Means algorithm to cluster based on the newly obtained representation. We compare MTSC-GE with six state-of-the-art benchmark methods on five public datasets, experimental results show that MTSC-GE has achieved good performance.

关键词： Deep learning Conferences Clustering methods Time series analysis Clustering algorithms Benchmark testing Stability analysis

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Unraveling the 3D Genome Architecture in Plants:Present and Future

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Molecular Plant 2020年第12期13卷 1676-1693页

作者： Weizhi Ouyang Dan Xiong Guoliang Li Xingwang Li National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data 3D Genomics Research CenterHuazhong Agricultural UniversityWuhan 430070China

The eukaryotic genome has a hierarchicalthree-dimensional(3D)organization with functional implications for DNA replication,DNA repair,and transcriptional *** the past decade,scientists have endeavored to elucidate the spatial characteristics and functions of plant genome architecture using high-throughput chromatin conformation capturing technologies such as Hi-C,ChlA-PET,and ***,we systematically review current understanding of chromatin organization in plants at multiple *** also discuss the emerging opinions and concepts in 3D genome research,focusing on state-of-the-art 3D genome techniques,RNA-chromatin interactions,liquid-liquid phase separation,and dynamic chromatin *** propose the application of single-cell/single-molecule multi-omics,multiway(DNA-DNA,DNA-RNA,and RNA-RNA interactions)chromatin conformation capturing methods,and proximity ligation-independent 3D genome-mapping technologies to explore chromatin organization structure and function in *** methods could reveal the spatial interactions between trait-related SNPs and their target genes at various spatiotemporal resolutions,and elucidate the molecular mecha-nisms of the interactions among DNA elements,RNA molecules,and protein factors during the formation of key traits in plants.

关键词： 3D genomic methods plant 3D genome dynamic chromatin organization liquid-liquid phase separation RNA-chromatin interaction

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Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic key

Communication Encryption Scheme of 5G Power Trading Private ...

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Electrical, Power and Computer engineering (EPCE), Asia Conference on

作者： Rui Zhou Yiyang Song Ying Xu Electrical Engineering and Automation School Harbin Institute of Technology Harbin China Information Data Department Guangdong Power Exchange Center Guangzhou China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency.

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs

Cloud-assisted Road Condition Monitoring with Privacy Protec...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Lemei Da Yujue Wang Yong Ding Bo Qin Xiaochun Zhou Hai Liang Huiyong Wang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Pengcheng Laboratory Cyberspace Security Research Center Shenzhen China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education School of Information Renmin University of China Beijing China Shenzhen Olym Information Security Technology Co. Ltd. Shenzhen China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation systems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.

关键词： Condition monitoring Privacy data privacy Roads Vehicular ad hoc networks Forgery Sensors

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A New Type Wireless data Center of Comb Topology 6th

A New Type Wireless Data Center of Comb Topology

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6th International Conference on data Science, ICDS 2019

作者： Zhang, Qingfang Han, Zhijie Du, Xiaoyu School of Computer and Information Engineering Henan University KaifengHenan475004 China Institute of Data and Knowledge Engineering Henan University KaifengHenan475004 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Network NanjingJiangsu210003 China

ISBN: (纸本)9789811528095

In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designed based on wires. Researchers have proposed Dcell, Bcube and other data center topologies successively. However, in the traditional data center network, the wired topology structure has the disadvantages of high overhead, complex deployment, high maintenance cost, slow heat dissipation and so on. Moreover, the existing wireless data center Flyways structure and Graphite structure have the problems of low connectivity and poor scalability. So this paper proposes and designs a new data center network topology Comb structure (Comb model is a multi-layer cellular structure model, which is a multi-layer structure composed of single-layer honeycomb-like structure interlaced with each other). The structure can effectively improve the connectivity of the data center, shorten the routing path, reduce the probability of node failure in the network, and ensure the smooth network. © Springer Nature Singapore Pte Ltd 2020.

关键词： Topology

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HSI: Human Saliency Imitator for Benchmarking Saliency-Based Model Explanations 10th

HSI: Human Saliency Imitator for Benchmarking Saliency-Based...

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10th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022

作者： Yang, Yi Zheng, Yueyuan Deng, Didan Zhang, Jindi Huang, Yongxiang Yang, Yumeng Hsiao, Janet H. Cao, Caleb Chen Huawei Research Hong Kong Hong Kong Department of Psychology University of Hong Kong Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Brain and Cognitive Sciences University of Hong Kong Hong Kong Institute of Data Science University of Hong Kong Hong Kong

ISBN: (纸本)9781577358787

Model explanations are generated by XAI (explainable AI)methods to help people understand and interpret machine learning models. To study XAI methods from the human perspective, we propose a human-based benchmark dataset, i.e., human saliency benchmark (HSB), for evaluating saliency based XAI methods. Different from existing human saliency annotations where class-related features are manually and subjectively labeled, this benchmark collects more objective human attention on vision information with a precise eyetracking device and a novel crowdsourcing experiment. Taking the labor cost of human experiment into consideration, we further explore the potential of utilizing a prediction model trained on HSB to mimic saliency annotating by humans. Hence, a dense prediction problem is formulated, and we propose an encoder-decoder architecture which combines multimodal and multi-scale features to produce the human saliency maps. Accordingly, a pretraining-finetuning method is designed to address the model training problem. Finally, we arrive at a model trained on HSB named human saliency imitator(HSI).We show, through an extensive evaluation, that HIS can successfully predict human saliency on our HSB dataset, and the HSI-generated human saliency dataset on Image Netshow cases the ability of benchmarking XAI methods both qualitatively and quantitatively. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Benchmarking

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Multi-Factor Information Matrix: A Directed Weighted Method to Identify Influential Nodes in Social Networks

SSRN

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SSRN 2023年

作者： Wang, Yan Zhang, Ling Yang, Junwen Yan, Ming Li, Haozhan State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China School of Data Science and Media Intelligence Communication University of China Beijing China School of Information and Communication Engineering Communication University of China Beijing China

Identifying super-propagators on social networks takes on critical significance in guiding and controlling public opinions on the internet. The influence of nodes in the propagation process not only depends on the network structure, but also is related to the dynamic interaction between nodes. To address this problem, from the perspectives of the network topology and dynamic interaction, a multi-factor information matrix centrality algorithm is developed in this study in accordance with the characteristics of information propagation process and three degrees of influence rule, with the node influence, neighbor influence, and mutual influence exerted by information feedback considered simultaneously. Based on this, the subjective and objective weighted methods are combined to enhance the accuracy of evaluation results, then the multi-factor matrix can be obtained. Five comparative experiments are employed to verify the effectiveness of proposed method with eight networks and six existing centrality applied. The results of the experiments confirm the validity and superiority of the proposed method in terms of differentiation, connectivity and propagation performance. Moreover, its low time complexity makes the algorithm can be used in large-scale social networks. In general, methodological and multidimensional support are provided for the accurate identification of super-propagators in online social networks. © 2023, The Authors. All rights reserved.

关键词： Social networking (online)

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Asymmetric Sandwiched Nanofibrillated Cellulose Composites Prepared by One-Pot Vacuum Filtration for Ultrahigh Electromagnetic Interference Shielding

SSRN

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SSRN 2023年

作者： Qi, Wenjing Zeng, Lingjun Xue, Bai Wu, Changmei Mai, Xiaoping Xie, Lan Zheng, Qiang Department of Polymer Materials and Engineering College of Materials and Metallurgy Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China National Engineering Research Center for Compounding and Modification of Polymer Materials National and Local Joint Engineering Research Center for Functional Polymer Membrane Materials and Membrane Processes Guiyang550014 China Department of Polymer Science and Engineering Zhejiang University Hangzhou310027 China

High-efficiency electromagnetic interference (EMI) shielding composite films (EMI shielding effectiveness (SE) value > 100 dB) with a small thickness are imminently desired for eliminating the serious electromagnetic wave pollution. Herein, a series of asymmetric sandwiched silver-coated four-needle zinc oxide whisker/nanofibrillated cellulose/Ti3C2Tx (F-ZnO&Ag/NFC/Ti3C2Tx) composite films consisting of the loose F-ZnO&Ag sedimentary layer and compact upper Ti3C2Tx layer were successful fabricated by a facile one-pot vacuum filtration method in combination with gravity-induced delamination. Arising from the favorable electrical conductivity and unique asymmetric sandwich structure, electromagnetic waves can be trapped and dissipated in the F-ZnO&Ag skeletons and strongly multiply reflected between top Ti3C2Tx layer and bottom F-ZnO&Ag layers. Thereby, F-ZnO&Ag/NFC/Ti3C2Tx-30.4 exhibits the ultrahigh averaged EMI SET of 103.9 dB (the maximal value of 123.8 dB) at a low thickness of merely 209 μm, which is far superior to that of almost all the reported NFC composites at the similar thickness to date. In the real EMI shielding application test, F-ZnO&Ag/NFC/Ti3C2Tx composite films are confirmed to effectively block the transmission of controlled signal and switch off the toy car system. Such simple and high-efficiency asymmetric sandwich structure design makes a great progress towards preparing and adjusting thin EMI shielding composite films. © 2023, The Authors. All rights reserved.

关键词： Zinc oxide

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NOMANet: A Graph Neural Network Enabled Power Allocation Scheme for NOMA

arXiv

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arXiv 2025年

作者： Hou, Yipu Lu, Yang Chen, Wei Ai, Bo Niyato, Dusit Ding, Zhiguo State Key Laboratory of Advanced Rail Autonomous Operation China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University 639798 Singapore Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates

This paper proposes a graph neural network (GNN) enabled power allocation scheme for non-orthogonal multiple access (NOMA) networks. In particular, a downlink scenario with one base station serving multiple users over several subchannels is considered, where the number of subchannels is less than the number of users, and thus, some users have to share a subchannel via NOMA. Our goal is to maximize the system energy efficiency subject to the rate requirement of each user and the overall budget. We propose a deep learning based approach termed NOMA net (NOMANet) to address the considered problem. Particularly, NOMANet is GNN-based, which maps channel state information to the desired power allocation scheme for all subchannels. The multi-head attention and the residual/dense connection are adopted to enhance the feature extraction. The output of NOMANet is guaranteed to be feasible via the customized activation function and the penalty method. Numerical results show that NOMANet trained unsupervised achieves performance close to that of the successive convex approximation method but with a faster inference speed by about 700 times. Besides, NOMANet is featured by its scalability to both users and subchannels. © 2025, CC BY.

关键词： Channel state information

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