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2020 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2020

作者： Zhang, Chengwen Li, Tang Gou, Yunqing Yang, Mo Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China International School Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781728170046

In recent years, with the rapid development of location-based social networks (LBSN) in the Internet, point of interest (POI) recommendation has become a hot spot. Most existing researches make use of contextual information to model users' interest preferences. However, the existing methods for extracting various auxiliary information still need to be improved, such as how to treat the users' social relations equally. In order to obtain users' actual preferences more accurately, in POI recommendation, we propose a deep learning framework KEAN (Knowledge Embedded and Attention Based Network) based on knowledge graph and attention model. The framework includes knowledge-graph embedding method, preference extraction network based on attention mechanism and recommendation network. Our study used knowledge-graph embedding method to get the embedding of each user and POI. In addition, an LSTM network based on attention mechanism was proposed, which uses LSTM network to learn the user's preferences according to the user's check-in sequence. Besides, the attention mechanism was used to extract friends' preferences and merge them with the user's preferences to generate end-user preferences. Finally, our model use fully-connected neural networks to realize recommendations. The effectiveness of the model was proved by the experimental results based on real LBSN datasets. © 2020 IEEE.

关键词： Knowledge graph

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: Multi-Dimensional Spatial Reuse Enhancement for Directional Millimeter-Wave Wireless Networks

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IEEE Transactions on Mobile Computing 2021年第12期21卷 4439-4455页

作者： Yi Yang Anfu Zhou Dongzhu Xu Kun Liang Huadong Ma Teng Wei Jianhua Liu Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison WI USA OPPO Mobile Communications Company Ltd. Research Institute Beijing China

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose MDSR, which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, MDSR takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, MDSR improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate MDSR on the testbed using commodity mmWave radios. The evaluation results demonstrate that MDSR improves network throughput by multi-folds compared with the state-of-the-art one.

关键词： Interference Throughput Signal to noise ratio Millimeter wave measurements Switches Sensors Particle measurements

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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Student Behavior Recognition in Remote Video Classrooms 16th

Student Behavior Recognition in Remote Video Classrooms

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16th International Conference on intelligent Information Hiding and multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Wang, Rongrong Zhang, Guanglong Zhang, Fuquan Dong, Zhiwei Qi, Mingzhu Computer School Beijing Institute of Technology Beijing100081 China Computer School Jilin University Changchun130012 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Botou Vocational College Botou062150 China Shandong Dezhou Experimental Middle School Dezhou253000 China

ISBN: (纸本)9789813367562

With the development of high-definition camera equipment and Internet communication technology, remote video teaching methods have emerged. This new type of teaching method has led to changes in traditional classrooms. On the one hand, remote video teaching has broken the constraints of time and space and effectively improved the inequality of educational resources. On the other hand, in actual video courses, teachers cannot effectively monitor students’ learning actions and states, so they cannot communicate well. In order to solve this problem, this paper proposes an algorithm based on deep learning to monitor the behaviors of students in real time during the learning process, understand the learning status of students, and strengthen the real-time communication between teachers and students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Students

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RDVC: Efficient Deep Video Compression with Regulable Rate and Complexity Optimization

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IEEE Transactions on multimedia 2025年

作者： Wei, Xiaojie Lin, Jielian Xu, Jiawei Gao, Wei Zhao, Tiesong Putian University School of Mechanical Electrical & Information Engineering PutianFujian China Shenzhen Graduate School Peking University School of Electronic and Computer Engineering Shenzhen518055 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Tsinghua University Department of Electronic Engineering Beijing100084 China

Deep video coding has paved a way to break through the performance bottleneck of reigning hybrid video coding. However, unlike hybrid video codecs, existing deep video codecs cannot offer both flexible rates and regulable complexities within one single codec, which limits their applications. In this paper, we propose a Regulable Deep Video Codec (RDVC) to address the above issue. First, we propose an Adaptive Feature Compression (AFC) network that generates variable rates while ensuring Rate-Distortion (RD) performance. The network introduces a two-stage coarse-to-fine rate adjustment that can be controlled by a user-specified rate level. Second, we propose a Spatio-Temporal Feature Propagation (STFP) mechanism to provide high-quality reference information for AFC process. Third, we also utilize slimmable convolutional components in our framework to adjust decoding complexity constrained by user configuration. Experimental results demonstrate that RDVC can adjust the codec structure flexibly according to different user configurations while maintaining advanced performance. On average, it reduces the bit-per-pixel (bpp) by 9.35%/58.12% while maintaining the same PSNR/MS-SSIM as the reference software VTM-13.2. Sourcecode is available at: https://***/WXJ0001/RDVC. © 2025 IEEE.

关键词： Signal distortion

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Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning

arXiv

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arXiv 2022年

作者： Dong, Xingping Ouyang, Tianran Liao, Shengcai Du, Bo Shao, Ling School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China College of Information Technology United Arab Emirates University Al Ain United Arab Emirates UCAS-Terminus AI Lab University of Chinese Academy of Sciences Beijing100049 China

Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit. To reduce the requirement of labels, a semi-supervised meta-training (SSMT) setting has been proposed for FSL, which includes only a few labeled samples and numbers of unlabeled samples in base classes. However, existing methods under this setting require class-aware sample selection from the unlabeled set, which violates the assumption of unlabeled set. In this paper, we propose a practical semi-supervised meta-training setting with truly unlabeled data to facilitate the applications of FSL in realistic scenarios. To better utilize both the labeled and truly unlabeled data, we propose a simple and effective meta-training framework, called pseudo-labeling based meta-learning (PLML). Firstly, we train a classifier via common semi-supervised learning (SSL) and use it to obtain the pseudo-labels of unlabeled data. Then we build few-shot tasks from labeled and pseudo-labeled data and design a novel finetuning method with feature smoothing and noise suppression to better learn the FSL model from noise labels. Surprisingly, through extensive experiments across two FSL datasets, we find that this simple meta-training framework effectively prevents the performance degradation of various FSL models under limited labeled data, and also significantly outperforms the representative SSMT models. Besides, benefiting from meta-training, our method also improves several representative SSL algorithms as well. We provide the training code and usage examples at https://***/ouyangtianran/PLML. © 2022, CC BY-NC-ND.

关键词： Self-supervised learning

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Exploring structure diversity in atomic resolution microscopy with graph neural networks

arXiv

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arXiv 2024年

作者： Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan Department of Materials Hunan Key Laboratory of Mechanism and Technology of Quantum Information College of Aerospace Science and Engineering National University of Defense Technology Changsha410000 China School of Computer State Key Laboratory of Complex & Critical Software Environment National University of Defense Technology Changsha410000 China College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Material Science and Engineering Peking University Beijing100871 China School of Physics Central South University Changsha410083 China College of Science National University of Defense Technology Changsha410000 China School of Advanced Materials Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China

The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, we describe a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.), showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. Copyright © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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Information Eye: A Hybrid Visualization Approach of Exploring Relational Information Space

Information Eye: A Hybrid Visualization Approach of Explorin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： L. Yang F. Zhou X.Y. Li Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

In this paper, we propose a novel hybrid layout called Information Eye for relational information space, which supports interactive exploration of information. It is a new visual metaphor, which uses the circular layout to represent the relationship information and the radial tree in the center to represent the attribute information. It combines the two traditional layout methods and presents the shape of the eye. This method draws on the idea of force-directed algorithm and introduces the concept of force into a circular layout. The distance from the node to the center of the circle is used instead of the node-link diagram to express the relationship, making the relationship more intuitive. In the center of the circular layout, the radial tree is used to characterize the attribute information of the nodes, which is more layered. Information Eye also introduces a round lens method, similar to the focusing function of human eyes, to realize the context change when the center of the circle is selected or not, and realizes a smooth transition of the information view. It combines multiple interactive technologies to realize the interactive exploration of two types of information. Finally, we apply this technique to movie data to illustrate its usefulness.

关键词： Visualization Shape Layout Data visualization Motion pictures Space exploration Lenses

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Prediction of Financial Big Data Stock Trends Based on Attention Mechanism

Prediction of Financial Big Data Stock Trends Based on Atten...

引用

IEEE International Conference on Big Knowledge (ICBK)

作者： Jiannan Chen Junping Du Zhe Xue Feifei Kou Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

Stock trend prediction has always been the focus of research in the field of financial big data. Stock data is complex nonlinear data, while stock price is changing over time. Based on the characteristics of stock data, this paper proposes a financial big data stock trend prediction algorithm based on attention mechanism (STPA). We adopt Bidirectional Gated Recurrent Unit (BGRU) and attention mechanism to capture the long-term dependence of data on time series. The attention mechanism is used to analyze the weight of the impact of data from different time periods on the trend prediction results, thereby reducing the error of stock data change trend prediction and improving the accuracy of trend prediction. We select the daily closing price data of 10 stocks for model training and performance evaluation. Experimental results demonstrate that the proposed method STPA achieves higher precision, recall rate and F1-Score in predicting stock change trends than the other methods. Compared with mainstream methods, STPA improves the precision by 4%, improves recall by 2.5%, and improves F1-Score by 3.2%.

关键词： Market research Big Data Communications technology Logic gates Prediction algorithms Hidden Markov models Time series analysis

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