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31st International Conference on Multimedia Modeling, MMM 2025

作者： Zhu, Yingqian Gao, Guanyu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9789819620630

Live video streaming demands high user Quality of Experience (QoE) and requires significant computing power and bandwidth for video encoding and transmission. The standard adaptive live streaming approach encodes the source video content at multiple predefined bitrates, allowing users to select the most appropriate bitrate for optimal rate adaptation. The current methods for encoding ladder design primarily focus on optimizing user QoE. However, the relationship between the encoding ladder and user QoE is complex due to the dynamic nature of live streaming, where the number of users fluctuates, and each user experiences varying and evolving bandwidth conditions. On the other hand, resource consumption in live streaming significantly impacts operational costs, as it requires substantial resources for both video encoding and delivery. To address this challenge, we propose QRALadder, a QoE and resource consumption-aware approach to encoding ladder design. Our method simultaneously optimizes user QoE while considering the impact of the encoding ladder on resource consumption in live streaming, and it can dynamically select the most suitable encoding ladder based on the current system state. We use the Dueling Double Deep Q Networks (Dueling DDQN) algorithm to learn the optimal encoding ladder, maximizing overall performance by analyzing user QoE feedback and resource consumption about system states. We conduct comparative experiments using various types of video content and real-world bandwidth traces to validate our approach. The results show that QRALadder can achieve higher overall performance compared to baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Performance Analysis of a Novel Relay Selection Scheme for Wireless-Powered Cluster-Based Multi-Hop Cognitive Relay Networks

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1551-1562页

作者： Sun, Hui Naraghi-Pour, Mort Qian, Yuwen Sheng, Weixing Han, Yubing Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering School of Electrical Engineering and Computer Science Baton RougeLA70803 United States

In this paper, we study the performance of wireless-powered cluster-based multi-hop cognitive relay networks (MCRNs), where secondary nodes harvest energy from multiple dedicated power beacons (PBs) and share the spectrum with multiple primary receivers (PRs) in the underlay paradigm. For this system, we propose a hop-by-hop relay selection scheme called the largest decoding set (LDS). In each stage, relay selection is based on the harvested energy from PBs and the channel state information (CSI) of both the interference links to PRs and the relaying links in the subsequent hop. Considering both harvested energy and maximum interference constraints, we derive the exact end-to-end outage probability and show that the results closely match those obtained from simulations. Moreover, the asymptotic end-to-end outage probabilities in two different scenarios are derived to provide more valuable insights. Numerical results show that the outage probability of the proposed LDS scheme is very close to that of the best-path scheduling (BPS) scheme, which provides a lower bound on outage probability but requires global CSI before the secondary source transmits. We also compare the LDS scheme with other relay selection schemes that have appeared recently in the literature and show that LDS has the best outage performance. © 2024 IEEE.

关键词： Cognitive radio

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Modeling contextual goals and detecting context conflicts from BDD-based user stories

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International Journal of computers and Applications 2024年第12期46卷 1057-1068页

作者： Zheng, Liwei Wang, Yan Cui, Zhanqi Computer School Software Engineering Research Center Beijing Information Science and Technology University Beijing China

Behavior-Driven Development (BDD) user stories are widely used in agile methods for capturing user requirements and acceptance criteria due to their simplicity and clarity. However, the concise structure of BDD-based user stories prevents capturing contextual information and connections between them, which help stakeholders understand requirements and uncover potential issues. The contextual goal models (CGMs) can provide explicit relationships between goals and contexts, but manually constructing models is effort-intensive. This paper proposes an automated approach called BUS2C to model contextual goals and detect context conflicts from BDD-based user stories. The aim is to improve requirements analysis by systematically eliciting goals and dependencies. BUS2C approach involves: (1) mapping BDD-based user story elements to CGMs, and (2) merging related models based on similarity. The context conflict detection algorithm checks if contexts associated with different goals are compatible using natural language metrics. We evaluated BUS2C approach through three experiments. First, we showed the effectiveness in merging scattered CGMs with common features. Second, we demonstrated the capability to construct CGMs from BDD-based user stories quickly, assisting modelers. Our merging method also produced models closer to manually built ones versus without merging. Third, we validated that the context conflict detection can successfully identify inconsistencies in small datasets, enhancing requirements quality. Further validation on larger datasets is needed. In conclusion, this work contributes automated techniques to systematically model and analyze contextual goals from BDD-based user stories. By providing unified goal understanding and detecting issues like conflicts, BUS2C approach aims to improve requirements analysis and support adaptive systems development. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： BDD-based user stories contextual goal model context conflict detection

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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

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Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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Res-SRNN: Residual Spiking Recurrent Neural Network for Efficient Video Recognition

Res-SRNN: Residual Spiking Recurrent Neural Network for Effi...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Zhang, Ya Luo, Xiaoling Sun, Qian Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

ISBN: (纸本)9798331504755

Action recognition in videos is a critical task in computer vision, with a wide range of applications including security surveillance, behavior analysis, and cooperative control. Despite significant advancements in action recognition accuracy achieved by existing video recognition methods, they commonly suffer from high computational complexity and substantial consumption of memory and computational resources. To address the urgent demand for concise, energy-efficient, and high-performance video recognition methods, we propose a residual spiking recurrent neural network (Res-SRNN). Firstly, we design the residual convolution module based on 2D convolution operations to effectively extract spatial information from input video frames. Moreover, drawing inspiration from the recurrent connections in the visual cortex, we incorporate the microscale spiking mechanism of the brain and put forward the spiking recurrent neural network (SRNN). Our SRNN not only excels at capturing complex temporal dependencies but also conserves computational energy costs. Experimental results demonstrate that our proposed Res-SRNN method significantly outperforms most comparative methods on benchmark datasets, achieving the best balance between parameter efficiency and performance. © 2024 IEEE.

关键词： Recurrent neural networks

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Group-PTP: A Pedestrian Trajectory Prediction Method Based on Group Features

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IEEE Transactions on Multimedia 2025年 27卷 3527-3541页

作者： Zhang, Chuanyang Zhang, Guijuan Zheng, Zhuoran Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250014 China State Key Laboratory of High-End Server Storage Technology Organization Jinan250358 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Group features have significant effects on pedestrian movement and constitute a focal point in pedestrian trajectory prediction research. In reality, pedestrians within a group exhibit notable consistency features due to their compact spatial positions, close destinations, and factors such as coordination within the group. In contrast, owing to the dispersed destinations among groups and the lack of coordination, there are significant differences in velocity and direction between the groups, leading to strong conflicts. However, existing pedestrian trajectory prediction models based on group features lack sufficient quantification of both within-group and between-group features. To address this problem, we propose Group-PTP, a novel pedestrian trajectory prediction model based on group features. Specifically, we first propose a group graph attention network-based group features aggregation method (Group-GAT). By quantifying and aggregating the intra-consistency and inter-conflict features exhibited by the groups, our method can better capture the features and interactions both within and between groups. Second, we propose a group multi-feature information representation model that fuses captured group aggregate features, pedestrian coordinates, surrounding pedestrian features, and obstacle features through fusion concatenation. Finally, we propose a multi-feature temporal convolutional network (MF-TCN) that embeds the impact weights of multi-feature information into pedestrian coordinates to obtain feature outputs and conducts temporal operations on feature outputs to predict future trajectories. The experimental results demonstrate that our proposed Group-PTP achieves state-of-the-art performance on several different trajectory prediction benchmarks. © 1999-2012 IEEE.

关键词： Convolution

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Exploring diversity and time-aware recommendations: an LSTM-DNN model with novel bidirectional dynamic time warping algorithm

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Soft Computing 2025年第4期29卷 2003-2013页

作者： Li, Te Chen, Liqiong Sun, Huaiying Hou, Mengxia Lei, Yunjie Zhi, Kaiwen School of Computer and Engineering East China University of Science and Technology Meilong road Shanghai200237 China School of Computer Science and Information Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China School of Chemistry and Environmental Engineering Shanghai Institute of Technology Haiquan road Shanghai201400 China

With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the temporal attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes an LSTM-DNN model based on the novel bidirectional Dynamic Time Warping (DTW) algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a novel bidirectional DTW algorithm to calculate the similarity between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" and "exploration" of information, and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs an Long Short-Term Memory-Deep Neural Networks (LSTM-DNN) to dynamically obtain user interests and preferences, and proposes a new metric Cumulative Self-System Diversity (CSSD) to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Time series

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Graph Convolutional Network-Based Repository Recommendation System

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computer Modeling in engineering & sciences 2023年第10期137卷 175-196页

作者： Zhifang Liao Shuyuan Cao Bin Li Shengzong Liu Yan Zhang Song Yu School of Computer Science and Engineering Central South UniversityChangshaChina School of Information Technology and Management Hunan University of Finance and EconomicsChangshaChina School of Engineering and Built Environment Glasgow Caledonian UniversityGlasgowUK

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

关键词： Repository recommendation graph convolutional network open-source software GitHub

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

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computers, Materials & Continua 2023年第9期76卷 2763-2777页

作者： Shuo Cai Tingyu Luo Fei Yu Pradip Kumar Sharma Weizheng Wang Lairong Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenUK School of Automotive and Mechanical Engineering Changsha University of Science and TechnologyChangsha410114China

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission *** Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy *** the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting ***,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the ***,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is *** approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing ***,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

关键词： BSN Internet of Things reliability analysis random isolation time

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