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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-11页

作者： Chen, Bingyue Chen, Binglian Zhang, Kang Xu, Binghui Jiang, Guoxin College of Wenling Taizhou Vocational College of Science & Technology Zhejiang Taizhou318020 China College of Agriculture and Bioengineering Taizhou Vocational College of Science & Technology Zhejiang Taizhou318020 China College of Mechanical and Mold Engineering Taizhou Vocational College of Science & Technology Zhejiang Taizhou318020 China Taizhou Vocational & Technical College Zhejiang Taizhou318000 China Digital office Zhejiang Gongshang University Zhejiang Hangzhou310000 China

This study aims to optimize the indoor thermal environment through sensor network technology to support the multimedia assisted physics teaching, improve the teaching effect and students’ learning experience. The sensor network technology is used in the study to arrange a variety of sensors in the classroom to monitor the thermal environment parameters in real time. Through data analysis, the thermal environment optimization model is established, and the numerical simulation technology is used to adjust different environmental factors. Combined with multimedia teaching, this paper explores the influence of thermal environment optimization on students’ learning effect. Studies have shown that reasonable adjustment of indoor temperature and air mobility can significantly improve students’ attention and learning motivation. The optimized classroom environment showed higher effectiveness in both physics experiment and theoretical learning, and students’ feedback satisfaction increased. Therefore, the indoor thermal environment optimization based on sensor network can effectively improve the quality of multimedia assisted physics teaching. By analyzing and processing the collected information, the cluster control end can reasonably arrange the playback content of multimedia courseware to meet the learning needs of students. This multimedia courseware assisted physics teaching application based on sensor networks and deep learning technology provides new help and support for physics teaching. Copyright © 2024 Chen et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Students

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Enhanced VR Experience with Edge Computing: The Impact of Decoding Latency

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6275-6292页

作者： Huang, Liang Li, Yuqi Liang, Hongyuan Chi, Kaikai Wu, Yuan Zhejiang University of Technology School of Computer Science and Technology China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Macau China

Virtual reality (VR) applications have revolutionized digital interaction by providing immersive experiences.360° VR video streaming has experienced significant growth and popularity as a pivotal VR application. However, the combination of limited network bandwidth and the demand for high-quality videos frequently hinders the achievement of a satisfactory quality of experience (QoE). Although prior methods have enhanced QoE, the effects of decoding latency have been poorly studied. It is technically challenging to design a quality adaptation algorithm that can balance the pursuit of high-quality videos and the limitation of limited bandwidth resources. To address this challenge, we propose an edge-end architecture for 360° VR video streaming and aim to enhance overall QoE by solving a performance optimization problem. Specifically, our experiments on commercial mobile devices in real-world situations reveal that decoding latency significantly influences QoE. First, decoding latency plays a major role in contributing to end-to-end latency, which exceeds the transmission latency. Second, decoding latency can differ considerably between devices with varying computational capabilities. Building on this insight, we propose a novel latency-aware quality adaptation (LAQA) algorithm. LAQA lies in developing a solution that can allocate video quality in real-time and enhance overall QoE. LAQA involves not only the quality of the received content, the transmission latency and the quality variance, but also the decoding latency and the fairness of the user quality. Subsequently, we formulate a combinatorial optimization problem to maximize overall QoE. Through extensive validation with experimental data from real-world situations, LAQA offers a promising approach to enhance QoE and ensure fairness performance in different devices. In particular, LAQA achieves 16.77% and 10.66% enhancement over the state-of-the-art combinatorial optimization and reinforcement learning algorithm

关键词： Bandwidth

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M-LSM:An Improved Multi-Liquid State Machine for Event-Based Vision Recognition

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Journal of computer science & technology 2023年第6期38卷 1288-1299页

作者：王蕾郭莎莎曲连华田烁徐炜遐 College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and ***,the existing event-based models for vision recogni-tion are faced with several issues,such as large network complexity and expensive training *** this paper,we propose an improved multi-liquid state machine(M-LSM)method for high-performance vision ***,we intro-duce two methods,namely multi-state fusion and multi-liquid search,to optimize the liquid state machine(LSM).Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal *** adapt network architecture search(NAS)to find the potential optimal architecture of the multi-liquid state *** also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity(STDP).Our M-LSM is evalu-ated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.

关键词： liquid state machine bio-inspired learning classification event-based vision

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Research on the impact of pointing gestures based on computer vision technology on classroom concentration

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Neural Computing and Applications 2024年 1-13页

作者： Shi, Jianyang Chen, Zhangze Zhu, Jia Zhou, Jian Wang, Qing Ma, Xiaodong College of Education Zhejiang Normal University No. 688 Yingbin Avenue Zhejiang Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University No. 688 Yingbin Avenue Zhejiang Jinhua321004 China

Classroom concentration is an essential manifestation of learners’ engagement in the classroom, and it is a critical factor in adjusting learning states and optimizing teaching processes. A thorough exploration of the factors that affect classroom concentration and their effects is of great significance for enhancing it. This study proposes a classroom concentration recognition model by integrating the two dimensions of emotional and behavioral concentration using computer vision technology. The model’s effectiveness in recognizing classroom concentration is verified through a comparative experiment with EEG equipment. Based on this model, this study further investigates the impact of teachers’ pointing gestures on learners’ classroom concentration. The results of ANOVA show that when teachers use pointing gestures, they can improve learners’ concentration in class in a short time, but this positive effect has boundary effects. Our research results can help improve teachers’ teaching behaviors and enhance learners’ learning effects to some extent. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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Path-Based Multicast Routing for Network-on-Chip of the Neuromorphic Processor

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Journal of computer science & technology 2023年第5期38卷 1098-1112页

作者：康子扬李石明王世英曲连华龚锐石伟徐炜遐王蕾 College of Computer Science and Technology National University of Defense TechnologyChangsha 410000China

Network-on-Chip(NoC)is widely adopted in neuromorphic processors to support communication between neurons in spiking neural networks(SNNs).However,SNNs generate enormous spiking packets due to the one-to-many traffic *** spiking packets may cause communication pressure on *** propose a path-based multicast routing method to alleviate the ***,all destination nodes of each source node on NoC are divided into several ***,multicast paths in the clusters are created based on the Hamiltonian path *** proposed routing can reduce the length of path and balance the communication load of each ***,we design a lightweight microarchitecture of NoC,which involves a customized multicast packet and a routing *** use six datasets to verify the proposed multicast *** with unicast routing,the running time of path-based multicast routing achieves 5.1x speedup,and the number of hops and the maximum transmission latency of path-based multicast routing are reduced by 68.9%and 77.4%,*** maximum length of path is reduced by 68.3%and 67.2%compared with the dual-path(DP)and multi-path(MP)multicast routing,***,the proposed multicast routing has improved performance in terms of average latency and throughput compared with the DP or MP multicast routing.

关键词： neuromorphic processor spiking neural network(SNN) Network-on-Chip(NoC) path-based multicast

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Exploring Correlations of Self-Supervised Tasks for Graphs 41

Exploring Correlations of Self-Supervised Tasks for Graphs

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41st International Conference on Machine Learning, ICML 2024

作者： Fang, Taoran Zhou, Wei Sun, Yifei Han, Kaiqiao Ma, Lvbin Yang, Yang College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang Huayun Information Technology Hangzhou China

Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-supervised learning remains limited, and the inherent relationships between various self-supervised tasks are still unexplored. Our paper aims to provide a fresh understanding of graph self-supervised learning based on task correlations. Specifically, we evaluate the performance of the representations trained by one specific task on other tasks and define correlation values to quantify task correlations. Through this process, we unveil the task correlations between various self-supervised tasks and can measure their expressive capabilities, which are closely related to downstream performance. By analyzing the correlation values between tasks across various datasets, we reveal the complexity of task correlations and the limitations of existing multi-task learning methods. To obtain more capable representations, we propose Graph Task Correlation Modeling (GraphTCM) to illustrate the task correlations and utilize it to enhance graph self-supervised training. The experimental results indicate that our method significantly outperforms existing methods across various downstream tasks. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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ChatGPT is a Remarkable Tool—For Experts

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Data Intelligence 2024年第1期6卷 240-296页

作者： Amos Azaria Rina Azoulay Shulamit Reches School of Computer Science Ariel UniversityIsrael Department of Computer Science Jerusalem College of TechnologyIsrael Department of Mathematics Jerusalem College of TechnologyIsrael

This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains,including scientific writing,mathematics,education,programming,and *** explore the potential of ChatGPT to enhance productivity,streamline problem-solving processes,and improve writing ***,we highlight the potential risks associated with excessive reliance on ChatGPT in these *** limitations encompass factors like incorrect and fictitious responses,inaccuracies in code,limited logical reasoning abilities,overconfidence,and critical ethical concerns of copyright and privacy *** outline areas and objectives where ChatGPT proves beneficial,applications where it should be used judiciously,and scenarios where its reliability may be *** light of observed limitations,and given that the tool's fundamental errors may pose a special challenge for non-experts,ChatGPT should be used with a strategic *** drawing from comprehensive experimental studies,we offer methods and flowcharts for effectively using *** recommendations emphasize iterative interaction with ChatGPT and independent verification of its *** the importance of utilizing ChatGPT judiciously and with expertise,we recommend its usage for experts who are well-versed in the respective domains.

关键词： limitations utilizing Tool

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Production of biliverdin by biotransformation of exogenous heme using recombinant Pichia pastoris cells

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Bioresources and Bioprocessing 2024年第1期11卷 263-272页

作者： Jianfeng Mei Yanchao Han Shihang Zhuang Zhikai Yang Yu Yi Guoqing Ying College of Pharmaceutical Science Zhejiang University of Technology18 Chaowang RoadGongshu DistrictHangzhou 310014ZhejiangChina.

Biliverdin,a bile pigment hydrolyzed from heme by heme oxygenase(HO),serves multiple functions in the human body,including antioxidant,anti-inflammatory,and immune response inhibitory *** has great potential as a clinical drug;however,no economic and efficient production method is available ***,the production of biliverdin by the biotransformation of exogenous heme using recombinant HO-expressing yeast cells was studied in this ***,the heme oxygenase-1 gene(HO1)encoding the inducible plastidic isozyme from Arabidopsis thaliana,with the plastid transport peptide sequence removed,was recombined into Pichia pasto-ris GS115 *** resulted in the construction of a recombinant *** GS115-HO1 strain that expressed active HO1 in the *** that,the concentration of the inducer methanol,the induction culture time,the pH of the medium,and the concentration of sorbitol supplied in the medium were optimized,resulting in a significant improvement in the yield of ***,the whole cells of GS115-HO1 were employed as catalysts to convert heme chloride(hemin)into *** results showed that the yield of biliverdin was 132 mg/L when hemin was added to the culture of GS115-HO1 and incubated for 4 h at 30°*** findings of this study have laid a good foundation for future applications of this method for the economical production of biliverdin.

关键词： Biliverdin Biotransformation Heme Heme oxygenase-1 Recombinant Pichia pastoris

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Social Robot Detection Method with Improved Graph Neural Networks

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computers, Materials & Continua 2024年第2期78卷 1773-1795页

作者： Zhenhua Yu Liangxue Bai Ou Ye Xuya Cong Institute of Systems Security and Control College of Computer Science and TechnologyXi’an University of Science and TechnologyXi’an710054China

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social *** social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social *** paper proposes a social robot detection method with the use of an improved neural ***,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships ***,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the ***,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph ***,social robots can be more accurately identified by combining user behavioral and relationship *** carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,*** with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two *** results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

关键词： Social robot detection social relationship subgraph graph attention network feature linear modulation behavioral gene sequences

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