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Energy Efficient Dual User Association for Large Scale IRS-Aided mmWave Communication Networks

China Communications 2025年第1期22卷 182-195页

作者： Chen Guolin Deng Yiqin Huang Xiaoxia Fang Yuguang School of Electronics and Communication Engineering Sun Yat-sen UniversityShenzhen 518107China School of Control Science and Engineering Shandong UniversityJinan 250100China Department of Computer Science City University of Hong KongHong Kong 999077China

The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention *** the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS ***,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and *** problem is efficiently addressed in two ***,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE *** reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and *** results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.

关键词： energy efficiency intelligent reflecting surface,millimeter-wave communication

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GraphSA: Smart Contract Vulnerability Detection Combining Graph Neural Networks and Static Analysis 26

GraphSA: Smart Contract Vulnerability Detection Combining Gr...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： He, Long Zhao, Xiangfu Wang, Yichen Yang, Jiahui Sun, Xuelei School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9781643684369

Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others. © 2023 The Authors.

关键词： Smart contract

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Proactive Perception of Preferences Evolution Based on Graph Neural Networks 16th

Proactive Perception of Preferences Evolution Based on Gra...

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16th International Conference on Service Science, ICSS 2023

作者： Pang, Lixin Liu, Zhizhong Meng, Lingqiang Li, Linxia Song, Xiaoyu School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9789819944019

User preferences play an important role in intelligent services, smart recommendations, and so on. Unfortunately, user preferences are dynamic and always evolving. How to proactively perceive users’ preferences evolution has become a critical issue. Recently, some wonderful research results about preference learning or preference prediction has been achieved. However, existing works can only perceive the change of preferences implicitly, cannot present users’ preferences change explicitly. To tackle this issue, we propose an approach for proactive perception of preferences evolution based on graph neural networks (PPGNN). Firstly, PPGNN constructs a directed sequence graph based on the user’s historical item interaction sequence, and learns the embedded vector representation of nodes in the directed sequence graph with the gated graph neural network (GGNN). Secondly, PPGNN learns the complex correlation between users and items in the interaction sequence using attention networks to obtain users’ long-term preferences. Meanwhile, PPGNN uses the location-aware network to preserve the location information in the sequence, and extracts user’s short-term preferences from the sequence with the attention network. Thirdly, PPGNN combines the user’s long-term and short-term preferences through a gated-fusion network and recommends an item that the user is most interested in. Finally, PPGNN perceives the evolution of user preferences by comparing the recommended items with the items that the user was previously interested in. Extensive experiments have been conducted to validate the proposed method and demonstrate its effectiveness. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graph neural networks

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A Weighted Slope One Collaborative Filtering Algorithm for Improved User Relevance

A Weighted Slope One Collaborative Filtering Algorithm for I...

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2023 International Conference on Artificial Intelligence and Automation control, AIAC 2023

作者： Li, Ruizhi Liu, Qicheng Yantai University School of Computer and Control Engineering Yantai264005 China

ISBN: (纸本)9798350383805

Considering the low accuracy of recommendation results of current collaborative filtering algorithms caused by data sparsity, this paper proposes a weighted Slope One collaborative filtering algorithm for improved user relevance (WSOCF-IUR). The proposed algorithm takes into account the influence of the number of different user comments and the number of common rating items on recommendation accuracy. In addition, by incorporating user activity and improving the Pearson correlation coefficient, the algorithm reduces the influence of interfering data on recommendations and improving the overall recommendation accuracy. Experimental results demonstrate that the proposed algorithm achieves better recommendation results than other collaborative filtering recommendation algorithms. © 2023 IEEE.

关键词： Collaborative filtering

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Multimodal Intent Recognition Based on Contrastive Learning 16th

Multimodal Intent Recognition Based on Contrastive Learning

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16th International Conference on Service Science, ICSS 2023

作者： Wu, Yuxuan Liu, Zhizhong Su, Zhaohui Song, Xiaoyu School of Computer and Control Engineering Yantai University Yantai264005 China

ISBN: (纸本)9789819944019

Multimodal intent recognition plays a critical role in service science as it enables the achievement of more intelligent and personalized services. Recently, multimodal intent recognition has become a hot topic, and some research has been carried out on this topic. However, most existing research methods focus on exploring intra-modal or inter-modal interactions, which ignore the relationship between multimodal samples. To address this issue, we propose an approach for Multimodal Intent Recognition Based on Contrastive Learning (MIRCL). Firstly, the original samples and augmented samples are separately fed into crossmodal Transformer modules for multimodal feature fusion. The resulting fused multimodal features are then used for contrastive learning. Meanwhile, the fused multimodal features of the original samples are passed into a multimodal information bottleneck (MIB) module for intent recognition. Finally, the contrastive learning task and intent recognition task are jointly trained to optimize the whole model. We carried out comprehensive validation tests based on the publically available dataset MIntRec to prove the superiority of our proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Statistical tests

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Slow Convergence of Interacting Kalman Filters in Word-of-Mouth Social Learning 60

Slow Convergence of Interacting Kalman Filters in Word-of-Mo...

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60th Annual Allerton Conference on Communication, control, and Computing, Allerton 2024

作者： Krishnamurthy, Vikram Rojas, Cristian R. School of Electrical and Computer Engineering Cornell University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden

ISBN: (纸本)9798331541033

We consider word-of-mouth social learning involving m Kalman filter agents that operate sequentially. The first Kalman filter receives the raw observations, while each subsequent Kalman filter receives a noisy measurement of the conditional mean of the previous Kalman filter. The prior is updated by the m-th Kalman filter. When m=2, and the observations are noisy measurements of a Gaussian random variable, the covariance goes to zero as k-1/3 for 7kappa;observations, instead of O(k-1) in the standard Kalman filter. In this paper we prove that for m agents, the covariance decreases to zero as k-(2m-1), i.e, the learning slows down exponentially with the number of agents. We also show that by artificially weighing the prior at each time, the learning rate can be made optimal as k-1. The implication is that in word-of-mouth social learning, artificially re-weighing the prior can yield the optimal learning rate. © 2024 IEEE.

关键词： Kalman filters

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Improvement Algorithm Analysis and Implementation of Plant Disease and Pest Recognition Based on YOLO 5

Improvement Algorithm Analysis and Implementation of Plant D...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wei, Yongxiang Pan, Xin Jilin Institute of Chemical Technology School of Information and Control Engineering Jilin China Changchun Institute of Technology School of Computer Technology and Engineering Changchun China Changchun Institute of Technology School of Computer Technology and Engineering Jilin Changchun China

ISBN: (纸本)9798350373820

Agriculture is one of the main economic pillars in many countries and regions, contributing significantly to the stable development of the national economy. However, the growth rate of agriculture is currently declining rapidly, and plant diseases and pests are increasingly prevalent due to various factors such as climate and pollution. In response to the detection and recognition of plant diseases and pests, we propose introducing a multi-channel BiFormer module into the basic YOLOv8 target detection algorithm, which can effectively improve recognition accuracy. In experiments, we tested our method using a cashew dataset, and the results showed that our method outperforms traditional methods in terms of precision (P), recall rate (R), and mean average precision (mAP), indicating its high effectiveness. © 2024 IEEE.

关键词： Object detection

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Research on Teaching Reform for Python Programming Curriculum Based on AIGC-CDIO-OBE Model 6

Research on Teaching Reform for Python Programming Curriculu...

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6th International Conference on computer Science and Technologies in Education, CSTE 2024

作者： Liao, Erchong School of Control and Computer Engineering North China Electric Power University Hebei Province Baoding China

ISBN: (纸本)9798350351804

This paper focuses on the characteristics of computer science general course, specifically the emphasis on comprehension and practical application in "Python Language Programming". Combining the contemporary Artificial Intelligence Generated Content (AIGC) technology with the teaching philosophy of Outcome-Based Education (OBE) and CDIO (Conceive, Design, Implement, Operate) in computer education, I propose the innovative AIGC-CDIO-OBE education model. Which can help students enhance computational thinking, design thinking, and programming skills in python language. Through teaching practices, the effectiveness and feasibility of this education model are demonstrated when compared to traditional teaching methods. Students have more interesting for learning, stronger motivation for coding, and stronger sense of achievement in their learning outcomes. © 2024 IEEE.

关键词： Python

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