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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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Multiple Time-Periods POI Recommendation Model Based on Distributed ELM 6

Multiple Time-Periods POI Recommendation Model Based on Dist...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Zhang, Zhen Gong, Yaqi Zhao, Xiangguo Chen, Jun Ren, Jianyu Ouyang, Jintao School of Electrical Engineering Yingkou Institute of Technology Yingkou China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China School of Software Northeastern University Shenyang China

ISBN: (纸本)9798350353174

To solve the problems of low efficiency of algorithm execution and not fully considering the POI needs of users in different time-periods in point-of-interest (POI) recommendation. In this paper, propose a Multiple time-periods POI recommendation model MP-DELM based on distributed extreme learning machine (DELM). According to the user's different periods and different types of POI access requirements, the data set was processed in different periods;extract POI preference features: distance preference feature, personalized preference feature, popularity preference feature, and friend preference feature, in which for the friend preference feature, social friend feature and the trajectory feature are integrated for the first time, and a new trajectory friend similarity calculation method is proposed;in order to improve the efficiency of POI recommendation, improve DELM classifier. And the online sequential extreme learning machine (OS-ELM) is used to update training data in real time. Finally, we prove that the MP-DELM recommendation model can effectively improve the accuracy and efficiency of POI recommendation in the real datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images 41

Tabular Insights, Visual Impacts: Transferring Expertise fro...

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41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Jun-Peng Ye, Han-Jia Wang, Leye Yang, Yang Jiang, Yuan Zhan, De-Chuan School of Artificial Intelligence Nanjing University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Ministry of Education & School of Computer Science Peking University Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the inherent complexity of accurately mapping diverse tabular data to visual contexts, coupled with the necessity to devise distinct strategies for numerical and categorical tabular attributes. We propose CHannel tAbulaR alignment with optiMal tranSport (CHARMS), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. Specifically, CHARMS measures similarity distributions across modalities to effectively differentiate and transfer relevant tabular features, with a focus on morphological characteristics, enhancing the capabilities of visual classifiers. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that CHARMS not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge. Copyright 2024 by the author(s)

关键词： Knowledge transfer

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Sybil attack simulation on Enhanced IdAPoS protocol with different network topology 12

Sybil attack simulation on Enhanced IdAPoS protocol with dif...

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12th IEEE International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2024

作者： Chen, Shengyu Jin, Shenghao Wei, Yigang Zhang, Hui Beihang University State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beijing China Beihang University School of economics and management Beijing China

ISBN: (纸本)9798331510633

This study mainly focus on Sybil attacks with the Identity-Augmented Proof-of-Stake (IdAPoS) protocol under different network topologies, including random, scale-free, and hierarchical networks. The study finds that scale-free networks are more resistant to Sybil attacks, delaying their effects. Furthermore, the research improves the IdAPoS protocol by introducing active strategies for honest nodes, which improves the behaviours of this protocol, and by enabling them to dynamically assess and update their trusted and suspicious authority lists based on on-ledger data, which improves the completeness of this protocol. © 2024 IEEE.

关键词： Network topology

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WiHunter: Enabling Real-time Small Object Detection via Wireless Sensing 31

WiHunter: Enabling Real-time Small Object Detection via Wire...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zou, Xiang Liu, Jianwei Han, Jinsong Xi, Wei Wang, Zhi School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Technology Zhejiang University China School of Software Engineering Xi'an Jiaotong University China

ISBN: (纸本)9798350399738

Rodent infestation is a great danger to human society, continuously threatening food safety and inducing disease spread. Existing methods to deal with rodent infestation are mainly based on passive bait traps and poisoning. These methods lack timeliness and effectiveness due to the missing of real-time detection. In this paper, we develop WiHunter, a new wireless sensing system to discover small objects (e.g., rat). Our idea is to exploit reflection signal effect of wireless channels induced by the movement of small objects around the receiver antenna. However, existing wireless sensing works usually employ customized or costly device-dependency Network Interface Cards(NIC), which are impractical to be densely deployed in reality. We implement WiHunter with several CSI-enabled standalone IoT nodes. We show how such devices enable moving small object detection via WiFi signal. The rationale behind this is 1) thanks to the widespread deployments of WiFi infrastructures and IoT devices, the WiFi signal covers almost every location of the corner, 2) the signal amplitude of each device is related to the small object near the receiver antenna. This ability gives us the opportunity to sense object as small as a rat. We implement WiHunter with ESP32 microcontroller on Espressif IoT Development Framework (both of them are cheap commodity off-the-shelf (COTS) devices) and design a practical small object intrusion detection system. Comprehensive and real-world experiments demonstrate that our system is effective in detecting the presence of small objects with an average accuracy of 92.1%. © 2023 IEEE.

关键词： Rats

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Multi-View Dynamic Kernelized Evidential Clustering

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2435-2450页

作者： Jinyi Xu Zuowei Zhang Ze Lin Yixiang Chen Weiping Ding the Software Engineering Institute East China Normal UniversityShanghai 200062 Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China IEEE the School of Automation Northwestern Polytechnical UniversityXi’an 710072China the School of Artificial Intelligence and Computer Science Nantong UniversityNantong 226019 the Faculty of Data Science City University of MacaoMacao 999078China

It is challenging to cluster multi-view data in which the clusters have overlapping *** multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering *** solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping *** offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective *** on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance.

关键词： Evidential clustering imprecision characterizing kernel technique multi-view clustering

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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Transformer-Based Named Entity Recognition for Power Research with Dependency Relationship Information 7

Transformer-Based Named Entity Recognition for Power Researc...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Deng, Jiewen Jiang, Wei Zhao, Hongda Ren, Qinghua Zhang, Li School of Software Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798350345094

The process of research activities in the electric power field produces a large number of articles, patents, and other results, which contain a large amount of information. Named entity recognition is a common method for obtaining textual information, and this study establishes a model that can efficiently recognize named entities of electric power research results. Firstly, the keywords of electric power literature are obtained for preprocessing and organization, and then the annotated dataset of keyword named entities of electric power scientific research is constructed. After that, this paper enhances the ability of the model to obtain contextual information through the Transformer encoder and enriches the expression of textual information by obtaining textual semantic structure information through dependent syntactic analysis. Meanwhile, in order to strengthen the feature extraction ability, the BILSTM-Multi-Head-Attention-CRF model is used to obtain text context information. IDCNN model is also used to make this paper's model acquire the ability to extract text semantic structure information. Finally, ablation experiments are conducted to show that the model proposed in this paper has a better recognition effect compared with the named entity recognition methods in the generic domain. © 2023 IEEE.

关键词： Semantics

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Fuzzy based Fall Risk Prediction in Older Adults 3

Fuzzy based Fall Risk Prediction in Older Adults

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mohan, Deepika Al-Hamid, Duaa Zuhair Chong, Peter Han Joo Gutierrez, Jairo Li, Hui Auckland University of Technology Department of Electrical and Electronic Engineering Auckland New Zealand Auckland University of Technology Department of Computer Science and Software Engineering Auckland New Zealand Peking University School of Electronic & Computer Engineering Shenzhen China

ISBN: (纸本)9798350372120

The global elderly population is increasing rapidly, leading to a rise in chronic illnesses and co-existing conditions, which in turn results in higher healthcare expenses. Accidental falls are among the leading causes of injury-related deaths in elderly individuals. This study aims to create a real-time monitoring system using vital signs to foresee a future fall in older adults by identifying abnormalities through continuous monitoring. The proposed fall prediction technique, employing the Fuzzy-based Fall Prediction Algorithm, utilizes Fuzzy rules to learn and execute tasks. The obtained results are then classified according to different levels of predicted risk indicators. The developed model is tested using data from older adults sourced from a public repository and compared with the results of the theoretical evaluation. The simulated outcomes demonstrate that the proposed algorithm achieves 96% accuracy, 93.75% sensitivity, and 100% specificity. Utilizing these advancements in the proposed heterogeneous technology allows for the early prediction of falls in the elderly and can potentially save lives. © 2024 IEEE.

关键词： Forecasting

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Constructing Knowledge Graph for Electricity Keywords Based on Large Language Model 7

Constructing Knowledge Graph for Electricity Keywords Based ...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Zhao, Honda Jiang, Wei Deng, Jiewen Ren, Qinghua Zhang, Li School of Software Southeast University Nanjing China School of Electrical Engineering Southeast University Nanjing China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

ISBN: (纸本)9798350345094

In the information age, the electric power industry, as a crucial pillar of modern society, has accumulated a wealth of valuable research literature. Knowledge graph technology offers the potential to tap into this knowledge repository, providing a better understanding of research outcomes in the electric power domain. However, due to the diversity and complexity of knowledge in the power industry, it is difficult to build a comprehensive and complete knowledge graph of power keywords. In recent years, large language models (LLMs) have made significant advancements. This paper harnesses LLM technology along with text similarity analysis and co-occurrence frequency analysis to establish a comprehensive framework for processing keyword knowledge in the field of electric power. Within this framework, various forms of information found in electric power research can be processed. This includes creating a thesaurus of electric power domain keywords;obtaining the individual attributes implied by the information keywords in this thesaurus and their interconnections;and generating a knowledge graph of electric power domain keywords. This knowledge graph includes attributes, interpretations, relationships, and associated literature for keywords. It serves as a valuable reference for the effective utilization of research outcomes in the electric power domain. © 2023 IEEE.

关键词： Knowledge graph

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