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Deep-Learning-Assisted Complete Targets Coverage in Energy-Harvesting IoT Networks

IEEE Internet of Things Journal 2025年第11期12卷 17780-17790页

作者： Wang, Kunsheng Yang, Changlin Chin, Kwan-Wu Xian, Jun Sun Yat-sen University School of Mathematics Guangdong Guangzhou510275 China Sun Yat-sen University School of Software Engineering Zhuhai519082 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Minnan Normal University School of Mathematics and Statistics Zhangzhou363000 China Sun Yat-sen University Guangdong Provincial Key Laboratory of Computational Science Guangzhou510275 China

Complete targets coverage is required by many Internet of Things (IoT) applications. In this respect, an important goal is to maximize the number of time slots with complete targets coverage. Achieving such coverage is challenging when devices experience spatio-temporal energy arrivals. To this end, this article outlines a deep learning assisted approach that has an offline stage whereby it determines and stores an exhaustive collection of optimal activation schedules based the energy levels and arrivals of devices. In addition, it presents a network partitioning and training strategy, and outlines an algorithm to mend coverage holes in its online stage. We have compared the proposed approach with the optimal solution, and also a state-of-the-art heuristic algorithm. The results show that our solution achieves 94% of the optimal coverage lifetime. Moreover, the proposed approach has a 35% smaller optimality gap as compared with the said heuristic algorithm. © 2014 IEEE.

关键词： Self-supervised learning

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Personalized Health Assistant with Reinforcement Learning 1

Personalized Health Assistant with Reinforcement Learning

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Jin, Jennifer Kim, Mira Kim, Soo Dong Computer Science and Engineering California State University San BernardinoCA92407 United States Department of Computer Science California State University FullertonCA92831 United States School of Software Soongsil University Seoul Korea Republic of

ISBN: (纸本)9798350371987

Integration of medical IoT devices with machine learning presents significant advantages for personalized healthcare. This research is to develop a software solution which leverages smart scale devices to measure health indexes, recommend personalized health-promoting activities. We designed the system with Q-learning elements and tailored the Q-learning policy to keep learning the personal effectiveness of health-promoting activities through users’ episodes. The system was implemented in Python, and the experiments demonstrated a substantial average enhancement of 25% across health indexes and 53% in optimizing the performance of the recommender policy. ©2024 IEEE.

关键词： Reinforcement learning

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Multi-turn dialogue model based on the improved hierarchical recurrent attention network

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International Journal for engineering Modelling 2021年第2期34卷 17-29页

作者： Miao, Jiawei Wu, Jiansheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114044 China

When considering the multi-turn dialogue systems, the model needs to generate a natural and contextual response. At present, HRAN, one of the most advanced models for multi-turn dialogue problems, uses a hierarchical recurrent encoder-decoder combined with a hierarchical attention mechanism. However, for complex conversations, the traditional attention-based RNN does not fully understand the context, which results in attention to the wrong context that generates irrelevant responses. To solve this problem, we proposed an improved hierarchical recurrent attention network, a self-attention network (HSAN), instead of RNN, to learn word representations and utterances representations. Empirical studies on both Chinese and English datasets show that the proposed model has achieved significant improvement. © 2021, University of Split. All rights reserved.

关键词： Speech processing

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SWISP: Distributed Convoy Mining via Sliding Window-based Indexing and Sub-track Partitioning 40

SWISP: Distributed Convoy Mining via Sliding Window-based In...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Chenxu Yang, Xin Li, Tianyi Wei, Jiaxing Wang, Pinghui Xiang, Hongzhen Jensen, Christain S. School of Software Engineering MOE Key Lab of INNS Xi'An Jiaotong University Xi'an China School of Software Engineering Xi'An Jiaotong University Xi'an China Aalborg University Department of Computer Science Aalborg Denmark MOE Key Lab of INNS Xi'An Jiaotong University Xi'an China

ISBN: (纸本)9798350317152

With the widespread deployment of location-aware mobile devices, a mass of trajectory data is being generated and collected. Mining co-movement patterns of people and vehicles from streaming and massive trajectory data has attracted much attention due to its wide applications in various fields. As a typical co-movement pattern, convoys describe objects moving together in consecutive timestamps. There are two challenges for efficient distributed convoy mining: object clustering and workload balancing. Clustering objects in each time snapshot is a time-consuming operation. In addition, on the basis of practical application scenarios, load balancing is an important consideration for distributed algorithms. To tackle the above challenges, we propose a novel method for distributed convoy mining via sliding window-based indexing and sub-track partitioning, abbreviated SWISP. We offer three major advancements. First, we develop a grid-based DBSCAN clustering algorithm named Grid-DBSCAN for distributed scenarios. It avoids the exhaustive calculation of pairwise distances for neighborhood search and thus improves computational efficiency in the clustering stage. Second, we propose a sliding window-based indexing scheme to filter out sub-tracks with less than k consecutive time snapshots, significantly reducing the number of candidate sub-tracks for convoy mining. Third, we develop a distributed convoy mining algorithm based on sub-track partitioning. It exploits both temporal and spatial information of sub-tracks for data partitioning and solves the data skewness problem caused by uneven data distributions. We conduct extensive experiments on four real-world datasets. The experimental results show that our distributed algorithm can handle large-scale trajectory data and is more efficient than state-of-the-art approaches. © 2024 IEEE.

关键词： Trajectories

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

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computer Modeling in engineering & sciences 2023年第4期135卷 91-108页

作者： Zhichen Hu Huali Ren Jielin Jiang Yan Cui Xiumian Hu Xiaolong Xu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institution of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou515021China School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210023China

An obviously challenging problem in named entity recognition is the construction of the kind data set of *** some research has been conducted on entity database construction,the majority of them are directed at Wikipedia or the minority at structured entities such as people,locations and organizational nouns in the *** paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in ***,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text ***,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity ***,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this *** show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition *** study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

关键词： Named entity recognition carbonate platform corpus entity extraction english literature detection

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Integration of Metaverse Recordings in Multimedia Information Retrieval 24

Integration of Metaverse Recordings in Multimedia Informatio...

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13th International Conference on software and computer Applications, ICSCA 2024

作者： Steinert, Patrick Wagenpfeil, Stefan Frommholz, Ingo Hemmje, Matthias L. University Of Hagen Faculty Of Mathematics And Computer Science Hagen Germany Pfh Private University Of Applied Sciences Faculty Of Business Computing And Software Engineering Göttingen Germany University Of Wolverhampton School Of Mathematics And Computer Science Wolverhampton United Kingdom

ISBN: (纸本)9798400708329

This paper addresses the challenges in integrating Metaverse Recordings in Multimedia Information Retrieval as a new type of multimedia. Specifically, we describe the characteristics of video content and explain the key differences of a metaverse to other videos. We present a specialized process to analyze and index Metaverse Recordings. We demonstrate that existing techniques can be used for metaverse-specific retrieval, but need content-specific adaptions. Furthermore, we evaluate a prototypical implementation based on the proposed methods with respect to the impact on the retrieval process. © 2024 Owner/Author.

关键词： Multimedia systems

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An Efficient Convolutional Multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks Multi-scale Vision Transformer

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Demystifying Mobile Extended Reality in Web Browsers: How Far Can We Go? 23

Demystifying Mobile Extended Reality in Web Browsers: How Fa...

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2023 World Wide Web Conference, WWW 2023

作者： Bi, Weichen Ma, Yun Tian, Deyu Yang, Qi Zhang, Mingtao Jing, Xiang School of Computer Science Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China School of Computer Science and Engineering University of New South Wales Australia School of Software and Microelectronics Peking University Beijing China

ISBN: (纸本)9781450394161

Mobile extended reality (XR) has developed rapidly in recent years. Compared with the app-based XR, XR in web browsers has the advantages of being lightweight and cross-platform, providing users with a pervasive experience. Therefore, many frameworks are emerging to support the development of XR in web browsers. However, little has been known about how well these frameworks perform and how complex XR apps modern web browsers can support on mobile devices. To fill the knowledge gap, in this paper, we conduct an empirical study of mobile XR in web browsers. We select seven most popular web-based XR frameworks and investigate their runtime performance, including 3D rendering, camera capturing, and real-world understanding. We find that current frameworks have the potential to further enhance their performance by increasing GPU utilization or improving computing parallelism. Besides, for 3D scenes with good rendering performance, developers can feel free to add camera capturing with little influence on performance to support augmented reality (AR) and mixed reality (MR) applications. Based on our findings, we draw several practical implications to provide better XR support in web browsers. © 2023 ACM.

关键词： Web browsers

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Saving The Elf: Immersive VR Experience Utilizing Multiple Relaxing Approaches

Saving The Elf: Immersive VR Experience Utilizing Multiple R...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Nie, Cheng Ding, Ding Wu, Jinhao Yang, Xu Ou, Ronghuang Du, Wei College of Software Engineering Southeast University Nanjing China Southeast University Computer Experimental Teaching Center Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence Southeast University Nanjing China School of Public Health Southeast University Nanjing China

ISBN: (纸本)9781665410205

The global prevalence of anxiety has seen a significant increase in the past few years. Anxiety, if left untreated, can lead to various detrimental effects, such as sleep disturbances, excessive fear, and even suicidal tendencies. Traditional face-to-face therapies have been employed to address this issue;however, their effectiveness is limited by time and space constraints, particularly during the COVID-19 pandemic. As a result, computer-based systems have emerged as a potential solution. However, most existing relaxation systems rely on a single method and have unnatural interactions, leading to a lack of variety and engagement. To overcome these limitations, this study proposes a virtual reality (VR) relaxation system that combines various relaxation techniques, a captivating storyline and natural interactions to enhance the user experience. To assess the effectiveness of the proposed system, a pilot study was conducted with 36 participants. The study utilized a between-subjects design, dividing the participants into three groups: a control group, a progressive muscular relaxation group, and a virtual reality relaxation group. The findings revealed that the virtual reality relaxation system significantly reduced anxiety levels among the participants. Importantly, this effect was observed to persist even after a period of time and was found to be more effective than using only one relaxation method in the long term. Moreover, the performance of our system is close to that of commercial systems. © 2024 IEEE.

关键词： Virtual environments

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