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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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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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DISENTANGLING LEARNING REPRESENTATIONS WITH DENSITY ESTIMATION 11

DISENTANGLING LEARNING REPRESENTATIONS WITH DENSITY ESTIMATI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Yeats, Eric Liu, Frank Li, Hai Department of Electrical and Computer Engineering Duke University United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States

Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencoder (GCAE), a method which achieves reliable disentanglement via flexible density estimation of the latent space. GCAE avoids the curse of dimensionality of density estimation by disentangling subsets of its latent space with the Dual Total Correlation (DTC) metric, thereby representing its high-dimensional latent joint distribution as a collection of many low-dimensional conditional distributions. In our experiments, GCAE achieves highly competitive and reliable disentanglement scores compared with state-of-the-art baselines. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Joint Access Selection and Computation Offloading in LEO Ubiquitous Edge Computing Networks: An Alternating DRL-Based Approach

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1870-1886页

作者： Yang, Junyi Zhang, Yuanjun Xiao, Zhenyu Han, Zhu Beihang University School of Electronic and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the increase in users' service diversity and demand for quality of experience (QoE), the utilization of low earth orbit (LEO) satellite networks for assisted or independent offloading of computation tasks has become a promising trend. However, due to the high mobility and uneven resource distribution of LEO satellites, it is difficult to meet the requirements of low delay and low overhead by using traditional optimization algorithms or common reinforcement learning (RL) algorithms. Therefore, in this paper, we consider a scenario in which terrestrial users offload delay-sensitive (DS) computation tasks to a LEO satellite network in order to study the joint access selection and computation offloading problem. First, we analyze the characteristics of the scenario and the computation tasks, and establish a generic mathematical model. Then, based on the block descent coordinate (BCD) principle, we propose a novel algorithm of alternating Dueling DQN (ADDQN) for the joint decision-making problem, where access selection and computation offloading are performed with corresponding independent agent respectively. Comprehensive simulations show that compared with other benchmark algorithms, the proposed method not only has better convergence, but also can maximize the number of successfully completed sub-tasks and the optimization objective value meanwhile reducing the unnecessary access handovers. © 2024 IEEE.

关键词： Satellites Computational modeling Decision making Low earth orbit satellites Handover Benchmark testing Mathematical models Quality of experience Optimization Convergence

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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Semantic-Based XAI: Leveraging Ontology Properties to Enhance Explainability

Semantic-Based XAI: Leveraging Ontology Properties to Enhanc...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Kosov, Pavel Kadhi, Nahla El Zanni-Merk, Cecilia Gardashova, Latafat Azerbaijan State Oil and Industry University Computer Engineering Department Baku Azerbaijan French-Azerbaijani University Computer Science Research Department Baku Azerbaijan INSA Rouen Normandie LITIS Laboratory Rouen France

ISBN: (纸本)9798350369106

The lack of explainability of machine learning models has been an issue for a long time. Experts in various fields of AI applications have sought explainable and trustworthy systems. DARPA has developed a modern approach to eXplainable AI to overcome this issue. Later, Bellucci et al. developed an Ontology-based Image Classifier by enriching the XAI of DARPA with a new method based on semantic web technologies. Using expert knowledge in the form of ontologies, explanations of their system are based on 'visible' properties discovered in images. Based on their ideas, Kosov et al. proposed new 'explanatory' properties, which showed the possibility of using other data types than images. However, we believe that the architecture of the previous system has to undergo several changes to be fully adapted to using various forms of data. The new architecture is proposed to improve the explanations of multiple data types, bringing us closer to the initial ideas of DARPA. Our approach provides a new flexible system where any machine learning model can use 'explanatory' properties in a new way by giving promising results and adapting the system to process tabular data by providing thorough explanations and laying down a strong foundation for improved explainability of other forms of data. The flexibility of the newly designed system allows it to be adapted to any machine learning model. It can work with any user interface tailored to fit each user's specific needs, mental model, and way of thinking. © 2024 IEEE.

关键词： Adversarial machine learning

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MeshWGAN: Mesh-to-Mesh Wasserstein GAN with Multi-Task Gradient Penalty for 3D Facial Geometric Age Transformation

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IEEE Transactions on Visualization and computer graphics 2024年第8期30卷 4927-4940页

作者： Zhang, Jie Zhou, Kangneng Luximon, Yan Lee, Tong-Yee Li, Ping The Hong Kong Polytechnic University Department of Computing The School of Design Hong Kong University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China The Hong Kong Polytechnic University School of Design Hong Kong Laboratory for Artificial Intelligence in Design Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications. © 1995-2012 IEEE.

关键词： Face recognition

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Intelligent edge CDN with smart contract-aided local IoT sharing

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High-Confidence Computing 2024年第4期4卷 56-68页

作者： Jiamin Fan Daming Liu Guoming Tang Kui Wu Xun Shao Department of Computer Science University of VictoriaVictoria BC V8W 2Y2Canada College of Computer Science and Technology Shanghai University of Electric PowerShanghai 200090China Peng Cheng Laboratory Shenzhen 518000China Department of Electrical and Electronic Information Engineering Toyohashi University of TechnologyToyohashi Aichi 441-8580Japan

A content delivery network(CDN)aims to reduce the content delivery latency to end-users by using distributed cache ***,deploying and maintaining cache servers on a large scale is very *** solve this problem,CDN providers have developed a new content delivery strategy:allowing end-users’s IoT edge devices to share their storage/bandwidth *** new edge CDN platform must address two core questions:(1)how can we incentivize end users to share IoT devices?(2)how can we facilitate a safe and transparent content transaction environment for end users?This paper introduces SmartSharing,a new content delivery network solution to address these *** smartSharing,the over-the-top(OTT)IoT devices belonging to end-users are used as mini-cache *** motivate end users to share the idle devices and storage/bandwidth resources,SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms(specifically,a tailored expectation-maximization(EM)algorithm).To facilitate content trading among end users,SmartSharing creates a secure and transparent transaction platform based on smart contracts in *** addition,SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing *** evaluation results show that CDN providers,end users and content providers can all benefit from our SmartSharing framework.

关键词： CDN over edge IoT Game theory EM algorithm Smart contracts

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TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering

TourSynbio: A Multi-Modal Large Model and Agent Framework to...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Shen, Yiqing Chen, Zan Mamalakis, Michail Liu, Yungeng Li, Tianbin Su, Yanzhou He, Junjun Liò, Pietro Wang, Yu Guang Johns Hopkins University Department of Computer Science Baltimore United States University of Cambridge Department of Computer Science and Technology Cambridge United Kingdom City University of Hong Kong Department of Computer Science Hong Kong Toursun Synbio Shanghai China Shanghai Ai Laboratory Shanghai China Shanghai Jiao Tong University Institute of Natural Sciences Shanghai China

ISBN: (纸本)9798350386226

The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored. Previous approaches have equipped LLMs with protein understanding capabilities by incorporating external protein encoders, but this fails to fully leverage the inherent similarities between protein sequences and natural languages, resulting in sub-optimal performance and increased model complexity. To address this gap, we present TourSynbio-7B, the first multi-modal large model specifically designed for protein engineering tasks without external protein encoders. TourSynbio-7B demonstrates that LLMs can inherently learn to understand proteins as language. The model is post-trained and instruction fine-tuned on InternLM2-7B using ProteinLM-Dataset, a dataset comprising 17.46 billion tokens of text and protein sequence for self-supervised pretraining and 893K instructions for supervised fine-tuning. TourSynbio7B outperforms GPT-4 on the ProteinLMBench, a benchmark of 944 manually verified multiple-choice questions, with 62.18% accuracy. Leveraging TourSynbio-7B's enhanced protein sequence understanding capability, we introduce TourSynbioAgent, an innovative framework capable of performing various protein engineering tasks, including mutation analysis, inverse folding, protein folding, and visualization. TourSynbio-Agent integrates previously disconnected deep learning models in the protein engineering domain, offering a unified conversational user interface for improved usability. Finally, we demonstrate the efficacy of TourSynbio-7B and TourSynbio-Agent through two wet lab case studies on vanilla key enzyme modification and steroid compound catalysis. Our results show that this combination facilitates protein engineering tasks in wet labs, leading t

关键词： Biochemical engineering

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Chen, Yen-Yi Wang, Jian-Hong Chan, Po-Cheng Liang, Kai-Wen Wang, Ze-Yu Wang, Jia-Ching School of Computer Science and Technology Shandong University of Technology Shandong Zibo China National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan

ISBN: (纸本)9798350353983

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications. © 2024 IEEE.

关键词： Echo suppression

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