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Modeling Long- and Short-Term Service Recommendations with a Deep Multi-Interest Network for Edge Computing

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Tsinghua Science and Technology 2024年第1期29卷 86-98页

作者： Rui Yuan Shunmei Meng Ruihan Dou Xinna Wang department of {1. Science and engineering Nanjing University of Science and TechnologyNanjing 210056China Faculty of Mathematics University of WaterlooWaterloo N2L 3G1Canada

Edge computing platforms enable application developers and content providers to provide context-aware services(such as service recommendations)using real-time wireless access network *** to recommend the most suitable candidate from these numerous available services is an urgent ***-through rate(CTR)prediction is a core task of traditional service ***,many existing service recommender systems do not exploit user mobility for prediction,particularly in an edge computing *** this paper,we propose a model named long and short-term user preferences modeling with a multi-interest network based on user *** uses a logarithmic network to capture multiple interests in different fields,enriching the representations of user short-term *** terms of long-term preferences,users'comprehensive preferences are extracted in different periods and are fused using a nonlocal *** experiments on three datasets demonstrate that our model relying on user mobility can substantially improve the accuracy of service recommendation in edge computing compared with the state-of-the-art models.

关键词： recommender system logarithmic network nonlocal network

Kinematic-mapping-model-guided analysis and optimization of 2-PSS&1.RR circular-rail parallel mechanism for fully steerable phased array antennas

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Defence Technology 2024年第8期38卷 136-154页

作者： Guodong Tan Xiangfei Meng Xuechao Duan Lulu Cheng Dingchao Niu Shuai He Dan Zhang Laboratory of Electromechanical Coupling in electronic Equipment Xidian University department of Mechanical {1. The Hong Kong Polytechnic University

This paper presents a systematic methodology for analyzing and optimizing an innovative antenna mount designed for phased array antennas, implemented through a novel 2-PSS&1.RR circular-rail parallel mechanism. Initially, a comparative motion analysis between the 3D model of the mount and its full-scale prototype is conducted to validate effectiveness. Given the inherent complexity, a kinematic mapping model is established between the mount and the crank-slider linkage, providing a guiding framework for subsequent analysis and optimization. Guided by this model, feasible inverse and forward solutions are derived, enabling precise identification of stiffness singularities. The concept of singularity distance is thus introduced to reflect the structural stiffness of the mount. Subsequently, also guided by the mapping model, a heuristic algorithm incorporating two backtracking procedures is developed to reduce the mount's mass. Additionally, a parametric finite-element model is employed to explore the relation between singularity distance and structural stiffness. The results indicate a significant reduction(about 1.%) in the antenna mount's mass through the developed algorithm, while highlighting the singularity distance as an effective stiffness indicator for this type of antenna mount.

关键词： Backtracking Circular rail Crank-slider linkage Innovative antenna mount Kinematic mapping model Stiffness singularity

IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

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computer Modeling in {1. & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed I.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia department of {1. engineering SRM University APGunturIndia College of computer and Information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 1..25% compared to SMRA and ACRA while achieving a 27.31. and 74.1.% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21. reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

关键词： Internet of Things edge computing offloading NOMA

Automatic bug assignments without texts: a study

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Frontiers of computer Science 2024年第4期18卷 211-213页

作者： Zexuan LI Kaixin HUANG department of {1. Science and engineering Shanghai Jiao Tong UniversityShanghai 200240China

1.Introduction Automatic bug assignment has been well-studied in the past *** textual bug reports usually describe the buggy phenomena and potential causes,engineers highly depend on these reports to fix ***,researchers spend much effort on processing bug reports,aiming for obtaining key information and/or clues for reproducing bugs,analyzing their root causes,assigning them to developers/maintainers,and fixing these ***,our previous research[1.reveals that noises in texts bring adverse impacts to automatic bug assignments unexpectedly,mainly due to insufficiency of classical Natural Language Processing(NLP)techniques.

关键词： bug assignment maintain

A survey on semantic communications:Technologies,solutions,applications and challenges

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Digital Communications and Networks 2024年第3期10卷 528-545页

作者： Yating Liu Xiaojie Wang Zhaolong Ning MengChu Zhou Lei Guo Behrouz Jedari School of Communication and Information engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Baotou Vocational&Technical College Baotou 014035China School of Information and electronic {1. Zhejiang Gongshang UniversityHangzhou 310018China department of Electrical and {1. engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Nokia Finland

Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity *** the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for *** this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are ***,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are *** of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are ***,some challenges and future research directions are presented to provide guidance for further research of SC.

关键词： Semantic communication Semantic coding Semantic extraction Semantic communication framework Semantic communication applications

A Study on the Explainability of Thyroid Cancer Prediction:SHAP Values and Association-Rule Based Feature Integration Framework

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computers, Materials & Continua 2024年第5期79卷 3111-3138页

作者： Sujithra Sankar S.Sathyalakshmi department of {1. Applications Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia department of {1. engineering Hindustan Institute of Technology and ScienceChennaiTamil NaduIndia

In the era of advanced machine learning techniques,the development of accurate predictive models for complex medical conditions,such as thyroid cancer,has shown remarkable *** predictivemodels for thyroid cancer enhance early detection,improve resource allocation,and reduce ***,the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and *** paper proposes a novel association-rule based feature-integratedmachine learning model which shows better classification and prediction accuracy than present *** study also focuses on the application of SHapley Additive exPlanations(SHAP)values as a powerful tool for explaining thyroid cancer prediction *** the proposed method,the association-rule based feature integration framework identifies frequently occurring attribute combinations in the *** original dataset is used in trainingmachine learning models,and further used in generating SHAP values *** the next phase,the dataset is integrated with the dominant feature sets identified through association-rule based *** new integrated dataset is used in re-training the machine learning *** new SHAP values generated from these models help in validating the contributions of feature sets in predicting *** conventional machine learning models lack interpretability,which can hinder their integration into clinical decision-making *** this study,the SHAP values are introduced along with association-rule based feature integration as a comprehensive framework for understanding the contributions of feature sets inmodelling the *** study discusses the importance of reliable predictive models for early diagnosis of thyroid cancer,and a validation framework of *** proposed model shows an accuracy of 93.48%.Performance metrics such as precision,recall,F1.score,and the area un

关键词： Explainable AI machine learning clinical decision support systems thyroid cancer association-rule based framework SHAP values classification and prediction

Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials and Continua 2024年第1期81卷 271-307页

作者： Tawfik N. Emara H.M. El-Shafai W. Soliman N.F. Algarni A.D. Abd El-Samie F.E. computers and Systems {1. Electronics Research Institute El Nozha Huckstep Cairo 12622 Egypt department of {1.s and Electrical Communications engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Security engineering Lab Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient screening methods. Advances in diagnostic techniques, particularly the use of computed tomography (CT) scans, have revolutionized the identification of lung cancer. CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small, calcified areas, crucial for identifying early-stage lung cancer. Consequently, there is growing interest in enhancing computer-aided detection (CAD) systems. These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis. This study aims to enhance the effectiveness of CAD systems through various methods. Initially, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm is employed to preprocess CT scan images, thereby improving their visual quality. Further refinement is achieved by integrating different optimization strategies with the CLAHE method. The CutMix data augmentation technique is applied to boost the performance of the proposed model. A comparative analysis is conducted using deep learning architectures such as Inception-V3, ResNet1.1. Xception, and EfficientNet. The study evaluates the performance of these architectures in image classification tasks, both with and without the implementation of the CLAHE algorithm. The em

关键词： CLAHE algorithm classification deep learning feature extraction healthcare applications Lung cancer detection optimization

Photoacoustic/ultrasound dual-modal imaging of human nails: A pilot study

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Journal of Innovative Optical Health Sciences 2024年第6期17卷 15-28页

作者： Yanting Wen Yijie Huang Lin Huang Dan Wu Renbin Zhong Shixie Jiang Yudi Zhang Ting Liu Xiaotian Liu Huabei Jiang department of Ultrasound Chengdu Fifth People's Hospital Chengdu 611137P.R.China School of computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 P.R.China School of electronic Science and {1. University of electronic Science and Technology Chengdu 61.731 P.R.China department of Psychiatry University of Florida College of Medicine GainesvilleFL 32610USA department of Medical {1. University of South Florida Tampa 33620USA

Traditional diagnostic techniques including visual examination,ultrasound(US),and magnetic resonance imaging(MRI)have limitations of in-depth information for the detection of nail disorders,resolution,and *** pilot study,for thefirst time,evaluates a dualmodality imaging system that combines photoacoustic tomography(PAT)with the US for the multiparametric quantitative assessment of human *** study involved a small cohort offive healthy volunteers who underwent PAT/US imaging for acquiring the nail unit *** PAT/US dual-modality imaging successfully revealed thefine anatomical structures and microvascular distribution within the nail and nail ***,this system utilized multispectral PAT to analyze functional tissue parameters,including oxygenated hemoglobin,deoxyhemoglobin,oxygen saturation,and collagen under tourniquet and cold stimulus tests to evaluate changes in the microcirculation of the nail *** quantitative analysis of multispectral PAT reconstructed images demonstrated heightened sensitivity in detecting alterations in blood oxygenation levels and collagen content within the nail bed,under simulated different physiological *** pilot study highlights the potential of PAT/US dual-modality imaging as a real-time,noninvasive diagnostic modality for evaluating human nail health and for early detection of nail bed pathologies.

关键词： Nail photoacoustic tomography ultrasound multi-modality quantitative parameter

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of {1. Science Air UniversityIslamabad44000Pakistan department of {1. Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia department of {1. Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia department of {1. engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC1.,have been *** analyzing the experimental results,it was found that the PASCAL-VOC1. dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network