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IEEE Symposium on computers and Communications (ISCC)

作者： Shitong Cheng Zhenghui Xu Xiuhua Li Xiongwei Wu Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial

arXiv

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arXiv 2023年

作者： Xiong, Haoyi Bian, Jiang Yang, Sijia Zhang, Xiaofei Kong, Linghe Zhang, Daqing Big Data Lab. Baidu Inc. No.10 Xibeiwang East Road Haidian Beijing100085 China Beijing University of Posts and Telecommunications 10 Xitucheng Rd Haidian Beijing100876 China Baidu Inc. No.10 Xibeiwang East Road Haidian Beijing100085 China Department of Computer Science and Engineering Shanghai Jiaotong University 800 Dongchuan Road Minghang Shanghai200240 China Télécom SudParis Institut Polytechnique de Paris Evry Courcouronnes 9 Rue Charles Fourier Essonne 91000 France

Large language models (LLMs) have become phenomenally surging1, since 2018 – two decades after introducing context-awareness into computing systems [1–4]. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning [5, 6]. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users’ requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users’ request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases – (1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner. Furthermore, we analyze several factors that might affect the performance of LLM-driven context-awareness, and then discuss the future research directions. © 2023, CC BY.

关键词： Modeling languages

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µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

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arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Boosting Time-series Domain Adaptation via A Time-Frequency Consensus Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial intelligence 2025年

作者： Yang, Wenmian Ragab, Mohamed Wu, Min Pan, Sinno Jialin Lin, Guosheng Jia, Weijia Chen, Zhenghua Institute of Technology Innovation Institute United Arab Emirates A∗STAR Singapore Department of Computer Science and Engineering Hong Kong Hong Kong Nanyang Technological University School of Computer Science and Engineering Singapore Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai519087 China BNUHKBU United International College Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China

Unsupervised Domain Adaptation (UDA) has proven to be effective in addressing the domain shift problem in computer vision. However, compared with visual applications, UDA for time series brings forth additional challenges. Potential domain shifts may have varying impacts on both time and frequency features, rendering conventional UDA methods less effective in this context. To address these challenges, we propose a Time-Frequency Consensus Domain Adaption (TFCDA) framework to enhance UDA methods for time-series data. TFCDA designs a frequency encoder, a trainable Time-Frequency Mapping (TFM), and a consensus loss, building upon conventional UDA methods to boost their performance. The TFM is trained on source domain data to learn the inherent time-frequency feature mapping, while the novel consensus loss ensures consistent feature transfer during UDA in the target domain, effectively reducing domain shifts in both time and frequency, and thus boosting overall performance. Experimental evaluations on four publicly available time-series datasets demonstrate TFCDA's effectiveness in enhancing existing UDA methods for time-series data, highlighting its potential for real-world applications. © 2025 IEEE.

关键词： Transfer learning

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Fully automatic HER2 tissue segmentation for interpretable HER2 scoring

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Journal of Pathology Informatics 2025年 17卷 100435-100435页

作者： Öttl, Mathias Steenpass, Jana Wilm, Frauke Qiu, Jingna Rübner, Matthias Lang-Schwarz, Corinna Taverna, Cecilia Tava, Francesca Hartmann, Arndt Huebner, Hanna Beckmann, Matthias W. Fasching, Peter A. Maier, Andreas Erber, Ramona Breininger, Katharina Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Institute of Pathology University Hospital Erlangen Erlangen Germany Department of Gynecology and Obstetrics Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Center for AI and Data Science (CAIDAS) Universität Würzburg Würzburg Germany Institute of Pathology Klinikum Bayreuth GmbH Bayreuth Germany Surgical Pathology Unit Azienda Sanitaria Locale Presidio Ospedaliero Ospedale San Giacomo Novi Ligure Italy Institute of Pathology University Regensburg Regensburg Germany

Breast cancer is the most common cancer in women, with HER2 (human epidermal growth factor receptor 2) overexpression playing a critical role in regulating cell growth and division. HER2 status, assessed according to established scoring guidelines, offers important information for treatment selection. However, the complexity of the task leads to variability in human rater assessments. In this work, we propose a fully automated, interpretable HER2 scoring pipeline based on pixel-level semantic segmentations, designed to align with clinical guidelines. Using polygon annotations, our method balances annotation effort with the ability to capture fine-grained details and larger structures, such as non-invasive tumor tissue. To enhance HER2 segmentation, we propose the use of a Wasserstein Dice loss to model class relationships, ensuring robust segmentation and HER2 scoring performance. Additionally, based on observations of pathologists' behavior in clinical practice, we propose a calibration step to the scoring rules, which positively impacts the accuracy and consistency of automated HER2 scoring. Our approach achieves an F1 score of 0.832 on HER2 scoring, demonstrating its effectiveness. This work establishes a potent segmentation pipeline that can be further leveraged to analyze HER2 expression in breast cancer tissue. © 2025 The Authors

关键词： Deep learning HER2 HER2 scoring Histopathology Semantic segmentation

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Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

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CheXMed: A Multimodal Algorithm to Improve Pneumonia Detection in the Elderly

SSRN

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SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Using Others' Tests to Identify Breaking Updates

Using Others' Tests to Identify Breaking Updates

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International Mining software Repositories, MSR

作者： Suhaib Mujahid Rabe Abdalkareem Emad Shihab Shane McIntosh Dept. of Computer Science and Software Engineering Data-driven Analysis of Software (DAS) Lab Concordia University Montreal Quebec Canada Software Analysis and Intelligence Lab (SAIL) School of Computing Queen's University Kingston Ontario Canada Dept. of Electrical and Computer Engineering Software Repository Excavation and Build Eng. Labs McGill University Montreal Quebec Canada

The reuse of third-party packages has become a common practice in contemporary software development. software dependencies are constantly evolving with newly added features and patches that fix bugs in older versions. However, updating dependencies could introduce new bugs or break backward compatibility. In this work, we propose a technique to detect breakage-inducing versions of third-party dependencies. The key insight behind our approach is to leverage the automated test suites of other projects that depend upon the same dependency to test newly released versions. We conjecture that this crowd-based approach will help to detect breakage-inducing versions because it broadens the set of realistic usage scenarios to which a package version has been exposed. To evaluate our conjecture, we perform an empirical study of 391,553 npm packages. We use the dependency network from these packages to identify candidate tests of third-party packages. Moreover, to evaluate our proposed technique, we mine the history of this dependency network to identify ten breakage-inducing versions. We find that our proposed technique can detect six of the ten studied breakage-inducing versions. Our findings can help developers to make more informed decisions when they update their dependencies.

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