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arXiv 2022年

作者： Chu, Dong Jaafar, Wael Yanikomeroglu, Halim School of Information and Communication Engineering The University of Electronic Science and Technology of China Chengdu China Software and Information Technology Engineering Department of École de Technologie Supérieure QC Canada Systems and Computer Engineering Department Carleton University ON Canada

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets. © 2022, CC BY.

关键词： Privacy-preserving techniques

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On the Design of Communication-Efficient Federated Learning for Health Monitoring

On the Design of Communication-Efficient Federated Learning ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Dong Chu Wael Jaafar Halim Yanikomeroglu School of Information and Communication Engineering of the University of Electronic Science and Technology of China Chengdu China department of École de Technologie Software and Information Technology Engineering Supérieure QC Canada Systems and Computer Engineering department Carleton University ON Canada

ISBN: (纸本)9781665435413

关键词： Training Costs Federated learning Pandemics Transfer learning Neural networks Optimized production technology

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deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques

arXiv

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arXiv 2022年

作者： Sa, Inkyu Lim, JongYoon Ahn, Ho Seok MacDonald, Bruce CSIRO Data61 Robotics and Autonomous Systems Group Robot Perception Team Switzerland CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches. Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper(capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127 k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45 k) and All seasons (180 k) subsets and applied a simple yet important conversion;digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms. We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported as various formats using cloud service. Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances of the dataset is 162 k and it is ready to use from cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.

关键词： Generative adversarial networks

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A Data-Driven Approach Towards software Regression Testing Quality Optimization

A Data-Driven Approach Towards Software Regression Testing Q...

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Information Technology (ACIT)

作者： Abdallah Moubayed Nouh Alhindawi Jamal Alsakran MohammadNoor Injadat Mohammad Kanan Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Department of Software Engineering Jadara University Irbid Jordan Fairleigh Dickinson University Canada Data Science & Artificial Intelligence Department Zarqa University Zarqa Jordan Department of Industrial Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees.

关键词： software testing Machine learning algorithms software algorithms software quality software systems software reliability Regression tree analysis Optimization Standards software development management

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ARCH-COMP20 repeatability evaluation report 7th

ARCH-COMP20 repeatability evaluation report

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7th International Workshop on Applied Verification of Continuous and Hybrid systems, ARCH 2020

作者： Johnson, Taylor T. Vanderbilt University Department of Electrical Engineering and Computer Science Institute for Software Integrated Systems NashvilleTN United States

This report presents the results of the repeatability evaluation for the 4th International Competition on Verifying Continuous and Hybrid systems (ARCH-COMP’20). The competition took place as part of the workshop Applied Verification for Continuous and Hybrid systems (ARCH) in 2020, affiliated with the IFAC World Congress. In its fourth edition, twenty-eight tools submitted artifacts through a Git repository for the repeatability evaluation, applied to solve benchmark problems for seven competition categories. The majority of participants adhered to the requirements for this year’s repeatability evaluation, namely to submit scripts to automatically install and execute tools in containerized virtual environments (specifically Dockerfiles to execute within Docker), and several categories used performance evaluation information from a common execution platform. The repeatability results represent a snapshot of the current landscape of tools and the types of benchmarks for which they are particularly suited and for which others may repeat their analyses. Due to the diversity of problems in verification of continuous and hybrid systems, as well as basing on standard practice in repeatability evaluations, we evaluate the tools with pass and/or failing being repeatable. © 2020 EasyChair. All rights reserved.

关键词： Hybrid systems

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A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

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IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation

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Game-Oriented Security Strategy Against Hotspot Attacks for Internet of Vehicles

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computers, Materials & Continua 2021年第8期68卷 2145-2157页

作者： Juan Guo Yanzhu Liu Shan Li Zhi Li Sonia Kherbachi Guilin University of Electronic Technology Beihai536000GuangxiChina Great Wall Computer Software and Systems Inc. Beijing100083China Department of Electronics and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing100070China State Grid Information&Telecommunication Group Co. Ltd.Beijing100031China Department of Management University of BejaiaBejaia06000Algeria

With the rapid development of mobile communication technology,the application of internet of vehicles(IoV)services,such as for information services,driving safety,and traffic efficiency,is growing *** businesses with low transmission delay,high data processing capacity and large storage capacity,by deploying edge computing in the IoV,data processing,encryption and decision-making can be completed at the local end,thus providing real-time and highly reliable communication *** roadside unit(RSU),as an important part of edge computing in the IoV,fulfils an important data forwarding function and provides an interactive communication channel for vehicles and server *** computing resources can be configured to accommodate the computing requirements of *** this study,a virtual traffic defense strategy based on a differential game is proposed to solve the security problem of user-sensitive information leakage when an RSU is *** incentive mechanism encourages service vehicles within the hot range to send virtual traffic to another *** attracting the attention of attackers,it covers the target RSU and protects the system from *** results show that the scheme provides the optimal strategy for intelligent vehicles to transmit virtual data,and ensures the maximization of users’interests.

关键词： Edge computing internet of vehicles differential games security defense

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Prediction of Dental Fluorosis Infection based on Particle Optimized Scored KNN Approach

Prediction of Dental Fluorosis Infection based on Particle O...

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Intelligent Methods, systems, and Applications (IMSA)

作者： Reem Kadry Montasser Osama Ismael Moataz El-Kholy Software Engineering Department Faculty of Computer Science October University for Modern Sciences and Arts Giza Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Oral Pathology Department Faculty of Dentistry October University for Modern Sciences and Arts Giza Egypt

Due to its simplicity of usage across a variety of applications, the K-Nearest Neighbor algorithm is usually utilized as a classification approach. The K-Nearest Neighbor algorithm's accuracy is greatly impacted by the occurrence of multidimensional and outlier data. This research uses a novel hybrid technique known as Particle Optimized Scored K-Nearest Neighbor (POSKNN) to build a model to predict the emergence of dental fluorosis. The proposed novel approach is implemented in two phases: the first phase helps in the resolution of multidimensional data by performing selection of feature using the Modified Particle Swarm Optimization algorithm, and the second phase helps in the resolution of the presence of outliers by making use of the results of the first phase and applying the newly proposed scored KNN technique to them. The suggested approach was tested using a real-world dataset acquired from three dentistry clinics in Egypt's El Baharia Oasis, which has 56 attributes, two classes, and 328 records. 10-fold cross validation was implemented to the model. The experimental evaluation was divided into two parts; evaluation for modified particle swarm optimization, evaluation for scored K nearest neighbor, and the hybrid particle optimized scored K nearest neighbor. The experiment’s results demonstrated that the suggested approach outperformed the traditional KNN and the modified KNN. POSKNN achieved an accuracy of 94.5% when K value is 11.

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs

arXiv

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arXiv 2023年

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023, CC BY-SA.

关键词： computer aided design

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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