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International Conference on software engineering (ICSE)

作者： Hao Guan Ying Xiao Jiaying Li Yepang Liu Guangdong Bai The University of Queensland Brisbane Australia Southern University of Science and Technology Shenzhen China UQ-SUSTech Joint Program Microsoft Software Technology Center Asia Beijing China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.

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System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing

System to Improve the Quality of Water Resources in Sri Lank...

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Advancements in Computing (ICAC), International Conference on

作者： M.H. Samindi Liyanage G.M.B. Gajanayake S Oshini Wijewickrama S.D.S. Fernando A Dinuka Wijendra Aruna Ishara Gamage Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department Computer Systems & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Water covers approximately 71% of the earth’s surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource.

关键词： Microorganisms Purification Image processing Optical character recognition Machine learning Turning Wastewater

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

arXiv

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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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Cognitive Twins for Predictive Maintenance and Security in IoT software systems

Cognitive Twins for Predictive Maintenance and Security in I...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： B. Venkata Swamy Pareshwar Prasad Barmola Senthil Thangavel S. Kaliappan Hardik Patel Girish Abhyankar Department of Chemistry B V Raju Institute of Technology Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Division of Research and Development Lovely Professional University Phagwara India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The increasing complexity and interconnectedness of Internet of Things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. This paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. The framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. The framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%.

关键词： Biological system modeling Decision making Reinforcement learning software systems Real-time systems Natural language processing Maintenance Internet of Things Security Predictive maintenance

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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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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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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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