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Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework

arXiv

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arXiv 2025年

作者： Mildner, Terje Hamelijnck, Oliver Giampouras, Paris Damoulas, Theodoros University of Warwick Department of Computer Science Coventry United Kingdom University of Warwick Department of Statistics Coventry United Kingdom

We introduce FEDGVI, a probabilistic Federated Learning (FL) framework that is provably robust to both prior and likelihood misspecification. FEDGVI addresses limitations in both frequentist and Bayesian FL by providing unbiased predictions under model misspecification, with calibrated uncertainty quantification. Our approach generalises previous FL approaches, specifically Partitioned Variational Inference (Ashman et al., 2022), by allowing robust and conjugate updates, decreasing computational complexity at the clients. We offer theoretical analysis in terms of fixed-point convergence, optimality of the cavity distribution, and provable robustness. Additionally, we empirically demonstrate the effectiveness of FEDGVI in terms of improved robustness and predictive performance on multiple synthetic and real world classification data sets. © 2025, CC BY.

关键词： Federated learning

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques 21

Early Fault Detection in Rolling Element Bearings Using Sign...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Saeed, Ali Akram, M. Usman Khan, Muazzam A. Afsar, M. Belal Quaid-i-Azam University ICESCO Big Data Analytics and Edge Computing Department of Computer Science Islamabad Pakistan National University of Sciences and Technology Department of Computer and Software Engineering Islamabad Pakistan Otto-von-Guericke University Faculty of Electrical Engineering and Information Technology Maddeburg Germany

ISBN: (纸本)9798350378078

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-To-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method. © 2024 IEEE.

关键词： Signal modulation

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Kurma Mahesh Paladi Aishwarya Sabavath Nithin Shanmugasundaram Hariharan Bandar Vaishnavi Vasantha S V Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance.

关键词： Heart Machine learning algorithms Accuracy Computational modeling Medical services Predictive models Prediction algorithms Boosting Classification algorithms Diseases

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Federated Learning for Privacy-Preserving data Mining

Federated Learning for Privacy-Preserving Data Mining

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Kale, Dattatray Raghunath Mane, Tushar S Buchade, Amar Patel, Prashant B Wadhwa, Lalit Kumar Pawar, Rajendra G. MIT Art Design and Technology University Computer Science and Engineering Department Pune India Vishwakarma Institute of Information Technology Artificial intelligence and Data Science Department BRACT's Pune India Dr.D Y Patil Institute of Technology Department of Instrumentation Engineering Pune India Dr.D Y Patil Institute of Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798331539948

Federated learning (FL) is an emerging approach that enables collaborative machine learning while preserving data privacy. Privacy has become a critical issue in the big data era, particularly in data mining applications that handle sensitive data. Classical machine learning necessitates gathering participant data for training, which could result in the illegal collection of personal information. FL removes the requirement for transferring raw data to a central server by training models across decentralized devices that hold local data samples. This paper discusses the methodology, implementation, and evaluation of federated learning as it relates to privacy-preserving data mining. The outcomes of our experiments show how FL can be used to achieve high model accuracy while protecting user privacy. The paper's conclusion outlines potential avenues for future research and enhancements to federated learning frameworks in order to strengthen privacy protections in data mining projects. © 2024 IEEE.

关键词： Federated learning

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Model Adaptation: Unsupervised Domain Adaptation without Source data

arXiv

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arXiv 2025年

作者： Li, Rui Jiao, Qianfen Cao, Wenming Wong, Hau-San Wu, Si Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Engineering South China University of Technology China Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong

In this paper, we investigate a challenging unsupervised domain adaptation setting — unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting. © 2025, CC BY-SA.

关键词： Prediction models

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Using GitHub Analytics to Assess the Quality of Collaboration in Software Engineering Teams

Using GitHub Analytics to Assess the Quality of Collaboratio...

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Frontiers in Education (FIE) Conference

作者： Quan Le Kiet Phan Bowen Hui Adara Putri Department of Computer Science Mathematics Physics and Statistics University of British Columbia Kelowna Canada

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice full paper investigates using team process analytics from GitHub to support team management. Effective teamwork is essential in higher education learning and workplace success. The role of educators in supporting proper team functioning includes helping students learn how to participate actively and communicate effectively in meetings, delegate work fairly, manage high-quality work throughput, and resolve conflicts if problems arise. Problems often emerge when team members have differing visions or individuals do not contribute equally to the work output. These problems are exacerbated in large classes involving many teams. Detecting these potential issues in teams and helping students work through them is important for team success. In software engineering projects, monitoring individual contributions can begin with mining activities on programming platforms such as GitHub, which makes much of the individual contributions more visible and quantifiable. In this work, we propose a framework for fairly assessing teamwork and present the development of team analytics to assist educators in detecting potential issues in team collaboration. We describe a pilot study involving this tool in the context of a software engineering capstone course with 104 students split into 22 teams managed by 4 teaching assistants. Our results show that the reports offer value in guiding the evaluation process and identifying where problems may be, but do not replace the actual repository analysis where needed. We discuss the potential value of using this tool to improve collaboration.

关键词： Measurement Education Employment Throughput Teamwork data mining Programming profession Monitoring Software engineering Software development management

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pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning 39

pFedGPA: Diffusion-based Generative Parameter Aggregation fo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lai, Jiahao Li, Jiaqi Xu, Jian Wu, Yanru Tang, Boshi Chen, Siqi Huang, Yongfeng Ding, Wenbo Li, Yang Tsinghua Shenzhen International Graduate School Tsinghua University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Ubiquitous Data Enabling Tsinghua University China

ISBN: (纸本)157735897X

Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server. Traditional methods, such as Federated Averaging (FedAvg), linearly aggregate these parameters which are usually trained on heterogeneous data distributions, potentially overlooking the complex, high-dimensional nature of the parameter space. This can result in degraded performance of the aggregated model. While personalized FL approaches can mitigate the heterogeneous data issue to some extent, the limitation of linear aggregation remains unresolved. To alleviate this issue, we investigate the generative approach of diffusion model and propose a novel generative parameter aggregation framework for personalized FL, pFedGPA. In this framework, we deploy a diffusion model on the server to integrate the diverse parameter distributions and propose a parameter inversion method to efficiently generate a set of personalized parameters for each client. This inversion method transforms the uploaded parameters into a latent code, which is then aggregated through denoising sampling to produce the final personalized parameters. By encoding the dependence of a client's model parameters on the specific data distribution using the high-capacity diffusion model, pFedGPA can effectively decouple the complexity of the overall distribution of all clients' model parameters from the complexity of each individual client's parameter distribution. Our experimental results consistently demonstrate the superior performance of the proposed method across multiple datasets, surpassing baseline approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence.

关键词： Federated learning

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