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arXiv 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng School of Computer Science University of Bristol United Kingdom Department of Information and Computing Science School of Mathematical Science Peking University China Department of Software Systems and Cybersecurity Monash University Australia School of Computing and Information Systems Singapore Management University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025, CC BY.

关键词： Federated learning

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IvyCross: a Privacy-Preserving and Concurrency Control Framework for Blockchain Interoperability

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IEEE Transactions on Mobile computing 2025年

作者： Li, Ming Weng, Jian Weng, Jiasi Li, Yi Wu, Yongdong Li, Dingcheng Xu, Guowen Deng, Robert Singapore Management University School of Computing and Information Systems 178902 Singapore Jinan University College of Cyber Security Guangzhou510632 China Nanyang Technological University School of Computer Science and Engineering Singapore

Interoperability is a fundamental challenge for longenvisioned blockchain applications. A mainstream approach is using Trusted Execution Environment (TEE) to support interoperable off-chain execution. However, this incurs multiple TEE configured with non-trivial storage capabilities running on fragile concurrent processing environments, rendering current strategies based on TEE far from being practical. This paper aims to fill this gap and design a practical interoperability mechanism with simplified TEE as the underlying architecture. Specifically, we present IvyCross, a TEE-based framework that achieves lowcost, privacy-preserving, and race-free blockchain interoperability. IvyCross allows running arbitrary smart contracts across heterogeneous blockchains atop two distributed TEE-powered hosts. We design an incentive scheme based on smart contracts to stimulate the honest behavior of two hosts, bypassing the requirement of the number of TEE and large memory need. We examine the conditions to guarantee the uniqueness of Nash Equilibrium via Game Theory. Furthermore, an extended optimistic concurrency control protocol is designed to ensure the correctness of concurrent contracts execution. We formally prove the security of IvyCross in the Universal Composability (UC) framework and implement a prototype atop Bitcoin, Ethereum, and FISCO BOCS. Extensive experimental results on end-to-end performance and concurrency control demonstrate the efficiency and practicality of IvyCross. © 2002-2012 IEEE.

关键词： Blockchain

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A Provably Secure Post-Quantum Based EDHOC Protocol 22

A Provably Secure Post-Quantum Based EDHOC Protocol

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Yadav, Awaneesh Kumar Shojafar, Mohammad Braeken, An School of Computing and Electrical Engineering Indian Institute of Technology Mandi India Institute for Communication Systems University of Surrey 5G/6GIC Guildford United Kingdom Vrije Universiteit Brussel Department of Engineering technology (INDI) Brussel Belgium

ISBN: (纸本)9798331508050

Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral-Diffie-Hellman-Over-COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. EDHOC consists of four authentication modes, each offering different security strengths and performance. However, these modes are not yet secure against quantum attacks. Since we are in a transition period and do not yet possess deeply analyzed post-quantum algorithms, we propose a generic hybrid protocol. As such, the protocol can easily update from one post-quantum algorithm to the other and becomes secure against either a post-quantum attack or a potential attack against a newly defined post-quantum algorithm. The proposed mode does not require any changes to the original EDHOC protocol and offers perfect backward compatibility. The security is ensured using Real-Or-Random (ROR) logic, and additional performance costs are analyzed using different variants of post-quantum algorithms. © 2025 IEEE.

关键词： Authentication EDHOC Key agreement Network Security OSCORE post-quantum Protocol ROR logic

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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DPS: Design Pattern Summarisation Using Code Features

arXiv

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arXiv 2025年

作者： Nazar, Najam Sikka, Sameer Treude, Christoph School of Computer and Mathematical Sciences The University of Adelaide Australia School of IT and Engineering Ada University Azerbaijan Faculty of Engineering and Information Technology The University of Melbourne Australia School of Computing and Information Systems Singapore Management University Singapore

Automatic summarisation has been used efficiently in recent years to condense texts, conversations, audio, code, and various other artefacts. A range of methods, from simple template-based summaries to complex machine learning techniques—and more recently, large language models—have been employed to generate these summaries. Summarising software design patterns is important because it helps developers quickly understand and reuse complex design concepts, thereby improving software maintainability and development efficiency. However, the generation of summaries for software design patterns has not yet been explored. Our approach utilises code features and JavaParser to parse the code and create a JSON representation. Using an NLG library on this JSON representation, we convert it into natural language text that acts as a summary of the code, capturing the contextual information of the design pattern. Our empirical results indicate that the summaries generated by our approach capture the context in which patterns are applied in the codebase. Statistical evaluations demonstrate that our summaries closely align with human-written summaries, as evident from high values in the ROUGE-L, BLEU-4, NIST, and FrugalScore metrics. A follow-up survey further shows that DPS summaries were rated as capturing context better than human-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Software design

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Instructors' Perspectives on Capstone Courses in computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in Computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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RS‐YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images

arXiv

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arXiv 2025年

作者： Yang, Lei Yuan, Guowu Zhou, Hao Liu, Hongyu Chen, Jian Wu, Hao School of Information Science and Engineering Yunnan University Kunming650504 China Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650504 China

Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS‐YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high‐performance remote sensing object detector, we combined the trained model with an open‐source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA‐v1.5, TGRS‐HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets. © 2025, CC BY-NC-ND.

关键词： Optical remote sensing

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Multi-Agent Performative Prediction Beyond the Insensitivity Assumption: A Case Study for Mortgage Competition

arXiv

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arXiv 2025年

作者： Wang, Guanghui Acharya, Krishna Lakshmikanthan, Lokranjan Muthukumar, Vidya Ziani, Juba College of Computing Georgia Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States

Performative prediction models account for feedback loops in decision-making processes where predictions influence future data distributions. While existing work largely assumes insensitivity of data distributions to small strategy changes, this assumption usually fails in real-world competitive (i.e. multi-agent) settings. For example, in Bertrand-type competitions, a small reduction in one firm’s price can lead that firm to capture the entire demand, while all others sharply lose all of their customers. We study a representative setting of multi-agent performative prediction in which insensitivity assumptions do not hold, and investigate the convergence of natural dynamics. To do so, we focus on a specific game that we call the "Bank Game", where two lenders compete over interest rates and credit score thresholds. Consumers act similarly as to in a Bertrand Competition, with each consumer selecting the firm with the lowest interest rate that they are eligible for based on the firms’ credit thresholds. Our analysis characterizes the equilibria of this game and demonstrates that when both firms use a common and natural no-regret learning dynamic—exponential weights—with proper initialization, the dynamics always converge to stable outcomes despite the general-sum structure. Notably, our setting admits multiple stable equilibria, with convergence dependent on initial conditions. We also provide theoretical convergence results in the stochastic case when the utility matrix is not fully known, but each learner can observe sufficiently many samples of consumers at each time step to estimate it, showing robustness to slight mis-specifications. Finally, we provide experimental results that validate our theoretical findings. © 2025, CC BY.

关键词： Prediction models

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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