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arXiv 2024年

作者： Li, Na Zhou, Chunyi Gao, Yansong Chen, Hui Fu, Anmin Zhang, Zhi Yu, Shui School of Cyberspace Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO CanberraACT2601 Australia Department of Computer Science and Software Engineering University of Western Australia PerthWA6009 Australia School of Computer Science University of Technology Sydney SydneyNSW2007 Australia

Personal digital data is a critical asset, and governments worldwide have enforced laws and regulations to protect data privacy. Data users have been endowed with the 'right to be forgotten' of their data. In the course of machine learning (ML), the forgotten right requires a model provider to delete user data and its subsequent impact on ML models upon user requests. Machine unlearning emerges to address this, which has garnered ever-increasing attention from both industry and academia. While the area has developed rapidly, there is a lack of comprehensive surveys to capture the latest advancements. Recognizing this shortage, we conduct an extensive exploration to map the landscape of machine unlearning including the (fine-grained) taxonomy of unlearning algorithms under centralized and distributed settings, debate on approximate unlearning, verification and evaluation metrics, challenges and solutions for unlearning under different applications, as well as attacks targeting machine unlearning. The survey concludes by outlining potential directions for future research, hoping to serve as a guide for interested scholars. Copyright © 2024, The Authors. All rights reserved.

关键词： Taxonomies

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Polarization Correction using Physics-guided Foundation Model for Photon-Counting CT

Polarization Correction using Physics-guided Foundation Mode...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： X. Yu W. Qin T. Zhong Q. Wu M. Su W. Wang X. Ji G. Quan Y. Chen X. Lai School of Biomedical Engineering ShanghaiTech University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China CT RPA Department United Imaging Healthcare Shanghai China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Photon-counting computed tomography (PCCT) represents a paradigm-shift advancement in CT technology. However, a significant challenge in PCCT is the polarization effect in photon-counting detectors (PCDs), especially in cadmium-based PCDs, which generate instable counting rate and inaccurate material decomposition results. In this paper, we introduce a polarization spectrum response model and propose a polarization correction method based on a physics-guided foundation model for photon-counting CT. Our approach is designed to accurately address the complexities of time-varying and dose-dependent polarization in PCDs. Preliminary results demonstrate accurate material decomposition that is free of significant band artifacts with proper polarization correction, underscoring the potential benefits in PCCT image quality.

关键词： Image quality Polarization Microwave integrated circuits Accuracy Semiconductor detectors Computed tomography Computational modeling

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RAT: A Refactoring-Aware Traceability Model for Bug Localization

RAT: A Refactoring-Aware Traceability Model for Bug Localiza...

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International Conference on software engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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A Two-stage Anomaly Detection System for Fog Computing Services 5

A Two-stage Anomaly Detection System for Fog Computing Servi...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Adeniyi Kamil, Ismaila Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando40601 Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai602105 India

ISBN: (纸本)9798331542559

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10-5. The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively. © 2024 IEEE.

关键词： Fog computing

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Employing a Deep Neural Network to Identify and Categorize Leaf Diseases in Tomato Plant in Their Early Stages

Employing a Deep Neural Network to Identify and Categorize L...

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International Conference on Advances in Information Technology (ICAIT)

作者： Chandradeep Bhatt Parveen Kumar Saini Vikash Sawan Sanjay Sharma Teekam Singh Taranath N L Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of CSE Chandigarh University Gharuan Mohali Punjab Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Associate Professor School of CSE Presidency University Rajanukunte Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Tomato production makes a good contribution to the world economy as an element of agriculture. Tomato plants are vulnerable to different types of diseases that might create a negative impact on the production of the Tomato yield. Identification of this type of vulnerable disease at its early stage is important. This research work proposed a 9-layer Deep Neural Network to identify the leaf disease in the tomato plant. Deep learning technology of neural network approach was used in this method to classify the different leaf diseases in the tomato plant. The performance accuracy was obtained at 97.32% through the proposed method.

关键词： Accuracy Artificial neural networks Production Transforms Pressing Agriculture Real-time systems Safety Diseases Testing

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Robustness Assessment of Hyperspectral Image Cnns Using Metamorphic Testing

SSRN

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SSRN 2022年

作者： Bouchoucha, Rached Ben Braiek, Houssem Khomh, Foutse Bouzidi, Sonia Zaatour, Rania Department of Computer Engineering and Software Engineering Polytechnique MontrealQCH3T 1J4 Canada Department of Computer Engineering and Software Engineering National Institute of Applied Science and Technology Tunis1080 Tunisia

Remote sensing has proven its utility in many critical domains, such as medicine, military, and ecology. Recently, we have been witnessing a surge in the adoption of deep learning (DL) techniques by the remote sensing community. DL-based classifiers, such as convolutional neural networks (CNNs), have been reported to achieve impressive predictive performances reaching 99\% of accuracy when applied to hyperspectral images (HSIs), a high-dimensional type of remote sensing data. However, these deep learners are known to be highly sensitive to even slight perturbations of their high-dimensional raw inputs. In real-world contexts, concerns can be raised about how robust they really are against corner-case scenarios. When HSI classifiers are applied in safety-critical applications, ensuring an adequate level of robustness is crucial to prevent unexpected system behaviors. Yet, there are few studies dealing with their robustness, nor are RGB-testing methods able to cover the HSI-specific challenges. This led us to propose a systematic testing method to assess the robustness of the CNNs trained to classify HSIs. First, we elaborate domain-specific metamorphic transformations that simulate naturally-occurring distortions of remote sensing HSIs. Then, we leverage metaheuristic search algorithms to optimize the fitness of synthetically-distorted inputs to stress the weaknesses of the on-testing CNN, while remaining in compliance with domain expert requirements, in order to preserve the semantic of the generated inputs. Relying on our metamorphic testing method, we assess the robustness of established and novel CNNs for HSI classification, and demonstrate their failure, on average, in 25\% of the produced test cases. Furthermore, we fine-tuned the tested CNNs on training data augmented with these failure-revealing metamorphic transformations. Results show that the fined-tuning successfully fixed at least 90\% of the CNN weaknesses, with less than 1\% of degradation in the origi

关键词： Remote sensing

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The Way to Check the Reliability Test in Smart Grids for the Smart Protection System

The Way to Check the Reliability Test in Smart Grids for the...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Hannah Jessie Rani R Manish Kumar Goyal Awakash Mishra Department of Electrical and Elceltronics Engineeirng Faculty of Engineering and Technology JAIN (Deemed to be University) Bangalore India Department of Computer Science & Application Vivekananda Global University Jaipur India Engineering Department Maharishi University of Information Technology Uttar Pradesh India

The complex operation of contemporary power systems is closely linked to the widespread integration of computer networks, or cyber networks. These applications fall into two general categories: direct and indirect interdependencies, which are the result of how they communicate with power networks. Past studies explored the area of direct interdependencies in cyber-power networks and presented a state mapping-based model that was carefully designed to assess its impact on power system dependability. The investigation of indirect relationships between cyber and electricity networks is the new emphasis of this article. A wide range of applications involving indirect interdependencies in modern power systems are examined, laying the foundation for the release of a model that is based on state updating. This model is carefully crafted to provide a numerical evaluation of cyber-power networks' dependability in the face of indirect interdependencies. Two separate models are used to maximize data communication inside the cyber network and minimize load limitation in the power network, respectively, in order to maximize the functioning of these integrated networks. In order to demonstrate the efficacy of the suggested solution model, the research focuses on a high-voltage converter equipped with both protection and monitoring devices. By taking a comprehensive approach, the research makes a substantial contribution to a deeper understanding of the complex interactions between cyber and electricity networks, providing priceless insights for enhancing the dependability of these interconnected systems.

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Robustface Recognition by Fusing Fuzzy Type 2 Induced Multiple Facial Fused Image 4

Robustface Recognition by Fusing Fuzzy Type 2 Induced Multip...

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4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021

作者： Ghosh, Manas Dey, Aniruddha Shaw, Rabindra Nath Ghosh, Ankush RCCIIT Department of Master of Computer Application Kolkata India MAKAUT Department of Information Technology Kolkata India Galgotias University Department of EC and Communication Engineering India School of Engineering and Applied Science Neotia University Kolkata India

ISBN: (纸本)9781728199511

This paper projects a new decision making model for face recognition from the original image fused with their true and partial diagonal images by integrating the type-2 fuzzy set based approach to mitigate the factors that pretend the face recognition accuracy. The G2DFLD based feature vectors corresponding to a test image are given input to neural network based classifier trained with the feature vectors of the fused images to generate the merit weights with respect to different classes (subjects) under consideration. A new scheme has been introduced in the present approach to generate a score by employing a fuzzy type-2 set based treatment. These scores with respect to each of the classes under consideration are rendered from the feature vectors of the test image and those of the diagonally fused training samples. For each class, the score is fused weighted by the corresponding merit weights to generate the concluding score. These class-wise concluding scores are deliberated in recognizing the test face image. Faces from the well-known databases (gallery) with varied pose, illumination and occlusion are used to evaluate the performance of the model. It has been found that our model exhibits more accurate classification performance than existing similar kind of image level fusion method. © 2021 IEEE.

关键词： Face recognition

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Robust Adversarial Defenses in Federated Learning: Exploring the Impact of Data Heterogeneity

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6005-6018页

作者： Li, Qian Wu, Di Zhou, Dawei Lin, Chenhao Liu, Shuai Wang, Cong Shen, Chao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an 710049 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an 710049 China Xidian University Xi’an 710071 China Xi’an Jiaotong University School of Software Xi’an 710049 China City University of Hong Kong Department of Computer Science Hong Kong

Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by exchanging local model parameters while preserving data privacy. In practice, FL faces two critical challenges. First, it is vulnerable to security issues as malicious clients would artificially harm the functionality of FL by launching poisoning attacks. Second, the inherent data heterogeneity among clients (termed Non-IID data in FL) naturally arises from distributed data ownership and significantly degrades model convergence and accuracy. However, with studies separately devoted to these two research lines, the interplay between data heterogeneity and security remains poorly understood. In this paper, we systematically investigate the relationship between data heterogeneity and adversarial robustness in FL. Specifically, we propose novel data partitioning algorithms that simulate Label-Conditional Non-IID and Feature-Conditional Non-IID with quantifiable heterogeneity levels. Further, we conduct extensive experiments to evaluate classical defense methods in the practical FL environment under state-of-the-art untargeted attacks. With results in various settings, we separately analyze the connection between Non-IID to defenses and attacks. Regarding attacks, with similar effects on models, Non-IID impacts the training in a different way compared with attacks. The interaction between attacks and Non-IID provides an opportunity to cause severe damage to FL. Regarding defenses, Non-IID induces heterogeneity in model distribution among clients which raises the difficulty of maintaining fidelity and robustness for defense methods. © 2005-2012 IEEE.

关键词： Adversarial Defenses Federated Learning Poisoning attacks Security

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