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IEEE International Conference on systems, Man and Cybernetics

作者： Yishuo Zhang Lanshan Zhang Zhizhen Zhang Ziyi Wang Xiaohui Xie Wendong Wang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Video Corpus Moment Retrieval aims to retrieve the relevant video from a large corpus and localize the corresponding moment within the target video based on a specific query. Existing methods have achieved promising accuracy and efficiency through dedicated model designs. However, we suggest these methods overly exploit dataset biases instead of semantics. This will lead to exaggerated performances on biased datasets and implicit a significant deficiency in generalizability, which is an important metric yet not considered in existing studies. In this paper, we observe the degradation caused by a spurious dependency and design a model to mitigate this harm. Specifically, we generate an Out-Of-Distributed (OOD) test set from a widely used TV Retrieval dataset, revealing the existing models' erroneous dependency on the temporal locations of target moments. Therefore, we utilize a theoretical Structural Causal Model (SCM) to dig into the roots of this dependency by constructing causal paths for the models. Furthermore, we propose a concrete Clip Location Deconfounding Model (CLDM) to disentangle the confounded video features into the content part and the location confounder part, then produce results with causal intervention. Experiments show that CLDM significantly alleviates the impact brought by dataset biases thus providing advanced generalizability among existing works.

关键词： Measurement Degradation TV Accuracy Semantics Cybernetics

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Programming by Example Made Easy

arXiv

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arXiv 2023年

作者： Wu, Jiarong Wei, Lili Jiang, Yanyan Cheung, Shing-Chi Ren, Luyao Xu, Chang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Computer Engineering McGill University Montreal Canada State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Key Lab of High Confidence Software Technologies Ministry of Education Department of Computer Science and Technology EECS Peking University Beijing China

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee’s effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers. Copyright © 2023, The Authors. All rights reserved.

关键词： Problem oriented languages

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

Securely Fine-tuning Pre-trained Encoders Against Adversaria...

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IEEE Symposium on Security and Privacy

作者： Ziqi Zhou Minghui Li Wei Liu Shengshan Hu Yechao Zhang Wei Wan Lulu Xue Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the *** this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six d

关键词： Training Adaptation models Sensitivity Accuracy Self-supervised learning Genetics Feature extraction

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A systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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Applying system Thinking to Learn from Accident of Modern Automatic Control systems

引用

Chinese Journal of Electronics 2025年第2期23卷 409-414页

作者： Ru Niu Yuan Cao Xiaocheng Ge Tao Tang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Department of Computer Science University of York York UK

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events, rather than a simple cause-effect chain. We take railway Train control systems (TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP - one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Sociotechnical systems Analytical models systematics Railway accidents Transportation Medical services Rail transportation systems thinking Research and development Accidents

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A deep learning-based approach for predicting ubiquitination sites using word embedding techniques

A deep learning-based approach for predicting ubiquitination...

引用

International Conference on computer and Information Technology (ICCIT)

作者： Md. Faruk Hosen Md. Abul Basar Md. Samim Hossain Sakib Sarker Md. Suhag Ali Monir Morshed Muhammad Shahin Uddin Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Ubiquitination is a prevalent and reversible post-translational modification (PTM) that controls apoptosis and plays a crucial role in protein breakdown and cell disorders. Ubiquitination also involved in the control of a number of physiological mechanisms such as cellular proliferation, gene transcription, intracellular signaling, DNA correction, and synthesis. However, experimental identification of ubiquitination sites prediction is time-consuming and costly, so it is an urgent need to construct effective predictors. In this paper, a novel predictor called UbiSite-DeepWET has been developed to accurately predict ubiquitination sites from sequences using a convolutional neural network (CNN) model. First, we perform nine feature extraction method, namely Amino Acid Composition (AAC), Dipeptide Compostion (DPC), Composition of k-spaced Amino Acid Pairs (CKSAAP), Pseudo-Amino Acid Composition (PAAC), Amino Acid index (AAindex) database, Word2Vec, GloVe and fastText, to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are used to remove unnecessary and irrelevant features for predicting ubiquitination sites. Finally, the best features are fed into the CNN classifier to build the UbiSite-DeepWET model for identifying the protein ubiquitination sites. Five-fold cross validation shows that the AUC values of Set1-Set6 datasets are 0.8846, 0.7764, 0.7881, 0.7740, 0.6870 and 0.7238, respectively. The Adaptive synthetic sampling approach (ADASYN) is applied in Set4 to Set6 unbalanced datasets, and the AUC values are 0.9929, 0.9975 and 0.9971, respectively. Additionally, we have created three independent test (IT) datasets which the AUC values are 0.8529, 0.6215 and 0.6001, respectively. The results demonstrate that the proposed technique UbiSite-DeepWET outperforms conventional ubiquitination prediction methods and offers new guidance for the identification of ubiquitination sites.

关键词： Protein engineering Additives Predictive models Amino acids Feature extraction Physiology Robustness Protein sequence Convolutional neural networks Bioinformatics

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GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

引用

arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature 39

Breaking Barriers in Physical-World Adversarial Examples: Im...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method's superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method's effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Imperceptible and Reliable Adversarial Attack 1

引用

4th International Conference on Frontiers in Cyber Security, FCS 2021

作者： Zhang, Jiawei Wang, Jinwei Luo, Xiangyang Ma, Bin Xiong, Naixue School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhenzhou450001 China Shandong Provincial Key Laboratory of Computer Networks and Qilu University of Technology Jinan250353 China Department of Computer Science and Mathematics Sul Ross State University AlpineTX79830 United States

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

Deep neural networks are vulnerable to adversarial examples, which can fool classifiers by adding small perturbations. Various adversarial attack methods have been proposed in the past several years, and most of them add the perturbation in a "sparse" or "global" way. Since the number of pixels perturbed by the "sparse" method and the perturbation intensity of each pixel added by the "global" method are both small, the adversarial property can be destroyed easily. Finally, it makes the adversarial attack and the adversarial training based on these samples unreliable. To address this issue, we present an "pixel-wise" method which is somewhere in between the "sparse" or "global" way. First, the perception of human eyes to the error of different image regions is different. Second, image processing methods have different effects on the different areas of the image. Based on these two considerations, we propose an imperceptible and reliable adversarial attack method, which projects the perturbation to the different areas differently. Extensive experiments demonstrate our method can preserve the attack ability while maintaining good view quality. More importantly, the proposed projection can be combined with existing attack methods to generate a stronger generation algorithm which improves the robustness of adversarial examples. Based on the proposed method, the reliability of adversarial attacks can be greatly improved. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Pixels

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