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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Hui Wang, Zhixiang Zhang, Kewei Hou, Jiaqi Liu, Yuanwei Tang, Hao Wang, Zheng National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China The University of Tokyo Japan School of Computer Science Peking University China

Physical adversarial attacks can deceive deep neural networks (DNNs), leading to erroneous predictions in real-world scenarios. To uncover potential security risks, attacking the safety-critical task of person detection has garnered significant attention. However, we observe that existing attack methods overlook the pivotal role of the camera, involving capturing real-world scenes and converting them into digital images, in the physical adversarial attack workflow. This oversight leads to instability and challenges in reproducing these attacks. In this work, we revisit patch-based attacks against person detectors and introduce a camera-agnostic physical adversarial attack to mitigate this limitation. Specifically, we construct a differentiable camera Image Signal Processing (ISP) proxy network to compensate for the physical-to-digital transition gap. Furthermore, the camera ISP proxy network serves as a defense module, forming an adversarial optimization framework with the attack module. The attack module optimizes adversarial patches to maximize effectiveness, while the defense module optimizes the conditional parameters of the camera ISP proxy network to minimize attack effectiveness. These modules engage in an adversarial game, enhancing cross-camera stability. Experimental results demonstrate that our proposed Camera-Agnostic Patch (CAP) attack effectively conceals persons from detectors across various imaging hardware, including two distinct cameras and four smartphones. © 2024 Neural information processing systems foundation. All rights reserved.

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Evaluating Generative Language Models with Prompt engineering for Categorizing User Stories to its Sector Domains 9

Evaluating Generative Language Models with Prompt Engineerin...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Alawaji, Batool Hakami, Mona Alshemaimri, Bader Qassim University Department of Computer Qassim Saudi Arabia King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350394474

In Agile software development, user stories capture requirements through a concise, user-centric approach. Manual categorization of these stories is both labor-intensive and error-prone. This study addresses the gap in existing research by exploring the application of generative language models, specifically GPT-style models, with prompt engineering for user story *** research introduces experiments utilizing two generative language models, gpt-3.5-turbo and Llama-2-chat-hf, emphasizing innovative prompt engineering. The evaluation of gpt-3.5-turbo and Llama-2-chat-hf demonstrates commendable performance in zero-shot experiments. Notably, gpt-3.5-turbo outperforms in few-shot prompting with a higher F-score of 67.93%. The gpt-3.5-turbo showcased superior compliance with instructions compared to Llama-2-chat-hf. The study underscores the promising capability of generative language models, with prompt engineering, to automate user story categorization *** work contributes to the potential of automating user story categorization in low-resource settings, showcasing how leveraging LLMs can enhance accuracy and streamline the categorization process in software development, ultimately leading to more efficient outcomes. © 2024 IEEE.

关键词： Classification (of information)

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QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization 38

QUEST: Quadruple Multimodal Contrastive Learning with Constr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Qi Gong, Tianxiang Gao, Shiqi Zhou, Haoyi Li, Jianxin School of Software Beihang University China School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework named QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95% on the CLIP model. © 2024 Neural information processing systems foundation. All rights reserved.

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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Enhanced Machine Learning for Real-Time Plant Replication in Embedded Systems 50

Enhanced Machine Learning for Real-Time Plant Replication in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chowdhury, Abhisek Jung, Jane Kuo, Matthew M.Y. Sinha, Roopak Computer Science and Software Engineering Department Auckland University of Technology Auckland New Zealand School of IT Deakin University Melbourne Australia

ISBN: (纸本)9781665464543

This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study. A comparative study was conducted on single-step and multi-step Dense models, Convolutional Neural Network (CNN), Recurrent Neural Net (RNN), and a Residual Neural Net (RNN2) models to investigate the most appropriate model for replicating the plant model. The study found that single-step models were consistently more accurate than multi-step models due to single-step model’s iterative nature. Whereas, multi-step models were better at revealing prediction patterns and identifying causes for large deviations. The best-performing model was the RNN2 model, however, signs of overfitting were observed. In general, all models were able to take into account minor random actuation, however, large changes such as the pendulum falling over caused the models to behave sporadically. © 2024 IEEE.

关键词： Inverted pendulum

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Robust copy-move detection and localization of digital audio based CFCC feature

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Multimedia Tools and Applications 2025年第11期84卷 9573-9589页

作者： Wang, Dongyu Li, Xiaojie Shi, Canghong Niu, Xianhua Xiong, Ling Wu, Hanzhou Qian, Qing Qi, Chao School of Computer and Software Engineering Xihua University Chengdu610039 China The College of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Information Shanghai University Shanghai200444 China School of Information Guizhou University of Finance and Economics Guiyang550000 China

Copy-move forgery is a common audio tampering technique in which users copy the contents of one speech and paste them into another region of the same speech signal, thus achieving the effect of tampering with the semantics. To verify the authenticity of the audio, this paper proposes a method to detect and localize audio copy-move forgery based on the cochlear filter of cochlear filter cepstral coefficients (CFCC) feature. The pitch tracking algorithm is used to distinguish the voiced and unvoiced segments in the audio, and then the CFCC coefficients are extracted for each voiced segment. The CFCC feature simulates the entire transmission process of signals in the cochlear basilar membrane using wavelet transformation. Finally, we use Pearson correlation coefficients (PCCs) and dynamic time warping (DTW) in combination to compare the similarity of voiced segments, accurately determining the tampered locations in the audio through threshold judgment. Through extensive experiments on relevant datasets, this algorithm achieves a precision rate of 98.39% and a recall rate of 98.00% in detecting audio without post-processing. Even when detecting audio that has undergone different post-processing, the precision and recall rates remain above 90%. Compared to existing methods, this approach not only achieves precise localization of replicated segments but also demonstrates superior experimental results. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of computer Science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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