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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity 22

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Unsupervised Community Discovery Algorithm via Reconstructed...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Wang, Jinghong Wang, Hui Yang, Jiateng Wang, Xizhao College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang050024 China College of Artificial Intelligence Big Data Hebei Polytechnic Institute Shijiazhuang050020 China College of Computer Science and Software Engineering ShenZhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability. © 2023 IEEE.

关键词： Graph neural networks

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Radio Map Estimation via Latent-Domain Plug-and-Play Denoisers

Radio Map Estimation via Latent-Domain Plug-and-Play Denoise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xu, Le Cheng, Lei Chen, Junting Pu, Wenqiang Fu, Xiao School of Electrical Engineering and Computer Science Oregon State University Corvallis United States College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9798350368741

Radio map estimation (RME) aims to construct a map of radio strength across multiple domains (e.g., space and frequency) from limited measurements. data-driven deep neural model-based RME showed promising performance, yet requiring excessive training resources. This work puts forth an RME approach that can effectively incorporate learned information without training over radio map data. Our idea is to employ the plug-and-play (PnP) denoising scheme from computational imaging. The PnP framework allows incorporating denoisers trained over natural images to handle other types of data, e.g., ocean sound fields and medical images, due to the similarity of their denoising processes. Conventional PnP methods mostly use the learned denoisers in the data domain. Instead, the proposed approach applies PnP in the latent domain through a tailored algorithm design and spatial-spectral factorization of radio maps. This way, the proposed RME method exhibits enhanced scalability and noise robustness. Simulations are used to illustrate the effectiveness of the proposed approach. © 2025 IEEE.

关键词： plug-and-play algorithm design radio map estimation Spectrum cartography

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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A survey on XR remote collaboration in industry

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Journal of Manufacturing Systems 2025年 81卷 49-74页

作者： Wang, Peng Yang, Huan Billinghurst, Mark Zhao, Shuang Wang, Yao Liu, Zhou Zhang, Yi School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Big Data Institute Baoshan University Yunnan 678000 China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand

This paper provides a survey of eXtended Reality (XR) remote collaboration in industry. The field of XR remote collaboration is currently at a critical point as collaborative XR systems are becoming more prevalent in industry. Moreover, research on XR remote collaboration in industry is a compelling and evolving field of study, especially because XR technology has recently reached a level of maturity that enables researchers and practitioners to use its capabilities to enhance remote collaboration, rather than focusing solely on the development of the underlying technology. However, to our knowledge, there has yet to be a comprehensive survey on XR remote collaboration in industry. Thus, this paper presents a systematic review of literature published between 2019 and 2024 in this domain. We identified a total of 161 papers, with more than 57 % published in the last three years, and all relevant studies are discussed in detail from fundamental requirements, industrial applications, toolkits/platforms, and system evaluations. Our findings indicate that XR remote collaboration holds great potential across various industrial domains, including design (e.g., design review and co-design), manufacturing (e.g., assembly and inspection), service (e.g., maintenance and monitoring), and training. Since XR remote collaboration is being implemented in real industrial environments, this paper aims to provide a comprehensive academic roadmap and valuable insights into the cutting-edge of XR remote collaboration in industry. This work will also serve as a resource for both current and future researchers who are interested in collaborative XR systems and applications in industry. © 2025 The Society of Manufacturing Engineers

关键词： Engineers

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Time Series data Cleaning Under Expressive Constraints on Both Rows and Columns

Time Series Data Cleaning Under Expressive Constraints on Bo...

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International Conference on data engineering

作者： Xiaoou Ding Genglong Li Hongzhi Wang Chen Wang Yichen Song School of Computer Science and Technology Harbin Institute of Technology National Engineering Laboratory for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy.

关键词： Measurement Costs data integrity Time series analysis Maintenance engineering Sensor phenomena and characterization Search problems

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Logo: A Novel Distributed Computing Framework for big data Analytics

SSRN

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SSRN 2023年

作者： Sun, Xudong He, Yu-Lin Huang, Joshua Zhexue Wu, Dingming Zhao, Lingxiang Cai, Yongda Big Data Institute College of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China Shenzhen518107 China

Spark reduces the I/O costs by introducing an in-memory data abstraction RDD. Nevertheless, the performance of Spark is still limited by the memory size. Moreover, since Spark follows the same parallelism strategy as Hadoop, the communication is a bottleneck of executing iterative algorithms. To have an I/O and communication efficient distributed computing facility, this paper proposes the sample independently parallel approach for big data analytics. It takes random sample partitions as the input, performs iterations independently and parallelly, and outputs an aggregation result. To obtain a practical solution of the sample independently parallel approach, we introduce Local Operations with Global Operations (LOGO) computing framework which is an extension of Spark. We also develop a machine learning library of LOGO that includes commonly used supervised learning, unsupervised learning, and frequent itemset mining algorithms. Extensive experiments are conducted to evaluate the performance of LOGO. The results demonstrate that LOGO outperforms the state-of-the-art Spark by orders of magnitude in terms of the running time. LOGO is capable to analyze terabyte-scale datasets and produce high-quality results. © 2023, The Authors. All rights reserved.

关键词： big data

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A Multiple Kernel-Based Kernel Density Estimator for Multimodal Probability Density Functions

SSRN

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SSRN 2023年

作者： He, Yu-Lin Chen, Jia-Qi Fournier-Viger, Philippe Huang, Joshua Zhexue Big Data Institute College of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China Shenzhen518107 China

To approximate multimodal PDFs, which are frequently encountered in real-world applications, this paper proposes a novel multiple kernel-based kernel density estimator (MK-KDE). It constructs a flexible KDE using the weighted average of multiple kernels by considering their kernel efficiencies. To achieve this, a novel efficient objective function is designed to obtain the optimized kernel weights and kernel bandwidths by minimizing both the global estimation error of MK-KDE and the local estimation errors of SK-KDEs. Moreover, a sophisticated $k$-nearest neighbor strategy is devised as a heuristic method to determine the unknown PDF values of the given data points, thereby optimizing the aforementioned objective function. A series of persuasive experiments were conducted to validate the feasibility, rationality, and effectiveness of MK-KDE for 10 multimodal PDFs. The experimental results demonstrated that MK-KDE is a viable approach to estimate multimodal PDFs. © 2023, The Authors. All rights reserved.

关键词： k-nearest neighbors

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