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Consistency preserving database watermarking algorithm for decision trees

Digital Communications and Networks 2024年第6期10卷 1851-1863页

作者： Qianwen Li Xiang Wang Qingqi Pei Xiaohua Chen Kwok-Yan Lam School of Telecommunications Engineering Xidian UniversityXi'an710071China School of Cyber Engineering Xidian UniversityXi'an710071China School of Telecommunications Engineering Xidian UniversityXi'anChina 2012 Laboratories Huawei TechnologesHuawei Technology CoLtdHangzhou310000China The School of Computer Science and Engineering Nanyang Technological University639798Singapore

Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data ***,when the watermarked database is used for data mining model building,such as decision trees,it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark *** watermarking algorithms mainly consider the statistical distortion of data,such as the mean square error,but very few consider the effect of the watermark on database ***,in this paper,a consistency preserving database watermarking algorithm is proposed for decision ***,label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision ***,the splitting values of the decision tree are adjusted according to the defined constraint ***,the adjusted database can obtain a decision tree consistent with the original decision *** experimental results demonstrated that the proposed algorithm does not corrupt the watermarks,and makes the watermarked decision tree consistent with the original decision tree with a small distortion.

关键词： Consistency preserving Decision tree Database watermarking Data mining

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Adversarial Geometric Attacks for 3D Point Cloud Object Tracking

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IEEE Transactions on Multimedia 2025年 27卷 3144-3157页

作者： Yao, Rui Zhang, Anqi Zhou, Yong Zhao, Jiaqi Liu, Bing ElSaddik, Abdulmotaleb China University of Mining and Technology School of Computer Science and Technology Mine Digitization Engineering Research Center Ministry of Education Xuzhou221116 China University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada

3D point cloud object tracking (3D PCOT) plays a vital role in applications such as autonomous driving and robotics. Adversarial attacks offer a promising approach to enhance the robustness and security of tracking models. However, existing adversarial attack methods for 3D PCOT seldom leverage the geometric structure of point clouds and often overlook the transferability of attack strategies. To address these limitations, this paper proposes an adversarial geometric attack method tailored for 3D PCOT, which includes a point perturbation attack module (non-isometric transformation) and a rotation attack module (isometric transformation). First, we introduce a curvature-aware point perturbation attack module that enhances local transformations by applying normal perturbations to critical points identified through geometric features such as curvature and entropy. Second, we design a Thompson sampling-based rotation attack module that applies subtle global rotations to the point cloud, introducing tracking errors while maintaining imperceptibility. Additionally, we design a fused loss function to iteratively optimize the point cloud within the search region, generating adversarially perturbed samples. The proposed method is evaluated on multiple 3D PCOT models and validated through black-box tracking experiments on benchmarks. For P2B, white-box attacks on KITTI reduce the success rate from 53.3% to 29.6% and precision from 68.4% to 37.1%. On NuScenes, the success rate drops from 39.0% to 27.6%, and precision from 39.9 to 26.8%. Black-box attacks show a transferability, with BAT showing a maximum 47.0% drop in success rate and 47.2% in precision on KITTI, and a maximum 22.5% and 27.0% on NuScenes. © 1999-2012 IEEE.

关键词： Iterative methods

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Simplified Kernel-Based Cost-Sensitive Broad Learning System for Imbalanced Fault Diagnosis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6629-6644页

作者： Yang, Kaixiang Chen, Wuxing Shi, Yifan Yu, Zhiwen Chen, C. L. Philip South China University of Technology School of Computer Science and Engineering Guangdong Guangzhou510006 China South China University of Technology School of Future Technology Guangdong Guangzhou510006 China Pengcheng Laboratory Guangdong Shenzhen518066 China Huaqiao University College of Engineering Fujian Quanzhou362021 China South China University of Technology School of Computer Science and Engineering Guangdong Guangzhou510650 China

In the field of intelligent manufacturing, tackling the classification challenges caused by imbalanced data is crucial. Although the broad learning system (BLS) has been recognized as an effective and efficient method, its performance wanes with imbalanced datasets. Therefore, this article proposes a novel approach named simplified kernel-based cost-sensitive broad learning system (SKCSBLS) to address these issues. Based on the framework of cost-sensitive broad learning system (CSBLS) that assigns distinctive adjustment costs for individual classes, SKCSBLS emphasizes the importance of the minority class while mitigating the impact of data imbalance. Additionally, considering the complexity introduced by noisy or overlapping data points, we incorporate kernel mapping into the CSBLS. This improvement not only improves the system's capability to handle overlapping classes of samples, but also improves the overall classification effectiveness. Our experimental results highlight the potential of SKCSBLS in overcoming the challenges inherent in unbalanced data, providing a robust solution for advanced fault diagnosis in intelligent systems. © 2020 IEEE.

关键词： Adversarial machine learning

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Applying an Information Retrieval Approach to Retrieve Relevant Articles in the Legal Domain

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Annals of Data Science 2024年第5期11卷 1563-1580页

作者： Kanapala, Ambedkar Pal, Sukomal Dara, Suresh Jannu, Srikanth Department of Computer Science and Engineering Geethanjali College of Engineering and Technology TS Hyderabad501301 India Varanasi India School of Technology Woxsen University TS Hyderabad502345 India Department of Computer Science and Engineering Vaagdevi Engineering College TS Warangal506005 India

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Information retrieval

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Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1579-1590页

作者： Shengao Lu Tong Wu Lixian Zhang Jianan Yang Ye Liang the School of Astronautics Harbin Institute of TechnologyHarbin 150080China the Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore IEEE

This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.

关键词： Asynchronous switching bumpless transfer H_∞control switched systems

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Skeleton-Based Action Recognition Using Graph Convolutional Network with Pose Correction and Channel Topology Refinement

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computers, Materials & Continua 2025年第4期83卷 701-718页

作者： Yuxin Gao Xiaodong Duan Qiguo Dai School of Engineering Guangzhou College of Technology and BusinessFoshan528138China School of Computer Science and Engineering Dalian Minzu UniversityDalian116000China SEAC key Laboratory of Big Data Applied Technology Dalian Minzu UniversityDalian116000China

Graph convolutional network(GCN)as an essential tool in human action recognition tasks have achieved excellent performance in previous ***,most current skeleton-based action recognition using GCN methods use a shared topology,which cannot flexibly adapt to the diverse correlations between joints under different motion *** video-shooting angle or the occlusion of the body parts may bring about errors when extracting the human pose coordinates with estimation *** this work,we propose a novel graph convolutional learning framework,called PCCTR-GCN,which integrates pose correction and channel topology refinement for skeleton-based human action ***,a pose correction module(PCM)is introduced,which corrects the pose coordinates of the input network to reduce the error in pose feature ***,channel topology refinement graph convolution(CTR-GC)is employed,which can dynamically learn the topology features and aggregate joint features in different channel dimensions so as to enhance the performance of graph convolution networks in feature ***,considering that the joint stream and bone stream of skeleton data and their dynamic information are also important for distinguishing different actions,we employ a multi-stream data fusion approach to improve the network’s recognition *** evaluate the model using top-1 and top-5 classification *** the benchmark datasets iMiGUE and Kinetics,the top-1 classification accuracy reaches 55.08%and 36.5%,respectively,while the top-5 classification accuracy reaches 89.98%and 59.2%,*** the NTU dataset,for the two benchmark RGB+Dsettings(X-Sub and X-View),the classification accuracy achieves 89.7%and 95.4%,respectively.

关键词： Pose correction multi-stream fusion GCN action recognition

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Alkali Atomic Density Detection with Arbitrarily Polarized Light

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IEEE Sensors Journal 2025年第11期25卷 20466-20473页

作者： Liu, Xuejing Wang, Menglu Li, Yuhan Wang, Yangtao Zhang, Xuedian Li, Yang University of Shanghai for Science and Technology Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education School of Optical-Electrical and Computer Engineering Shanghai200093 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai200093 China

The atomic density of an alkali vapor cell is a critical factor in determining the performance of atomic magnetometers. This paper presents a novel method to detect the density of an alkali atomic ensemble under arbitrary polarization states. By establishing a comprehensive model that correlates the absorption spectrum with the incident light’s polarization, our approach enables real-time and flexible measurements across varying temperatures and polarization angles. Experimental validations are conducted by systematically rotating a quarter-wave plate and controlling the vapor cell temperature, thereby collecting transmission spectra at multiple polarizations. The results demonstrate that the proposed method simplifies density measurements while preserving high accuracy, laying a solid foundation for further research on Spin-Exchange Relaxation-Free (SERF) atomic magnetometers. This technique serves as a versatile and effective tool for in-situ monitoring of alkali metal vapor density in various sensor applications. © 2001-2012 IEEE.

关键词： Absorption spectroscopy

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ISHD:Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

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computer Modeling in engineering & Sciences 2023年第10期137卷 509-526页

作者： Wu Song Yayuan Tang Wenxue Tan Sheng Ren School of Computer and Electrical Engineering Hunan University of Arts and SciencesChangde415000China School of Information Engineering Hunan University of Science and TechnologyYongzhou425119China School of Computer Science and Engineering Central South UniversityChangsha410083China

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.

关键词： Deep learning object detection video surveillance of exam room smart examination environment

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Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

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CAAI Transactions on Intelligence technology 2023年第3期8卷 824-835页

作者： Min Yang Zhenwei Wang Qiancheng Xu Chengming Li Ruifeng Xu Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenChina School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaUSA School of Intelligent Systems Engineering Sun Yat‐Sen UniversityGuangzhouChina School of Computer Science Harbin Institute of Technology(Shenzhen)ShenzhenChina

Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable ***,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic *** this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training *** learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation *** experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual *** reproducibility,we release the code and data publicly at:https://***/siat‐nlp/HSEMEC‐code‐data.

关键词： deep learning emotional conversation generation semantic‐emotional memory

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