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17th International Conference on Learning and Intelligent Optimization, LION-17 2023

作者： Zhu, HongBin Xia, YuXiao Li, YunZhao Li, Wei Liu, Kang Gao, Xianzhou Big Data Center State Grid of China Beijing100012 China State Grid Smart Grid Research Institute Shanghai200016 China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing200016 China

ISBN: (数字)9783031445057

ISBN: (纸本)9783031445040

In recent years, deep neural networks have enjoyed tremendous success in industry and academia, especially for their applications in visual recognition and natural language processing. While large-scale deep models bring incredible performance, their massive data requirements pose a huge threat to data privacy protection. With the growing emphasis on data security, the study of data privacy leakage in machine learning, such as machine unlearning, has become increasingly important. There have been many works on machine unlearning, and other research has proposed training several submodels to speed up the retraining process, by dividing the training data into several disjoint fragments. When the impact of a particular data point in the model is to be removed, the model owner simply retrains the sub-model containing this data point. Nevertheless, current learning methods for machine unlearning are still not widely used due to model applicability, usage overhead, etc. Based on this situation, we propose a novel hierarchical learning method, Hierarchical Machine Unlearning (HMU), with the known distribution of unlearning requests. Compared with previous methods, ours has better efficiency. Using the known distribution, the data can be partitioned and sorted, thus reducing the overhead in the data deletion process. We propose to train the model using the hierarchical data set after partitioning, which further reduces the loss of prediction accuracy of the existing methods. It is also combined with incremental learning methods to speed up the training process. Finally, the effectiveness and efficiency of the method proposed in this paper are verified by multiple experiments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data privacy

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Heuristic Elastic Scaling for Kubernetes Heterogeneous Microservices 21

Heuristic Elastic Scaling for Kubernetes Heterogeneous Micro...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Song He, Sheng Ning, Yishuang Gao, Xiang Ding, Zhijun The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China The Department of Computer Science and Technology Tongji University Shanghai201804 China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen518057 China Intelligent Computing Facility Innovation Center ZhejiangLab Hangzhou30012 China

ISBN: (纸本)9798350365221

Kubernetes has become the basic platform for building cloud native applications. However, existing horizontal scaling methods based on Kubernetes have problems with resource redundancy. Furthermore, the combined horizontal and vertical scaling based on Kubernetes takes a long time. Regarding the issue above, first, we describe the execution processes of three scaling techniques and compare them in different dimensions;second, we construct a formal elastic scaling model for expansion scenarios, with the goal of minimizing the total cost;and third, we introduce a heuristic expansion algorithm after balancing resource redundancy and execution time as the heuristic information. The experiment results show that the elastic scaling algorithm proposed in this paper achieves better performance than other algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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An Enhanced Authentication Protocol Suitable for Constrained RFID Systems

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IEEE Access 2024年 12卷 61610-61628页

作者： Hosseinzadeh, Mehdi Servati, Mohammad Reza Rahmani, Amir Masoud Safkhani, Masoumeh Lansky, Jan Janoscova, Renata Ahmed, Omed Hassan Tanveer, Jawad Lee, Sang-Woong Duy Tan University Institute of Research and Development Da Nang550000 Viet Nam Duy Tan University School of Medicine and Pharmacy Da Nang550000 Viet Nam Shahid Rajaee Teacher Training University Department of Computer Engineering Tehran16788-15811 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliou Yunlin64002 Taiwan School of Computer Science Tehran19395-5746 Iran University of Finance and Administration Faculty of Economic Studies Department of Computer Science and Mathematics Prague101 00 Czech Republic University of Human Development Department of Information Technology Sulaymaniyah0778-6 Iraq Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of Gachon University Pattern Recognition and Machine Learning Laboratory Department of Ai Software Seongnam-si13557 Korea Republic of

RFID technology offers an affordable and user-friendly solution for contactless identification of objects and individuals. However, the widespread adoption of RFID systems raises concerns regarding security and privacy. Vulnerabilities such as message tampering, interception, and eavesdropping pose significant risks to the integrity of the system. This study examines the effectiveness of two recently proposed ultra-lightweight RFID authentication protocols, URASP and KUAJB, in addressing these security challenges. Surprisingly, our findings reveal that both URASP and KUAJB protocols are susceptible to secret disclosure attacks, despite their claims of providing robust security. Consequently, the development of a truly ultra-lightweight protocol that ensures adequate security becomes a formidable task. As a potential solution, the enhancement of the URASP protocol through the integration of a simple cryptographic primitive is suggested to bolster its security measures. © 2013 IEEE.

关键词： Hash functions

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SG-LSBA: Semantic Latent Space Backdoor Attack on Stable Diffusion 3rd

SG-LSBA: Semantic Latent Space Backdoor Attack on Stable Di...

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3rd International Conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Pan, Yu Chen, Jiahao Wang, Lin Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Shanghai Development Center of Computer Software Technology Shanghai201112 China

ISBN: (纸本)9789819645084

Stable Diffusion (SD) models have achieved remarkable success in text-to-image synthesis, but their security vulnerabilities remain largely unexplored. In this paper, we introduce a novel semantic-guided latent space backdoor attack (SG-LSBA) that leverages the semantic information in the text input to inject stealthy and semantically coherent backdoors into SD models. Our approach outperforms existing methods by crafting context-aware semantic triggers, identifying target visual features in the latent space, and employing an adversarial optimization framework. Extensive evaluations demonstrate the high success rates, strong semantic relevance, and exceptional stealthiness of SG-LSBA. Our findings highlight the urgent need for considering the complex interplay between semantics and latent representations in developing robust defenses against back-door attacks in SD models. We make our code and datasets publicly available to facilitate further research and development of secure and reliable text-to-image synthesis models. The code is available at https://***/paoche11/SG-LSBA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Latent space backdoor attack Semantic guidance Semantic triggers Stable Diffusion

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Deep User and Item Inter-matching Network for CTR Prediction 28th

Deep User and Item Inter-matching Network for CTR Predicti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yuan, Zhiyang Xiao, Yingyuan Yang, Peilin Hao, Qingbo Wang, Hongya Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China College of Computer Science and Engineering Donghua University Shanghai China

ISBN: (纸本)9783031306716

CTR prediction plays an important role in increasing company revenue and user experience, and many efforts start with historical behavior to uncover user interest. There are two main problems with previous works: (1) When most previous works mined interests from users’ historical behaviors, they only focus on implicit or explicit interests. (2) When most previous works mined user interests through the relationship between target users and similar users, the representation of target users was not rich and accurate enough, resulting in less accurate item recommendations. Therefore the Deep User and Item Inter-Matching Network (DUIIN) is proposed in this paper to solve the above problems. First, we design Item-to-Item Network (IIN), using two different sub-networks Evolving Interest Network (EIN) and Feature Interaction Network (FIN) to mine the hidden interests and explicit interests shown by users’ historical behaviors, respectively. Then the User-to-User Network (UUN) is designed to mine user interests through the relationship between target users and similar users after representing the target users more accurately and richly. The experimental results show that the DUIIN model proposed in this paper performs better than other state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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MAKT: Multichannel Attention Networks based Knowledge Tracing with Representation Learning 11

MAKT: Multichannel Attention Networks based Knowledge Tracin...

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11th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022

作者： Jiang, Xuyang Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang University Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beijing China

ISBN: (纸本)9781665491174

As an effective and emerging component of intelligent education, Knowledge Tracing(KT) achieves the combination of artificial intelligence and individualized learning, whose aim is to assess students' mastery of knowledge concepts and assist in developing learning plans. Several existing KT models either use concepts sequence as input and evaluate students' knowledge state or treat exercise as input to predict students' future performance. In this paper, we introduce a constraint factor to extract concepts' and exercises' relation matrix, design three methods in representation learning, and propose a Multichannel Attention Networks based KT model(MAKT). Specifically, we restrict the co-occurrence relationship within a time window to extract the relation matrix and then train their representations via graph generative learning, graph contrastive learning, and matrix decomposition, respectively. In MAKT, a sliding window is implemented by multichannel where input sequence is sequentially lagged in turn by one position and attention mechanism is applied. We conduct experiments on several benchmark datasets and demonstrate that MAKT with concepts' and exercises' representation trained by matrix decomposition outperforms state-of-the-art models. © 2022 IEEE.

关键词： Students

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software Defect Prediction via GCN based on Structural and Context Information 9

Software Defect Prediction via GCN based on Structural and C...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Tang, Lijin Tao, Chuanqi Guo, Hongjing Zhang, Jingxuan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software quality plays an important role in the software engineering. To improve software reliability, software defect prediction which predicts code region is buggy or not aims to assist developers find bugs and allocate their test resources reasonably. Traditional defect prediction studies focus on handcrafted features and train machine learning classifiers to identify the defective code area. Since the source code contains rich semantic and structural information, and defective code is closely related to its context, now many researchers try to utilize semantic information of programs and build more accurate defect prediction model. Existing methods mainly analyze Abstract Syntax Tree (AST) which is obtained from source code and transform it into token sequences. But these methods fail to capture rich information of structural and context information of code. At the same time, some traditional features(e.g. The number of methods in the class(WMC), Lines of code(LOC)) are also very valuable in defect prediction. Therefore in this paper, we propose to a method to build the graph by connecting parent and child nodes of the AST and connecting leaf nodes from left to right to preserve the context information that contained in source code. Then we use Graph Convolutional Network(GCN) to automatically generate semantic and structural features from source code. In addition, we combine GCN learned features with handcrafted features for more accurate prediction model. Based on the above model, our evaluation on 12 data sets shows that our proposed method is superior to the state-of-the-art software defect prediction method by 10.37 % in recall, 1.62 % in precision, 5.15% in F-measure, and 8.11 % in MCC. © 2022 IEEE.

关键词： software reliability

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Reinforcement Learning-Based Explainable Recommendation over Knowledge Graphs with Negative Sampling 19

Reinforcement Learning-Based Explainable Recommendation over...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Siyuan Ouyang, Yuanxin Liu, Zhuang Rong, Wenge Xiong, Zhang Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang University Engineering Research Center of Advanced Computer Application Technology Ministry of Education Beijing China

ISBN: (纸本)9798350346558

Introducing knowledge graphs (KGs) into the recommender systems not only improves their performance but also enhances the interpretability. However, most KG-based recommendation methods have the problem of inefficiency and ex-post explanation, which reinforcement learning (RL) methods can solve properly. Most existing RL-based methods for explainable recommendations only consider positive rewards when designing the reward part of the RL environment, which is defective and misleads the policy of the RL agent. To address this problem, we propose Reinforced Knowledge Graph Reasoning with Reinforced Negative Sampling (RKGR-RNS) by introducing a negative sampling method into RL-based recommendation, which refines the reward mechanism to help optimize the agent's policy. And a judge module is proposed to improve the performance of the recommender system further. Experiments on three real datasets demonstrate that our method is better than the state-of-the-art baseline. © 2022 IEEE.

关键词： Reinforcement learning

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DUALWISE-MAR: Dual-domain Multi-view Weakly supervised Segmentation Network for CBCT Metal Artifact Reduction

DUALWISE-MAR: Dual-domain Multi-view Weakly supervised Segme...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhong, Xinyun Wu, Zhan Yang, Yang Lyu, Tianling Yu, Wenxue Xi, Yan Chen, Yang Zhejiang Lab Research Center for Augmented Intelligence Hangzhou311121 China First-Imaging Technology Shanghai200120 China Southeast University School of Software Nanjing211189 China Southeast University School of Computer Science and Engineering Nanjing210096 China

ISBN: (纸本)9798350337488

Intraoperative Cone-Beam Computed Tomography (CBCT) provides fast multiplanar cross-sectional imaging and three-dimensional reconstructions in clinical scene. However, high-attenuation metallic implants result in the obstruction of low-energy X-rays and further lead to metal artifacts in the reconstructed CT images. Existing metal artifact reduction approaches do not consider automated fine-grained metallic implants segmentation. Imprecise metallic implants segmentation severely limits the clinical practical applicability of MAR approaches. To address the above challenge, in this study, we present a novel DUAL-domain multi-view Weak supervIsed SEgmentation network for Metal Artifact Reduction (DUALWISE-MAR) to coarse-to-fine segment metallic implants without segmentation ground-truth. First, an Iterative Segmentation Refinement Mechanism is constructed to progressively enhance initial coarse label and guide segmentation network close to the ideal metal mask. Second, a Multi-view Consistent Segmentation Network is constructed for data-consistent segmentation to capture the metal shape relationship between adjacent views in projection-domain. Third, a Segmentation Recalibration Module is proposed to calibrate the inaccurate prediction regions in the imagedomain. Extensive experiments have been conducted on the real clinical CBCT dataset and demonstrate that the proposed DUALWISE-MAR framework achieves excellent performance in metal segmentation compared to the state-of-the-art methods. © 2023 IEEE.

关键词： CBCT metal artifact reduction dual-domain metal segmentation weakly supervised learning

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