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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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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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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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An Application Level Synchronization Method for Heterogeneous Redundant Structures

An Application Level Synchronization Method for Heterogeneou...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hong Yu Qinrang Liu Chao Cui Shuai Wei Information & Technology Research Center Information Engineering University Zhengzhou China Software Research & Development Department Phytium Technology Co. Ltd Tianjin China

In heterogeneous redundant architecture devices, programs with equivalent functions are implemented on heterogeneous executers, and the scheduler needs to compare their outputs to identify abnormality and decide the final output. Due to slight differences in the scheduling order of threads among different executers, the outputs of programs are likely to be different, which will have a significant impact on the arbitration of the scheduler, causing false error report and affecting the normal processing. We propose an application-level state synchronization method model to effectively mitigate the problem of program state synchronization of multiple heterogeneous executers. And we implemented a hardware-software codesign of the method in a DHRA switch and tested its effectiveness and latency. Tests show that the method is effective with latency within several milliseconds.

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Efficient Cutting of Submarine Pipelines by Linear-Shaped Charge 30th

Efficient Cutting of Submarine Pipelines by Linear-Shaped Ch...

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30th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2024

作者： Zhang, Shenhe Wang, Longkan Yang, Shuxin Zong, Zhi Zhang, Zhifan State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian116024 China China Ship Research and Development Academy Beijing100192 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China

ISBN: (纸本)9783031687747

Directional explosive cutting method has been applied to the cutting, maintenance and even destruction of submarine pipelines because of the high cutting speed and low risk to adjacent components. This paper develops a three-dimensional numerical model for the damage effects on the pipe induced by the Linear-Shaped Charge (LSC). The explosive is 50 kg TNT. The internal diameter of the pipe is 871 mm, and the thickness of the steel wall is 34 mm. The standoff distance is set to 1.0 times the height of the LSC and an air cavity is added in front of the liner for better jet formation effect. The pipe is filled with air, and the bottom of the pipeline is anchored to the seabed. Due to the symmetry of the numerical model, a quarter of the numerical model is established in order to improve the calculating efficiency. The FSI (Fluid-Structure Interaction) is taken into consideration. Based on structured-arbitrary Lagrange Euler (S-ALE) method, the damage process of the pipeline under the coupling load of the jet penetration, hydrostatic pressure, shock wave and bubble is investigated. According to the numerical simulation results, the pipe is immediately destroyed under the combined effect of the load. The results indicate that it is possible to effectively cut the pipelines by using a linear-shaped charge technique. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Submarine pipelines

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KRLGI: Knowledge Representation Learning Based on Global Information for Reasoning

KRLGI: Knowledge Representation Learning Based on Global Inf...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Bo Wu Yingchi Mao Fudong Chi Yingjie Liu Silong Ding Rongzhi Qi College of Computer Science and Software Engineering Hohai University Nanjing China Technology Research and Development Center Huaneng Lancang River Hydropower Inc Kunming China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Knowledge Graph Reasoning based on Representation Learning maps the entities and relations into a vector space, assessing entity-pair similarities to deduce unknown facts. However, existing methods focus solely on the local importance of entities and ignore isolated entities, leading to the issue of missing feature information. To solve this, we propose Knowledge Representation Learning based on Global Information (KRLGI). KRLGI adopts an attention-based biased random walk algorithm to obtain global information and determine the importance of global entities. The global entity importance goes through a conversion into attention weights, and these weights are integrated with the local entity importance. Subsequently, the local and global entity importance can be used together to represent entity embeddings. Globally integrated representations can reveal richer semantics and enhance reasoning capabilities. Experimental results indicate that KRLGI outperforms other baselines in reasoning accuracy on four public datasets. Notably, on the FB15k-237 dataset, KRLGI shows significant improvements over the best baseline, with increases of 6.37% in MRR, 2.5% in HITS@1, and 5.5% in HITS@10.

关键词： Representation learning Accuracy Semantics Knowledge graphs Learning (artificial intelligence) Cognition Vectors

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LRIRL: Improving Knowledge Graph Reasoning through Representation Learning-Based Rule Induction

LRIRL: Improving Knowledge Graph Reasoning through Represent...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yingjie Liu Yingchi Mao Fudong Chit Bo Wu Silong Ding Rongzhi Qi College of Computer Science and Software Engineering Hohai University Nanjing China Technology Research and Development Center Huaneng Lancang River Hydropower Inc Kunming China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Rule induction is an important approach for reasoning over Knowledge Graphs. Existing works mainly rely on searching for rule instances within the Knowledge Graph to induce rules. However, this approach may generate a vast search space, leading to inefficiency and difficulty in discovering rules that lack instance support. We propose a Logical Rule Induction based on Representation Learning (LRIRL) method, which can mine rules at the pattern level. By computing rule scores through vector representations of the rules, LRIRL can avoid the inefficiency caused by directly searching for rule instances in a vast search space. Furthermore, by incorporating the deductive nature of logical rules into the rule induction process, LRIRL can mine and evaluate rules even in the absence of rule instances. The generated rules can be used to perform more efficient and accurate reasoning tasks on the Knowledge Graph. Experimental results demonstrate that LRIRL outperforms baselines in both reasoning accuracy and rule mining efficiency on public datasets. Compared to the best baseline, LRIRL can achieve an average accuracy improvement of 3.43% in MRR, 1.40% in HITS@1, and 1.80% in HITS@10. Moreover, LRIRL can reduce rule mining time by an average of approximately 35% compared to the best baseline.

关键词： Representation learning Accuracy Knowledge graphs Cognition Vectors Logic Computational complexity Artificial intelligence

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GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

arXiv

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

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A CPK-Based Identity Authentication Scheme for IoT

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Computer Systems Science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams 30

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

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30th International Symposium on Semiconductor Manufacturing, ISSM 2024

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Ibaraki Tsukuba305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Nara Ikoma630-0192 Japan Tokyo Electron Technology Solutions Ltd. S-Technology Development Center Yamanashi Nirasaki407-0192 Japan Tokyo Electron Technology Solutions Ltd. Development Department 1 Yamanashi Nirasaki407-0192 Japan Tokyo Electron Ltd. Digital Design Center Hokkaido Sapporo060-0003 Japan Tokyo Electron Technology Solutions Ltd. Software and Data Engineering Division Yamanashi Nirasaki407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Ibaraki Tsukuba305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Ibaraki Tsukuba305-8568 Japan

ISBN: (纸本)9798331510589

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO2 layers were studied by monoenergetic positron beams. The HfO2 layers with 430 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (VHf) coupled with oxygen vacancies (VOs) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2024 IEEE.

关键词： Positron annihilation

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