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Semantic Template-based Convolutional Neural Network for Text Classification

ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Data-driven modeling of dislocation mobility from atomistics using physicsinformed machine learning

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npj Computational Materials 2024年第1期10卷 937-948页

作者： Yifeng Tian Soumendu Bagchi Liam Myhill GiacomoPo Enrique Martinez Yen Ting Lin Nithin Mathew Danny Perez Information Sciences Group ComputerComputational and Statistical Sciences Division(CCS-3)Los Alamos National LaboratoryLos Alamos87545 NMUSA Physics and Chemistry of Materials Theoretical Division(T-1)Los Alamos National LaboratoryLos Alamos87545 NMUSA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeUSA Department of Materials Science and Engineering Clemson UniversityClemson29623 SCUSA Department of Mechanical and Aerospace Engineering University of MiamiMiami33146 FLUSA

Dislocationmobility,which dictates the response of dislocations to an applied stress,is a fundamental property of crystalline materials that governs the evolution of plastic *** approaches for deriving mobility laws rely on phenomenological models of the underlying physics,whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and *** tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress,temperature,and local environment,such as body-centered cubic crystals(BCC)metals and *** this paper,we present a novel,uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations,using Graph Neural Networks(GNN)with a physics-informed *** demonstrate that this Physics-informed Graph Neural Network(PI-GNN)framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

关键词： stress dislocation consuming

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Interval Markov Decision Processes with Continuous Action-Spaces 26

Interval Markov Decision Processes with Continuous Action-Sp...

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26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Delimpaltadakis, Giannis Lahijanian, Morteza Mazo, Manuel Laurenti, Luca Control Systems Technology Group Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Aerospace Engineering Sciences Computer Science University of Colorado Boulder BoulderCO United States Delft Center for Systems Control Mechanical Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400700330

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |Q| max problems, where |Q| is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set A is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of A, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis. © 2023 Copyright held by the owner/author(s).

关键词： Markov processes

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Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

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Blockchain-based indoor location paging and answering service with truncated-geo-indistinguishability

IET Blockchain

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IET Blockchain 2021年第2-4期1卷 105-117页

作者： Yang, Changxin Ju, Xuan Liu, Erwu Geng, Yuanzhe Wang, Rui Shanghai Engineering Research Center for Blockchain Applications and Services Shanghai China College of Electronics and Information Engineering Tongji University Shanghai China Computer Science Tongji University Shanghai China

Indoor location-based services have numerous applications built on indoor navigation. Over the years, researchers have proposed various indoor navigation methods to find a certain point of interest. This paper proposes location paging and answering service (LPAS) for finding out someone in an indoor environment. However, it may face serious problems of privacy disclosure of the clients' indoor location data. To ensure security, privacy and convenience, the authors leverage blockchain to model the indoor environment, and build blockchain-based indoor LPAS (BILPAS) on blockchain by embedding our three-way-handshake Diffie–Hellman Key Agreement procedures. Our BILPAS can automatically establish a secure and privacy communication tunnel between any two users, thus greatly liberating participants from unnecessary self-determining interactions. On the premise that the certain person can also be found, the authors introduce Truncated-Geo-Indistinguishability (TGeoI) to perturb the actual location within a limited, valid, user-defined distance, thereby allowing users to obtain reasonable location privacy. The authors have put a strong emphasis in the formal treatment of the reasonable privacy guarantees, giving a rigorous definition of TGeoI and providing a comparison with Geo-Indistinguishability that the authors' method satisfies such property. Then, the authors present the BILPAS prototype and implement it on Hyperledger Fabric. Finally, the authors conduct the security and privacy analysis and evaluate the time performance. The experiment results validate the reasonable privacy, high security and convenience of the authors' framework. © 2021 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Blockchain

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The Immense Impact of Reverse Edges on Large Hierarchical Networks

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engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges Information flows Complex networks

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Lumbar spine localisation method based on feature fusion

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 931-945页

作者： Yonghong Zhang Ning Hu Zhuofu Li Xuquan Ji Shanshan Liu Youyang Sha Xiongkang Song Jian Zhang Lei Hu Weishi Li Robotics Institute School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina Beijing Zhuzheng Robot Co. LTDBeijingChina Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeKnoxvilleTennesseeUSA Department of Orthopaedics Peking University Third HospitalBeijingChina Engineering Research Center of Bone and Joint Precision Medicine Ministry of EducationBeijingChina Beijing Key Laboratory of Spinal Disease Research BeijingChina School of Biological Science and Medical Engineering Beihang UniversityBeijingChina Department of Computer Science University of WarwickCoventryUK

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is ***,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft *** the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent *** 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning *** an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is *** addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s ***,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

关键词： CT image lumbar spatial orientation multi‐scale information fusion

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A mesh optimization method using machine learning technique and variational mesh adaptation

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Chinese Journal of Aeronautics 2022年第3期35卷 27-41页

作者： Tingfan WU Xuejun LIU Wei AN Zenghui HUANG Hongqiang LYU MIITKey Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical *** light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational *** the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD ***,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is *** regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression *** the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area *** examples are first used to verify the feasibility and effectiveness of the proposed *** then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our *** show that the proposed method significantly improves the accuracy of the local flow features on the adaptive *** work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

关键词： CFD Flow field Machine learning Moving mesh method Regression models Variational mesh adaptation

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A Metaverse-based Student's Spatiotemporal Digital Profile for Representing Learning Situation 8

A Metaverse-based Student's Spatiotemporal Digital Profile f...

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8th International Conference of the Immersive Learning Research Network, iLRN 2022

作者： Liu, Fang Zhang, Yifan Zhao, Liang Dai, Qin Liu, Xiaona Shi, Xiangbin Shenyang Aerospace University School of Computer Science Shenyang China College of Information Shenyang Institute of Engineering Shenyang China Innovation Promotion Center Yantai Science and Technology Yantai China

ISBN: (纸本)9781734899528

In recent years, the metaverse becomes a promising method to provide an intelligent teaching platform for the teaching-learning process. Existing teaching evaluation methods rely on the exam results and the educator's teaching experience, which is hard to reflect the detailed teaching outcomes and each student's learning situation. Therefore, this paper proposes a four-layer metaverse architecture to build students' virtual entities of learning situations, containing a data acquisition layer, a technology layer, a model building layer, and an application layer. Furthermore, the virtual entities are constructed on the event logs recorded in the Learning Management System (LMS) and visualized as spatiotemporal digital profiles for students. In the spatial dimension, the profile can reflect a student's learning situation in different aspects, including the completion of an assignment, the mastery of knowledge, the practical ability, etc. In the temporal dimension, it can reflect a student's learning situation at different learning stages. Students' practical abilities are obtained by the Machine Learning method GBDT (Gradient Boosting Decision Tree), and other dimensions of the profile are generated by the Knowledge Graph technology. With these profiles, educators can do teaching intervention, teaching evaluation, and personalized cultivation, providing a new path for intelligent teaching. We take the CG (Course Grading) platform and Data Structure and Algorithm course as examples to validate our model strategy. The experimental results show that the spatiotemporal digital profiles can better describe students' learning situations, providing data support for teaching evaluation. © 2022 Immersive Learning Research Network.

关键词： Students

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Optimisation of alloy composition for highly-formable magnesium sheet

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International Journal of Minerals,Metallurgy and Materials 2022年第7期29卷 1388-1395页

作者： Zhuoran Zeng Mingzhe Bian Shiwei Xu Weineng Tang Chris Davies Nick Birbilis Jianfeng Nie College of Engineering and Computer Science Australian National UniversityACT.2601Australia Department of Materials Science and Engineering Monash UniversityVIC 3800Australia State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082China Research Institute(R&D Center) Baosteel Group CorporationShanghai 201999China Department of Mechanical and Aerospace Engineering Monash UniversityVIC 3800Australia

The effectiveness of Ca or Gd addition on ductility and formability of Mg-Zn-Zr based dilute alloys in deep drawing has not been systematically compared *** this study,formable Mg-Zn-Gd-Zr and Mg-Zn-Ca-Zr sheet alloys are produced by hot *** sheets have similarly weakened basal texture,but the sheet of the Mg-Zn-Gd-Zr alloys has higher ductility and formability than that of Mg-Zn-Ca-Zr *** combined addition of 0.2wt%Ca and 0.4wt%Gd to the Mg-1Zn-0.5Zr(wt%)alloy leads to a Mg-1Zn-0.4Gd-0.2Ca-0.5Zr alloy that has even better ductility,and its formability during deep drawing is comparable to the benchmark Al6016 *** increase in Ca content from 0.2wt%to 0.5wt%leads to decreased sheet ductility and formability,predominantly due to grain boundary embrittlement.

关键词： magnesium sheet alloy texture microstructure formability solute segregation

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