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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Alsharef, Ahmad Sonia Nassour, Hasan Sharma, Jitender Yogananda School of AI Computer and Data Science Shoolini University Solan India School of Management Sciences and Liberal Arts Shoolini University Solan India

ISBN: (纸本)9789380544472

With many online recruitment portals requesting job applicants to upload their resumes, the automated process of screening and shortlisting candidates can accelerate selection and decision-making. This study explores the use of text similarity measures as an alternative to experienced human hiring managers in processing resumes. Three text similarity measures: Cosine, Sqrt-Cosine, and Improved Sqrt-Cosine (ISC) similarity were utilized as computer programs in scanning resumes for the recruitment of a business development manager and a software engineer. The decisions of the algorithms were compared to those of an expert hiring manager within the same scenarios. The findings indicate that ISC and Sqrt-Cosine were closer to the expert-human decision than Cosine similarity. These specific text-similarity algorithms can also make acceptable decisions even when recruiting for high-level positions and can do so in seconds when executed as a program on a normal CPU processor. This study suggests that these algorithms can efficiently facilitate the process of decision-making in recruitment and shortlisting candidates and can be an effective alternative to expert hiring managers. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Decision making

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Global and Local Mixer for Micro-Expression Recognition 6

Global and Local Mixer for Micro-Expression Recognition

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Tang, Huaying Zhang, Xiaorong Mao, Xinglong Liu, Shifeng Zhao, Sirui Chen, Enhong University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology Hefei China Southwest University of Science and Technology School of Computer Science and Technology Mianyang China University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence School of Data Science Hefei China University of Science and Technology of China & Southwest University of Science and Technology School of Computer Science and Technology Hefei Mianyang China

ISBN: (纸本)9798350325485

Micro-expressions (MEs) refer to spontaneous, subtle, and fleeting facial movements that usually only occur in local facial areas, which can effectively reveal genuine emotions that humans try to hide. However, micro-expression recognition (MER) is still a difficult task due to its low intensity and local movements, and existing datasets are usually small-scale with class imbalance problem. To address these problems, we propose a novel MER framework named Global and Local Mixer (GL-Mixer) based on MLP-Mixer, which can focus on local regions where MEs appear and reduce the influence of ME-irrelevant regions. Specifically, the GL-Mixer mainly consists of three parts: Global Feature Learning (GFL) module, Local Feature Learning (LFL) module, and Feature Refinement (FR) module. The GFL module is designed to learn the global ME features from the whole face region. Meanwhile, the LFL module is composed of multiple parallel local mixers, and each local mixer extracts fine-grained features in a specific facial region. Then, Feature Refinement (FR) module aims to refine the fin e-grained ME features by focusing more on the local features of the effective ME regions and suppressing the features of the irrelevant region under the guidance of the global feature. Last, the model fuses the global and fine-grained ME features for classification. In addition, to solve the problem of insufficient data and class imbalance in ME datasets, we introduce class-balanced implicit semantic data augmentation. Extensive experiments are conducted on CASMEII, SAMM, and SMIC datasets, and the experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Semantics

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Improvement of semantic segmentation algorithm based on ResNeSt network

Improvement of semantic segmentation algorithm based on ResN...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Mian, Jia Yaping, Wan Wei, Yan Jianbin, Xie Hua, Zhong Yue, Wang University of South China School of Computer Hengyang China National University of Defense Technology School of Electronic Science Changsha China North University of China School of Data Science and Technology Taiyuan China

ISBN: (纸本)9798350399028

This paper proposes an improved semantic segmentation algorithm based on the split attention network resnest. The original icnet semantic segmentation model has some defects in the segmentation of foreground objects. In view of these problems, we optimized the icnet model with resnest network and CBAM convolution attention module. On the one hand, we replace the backbone network in the original model with resnest network, which enhances the feature extraction capability of the model. On the other hand, we introduce the CBAM convolution attention module to improve the segmentation ability of the model to the target in the foreground region. Without affecting the inference rate of the original model, the training effect of the improved idea proposed in this paper on the cityscapes open data set is improved separately from 71.0% to 71.3% and 71.9%. Respectively compared with the MIoU of the baseline model, the MIoU of the icnet model combined with the resnest backbone network and the CBAM convolution attention module on the cityscapes open data set is increased from 71.0% to 72.1%, and the segmentation effect of the foreground area target is significantly optimized in the visualization images, which has certain positive significance for the application of the semantic segmentation algorithm in the field of unmanned driving. © 2023 IEEE.

关键词： Semantic Segmentation

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Decentralized Metaverse: Towards a Secure, Autonomous, and Inclusive Virtual World

Decentralized Metaverse: Towards a Secure, Autonomous, and I...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Alsamhi, Saeed Hamood Hawbani, Ammar Kumar, Santosh Porwol, Lukasz Curry, Edward Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Computer Science and Engineering Iiit Naya Raipur Chhattisgarh India

ISBN: (纸本)9798350327816

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration. © 2023 IEEE.

关键词： Blockchain Cyber-Physical-Social Systems Decentralization Metaverse Situation Awareness

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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Recognition of data Breach Method from Story Using Advanced Deep Learning 3

Recognition of Data Breach Method from Story Using Advanced ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jeba, Samiha Maisha Aurpa, Tanjim Taharat Alom, Md Samiul Joy, Md. Wadud Mahmud Ahamed, Asm Shakil Dash, Satya Ranjan Ahmed, Md Shoaib International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Bangladesh Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science Bangladesh Kiit University School of Computer Applications Odisha India Boise State University Department of Computer Science BoiseID United States

ISBN: (纸本)9798350381931

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people's privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%. © 2024 IEEE.

关键词： Deep learning

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Guest Editorial: Special Issue on Evolutionary Computation for Games

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IEEE Transactions on Games 2023年第1期15卷 1-4页

作者： Schrum, Jacob Liu, Jialin Browne, Cameron Ekart, Aniko Gallagher, Marcus Southwestern University Department of Mathematics and Computer Science GeorgetownTX United States Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Aston University Department of Computer Science United Kingdom The University of Queensland School of Information Technology and Electrical Engineering Australia

The eight papers in this special section focus on applications of evolutionary computation to games to demonstrate several ways in which evolution can push boundaries and explore new areas of what is possible in the realm of games research, with a focus on game-playing, automatic agent parameter tuning, automatic game testing, and procedural content generation.

关键词： Special issues and sections Evolutionary computation Games

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Knowledge-Concept Diagnosis from fMRIs by Using a Space-Time Embedding Graph Convolutional Network 1

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Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Lei, Ye Zhang, Yupei Lin, Yi Shang, Xuequn School of Computer Science Northwestern Polytechnical University Xi’an China MIIT Big Data Storage and Management Lab Xi’an China

ISBN: (数字)9789819962228

ISBN: (纸本)9789819962211

Diagnosing the contents of learning in brain activities is a long-standing research task in cognitive sciences. The current studies on cognitive diagnosis (CD) in education determine the status of knowledge concept (KC) based on the observed responses to test items. However, the learning process of KC in the brain is left with no touch. This paper proposes to solve the problem of knowledge-concept diagnosis (KCD) from fMRIs by identifying the concepts a student focuses on in learning activities. Using the graph convolutional network (GCN), we introduce the STEGCN approach composed of a spatial GCN for brain-graph structure, a temporal GCN for brain-activity sequence, and a fully connected network for KCD. To evaluate STEGCN, we acquired an fMRI dataset that was collected on five concepts when students were learning a computer course. The experiment results demonstrate that our proposed method yields better performance than traditional models, showing the effectiveness of STEGCN in concept classification. This study contributes to a new fMRI-based route for knowledge-concept diagnosis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

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computers, Materials & Continua 2023年第4期75卷 2101-2117页

作者： Yanping Chen Xuyang Bai Xiaomin Jin Zhongmin Wang Fengwei Wang Li Ling School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an710121China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’anShaanxi710121China ZTE Corporation Shenzhen51805China

Deploying task caching at edge servers has become an effectiveway to handle compute-intensive and latency-sensitive tasks on the industrialinternet. However, how to select the task scheduling location to reduce taskdelay and cost while ensuring the data security and reliable communicationof edge computing remains a challenge. To solve this problem, this paperestablishes a task scheduling model with joint blockchain and task cachingin the industrial internet and designs a novel blockchain-assisted cachingmechanism to enhance system security. In this paper, the task schedulingproblem, which couples the task scheduling decision, task caching decision,and blockchain reward, is formulated as the minimum weighted cost problemunder delay constraints. This is a mixed integer nonlinear problem, which isproved to be nonconvex and NP-hard. To solve the optimal solution, thispaper proposes a task scheduling strategy algorithm based on an improvedgenetic algorithm (IGA-TSPA) by improving the genetic algorithm initializationand mutation operations to reduce the size of the initial solutionspace and enhance the optimal solution convergence speed. In addition,an Improved Least Frequently Used algorithm is proposed to improve thecontent hit rate. Simulation results show that IGA-TSPA has a faster optimalsolution-solving ability and shorter running time compared with the existingedge computing scheduling algorithms. The established task scheduling modelnot only saves 62.19% of system overhead consumption in comparison withlocal computing but also has great significance in protecting data security,reducing task processing delay, and reducing system cost.

关键词： Edge computing task scheduling blockchain task caching industrial security

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