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8th International Conference on Cyber Security and information Engineering, ICCSIE 2023

作者： Wen, Siyao Li, Dandan Yuan, Junlong Huang, Jingbin Zhou, Long School of Computer and Information Engineering Hubei Normal University Huangshi China National Engineering Research Center of Educational Big Data Application Technology Central China Normal University Wuhan China

ISBN: (纸本)9798400708800

With the rise in popularity of multiplayer online games, efficient game network communication has become increasingly vital. This research paper presents a comprehensive design and implementation of a network session plug-in, focusing on framework construction, data module integration, intermediate module development, and plug-in functionality testing. The primary objective of the plug-in is to enhance the convenience and efficiency of game network communication. Extensive testing, including performance evaluation, stress testing, and functional testing, has been conducted to validate the effectiveness and feasibility of the plug-in. The results showcase the successful development of a robust network session plug-in for multiplayer online games, offering valuable insights for future plug-in advancements. This research contributes significantly to the enhancement of game network technology, ultimately improving the overall player experience in multiplayer online gaming environments. © 2023 ACM.

关键词： Integration testing

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Frontiers in Artificial Intelligence and applications 3

Frontiers in Artificial Intelligence and Applications

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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Yadav, Jaya Shukla, Shruti Sharma, Kanika Soni, Nupur Agarwal, Shruti Pathak, Prabhash Chandra Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Mangalmay Institute of Management and Technology Department of Computer Application Greater Noida India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world's industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including Machine Learning (ML), Deep Learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence. © 2022 IEEE.

关键词： Genetic algorithms

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Graph Neural Network for Metapath Aggregation based on Neighborhood Similarity

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第2期8卷 478-490页

作者： Wang, Dong-Yang Lin, Kai-Biao Wu, Zhi-Rong Lu, Jiang-Tao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China Xiamen Jingpei Software engineering Co. LTD Xiamen361008 China

Most graphs or networks in the real world are inherently heterogeneous and involve different types of nodes and relationships. Typically, existing models define multiple metapaths in the heterogeneous graph to capture complex semantic relationships and guide neighbor selection. However, such models ignore node characteristics and do not consider information about intermediate nodes. Alternatively, in such models, the target node is assimilated by neighboring nodes or only information about a metapath is considered. Thus, in this paper, we proposed the neighborhood similarity-based metapath aggregation graph neural network (MANS-GNN) model. The proposed MANS-GNN first uses node mapping for the input of node features. Second, to avoid embedding and exces-sive assimilation between different node types, a similarity-aware neighborhood selector based on reinforcement learning is employed to select the most similar neighborhood for the target node. Then, a local aggregation module is employed to merge intermediate semantic nodes. Finally, global aggregation is employed to merge information from in-dividual metapaths. The proposed MANS-GNN model was evaluated experimentally in node classification and node clustering tasks on two heterogeneous graph datasets. Experimental results demonstrated that the proposed MANS-GNN model obtains more accurate predictions than the baseline model. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Reinforcement learning

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Personalization Exercise Recommendation Based on Cognitive Diagnosis 22

Personalization Exercise Recommendation Based on Cognitive D...

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6th International Conference on computer Science and application Engineering, CSAE 2022

作者： Du, Hongle Li, Na Ma, Famin Palaoag, Thelma College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines School of Mathematics and Computer Application Shangluo University Shaanxi Province Shangluo China

ISBN: (纸本)9781450396004

Online education has become an important part of education services. Faced with the vast online learning resources, how to provide students with personalized learning resources and achieve the goal of precise teaching is an important part of the intelligent teaching system. The premise of personalized recommendation is accurate personalized cognitive diagnosis and accurate analysis of each student’s mastery of knowledge. Since cognitive diagnosis can diagnose and evaluate individual knowledge structure or cognitive process, combined with knowledge space theory, this paper designs a personalized exercise recommendation method based on cognitive diagnosis. The sequential dependency between course knowledge points has an impact on personalized exercise recommendation. Therefore, firstly, according to the knowledge space theory, the relationship graph between the knowledge concepts of the course is constructed, that is, the course knowledge topology map;Students conduct cognitive diagnosis and analyze the knowledge mastery status of each student;finally, the knowledge structure of each student is visualized on the knowledge map, and personalized test questions are recommended based on students’ knowledge structure, question difficulty and course knowledge map. While improving the accuracy and reliability of the recommended test items, the interpretability of test item recommendation is guaranteed. The experimental comparison proves the accuracy and interpretability of the method in test item recommendation. © 2022 Association for Computing Machinery.

关键词： Students

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Iterative Equalization of CPM with Unitary Approximate Message Passing

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9343-9356页

作者： Liu, Zilong Song, Yi Guo, Qinghua Sun, Peng Gong, Kexian Wang, Zhongyong Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan Zhengzhou450001 China Henan University of Technology Henan Engineering Research Center of Grain Condition Intelligent Detection and Application Henan Zhengzhou450001 China Henan University of Technology College of Science and Engineering Henan Zhengzhou450001 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia

Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection in frequency selective fading channels. This paper proposes an iterative receiver tailored for the detection of CPM signals over frequency selective fading channels. This design leverages the factor graph framework to integrate equalization, demodulation, and decoding functions. The equalizer employs the unitary approximate message passing (UAMP) algorithm, while the unitary transformation is implemented using the fast Fourier transform (FFT) with the aid of a cyclic prefix (CP), thereby achieving low computational complexity while with high performance. For CPM demodulation and channel decoding, with belief propagation (BP), we design a message passing-based maximum a posteriori (MAP) algorithm, and the message exchange between the demodulator, decoder and equalizer is elaborated. With proper message passing schedules, the receiver can achieve fast convergence. Simulation results show that compared with existing turbo receivers, the proposed receiver delivers significant performance enhancement with low computational complexity. © 1967-2012 IEEE.

关键词： Demodulation

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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Big Data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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Security Challenges and Future Privacy Perspectives in Cloud Computing for Smart City Environments

Security Challenges and Future Privacy Perspectives in Cloud...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Tayal, Ankit Bhatia, Gaurav Singh, Jay Bansal, Mohit Anitha, G. Gl Bajaj Institute of Technology & Management Electrical Engineering Department Gr. Noida India Gl Bajaj Institute of Technology & Management Computer Application Department Gr. Noida India Institute of Aeronautical 5Engineering Department of Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798350357769

The context of smart cities and cloud computing is essential to understanding the broader landscape in which security challenges and privacy perspectives play pivotal roles in enhancing the quality of life for the residents in smart cities. Smart cities use information and communication technologies (ICT) to optimize resource efficiency. They leverage data, sensors, and connectivity to improve various aspects of urban living, including transportation, energy usage, public services, and sustainability. Cloud computing is an integral part of the infrastructure of smart cities that enables the collection, storage, analysis, and sharing of data and services. This research paper indicates many factors and challenges in the development of cloud-enabled smart city areas and provides potential recommendations. A guideline for future studies on this research topic has also been provided to serve the comparative analysis of the various findings and results. © 2023 IEEE.

关键词： Cloud computing Cyber Security Data computing Internet of Things (IoT) Smart City

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Peng, Wei Ping, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350386226

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM © 2024 IEEE.

关键词： Contrastive Learning

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Secure Data Transmission Based on Artificial Intelligence Using IoT in Security Systems 5

Secure Data Transmission Based on Artificial Intelligence Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Senthil Kumar, S. Maruthaveni, R. Jayanthi, N. Yadav, Ajay Kumar Kalaiarasan, T.R. Sri Ramakrishna Mission Vidyalaya College of Arts and Science Department of Information Technology Coimbatore India Dr.S N S Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore India Deemed to Be University Periyar Maniammai Institute of Science and Technology Department of Commerce Thanjavur India United Institute of Management Department of Computer Application U.P. Prayagraj India Sri Krishna College of Engineering and Technology Dept of Information Technology Coimbatore India

ISBN: (纸本)9798350383522

The Internet of Things (IoT), a rapidly expanding and constant technology today, enables the efficient exchange of information and control of electronic equipment. As technology grows more and more integrated into our daily lives, IoT serves as a bridge between human intelligence and artificial intelligence. Although the Internet of Things (IoT) presents numerous benefits for efficiency and creativity, it also poses serious privacy and security concerns. The main topic of the research is the enormous challenges in protecting IoT environments caused by the sheer number and variety of connected devices. This study's main goal is to look into how machine learning methods may be used to lessen the ongoing security vulnerabilities in IoT environments. We intend to create more resilient and intelligent security systems that can adapt to the changing threat environment by utilising machine learning's capabilities. The proposed study provides a thorough examination of the various elements and traits that make up IoT systems, with a focus on the current security measures. By taking a look at these elements, we may determine which strategies and techniques stand the highest chance of improving IoT security. This makes it possible for IoT deployments to be safer and more dependable in a technological setting that is quickly changing. © 2024 IEEE.

关键词： Camera Drowsiness Eye Face detection Hardware device Opencv Python

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