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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shu, Honglin Gao, Pei Yang, Ziwei Li, Chen Wu, Man Hong Kong Polytechnic University Department of Computing Hong Kong Nara Institute of Science and Technology Department of Information Science Nara Japan Kyushu Institute of Technology Department of Bioscience and Bioinformatics Iizuka Japan Keio University Deparment of Information and Computer Science Tokyo Japan

ISBN: (纸本)9798350319934

Professional question answering communities, such as Stack Overflow, are becoming a significant aspect of many intellectual endeavors. As a result, developing a strategy for swiftly locating relevant questions and answers can effectively assist experts in issue solving. Various detection models have been presented to address the problem of question-relatedness prediction, but all are sub-optimal since they cannot effectively capture the long-distance dependency of a long sequence. With the invention of the self-attention mechanism and transformer, a better approach to dealing with question-relatedness prediction has just emerged. The primary objective of this study is to investigate the feasibility of the transformer-based model for question-relatedness problems. We turn the question relatedness problem into a text classification problem and introduce a representative transformer-based model, namely Bidirectional Encoder Representation from Transformers (BERT), to deal with text classification. In our experiment, we show that BERT outperforms both SoftSVM and BiDotLSTM, implying that the transformer-based model has considerable potential to address the challenge in the question-relatedness problem. Furthermore, since we can re-formulate text classification problem into a link prediction problem, we discuss the possibility of incorporating the transformer-based approach and graph representation learning. © 2022 IEEE.

关键词： Forecasting

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Contact Tracing over Uncertain Indoor Positioning Data (Extended Abstract)

Contact Tracing over Uncertain Indoor Positioning Data (Exte...

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International Conference on Data Engineering

作者： Tiantian Liu Huan Li Hua Lu Muhammad Aamir Cheema Harry Kai-Ho Chan Department of Computer Science Aalborg University Denmark College of Computer Science and Technology Zhejiang University China Deparment of People and Technology Roskilde University Denmark Faculty of Information Technology Monash University Australia Information School University of Sheffield United Kingdom

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object $o$ , e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with $o$ for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals.

关键词： COVID-19 Pandemics Query processing Contact tracing Data engineering Data models Proposals

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Khan, Ijaz Zabil, Mohammed Hazli Mohamed Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparment of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. © 2023 IEEE.

关键词： Students

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Recognition of Secure Data Transmission in Cloud Platform using Deep Learning

Recognition of Secure Data Transmission in Cloud Platform us...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： R Bhavya M. Guru Vimal Kumar U.M. Ramya R Janagi M. Ganesan S. Deepa Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Chennai India Deparment of Information Technology Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Deparment of Information Technology Hindusthan College of Engineering and Technology Coimbatore India Department of Computer Technology-PG Kongu Engineering College Perundurai Erode India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission.

关键词： Deep learning Accuracy Decision making Organizations Feature extraction Robustness Safety

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Distributed Multi-agent Coordination over Cellular Sheaves

arXiv

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arXiv 2025年

作者： Hanks, Tyler Riess, Hans Cohen, Samuel Gross, Trevor Hale, Matthew Fairbanks, James Department of Computer and Information Science and Engineering United States Department of Mechanical and Aerospace Engineering University of Florida United States Deparment of Electrical and Computer Engineering Georgia Institute of Technology United States

Techniques for coordination of multi-agent systems are vast and varied, often utilizing purpose-built solvers or controllers with tight coupling to the types of systems involved or the coordination goal. In this paper, we introduce a general unified framework for heterogeneous multi-agent coordination using the language of cellular sheaves and nonlinear sheaf Laplacians, which are generalizations of graphs and graph Laplacians. Specifically, we introduce the concept of a nonlinear homological program encompassing a choice of cellular sheaf on an undirected graph, nonlinear edge potential functions, and constrained convex node objectives, which constitutes a standard form for a wide class of coordination problems. We use the alternating direction method of multipliers to derive a distributed optimization algorithm for solving these nonlinear homological programs. To demonstrate the applicability of this framework, we show how heterogeneous coordination goals including combinations of consensus, formation, and flocking can be formulated as nonlinear homological programs and provide numerical simulations showing the efficacy of our distributed solution *** Codes 93A16, 93B45, 55N30 © 2025, CC BY.

关键词： Laplace transforms

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Development of Virtual Reality Platform through Human computer Interaction using Artificial Intelligence

Development of Virtual Reality Platform through Human Comput...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： S Saranya B Channarayapriya U Harshavardhini A.Sunitha Nandhini J. Revathi R Venkatesan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Deparment of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Chennai India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Mathematics Kongu Engineering College Erode India Deparment of Information Technology Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments.

关键词： Human computer interaction Deep learning Technological innovation Heuristic algorithms Virtual environments Collaboration Real-time systems

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Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning

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computers, Materials & Continua 2021年第11期69卷 2217-2230页

作者： Muzamil Ahmed Muhammad Ramzan Hikmat Ullah Khan Saqib Iqbal Muhammad Attique Khan Jung-In Choi Yunyoung Nam Seifedine Kadry Deparment of Computer Science and Information Technology The University of LahoreSargodha CampusSargodha40100Pakistan Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan School of System and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science and Information Technology University of SargodhaSargodha40100Pakistan College of Engineering Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Science HITEC University TaxilaTaxilaPakistan Applied Artificial Intelligence Ajou UniversitySuwonKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab UniversityLebanon

Violence recognition is crucial because of its applications in activities related to security and law *** semi-automated systems have issues such as tedious manual surveillances,which causes human errors and makes these systems less *** approaches have been proposed using trajectory-based,non-object-centric,and deep-learning-based *** studies have shown that deep learning techniques attain higher accuracy and lower error rates than those of other ***,the their performance must be *** study explores the state-of-the-art deep learning architecture of convolutional neural networks(CNNs)and inception V4 to detect and recognize violence using video *** the proposed framework,the keyframe extraction technique eliminates duplicate consecutive *** keyframing phase reduces the training data size and hence decreases the computational cost by avoiding duplicate *** feature selection and classification tasks,the applied sequential CNN uses one kernel size,whereas the inception v4 CNN uses multiple kernels for different layers of the *** empirical analysis,four widely used standard datasets are used with diverse *** results confirm that the proposed approach attains 98%accuracy,reduces the computational cost,and outperforms the existing techniques of violence detection and recognition.

关键词： Violence detection violence recognition deep learning convolutional neural network inception v4 keyframe extraction

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Dual-image reversible data hiding based on encoding the numeral system of concealed information

Dual-image reversible data hiding based on encoding the nume...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Tzu Chuen Lu Thanh Nhan Vo Biswapati Jana Deparment of Information Managerment Chaoyang University of Technology Taichung Taiwan R.O.C Department of Computer Science Vidyasagar University Midnapore India

The dual-image reversible data hiding technique is dominant in terms of security and embedding capacity. Because the secret data is encoded and then embedded into two cover images instead of a single image. In this paper, the proposed method based on the dual-image RDH scheme utilizes a transferring numeral system to enhance the embedding rate. The secret data is encoded and converted to 2-digit base-9 numbers, with the encoding values hidden in two cover images. The results of the experiments reveal that the strategy achieves considerable results in terms of visual quality and payload. This method provides several options in the application.

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The Novel Protocol Uses Low Computational Complexity to Authenticate Edge Devices on Smart Grid Systems

The Novel Protocol Uses Low Computational Complexity to Auth...

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India International Conference on Power Electronics (IICPE)

作者： Prakash Kuppuswamy Sayed QY Al Khalidi Purushottam Kumar Ahmed Ali Shaik Meeran Sonia Vijaya Varshini Computer Science Engineering SRM University Delhi-NCR Haryana India Department of Information Technology King Khalid University Abha KSA Department of Information Technology Jazan University Jazan KSA Computer Science Deparment Bharathiyar University Coimbatore Tamil Nadu India

Grid computing has emerged as a powerful paradigm for harnessing distributed computing resources to solve complex problems. In grid computing, various devices across different administrative domains collaborate to provide computational power and storage capacity. Sensor agent devices have become the backbone of modern technological systems such as Internet of Things (IoT), smart homes, industrial automation, and autonomous vehicles. These devices collect and process real-time data from the surrounding environment, providing crucial insights for decision-making, monitoring, and control purposes. However, the performance of these devices depends on space complexity and time complexity. Achieving optimum performance in sensor agent devices requires a delicate balance between space complexity and time complexity. Reducing one complexity may increase the other, and vice versa. Light weight authentication device-based algorithms play a vital role in verifying the identity of devices and ensuring secure communication within the grid and that needs to satisfy complexity. These protocols verify the identity of devices and users, ensuring that only authorized entities can access and interact with the data. In this article, we will explore a new light weight authentication protocol based on a block cipher i.e ELBC (Extended Linear Block cipher algorithm). The new authentication protocols are easy to implement and deploy in any kind of environment. The algorithm divides the authentication message into blocks and encrypts them using matrix multiplication. By generating an invertible key matrix and applying encryption and decryption equations, the proposed algorithm ensures secure communication.

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MADCR:Mobility Aware Dynamic Clustering-Based Routing Protocol in Internet of Vehicles

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China Communications 2021年第7期18卷 69-85页

作者： Sankar Sennan Somula Ramasubbareddy Sathiyabhama Balasubramaniyam Anand Nayyar Chaker Abdelaziz Kerrache Muhammad Bilal Deparment of of Computer Science and Engineering Sona College of TechnologySalem 636005India Deparment of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabad 500090India. Graduate School Duy Tan UniversityDa Nang 550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang 550000Vietnam Laboratoire d’Informatique et de Mathématiques UniversitéAmar Telidji de LaghouatLaghouatAlgeria. Department of Computer Engineering Hankuk University of Foreign Studies17035Yongin-SiSouth Korea

Internet of Vehicles(IoV)is an evolution of the Internet of Things(IoT)to improve the capabilities of vehicular ad-hoc networks(VANETs)in intelligence transport *** network topology in IoV paradigm is highly *** is one of the promising solutions to maintain the route stability in the dynamic ***,existing algorithms consume a considerable amount of time in the cluster head(CH)selection ***,this study proposes a mobility aware dynamic clustering-based routing(MADCR)protocol in IoV to maximize the lifespan of networks and reduce the end-to-end delay of *** MADCR protocol consists of cluster formation and CH selection processes.A cluster is formed on the basis of Euclidean *** CH is then chosen using the mayfly optimization algorithm(MOA).The CH subsequently receives vehicle data and forwards such data to the Road Side Unit(RSU).The performance of the MADCR protocol is compared with that ofAnt Colony Optimization(ACO),Comprehensive Learning Particle Swarm Optimization(CLPSO),and Clustering Algorithm for Internet of Vehicles based on Dragonfly Optimizer(CAVDO).The proposed MADCR protocol decreases the end-toend delay by 5–80 ms and increases the packet delivery ratio by 5%–15%.

关键词： clustering protocol Internet of things Internet of vehicles optimization algorithm Mayfly algorithm

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