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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ali, Muhammad Cheema, Sehrish Munawar Aslam, Zaheer Naz, Ammerha Ayub, Nasir The Superior University Lahore Department of Software Engineering Lahore Pakistan University of Management and Technology Department of Computer Science Sialkot Pakistan University of Sialkot Department of Computer Science Sialkot Pakistan

ISBN: (纸本)9798350335316

Distributed agile development comes with a lot of challenges in particular as it has to do with agile teams working together from different geological locations on the same project. Most probably it happens due to a lack of visibility in the complex development and deployment process, poor communication, and unavailability of the development team and corresponding customer in the same place. These factors affect the performance of the team and increase the overall cost of development. To mitigate all these aspects, we proposed a cloud computing-based Infrastructure which is a combination of both agile as well as cloud computing technology named 'CBAI'. The proposed Infrastructure assists the team members to work efficiently even if they are different geo-locations without burdening the cost. It provides the basic structure for global agile development and is also efficient in reducing the technical liability, and the need for project backlog. © 2023 IEEE.

关键词： Cloud computing

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Robust Car-Following Control of Connected and Autonomous Vehicles: A Stochastic Model Predictive Control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive Control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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Pedestrian Physical Education Training Over Visualization Tool

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computers, Materials & Continua 2022年第11期73卷 2389-2405页

作者： Tamara al Shloul Israr Akhter Suliman A.Alsuhibany Yazeed Yasin Ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

E-learning approaches are one of the most important learning platforms for the learner through electronic *** study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and *** of them are currently working on this domain to fulfill the requirements of industry and the *** this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural *** input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is *** flow features are extracted to find more accurate ***,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior *** proposed method shows 84.50%of mean accuracy and a 15.50%of error ***,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).

关键词： Artificial intelligence behavior mining convolution neural network(CNN) deep flow e-learning environment

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Intelligent Sign Language Recognition System for E-Learning Context

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computers, Materials & Continua 2022年第9期72卷 5327-5343页

作者： Muhammad Jamil Hussain Ahmad Shaoor Suliman A.Alsuhibany Yazeed Yasin Ghadi Tamara al Shloul Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-si237Gyeonggi-doKorea

In this research work,an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and *** feature set has the ability to increase the overall performance of machine learning algorithms in an efficient *** hand gesture recognition based on these features has been implemented for usage in *** feature set used hand landmarks,which were generated using media-pipe(MediaPipe)and open computer vision(openCV)on each frame of the incoming *** overall algorithm has been tested on two well-known ASLalphabet(American Sign Language)and ISL-HS(Irish Sign Language)sign language *** machine learning classifiers including random forest,decision tree,and naïve Bayesian have been used to classify hand gestures using this unique feature set and their respective results have been *** the random forest classifier performed better,it has been selected as the base classifier for the proposed *** showed 96.7%accuracy with ISL-HS and 93.7%accuracy with ASL-alphabet dataset using the extracted features.

关键词： Decision tree feature extraction hand gesture recognition landmarks machine learning palm detection

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Integrating Real-Time and Non-Real-Time Collaborative Programming: Workflow, Techniques, and Prototypes

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Proceedings of the ACM on Human-computer Interaction 2023年第GROUP期7卷 1-19页

作者： Ma, Yifan Qi, Batu Xu, Wenhua Wang, Mingjie Du, Bowen Fan, Hongfei School of Software Engineering Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom

Real-time collaborative programming enables a group of programmers to edit shared source code at the same time, which significantly complements the traditional non-real-time collaborative programming supported by version control systems. However, one critical issue with this emerging technique is the lack of integration with non-real-time collaboration. Specifically, contributions from multiple programmers in a real-time collaboration session cannot be distinguished and accurately recorded in the version control system. In this study, we propose a scheme that integrates real-time and non-real-time collaborative programming with a novel workflow, and contribute enabling techniques to realize such integration. As a proof-of-concept, we have successfully implemented two prototype systems named CoEclipse and CoIDEA, which allow programmers to closely collaborate in a real-time fashion while preserving the work's compatibility with traditional non-real-time collaboration. User evaluation and performance experiments have confirmed the feasibility of the approach and techniques, demonstrated the good system performance, and presented the satisfactory usability of the prototypes. © 2023 ACM.

关键词： Control systems

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An Intelligent Framework for Recognizing Social Human-Object Interactions

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computers, Materials & Continua 2022年第10期73卷 1207-1223页

作者： Mohammed Alarfaj Manahil Waheed Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-si15073Gyeonggi-doKorea

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public *** involves localizing the human and object targets and then identifying the interactions between ***,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient ***,the proposed system offers an automated body-parts-based solution for HOI *** system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed ***,a convex hullbased approach for extracting key body parts has also been *** identifying the key body parts,two types of features are ***,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system *** results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

关键词： Dimensionality reduction human-object interaction key point detection machine learning watershed segmentation

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Opinion Spam Detection with Attention-Based LSTM Networks 20th

Opinion Spam Detection with Attention-Based LSTM Networks

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20th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2019

作者： Sedighi, Zeinab Ebrahimpour-Komleh, Hossein Bagheri, Ayoub Kosseim, Leila Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Engineering University of Kashan Kashan Iran Department of Methodology and Statistics Utrecht University Utrecht Netherlands

ISBN: (纸本)9783031243394

Today, online reviews have a great influence on consumers’ purchasing decisions. As a result, spam attacks, consisting of the malicious inclusion of fake online reviews, can be detrimental to both customers as well as organizations. Several methods have been proposed to automatically detect fake opinions;however, the majority of these methods focus on feature learning techniques based on a large number of handcrafted features. Deep learning and attention mechanisms have recently been shown to improve the performance of many classification tasks as they enable the model to focus on the most the important features. This paper describes our approach to apply LSTM and attention-based mechanisms for detecting deceptive reviews. Experiments with the Three-domain data set [15] show that a BiLSTM model coupled with Multi-Headed Self Attention improves the F-measure from 81.49% to 87.59% in detecting fake reviews. © 2023, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Network Traffic Classifiers for Internet of Things Intrusion Detection using Machine Learning Classifiers 3

Network Traffic Classifiers for Internet of Things Intrusion...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Uniyal, Anita Mishra, Amit Kumar Manushi Pant, Yogesh Computer Science & Engineering GRD IMT Dehradun India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India GRD IMT Department of Computer Science & Engineering Dehradun India Software Business Systems Consultanting India Private Limited India

ISBN: (纸本)9798350303360

In today's era smart devices are integral part of our life and every device becomes smart if it is equipped with sensors and having ability to connect with internet. Without any hindrance we can connect and interact with soundings via smart devices, this is possible because of the technological advancement such as cloud computing and Internet of things. With the latest technologies we are using advance algorithms such as machine learning to make our decision more effective. The importance and need of smart devices in life have more vulnerability attacks so it needs concrete security mechanism and privacy policies to safeguard from cyber security and threats via intrusion detection. The existing IoT security literature is thoroughly analysed in this paper, outlining the many vulnerabilities and possible defences. The paper also looks into the use of machine learning techniques for traffic inflow classification, highlighting comparisons of different classifiers for problems that are special to different domains and different machine learning techniques for early identification of intrusion detection. The paper is concluded with a review of the current problems and potential future directions for IoT security research. © 2023 IEEE.

关键词： Intrusion detection

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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Privacy-Preserving Edge-Aided Eigenvalue Decomposition in Internet of Things

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IEEE Internet of Things Journal 2025年第12期12卷 19901-19914页

作者： Zhao, Xiaotong Zhang, Hanlin Lin, Jie Liang, Fan Kong, Fanyu Xu, Hansong Hua, Kun Qingdao University College of Computer Science and Technology Qingdao266071 China Xian Jiaotong University School of Electronic and Information Engineering Xian710049 China Sam Houston State University Department of Computer Science HuntsvilleTX77340 United States Shandong University School of Software Jinan250101 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China California Polytechnic State University Department of Electrical Engineering San Luis ObispoCA93407 United States

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in IoT. When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this paper, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users' private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives. © 2025 IEEE.

关键词： Differential privacy

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