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Intelligent Sign Language Recognition System for E-Learning Context

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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Programming Education for Young People using the Falling-Puzzle Game, 'Puyo Puyo' 14

Programming Education for Young People using the Falling-Puz...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Saito, Daisuke Tian, Ruochen Washizaki, Hironori Fukazawa, Yoshiaki Waseda University Global Software Engineering Laboratory Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350399431

This study utilizes the game rules of a falling-puzzle game, developed as a consumer-oriented digital game, in programming education for young people. When digital games are used in programming education, they are often designed specifically for that purpose. In this study, we focused on the game rules of the consumer falling-object puzzle game, 'Puyo Puyo.' Learning impact was investigated on 23 Japanese elementary, junior high, and senior high school students. The results show that the concepts of branching and arraying in programming were taught effectively;thus, consumer digital game rules can be used as a case study for programming education. © 2023 IEEE.

关键词： E-learning

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Efficient adaptive rendering of planetary-scale terrains

Efficient adaptive rendering of planetary-scale terrains

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作者： Masood, Zafar Jiangbin, Zheng Irfan, Muhammad Ahmad, Idress School of Software Northwestern Polytechnical University Shaanxi Xi'an China Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

Planetary-scale terrain requires adaptive simplification and tessellation for highperformance rendering. Real-world visualisation requires adaptive rendering of the world-scale geometric model, which is a challenging task especially on consumer-level notebooks. In this work, we propose a method for the adaptive rendering of the Earth's surface model using modern GPU features. The multi-resolution model is partitioned into patches and is submitted efficiently using the geometry-instancing feature for high-performance rendering. Patch-based algorithm culls worldscale model in graphics pipeline to reduce simplification and rendering load. The proposed method performs the adaptive simplification of patches with geometric-error control in image space for optimum triangulation. Simplified patches are tessellated using power-of-two tessellation factors to avoid boundary cracks. After tessellation, a three-dimensional surface is constructed by displacement mapping using a regular height data grid. Comparative evaluation of the proposed method with clipmaps and hardware-based tessellation methods is performed and results are presented. © 2023 Inderscience Enterprises Ltd.

关键词： Geographic information systems

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Intelligent Sorting System 7

Intelligent Sorting System

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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Mao, Yanghong Chen, Chuixin Pan, Zhengjun Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Institute of Guangzhou Department of Software Engineering Software Engineering Guangzhou China Department of Software Engineering Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9781665487115

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. The system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through trajectory planning. Aiming at the problem that the deep learning algorithm takes a long time, The traditional image processing algorithm and deep learning target detection algorithm are organically combined to reduce the use delay of image processing in the system. Because the training of deep learning model requires a large number of data sets, but the amount of data in manufacturing factories is small, and data labeling is time-consuming and laborious, YOLO v3 algorithm is used to improve the efficiency of training model under a small number of labeled data sets, SIFT algorithm is used to extract image features, and K-D tree algorithm is used to match image features. In the algorithm of position, the centroid is extracted as the positioning reference coordinate, the kinematics model of the manipulator is established. trajectory planning, straight-line and arc interpolation algorithms are used. The experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Deep learning Training Trajectory planning Target recognition Object detection Feature extraction Manipulators

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WIP: Concept Maps for computer Organization: A Systematic Study 54

WIP: Concept Maps for Computer Organization: A Systematic St...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Belkhouche, F. Bendjilali, K. Shippensburg University of Pennsylvania Department of Computer Electrical and Software Engineering ShippensburgPA United States Raritan Valley Community College Department of Mathematics and Computer Science BranchburgNJ United States

ISBN: (纸本)9798350351507

This research-to-practice WIP paper describes concept maps for computer organization. A concept map is a tool that can be used to describe the relationship between the topics (or concepts) in a specific field of study. Concept maps are used in a variety of applications to improve curricula and assessment, particularly in K12 and undergraduate STEM education. In this paper we use concept maps for modeling computer organization topics and assess their importance with respect to the depth and breadth levels required for computer science and engineering graduates. The proposed concept maps focus on two aspects: The first one is the idea of the centrality of the topics. Centrality is an important concept in graph theory. Each topic in the concept map is given a level of centrality or a centrality index. The second aspect is the relationship between the topics, particularly between the topics with high level of centrality. The objective of the modeling is to identify the most central topics and come up with a set of more standard learning outcomes for undergraduate level computer organization courses. © 2024 IEEE.

关键词： Students

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