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9th International Conference on Computer and Communications, ICCC 2023

作者： Dong, Shuhan Lu, Yijun Wang, Yu Dong, Jiong Hanyang University Department of Physical Education Seoul Korea Republic of Muroran Institute of Technology Department of Sciences and Informatics Muroran Japan Zhejiang University College of Control Science and Engineering Hangzhou China Xuchang University Department of Information Engineering Xuchang China

ISBN: (纸本)9798350317251

The increasing popularity of VR games has sparked a notable surge in interest toward VR exergames, particularly in the context of the COVID-19 pandemic. To uphold the quality of VR exergames and provide valuable feedback to developers, it is imperative to consider the perspectives and concerns of players regarding VR exergames and conduct a comprehensive analysis of their reviews. This study employs the Latent Dirichlet Allocation (LDA) algorithm to effectively discern various topics within reviews, thus elucidating players' reservations concerning VR exergames. Empirical investigations were conducted using a dataset comprising 15,917 English-language reviews of 16 VR exergames on the Steam platform. The experimental findings unequivocally indicate that players exhibit a favorable disposition toward VR exergames (88.31% vs. 11.69%). The analysis encompasses eight distinct topics including user experience, graphics, music, and gameplay. © 2023 IEEE.

关键词： Virtual reality

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Maritime radar odometry inspired by visual odometry

Maritime radar odometry inspired by visual odometry

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International Conference on Information Fusion

作者： Henrik D. Flemmen Rudolf Mester Annette Stahl Torleiv H. Bryne Edmund Førland Brekke Department of Engineering Cybernetics Norwegian University of Science and Technology Department of Informatics and computer science Norwegian University of Science and Technology

Future autonomous ships will need several redundant positioning systems to navigate reliably. Global Navigation Satellite Systems are highly accurate but they are susceptible to disruptions and intentional jamming. Maritime radars have long range and are robust against bad weather and darkness, but the use for ownship motion estimation has received relatively little attention in the research field. In this work, we present a radar odometry estimation method inspired by advances in visual odometry and simultaneous localization and mapping. The method works on raw radar data in a coastal environment and combines the Kanade-Lucas-Tomashi tracker with a factor graph back-end. We test it on data from a large ship with a maritime radar with a range of 19 km. We find that it is robust with only a small drift and no erroneous jumps in the estimate.

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Trajectory Adaptation with Impedance Control for Human-Robot Collaboration Tasks 8

Trajectory Adaptation with Impedance Control for Human-Robot...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Chaoyi Luo, Jing Zeng, Chao Tang, Biwei Pang, Muye Yang, Chenguang School of Automation Wuhan University of Technology Wuhan430070 China University of Hamburg Department of Informatics Hamburg22527 Germany School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

ISBN: (纸本)9798350300178

In human-robot collaboration tasks, an operator usually interacts with a robot physically and guides the robot to move by employing the interaction force in the model of impedance control. However, in physical human-robot interaction (pHRI), the trade-off between the position accuracy and interaction force is one of the main disadvantages of impedance control. In order to achieve compliant control with a high position control accuracy, a method of trajectory adaptation based on impedance control is proposed with minimized interaction force and small position error in pHRI and the effectiveness of the collaboration tasks is guaranteed. We use impedance control to generate the robot's movement for compliant control and use trajectory adaptation to regulate the robot's reference trajectory. By employing the proposed method, the interaction force can be reduced with small position error. The results of experiment verify the effectiveness of the proposed method. © 2023 IEEE.

关键词： Trajectories

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Real Time Prediction Model for Air Pollution and Air Quality Index based on Machine Learning 1

Real Time Prediction Model for Air Pollution and Air Quality...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kumar, Rakesh Sharma, Bhisham Shekhar, Shashi Dhaou, Imed Ben Singhal, Saurabh GLA University Department of Computer Engineering & Applications Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Controlling air pollution is a difficult issue for governments in densely populated and developing nations. The burning of fossil fuels, industrial parameters and traffic assume critical parts in contamination of air. There is distinctive particulate matter which decide the nature of the air however among all the particulate matter, consideration towards particulate matter (PM 2.5) is become a necessity. In this paper we detect the PM value using image processing technology. Image processing uses edge detection and depth estimation techniques to get the contaminated regions of the picture. Accordingly, image processing is used to detect air pollution. It detects and quantifies contamination in the air with the image features like time, day/night, outdoor conditions for determining the correlation. The proposal uses the learning model based on these parameters to predict PM level on collected photos. High-level of PM can cause major issues on individuals' wellbeing. As a result, regulating it by just being vigilant on its overall visibility is critical. This paper proposes a method for identifying and evaluating PM contamination by distinguishing six image features: transmission, sky perfection and shading, complete and neighborhood picture difference, and picture entropy. To assess the association between PM level and numerous elements, we also analyze the time, terrain, and climate state of each image. We created a relapse model based on these data to forecast PM2.5 levels in a specific city. © 2023 IEEE.

关键词： Forecasting

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ReviewRanker: A Semi-supervised Learning-Based Approach for Code Review Quality Estimation 6th

ReviewRanker: A Semi-supervised Learning-Based Approach for ...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Mahbub, Saifullah Easin Arafat, Md. Rahman, Chowdhury Rafeed Ferdows, Zannatul Hasan, Masum Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh School of Computing National University of Singapore Singapore Singapore Department of Computer Science University of Rochester RochesterNY United States

ISBN: (纸本)9789819734412

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning-based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and well-defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection by reducing human bias and the effort required for such a task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://***/saifarnab/code_review. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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Faba Bean Disease Detection Using Deep Learning Techniques

Faba Bean Disease Detection Using Deep Learning Techniques

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2023 International Conference on Cyber Management and engineering, CyMaEn 2023

作者： Salau, Ayodeji Olalekan Abeje, Bekalu Tadele Faisal, Abeer Naser Asfaw, Tesfa Tegegne Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Haramaya University College of Computing and Informatics Department of Information Technology Ethiopia Institute of Technology Faculty of Computing Bahir Dar University Department of Computer Science Ethiopia University of Sumer College of Computer Science and Information Technology Al-Rifai Iraq

ISBN: (纸本)9781665493291

Faba bean (Vicia faba L.) is an important cash crop for animal and human consumption in many countries, especially Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria, which forms nodules on its roots. Ethiopia is the second largest faba bean producer;however, according to research, the yield is generally low. Among the constraints contributing to low productivity of the crop, disease is the most important. Even though there are countermeasures such as the use of chemical sprays, which is used to reduce the damage or eliminate the disease, an effective disease identification technique is still required. Chocolate spot, faba bean gal, and rust are the most common diseases of faba beans. To effectively detect the disease, in this paper, we propose an end-to-end CNN model for training before and after the dataset is preprocessed. The CNN end-to-end model is trained with raw images and achieves 92.1% training accuracy. Also, the model is trained with preprocessed data and it achieves a training accuracy of 98.14% for the end-to-end CNN classifier. The results show that the proposed approach is suitable for faba bean disease detection and can be employed to detect diseases of other crops. © 2023 IEEE.

关键词： Convolutional neural networks

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Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network

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Computers, Materials & Continua 2021年第9期68卷 3391-3410页

作者： Ali Abdi Seyedkolaei Seyed Amin Hosseini Seno Rahmat Budiarto Faculty of Engineering Ferdowsi University of MashhadMashhadIran Department of Informatics Faculty of Science and TechnologyUniversitas Alazhar IndonesiaJakartaIndonesia

In software-defined networking(SDN)networks,unlike traditional networks,the control plane is located separately in a device or *** of the most critical problems in these networks is a controller placement problem,which has a significant impact on the network’s overall *** paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load *** researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement *** demonstrates through careful computational studies that the formulations can design networks with much less installation cost while accepting a general connected topology among controllers and user-defined survivability *** researchers used the proposed framework on six different topologies then analyzed and compared with Iterated Local Search(ILS)and Expansion model for the controller placement problem(EMCPP)along with considering several evaluation *** results show that the proposed framework outperforms the ILS and ***,the proposed framework has a 38.53%and 38.02%improvement in reducing network implementation costs than EMCPP and ILS,respectively.

关键词： Software-defined network controller placement kernel search integer programming survivability cost

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An Efficient Lightweight Authentication and Key Agreement Protocol for Patient Privacy

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Computers, Materials & Continua 2021年第12期69卷 3495-3512页

作者： Seyed Amin Hosseini Seno Mahdi Nikooghadam Rahmat Budiarto Department of Computer Engineering Ferdowsi University of MashhadMashhad***Iran Department of Informatics Faculty of Science and TechnologyUniversitas Alazhar IndonesiaJakarta12110Indonesia

Tele-medical information system provides an efficient and convenient way to connect patients at home with medical personnel in clinical *** this system,service providers consider user authentication as a critical *** address this crucial requirement,various types of validation and key agreement protocols have been *** main problem with the two-way authentication of patients and medical servers is not built with thorough and comprehensive analysis that makes the protocol design yet has *** paper analyzes carefully all aspects of security requirements including the perfect forward secrecy in order to develop an efficient and robust lightweight authentication and key agreement *** secureness of the proposed protocol undergoes an informal analysis,whose findings show that different security features are provided,including perfect forward secrecy and a resistance to DoS ***,it is simulated and formally analyzed using Scyther *** results indicate the protocol’s robustness,both in perfect forward security and against various *** addition,the proposed protocol was compared with those of other related protocols in term of time complexity and communication *** time complexity of the proposed protocol only involves time of performing a hash function Th,i.e.,:O(12Th).Average time required for executing the authentication is 0.006 seconds;with number of bit exchange is 704,both values are the lowest among the other *** results of the comparison point to a superior performance by the proposed protocol.

关键词： Authentication key agreement protocol tele-medical Scyther perfect forward secrecy

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Leukocoria Identification: A 5-Fold Cross Validation CNN and Adaboost Hybrid Approach 6

Leukocoria Identification: A 5-Fold Cross Validation CNN and...

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6th International Seminar on Research of Information technology and Intelligent Systems, ISRITI 2023

作者： Sari, Indah Purnama Al-Khowarizmi Ramadhani, Fanny Satria, Andy Sulaiman, Oris Krianto Universitas Muhammadiyah Sumatera Utara Department of Information Technology Medan Indonesia Universitas Negeri Medan Department of Computer Science Medan Indonesia Universitas Dharmawangsa Department of Information Technology Medan Indonesia Universitas Islam Sumatera Utara Department of Informatics Engineering Medan Indonesia

ISBN: (纸本)9798350358346

Leukocoria, which is distinguished by an unusual white reflection in the pupil, is an important sign of several eye conditions. Early detection and correct alleviation of leukocoria are critical for prompt diagnosis and therapy. In this article, we propose a hybrid method for leukocoria classification utilizing Convolutional Neural Networks (CNN) and Adaptive Boosting (AdaBoost). The performance of the suggested strategy is assessed using the 5-Fold Cross Validation method in the proposed work. In order to increase classification accuracy, CNN is utilized for feature extraction and AdaBoost is employed as a boosting technique. The experimental results reveal that the suggested combined strategy is more effective in relieving leukocoria than the CNN or AdaBoost single models. In comparison to the conventional CNN technique, the performance results of the model utilizing the CNN + Adaboost method with 5-Fold Cross Validation offer better and superior outcomes in diagnosing leukocoria. Results from the CNN + Adaboost model using 5-Fold Cross Validation show very high accuracy values of 95%, precision values of 95%, recall values of 95%, and Fl scores of 94%. © 2023 IEEE.

关键词： Adaptive boosting

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