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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Ahn, Taehoon Bae, Sang Won Graduate School of Artificial Intelligence Pohang University of Science and Technology Korea Republic of Division of Artificial Intelligence and Computer Science Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9783959773546

Given a set P of n points in the plane, the two-line center problem asks to find two lines that minimize the maximum distance from each point in P to its closer one of the two resulting lines. The currently best algorithm for the problem takes O(n2 log2 n) time by Jaromczyk and Kowaluk in 1995. In this paper, we present faster algorithms for three variants of the two-line center problem in which the orientations of the resulting lines are constrained. Specifically, our algorithms solve the problem in O(n log n) time when the orientations of both lines are fixed;in O(n log3 n) time when the orientation of one line is fixed;and in O(n2α(n) log n) time when the angle between the two lines is fixed, where α(n) denotes the inverse Ackermann function. © Taehoon Ahn and Sang Won Bae.

关键词： Constrained optimization

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Dynamic Graph Neural Networks with Neighborhood Constraints 21

Dynamic Graph Neural Networks with Neighborhood Constraints

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Ren, Jiankang Lv, Xian Zhang, Jun Li, Xu Ni, Zheng Zhang, Haijun School of Computer Science and Technology Dalian University of Technology Dalian China Dalian University of Technology Key Laboratory of Social Computing and Cognitive Intelligence Dalian China School of Computer Science and Technology Xinjiang Normal University Xinjiang China Graduate School of Education Dalian University of Technology Dalian China

ISBN: (纸本)9798350365221

Graph data are well suited to represent complex interactions and dependencies owing to its inherent relational structure. Graph neural networks (GNNs) can effectively explore the topological structure and node feature information of graph data to perform perceptual inference tasks. In GNNs, the state of each node is iteratively updated by aggregating information from its neighbors. Note that some noise may be introduced due to the information aggregation of irrelevant neighbor nodes, leading to poor performance with higher computational overhead. This paper proposes a dynamic graph generation method based on neighborhood constraints, which learns the degrees of the nodes and imposes the neighborhood constraints to limit the neighbor-hood space of the nodes, resulting in a sparse graph with dynamic neighborhoods. Based on the sparse graph, the dynamic graph neural network selectively aggregates the information of the most important neighbors to improve the performance of the model. Furthermore, to ensure neighborhood constraints satisfaction during the learning process, a loss function is designed to penalize the nodes whose degrees exceed a specific threshold. The evaluation results demonstrate that our proposed method achieves a higher inference accuracy with the same or higher sparsity compared to state-of-the-art methods. © 2024 IEEE.

关键词： Graph neural networks

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Design of Household Robotic Arm System to sort Recyclable Resources based on Deep Learning

Design of Household Robotic Arm System to sort Recyclable Re...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Guo, Yuning Kameda, Hiroyuki Wu, Bo Graduate School of Bionics Computer and Media Tokyo University of Technology Tokyo Japan School of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (纸本)9798350304602

As society progresses and living conditions enhance, global domestic waste generation has witnessed a significant surge., and the problem of domestic waste separation has attracted more and more attention from the viewpoint of creating a circular economy (CE) society. Advances in machine learning and robotics offer a promising solution to this problem. Particularly, the intricacies of waste classification at the household level result in high misclassification rates, leading to inefficient waste management and recycling. Our research focuses on utilizing deep learning and robotic technologies to mitigate these issues. In this paper, we aim to design a domestic waste resource classification system built on the YOLOv5 algorithm and a household robotic arm. By integrating computer vision and machine learning with the YOLOv5 target detection algorithm and precise robotic arm operations, the system can automatically identify, classify, and sort different types of waste resources. The critical challenges that the system effectively addresses include reducing manual intervention, minimizing time consumption, and improving the accuracy of household recycling classification, while promoting the development of environmental awareness. To effectively reduce manual intervention, decrease time consumption, and enhance the accuracy of household waste classification, our main contribution is the development of an integrated system for more efficient household waste resource management. © 2023 IEEE.

关键词： Robotic arms

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Energy Optimization of Distributed Video Processing System in Dynamic Environment 21

Energy Optimization of Distributed Video Processing System i...

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21st IEEE Consumer Communications and Networking Conference, CCNC 2024

作者： Lou, Yang Shimonishi, Hideyuki Murata, Masayuki Techasarntikul, Nattaon Graduate School of Information Science and Technology Osaka University Osaka Japan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350304572

To create a future society based on cyber-physical systems, we need real-time digital twins made with cameras and sensors. The challenge is making this energy-efficient. A model in [1] suggests dividing video analysis tasks among terminals, edge servers, and cloud servers to minimize power consumption. This paper addresses energy optimization in dynamic environments with a two-level approach: detecting and categorizing environmental changes (LEC and SEC) and using a two-level adaptive evolutionary algorithm (TAEA) to make corresponding adjustments. A case study with a differential evolution algorithm demonstrates its effectiveness in minimizing energy costs and improving processing accuracy and latency violations. © 2024 IEEE.

关键词： differential evolution dynamic system energy efficiency genetic algorithm optimization Video analysis

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Relationship between Embedment and Compressive Properties with Grain Direction of Hardwood Species

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Zairyo/Journal of the Society of Materials science, Japan 2024年第8期73卷 669-674页

作者： Takuro, Mori Tagawa, Riho Inoue, Ryo Tanaka, Kei Hiroshima University Graduate School of Advanced Science and Engineering Kagamiyama Higashi-Hiroshima739-8527 Japan Kumamoto University Faculty of Advanced Science and Tchnology Kurokami Chuo-ku Kumamoto860-8555 Japan Oita University Faculty of Science and Technology Architecture Program Dannoharu Oita870-1124 Japan

In general, softwoods are used as the main structural material for wooden structures, while hardwoods are less used. Some hardwoods show superior mechanical performance due to the high density about 1.0 x 103 kg/m3. Therefore, the utilizing hardwoods as structural materials are supposed to more flexible structural design proposals. Studies on the mechanical performance of hardwoods for structural design are not enough, especially as an embedment performance. In this study, it was conducted embedment and compression tests related to the joint performance of wooden structures using three species of hardwoods (Ulin, Buna, and Kuri) and a softwood (Sugi) to investigate the effects of strength performances such as density and woody structure. As a result, hardwoods with higher density showed higher strength performance. Embedment strength relative to compressive strength was 0.94 to 1.49 times higher in the direction parallel to the grain and 3.46 to 5.08 times higher in the direction perpendicular to the grain. About embedment strength, the ring-porous wood, Kuri, showed a similar trend (TR⨫RT, TL⨫RL) to that of a softwood, Sugi, while the diffuse-porous wood, Ulin and Buna, showed different trends (RTRL). © 2024 Society of Materials science Japan. All rights reserved.

关键词： Hardwoods

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Recent Trends for Text Summarization in Scientific Documents 9

Recent Trends for Text Summarization in Scientific Documents

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Sukmandhani, Arief Agus Arifin, Yulyani Zarlis, Muhammad Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350370126

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required;therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization. © 2023 IEEE.

关键词： Text processing

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Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model 8

Sentiment Analysis for TikTok Review Using VADER Sentiment a...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app;there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80. © 2023 The Authors. Published by Elsevier B.V.

关键词： Google Play Store Sentiment Analysis SVM TikTok VADER Sentiment

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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