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1st International Conference on Robotics, Engineering, science, and technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Source Channel Model Selection for Downlink CSI Feedback Estimation using Transfer Learning in Massive MIMO System

Source Channel Model Selection for Downlink CSI Feedback Est...

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2025 International Conference on Computing, Networking and Communications, ICNC 2025

作者： Jingyu, Liu Bouazizi, Mondher Ohtsuki, Tomoaki Keio Univerity Graduate School of Science and Technology Yokohama Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Yokohama Japan

ISBN: (纸本)9798331520960

Transfer learning-based methods for Channel State Information (CSI) feedback can achieve a good CSI reconstruction performance with small amounts of computation time and cost. However, for higher efficacy, such methods need to fine-tune a properly selected source channel model each time. This is because fine-tuning a model trained on drastically different data will suffer from slow convergence and non-optimal performance, which defies the purpose of transfer-learning. In this paper, we use a one-stage algorithm utilizing k-nearest neighbors (k-NN) and Deep Neural Network (DNN) methods to classify the downlink Clustered Delay Line (CDL) channels into one of 5 channel models with the help of a selected set of features. Based on the classification results, the appropriate source channel model for fine-tuning is selected, and the model is fine-tuned on the received data. To demonstrate the usefulness of our method, we compare its results to those of existing methods. Through simulation, we show that the overall classification accuracy for 5 CDL channel models using the one-step algorithm is 80.73%. Upon classification, and by selecting the proper source model, our method can reach a better normalized mean square error (NMSE) of CSI feedback performance in a shorter time and with fewer samples. © 2025 IEEE.

关键词： Deep neural networks

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Conversational Agents to Support People with Communication Disability: A Co-design Study with Speech Pathologists

Conversational Agents to Support People with Communication D...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Khan, Fahad Wu, Yufeng Dray, Julia Hemsley, Bronwyn Kocaballi, A. Baki Commonwealth Bank of Australia SydneyNSW Australia School of Computer Science University of Technology Sydney SydneyNSW Australia Graduate School of Health University of Technology Sydney SydneyNSW Australia

ISBN: (纸本)9798400713958

Communication disabilities can limit individuals’ social interaction, often leading to isolation and reduced quality of life. Speech-language pathologists (SLPs) can address these challenges through tailored interventions, often supported by assistive technologies. This study explores how conversational agents (CAs) can support individuals with communication disabilities by incorporating SLP expertise through a co-design approach. We conducted a two-phase study: a co-design workshop to understand the needs of the target population and potential roles of CAs to support communication skill training, and a subsequent evaluation session with a CA prototype by a group of SLPs. Our findings highlighted the importance of designing accessibility, personalization, and motivation features of CAs with greater depth, augmentation, and precision, emphasizing the need for multiple interaction modalities, real-time and deeply tailored personalization, and more intrinsic motivational strategies. Our work contributes to the design and application of inclusive CAs in communication therapy, with implications for clinical and home settings. © 2025 Copyright held by the owner/author(s).

关键词： Intellectual property core

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Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites 12

Developing a Component Comment Extractor from Product Review...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Anda, Shogo Kikuchi, Masato Ozono, Tadachika Nagoya Institute of Technology Graduate School of Engineering Department of Computer Science Aichi466-8555 Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

Consumers often read product reviews to inform their buying decision, as some consumers want to know a specific component of a product. However, because typical sentences on product reviews contain various details, users must identify sentences about components they want to know amongst the many reviews. Therefore, we aimed to develop a system that identifies and collects component and aspect information of products in sentences. Our BERT-based classifiers assign labels referring to components and aspects to sentences in reviews and extract sentences with comments on specific components and aspects. We determined proper labels based for the words identified through pattern matching from product reviews to create the training data. Because we could not use the words as labels, we carefully created labels covering the meanings of the words. However, the training data was imbalanced on component and aspect pairs. We introduced a data augmentation method using WordNet to reduce the bias. Our evaluation demonstrates that the system can determine labels for road bikes using pattern matching, covering more than 88% of the indicators of components and aspects on e-commerce sites. Moreover, our data augmentation method can improve the macro-F1-measure on insufficient data from 0.66 to 0.76. © 2022 IEEE.

关键词： Pattern matching

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Adopting Augmented Reality (AR) technology in Retail Sector: Systematic Review of Prisma Methods 7

Adopting Augmented Reality (AR) Technology in Retail Sector:...

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7th International Conference on Information Management and technology, ICIMTech 2022

作者： Maryani Prabowo, Harjanto Gaol, Ford Lumban Hidayanto, Ahmad Nizar School of Information Systems BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Information Systems Department Computer Science Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Computer Science Universitas Indonesia Depok16424 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

One of the most popular technologies nowadays is augmented reality (AR). popular technologies in various industries. Many industries have adopted this AR technology, one of which is with the aim of marketing the products offered to their customers. With more and more adopting this technology, retailers are also using it. The goal of this research is to determine out what important variables are often encountered in the application of AR technology. A qualitative technique is used in this study, as well as a Systematic Literature Review Prism approach. The result are 56 papers, 35 candidate papers and 26 papers were selected. We examine where and how in retail, augmented reality (AR) is used, the aspects of Augmented reality technology that are frequently examined, and the psychological and behavioral effects that AR can have. © 2022 IEEE.

关键词： Augmented reality

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Sentiment Analysis of Makeup Alley Website Cosmetic Reviews Using SVM and TF-IDF

Sentiment Analysis of Makeup Alley Website Cosmetic Reviews ...

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International Conference on Information Management and technology (ICIMTech)

作者： Christine Putri Claudya Salim Veronica Dwiyanti W. K Said Achmad Jurike V Moniaga Computer Science Department Mobile Application & Technology Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

This paper focuses on performing sentiment analysis of cosmetic reviews using a machine learning algorithm. The study aims to classify reviews into positive, neutral, and negative opinions using aspect-based sentiment analysis with TF-IDF and SVM algorithms. The research questions include understanding user opinions on cosmetic products and identifying the most common positive and negative sentiments in reviews. Data cleaning, tokenization, stopwords removal, normalization, and lemmatization techniques are employed to achieve these goals. The study uses the Makeup Alley website as a data source and employs web scraping to obtain the data. The Natural Language Toolkit (NLTK) is used to simplify the removal of stopwords. The research seeks to document the step-by-step process of developing a sentiment analysis application tailored to cosmetic product reviews.

关键词： Support vector machines Sentiment analysis Machine learning algorithms Accuracy Reviews Soft sensors Tokenization Cleaning Vectors Classification algorithms

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Effect of Varied Datasets on Training of a Segmentation Model Used in Visual Navigation

Effect of Varied Datasets on Training of a Segmentation Mode...

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IEEE International Conference on Big Data

作者： Marin Wada Miho Adachi Ryusuke Miyamoto Dept. of Computer Science Graduate School of Science and Technology Dept. of Computer Science School of Science and Technology Meiji University Kawasaki Japan

A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use.

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Special Issue on Robotics and Mechatronics technology for Aerial Robots

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Journal of Robotics and Mechatronics 2024年第5期36卷 999-1000页

作者： Ohtake, Hiroshi Suzuki, Satoshi Takesue, Naoyuki Miwa, Masafumi Fujikawa, Taro Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan Faculty of Engineering Chiba University 1-33 Yayoi-cho Inage-ku Chiba Chiba263-8522 Japan Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan Graduate School of Technology Industrial and Social Sciences Tokushima University 2-1 Minamijosanjima-cho Tokushima Tokushima770-8506 Japan Graduate School of Science and Technology for Future Life Tokyo Denki University 5 Senju Asahi-cho Adachi-ku Tokyo120-8551 Japan

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Wandy, Wandy Adi, Kusworo Ayu, Media Anugerah Diponegoro University Doctoral Program of Information Systems School of Postgraduate Studies Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science South Jakarta Indonesia

ISBN: (纸本)9798331505530

Full-marathon and Half-marathon distances are categorized as road running. Full-marathon running is becoming increasingly popular, and Half-marathon is increasing worldwide in both sexes and all age groups. Some aspects might relate to Full-marathon and Half-marathon running performance during training and races. technology also plays an essential role in supporting runners and running races. technology like artificial intelligence (AI) now supports the running athlete, not only predicting performance and results. It can also be used later to help the coach generate training programs for the athlete. This research aimed to find many aspects of marathons and performance and analyze them to see if artificial intelligence could later support them. It used secondary data and a systematic literature review proposed by Kitchenham. Out of the 58 articles, 21 of them (36.21%) received a score of 1 from Q1. Additionally, 19 articles (32.76%) received a score of 1 from both Q2 and Q3. Among the 58 articles, 9 (15.52%) received a total score of 3, with all three Q1, Q2, and Q3 scores being 1. This indicates that artificial intelligence will likely support the content of these nine articles. Several factors were also discovered to be connected to marathons and athletic performance. These findings suggested that additional investigation into marathons and performance, later backed by artificial intelligence, remained pertinent and essential. © 2024 IEEE.

关键词： artificial intelligence aspects marathon performance

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