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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

Procedia computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Movement Impacts on Eyeblink Detection Using FMCW Radar

Movement Impacts on Eyeblink Detection Using FMCW Radar

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Smart World Congress (SWC), IEEE

作者： Xinze Zhang Walid Brahim Jianhua Ma Bernady O. Apduhan Graduate School of Computer and Information Sciences Hosei University Tokyo Japan Department of Information Science Faculty of Science and Engineering Kyushu Sangyo University Fukuoka Japan

Eyeblink detection is essential for assessing physiological and psychological states in various applications. Radar technology offers a promising solution for non-intrusive and reliable eyeblink detection. This ongoing work investigates the effects of movements on eyeblink detectability using FMCW radar. The proposed methodology relies on the use of Adaptive Variational Mode Decomposition algorithm (AVMD) for blink signal extraction. Extensive experiments were performed to assess the performance under challenging conditions, analyzing the influence of body, head, and mouth movements on blink detectability. An in-depth analysis of movement impacts, identification of underlying reasons, and potential mitigation strategies is presented. This research provides a comprehensive evaluation, useful for the next step of research to provide a more reliable radar-based blink detection solution.

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MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition

MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Netwo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Linhuang Wang Xin Kang Fei Ding Satoshi Nakagawa Fuji Ren Advanced Technology and Science University of Tokushima Tokushima Japan Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we propose a MultiScale Spatio-temporal CNN-Transformer network (MSST-Net). Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer). The MELayer extracts multi-scale spatial information and encodes these features before sending them into a Temporal Transformer (T-Former). The T-Former simultaneously extracts temporal information while continually integrating multi-scale spatial information. This process culminates in the generation of multi-scale spatio-temporal features that are utilized for the final classification. Our method achieves state-of-the-art results on two in-the-wild datasets. Furthermore, a series of ablation experiments and visualizations provide further validation of our approach's proficiency in leveraging spatio-temporal information within DFER.

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No-Reference Image Quality Assessment Based on Image Naturalness and Semantics 18th

No-Reference Image Quality Assessment Based on Image Natural...

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18th International Forum of Digital Multimedia Communication, IFTC 2021

作者： Hu, Runze Shi, Wuzhen Liu, Yutao Li, Xiu Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Ocean University of China Qingdao China

ISBN: (纸本)9789811922657

Automatically providing feedback about the quality of natural images could be of great interest for image-driven applications. Toward this goal, this paper proposes a novel no-reference image quality metric capable of effectively evaluating the image quality without requring the information of the original image. The proposed method delivers a comprehensive analysis of the image quality through exploring its statistical natural properties and high-level semantics. Specifically, we adopt the NSS regularities based method to characterize the image naturalness, and take the semantic information of the image through the deep neural networks. The quality prediction model is then derived by SVR to analyze the relationship between these extracted features and the image quality. Experiments conducted on LIVEC and CID2013 databases manifest the effectiveness of the proposed metric as compared to existing representative image quality assessment techniques. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Automatic Adaptation Rule Optimization via Large Language Models

arXiv

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arXiv 2024年

作者： Ishimizu, Yusei Li, Jialong Xu, Jinglue Cai, Jinyu Iba, Hitoshi Tei, Kenji School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

Rule-based adaptation is a foundational approach to self-adaptation, characterized by its human readability and rapid response. However, building high-performance and robust adaptation rules is often a challenge because it essentially involves searching the optimal design in a complex (variables) space. In response, this paper attempt to employ large language models (LLMs) as a optimizer to construct and optimize adaptation rules, leveraging the common sense and reasoning capabilities inherent in LLMs. Preliminary experiments conducted in SWIM have validated the effectiveness and limitation of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Explainable Learning Outcomes Prediction: information Fusion Based on Grades Time-Series and Student Behaviors

Explainable Learning Outcomes Prediction: Information Fusion...

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

作者： Jiang, Yuan-Hao Chen, Zi-Wei Zhao, Cong Tang, Kezong Duan, Jicong Zhou, Yizhou Shanghai Institute of Artificial Intelligence for Education East China Normal University Shanghai China Graduate School Shanghai Jiao Tong University Shanghai China Jingdezhen Ceramic University School of Information Engineering Jingdezhen China School of Computer Jiangsu University of Science and Technology Zhenjiang China College of Automation Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798400713958

Accurately and timely predicting learners’ outcomes can assist educators in making instructional decisions or interventions. This helps prevent students from falling into a vicious cycle of decreased academic achievement and increased aversion to learning, potentially leading to dropout. Data-driven models often outperform eXplainable Artificial Intelligence (XAI) models in predicting learning outcomes, yet their lack of interpretability can hinder trust from educators. Therefore, this study developed an XAI information fusion framework that not only extracts potential trends from the time series of student grades to enhance predictive performance but also mines explicit relationships between classroom behaviors and learning outcomes. This reveals the behavioral causes behind changes in grades. Furthermore, we have made public the Dataset for Predicting Outcomes from Time sequences and Student behaviors (DPOTS), and validated the effectiveness of the developed XAI information fusion framework based on DPOTS. The results indicate that, the Mean Absolute Error (MAE) of CEO-IF was reduced by an average of 26.32% compared to the baseline algorithms, and it showed a 22.63% reduction compared to the averaging-based information fusion method. The homepage for the project can be accessed at https://***/10.5281/zenodo.14958102. © 2025 Copyright held by the owner/author(s).

关键词： agent role play AI agent AI for education educational dataset Large language model sustainable development goals

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Verification of State Based Slow HTTP DDoS Prevention Method

Verification of State Based Slow HTTP DDoS Prevention Method

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International Conference on Control, Automation and information sciences (ICCAIS)

作者： Dau Anh Dung Yasuhiro Nakamura Department of Information and Mathematical Sciences Graduate School of Science and Engineering National Defense Academy Yokosuka Japan Department of Computer Science School of Electrical and Computer Engineering National Defense Academy Yokosuka Japan

S1ow HTTP DDoS attacks involve sending numerous requests to a web server at a low rate, prolonging connection durations to saturate the server’s connection pool and disrupt legitimate client access. Detection based on traffic volume monitoring is challenging because of the low traffic volume generated per unit time. Furthermore, attackers employ tactics, such as controlling request transmission rates, timing, and connection durations, to make detection more difficult. Existing mitigation methods statistically process the packet characteristics of an attack, taking at least approximately 20 s for identification and mitigation. Additionally, the false-positive rate for legitimate connections has not been considered, necessitating validation in real-world environments. We proposed a fast detection method that observes the state transitions for each flow of HTTP communication on the network path, cutting off incomplete connections that exceed predefined thresholds for connection duration and idle time. Using data captured from actual attacks, we conducted detection experiments, achieving an average detection and mitigation time of 10 s from the start of the attack. The identification accuracy, as measured by the F1 Score, was 0.986, and the false-positive rate was 0.0096. However, there are concerns about accidentally disconnecting legitimate traffic when applying this method to web servers handling a large number of HTTP communications. Therefore, in this study, we used a web server simulating real-world operations to verify the effectiveness of the proposed method and measured a false positive rate of 0.0013, confirming the efficacy of the proposed approach.

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Integrating Spatial Prior Adapter for Enhancing SAM Performance in Medical Image Segmentation

Integrating Spatial Prior Adapter for Enhancing SAM Performa...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jihong Hu Yinhao Li Lanfen Lin Yen-Wei Chen Graduate School of Information Science and Engineering Ritsumeikan University Kusatsu Japan College of Computer Science and Technology Zhejiang University Hangzhou China

The Segment Anything Model (SAM) has emerged as a focal point of interest within the domain of image segmentation. Capitalizing on extensive pretraining with vast datasets and an interactive prompting mechanism, SAM stands out as a formidable base model for natural image segmentation tasks. However, a number of empirical studies indicate that SAM's performance diminishes substantially when applied to standard medical segmentation tasks. This is largely attributed to the modal and feature disparities between natural and medical images. In response to this challenge, this study introduces a novel, parameter-efficient fine-tuning approach termed the Spatial Prior Adapter (SPA). SPA leverages convolutional neural networks (CNNs) to capture multi-scale features, which are seamlessly amalgamated with the features harnessed from the SAM image encoder. Notably, during the model's fine-tuning phase, the SAM image encoder is frozen, enabling exclusive refinement of the SPA for optimal parameter calibration. Experimental evaluations on two medical segmentation datasets validate the effectiveness of our proposed method.

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3D geometric shape assembly via efficient point cloud matching 24

3D geometric shape assembly via efficient point cloud matchi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Nahyuk Lee Juhong Min Junha Lee Seungwook Kim Kanghee Lee Jaesik Park Minsu Cho Department of Computer Science and Engineering POSTECH Pohang Korea Graduate School of Artificial Intelligence POSTECH Pohang Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Department of Computer Science and Engineering POSTECH Pohang Korea and Graduate School of Artificial Intelligence POSTECH Pohang Korea

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr.

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Identifying english proficiency by frontal theta activity during english learning

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Discover Education 2025年第1期4卷 1-7页

作者： Emsawas, Taweesak Kimura, Tsukasa Ogura, Shintaro Morita, Hironori Abdullajon, Ergashov Grundlehner, Bernard Ogura, Mototsugu Numao, Masayuki Graduate School of Information Science and Technology Osaka University Osaka Japan SANKEN (The Institute of Scientific and Industrial Research) Osaka University Osaka Japan Graduate School of Human Sciences Osaka University Osaka Japan Shinhodo Inc Tokyo Japan imec the Netherlands/Holst Centre Eindhoven Netherlands Faculty of Engineering Department of Information and Computer Science Kyoto Tachibana University Kyoto Japan

Second-language (L2) knowledge of English is an essential communication tool in the contemporary era of globalization in many research and business fields. Among the important tasks in English education is the measurement of student proficiency. While various tests purport to measure proficiency in learning English, repeated preparation and implementation of tests remain major costs in providing English education. We propose that measurement with an electroencephalogram (EEG) might be used for measuring proficiency in L2 learning of English without the repeated preparation and implementation of English tests. In this study, we proposed the use of an EEG as an index of the frontal theta band activity involved in concentration with the use of English materials without the need for the repeated preparation and implementation of English tests. Our experiments recorded the EEG signals of 52 participants, divided into two groups of those with beginner and advanced knowledge of English. All participants were asked to attend three sessions: before, during, and after the English lesson and the frontal theta band activities were analyzed as index of concentration for these lessons. The results showed that the frontal theta band activity for advanced students during the English lesson was larger than that for beginner students. Frontal brain activity reflects a common computation recognizing the need for cognitive control, such as concentration on L2 learning. Without dependence on existing tests, this study proposes a method to measure L2 proficiency more quickly than the methods used in previous neuroscience studies. © The Author(s) 2025.

关键词： Concentration Electroencephalogram (EEG) Second-language acquisition Second-language education Time-frequency analysis

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