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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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Critical success factor in Monetizing Blog

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Telkomnika (Telecommunication Computing Electronics and Control) 2016年第2期14卷 757-761页

作者： Rizky, Andika Pardamean, Bens Information System Management Graduate Program Bina Nusantara University Jakarta Indonesia

Blog usage has transformed from its initial function as a digital diary into a legitimate form of mainstream media. Nowadays any member of the public can generate income from blogging through monetization even though various factors affect the results of this process. Four factors were identified: traffic, search engine optimization (SEO), post frequencies, and media usage. This study examines the level of importance of these factors through practical implementation into blogs then quantitatively determine which factor is critical for the success of blog monetization. An empirical analysis based on thirty samples of blogs were performed to assess the impact of the received income. The finding showed that the revenue for most of the blogs increased after the implementation of critical factors with SEO being the most critical of all the factors. © 2016 Universitas Ahmad Dahlan.

关键词： Blogs

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Focal control of non-invasive deep brain stimulation using multipolar temporal interference

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Bioelectronic Medicine 2025年第1期11卷 1-15页

作者： Botzanowski, Boris Acerbo, Emma Lehmann, Sebastian Kearsley, Sarah L. Steiner, Melanie Neufeld, Esra Missey, Florian Muller, Lyle Jirsa, Viktor Corneil, Brian D. Williamson, Adam UMR_1106 INSERM Aix-Marseille Université Marseille France St. Anne’S University Hospital Brno Czech Republic Department of Physiology and Pharmacology Western University LondonONN6A 5B7 Canada Graduate Program in Neuroscience Western University LondonONN6A 5B7 Canada IT’IS Foundation for Research on Information Technologies in Society Zurich8004 Switzerland Department of Mathematics Western University LondonONN6A 5B7 Canada Department of Psychology Western University LondonONN6A 5B7 Canada Robarts Research Institute Western University LondonONN6A 5B7 Canada Center for Social and Affective Neuroscience Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Temporal interference (TI) is a method of non-invasive brain stimulation using transcutaneous electrodes which allows the targeting and modulation of deeper brain structures, not normally associated with non-invasive simulation, while avoiding unwanted stimulation of shallower cortical structures. The properties of TI have been previously demonstrated, however, the problem of decoupling stimulation focality from stimulation intensity has not yet been well addressed. In this paper, we provide a possible novel solution, multipolar TI (mTI), which allows increased independent control over both the size of the stimulated region and the stimulation intensity. The mTI method uses multiple carrier frequencies to create multiple overlapping amplitude-modulated envelopes, rather than using one envelope as in standard TI. The study presents an explanation of the concept of mTI along with experimental data gathered from Rhesus macaques and mice. We improved the focality at depth in anesthetized mice and monkeys, and using the new focality in awake monkeys, evoked targeted activity at depth in the superior colliculus. The mTI method could be an interesting and potentially useful new tool alongside other forms of non-invasive brain stimulation. Teaser Multipolar Temporal Interference Stimulation can produce a more focal brain stimulation at depth compared to Temporal Interference. © The Author(s) 2025.

关键词： Electric fields

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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An approach to parameter optimizer for music mood classification in machine learning

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AIP Conference Proceedings 2025年第1期3223卷

作者： Nunik Destria Arianti T. Thoyyibah Achmad Udin Zailani Neny Rosmawarni Rinna Rachmatika Achmad Lutfi Fuadi Information System University of Nusaputra Sukabumi Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten Indonesia Faculty of Information Technology Veterans National Development University Indonesia

Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.

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Classification machine learning with chromagram and word embedding feature

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AIP Conference Proceedings 2025年第1期3223卷

作者： Thoyyibah Ines Heidiani Ikasari Perani Rosyani Wasis Haryono Sofa Sofiana Neny Rosmawarni Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten South Tangerang Indonesia Faculty of Information Technology Veterans National Development University Jakarta Jakarta Indonesia

Indonesia is a country that is rich in artistic diversity. This art can be a song. In addition to traditional songs, Indonesia is rich in songs from various genres. Some of the genres used by the author are pop, ballad, dangdut. Song or music has become an interesting area of research in computer science. Music recognition is a computational problem in the field of computer vision. Music introduction aims to classify music into several categories such as genre, mood, or music generation. Although in the last ten years a number of studies have been published, the problem of music recognition is still a difficult problem to solve by computers. In addition, it is necessary to explore several features that represent music, including audio spectrograms, chromagrams, and song lyrics. The author processes the lyrics and audio features in the form of a chromagram using many songs. Music can be a collection of data that can be processed by machine learning. The data used for classification is song data in the form of datasets. The author uses his own dataset or manual methods in collecting and processing datasets. The song is downloaded from YouTube after that it is converted into mp3 and cut. The author processed 1181 songs from the 70’s and 80’s era which only took the chorus part. Lyrics in chorus form are saved in excel. The audio chorus is saved in .wav. The results of the Mood classification used by the author in music consist of 3, namely happy, sad and neutral which are processed using machine learning. Using 2 experiments on Chromagram and word embedding feature has a higher value of 81%.

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EUC training: comparison of methods and the role of individual differences

EUC training: comparison of methods and the role of individu...

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Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences: Emerging Technologies and Applications Track

作者： Olfman, Lorne Sein, Maung K. Bostrom, Robert P. Information Science Program Claremont Graduate Sch CA USA

ISBN: (纸本)0818619147

The research reported here is based on a study that compared two seminar/classroom software end-user-computing (EUC) training methods in terms of understanding outcomes. Understanding of the target software, IFPS, was measured in terms of architecture, function, and syntax. Results showed that for function understanding, construct-based training was superior. For syntax understanding, those with a high level of previous experience and low levels of 'crystallized' intelligence had the highest levels of understanding. There were also some near significant interactions between training methods and individual differences. Suggested directions for researchers and implications for practitioners are discussed.

关键词： Computer Software

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Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

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IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

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USING JUPYTER NOTEBOOKS for VIEWING and ANALYSING GEOSPATIAL DATA: TWO EXAMPLES for EMOTIONAL MAPS and EDUCATION DATA

USING JUPYTER NOTEBOOKS for VIEWING and ANALYSING GEOSPATIAL...

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FOSS4G 2021 - Academic Track

作者： Camara, Gabriele Silveira Camboim, Silvana Philippi Bravo, João Vitor Meza Graduate Program in Geodetic Science Federal University of Parana Brazil Institute of Geography Graduate Program in Geography and Graduate Program in Agriculture and Geospatial Information Federal University of Uberlandia Brazil

This article presents two applications developed using Jupyter Notebook in the Google Colab, combining several Python libraries that enable an interactive environment to query, manipulate, analyse, and visualise spatial data. The first application is from an educational context within the MAPFOR project, aiming to elaborate an interactive map of the spatial distributions of teachers with higher education degrees or pedagogical complementation per vacancies in higher education courses. The Jupyter solutions were applied in MAPFOR to better communicate within the research team, mainly in the development area. The second application is a framework to analyse and visualise collaborative emotional mapping data in urban mobility, where the emotions were collected and represented through emojis. The computational notebook was applied in this emotional mapping to enable the interaction of users, without a SQL background, with spatial data stored in a database through widgets to analyse and visualise emotional spatial data. We developed these different contexts in a Jupyter Notebook to practice the FAIR principles, promote the Open Science movement, and Open Geospatial Resources. Finally, we aim to demonstrate the potential of using a mix of open geospatial technologies for generating solutions that disseminate geographic information. Copyright © 2021 G. S. Camara et al.

关键词： Python

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Resampling in neural networks with application to spatial analysis

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Geo-Spatial information Science 2022年第3期25卷 413-424页

作者： Bruno Póvoa Rodrigues Vinicius Francisco Rofatto Marcelo Tomio Matsuoka Talita Teles Assunção Graduate Program in Agriculture and Geospatial Information Federal University of UberlândiaMonte CarmeloBrazil Institute of Geography Federal University of UberlândiaMonte CarmeloBrazil

In developing Artificial Neural Networks(ANNs),the available dataset is split into three categories:training,validation and ***,an important problem arises:How to trust the predic-tion provided by a particular ANN?Due to the randomness related to the network itself(architecture,initialization and learning procedure),there is usually no best *** this issue,we provide a framework,which captures the randomness related to the network *** idea is to perform several training and test trials based on the Jackknife resampling *** consists of iteratively deleting a single observation each time from the sample and recomputing the ANN on the rest of the sample ***,interval prediction is available instead of point *** proposed method was applied and tested using pH,Ca and P data obtained by analyzing 118 georeferenced soil *** results,based on the dataset size simulation,showed that 60%reduction in available dataset offers compatible accuracy in relation to full dataset,and therefore a higher cost of sampling in the field would not be *** re-sampling method spatially characterizes the points of greater or lesser accuracy and *** re-sampling method increased the success rate by using interval prediction instead of using the mean as the most probable *** we restrict it to the regression neural network model,the resampling method proposed can also be extended to other modern statistical tools,such as Kriging,Least Squares Collocation(LSC),Convolutional Neural Network(CNN),and so on.

关键词： Artificial Neural Network(ANN) data splitting resampling delete-1 Jackknife spatial analysis

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