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4th Asia Service sciences and Software engineering Conference, ASSE 2023

作者： Cowan, Brendan Watanobe, Yutaka Shirafuji, Atsushi Graduate Department of Computer and Information Systems University of Aizu Aizu-Wakamatsu Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798400708534

Due to their robustness, large language models (LLMs) are being utilized in many fields of study, including programming and education. Notably, they can be used by programmers by interfacing with their IDEs to assist with development, and in education by giving students meaningful and immediate feedback. In this paper, we propose and explore the groundwork of a framework designed to combine these two applications of LLMs. The framework acts as a facilitator between the LLM and the student by reading the student's prompts before filtering and modifying them and sending them to the LLM. The intent is that this will improve the responses from the LLM, thereby improving the student's learning experience. We discuss the framework in detail and analyze the value of individual responses returned from the LLM as a result of our framework. We conclude that the framework causes the LLM to give helpful responses in comparison to how it would respond without the framework. © 2023 Owner/Author.

关键词： Students

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Control and Data Integrity are Important Factors of Data Governance Technology 10

Control and Data Integrity are Important Factors of Data Gov...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Chandra, Yakob Utama Prabowo, Harjanto Gaol, Ford Lumban Purwandari, Betty School of Information Systems Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia BINUS Business School Bina Nusantara University Doctor of Research Management Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350339543

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: 'What are the important factors that determine the utilization of data governance technology?' Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity. © 2023 IEEE.

关键词： Digital storage

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design 9

Confidentiality. Integrity, and Availability (CIA) Approach ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Ariansyah, Dedy Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used. © 2024 IEEE.

关键词： Data integrity

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Greeting Gesture Analysis Using Boosting Algorithms and SHAP 13

Greeting Gesture Analysis Using Boosting Algorithms and SHAP

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Wibowo, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of Systems Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798350373332

Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classification and prediction tasks. However, the first problem is that there is rarely any research on human activities that reflect the culture of a region, such as greeting gestures. Second, there are few studies that do not provide models resulting from the classification or prediction process using machine learning, which can result in a lack of understanding of how a model from machine learning is generated. This study aims to predict Japanese greeting gestures (eshaku, keirei, saikeirei, waving hand) and Indonesian (placing both hands in front of the chest) using Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting. Then, represent the model of the learning machine using SHapley Additive exPlanations (SHAP) to find out how the learning machine obtain a model. Our findings show the efficacy of this method and show that the model not only predicts greeting gestures with high accuracy but also offers a transparent understanding of the factors influencing its predictions, thus enhancing the interpretability and applicability of machine learning in recognizing culturally significant human activities. In the future, we plan to use machine learning and other Explainable Artificial Intelligence (XAI) methods to study human activities with cultural concepts such as greeting gestures in different styles, create more greeting gesture-based human activity datasets, and examine the ethical implications or considerations of using machine learning in cultural contexts to respect cultural differences. © 2024 IEEE.

关键词： Contrastive Learning

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Effect of Sn addition on electrochemical activity of ribbons of Mg-Al-Ca anode material for rechargeable magnesium batteries fabricated by rapid solidification

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Keikinzoku/Journal of Japan Institute of Light Metals 2025年第1期75卷 9-13页

作者： Fukuta, Yoshitaka Kikura, Kensei Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawagun Toyama Japan Saitama Industrial technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium-based batteries are potential candidates for next-generation rechargeable batteries due to the divalent nature of magnesium cations and the natural abundance of magnesium resources. In this study, the electrochemical activity of Mg-6mass%Al-3mass%Ca anodes combined with Sn was investigated. Thin ribbons of the anode specimens were prepared by a single-roll rapid solidification method. The results showed that the electrochemical activity increased upon adding 1 mass% Sn, while it decreased for 3 mass% Sn. Based on the results of an X-ray diffraction analysis, the addition of Sn led to the formation of Mg2Sn and Ca2Sn phases, which increased the lattice distortion. Scanning electron microscopy observations indicated that these secondary phases were homogeneously distributed for 1 mass% Sn addition, while they were coarsely distributed for 3 mass% Sn. © 2025 The Japan Institute of Light Metals.

关键词： X ray diffraction analysis

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Integrated preparation and manipulation of high‑dimensional flying structured photons

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eLight 2024年第1期4卷 185-192页

作者： Haoqi Zhao Yichi Zhang Zihe Gao Jieun Yim Shuang Wu Natalia MLitchinitser Li Ge Liang Feng Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Materials Science and Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering and Department of Physics Duke UniversityDurhamNC 27708USA Department of Engineering Science and Physics College of Staten IslandCUNYStaten IslandNY 10314USA The Graduate Center CUNYNew YorkNY 10016USA

The hope for a futuristic global quantum internet that provides robust and high-capacity quantum information transfer lies largely on qudits,the fundamental quantum information carriers prepared in high-dimensional superposition ***,preparing and manipulating N-dimensional flying qudits as well as subsequently establishing their entanglement are still challenging tasks,which require precise and simultaneous maneuver of 2(N-1)parameters across multiple degrees of ***,using an integrated approach,we explore the synergy from two degrees of freedom of light,spatial mode and polarization,to generate,encode,and manipulate flying structured photons and their formed qudits in a four-dimensional Hilbert space with high quantum fidelity,intrinsically enabling enhanced noise resilience and higher quantum data *** four eigen spin–orbit modes of our qudits possess identical spatial–temporal characteristics in terms of intensity distribution and group velocity,thereby preserving long-haul coherence within the entirety of the quantum data transmission *** leveraging the bi-photon entanglement,which is well preserved in the integrated manipulation process,we present versatile spin–orbit cluster states in an extensive dimensional Hilbert *** cluster states hold the promise for quantum error correction which can further bolster the channel robustness in long-range quantum communication.

关键词： cluster thereby preparing

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. © 2024 The Authors.

关键词： Prediction models

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Classification of Alzheimer's Disease using Random Oversampling and Albumentations on Convolutional Neural Network 8

Classification of Alzheimer's Disease using Random Oversampl...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

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Disaster recovery plan implementation evaluation model at the corporation 7

Disaster recovery plan implementation evaluation model at th...

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7th Information systems International Conference, ISICO 2023

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Putra, Gustian Rama Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

The application of information technology in addition to making internal business processes more effective and efficient, is also to improve customer service. This role is important for companies to maintain the sustainability of information systems as a strategic tool for competitive advantage so system availability is absolutely essential. Research related to the Disaster Recovery Plan (DRP) needs to be carried out to see the readiness of a corporation to build a backup system in different places and locations. By using the method of factor analysis and regression analysis to form an evaluation model, it will obtain an overview of what factors must be considered and what the current conditions are. The results of the study show that there are Core Technical DRP, Organizational Preparation, and Costing factors which are the main variables in supporting DRP readiness. The conclusion from this study, through these three new factors a mathematical model can be built as a reference for improving DRP implementation in corporations. © 2023 The Authors. Published by Elsevier B.V.

关键词： Factor analysis

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