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

作者： Ming-Yi Hong Shih-Yen Chang Hao-Wei Hsu Yi-Hsiang Huang Chih-Yu Wang Che Lin Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis.

关键词： Training Knowledge engineering Deep learning Signal processing Feature extraction Graph neural networks Decision trees

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

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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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Analysis of Shoping Intention Using E-Commerce during Pandemic 8

Analysis of Shoping Intention Using E-Commerce during Pandem...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Wijaya, Alessandro Bryan Tio, Fendy Malaysiani, Liza Azzahra Sanjaya, Sandi Martin, Yessin Rio Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665416979

This study aims to investigate people's shopping intentions using e-commerce during the pandemic in Indonesia. The method used in this research is the quantitative method in which the results of the study are used as an assumption and research discussion. Data collection was carried out by questionnaires then measure using t-test. The result can be concluded that the intention to shop using e-commerce during the COVID-19 pandemic is influenced using e-commerce features and e-commerce techniques. These results indicate that the intention to shop using e-commerce can increase if the availability of goods in e-commerce is quite complete, the quality of goods sold is very high, normal prices and technical requirement, such as speed, security, and a good e-commerce design system. © 2021 IEEE.

关键词： COVID-19

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

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Procedia Computer Science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

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Big Data Approach for Sustainable Environmental Design

Big Data Approach for Sustainable Environmental Design

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Kruti Jani M. Rajyalaxmi Anita Gehlot Marwah A. Halwani Aparna Marwah Daljeet Singh Bawa Shri Chimanbhai Patel Post Graduate Institute Of Computer Application Ahmedabad School of Business SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India Management Information System Department King Abdulaziz University Rabigh Department of Management Studies BVIMR Delhi

The concept of sustainable environment enhancement has drawn attention on a global scale. This study examines the connection between environmental design as well as the long-term development of a sustainable environment in the modern era using a big data approach. Big data analytics is the challenging process of sifting through vast amounts of data to discover subtle data like market patterns, connections, and client preferences. To evaluate commercial big data, we proposed a novel, highly efficient approach for controlling the sustainable environmental supply system. Additionally, this lessens the hazard that comes with using hazardous substances. This research covers both economic expenses and carbon emissions in 3 contexts. The results show that the environment's sustainability scale will expand as the Binhai region of China is further developed, the commercial structure is changed, and the public's view of urban sustainable advancement steadily rises.

关键词： Costs Green design Carbon dioxide Computer architecture Big Data Market research Hazards

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FincGAN: A Gan Framework of Imbalanced Node Classification on Heterogeneous Graph Neural Network

FincGAN: A Gan Framework of Imbalanced Node Classification o...

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

作者： Hung Chun Hsu Ting-Le Lin Bo-Jun Wu Ming-Yi Hong Che Lin Chih-Yu Wang Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Department of Accounting and Statistics National ChengChi University Taiwan Department of Risk Management and Insurance National ChengChi University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images 5

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement

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Optimization and Computing Model of Fish Resource Supply Chain Distribution Network 5

Optimization and Computing Model of Fish Resource Supply Cha...

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5th International Conference on Computing and Applied Informatics, ICCAI 2020

作者： Nurdin Zarlis, M. Tulus Efendi, S. Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sumatera Utara Medan Indonesia

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers. © Published under licence by IOP Publishing Ltd.

关键词： Supply chains

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Model of Sustainable Development Based on FinTech in Financial and Banking Industry: A Mixed-Method Research 3

Model of Sustainable Development Based on FinTech in Financi...

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3rd International Conference on Computer and Informatics Engineering, IC2IE 2020

作者： Legowo, Mercurius Broto Subanidja, Steph Sorongan, Fanky Antoneus Information Technology Faculty Institute of Perbanas Information System Study Program Jakarta Indonesia Post Graduate School Institute of Perbanas Master of Management Program Jakarta Indonesia Economic and Business Faculty Institute of Perbanas Accounting Study Program Jakarta Indonesia

ISBN: (纸本)9781728182476

In the past decade, it stated that the discourse on Sustainable Development and the Financial technology(FinTech) role in the financial and banking industries, has attention on researchers, practitioners, regulators, and policymakers. The real problem arises from the discourse on whether FinTech has an effect on sustainable development in the financial and banking industry in Indonesia. This discourse needed to be understood better and more deeply. For credible research results, it is necessary to use a mixedmethod research approach. This research aims to develop a model that will help uncover the phenomenon of FinTech to sustainable development in the Financial and Banking Industry in Indonesia, using mixed methods research. In this study, Exploratory Sequential Mixed Method used as a type of mixedmethod approach. The main results of this study reveal that FinTech, which is influenced by business drivers, has a significant role in sustainable development in the Financial and Banking Industry in Indonesia. The contribution of this research can provide insight and understanding for researchers and practitioners in the financial and banking industry who want to implement a sustainable development model based on FinTech in more depth. © 2020 IEEE.

关键词： Sustainable development

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Facilitating decision making in authentic contexts: an SVVR-based experiential flipped learning approach for professional training

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Interactive Learning Environments 2023年第8期31卷 5219-5235页

作者： Hsin Huang Gwo-Jen Hwang Shao-Chen Chang a Department of Nursing Tri-Service General Hospital National Defense Medical Center Taipei Taiwan b Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan c International Bachelor Program in Informatics and the Department of Information Communication Yuan Ze University Chung-Li Taiwan

ABSTRACTFacilitating decision making in authentic contexts is an important educational objective of professional training. To enable learners to have more opportunities to practice in authentic contexts under the guidance of instructors, the flipped learning mode of shifting the lectures to be the pre-class activity with the presentation of digital media has gradually been receiving attention. However, the conventional flipped learning mostly uses videos to present teaching content. In such a learning environment with one-way information and lack of experience, it is not easy for most learners to experience the actual situation encountered in the nursing procedure, which affects their judgment and performance when dealing with actual scenarios. To solve this problem, the present study adopted spherical video-based virtual reality (SVVR) to provide an experiential learning mode in flipped learning. To verify the effects of this teaching mode, this study conducted an experiment in a blood transfusion safety training course. The experimental group used the SVVR-based experiential flipped learning (SVVR-EFL) mode, while the control group used the conventional flipped learning mode. The results showed that using the SVVR-EFL mode not only enhanced new nursing staff’s learning achievement, but also increased their decision-making performance, meta-cognition tendency, problem-solving tendency and classroom engagement.

关键词： Flipped learning virtual reality experiential learning decision making professional training

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