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International Conference on Information Management and Technology (ICIMTech)

作者： Gede Indra Raditya Martha Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.

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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait

Multilabel Machine Learning System for Predicting Gender and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Vincent Yono Dimitar Kazakov Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department University of York United Kingdom

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm.

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Determinant Factors Affecting the Information System Success of Community-Based Application to Fight Covid-19 Pandemic

Determinant Factors Affecting the Information System Success...

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2021 International Conference on computer, Control, Informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Purwanto, Adnan Asih, Dybio Dompu Hot Nurjabar, Ilham Cahyani, Trisiwi Indra Hidayat, Deni Manik, Lindung Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

National governments around the world have made every effort to fight Covid-19. One of which is by building mobile applications that can be used to trace and monitor the citizens' health during this pandemic. These applications are expensive. It requires a significant amount of time and effort to develop and maintain the information systems. Therefore, there is a continuous struggle to identify the key factors influencing the information systems' adoption, utilization, and success. This study was performed to address the problem by identifying parameters that affect the information system success of "10 Rumah Aman (10 Safe Houses)", a community-based application in fighting the Covid-19 pandemic, using the DeLone and McLean (D&M) model. As a result, system quality positively affected the use and user satisfaction. Furthermore, service quality positively influenced use but not user satisfaction. Meanwhile, information quality had no significant effect either on use or satisfaction. Both use and user satisfaction had a significant effect on net benefits. © 2021 ACM.

关键词： Information systems

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Mobile Application as Community Social Media

Mobile Application as Community Social Media

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International Conference on Inventive Computation Technologies (ICICT)

作者： Teuku Ryan Pahlevi Johannes Haryanto Rahmat Adha Bayu Alfian Harco Leslie Hendric Spits Warnars Rahayu Widiono Computer Science Department School of Computer Science. Bina Nusantara University Jakarta Indonesia Information Systems department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia

To day's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle of today's people, which of course almost half of their daily life is spent in front of gadgets. On the one hand, this is undoubtedly very good and supports humans in carrying out their daily life activities, but on the other hand, this raises new problems where humans start to lack time to communicate and socialize with each other and often making them stressed and anxious because they are already tired and do not know how to socialize anymore. This paper begins with a model and implementation of a very simple mobile application that behaves as a social media inspired by *** as a social media for academics. The design of this mobile application is done by using use case diagrams to describe existing processes and class diagrams to describe class relationships.

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XPACE and TASC Modeler: Tool support for data-driven context modeling

arXiv

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arXiv 2022年

作者： Falcão, Rodrigo King, Rafael Carvalho, Antônio Lázaro Fraunhofer IESE Kaiserslautern Germany Computer Science Graduate Program Federal University of Bahia Salvador Brazil

From a requirements engineering point of view, the elicitation of contextaware functionalities calls for context modeling, an early step that aims at understanding the application contexts and how it may influence user tasks. In practice, however, context modeling activities have been overlooked by practitioners due to their high complexity. To improve this situation, we implemented xPACE and TASC Modeler, which are tools that support the automation of context modeling based on existing contextual data. In this demonstration paper, we present our implementation of a data-driven context modeling approach, which is composed of a contextual data processor (xPACE) and a context model generator (TASC Modeler). We successfully evaluated the results provided by the tools in a software development project. © 2022, CC BY.

关键词： Software design

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Microprocessor Instruction Design Tool for RISC-V Architecture

Microprocessor Instruction Design Tool for RISC-V Architectu...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Luo Jiahui Tomoyuki Morimoto Tadahiro Ogita Ryota Kawamata Wang Ziming Toshiyuki Tsutsumi Computer Science Program Graduate School of Science and Technology Meiji University Kanagawa Japan VIAX Co.Ltd. Department of Computer Science School of Science and Technology Meiji University Kanagawa Japan TOYOTA MOTOR CORPORATION. SUS Co. Ltd.

We have been developing MEIMAT (MEiji Microprocessor Architecture Design Tools). The MEIMAT is essentially designed to be able to represent any instruction of various processors by the MEIMAT meta-instruction in two ways of semantic and functional expressions. In this paper, we have verified the RISC-V support to the MEIMAT, and have improved so that MEIMAT further supports RISC-V. First, we have verified whether the RISC-V instructions can be expressed by the MEIMAT semantic expression. It is also verified that the functional expression is automatically generated from the semantic instruction, and the corresponding correct circuit diagrams are generated in the MEIMAT instruction visualization tool. Then, we have newly developed a stage configuration mechanism to implement the RISC-V instructions. Because the RISC-V architecture is implemented by 4 or 5 processing stages in most actual processors. To develop further an easy-to-understand design tool, we have also introduced illustration windows for RISC-V specified hardware modules such as a register file, especially a status register in the instruction visualization tool. Finally, through these verifications and improvements, we have successfully confirmed that the 43 instructions in the RISCV (RV32I) instructions set of the RISC-V architecture can be converted into MEIMAT meta-instructions, and then they can be represented as circuit diagrams with the RISC-V stage configuration in the MEIMAT instruction visualization tool.

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UAV-Aided Data Acquisition Using Gaining-Sharing Knowledge Optimization Algorithm

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computers, Materials & Continua 2022年第9期72卷 5999-6013页

作者： Rania M Tawfik Hazem A.A.Nomer M.Saeed Darweesh Ali Wagdy Mohamed Hassan Mostafa Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt School of Information Technology and Computer Science(ITCS) Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Science and EngineeringThe American University in CairoEgypt Electronics and Communications Engineering Department Cairo UniversityEgypt University of Science and Technology Nanotechnology and Nanoelectronics ProgramZewal City of Science and TechnologyOctober Gardens6th of OctoberGiza12578Egypt

Unmanned Aerial Vehicles(UAVs)provide a reliable and energyefficient solution for data collection from the Narrowband Internet of Things(NB-IoT)***,the UAV’s deployment optimization,including locations of the UAV’s stop points,is a necessity to minimize the energy consumption of the UAV and the NB-IoT devices and also to conduct the data collection *** this regard,this paper proposes GainingSharing Knowledge(GSK)algorithm for optimizing the UAV’s *** GSK,the number of UAV’s stop points in the three-dimensional space is encapsulated into a single individual with a fixed length representing an entire *** superiority of using GSK in the tackled problem is verified by simulation in seven *** provides significant results in all seven scenarios compared with other four optimization algorithms used before with the same ***,the NB-IoT is proposed as the wireless communication technology between the UAV and IoT devices.

关键词： NB-IoT UAV GSK stop points optimization

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Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube

Robot Finder v2: Search and Ingestion of Educational Robotic...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Gabriel Batistuta Urbano Lopes Ryllari Raianne Marques de Santana Costa Sebastião Emidio Alves Filho B.Sc. in Computer Science Rio Grande do Norte State University Mossorô RN Brazil Graduate Program in Computer Science - PPgCC UERN/UFERSA Rio Grande do Norte State University and Federal University of the Semi-Arid Region Mossoró RN Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Educational Robotics offers an interactive learning approach where students can turn abstract theories into concrete projects. With the exponential growth in content creation on internet platforms, finding specific, high-quality learning objects about robotics in education can be challenging. Platforms like YouTube, for example, have a vast collection of educational videos that cover various topics related to educational robotics. However, they are scattered among a massive amount of available data, making it difficult to have more assertive searches and identify the best resources. This work aims to present Robot Finder v2, a software that uses an automated approach to feed RepositORE, a repository of Learning Objects in Educational Robotics. The Robot Finder v2 searches and stores data from YouTube videos to make the search for this content simpler and more effective. The collected data feeds the RepostiORE, where these objects can be adapted and researched by users looking to acquire skills in educational robotics. For the objects to be found accurately, the software uses the YouTube Data API v3, which has a set of metadata in addition to the descriptions of the videos provided by their creators. The processing done in the Robot Finder optimizes the search and registration of information by mapping API endpoints and seeking correlations of metadata between RepositORE objects and YouTube videos.

关键词： Video on demand Correlation Education Metadata Search problems Software Web sites Feeds Robots Videos

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Security Risks and Best Practices for Blockchain and Smart Contracts: A Systematic Literature Review

Security Risks and Best Practices for Blockchain and Smart C...

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International Conference on Information Management and Technology (ICIMTech)

作者： Semi Yulianto Harco Leslie Hendric Spits Warnars Harjanto Prabowo Meyliana Achmad Nizar Hidayanto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

The rise of blockchain technology and smart contracts has brought widespread attention due to their capacity to transform multiple industrial sectors through decentralized, transparent, secure transactions. However, despite their promise to revolutionize various fields worldwide, lingering concerns regarding security risks impede their adoption rate. Addressing these concerns is crucial now more than ever; therefore, we conducted a comprehensive literature review within our study's scope that focused on published papers between 2014-2023 centered around security risks concerning blockchain and smart contracts. Our systematic approach using the PRISMA checklist analyzed nine categorized research model-based primary studies while recognizing vulnerabilities in smart contract development and providing best practices to mitigate such issues. These findings benefit both researchers and practitioners as they showcase how acknowledging these vulnerabilities can further develop into exploring more significant aspects of blockchain technology's security issues and smart contract development processes. Our study contributes significantly by expanding knowledge in this field while providing novel insights valuable for individuals involved in designing or implementing blockchain technologies.

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： computerized tomography

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