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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Horisaki, Shota Matama, Kazushige Goto, Ren Naito, Katsuhiro Suzuki, Hidekazu Meijo University Graduate School of Science and Technology Nagoya Japan Aichi Institute of Technology Graduate School of Business Administration and Computer Science Toyota Japan Aichi Institute of Technology Faculty of Information Science Toyota Japan Meijo University Faculty of Information Engineering Nagoya Japan

ISBN: (纸本)9798350324136

Internet of Things (IoT) devices are installed in various locations to realize smart cities, smart factories, smart homes, etc., and are connected to the Internet for data communication. Most current IoT systems are built on a client-server model using servers built on the cloud. On the other hand, edge computing services are increasing and there are still demands to manage sensitive data in a local environment, and a local edge computing is emerging as a new concept to address these demands. Next-generation IoT services are expected to increase the importance of interoperability between different IoT systems, and connectivity to the local edge computing environment will be required. The authors have proposed QUIC-based CYPHONIC (CYber PHysical Overlay Network over Internet Communication) as a communication architecture that simultaneously achieves communication connectivity and mobility transparency in a mixed IPv4/IPv6 environment. This paper shows that QUIC-based CYPHONIC can be used to realize end-To-end encrypted communication between local edge devices located in different local networks over the actual Internet environment with practically acceptable performance. © 2024 IEEE.

关键词： End-To-End Connectivity Local Edge Computing Overlay Network QUIC

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Spacecraft potential effects on low-energy ion measurements to be made by probe B1 of Comet Interceptor

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RAS Techniques and Instruments 2024年第1期3卷 333-346页

作者： Bergman, S. Kasahara, S. Stenberg Wieser, G. Division of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Earth and Planetary Science Graduate School of Science The University of Tokyo Swedish Institute of Space Physics

Spacecraft charging causes notorious issues for low-energy plasma measurements. The charged particles are accelerated towards or repelled from the spacecraft surface, affecting both their energy and travel direction. The latter results in a distortion of the effective field of view (FOV) of the instrument making the measurements. The Comet Interceptor mission, planned to be launched in 2029, will make a flyby of a long-period or interstellar comet that ideally is dynamically new. The mission comprises one main spacecraft A, developed by the European Space Agency (ESA), and two sub-probes B1 and B2, developed by the Japan Aerospace Exploration Agency and ESA, respectively. The low-energy plasma measurements made by Comet Interceptor will likely be affected by the spacecraft potential in the case of low relative flyby velocities. On probe B1, the Cometary Ion Mass Spectrometer (CIMS) of the Plasma Suite is an ion mass spectrometer, capable of measuring ions with energies down to 10 eV/q. In this work, we use the Spacecraft Plasma Interaction Software to study the influence of the spacecraft potential on the low-energy ion measurements to be made by CIMS in the inner cometary magnetosphere. The results show that the effective FOV of CIMS is distorted at low energies when the flyby velocity is low. The distortion level is highly geometry dependent, and the largest distortions are caused by the magnetometer boom. Furthermore, the results show that cold ions with bulk velocities in the range 1-10 km s-1, flowing both radially away from and inward towards the nucleus, are detectable by the instrument considering the nominal observation geometry. © 2024 The Author(s).

关键词： Comets Data Methods Instrumentation Numerical Methods Plasma SPIS

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Rethinking MedSAM: Performance Discrepancies in Clinical Applications

Rethinking MedSAM: Performance Discrepancies in Clinical App...

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2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Nouman, Muhammad Khoriba, Ghada Rashed, Essam A. Graduate School of Information Science University of Hyogo Kobe650-0047 Japan School of Information Technology and Computer Science Nile University Giza12677 Egypt

ISBN: (纸本)9798350391213

Segment Anything Model (SAM) has gained significant attention for its versatility and effectiveness in solving various image segmentation problems. Medical image segmentation (MIS) is a complicated problem compared to natural image segmentation, considering the variability in imaging modalities, applications, and clinical requirements. We conduct an evaluation study to validate the usage of MedSAM as a foundation model for the segmentation of medical images, examining its performance in both zero-shot and fine-tuned settings. This paper critically evaluates MedSAM's performance across diverse clinical applications, highlighting performance gaps that question its generalization ability in MIS. Experiments on multiple datasets, including MRI, CT, ultrasound, endoscopy, and retinal fundus images, have shown that MedSAM struggles with complex anatomical structures. Furthermore, the results show that it is sensitive to the bounding box accuracy. These findings suggest that its current implementation lacks the robustness necessary for widespread adoption in clinical practice without fine-tuning. This paper discusses these limitations and proposes strategies for enhancing the MedSAM to address the complexities of MIS. © 2024 IEEE.

关键词： Image segmentation

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Fostering Digital Innovation in Indonesia: The Role of STEM-AI Skills in Overcoming Talent Deficits and Gender Stereotypes 4

Fostering Digital Innovation in Indonesia: The Role of STEM-...

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4th International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2024

作者： Halim, Erwin Zahra, Amalia Maryani Wulandhari, Lili Ayu Fitrianah, Devi Puspita, Virienia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Communication Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

This research explores the optimization of digital talent in advanced industries, particularly in the context of rapid digital transformation. Despite the increasing importance of digital talent for gaining a competitive edge, several challenges persist. In Indonesia, for example, there is a significant shortage of digitally skilled workers, with the World Bank reporting a gap of 9 million skilled individuals over the past 15 years. Additionally, artificial intelligence (AI) is expected to impact 9.5 million jobs, further exacerbating the demand for digital skills. Furthermore, the employability of graduates remains low, largely due to inadequate training, poor language proficiency, and limited cultural sensitivity. Many companies face difficulties in finding qualified individuals, quickly upskilling their workforce, and fostering an innovative organizational culture. This study seeks to evaluate the factors influencing digital talent performance in the context of these challenges. It examines seven key variables: digital readiness, digital technology adoption, relevance of AI, digital skills, individual performance, problem-solving ability, and overall digital talent performance. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data was collected from 378 participants, primarily students with STEM backgrounds, in the Jabodetabek area of Indonesia in November 2024. The findings reveal that all hypotheses are significant, with results indicating that user satisfaction and the perceived value of digital talent in Indonesia have a notable impact on continued usage intentions in digital roles. © 2024 IEEE.

关键词： digital readiness digital talent performances digital technology individual performance STEM-AI

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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2023 International Conference on Informatics Engineering, science and technology, INCITEST 2023

作者： Mimboro, Prasetyo Soewito, Benfano Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Reliability and Incentive of Performance Assessment for Decentralized Clouds

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Journal of computer science & technology 2022年第5期37卷 1176-1199页

作者： Jiu-Chen Shi Xiao-Qing Cai Wen-Li Zheng Quan Chen De-Ze Zeng Tatsuhiro Tsuchiya Min-Yi Guo Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science China University of GeosciencesWuhan 430074China Graduate School of Information Science and Technology Osaka UniversityOsaka 565-0871Japan

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing *** any user or enterprise can be the cloud provider in the decentralized cloud,the performance assessment of the heterogeneous computing resources is of vital ***,with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric,the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized *** this paper,we present a robust performance assessment solution RODE to solve this *** mainly consists of a performance monitoring mechanism and an assessment of the claimed performance(AoCP)*** performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud *** on the performance monitoring results,the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as *** extensive experiments,we show RODE can accurately monitor the performance of cloud providers on the premise of reliability,and incentivize cloud providers to honestly present the performance information and maintain the performance stability.

关键词： decentralized cloud computing robust performance assessment trusted execution environment(TEE)

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Transfer Learning Method for Malaria Identification 8

Transfer Learning Method for Malaria Identification

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herli, Cecilia Audrey Wijaya, Daniel Zerge Hansen Angela, Michelle Pragusma, Zakaria Berlam Wulandari, Ajeng Anggreainy, Maria Susan Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta11530 Indonesia

ISBN: (纸本)9798350342604

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase. © 2023 IEEE.

关键词： Convolutional neural networks

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Anonymous TollPass: A Blockchain-Based Privacy-Preserving Electronic Toll Payment Model

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computers, Materials & Continua 2024年第6期79卷 3495-3518页

作者： Jane Kim Soojin Lee Chan Yeob Yeun Seung-Hyun Seo Department of Electronic&Electrical Engineering Graduate SchoolHanyang UniversitySeoul04763Korea Department of Electrical Engineering and Computer Science Khalifa University of Science and TechnologyAbu Dhabi127788United Arab Emirates School of Electrical Engineering Hanyang UniversityERICAAnsan15588Korea

As big data,Artificial Intelligence,and Vehicle-to-Everything(V2X)communication have advanced,Intelligent Transportation Systems(ITS)are being developed to enable efficient and safe transportation *** Toll Collection(ETC),which is one of the services included in ITS systems,is an automated system that allows vehicles to pass through toll plazas without stopping for manual *** ETC system is widely deployed on highways due to its contribution to stabilizing the overall traffic system *** ensure secure and efficient toll payments,designing a distributed model for sharing toll payment information among untrusted toll service providers is ***,the current ETC system operates under a centralized ***,both toll service providers and toll plazas know the toll usage history of *** raises concerns about revealing the entire driving routes and patterns of *** address these issues,blockchain technology,suitable for secure data management and data sharing in distributed systems,is being applied to the ETC *** enables efficient and transparent management of ETC ***,the public nature of blockchain poses a challenge where users’usage records are exposed to all *** tackle this,we propose a blockchain-based toll ticket model named AnonymousTollPass that considers the privacy of *** proposed model utilizes traceable ring signatures to provide unlinkability between tickets used by a vehicle and prevent the identity of the vehicle using the ticket from being identified among the ring members for the ***,malicious vehicles’identities can be traced when they attempt to reuse *** conducting simulations,we show the effectiveness of the proposed model and demonstrate that gas fees required for executing the proposed smart contracts are only 10%(when the ring size is 50)of the fees required in previous studies.

关键词： Blockchain electronic toll collection smart contract traceable ring signature

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Applications of Artificial Intelligence in Human Resource Management: Theoretical Framework and Future Research Directions 4

Applications of Artificial Intelligence in Human Resource Ma...

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4th International Conference on computer science and Management technology, ICCSMT 2023

作者： Zhang, Xuming Wang, Shilong Peng, Ling School of Management Guangdong University of Science and Technology Dongguan523083 China Graduate School of Computer Science and Technology Harbin Institute of Technology Harbin150000 China

ISBN: (纸本)9798400709517

With the rapid advancement of artificial intelligence (AI) technologies, their incorporation into human resource management (HRM) has grown increasingly prevalent. AI tools have substantially enhanced the precision of talent acquisition, predictive performance analytics, and the formulation of responsive HR strategies and human resource management (HRM). However, challenges persist, including data privacy concerns, potential biases in AI algorithms, and retaining a human-centric approach amidst automation. This paper delves into the diverse applications of AI within HRM, encompassing areas such as recruitment screening, employee engagement analysis, and forward-thinking HR planning. Looking ahead, organizations must deeply contemplate the ethical implications of these technologies, endeavoring to design transparent, accountable, and unbiased AI systems while striking a balance between technological efficiency and human intuition and judgment. © 2023 ACM.

关键词： Human resource management

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