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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Machine Learning Approach to Malware Classification Using Byte n-grams on IoT Devices 19

Machine Learning Approach to Malware Classification Using By...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yama, Yuto Uda, Ryuya Graduate School of Bionics Computer and Media Science Tokyo University of Technology Graduate School 1404–1 Katakuramachi Hachioji Tokyo192–0982 Japan

ISBN: (纸本)9798331507817

Malware for IoT devices has become popular in recent years. Many detection methods have been proposed to detect the malware, mainly using machine learning, but they are not designed to work on IoT devices and are often implemented using Python. Running a Python program requires the installation of a package, which is impractical given the memory and storage size of IoT devices. In addition, methods that require decompilation, static and dynamic analysis of test samples are difficult to run on IoT devices. Therefore, in this research, we propose a method implemented in C/C++ so that malware detection using byte n-grams and machine learning can be run on IoT devices, and then resource consumption can be measured. Byte n-gram methods are good approaches to detect malware without knowing the execution environment. Moreover, the Top-L approach to information gain can be effectively applied to reduce storage and memory consumption. As a result of the evaluation, when using an SVM model with Top-50000 that maintains effective classification accuracy, the memory consumption was 71.91MB and the storage consumption was 1.3GB, which can be implemented in IoT devices. © 2025 IEEE.

关键词： C++ (programming language)

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Security and Privacy Issues in Cloud-Based Databases: A Literature Review 10

Security and Privacy Issues in Cloud-Based Databases: A Lite...

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

作者： Marpaung, Oliver Sebastian Alvyn, David Adriel William, Vincent Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

Nowadays, technology is getting more sophisticated. It can be seen with the emergence of Cloud Computing. One application of Cloud Computing is Cloud databases. In a cloud environment, there will be privacy and security issues. From the literature results, data leaks and attacks from outsiders are still found when using cloud databases. Cloud service providers already have the technology to maintain data privacy and security. Three procedures can be carried out to maintain data privacy and security: encryption, access control, and third-party audits. Data privacy and security will be maintained with cloud service providers and procedures technology. © 2023 IEEE.

关键词： Cryptography

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Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5791-5804页

作者： Cheng, Hailun Huang, Shenjin Cai, Linghan Xu, Yangfan Wang, Runming Zhang, Yongbing Tsinghua University Institute of Biopharmaceutical and Health Engineering Shenzhen International Graduate School Shenzhen518055 China Harbin Institute of Technology Faculty of Computing Harbin150001 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally employ attention mechanisms to aggregate instance-level features. However, the weakly supervised signal and the imbalanced instance distribution often lead to inaccurate attention localization, compromising the performance and generalization capability of the MIL framework. To address these problems, this paper presents a novel MIL framework called FAMIL that focuses on inaccurate attention and refines them. FAMIL adopts a dual-branch structure and incorporates two innovative online data augmentation strategies: attention-based Mixup (ABMix) and attention-based Masking (ABMask). ABMix emphasizes the significance of positive instances, generalizing Mixup in the MIL scenarios, while ABMask flexibly identifies challenging positive instances to optimize the feature representation. Moreover, these two methods are plug-and-play and can be easily embedded into attention-based MIL methods. Extensive experiments on three public benchmarks demonstrate the superiority of our FAMIL, outperforming current state-of-the-art methods. The test AUC for the binary tumor classification can be up to 92.61% over CAMELYON16. And the AUC over the cancer subtype classification can be up to 93.81% and 98.41% on TCGA-NSCLC and TCGA-RCC datasets, respectively. © 1991-2012 IEEE.

关键词： Image classification

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Social Network Analysis of Cryptocurrency using Business Intelligence Dashboard

HighTech and Innovation Journal

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HighTech and Innovation Journal 2022年第2期3卷 220-229页

作者： Setyono, Jonathan C. Suryawidjaja, William S. Girsang, Abba S. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

There are currently more than 10.000 cryptocurrencies available to buy from the online market, with a vast range of prices for each coin it sells. The fluctuation of each coin is affected by any social events or by several important companies or people behind it. The aim of this research is to compare three cryptocurrencies, which are Bitcoin, Ethereum, and Binance Coin, using Social Network Analysis (SNA) by visualizing them using Business Intelligence (BI Dashboard). This study uses the SNA parameters of degree, diameter, modularity, centrality, and path length for each network and its actors and their actual market price by crawling (data collecting process) from Twitter as one of the social media platforms. From the research conducted, the popularity of cryptocurrencies is affected by their market price and the activeness of their actors on social media. These results are important because they could help in the decision-making to buy cryptocurrencies with high popularity on social media because they tend to retain their value over time and could benefit from price spikes from influential people. © Authors retain.

关键词： Decision making

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Retinal Arteries and Veins Semantic Segmentation Using Modified U-Net Architecture

Retinal Arteries and Veins Semantic Segmentation Using Modif...

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

作者： Holiel, Heidi Ahmed El-Beheiry, Aya Rashed, Essam A. School of Information Technology and Computer Science Nile University Giza Egypt Graduate School of Information Science University of Hyogo Kobe650-0047 Japan

ISBN: (纸本)9798350391213

This paper presents a modified U-Net architecture designed for the semantic segmentation of retinal arteries and veins using infrared reflectance retinal imaging. Leveraging the RAVIR dataset, which comprises 46 high-quality retinal images, we focus on addressing the challenges of low contrast and varying lighting conditions often found in retinal images. Our approach incorporates advanced features such as bilinear interpolation and attention mechanisms to enhance segmentation accuracy. The modified U-Net architecture achieves a mean Intersection over Union (mIoU) of 0.8855 and a Dice coefficient of 0.9382, showing a significant improvement over the traditional U-Net. These results demonstrate its potential for practical applications in retinal disease diagnosis and monitoring. © 2024 IEEE.

关键词： Semantic Segmentation

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Observer-Structured PID Controller: Flight Controller Design Examples 4

Observer-Structured PID Controller: Flight Controller Design...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Sato, Masayuki Sebe, Noboru Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (纸本)9798350391183

This note shows two design examples of PID con-Trollers, whose state variables estimate the selected state vari-Ables of the controlled plant systems, for LTI Parametrically Dependent (LTIPD) systems. The authors have already proposed observer-structured controllers, whose structure is not the same as but is very similar to the well-established Luenberger observer-based controllers, and have also proposed a conversion method from a priori designed unstructured LTI or Linear Parameter-Varying (LPV) controllers to observer-structured LTI/LPV con-Trollers with the plant state estimation ability implemented. How-ever, the successful conversion has been reported only for full-order controllers. In this note, we show two examples, in which a priori designed LTI PID controllers are successfully converted to observer-structured PID controllers, and the designed observer-structured PID controllers well estimate the selected plant state variables in numerical simulations. © 2024 IEEE.

关键词： Three term control systems

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EarLock: Personal Authentication System for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6183-6196页

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Yokohama108-0073 Japan Future University Hakodate Department of Media Architecture Hokkaido 041-0803 Japan Hokkaido University Graduate School of Information Science and Technology Sapporo060-0808 Japan University of Tsukuba Department of Computer Science Tsukuba305-8577 Japan

Earphone-type wearable devices, also known as "hearables," will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock’s authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2–93.7%, 83.4– 94.7%, and 85.9–90.0%. © 2002-2012 IEEE.

关键词： Information leakage

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Multi-dimensional ability diagnosis for machine learning algorithms

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science China(Information sciences) 2024年第12期67卷 321-322页

作者： Qi LIU Zheng GONG Zhenya HUANG Chuanren LIU Hengshu ZHU Zhi LI Enhong CHEN Hui XIONG State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Analytics and Statistics The University of Tennessee Computer Network Information Center Chinese Academy of Sciences Shenzhen International Graduate School Tsinghua University Hong Kong University of Science and Technology (Guangzhou)

A significant proportion of noticeable improvement in machine learning architectures actually benefits from the consistent inspiration of the way human learning [1]. For instance, curriculum learning [2] is inspired by highly organized human education systems, i.e., training the algorithms with easy samples first and gradually transforming to the hard examples can contribute to faster convergence and lower generalization error.

关键词： Adversarial machine learning

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Multimodal Large Models are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230022 China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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