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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Ogawa, Ryuhei Sato, Naoto Saito, Atsushi Singh, Mithun Kuniyil Ajith Kawamoto, Hiroaki Sankai, Yoshiyuki Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Tsukuba Japan Research & Development Division Cyberdyne Inc. Tsukuba Japan Research & Business Development Division Cyberdyne Inc. Rotterdam Netherlands Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781510683860

LED-based photoacoustic imaging (PAI) with linear array ultrasound probes has gained attention due to its cost-effectiveness, safety, and potential for portable applications. However, its low signal-to-noise ratio (SNR) and limited visibility of blood vessel lumens remain challenges. To address these issues, we propose an innovative deconvolution approach that leverages the differential of the LED light pulse to enhance photoacoustic image quality. Our optimized reconstruction algorithm improves blood vessel visualization by applying deconvolution of the PA signals with the differential of the LED pulse, leading to improved lumen visibility and enhanced imaging of peripheral blood vessels in both 2D and potentially 3D. Our method precisely measures the LED pulse profile using the LED arrays integrated into our AcousticX system. The second differential of this pulse is then utilized as the point spread function (PSF) in the deconvolution process. This technique effectively enhances PA signals by emphasizing the leading and trailing edges of the pulse, thereby improving blood vessel visibility and overall image quality. To validate our approach, we conducted experiments using tissue-mimicking phantoms and human volunteers with the AcousticX system. The algorithm was tested offline in MATLAB and in real-time on the AcousticX platform. Results demonstrate substantial artifact suppression, improved SNR, and enhanced image contrast, ultimately enabling clearer visualization of the blood vessels. This advancement improves the competitiveness of LED-based PAI systems compared to laser-based alternatives and broadens their potential for clinical applications, accelerating their translation into medical practice. © 2025 SPIE.

关键词： Ultrasonic imaging

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer Knowledge Transfer, and the Mediating Role of IS/IT

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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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A Secure NFC Cardless Cash Withdrawal System 5th

A Secure NFC Cardless Cash Withdrawal System

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5th International Conference on Financial Technology, ICFT 2024

作者： Hwang, Min-Shiang Lin, Cheng-Ying Chang, Chun-Hsien Yang, Cheng-Ying Department of Computer Science and Information Engineering Asia University Taichung Taiwan Fintech and Blockchain Research Center Asia University Taichung Taiwan The Ph.D. Program in Artificial intelligence Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science University of Taipei Taipei Taiwan

ISBN: (纸本)9789819638109

As IoT technology grows, several different payment methods are available now. NFC (Near Field Communication) as a short-range communication technology has been applied in many fields, such as contactless payment and electronic tickets. ATM (Auto Teller Machine) also benefits from NFC technology as one way of cardless cash withdrawal. However, there are many security problems, and convenience problems exist. In security, certain kinds of attacks, such as man-in-the-middle, replay, and brute-force attacks, must be considered. These attacks may influence user privacy. Furthermore, considering user experience, existing methods may not make users feel enough convenience when using. To enhance security and user experience, we proposed a secure NFC cardless cash withdrawal system using time stamps and nonce to reduce the risk of attack and design more effortless operations for users to enhance user experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic teller machines

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

引用

arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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Generative AI-Driven Data Augmentation for Robust Virtual Metrology: GANs, VAEs, and Diffusion Models

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Lin, Chin-Yi Tseng, Tzu-Liang Emon, Solayman Hossain Tsai, Tsung-Han Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El Paso 79968 TX United States Computational Science Program University of Texas at El Paso El Paso 79968 TX United States Institute of Information and Decision Sciences National Taipei University of Business Taipei 10051 Taiwan

Advanced semiconductor manufacturing increasingly depends on Virtual Metrology (VM) for real-time quality monitoring, yet conventional data-driven models rarely capture the scarce or extreme process conditions critical for robust predictions. We propose a Multi-Stage Constrained Data Generative Augmentation (MSC-DGA) framework that integrates Variational Autoencoders (VAE), Normalizing Flows, and Constrained Diffusion to systematically expand coverage of seldom-seen regimes. By embedding strict engineering constraints during generation and applying a two-stage quality filter, MSCDGA ensures physically plausible synthetic samples. We further present theoretical proofs showing that multi-stage generation can approximate complex sensor distributions while enforcing domain validity, thereby improving coverage and preserving essential process physics. Empirically, we demonstrate the approach on real WBG SiC data, incorporating these curated samples into a Generative Foundation Model (GFA-VM) with few-shot finetuning and uncertainty-based active sampling, yielding significant accuracy gains for rarely observed conditions. Experiments confirm notable performance improvements over single-stage augmentation and naive oversampling. By rigorously balancing distribution realism with engineering feasibility, MSC-DGA offers a practical and theoretically grounded advancement for VM, enhancing adaptive control and product quality in next-generation power semiconductor manufacturing. © 2024 IEEE.

关键词： Advanced Process Control (APC) Data augmentation Diffusion models Generative models Physics-informed constraints Rare event coverage Semiconductor manufacturing Variational Autoencoders (VAE) Virtual Metrology (VM)

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Entity-relationship and object-oriented data modeling - An experimental comparison of design quality

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DATA & KNOWLEDGE engineering 1997年第3期21卷 297-315页

作者： Shoval, P Shiran, S Information Systems Program Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva 84105 Israel

We compare EER and OO data models from the point of view of design quality. Quality is measured in terms of (a) correctness of the conceptual schemas being designed, (b) time to complete the design task, and (c) designers' preferences of the models. Result of an experimental comparison of the two models reveal that the EER model surpasses the OO model for designing unary and ternary relationships, it takes less time to design EER schemas, and the EER model is preferred by designers. We conclude that even if the objective is to implement an OO database schema, the recommended procedure is to: (1) create an EER conceptual scheme, (2) map it to an OO schema, and augment the target schema with behavioral constructs that are unique to the 00 approach.

关键词： data modeling entity relationship model object oriented model database design conceptual schema experimentation schema transformation quality of design

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STRUCTURED METHOD FOR DESIGNING SUBsystems

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information AND SOFTWARE TECHNOLOGY 1995年第2期37卷 79-85页

作者： SHOVAL, P Information Systems Program Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva 84105 Israel

This paper describes a structured method for designing subsystems from a whole system, which extends ADISSA methodology for systems analysis, design and prototyping, The design of a subsystem includes: (a) definition of the transactions, or processes, that belong to the subsystem, thus determining the boundaries of the subsystem;(b) design of the subsystem user-interface, a sub-menu-tree, derivable from the menu-tree interface of the global system;and (c) design of the database sub-schema, derivable from the global database schema. The method is supported by a CASE tool, enabling automatic and interactive design and prototyping of the subsystems.

关键词： SUBSYSTEM ANALYSIS AND DESIGN METHODOLOGY CASE PROTOTYPING TRANSACTIONS MAN-MACHINE INTERFACE MENU-TREE DATABASE SCHEMA SUB-SCHEMA

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