Procedia Computer Science最新文献_第10页

Securing the Industrial Internet of Things: Threats, Vulnerabilities, and Recommendations 保护工业物联网：威胁、漏洞和建议

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.procs.2025.10.258

Akzhibek Amirova , Laura Aldasheva , Aliya Abdiraman

The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, enabling intelligent automation, real-time data exchange, and integration of cyber-physical systems in industrial environments. However, the increasing complexity and interconnectivity of IIoT infrastructures introduce significant cybersecurity challenges. This paper provides a comprehensive overview of the current state of information security in IIoT systems. It begins with a conceptual analysis of IIoT and its distinctions from general IoT applications. The study reviews recent scientific research from 2018 to 2023, focusing on threat models, asset classification, and risk assessment frameworks. A detailed taxonomy of threats and vulnerabilities specific to IIoT environments is presented, emphasizing the insufficiency of traditional IT security strategies in industrial settings. Furthermore, the article analyzes protected asset categories and their corresponding attack vectors. Drawing on literature analysis and empirical observations from Kazakhstan’s industrial digitalization initiatives, this work identifies pressing security gaps and proposes practical recommendations for enhancing cybersecurity at various stages - from system design and data protection to incident response and personnel training. The findings aim to contribute to the development of robust and adaptive security strategies tailored to the unique needs of IIoT systems

工业物联网（IIoT）是工业4.0的关键组成部分，可实现工业环境中的智能自动化、实时数据交换和网络物理系统集成。然而，工业物联网基础设施日益增加的复杂性和互联性带来了重大的网络安全挑战。本文全面概述了工业物联网系统中信息安全的现状。首先是对工业物联网的概念分析及其与一般物联网应用的区别。该研究回顾了2018年至2023年的最新科学研究，重点关注威胁模型、资产分类和风险评估框架。提出了针对工业物联网环境的威胁和漏洞的详细分类，强调了传统IT安全策略在工业环境中的不足。此外，本文还分析了受保护资产类别及其对应的攻击向量。根据文献分析和哈萨克斯坦工业数字化举措的实证观察，这项工作确定了紧迫的安全漏洞，并提出了从系统设计和数据保护到事件响应和人员培训等各个阶段加强网络安全的实用建议。研究结果旨在为开发针对工业物联网系统独特需求的强大和自适应安全策略做出贡献

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引用次数: 0

A proposal for standardized PREMs implementation in hospital context in Portugal 关于在葡萄牙医院实施标准化PREMs的建议

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.procs.2025.10.245

Madalena Passos , Inês Mendes , Tayan Peler , Hugo Peixoto

Patient Reported Experience Measures (PREMs) are essential tools to assess healthcare quality from the patients’ perspective, complementing traditional outcome-based evaluations. In Portugal, although healthcare institutions are required to conduct satisfaction surveys, the lack of standardized and interoperable PREMs limits comparability and benchmarking. This paper proposes a framework for the standardized implementation of PREMs in hospitals, supported by interoperability through the HL7 FHIR standard. The solution integrates with hospital information systems to automatically identify clinical encounters, distribute tailored questionnaires, ensure anonymized data collection, and provide real-time analytics through a dashboard with alert mechanisms. The framework was validated in a simulated environment, demonstrating full automation of survey distribution and response analysis, while ensuring patient privacy and usability for healthcare staff. Results indicate that interoperability is a strategic enabler for scalable and sustainable PREMs adoption, fostering continuous quality improvement in healthcare services. Limitations and future work include testing with real hospital systems, expanding to additional clinical contexts, and scaling to national benchmarking.

患者报告体验测量（prem）是从患者角度评估医疗保健质量的重要工具，补充了传统的基于结果的评估。在葡萄牙，尽管要求医疗保健机构进行满意度调查，但缺乏标准化和可互操作的预置管理系统限制了可比性和基准。本文提出了一个通过HL7 FHIR标准支持互操作性的医院PREMs标准化实施框架。该解决方案与医院信息系统集成，可自动识别临床就诊情况，分发量身定制的问卷，确保匿名数据收集，并通过带有警报机制的仪表板提供实时分析。该框架在模拟环境中进行了验证，展示了调查分发和响应分析的完全自动化，同时确保了患者隐私和医护人员的可用性。结果表明，互操作性是采用可扩展和可持续的prem的战略推动因素，促进了医疗保健服务的持续质量改进。局限性和未来的工作包括在真实的医院系统中进行测试，扩展到其他临床环境，并扩展到国家基准。

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引用次数: 0

Sleep, Neuromodulation, and Avoiding Forgetting 睡眠、神经调节和避免遗忘

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-11-25 DOI: 10.1016/j.procs.2025.10.178

Peter Roberts, Elhadi Shakshuki

Catastrophic forgetting is the current obstacle for building robust continual machine learning models. Promising approaches incorporate methods inspired by the mechanisms the brain uses to learn. The most notable mechanisms are sleep, synaptic plasticity and consolidation, neuromodulation, and associative memory. This work aims to define these mechanisms and how they operate within the brain for learning, explore how they’ve been implemented in prior works, and begin to construct its own approach in mitigating catastrophic forgetting.

灾难性遗忘是目前构建稳健的持续机器学习模型的障碍。有希望的方法包括受大脑学习机制启发的方法。最显著的机制是睡眠、突触可塑性和巩固、神经调节和联想记忆。这项工作旨在定义这些机制，以及它们如何在大脑中进行学习，探索它们在之前的工作中是如何实现的，并开始构建自己的方法来减轻灾难性遗忘。

引用次数: 0

LLM-Assisted Qualitative Data Analysis: Security and Privacy Concerns in Gamified Workforce Studies 法学硕士辅助定性数据分析：游戏化劳动力研究中的安全和隐私问题

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.011

Aisvarya Adeseye , Jouni Isoaho , Tahir Mohammad

Large language models (LLMs) have transformed textual or qualitative data processing and analysis by automating and enhancing interpretive accuracy, particularly in complex areas like cybersecurity, ethics, and compliance. This study examines the effective-ness of local LLMs in analyzing qualitative research using the data gathered from the case study on “perspectives on security and privacy issues associated with the introduction of gamified workforce studies”. The research presented in this paper utilized 23 interview transcripts to evaluate three popular LLMs, namely LLaMA, Gemma, and Phi, running on a local infrastructure. We observed that LLaMA focuses on practical data security, Gemma on regulatory compliance, and Phi on ethical transparency and trust-building. By combining these models, researchers can gain a more comprehensive understanding of the complex implications of gamification in workforce studies. Local LLMs provide the added benefit of enhanced data privacy and security by processing sensitive data entirely within a controlled environment. This study explores the system and user prompts that can improve the interpretive accuracy of various qualitative research approaches, such as thematic analysis, frequency analysis, impact level analysis, sensitivity analysis, and disclosure analysis, demonstrating the potential of local LLMs for qualitative analysis for sensitive data. This study recommends the usage of LLMs for the initial stage of the qualitative analysis process to enhance the efficiency and effectiveness of subsequent completely manual or software-assisted manual analysis.

大型语言模型（llm）已经通过自动化和提高解释准确性来改变文本或定性数据处理和分析，特别是在网络安全、道德和合规等复杂领域。本研究利用从“引入游戏化劳动力研究相关的安全和隐私问题视角”的案例研究中收集的数据，检验了本地法学硕士在分析定性研究方面的有效性。本文的研究利用23个访谈记录来评估在当地基础设施上运行的三种流行的llm，即LLaMA， Gemma和Phi。我们观察到，LLaMA侧重于实际数据安全，Gemma侧重于监管合规，Phi侧重于道德透明度和信任建立。通过结合这些模型，研究人员可以更全面地了解劳动力研究中游戏化的复杂含义。本地llm通过完全在受控环境中处理敏感数据，提供了增强数据隐私和安全性的额外好处。本研究探讨了可以提高专题分析、频率分析、影响水平分析、敏感性分析和披露分析等各种定性研究方法解释准确性的系统和用户提示，展示了地方法学硕士对敏感数据进行定性分析的潜力。本研究建议在定性分析过程的初始阶段使用llm，以提高后续完全手工或软件辅助手工分析的效率和有效性。

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引用次数: 0

Incorporating the habit effect into scoring function of agent-based transport simulation 将习惯效应纳入基于智能体的交通仿真评分函数

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.035

Chansung Kim , Kyoungju Kim , Jiyoung Park

In studies of travel behavior using small-scale stated preference (SP) survey data, mode choice models have been used to predict the effects of modal shifts when the travel time or cost of a target transport mode changes. Recently, however, some studies have shown the existence of state dependence (also referred to as inertia or habit) in transport mode use, suggesting that failing to account properly for the effects of state dependence may lead to overestimated predictions of the impact of transport policies. State dependence refers to the inertia of travelers who continue using the same mode of transport even when their residential locations or the levels of transport service change. By contrast, large-scale origin-destination (OD)-based transport demand models (such as agent-based models [ABMs]) have not identified the need to control for state dependence, nor have there been related studies on this issue. This gap is due to the difficulty of implementing state-dependent models within OD-based or ABMs, whereas state-dependent mode choice models can be developed using small-scale SP data. Additionally, there is concern that failing to control for state dependence in ABMs may lead to an overestimation of the effects of transport policies. This study proposes a method to incorporate the habit effect into the scoring function of ABMs and implements the related code. Using a public transportation enhancement policy scenario, the study demonstrates that the habit effect influences the results.

在使用小规模陈述偏好（SP）调查数据的出行行为研究中，模式选择模型被用来预测当目标交通方式的出行时间或成本发生变化时，模式转换的影响。然而，最近的一些研究表明，在运输方式的使用中存在着国家依赖（也被称为惯性或习惯），这表明如果不能适当地考虑到国家依赖的影响，可能会导致对运输政策影响的高估预测。状态依赖是指即使居住地点或交通服务水平发生变化，旅行者仍继续使用同一种交通方式的惯性。相比之下，基于大规模始发目的地（OD）的运输需求模型（如基于agent的模型[ABMs]）并没有确定控制状态依赖的必要性，也没有对此问题的相关研究。这种差距是由于在基于od或abm中实现状态依赖模型的困难，而状态依赖模式选择模型可以使用小规模SP数据开发。此外，有人担心，未能控制ABMs中的状态依赖性可能导致对运输政策影响的高估。本文提出了一种将习惯效应纳入ABMs评分函数的方法，并实现了相关代码。利用公共交通改善政策情景，研究表明习惯效应影响结果。

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引用次数: 0

Webcam Eye-Tracking Browser Extension For General Navigation 网络摄像头眼动追踪浏览器扩展一般导航

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.072

Quinth Anthony A. Razuman, Brixzel John Q. Mabala, Malikey M. Maulana

Webcam eye-tracking has emerged as a cost-effective alternative to traditional eye-tracking technologies, offering a promising solution for individuals with disabilities who face challenges in using standard input devices. In this study, the researchers developed a web browser extension leveraging webcam eye-tracking for web browsing and navigation. The extension was created using Webgazer.js, a webcam eye-tracking JavaScript library, and complemented by the development of four web applications optimized for webcam eye-tracking. Through user evaluations and testing, the system demonstrated encouraging results. Users found the system to be both useful and user-friendly, as evidenced by a progressive decline in error rates over time. This trend indicated successful iterative system improvements and increased user proficiency. Although variations in error rates were observed across different applications, suggesting varying levels of intuitiveness and user-friendliness, the overall findings affirmed the system’s utility and usability. This research achieved its objectives by effectively showcasing the extension’s potential as a robust tool for individuals with hand disabilities.

网络摄像头眼动追踪已经成为传统眼动追踪技术的一种经济有效的替代方案，为那些在使用标准输入设备方面面临挑战的残疾人提供了一个有希望的解决方案。在这项研究中，研究人员开发了一个网络浏览器扩展，利用网络摄像头的眼球追踪来浏览和导航网页。该扩展是使用Webgazer.js创建的，这是一个网络摄像头眼动追踪JavaScript库，并辅以为网络摄像头眼动追踪优化的四个web应用程序的开发。通过用户评价和测试，该系统取得了令人鼓舞的效果。用户发现该系统既有用又用户友好，随着时间的推移错误率逐渐下降就是明证。这种趋势表明成功的迭代系统改进和用户熟练程度的提高。虽然在不同的应用程序中观察到错误率的变化，表明不同程度的直观性和用户友好性，但总体结果肯定了系统的实用性和可用性。这项研究通过有效地展示扩展的潜力作为一个强大的工具，手残疾的个人实现了其目标。

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引用次数: 0

A Conceptual Framework for Digital Twins of Multi-Agent Systems 多智能体系统数字孪生的概念框架

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.043

Hui Min Lee , Ruhollah Jamali , Sanja Lazarova-Molnar

Multi-agent Systems (MASs) are complex systems made up of agents that can be any entities with the ability to interact autonomously and make decentralized decision-making to solve complex problems. Data-driven Agent-based Modeling and Simulation (DDABMS) equips MASs with access to decisions based on near-real-time data, allowing for more informed decisions for systems’ enhancements. Digital Twins (DTs) can further enhance MASs by serving as virtual replicas that enable what-if scenarios exploration and allow continuous validation and refinement of the underlying models with real-time data from MASs. However, we discovered a gap in systematically integrating DTs with DDABMS, as existing efforts focus on specific problems and domains rather than providing a generalized framework to develop DTs with DDABMS. This paper addresses this gap by proposing a generalized framework to develop DTs for MASs with DDABMS. To demonstrate the practicability of our proposed framework for modeling and simulation of complex systems, we present an illustrative case study based on an epidemiological Susceptible-Infected-Recovered model.

多智能体系统（MASs）是由智能体组成的复杂系统，这些智能体可以是具有自主交互能力的任何实体，并可以做出分散的决策来解决复杂问题。数据驱动的基于代理的建模和仿真（DDABMS）使MASs能够访问基于近实时数据的决策，从而为系统的增强提供更明智的决策。数字孪生（Digital Twins, dt）可以作为虚拟副本进一步增强MASs，从而实现假设场景的探索，并允许使用来自MASs的实时数据对底层模型进行持续验证和改进。然而，我们发现在系统地集成DTs和DDABMS方面存在差距，因为现有的工作集中在特定的问题和领域，而不是提供一个通用的框架来开发DTs和DDABMS。本文通过提出一个使用DDABMS为MASs开发DTs的通用框架来解决这一差距。为了证明我们提出的复杂系统建模和模拟框架的实用性，我们提出了一个基于流行病学易感-感染-恢复模型的说明性案例研究。

引用次数: 0

The Impact of Gender and Age on Thermal Comfort 性别和年龄对热舒适的影响

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.042

Christos Mountzouris , Grigorios Protopsaltis , John Gialelis

Thermal comfort is a highly subjective experience, reflecting an individual’s satisfaction with the prevailing thermal conditions in indoor spaces. Indoor climatic conditions are not the sole determinants shaping thermal experiences. Anthropogenic factors also exert a significant influence on them, underscoring the need for personalized approaches to enhance thermal comfort. This study investigated whether the anthropogenic factors of gender and age influence an individual’s thermal perception, analyzing data from multiple field studies on thermal comfort, which conducted across diverse climatic zones, geographical regions, and building types. The results demonstrated significant differences in thermal perception between males and females, with females exhibiting a greater propensity to report “cold” sensations and a reduced likelihood to report “neutral” sensations compared to males. Adolescents and young adults reported higher discomfort levels, while middle-aged and elderly exhibited a more evenly distributed balance between comfort and discomfort. Overall, the findings highlight the necessity to account for demographic diversity in thermal comfort assessments.

热舒适是一种高度主观的体验，反映了个人对室内热环境的满意度。室内气候条件不是形成热体验的唯一决定因素。人为因素也对其产生重大影响，强调需要个性化的方法来提高热舒适。本研究调查了性别和年龄等人为因素是否会影响个体的热感知，分析了来自不同气候带、地理区域和建筑类型的多个热舒适实地研究的数据。结果表明，男性和女性在热感知上存在显著差异，与男性相比，女性表现出更大的报告“冷”感觉的倾向，而报告“中性”感觉的可能性更低。青少年和年轻人报告的不适程度更高，而中年和老年人在舒适和不适之间表现出更均匀的平衡。总的来说，研究结果强调了在热舒适评估中考虑人口多样性的必要性。

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引用次数: 0

Developing Skeletal Activity Scheduler using Machine Learning 使用机器学习开发骨骼活动调度程序

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.054

Sagar Bhandari , Muhammad Ahsanul Habib

Understanding human mobility patterns is crucial for sustainable urban planning. This study presents a novel approach for predicting daily activity sequences using machine learning techniques, specifically Long Short-Term Memory (LSTM) networks and Explainable Boosting Machines (EBM). Utilizing data from the 2022 Halifax Travel Activity (HaliTRAC) Survey, we train these models to predict sequences of activities based on individual and household characteristics, aiming to balance predictive performance with interpretability. The LSTM model effectively captures complex temporal dependencies, while EBM provides clear insights into the significance of individual features, addressing the "black box" nature of Machine Learning models. By simplifying activity sequences into five primary activity types, the refined LSTM and EBM models achieve accuracies of 70.25% and 73.73%, respectively. Key findings highlight employment status, age, and education level as major determinants of activity patterns, with household characteristics like size playing a secondary role. This research demonstrates the potential of utilizing advanced machine learning techniques in mobility analysis, offering both accurate predictions and actionable insights. The proposed framework provides a foundation for developing transparent and reliable tools to inform transportation policies and urban development strategies.

了解人类流动模式对可持续城市规划至关重要。本研究提出了一种使用机器学习技术预测日常活动序列的新方法，特别是长短期记忆（LSTM）网络和可解释增强机器（EBM）。利用2022年哈利法克斯旅行活动（HaliTRAC）调查的数据，我们训练这些模型来预测基于个人和家庭特征的活动序列，旨在平衡预测性能和可解释性。LSTM模型有效地捕获了复杂的时间依赖性，而EBM则对单个特征的重要性提供了清晰的见解，解决了机器学习模型的“黑箱”性质。通过将活动序列简化为5种主要活动类型，改进后的LSTM和EBM模型的准确率分别达到70.25%和73.73%。主要研究结果强调，就业状况、年龄和教育水平是活动模式的主要决定因素，家庭规模等特征起次要作用。这项研究展示了在流动性分析中利用先进的机器学习技术的潜力，提供了准确的预测和可操作的见解。拟议的框架为制定透明和可靠的工具提供了基础，为交通政策和城市发展战略提供信息。

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引用次数: 0

Data-driven machinery faults detection techniques using Artificial Intelligence in Industry 4.0 concept 工业4.0概念中使用人工智能的数据驱动机械故障检测技术

Procedia Computer Science

Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1016/j.procs.2025.03.053

Galina Samigulina , Zarina Samigulina , Daulet Bekeshev , Diana Butakova

The research is devoted to the development of an intelligent technology for diagnosing industrial equipment of oil and gas facilities based on an improved FMEA methodology (Analysis of Modes, Failures of their Influence, Degree of Criticality) in combination with a unified artificial immune system (UIIS) and the principles of immunological homeostasis. The main trends in the development of bioinspired artificial intelligence technologies are considered. A unified artificial immune system is built on the basis of modified algorithms of the artificial immune system (AIS) in order to identify the most effective ones (in data processing and forecasting) for a certain set of production data. The application of the principles of immunological homeostasis to assess modified algorithms allows identifying the «homeostasis area» in which the algorithms have the best predictive properties and can form an adequate immune response. The extension of the FMEA methodology with an intelligent block based on UAIS allows to automate the information processing previously carried out manually by experts, reduce time and resources when diagnosing equipment, and eliminate errors associated with the «human factor». The technology has been approbated on real data on equipment failures at TengizChevroil company (oil and gas industry) and on experimental data on equipment from Schneider Electric (Industrial Automation Lab).

该研究致力于开发基于改进的FMEA方法（模式分析，故障影响，临界程度），结合统一的人工免疫系统（UIIS）和免疫稳态原理的石油和天然气设施工业设备诊断智能技术。考虑了生物人工智能技术发展的主要趋势。在对人工免疫系统（AIS）算法进行改进的基础上，建立了统一的人工免疫系统，以识别某一组生产数据中最有效的（数据处理和预测）算法。应用免疫稳态原理来评估改进的算法，可以确定算法具有最佳预测特性并可以形成适当免疫反应的“稳态区域”。基于UAIS的智能模块扩展了FMEA方法，使以前由专家手动执行的信息处理自动化，减少了诊断设备时的时间和资源，并消除了与“人为因素”相关的错误。该技术已在tengizchevron公司（石油和天然气行业）设备故障的实际数据以及Schneider电气（工业自动化实验室）设备的实验数据中得到认可。

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引用次数: 0