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Machine learning approaches for predicting Ankle Dorsi Plantar Moments in Cerebral Palsy gait analysis: A comparative study

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Gait & Posture 2024年 113卷 198-199页

作者： Mustafa Erkam Özates Firooz Salami Sebastian I. Wolf Yunus Ziya Arslan Turkish German University Department of Electrical and Electronics Engineering- Faculty of Engineering Istanbul Turkey Heidelberg University Hospital Clinic for Orthopedics and Trauma Surgery Heidelberg Germany Turkish German University Department of Robotics and Intelligent Systems- Institute of Graduate Studies in Science and Engineering- Istanbul Turkey

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Absolute longitudinal distance measurement verification of a standard polystyrene nanoparticle near a surface in water by means of multi-wavelength evanescent field 22

Absolute longitudinal distance measurement verification of a...

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22nd International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2022

作者： Blattler, Aran Khajornrungruang, Panart Suzuki, Keisuke Baba, Akiyoshi Goto, Daiki Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Advanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan Center of Microelectronic System Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

In the wet semiconductor process, abrasive nanoparticles are widely used in the chemical mechanical polishing (CMP) process to polish wafer surfaces for high planarization. During polishing, nanoparticles move with a high-speed three-dimensional (3D) position in the solution. In the 3D particles's moving positions, Z is a perpendicular longitudinal distance between the surface to the nanoparticle that plays a significant role in polishing, which could not be measured by general microscopy without longitudinal scanning. To understand nanoparticle phenomena near-surface, we have proposed and developed the apparatus with real-time fastmotion tracking on optical multi-wavelength evanescent fields microscopy to measure the 3D motion of the nanoparticle in water without scanning. In euspen's 2020, we could verify the relative distance ?Z of nanoparticles in water, but the absolute distance Z is unknown yet. In this manuscript, we have therefore developed an invisible nano-step height in water to verify the absolute height Z of an individual nanoparticle. The invisible nano-step height in water is made from an optical resin with a refractive index of 1.35 that is close to a refractive index of water to avoid unnecessary light during experimental verification. The nano-step height was less than 100 nm on a glass surface, fabricated using nanoimprint lithography. Polystyrene f100 nm standard particles in water were used to verify the height Z. The results of height Z of nanoparticles could be measured less than 100 nm with the measurement uncertainty ±10 nm (2s), which is related to the interferometer result of the nano-step height. © European Society for Precision Engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Nanoparticles

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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intelligent sensing devices and systems for personalized mental health

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Med-X 2025年第1期3卷 1-22页

作者： Xing, Yantao Yang, Yang Yang, Kaiyuan Lu, Albert Xing, Luyi Mackie, Ken Guo, Feng Department of Intelligent Systems Engineering Indiana University Bloomington Bloomington USA Culver Academies High School Culver USA Department of Computer Science Indiana University Bloomington Bloomington USA Department of Psychological and Brain Sciences Gill Institute for Neuroscience Indiana University Bloomington Bloomington USA

Mental disorders disturb the cognition, emotion, and behavior of a diverse patient population, and can reduce their quality of life and even cause death. Despite significant advances in the diagnosis and treatment of mental disorders, challenges remain in achieving objective understanding, accurate assessment, and timely intervention for personalized conditions. Here, we review the recent development of intelligent sensing devices and systems for advancing the diagnosing, monitoring, and managing of mental disorders, with a special emphasis on personalized mental healthcare. We first introduce the mechanisms and clinical symptoms of mental disorders and related diagnostic principles. Then, we discuss the working principle and application of wearable sensors and systems to track various physiological parameters and markers for long-term monitoring, early screening, and treatment evaluation. Furthermore, we highlight recent emerging advancements in Artificial Intelligence (AI) and digital health and give perspectives on their integration with sensing technologies to address the emergent challenges of personalized mental healthcare. We believe innovative intelligent sensing technologies may significantly improve the patient's quality of life, enhance the efficiency and robustness of current healthcare systems, and reduce the socioeconomic burden for mental disorders and other diseases. • Summary of innovative intelligent sensing devices and systems for personalized mental healthcare. • Highlight of recent advancement of physiological and biochemical sensing technologies for objective assessment of mental disorders. • Introduction of emergent Al and digital health for advancing long-term monitoring, early screening, and treatment evaluation of mental disorders. • Perspectives of data, privacy, and safety for personalized mental healthcare.

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Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches 21

Privacy-preserving Biometric-driven Data for Student Identit...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Fidas, Christos Belk, Marios Portugal, David Pitsillides, Andreas University of Patras Department of Electrical and Computer Engineering Greece Cognitive Ux GmbH Germany University of Coimbra Institute of Systems and Robotics Portugal University of Cyprus Department of Computer Science Cyprus

ISBN: (纸本)9781450383677

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data. © 2021 Owner/Author.

关键词： Blockchain

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The Validity of a Machine Learning-Based Video Game in the Objective Screening of Attention Deficit Hyperactivity Disorder in Children Aged 5 to 12 Years

arXiv

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arXiv 2023年

作者： Zakani, Zeinab Moradi, Hadi Ghasemzadeh, Sogand Riazi, Maryam Mortazavi, Fatemeh Department of Psychology University of Tehran Iran Department of Robotics and Artificial Intelligence University of Tehran Iran Intelligent Systems Research Institute SKKU Iran Department of Electrical and Computer Engineering Boston University United States Department of Robotics and Artificial Intelligence University of Tehran Tehran Iran

Objective: Early identification of ADHD is necessary to provide the opportunity for timely treatment. However, screening the symptoms of ADHD on a large scale is not easy. This study aimed to validate a video game (FishFinder) for the screening of ADHD using objective measurement of the core symptoms of this disorder. Method: The FishFinder measures attention and impulsivity through in-game performance and evaluates the child's hyperactivity using smartphone motion sensors. This game was tested on 26 children with ADHD and 26 healthy children aged 5 to 12 years. A Support Vector Machine was employed to detect children with ADHD. results: This system showed 92.3% accuracy, 90% sensitivity, and 93.7% specificity using a combination of in-game and movement features. Conclusions: The FishFinder demonstrated a strong ability to identify ADHD in children. So, this game can be used as an affordable, accessible, and enjoyable method for the objective screening of ADHD. © 2023, CC BY-NC-ND.

关键词： Serious games

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling Software Security Software development management

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Integrating Self Sovereign Identity in XACML: the MERGE Approach

Integrating Self Sovereign Identity in XACML: the MERGE Appr...

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IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Andrea De Salve Damiano Di Francesco Maesa Paolo Mori Giulio Piva Laura Ricci National Research Council Institute of Applied Sciences and Intelligent Systems Lecce Italy Department of Computer Science University of Pisa Pisa Italy National Research Council Institute of Informatics and Telematics Pisa Italy

ISBN: (数字)9798331505875

ISBN: (纸本)9798331505882

Attribute-Based Access Control (ABAC) systems are commonly used for protecting resources from unauthorized accesses. They perform the access decision process taking into account a set of attributes describing the features of the subjects executing the accesses, the resources being accessed, and the environment. In this paper, we propose to enhance traditional ABAC systems by integrating them with the novel Self Sovereign Identity (SSI) paradigm. More precisely, we propose a novel access control framework, leveraging the well known eXtensible Access Control Markup Language (XACML) standard for ABAC systems, which allows to exploit SSI credentials as sources of users’ attributes for policy evaluation. Finally, we showcase a prototype implementation of our proposal, and we analyze its performance when evaluating XACML policies integrating SSI credentials with a variable number of attributes.

关键词： Access control Markup languages Prototypes Collaboration Permission Proposals Standards Periodic structures

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INFLUENCE FUNCTIONS FOR SCALABLE DATA ATTRIBUTION IN DIFFUSION MODELS

arXiv

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arXiv 2024年

作者： Mlodozeniec, Bruno Eschenhagen, Runa Bae, Juhan Immer, Alexander Krueger, David Turner, Richard Department of Engineering University of Cambridge United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science University of Toronto Canada Vector Institute Toronto Canada Department of Computer Science ETH Zurich Switzerland Mila – Quebec AI Institute Montreal Canada The Alan Turing Institute London United Kingdom

Diffusion models have led to significant advancements in generative modelling. Yet their widespread adoption poses challenges regarding data attribution and interpretability. In this paper, we aim to help address such challenges in diffusion models by developing an influence function framework. Influence function-based data attribution methods approximate how a model’s output would have changed if some training data were removed. In supervised learning, this is usually used for predicting how the loss on a particular example would change. For diffusion models, we focus on predicting the change in the probability of generating a particular example via several proxy measurements. We show how to formulate influence functions for such quantities and how previously proposed methods can be interpreted as particular design choices in our framework. To ensure scalability of the Hessian computations in influence functions, we systematically develop K-FAC approximations based on generalised Gauss-Newton matrices specifically tailored to diffusion models. We recast previously proposed methods as specific design choices in our framework, and show that our recommended method outperforms previous data attribution approaches on common evaluations, such as the Linear Data-modelling Score (LDS) or retraining without top influences, without the need for method-specific hyperparameter tuning. Copyright © 2024, The Authors. All rights reserved.

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Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation

arXiv

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arXiv 2023年

作者： Yang, Jingwei Xue, Bohuan Feng, Yi Wang, Deming Fan, Rui Chen, Qijun The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong

This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N+ achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85◦, 8.95◦, 9.25◦, and 11.98◦ on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N+ into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://***/3F2Nplus. Note to Practitioners—The primary motivation behind this work arises from the need to develop a high-performing surface normal estimator for practical robotics and computer vision applications. While geometry-based surface normal estimators have been widely used in these domains, the existing solutions focus merely on discontinuity discrimination. To tackle this problem, this article introduces a plug-and-play module that leverages both depth curvature and correlation coefficient to quantify discontinuity levels, thereby optimizing surface normal estimation, particularly near or on discontinuous regions. Moreover, this article also introduces a large-scale public dataset wit

关键词： Gaussian noise (electronic)

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