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IEEE International Conference on Big Data

作者： Nathan Blackthorn Andrew Arash Mahyari Ashok Srinivasan Department of Computer Science University of West Florida Pensacola FL USA Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The importance of social determinants of health in shaping equitable public health policies is gaining increasing recognition. Emerging data sources, such as mobility and social media data, are becoming key to public health models. However, privacy concerns often limit access to these sensitive data, as even anonymized datasets are vulnerable to deductive disclosure. A promising solution to this challenge is the use of synthetic populations, which not only safeguard privacy but also enable the exploration of various "what-if" scenarios. Most existing studies on synthetic populations assume full access to complete datasets for training. However, in many public health applications, such as census data, this assumption is unrealistic due to incomplete responses, especially regarding sensitive questions like wealth or education. This paper introduces a novel approach for training Variational AutoEncoders (VAEs) with incomplete data, without resorting to missing value imputation. Instead, the VAEs are trained solely on the observed data. Using the 2019 PUMS dataset for Florida, we successfully train VAEs to generate diverse and flexible synthetic populations. By comparing marginal distributions and utilizing t-SNE for analysis, the results highlight the effectiveness of this method in addressing missing data challenges. This work demonstrates the potential of VAEs in generating synthetic populations for health research, even when complete datasets are unavailable, thereby offering a robust solution to advance public health studies while preserving data privacy.

关键词： Training Data privacy Privacy Social networking (online) Soft sensors Autoencoders Refining Imputation Data models Public healthcare

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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The Smart Hospital: Data and AI Challenges

The Smart Hospital: Data and AI Challenges

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Plati, Daphni K. Konstantakopoulos, Fotios S. Kalatzis, Theofanis Manousos, Dimitrios Kassiotis, Thomas Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Forth Institute of Computer Science Crete HeraklionGR 70013 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy

ISBN: (纸本)9798350383386

Healthcare systems generally constitute a combination of healthcare facilities and providers that collaborate in order to offer a complete range of healthcare services. As the primary providers of healthcare, hospitals hold an essential place in the healthcare system. This study introduces three novel automated Artificial Intelligence (AI) systems by using the robotic platform of the TIAGo robot: (i) an image-based system that monitors the food consumption of the patients and provides information regarding their energy intake to prevent malnutrition;(ii) a rehabilitation system that monitors patients as they perform prescribed exercises to prevent loss of mobility, and (iii) a monitoring system for the correctness of the oxygen therapy in terms of the proper positioning of the oxygen mask to prevent hypoxia complications. A well-designed observational clinical study is planned to evaluate and validate the utility and effectiveness of the proposed AI systems in improving patient stay in the hospital. For the abovementioned systems, three novel datasets were *** Relevance: Through the implementation of AI systems, the proposed research intends to provide specialists with an extra tool to aid in the monitoring of hospitalized patients, thus helping to prepare the way for the Smart Hospital. © 2023 IEEE.

关键词： Hospitals

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Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection

arXiv

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arXiv 2024年

作者： Rahman, Md Abdur Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States iDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. These attacks target LLMs applications through using carefully designed input prompts to divert the model from adhering to original instruction, thereby it could execute unintended actions. These manipulations pose serious security threats which potentially results in data leaks, biased outputs, or harmful responses. This project explores the security vulnerabilities in relation to prompt injection attacks. To detect whether a prompt is vulnerable or not, we follows two approaches: 1) a pre-trained LLM, and 2) a fine-tuned LLM. Then, we conduct a thorough analysis and comparison of the classification performance. Firstly, we use pre-trained XLM-RoBERTa model to detect prompt injections using test dataset without any fine-tuning and evaluate it by zero-shot classification. Then, this proposed work will apply supervised fine-tuning to this pre-trained LLM using a task-specific labeled dataset from deepset in huggingface, and this fine-tuned model achieves impressive results with 99.13% accuracy, 100% precision, 98.33% recall and 99.15% F1-score thorough rigorous experimentation and evaluation. We observe that our approach is highly efficient in detecting prompt injection attacks. © 2024, CC BY.

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Self-Organized Sensor Eggs for Decentralized Localization and Sensing on Vulcano Island – A Glimpse into Future Space Exploration with Swarms

Self-Organized Sensor Eggs for Decentralized Localization an...

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International Conference on Information Fusion

作者： Fabio Broghammer Thomas Wiedemann Siwei Zhang Armin Dammann Christian Gentner Petar Djuric Institute of Communications and Navigation German Aerospace Center (DLR) Germany Perception for Intelligent Systems Munich Institute of Robotics and Machine Intelligence TU Munich Germany Department of Electrical and Computer Engineering Stony Brook University USA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized “sensor eggs”. These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Location awareness Volcanic activity Probability density function Robot sensing systems Particle filters Transceivers Sensors Timing Surface cracks Ultra wideband technology

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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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Verifying Nonlinear Neural Feedback systems using Polyhedral Enclosures

arXiv

引用

arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Towards Designing a Medication Assistive intelligent System with Automated Insulin-dose Prediction for Elderlies

Towards Designing a Medication Assistive Intelligent System ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Abrar Ahmed Minhazul Islam Mahi M. Akhtaruzzaman Jamal Uddin Tanvin Fahim Shahryer Sachitra Halder Muhammad Towfiqur Rahman Md Shofiqul Islam Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh Department of Computer Science & Engineering University Asia Pacific (UAP) Dhaka Bangladesh Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Geelong Victoria

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Medication management poses significant challenges for many patients, particularly the elderlies, who often struggle with keeping track of their medication schedules and taking the correct dosages. To address this issue, this study aims to design an AI-integrated Medication Management and Assistive Unit (AMMAU) for elderlies with some important features like, schedule reminder, medicine recognition and count, early indication of shortage, sorted slots, and so on. Automated insulin-dose prediction and alert system makes the system unique and more demanding at these current scenarios. For this study, a suitable machine learning model is designed, analyzed, verified, and embedded in the proposed system so that the elderly diabetic patients can get alert, further compare with the current insulin-doses. Though, the proposed system is currently focusing on only the Basal insulin doses prediction, the system will definitely reduce the risks associated with wrong management of medication by degrading the chances of missed doses or taking wrong pills for elderlies at home.

关键词： Training Schedules Analytical models Prototypes Machine learning Predictive models Internet of Things Older adults Insulin Physical design

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Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

Fuzzy Object Ambiguity Determination and Human Attention Ass...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability

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Enhancing HIPAA Compliance in AI-driven mHealth Devices Security and Privacy

Enhancing HIPAA Compliance in AI-driven mHealth Devices Secu...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： ABM Kamrul Islam Riad Md Abdul Barek Md Mostafizur Rahman Mst Shapna Akter Tahia Islam Md Abdur Rahman Md Raihan Mia Hossain Shahriar Fan Wu Sheikh Iqbal Ahamed 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 Department of Computer Science Georgia State University USA Dept. of Computer Science Tuskegee University USA Department of Computer Science Marquette University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

-The integration of Artificial Intelligence (AI) in mobile health (mHealth) devices offers significant advancements in patient care but also raises complex security and privacy concerns for sensitive health data. This paper explores the evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024. It examines the current state of HIP AA compliance in AI-driven mHealth, identifying potential vulnerabilities and gaps among mHealth devices in considering privacy and security. The study outlines key security and privacy considerations specific to AI-powered mHealth technologies, emphasizing risks such as data breaches, ransomware attack and unauthorized access. The paper concludes by highlighting the importance of continual adaptation to the dynamic nature of AI technologies and offers insights for stakeholders to navigate the regulatory landscape and ensure the responsible deployment of AI in healthcare.

关键词： Privacy Data privacy Navigation Software Security Stakeholders Ransomware

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