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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yenho Chen Noga Mudrik Kyle A. Johnsen Sankaraleengam Alagapan Adam S. Charles Christopher J. Rozell Machine Learning Center Georgia Institute of Technology and Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Machine Learning Center Georgia Institute of Technology and School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9798331314385

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. Code is available at: https://***/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

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Study of the Efficiency Coefficient of the Steganographic Embedding in Text Containers Using the Letter Replacement Method 3

Study of the Efficiency Coefficient of the Steganographic Em...

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3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Zhelezov, Stanimir Kordov, Krasimir Nachev, Atanas Stoyanova, Teodora Uzunova-Dimitrova, Boryana Pavlova, Daniela Konstantin Preslavsky University of Shumen Dept. Computer Science and Technologies Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. Computer Informatics Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria

ISBN: (纸本)9781665439817

The embedding efficiency is one of the important indicators for the steganographic methods. This article presents a study of this indicator when embedding hidden information in text containers using the letter replacement method. Containers in four different languages were studied and a comparison of the embedding efficiency in each of these languages was made. © 2021 IEEE.

关键词： Containers

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AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fall Risk and Treatment Efficacy in Balance Disorders 24

AI-Enhanced Tele-Rehabilitation: Predictive Modeling for Fal...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Karapintzou, Efterpi Tsakanikas, Vassilios Kikidis, Dimitrios Nikitas, Christos Nairn, Brooke Pavlou, Marousa Bamiou, Doris-Eva Exarchos, Themis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece Dept. of Otorhinolaryngology Head and Neck Surgery National and Kapodistrian University of Athens Hippocrateion General Hospital Athens Greece Ear Institute Faculty of Brain Sciences University College London London United Kingdom University College London Hospitals Dept. of Neuro-Otology London United Kingdom Centre for Human and Applied Physiological Sciences Faculty of Life Sciences and Medicine School of Basic and Medical Biosciences King's College London London United Kingdom Ionian University Dept. of Informatics Corfu Greece Biomedical Research Institute Foundation for Research and Technology Hellas FORTH Ioannina Greece

ISBN: (纸本)9798331518622

This study focuses on the development of artificial intelligence models to enhance telerehabilitation practices. We utilized diverse datasets to create clinically relevant models for predicting two critical outcomes: fall risk and treatment effectiveness. By applying various machine learning techniques, including K-Nearest Neighbors, Random Forest, Decision Tree, Support Vector Machine, and XGBoost, our models demonstrated high accuracy, sensitivity, and specificity. Notably, the Random Forest model achieved an accuracy of 0.97 in predicting fall risk and 0.96 in assessing treatment effectiveness. These models equip clinicians with powerful tools for data-driven decision-making, ultimately improving patient outcomes in rehabilitation settings. © 2024 IEEE.

关键词： Prediction models

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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A Framework for Digital Twin Collaboration

A Framework for Digital Twin Collaboration

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Simulation Winter Conference

作者： Zhengchang Hua Karim Djemame Nikos Tziritas Georgios Theodoropoulos Dept. of Computer Science and Eng. Southern University of Science and Technology (SUSTech) Shenzhen China School of Computing University of Leeds Leeds UK Dept. of Informatics and Telecommunications University of Thessaly Lamia GREECE Research Institute of Trustworthy Autonomous Systems (RITAS) SUSTech Shenzhen China

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework's effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system's objectives across various test cases and show scalability for large deployments.

关键词： Accuracy Federated learning Scalability Decision making Collaboration Virtual power plants Predictive models Robustness Digital twins Complex systems

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The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation

The Effect of Normalization for Bi-directional Amharic-Engli...

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2022 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2022

作者： Belay, Tadesse Destaw Tonja, Atnafu Lambebo Kolesnikova, Olga Yimam, Seid Muhie Ayele, Abinew Ali Haile, Silesh Bogale Sidorov, Grigori Gelbukh, Alexander Wollo University College of Informatics Kombolcha Ethiopia Centro de Investigación en Computación Instituto Politécnico Nacional Mexico City Mexico Universität Hamburg Dept. of Informatics Hamburg Germany Bahir Dar University ICT4D Research Center Bahir dar Ethiopia Assosa University Dept. of Computer Science Assosa Ethiopia

ISBN: (数字)9781665455879

ISBN: (纸本)9781665455879

Machine translation (MT) is one of the prominent tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT task has received a great deal of attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English translation and 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions. © 2022 IEEE.

关键词： Neural machine translation

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False Negative/Positive Control for SAM on Noisy Medical Images

arXiv

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arXiv 2023年

作者： Yao, Xing Liu, Han Hu, Dewei Lu, Daiwei Lou, Ange Li, Hao Deng, Ruining Arenas, Gabriel Oguz, Baris Schwartz, Nadav Byram, Brett C. Oguz, Ipek Dept. of Computer Science Vanderbilt University NashvilleTN United States Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Dept. of Biomedical Engineering Vanderbilt University NashvilleTN United States Dept. of Obstetrics and Gynecology University of Pennsylvania PhiladelphiaPA United States

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM’s performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM’s performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon at: https://***/xyimaging/FNPC Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Integrating and validating maritime transport security services: Initial results from the CS4EU demonstrator 21

Integrating and validating maritime transport security servi...

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13th International Conference on Contemporary Computing, IC3 2021

作者： Grigoriadis, Christos Papastergiou, Spyridon Kotzanikolaou, Panayiotis Douligeris, Christos Dionysiou, Antreas Elias, Athanasopoulos Bernsmed, Karin Hakon Meland, Per Kamm, Liina SecLab Dept. of Informatics Univ.of Piraeus Greece SREC Dept. of Computer Science Univ.of Cyprus Cyprus SINTEF Digital Trondheim Norway Cybernetica AS Tartu Estonia

ISBN: (纸本)9781450389204

Maritime transport is a characteristic example of a collaborative and complex cyber-physical environment, involving various stakeholders and actors, with different goals and requirements. Securing such a complex ecosystem is a challenging task and has recently attracted various research efforts in different areas including, threat management, system hardening, trust management and communication security. However, the integration and validation of such targeted maritime transport security services is a complex task that has its own challenges. In this paper we present the preliminary results of the maritime transport security services demonstrator, developed under the CyberSecurityForEurope (CS4EU) pilot project. We have set up a demonstrator to integrate, extend and validate four maritime-specific security services, covering risk and threat management, system hardening, trust management and secure communications. Our goal is to enhance the provisioning of these services and to identify possible research and implementation gaps. © 2021 Owner/Author.

关键词： Risk assessment

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Delayed dynamic-feedback controller design for multi-frequency vibration suppression

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IFAC-PapersOnLine 2024年第27期58卷 196-201页

作者： Adrian Saldanha Adam Peichl Wim Michiels Tomas Vyhlidal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.

关键词： vibration suppression time-delay DDAE

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Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

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IEEE International Requirements Engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

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