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SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPING REVIEW TOWARDS AN EFFECTIVE BENCHMARKING APPROACH 22

SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPIN...

22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafyllou, Vassilis University of Peloponnese Electrical and Computer Engineering Department Patras Greece Health Physics & Computational Intelligence Laboratory Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation lab Electrical and Computer Engineering Dpt. University of Peloponnese Patras Greece

Healthcare has undoubtedly brought many advancements through information technology. Specifically, healthcare informatics involves the use of various technologies, data management and communication systems to collect, store, analyze and effectively disseminate information. Therefore, the above serves to improve care as well as patient outcomes, improve efficiency, promote innovation and data analysis, as digitization has become an integral part of our daily lives in today's world. However, with the increased use of interconnected systems and electronic health records, hospitals have become prime targets for cyber-attacks. In this article we will delve into the critical topic of cybersecurity in hospitals as well as nursing facilities. The deepening of this issue was carried out with extensive analysis of the security systems of Greek hospitals. We investigated all their web sites and the security systems already in place on those sites. By checking we found a number vulnerabilities and potential risks. The most important weakness is the lack of legal as well as technical information for all interested citizens and patients. More organizational as well as technical work concerning internal architecture of information systems is needed, towards the improvement of security. In conclusion, we present our proposals on strategies to strengthen healthcare facilities against cyber threats. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Cybersecurity Digitization Healthcare Information Patients Systems

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Cascade Temporal Convolutional Network for Multitask Learning 6

Cascade Temporal Convolutional Network for Multitask Learnin...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Zhang, Huiyun Huang, Heming Han, Henry Baylor University School of Engineering and Computer Science Department of Computer Science United States Qinghai Normal University School of Computer Science Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9781665491259

Multi-task learning (MTL) is a powerful technique in machine learning that enables the simultaneous training of multiple tasks. In this paper, we propose a novel MTL approach for the recognition of emotions, speakers, and gender in speech signals. Our approach presents a novel deep learning model called the Cascade Temporal Convolutional Network (CTCN to handle MTL efficiently. CTCN is designed to effectively improve the parallel processing ability of the model in multitasking learning. It provides an effective solution for improving the performance of deep learning in multitasking scenarios. CTCN is particularly adept at processing sequences of data with varying lengths and extracting features that are relevant to multiple tasks. Our experimental results demonstrate that the proposed CTCN approach for MTL achieves significantly superior performance on all tasks on benchmark datasets compared to other deep learning models. This study will inspire more future work in the MTL field. © 2023 IEEE.

关键词： Speech recognition

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on Information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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A study on the impact of pre-trained model on Just-In-Time defect prediction 23

A study on the impact of pre-trained model on Just-In-Time d...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Guo, Yuxiang Gao, Xiaopeng Zhang, Zhenyu Chan, W.K. Jiang, Bo Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350319583

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone's pre-training model is similar, the training resources that need to be consumed are closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data. © 2023 IEEE.

关键词： Forecasting

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Deep Learning Techniques for Enhancing Near-Field Spatially Varying Channels In Wireless Communications

Deep Learning Techniques for Enhancing Near-Field Spatially ...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Srividhya, Ch. Sapthami, I. Nagpal, Amandeep Pachauri, Praveen Chandrashekar, Rakesh Jabbar, Ahmed Sabah Abed AL-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Government Polytechnic Siwan India New Horizon College Of Engineering Bangalore India College Of Medical Technology The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

In the ever-growing field of radio communications, better ways of modeling and boosting channels are needed, especially for channels that change location in the near field. Here, we present a new way to use deep learning to improve the performance of radio communication channels. Our plan, called the 'Proposed Method,' uses complex neural network structures to model and improve channel features. We tested the proposed method against six well-known ones, including Orthogonal Matching Pursuit, Maximum Likelihood Estimation, Least Squares, Compressive Sensing, Singular Value Decomposition, and Beamforming, to see how well it works. To rate how well something works, we use things like Mean Absolute Error (MAE), Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Computational Complexity, and Root Mean Squared Error (RMSE). The results show that the Proposed Method is better because it models and estimates channels more accurately and has lower RMSE, MAE, BER, and MSE. The Proposed Method also has a higher signal-to-noise ratio (SNR), which means the quality of the data is better. In addition, because it is simple to compute, it is a good answer for real-world problems. © 2024 IEEE.

关键词： Beamforming

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Assessment of Neuromuscular Function in Patients with Long-Term Symptoms of COVID-19 9th

Assessment of Neuromuscular Function in Patients with Lon...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Cremasco, Emilia F. Zambalde, Ellen P. Germer, Carina M. Elias, Leonardo A. Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783031494062

Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifestations, and although chronic fatigue has been reported as a long-term effect of other viral infections, the etiology of this complaint is still unclear. In this work, we employed an experimental protocol to assess the neuromuscular function in participants who had COVID-19 (12 women, 5–30 weeks after COVID-19 diagnosis) and age-matched controls (9 women). The COVID participants presented a high degree of variability in their symptoms, and the majority (91%) had mild to moderate manifestations. Participants performed an abduction force of the index finger in three different motor tasks, namely isometric force steadiness, reaction time, and time-to-task failure (fatigue task). The experiment was performed in three stages: pre-fatigue, post-fatigue, and after a resting interval of 5 min. We found significant differences between groups for the maximum voluntary contraction, indicating the COVID group had a lower force generation capacity. Nonetheless, no significant difference was found in measures related to force control;however, a larger and more homogeneous number of participants is needed to confirm these observations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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Human-Inspired Anticipatory Motion Planning for Robotic Manipulators in Contact Tasks

Human-Inspired Anticipatory Motion Planning for Robotic Mani...

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IEEE International Conference on Robotics and Biomimetics

作者： Malak Slim Naseem Daher Imad H. Elhajj Electrical and Computer Engineering Department Vision and Robotics Lab American University of Beirut Lebanon

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this work, we present a novel solution aimed at improving robotic manipulators' performance in contact tasks. Inspired by the human motor control system, which relies on a feedforward mechanism to anticipate and plan movements based on the physical properties of the target environment, our approach plans the robot's motion during the reaching phase, prior to contact. To validate our approach, we conducted experiments using the KUKA youBot arm in two distinct environments, represented by soft and hard materials. Results showed that the robot exhibited compliant behavior, with an average reduction of 71% in overshoot, 60% in rise-time, and 68% steady-state error of the force control response during contact.

关键词： Robot motion Motor drives computer vision Estimation Manipulators Planning Steady-state Force control Feedforward systems Robots

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Rechargeable Battery State Estimation Based on Adaptive-Rate Processing and Machine Learning 1

Rechargeable Battery State Estimation Based on Adaptive-Rate...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alyoucef, Afnan Qaisar, Saeed Mian Hafsi, Meriem Effat University Electrical and Computer Engineering Department Jeddah22332 Saudi Arabia LINEACT Laboratory CESI Engineering School Villeurbanne69100 France

ISBN: (纸本)9781665472753

The generalization of the use of electronic systems and their integration in industrial systems and different aspects of modern life (internet of things, electric vehicles, robotics, smart grids), give rise to new challenges related to the storage and optimized management of energy. Lithium-on batteries perfectly meet this objective due to their good qualities such as high energy density, small installation size, low self-discharge and high supply capacity. However, their wide application requires further research on battery failure prediction and health management. Intelligent 'battery management systems' (BMSs) employ the real-time estimation and control algorithms to improve the battery safety while enhancing its performance. Nevertheless, BMS are complex and require increased processing power which could lead to more power consumption. In this context, the present article provides a new approach for efficient prediction of the 'Lithiumion' (Li-ion) battery cells capacities by analysing and exploiting the battery parameters, acquired by an event-driven module. It acquires the intended cells voltages, currents and temperature values during the charge-discharge cycles. The solution is based on the machine learning algorithms and event-based segmentation. The 'National Aeronautics and Space Administration' (NASA) has provided a high-power Li-Ion cells dataset for the purpose of research and innovation. This dataset is used to test and evaluate the suggested approach. The evaluation of the overall performance of the chain has shown encouraging results of the proposed approach. © 2023 IEEE.

关键词： Battery management systems

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Feature Model Configuration Reuse Scheme for Self-Adaptive Systems

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computers, Materials & Continua 2022年第4期71卷 1249-1262页

作者： Sumaya Alkubaisi Said Ghoul Oguz Ata Electrical and Computer Engineering Department Altinbas UniversityIstanbul34218Turkey Faculty of Information Technology Research Laboratory on Bio-Inspired Software EngineeringPhiladelphia UniversityAmman26160Jordan

Most large-scale systems including self-adaptive systems utilize feature models(FMs)to represent their complex architectures and benefit from the reuse of commonalities and variability ***-adaptive systems(SASs)are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite ***,reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and *** process of configuration reuse can be a better alternative to some contexts to reduce computational time,effort and ***,systems’complexity can be reduced while the development process of systems by reusing elements or *** are considered one of the new ways of reuse process that are able to introduce new opportunities for the reuse process beyond the conventional system *** current FM-based modelling techniques represent,manage,and reuse elementary features to model SASs concepts,modeling and reusing configurations have not yet been *** this context,this study presents an extension to FMs by introducing and managing configuration features and their reuse *** results demonstrate that reusing configuration features reduces the effort and time required by a reconfiguration process during the run time to meet the required scenario according to the current context.

关键词： Self-adaptive system feature model system reuse configuration management variability modeling

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