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34th Medical Informatics Europe Conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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Transformer-Based Multivariate Time Series Anomaly Localization

Transformer-Based Multivariate Time Series Anomaly Localizat...

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Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Charalampos Shimillas Kleanthis Malialis Konstantinos Fokianos Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decisionmaking. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the SpaceTime Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and spacetime statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.

关键词： Location awareness Measurement Time series analysis Transformers Sensor systems Safety Internet of Things Reliability Monitoring Synthetic data

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Transformer-based Multivariate Time Series Anomaly Localization

arXiv

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arXiv 2025年

作者： Shimillas, Charalampos Malialis, Kleanthis Fokianos, Konstantinos Polycarpou, Marios M. KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decision-making. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism’s learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the Space-Time Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and space-time statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model’s superiority over state-of-the-art methods in both detection and localization tasks. © 2025, CC BY-SA.

关键词： Time series

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Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

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Digital Communications and Networks 2021年第4期7卷 492-504页

作者： Nyothiri Aung Weidong Zhang Kashif Sultan Sahraoui Dhelim Yibo Ai National Center for Materials Service Safely University of Science and Technology BeijingBeijing100083China Department of Software Engineering Bahria University Islamabad CampusIslamabadPakistan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic *** congestion pricing and electric vehicle charging policies have been introduced in recent ***,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management *** this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city *** proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging *** propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging *** proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection *** large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

关键词： IoV EV VANET Smart city Congestion pricing Congestion avoidance EV charging Traffic optimization

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AMC Integrated Multilayer Wearable Antenna for Multiband WBAN Applications

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computers, Materials & Continua 2022年第5期71卷 3227-3241页

作者： Iqra Aitbar Nosherwan Shoaib Akram Alomainy Abdul Quddious Symeon Nikolaou Muhammad Ali Imran Qammer H.Abbasi Research Institute for Microwave and Millimeter-Wave Studies(RIMMS)National University of Sciences and Technology(NUST) H-1244000IslamabadPakistan School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK KIOS Research and Innovation Center of Excellence University of Cyprus2109NicosiaCyprus Frederick Research Center(FRC)and Department of Electrical Engineering Frederick University1036NicosiaCyprus James Watt School of Engineering University of GlasgowGlasgowG128QQU.K

In this paper,a compact,efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor(AMC)is *** of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band *** proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional,thus,considerably reducing the Specific Absorption Ratio(SAR).The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 *** proposed design operates over a wide frequency band of 2.9 to 12 GHz(exceeding the designated 3.1−10.6 GHz Ultra-Wideband(UWB)band).The band notches were created using slots on the radiating patch in the sub-bands from 5.50 to 5.67 GHz and 7.16 to 7.74 *** overall dimensions of the structure are 33×33×6.75 *** antenna’s radiation performance increased considerably with the addition of the AMC *** SAR values for the antenna are reduced by 85.3%when the AMC is used and are 0.083 W/kg which is well below the FCC SAR *** simple design,miniaturized profile,low SAR and wide operating bands with multiple band notches make the presented antenna an appealing choice for several UWB wearable body area network(WBAN)applications.

关键词： Ultra-wideband(UWB) monopole antenna wearable antenna specific absorption ratio(SAR)

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An Artificial Bee Colony Based Mapping Method for Three Dimensional Network-on-Chip

An Artificial Bee Colony Based Mapping Method for Three Dime...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Pinar Kullu Sevgi Yigit-Sert Merve Ozkan-Okay Yilmaz Ar Artificial Intelligence Center of Excellence University of Delaware Newark U.S.A Department of Computer Engineering Faculty of Engineering Ankara University Ankara Turkey

The total number of cores on a single chip is increased due to the rapidly decreasing size of transistors. In order to meet the performance demands of these designs, many designers prefer to use three-dimensional network-on-chips (3D-NoCs). These designs are good alternatives to traditional mesh structures since they consume less energy, ensure better performance, and need less area. However, new design algorithms are needed to map a given application to 3D-NoCs. In this paper, we propose an Artificial Bee Colony (ABC) based method for mapping application cores to the 3D-NoC architectures. We represent each possible solution with a different food source which produces a computed energy consumption. We aim to reduce the total energy consumption of the system. We compare our results against an existing study through several multimedia benchmarks in terms of the total energy consumption of the system. Our proposed approach produces comparable results in less time to an integer linear programming (ILP) based model which generates the optimum solution in a very long time.

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Impact of Extreme Class-Imbalance on Landslide-Risk Prediction and Mitigation Using Two-Stage Deep Neural Network 14

Impact of Extreme Class-Imbalance on Landslide-Risk Predicti...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Tengtrairat, N. Woo, W.L. Parathai, P. Sundaranaga, C. Na Ayutthaya, T. Kridakorn Rinchumphu, D. School of Software Engineering Payap University Chiang Mai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom Chiang Mai University City Research and Development Center Faculty of Engineering Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Civil Engineering Chiang Mai Thailand

ISBN: (纸本)9798350336313

The classification of landslides is the one of the most challenging topics because of the complexity of the relationships of various dynamic and uncertain factors and the physical gaining data processes. Landslide incidents frequently occur in the upper northern region of Thailand due to its topography. The landslide classification method is proposed to capture the significant features from an extreme case of class-imbalance dataset. The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. A study area of this research covers an area of 25 square kilometers at Chiang Rai, in Thailand which contains 30,408 non-landslide data and 1,077 landslide data. The percentage of landslide data is 3.54% of the total data. This paper proposed the solution of landslide classification given by extreme class-imbalance dataset. The proposed method has two main steps i.e., firstly, mitigating class-imbalance dataset. and secondly two-stage learning. Performance of the proposed method benchmarks the baseline, the logistic regression (LR), the random forest classifier (RFC) methods given by enhanced dataset. In the case of imbalance dataset, the one-class method is assessed against the proposed method along with the LR and the RFC methods. Experimental results demonstrate that the proposed method has improved the landslide-risk prediction performance over the baseline, the LR, the RFC and the one-class SVM methods in terms of an average area under the curve scores by 0.48, 0.48, 0.03, and 0.06, respectively, in both enhanced dataset and imbalance dataset. © 2022 IEEE.

关键词： Landslides

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Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care

Using Formal Knowledge to Support Episodic Evidence-Based Nu...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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Seg-CapNet:A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

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Journal of computer Science & Technology 2021年第2期36卷 323-333页

作者： Yang-Jie Cao Shuang Wu Chang Liu Nan Lin Yuan Wang Cong Yang Jie Li School of Software Zhengzhou UniversityZhengzhou 450000China Center of Modern Analysis and Gene Sequencing Zhengzhou UniversityZhengzhou 450000China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200000China

Deep neural networks(DNNs)have been extensively studied in medical image ***,existing DNNs often need to train shape models for each object to be segmented,which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging(MRI).In this paper,we propose a capsulebased neural network,named Seg-CapNet,to model multiple regions simultaneously within a single training *** Seg-CapNet model consists of the encoder and the *** encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers,capsule layers,and fully-connected *** the decoder transforms the feature vectors into segmentation masks by *** maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers,which are conducive to the backpropagation of the *** output vectors of Seg-CapNet contain low-level image features such as grayscale and texture,as well as semantic features including the position and size of the objects,which is beneficial for improving the segmentation *** proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017(ACDC 2017)and the Sunnybrook Cardiac Magnetic Resonance Imaging(MRI)segmentation *** results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7%and the average Hausdorff distance is reduced by 22%.The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.

关键词： capsule neural network image segmentation left ventricle segmentation cardiac magnetic resonance imaging

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Privacy-Enhancing Paradigms within Federated Multi-Agent Systems

arXiv

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arXiv 2025年

作者： Shi, Zitong Wan, Guancheng Huang, Wenke Zhang, Guibin Shao, Jiawei Ye, Mang Yang, Carl National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China National University of Singapore Singapore China Department of Computer Science Emory University United States

LLM-based Multi-Agent Systems (MAS) have proven highly effective in solving complex problems by integrating multiple agents, each performing different roles. However, in sensitive domains, they face emerging privacy protection challenges. In this paper, we introduce the concept of Federated MAS, highlighting the fundamental differences between Federated MAS and traditional FL. We then identify key challenges in developing Federated MAS, including: 1) heterogeneous privacy protocols among agents, 2) structural differences in multi-party conversations, and 3) dynamic conversational network structures. To address these challenges, we propose Embedded Privacy-Enhancing Agents (EPEAgents), an innovative solution that integrates seamlessly into the Retrieval-Augmented Generation (RAG) phase and the context retrieval stage. This solution minimizes data flows, ensuring that only task-relevant, agent-specific information is shared. Additionally, we design and generate a comprehensive dataset to evaluate the proposed paradigm. Extensive experiments demonstrate that EPEAgents effectively enhances privacy protection while maintaining strong system performance. The code will be availiable at https://***/ZitongShi/EPEAgent Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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