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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Real-Time Emotion Recognition Using Deep Learning Algorithms

Real-Time Emotion Recognition Using Deep Learning Algorithms

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IEEE Conference on Vehicular Technology (VTC)

作者： Abderrahmane El Mettiti Mohammed Oumsis Abdellah Chehri Rachid Saadane LRIT Laboratory Mohammed V University Rabat Morocco High School of Technology Mohammed V University Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Ontario Canada Laboratory Engineering Systems SIRC/LAGeS-EHTP Hassania School of Public Works Casablanca Morocco

ISBN: (纸本)9781665454698

Machine learning (ML) and deep learning (DL) techniques have been used to study the changes in human physiological and non-physiological properties. DL has proven his efficiency when perceiving positive emotions (joy, surprise, pride, emotion) and negative emotions (anger, sadness, fear, disgust). Furthermore, the DL is used to identify the emotions accordingly. First, this paper describes the different DL and ML algorithms applied in the emotion recognition field. Then, as a perspective, it proposes a three-layered emotion recognition architecture that leverages the massive data generated by IoT devices such as mobile phones, smart homes, and health monitoring. Finally, the potential of emerging technologies, such as 5G and 6G communication systems in a parallel Big Data infrastructure, were discussed.

关键词： Deep learning 6G mobile communication Emotion recognition Vehicular and wireless technologies 5G mobile communication Smart homes Big Data

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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Managing Control software Variability in Cyber-Physical Production systems: The V4rdiac Approach

SSRN

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SSRN 2024年

作者： Fadhlillah, Hafiyyan Sayyid Meixner, Kristof Greiner, Sandra Fernández, Antonio Manuel Gutiérrez Rabiser, Rick Christian Doppler Laboratory VaSiCS Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Christian Doppler Laboratory SQI TU Wien Favoritenstraße 9-11 Vienna1040 Austria Information Systems Engineering TU Wien Favoritenstraße 9-11 Vienna1040 Austria Software Engineering Group Institute of Computer Science University of Bern Neubrückstraße 10 Bern3012 Switzerland Department of Computer Languages and Systems Universidad de Sevilla Avenida Reina Mercedes s/n Sevilla41012 Spain

Cyber-Physical Production systems (CPPSs) are highly configurable and versatile production systems that utilize diverse hardware components through control software. Managing control software variants in CPPS development is challenging as variation points in the software stem from multiple engineering disciplines. Furthermore, there is a lack of mechanisms to explicitly describe control software variants as part of the software. In this paper, we present an extension of the Variability for 4diac (V4rdiac) approach and investigate its usefulness in managing control software variants based on multidisciplinary variability in CPPS development and delta modeling. We extend the approach by considering its application engineering activities to produce client-specific control software variants. We also present a novel mechanism to visualize deltas in control software to reduce the effort of implementing and maintaining deltas. We apply V4rdiac in two case studies and conduct an additional user study with four participants from industry to evaluate its usefulness. The evaluation results confirm that the engineers were able to successfully manage the control software variants. The results also show that V4rdiac can be easily learned and increases industrial practitioners’ productivity in managing control software. © 2024, The Authors. All rights reserved.

关键词： Application programs

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Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Independent and Identically Distributed (IID) Data Assessmen...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mohamad Arafeh Ahmad Hammoud Hadi Otrok Azzam Mourad Chamseddine Talhi Zbigniew Dziong Department of Software Engineering Ecole de Technologie Superieure (ETS) Montreal QC Canada Department of Electrical Engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer science and Mathematics Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

关键词： Data privacy Federated learning Distributed databases Coherence Global communication Convergence

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Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

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Journal of Modern Power systems and Clean Energy 2020年第5期8卷 906-918页

作者： Jaber Valinejad Mousa Marzband Mert Korkali Yijun Xu Ameena Saad Al-Sumaiti IEEE Bradley Department of Electrical and Computer Engineering Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Computer Science Virginia TechNorthern Virginia CenterGreater Washington D.C.VA 22043USA Department of Mathematics Physicsand Electrical EngineeringNorthumbria UniversityNewcastleUK Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21589Saudi Arabia Computational Engineering Division Lawrence Livermore National LaboratoryLivermoreCA 94550USA Advanced Power and Energy Center Electrical Engineering and Computer ScienceKhalifa UniversityAbu DhabiP.O.Box 127788UAE

Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other ***,there is the need for a smart structure to handle the new *** paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other *** upper-level problem is decomposed into a set of low-level market clearing *** electricity and heat markets are simultaneously modeled in this ***,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity *** addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat *** show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

关键词： Microgrids distributed energy resource(DER) electricity market heat market demand response(DR) coalition formation energy storage

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Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms

arXiv

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arXiv 2023年

作者： Wang, Ziyue Liu, Ya-Feng Wang, Zhaorui Yu, Wei The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit their signature sequences to multiple base stations (BSs), and the BSs cooperatively detect the active devices based on the received signals. While the scaling law for the covariance-based activity detection in the single-cell scenario has been extensively analyzed in the literature, this paper aims to analyze the scaling law for the covariance-based activity detection in the multi-cell massive MIMO system. Specifically, this paper demonstrates a quadratic scaling law in the multi-cell system, under the assumption that the path-loss exponent of the fading channel γ > 2. This finding shows that, in the multi-cell massive MIMO system, the maximum number of active devices that can be correctly detected in each cell increases quadratically with the length of the signature sequence and decreases logarithmically with the number of cells (as the number of antennas tends to infinity). Moreover, in addition to analyzing the scaling law for the signature sequences randomly and uniformly distributed on a sphere, the paper also establishes the scaling law for signature sequences based on a finite alphabet, which are easier to generate and store. Finally, this paper proposes two efficient accelerated coordinate descent (CD) algorithms with a convergence guarantee for solving the device activity detection problem. The first algorithm reduces the complexity of CD by using an inexact coordinate update strategy. The second algorithm avoids unnecessary computations of CD by using an active set selection strategy. Simulation results show that the proposed algorithms exhibit excellent performance in terms of computational efficiency and detection error probability. Copyright © 2023, The Authors. All rights reserved.

关键词： Fading channels

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Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting

arXiv

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arXiv 2022年

作者： Xing, Wei W. Zhang, Ziyang Shah, Akeel A. Key Laboratory of Low-grade Energy Utilisation Technologies and Systems MOE Chongqing University Chongqing400030 China School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Predicting the end-of-life or remaining useful life of batteries in electric vehicles is a critical and challenging problem, predominantly approached in recent years using machine learning to predict the evolution of the state-of-health during repeated cycling. To improve the accuracy of predictive estimates, especially early in the battery lifetime, a number of algorithms have incorporated features that are available from data collected by battery management systems. Unless multiple battery data sets are used for a direct prediction of the end-of-life, which is useful for ball-park estimates, such an approach is infeasible since the features are not known for future cycles. In this paper, we develop a highly-accurate method that can overcome this limitation, by using a modified Gaussian process dynamical model (GPDM). We introduce a kernelised version of GPDM for a more expressive covariance structure between both the observable and latent coordinates. We combine the approach with transfer learning to track the future state-of-health up to end-of-life. The method can incorporate features as different physical observables, without requiring their values beyond the time up to which data is available. Transfer learning is used to improve learning of the hyperparameters using data from similar batteries. The accuracy and superiority of the approach over modern benchmarks algorithms including a Gaussian process model and deep convolutional and recurrent networks is demonstrated on three data sets, particularly at the early stages of battery lifetime. Copyright © 2022, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Regional Controllability and Minimum Energy Control of Delayed Caputo Fractional-Order Linear systems

arXiv

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arXiv 2022年

作者： Karite, Touria Khazari, Adil Torres, Delfim F.M. Laboratory of Engineering Systems and Applications Department of Electrical Engineering & Computer Science National School of Applied Sciences Sidi Mohamed Ben Abdellah University Avenue My Abdallah Km 5 Route d'Imouzzer BP 72 Fez Morocco Laboratory of Analysis Mathematics and Applications National School of Commerce & Management Sidi Mohamed Ben Abdellah University Fez Morocco Department of Mathematics University of Aveiro Aveiro3810-193 Portugal

We study the regional controllability problem for delayed fractional control systems through the use of the standard Caputo derivative. First, we recall several fundamental results and introduce the family of fractional-order systems under consideration. Afterwards, we formulate the notion of regional controllability for fractional systems with control delays and give some of their important properties. Our main method consists in defining an attainable set, which allow us to prove exact and weak controllability. Moreover, main results include not only those of controllability but also a powerful Hilbert uniqueness method that allow us to solve the minimum energy optimal control control problem. Precisely, an explicit control is obtained that drives the system from an initial given state to a desired regional state with minimum energy. Examples are given to illustrate the obtained theoretical *** Codes 26A33, 49J20, 93B05 Copyright © 2022, The Authors. All rights reserved.

关键词： Controllability

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent systems (ICTIS), International Conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling Data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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