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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos School of Computer Science University of Birmingham Birmingham UK Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

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Early Prediction of Depressive Episodes in Mood Disorders Using Circadian Rhythm Indicators and Deep Learning

Early Prediction of Depressive Episodes in Mood Disorders Us...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Byeongsu Kim Minsu Chae Yihyun Kim Seokjin Kong Yeongmin Kim Taewon Jung Jaegwon Jeong Soohyun Park Chul-Hyun Cho Ji Won Yeom Taek Lee Heon-Jeong Lee Hwamin Lee Dept. of Biomedical Informatics College of Medicine Korea University Seoul Republic of Korea Dept. of Psychiatry College of Medicine Korea University Seoul Republic of Korea Chronobiology Institute Korea University Seoul Republic of Korea Dept. of Computer Science and Engineering SunMoon University Asan Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The early prediction of depressive mood episodes is crucial for effective intervention in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD). This study explores a predictive framework leveraging digital phenotypic data collected from smartphones and smartwatches, with a focus on circadian rhythm indicators such as Dim Light Melatonin Onset (DLMO). Using data from 164 participants within the Mood Disorder Cohort Research Consortium in Korea, time-series features related to sleep, heart rate, activity levels, and light exposure were processed to predict mood episodes seven days in advance. Deep learning models, including LSTM, GRU, and an LSTM-GRU hybrid, were applied to analyze this data, with the GRU model achieving the highest recall (0.767) and the LSTM model displaying superior robustness across metrics. SHAP value analysis of DLMO-related variables further underscored the association between circadian rhythm disruptions and depressive episodes, with delayed wake-up times relative to ideal schedules linked to increased depressive symptoms. Our findings demonstrate the feasibility of using digital phenotypes for early detection of mood episodes. These results highlight the potential of automated monitoring systems in clinical practice, which enable proactive intervention strategies through continuous, objective monitoring of patient conditions.

关键词： Analytical models Phenotypes Mood Predictive models Data models Robustness Circadian rhythm Long short term memory Smart phones Monitoring

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Context-aware Container Orchestration in Serverless Edge Computing

arXiv

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arXiv 2024年

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge United Kingdom Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States School of Information Science and Technology Northwest Univesity Xi'An China

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024, CC BY.

关键词： Quality of service

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Fairness-aware Classifier Design via Multi-objective Fuzzy Genetics-based Machine Learning

Fairness-aware Classifier Design via Multi-objective Fuzzy G...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Takeru Konishi Naoki Masuyama Jorge Casillas Yusuke Nojima Graduate School of Informatics Osaka Metropolitan University Sakai Japan Dept. of Computer Science and AI University of Granada Granada Spain

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

There is a growing interest in fairness and transparency in Artificial Intelligence (AI) in order to use AI with confidence. In addition, the consideration of fairness in Explainable AI (XAI) has attracted much attention. A fuzzy classifier is one of the most representative XAI methods that can linguistically explain the basis of classification. Multi-objective Fuzzy Genetics-based Machine Learning (MoFGBML) can efficiently obtain a set of fuzzy classifiers considering the maximization of classification accuracy and minimization of model complexity simultaneously by Evolutionary Multi-objective Optimization Algorithms (EMOAs). In this study, we investigate the effect of the conventional MoFGBML on the fairness of the obtained fuzzy classifiers and examine the effect of fairness-aware optimization on the search performance of MoFGBML. Analysis of the results provided many insights into fairness-aware MoFGBML.

关键词： Accuracy Machine learning algorithms Explainable AI Data visualization Minimization Complexity theory Classification algorithms

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Visualizing Research on Explainable Artificial Intelligence for Medical and Healthcare

Visualizing Research on Explainable Artificial Intelligence ...

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International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Subhan Ali Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Dept of Computer Science Norwegian University of Science & Techology (NTNU) Gjϕvik Norway Dept. of Informatics Linnaeus University Växjö Sweden Dept. of Computer Science Sukkur IBA University Sukkur Pakistan

Understanding complex machine learning and artificial intelligence models have always been challenging because these models are black-box, and often we don't know what information models rely upon to infer. Explainable Artificial Intelligence (XAI) has emerged as a new exciting field to explain and understand these machine learning models as humans can understand and improve them. In the past few years, there have been numerous research articles on explainable artificial intelligence for medical and healthcare. 1687 documents are being studied and analysed using bibliometric methods in this work. There are certain systematic reviews on the same topic, but this study is the first of its kind to use a quantitative method to analyze a large number of publications. The results of this study show that the research in this field took pace in 2011, and there have been quite many publications in the following years. We have also identified top-cited journals and articles. Through thematic analysis, we have found some important thematic areas of research in the field of XAI for medical and healthcare. The findings showed that the USA is the global leader in XAI research, followed by China and Canada at second and third place, respectively.

关键词： Visualization Image segmentation Systematics Bibliometrics Medical services Machine learning Market research

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Classification of high-resolution satellite images from urban areas based hybrid supporting vector machines and Multi-instance learning

Classification of high-resolution satellite images from urba...

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2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Mahmoud, Magdy Shayboub Ali Suez Canal University Faculty of Computers and Informatics Computer Science Dept. Ismailia Egypt

ISBN: (纸本)9781665445733

Remotely sensed image grading advanced considerably;takes into account the availability and abundance of various resolution image grading algorithms. Several works were successful by the fusion of space-spectrum knowledge with supporting vector machines (SVM). To incorporate all these data with the composite approach, we suggest a technique using a hybrid multi-spectral and multi-instance procedure. This paper introduces a groundbreaking approach to exploring urban buildings through the implementation of the SVM-based support classification and Multi-instance learning (MIL). In this paper, we present the use of this model, the classification of images. Use high-resolution technology from Quickbird. This practice and archery have contributed to the performance, efficiency, and power. The suggested solution was tested in traditional urban imagery scenes. The results show a major improvement the classification performance compared to the two separately used attributes. The results of the experiments indicate a very promising accuracy of 91, 24%. © 2021 IEEE.

关键词： Grading

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Towards Motor Imagery Classification of Finger Tapping by Both Hands for Controlling a Finger-Arm Bionic Robot

Towards Motor Imagery Classification of Finger Tapping by Bo...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Seong-Hyun Yu Young-Min Go Hyeong-Yeong Park Seo-Jin Lee Minji Lee Ji-Hoon Jeong Dept. Computer Science Chungbuk National University Cheongju Republic of Korea School of Computer Science Chungbuk National University Cheongju Republic of Korea Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon South Korea

In motor imagery, the greatest attention was paid to decoding signals from large muscles. However, there is a missing piece in helping people with finger issues or paralysis. This study investigates the potential advancements in neurorehabilitation by exploring the discriminative capabilities of electroencephalography (EEG) based classification in finger tapping. To improve neurorehabilitation strategies, we performed a comparative analysis of finger tapping classification during motor imagery and execution tasks. Using EEG signals, we employed machine learning and deep learning techniques, including support vector machines (SVM), linear discriminant analysis (LDA) and EEGNet. Our results indicate promising differences in EEG signatures between the two paradigms, with SVM, LDA, and EEGNet demonstrating varying degrees of efficacy in classification. These findings suggest potential implications for customized neurorehabilitation interventions. The study contributes to the affirmative possibility of effective rehabilitation strategies and is of particular relevance for patients with impaired motor function.

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Robust Privacy amidst Innovation with Large Language Models through a Critical Assessment of the Risks

arXiv

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arXiv 2024年

作者： Chuang, Yao-Shun Sarkar, Atiquer Rahman Hsu, Yu-Chun Mohammed, Noman Jiang, Xiaoqian McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States Dept. of Computer Science University of Manitoba WinnipegR3T 5V6 Canada

Objective This study evaluates the integration of Electronic Health Records (EHRs) and Natural Language Processing (NLP) with large language models (LLMs) to enhance healthcare data management and patient care, focusing on using advanced language models to create secure, HIPAA-compliant synthetic patient notes for global biomedical research. Materials and Methods The study used de-identified and re-identified versions of the MIMIC III dataset with GPT-3.5, GPT-4, and Mistral 7B to generate synthetic clinical notes. Text generation employed templates and keyword extraction for contextually relevant notes, with one-shot generation for comparison. Privacy was assessed by analyzing PHI occurrence and co-occurrence, while utility was evaluated by training an ICD-9 coder using synthetic notes. Text quality was measured using ROUGE and cosine similarity metrics to compare synthetic notes with source notes for semantic similarity. Results and Discussion Analysis of PHI occurrence and text utility via the ICD-9 coding task showed that the keyword-based method had low risk and good performance. One-shot generation showed the highest PHI exposure and PHI co-occurrence, especially in geographic location and date categories. The Normalized One-shot method achieved the highest classification accuracy. Privacy analysis revealed a critical balance between data utility and privacy protection, influencing future data use and sharing. Re-identified data consistently outperformed de-identified data. Conclusion This study shows that keyword-based methods can create synthetic clinical notes that protect privacy while retaining data usability, potentially improving clinical data sharing. The use of dummy PHIs to counter privacy attacks may offer better utility and privacy than traditional de-identification. © 2024, CC BY.

关键词： Electronic health record

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GPU-accelerated PostgreSQL for Scalable Management and Processing of Irregular Time-Series Data using SPI

GPU-accelerated PostgreSQL for Scalable Management and Proce...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Saied-Walker, Jamal Gupta, Pallavi Yi, Ruhan Marchal, Noah Skubic, Marjorie Scott, Grant J. University of Missouri Dept. of Ee & Computer Science ColumbiaMO65211 United States University of Missouri Institute for Data Science & Informatics ColumbiaMO65211 United States University of Missouri Center to Stream Healthcare in Place ColumbiaMO65211 United States

ISBN: (纸本)9798350324457

As the demand for real-time signal processing increases in various fields, such as healthcare, artificial intelligence, machine learning, and scientific research, there is a need for more efficient methods to analyze large amounts of data. To address this challenge and explore the opportunities to accelerate different signal processing algorithms, this paper proposes the integration of graphics processing units (GPUs) with database management systems (DBMS) using the PostgreSQL server programming interface (SPI). The performance of the proposed method is evaluated by comparing central processing unit (CPU) and GPU approaches for feature extraction using a data processing pipeline for heart rate estimation from hydraulic bed sensor data. Furthermore, the paper analyzes timing metrics, usability, adaptability, and discusses precision differences between CPU and GPU code by performing different thread and block configurations. © 2023 IEEE.

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