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Hybrid Programming-based Scheduling Approach for Many Heterogeneous Computing Tasks with Asynchronous Generation in IIoT

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IEEE Internet of Things Journal 2025年

作者： Hu, Bingtao Zhong, Ruirui Wang, Yong Wang, Tianyue Feng, Yixiong Zhou, MengChu Tan, Jianrong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Hangzhou Dianzi University Institute of Industrial Internet Hangzhou310018 China City University of Hong Kong Department of Systems Engineering China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated Heterogeneous Computing Tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily-verified propositions to either identify feasible solutions or demonstrate the infeasibility of the ***, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines. © 2014 IEEE.

关键词： Constrained optimization

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Data privacy quantification and de-identification model based on information theory

Data privacy quantification and de-identification model base...

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2019 International Conference on Networking and Network Applications, NaNA 2019

作者： Zhang, Zeyu Lu, Zhiyang Tian, Youliang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China College of Electrical Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9781728126296

De-identification enables to protect privacy of data from different attacks. At present, the specific de-identification standards and privacy quantification methods are given in many models, such as K-anonymity model and differential privacy model. But, the K-anonymity model does not provide an effective method to prove its degree of privacy protection, and when the model parameters change, the degree of privacy protection cannot be quantified. And due to rigorous calculation method of the differential privacy model, which quantifies the degree of privacy protection based on a mathematical basis, it is difficult to be used widely in organizations and institutions. So, this paper proposes a de-identification model, which includes quantification solution of identifying the sensitivity of the personal information, de-identification approach for adaptively matching different standard de-identification methods to personal information with different sensitivities and de-identification effect detection function. The objective is to provide an automatic, efficient and widely used de-identification model, which is built by quantitatively analyzing degree of privacy protection based on conditional entropy. Finally, performance analysis results show that the model makes the trade-off between privacy and secure of data and the widespread use of data. © 2019 IEEE.

关键词： Data privacy

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Towards deployment-centric multimodal AI beyond vision and language

arXiv

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

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Abnormal association between neural activity and genetic expressions of impulsivity in attention deficit hyperactivity disorder: an Adolescent Brain Cognitive Development study

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年

作者： Soohyun Jeon Jae-eon Kang Jundong Hwang Vince D. Calhoun Jong-Hwan Lee Department of Brain and Cognitive Engineering Korea University Seoul South Korea Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University Atlanta USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Interdisciplinary Program in Precision Public Health Korea University Seoul South Korea McGovern Institute for Brain Research Massachusetts Institute of Technology Boston MA

Background Impulsivity in highly heritable attention deficit hyperactivity disorder (ADHD) has been studied using neural activity via fMRI or genetic data, but rarely with multivariate methods linking both. We investigated coupled neural activity and gene expression signatures, using parallel independent component analysis (pICA) and Adolescent Brain Cognitive Development data. Methods Children with ADHD ( n = 394; 63% males) and healthy controls ( n = 1,000; 47% males) of European ancestry were included. The subjects were randomly divided into 80% discovery and 20% replication datasets with demographic stratification. We analyzed neural activity and gene expressions from the discovery datasets using pICA and extracted paired independent components (pICs). The loading coefficients of the pICs were utilized to predict behavioral and cognitive data for stop signal task (SST) in replication datasets. Results We identified three pICs estimated from gene expression in the cortex, cerebellum, and nucleus accumbens. Significant neural activity was mainly localized to the orbital/inferior/middle frontal gyri, rectal gyrus, precuneus, inferior temporal gyrus, inferior parietal lobule, and cerebellum. Significant gene components were associated with immunoglobulin, taste receptor, and immunity-related terms and were overlapped with ADHD-related genes. The extracted fMRI-/Gene-ICs were significantly correlated with mean reaction time, stop signal reaction time of SST, and behavioral inhibition with a large boost in sensitivity when both the paired fMRI-/Gene-ICs and their interaction were used in a multimodal regression analysis. Conclusion We reported biologically plausible pairs of neural activity and gene sets using pICA, which were significantly associated with ADHD impulsivity-related behavioral and cognitive data.

关键词： Attention deficit hyperactivity disorder functional magnetic resonance imaging gene expression imaging genetics parallel independent component analysis stop signal task

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Eight Become One: the EURECA-PRO University Network

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BHM Berg- und Huttenmannische Monatshefte 2022年第10期167卷 462-471页

作者： Kircher, Volkmar Feiel, Susanne Bongaerts, Jan C. Sishchuk, Julia Drebenstedt, Carsten Lagoudakis, Michail Fragkedaki, Konstantina García-Llamas, Paula Suárez-Corona, Adriana Fernández-Raga, María Baelo-Álvarez, Roberto Górski, Marcin Pawelczyk, Marek Hochstein, Synnöve Walter, Stefanie Speck, Maria Ravas, Oana Van Hellemont, Elke Vancauwenbergh, Sadia Resources Innovation Center Leoben Montanuniversität Leoben Leoben Austria Public Relations Office International Relations and University Cooperation Montanuniversität Leoben Leoben Austria Faculty of Business Administration Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of work package education and studies Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of educational council Technische Universität Bergakademie Freiberg Freiberg Germany School of Electrical and Computer Engineering Technical University of Crete Chania Greece EURECA-PRO European University Technical University of Crete Chania Greece Department of General and Specific Didactics and Educational Theory University of León Leon24071 Spain Department of Mathematical Sciences Universidad de León León24071 Spain Department of Applied Physics University of León León24071 Spain Faculty of Civil Engineering Silesian University of Technology Gliwice Poland Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Fakultät Medien Hochschule Mittweida Mittweida Germany International Relation Office University of Petroşani Petroşani Romania Rector’s Office Hasselt University Hasselt Belgium Directorate Research Library and Internationalisation Hasselt University Hasselt Belgium

The United Nations Sustainable Development Goal 12 is the foundation of the European University on Responsible Consumption and Production consortium (EURECA-PRO). This article introduces the eight EURECA-PRO partner universities: Montanuniversität Leoben (Austria), Technische Universität Bergakademie Freiberg (Germany), Technical University of Crete (Greece), University of León (Spain), Silesian University of Technology (Poland), Mittweida University of Applied Sciences (Germany), University of Petroşani (Romania), and Hasselt University (Belgium). In addition, each university’s role within the alliance and unique research and study programmes are outlined. The synergy created by EURECA-PRO enables the pursuit of an ambitious research agenda with five research "Lighthouse Missions" as well as the implementation of joint study programmes. © 2022, The Author(s).

关键词： Sustainable development

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Pandemic Vulnerability Index of US Cities: A Hybrid Knowledge-based and Data-driven Approach

arXiv

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arXiv 2022年

作者： Shahinoor Rahman, Md. Paul, Kamal Chandra Mokhlesur Rahman, Md. Samuel, Jim Thill, Jean-Claude Amjad Hossain, Md. Nawaz Ali, G.G.Md. Department of Earth and Environmental Sciences New Jersey City University Jersey CityNJ07305 United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States The William States Lee College of Engineering University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States E.J. Bloustein School of Planning & Public Policy Rutgers University NJ08901 United States Department of Geography and Earth Sciences University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States Department of Accounting Information Systems and Finance Emporia State University EmporiaKS66801 United States Department of Computer Science and Information Systems Bradley University PeoriaIL61625 United States Khulna9203 Bangladesh School of Data Science University of North Carolina at Charlotte 9201 University City Blvd NC28223 United States

Cities become mission-critical zones during pandemics and it is vital to develop a better understanding of the factors that are associated with infection levels. The COVID-19 pandemic has impacted many cities severely;however, there is significant variance in its impact across cities. Pandemic infection levels are associated with inherent features of cities (e.g., population size, density, mobility patterns, socioeconomic condition, and health & environment), which need to be better understood. Intuitively, the infection levels are expected to be higher in big urban agglomerations, but the measurable influence of a specific urban feature is unclear. The present study examines 41 variables and their potential influence on COVID-19 cases and fatalities. The study uses a multi-method approach to study the influence of variables, classified as demographic, socioeconomic, mobility and connectivity, urban form and density, and health and environment dimensions. This study develops an index dubbed the PVI-CI for classifying the pandemic vulnerability levels of cities, grouping them into five vulnerability classes, from very high to very low. Furthermore, clustering and outlier analysis provides insights on the spatial clustering of cities with high and low vulnerability scores. This study provides strategic insights into levels of influence of key variables upon the spread of infections as well as fatalities, along with an objective ranking for the vulnerability of cities. Thus it provides critical wisdom needed for urban healthcare policy and resource management. The pandemic vulnerability index calculation method and the process present a blueprint for the development of similar indices for cities in other countries, leading to a better understanding and improved pandemic management for urban areas and post-pandemic urban planning across the world. © 2022, CC BY.

关键词： Population statistics

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From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

arXiv

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arXiv 2024年

作者： Shi, Suwen Huang, Ziwei Gu, Xingxin Lin, Xu Zhong, Chaoying Hang, Junjie Lin, Jianli Zhong, Claire Chenwen Zhang, Lin Li, Yu Huang, Junjie Department of Chemistry Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States College of Professional Studies Northeastern University BostonMA02215 United States Department of Thoracic Surgery The First Affiliated Hospital School of Medicine Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis. © 2024, CC BY.

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Learning Eating Environments Through Scene Clustering

Learning Eating Environments Through Scene Clustering

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sri Kalyan Yarlagadda Sriram Baireddy David Guera Carol J. Boushey Deborah A. Kerr Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette IN USA University of Hawaii Cancer Center Honolulu HI USA School of Public Health Curtin University Perth Western Australia

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health agencies around the world have recognized the eating environment as a promising context for improving diet and health. In this paper, we propose an image clustering method to automatically extract the eating environments from eating occasion images captured during a community dwelling dietary study. Specifically, we are interested in learning how many different environments an individual consumes food in. Our method clusters images by extracting features at both global and local scales using a deep neural network. The variation in the number of clusters and images captured by different individual makes this a very challenging problem. Experimental results show that our method performs significantly better compared to several existing clustering approaches.

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A back of the envelope estimate of the average non-residential outage cost in the US

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Electricity Journal 2021年第4期34卷 106930-106930页

作者： Woo, C.K. Tishler, A. Zarnikau, J. Chen, Y. Department of Asian and Policy Studies Education University of Hong Kong New Territories Hong Kong Coller School of Management Tel Aviv University Tel Aviv69978 Israel Department of Economics University of Texas at Austin AustinTX78712 United States Department of Electrical and Computer Engineering Department of Environmental Studies University of California Santa Cruz Santa CruzCA95064 United States

Using the Envelope Theorem, this paper derives a simple formula for market-based estimation of an average non-residential outage cost. Its key finding is that the 2019 estimate for the US is about US$10 per kWh unserved, below the regional estimates of approximately US$20 to US$40 per kWh unserved previously found by a large national study that uses contingent valuation (CV) survey data. This finding's policy implication is reductions in marginal-cost-based electricity prices, an electric grid's need for transmission and generation capacity and demand response resources, a local distribution company's reliability-related investment, an independent system operator's cap for wholesale prices, and a regulator's penalty for reliability deterioration under performance-based regulation. © 2021 Elsevier Inc.

关键词： Investments

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