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Innovation, Research and Development in engineering Education. Activities of the Spanish Chapter of the IEEE Education Society: Directive Board of the IEEE Education Society Spanish Chapter 2020-2021

Innovation, Research and Development in Engineering Educatio...

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Technologies Applied to Electronics Teaching (TAEE)

作者： Ó scar Martí nez Bonastre Cristina Ferná ndez Lluí s Vicent Francisco Mur Manuel Castro Gabriel Diaz Jose Carpio Elio Sancristobal Sergio Martin Jose Sá nchez Francisco Javier Arcega Solsona Juan Suardiaz Muro Manuel Caeiro-Rodrí guez Martí n Llamas Inmaculada Plaza Baltasar Ferná ndez Manjó n Pedro Muñ oz-Merino Edmundo Tovar Telmo Zarraonandia Ayo Javier Garcia Zubia José Antonio Delgado Pení n Department of Statistics Mathematics and Computing Science Operations Research Centre Universidad Miguel Hern&#x00E1 ndez Elche Spain Department of Electronic Technology Universidad Carlos III de Madrid Madrid Spain UPF Barcelona School of Management Barcelona Spain Department of Electrical Electronic and Control Engineering UNED Madrid Spain Department of Electrical Engineering Universidad de Zaragoza Zaragoza Spain Department of Automatics Electrical Engineering and Electronic Technology Technical University of Cartagena Cartagena Spain atlanTTic Research Center University of Vigo Vigo Spain Department of Electronics and Communication Universidad de Zaragoza Zaragoza Spain Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Madrid Spain Department of Telematic Engineering Universidad Carlos III de Madrid Madrid Spain Department of Computer Languages and Systems and Software Engineering Universidad Polit&#x00E9 cnica de Madrid Madrid Spain Department of Computer Science and Engineering Universidad Carlos III de Madrid Madrid Spain Department of Electronic Engineering Universidad de Deusto Bilbao Spain Universitat Polit&#x00E8 cnica de Catalunya Barcelona Spain

ISBN: (数字)9781665421614

ISBN: (纸本)9781665421621

The Technical Committee of the CESEI (Spanish Chapter of the IEEE Education Society) is focused on the Innovation, Research and Development (I+R+D) of the education in engineering, mainly Electric and computer engineering. As in other interdisciplinary fields, the I+R+D involves several sources of information and different stakeholders, each one with its own focus and purpose. As a result, sometimes it may be complex for practitioners and researchers to identify the more appropriate entities for their interests. This paper shows how the CESEI Technical Committee tries to solve this difficulty by providing updated information regarding the main publications (journals), events (conferences) and awards to the best academic works. The paper focuses special attention on the activities held during 2020–2021.

关键词： Technological innovation Collaboration Stakeholders engineering education Research and development

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Distributed Feedback Control on the SIS Network Model: An Impossibility Result ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10955-10962页

作者： Mengbin Ye Ji Liu Brian D.O. Anderson Ming Cao Optus-Curtin Centre of Excellence in Artificial Intelligence Curtin University Perth Australia Faculty of Science and Engineering ENTEG University of Groningen Groningen 9747 AG Netherlands Department of Electrical and Computer Engineering Stony Brook University New York USA Research School of Electrical Energy and Material Engineering Australian National University Canberra Australia Hangzhou Dianzi University Hangzhou Zhejiang China Data61-CSIRO Canberra A.C.T. Australia

This paper considers the deterministic Susceptible-Infected-Susceptible (SIS) epidemic network model, over strongly connected networks. It is well known that there exists an endemic equilibrium (the disease persists in all nodes of the network) if and only if the effective reproduction number of the network is greater than 1. In fact, the endemic equilibrium is unique and is asymptotically stable for all feasible nonzero initial conditions. We consider the recovery rate of each node as a control input. Using results from differential topology and monotone systems, we establish that it is impossible for a large class of distributed feedback controllers to drive the network to the healthy equilibrium (where every node is disease free) if the uncontrolled network has a reproduction number greater than 1. In fact, a unique endemic equilibrium exists in the controlled network, and it is exponentially stable for all feasible nonzero initial conditions. We illustrate our impossibility result using simulations, and discuss the implications on the problem of control over epidemic networks.

关键词： deterministic epidemic models Susceptible-Infected-Susceptible (SIS) model complex networks control of networked systems differential topology monotone systems

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Copy and paste GAN: Face hallucination from shaded thumbnails

arXiv

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arXiv 2020年

作者： Zhang, Yang Tsang, Ivor W. Luo, Yawei Hu, Changhui Lu, Xiaobo Yu, Xin School of Automation Southeast University China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China Centre for Artificial Intelligence University of Technology Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology China School of Automation Nanjing University of Posts and Telecommunications China Australian Centre for Robotic Vision Australian National University Australia

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and nonuniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details;while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternately in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

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Quantum linear algebra is all you need for Transformer architectures

arXiv

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arXiv 2024年

作者： Guo, Naixu Yu, Zhan Choi, Matthew Agrawal, Aman Nakaji, Kouhei Aspuru-Guzik, Alán Rebentrost, Patrick Centre for Quantum Technologies National University of Singapore 117543 Singapore Department of Computer Science University of Toronto TorontoONM5S 2E4 Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Mathematics National University of Singapore 119076 Singapore Department of Chemistry University of Toronto TorontoONM5G 1Z8 Canada 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Department of Materials Science & Engineering University of Toronto TorontoONM5S 3E4 Canada Department of Chemical Engineering & Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada School of Computing National University of Singapore 117417 Singapore

Generative machine learning methods such as large-language models are revolutionizing the creation of text and images. While these models are powerful they also harness a large amount of computational resources. The transformer is a key component in large language models that aims to generate a suitable completion of a given partial sequence. In this work, we investigate transformer architectures under the lens of fault-tolerant quantum computing. The input model is one where trained weight matrices are given as block encodings and we construct the query, key, and value matrices for the transformer. We show how to prepare a block encoding of the self-attention matrix, with a new subroutine for the row-wise application of the softmax function. In addition, we combine quantum subroutines to construct important building blocks in the transformer, the residual connection and layer normalization, and the feed-forward neural network. Our subroutines prepare an amplitude encoding of the transformer output, which can be measured to obtain a prediction. Based on common open-source large-language models, we provide insights into the behavior of important parameters determining the run time of the quantum algorithm. We discuss the potential and challenges for obtaining a quantum advantage. © 2024, CC BY.

关键词： Network architecture

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Benchmarking bias: Expanding clinical AI model card to incorporate bias reporting of social and non-social factors

arXiv

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arXiv 2023年

作者： Heming, Carolina A.M. Abdalla, Mohamed Mohanna, Shahram Ahluwalia, Monish Zhang, Linglin Trivedi, Hari Woo, MinJae Fine, Benjamin Gichoya, Judy Wawira Celi, Leo Anthony Seyyed-Kalantari, Laleh University of Iowa Iowa CityIA United States Institute for Better Health Trillium Health Partners 100 Queensway West 6th floor MississaugaONL5B 1B8 Canada Kingston Health Sciences Centre Queen's University 76 Stuart St. KingstonONK7L 2V7 Canada School of Data Science and Analytics Kennesaw State University 3391 Town Point Dr NW KennesawGA30144 United States Department of Radiology and Imaging Sciences Emory University 1364 E Clifton Rd NE AtlantaGA30322 United States Vector Institute of Artificial Intelligence 661 University Ave Suite 710 TorontoONM5G 1M1 Canada Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Harvard Medical School 25 Shattuck St BostonMA02115 United States Department of Electrical Engineering and Computer Science York University 4700 Keele St TorontoONM3J 1P3 Canada Department of Medicine University of Alberta Walter C Mackenzie Health Sciences Centre St. NW Edmonton Alberta 8440 112 Canada

The model facts labels are often used to communicate artificial intelligence (AI) model performance to the end users for proper deployment and often report the overall performance averaged across all populations. However, the overall performance is not generalizable to different subgroups of patients. In this paper, we highlight the importance of currently unforeseen aspects, essential for AI models in healthcare, that are not included in the model fact cards. We suggest including through bias and fairness analysis outcomes in model fact labels in the health care AI model including: (a) socio-demographic fairness factors (e.g., sexes, race, socioeconomic status, etc.) and (b) Technical, Environmental and Biological Bias Factors (e.g. disease-dependent, anatomical, or instrumental factors, etc.). The socio-demographic fairness factors have been explored in fairness analysis of AI models in healthcare but have not often been listed in model fact labels. Besides, the technical, environmental, and biological bias factors have not often been explored in the bias analysis of AI models in healthcare and have not been reported in model fact cards. Here, we demonstrate disparities with respect to both sets of factors. We suggest that clinical AI model reporting cards need to be expanded to communicate such a wider range of disparities to end users for precise patient care. Analysis and communication of such a wider range of biases identify potential drivers of bias, which pave the way to debiasing. As well, it highlights the model caveats and guides the end user and policymaker on how, when, and under what circumstances the model works or fails. Copyright © 2023, The Authors. All rights reserved.

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, artificial intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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Author Correction: PLAS-20k: Extended dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications

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Scientific data 2024年第1期11卷 730页

作者： Divya B Korlepara C S Vasavi Rakesh Srivastava Pradeep Kumar Pal Saalim H Raza Vishal Kumar Shivam Pandit Aathira G Nair Sanjana Pandey Shubham Sharma Shruti Jeurkar Kavita Thakran Reena Jaglan Shivangi Verma Indhu Ramachandran Prathit Chatterjee Divya Nayar U Deva Priyakumar IHub-Data International Institute of Information Technology Hyderabad 500032 India. Divison of Physics School of Advanced Sciences Vellore Institute of Technology Chennai 600127 India. Department of Artificial Intelligence School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bengaluru 560035 India. Centre for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India. Department of Materials Science and Engineering Indian Institute of Technology Delhi Hauz Khas New Delhi 110016 India. divyanayar@mse.iitd.ac.in. IHub-Data International Institute of Information Technology Hyderabad 500032 India. deva@iiit.ac.in. Centre for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Hyderabad 500032 India. deva@iiit.ac.in.

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Corrigendum to “ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models” [Robotics and Autonomous Systems Volume 157, November 2022, 104234]

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Robotics and Autonomous Systems 2024年 178卷

作者： Arodh Lal Karn Sudhakar Sengan Ketan Kotecha Irina V Pustokhina Denis A Pustokhin V Subramaniyaswamy Dharam Buddhi Department of Financial and Actuarial Mathematics School of Science Xi'an Jiaotong-Liverpool University China Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Symbiosis Centre for Applied Artificial Intelligence Symbiosis International (Deemed University) Pune Maharashtra 412115 India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics 117997 Moscow Russia Department of Logistics State University of Management 109542 Moscow Russia School of Computing SASTRA Deemed University Thanjavur 613401 Tamil Nadu India Division of Research & Innovation Uttaranchal University Uttarakhand 248007 Dehradun India

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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