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arXiv 2021年

作者： Bokor, Peter Hudec, Lukas Fabian, Ondrej Benesova, Wanda Slovak University of Technology Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Ilkovicova 2 Bratislava84216 Slovakia Institute for Clinical and Experimental Medicine Clinical and Transplant Pathology Centre Videnska 1958/9 Prague 4140 21 Czech Republic Charles University Thomayer University Hospital 3rd Faculty of Medicine Department of Pathology and Molecular medicine Videnska 800 Prague 4140 59 Czech Republic

Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSIs), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSIs based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%. © 2021, CC BY.

关键词： Medical imaging

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MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing 24

MRP 2020: The Second Shared Task on Cross-Framework and Cros...

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Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020

作者： Oepen, Stephan Abend, Omri Abzianidze, Lasha Bos, Johan Hajič, Jan Hershcovich, Daniel Li, Bin O’Gorman, Tim Xue, Nianwen Zeman, Daniel University of Oslo Department of Informatics Norway The Hebrew University of Jerusalem School of Computer Science and Engineering Israel Utrecht University UiL OTS Netherlands University of Groningen Center for Language and Cognition Netherlands Charles University Prague Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic University of Copenhagen Department of Computer Science Denmark Nanjing Normal University School of Chinese Language and Literature China University of Massachusetts at Amherst College of Information and Computer Sciences United States Brandeis University Department of Computer Science United States

ISBN: (纸本)9781952148644

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization;for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://***. © 2020 Association for Computational Linguistics.

关键词： Directed graphs

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Biometrics and quality of life of lymphoma patients: A longitudinal mixed-model approach

Biometrics and quality of life of lymphoma patients: A longi...

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作者： Oliveira, Alexandra Silva, Eliana Aguiar, Joyce Faria, Brígida Mónica Reis, Luís Paulo Cardoso, Henrique Gonçalves, Joaquim Oliveira e Sá, Jorge Carvalho, Victor Marques, Herlander LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal ESS-IPP - Department of Biomathematics Biostatistics and Bioinformatics School of Health Polytechnic Institute of Porto Porto Portugal DEI-FEUP - Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal Centro ALGORITMI – University of Minho Guimarães Portugal LITEC - Lab. de Inovação Tecnológica e Engenharia do Conhecimento Porto Portugal 2Ai - Applied Artificial Intelligence Laboratory School of Technology of Polytechnic Institute of Cávado and Ave Barcelos Portugal CINTESIS - Centro de Investigação em Tecnologias e Serviços de Saúde Porto Portugal Centro Hospitalar de Braga Braga Portugal

Knowledge engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health-related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato-oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real-time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed-effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life. © 2020 John Wiley & Sons Ltd

关键词： Regression analysis

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Pruning neural networks without any data by iteratively conserving synaptic flow

arXiv

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arXiv 2020年

作者： Tanaka, Hidenori Yamins, Daniel L.K. Kunin, Daniel Ganguli, Surya Physics & Informatics Laboratories NTT Reserach Inc Department of Applied Physics Stanford University Department of Psychology Department of Computer Science Stanford University Institute for Computational and Mathematical Engineering Stanford University Department of Applied Physics Stanford University

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.9 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that data must be used to quantify which synapses are important. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth

SmartCHANGE: AI-based long-term health risk evaluation for d...

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International Conference on applied Mathematics & computer Science (ICAMCS)

作者： Nina Reščič Janna Alberts Teatske M. Altenburg Mai J. M. Chinapaw Antonio De Nigro Dario Fenoglio Martin Gjoreski Anton Gradišek Gregor Jurak Athanasios Kiourtis Dimosthenis Kyriazis Marc Langheinrich Elena Mancuso Argyro Mavrogiorgou Mykola Pechenizkiy Roberto Pratola José Ribeiro Maroje Sorić Fawad Taj Tuija H. Tammelin Martijn Vastenburg Anna Vilanova Tanja G. M. Vrijkotte Mitja Luštrek Department of Intelligent Systems Jožef Stefan Institute Ljubljana Slovenia Research & Development ConnectedCare Nijmegen The Netherlands Department of Public and Occupational Health Amsterdam UMC Amsterdam Public Health Research Institute Vrije Universiteit Amsterdam Amsterdam The Netherlands Engineering Ingegneria Informatica S.p.A. Rome Italy Faculty of Informatics Università Della Svizzera Italiana Lugano Switzerland Faculty of Sports University of Ljubljana Ljubljana Slovenia Department of Digital Systems University of Piraeus Piraeus Greece Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Faculty of Sport University of Porto Porto Portugal Likes School of Health and Social Studies Jamk University of Applied Sciences Jyväskylä Finland

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project’s proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes.

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Automating the Practice of Science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Accelerating SDN control plane with GPGPU-based packet classification

Accelerating SDN control plane with GPGPU-based packet class...

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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Martin Baláž Pavol Helebrandt Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9781728149677

ISBN: (纸本)9781728149684

The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms.

关键词： Graphics processing units Acceleration Classification algorithms Firewalls (computing) Telecommunication traffic Process control

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Pruning neural networks without any data by iteratively conserving synaptic flow 20

Pruning neural networks without any data by iteratively cons...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Hidenori Tanaka Daniel Kunin Daniel L. K. Yamins Surya Ganguli Physics & Informatics Laboratories NTT Research Inc. Department of Applied Physics Stanford University Institute for Computational and Mathematical Engineering Stanford University Department of Psychology Department of Computer Science Stanford University Department of Applied Physics Stanford University

ISBN: (纸本)9781713829546

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradient-based pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently competes with or outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.99 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that, at initialization, data must be used to quantify which synapses are important.

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