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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Masuda-Katsuse, Ikuyo Hirashima, Yuiko Kindai University Faculty of Humanity-Oriented Science and Engineering Department of Information and Computer Science Fukuoka Japan Fukuoka International University of Health and Welfare Postgraduate Speech and Hearing Sciences Course Fukuoka Japan

ISBN: (纸本)9781728173979

We developed a remote articulation test system to support special education classes for language-disabled children. We compared the diagnostic performances of remote articulation test sessions using our system and face-to-face test sessions or remote test sessions using an existing web video system. In the experiment, an external expert and teachers performed articulation tests on the children in a special education class. They also assessed their diagnostic confidence for each phoneme. Their diagnosis results were sufficiently consistent between the face-to-face and remote sessions and among the inspectors. The diagnostic confidence of the remote test using our system was significantly higher than the face-to-face session as well as the session using the existing web video system. In addition, our system was evaluated as having greater usability as a remote articulation system than the existing web video system. © 2020 IEEE.

关键词： articulation test remote diagnostic system special education class WebRTC

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An Advance Implementation of Machine Learning Techniques for the Prediction of Cervical Cancer

An Advance Implementation of Machine Learning Techniques for...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Vandana Roy Lipika Roy Ritu Ahluwalia Geetanjli Khambra M. Ramesh K. Rajasekhar Dept. of EC Gyan Ganga Institute of Technology and Sciences Jabalpur M.P. India UG Course Gyan Ganga Institute of Technology and Sciences Jabalpur M.P. India Department of Computer Applications The Bhopal School of Social Sciences Bhopal M.P India Dept. of IT St. Martin's Engineering College Secunderabad Telangana Bharat Dept. of ECE B V C Engineering College Odalarevu AP Bharat

Cancer has now become a widespread issue, and it might be difficult to diagnose it. The analysis and diagnosis of disease in a patient through a monitoring system has advanced significantly, but there are still some obstacles to overcome. Technology-assisted disease discovery is urgently required since it could enable clinicians to make an accurate diagnosis more quickly. The process makes use of event-driven datasets produced by the Internet of Things (IoT). Rarely are the cloud and IoT used to process the real-time dataset for illness discovery. Cancer detection is a serious difficulty since the data generated by sensors constitutes huge data and cannot be quickly processed without the use of data mining and machine learning. A precise and intelligent method is needed for cancer prognosis because it has been discovered that cancer cannot be accurately predicted from offline datasets. The International Federation of Obstetrics and Gynecology (IFOG) realworld data set has been used to predict cervical cancer using machine learning. Five metrics, including true-positive-rate, false-positiverate, f-measure, MCC, and accuracy, have been used to examine the results of the training of six machine learning models using the latest UCI repository. The outcomes demonstrate that the tree-based decision stump machine learning classifier and the meta-based iterative classifier optimizer achieved the maximum prediction accuracy of 77.97%, which is the greatest among the other ML classifiers. The proposed model demonstrated noteworthy performance in terms of prediction accuracy.

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Verification of Usefulness of Student Modeling with Real Educational Data using Convex Factorization Machines

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Procedia computer Science 2021年 192卷 804-811页

作者： Shinichi Oeda Daiki Shimizu Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

Extracting useful information generated from educational settings involves the application of data mining, machine learning, and statistics to the large amount of electronic data collected by educational systems. To generate better higher learning outcomes using an intelligent tutoring system, such as an e-learning system, it is necessary to more accurately understand the state of student knowledge. The purpose of student modeling is to estimate the students’ skills from log data, such as examination results, and to predict whether or not a student will be able to solve a problem. In this study, we propose a student performance prediction method using convex factorization machines. Factorization machines offer a combination of the advantages of support vector machines and factorization models such as matrix factorization. The results of conventional methods, which predict student performance using factorization machines, exhibited better results than they have before. However, because factorization machines are not convex optimizations, they acquire local minima, which is a disadvantage. Therefore, we used convex factorization machines in order to improve the performance of student modeling predictions.

关键词： Educational Data Mining Student Modeling Convex Factorization Machines Factorization Machines Convex Optimization Recommendation Intelligent Tutoring Systems

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A Hybrid BRKGA Approach for the Two Stage Capacitated Facility Location Problem

A Hybrid BRKGA Approach for the Two Stage Capacitated Facili...

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Congress on Evolutionary Computation

作者： Gabriel Souto Igor Morais Liss Faulhaber Glaydston Mattos Ribeiro Pedro Henrique González Undergraduate Course in Computer Science CEFET/RJ Rio de Janeiro Brazil Graduate Program in Computer Science CEFET/RJ Rio de Janeiro Brazil Transport Engineering Program UFRJ Rio de Janeiro Brazil CEFET/RJ Rio de Janeiro Brazil

Finding optimal locations for installing factories and warehouses, fulfilling customers’ demand is the goal of the Two-Stage Capacitated Facility Location, which compounds a supply chain class of problems. Besides locating factories and warehouses, the problem’s objective is to minimize the operational costs: opening facilities and product flow, satisfying customer demand, and without disregarding the capacity of factories and warehouses. A Hybrid Biased Random-Key Genetic Algorithm (HBRKGA), combining BRKGA with Local Search and Local Branching, was proposed to solve the problem. The computational experiments showed that the method was reliable and stood out among other approaches to the problem.

关键词： Supply chains Evolutionary computation Search problems Production facilities Reliability Compounds Genetic algorithms

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Illness duration and symptom profile in symptomatic UK school-aged children tested for SARS-CoV-2 (vol 5, pg 708, 2021)

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LANCET CHILD & ADOLESCENT HEALTH 2021年第10期5卷 708-718页

作者： Molteni, E. Sudre, C. H. Canas, L. S. School of Biomedical Engineering and Imaging Sciences Faculty of Life Sciences and Medicine School of Life Course Sciences King's College London London UK MRC Unit for Lifelong Health and Ageing Department of Population Health Sciences and Centre for Medical Image Computing Department of Computer Science University College London London UK Population Health Sciences Institute Faculty of Medical Sciences Newcastle University Newcastle upon Tyne UK Department of Population Health Faculty of Epidemiology and Population Health London School of Hygiene & Tropical Medicine London UK Zoe Limited London UK King's College London and Guy's and St Thomas' PET Centre London UK Department of Twin Research and Genetic Epidemiology Faculty of Life Sciences and Medicine School of Life Course Sciences King's College London London UK Department of Endocrinology Guys and St Thomas' NHS Foundation Trust London UK

Background In children, SARS-CoV-2 infection is usually asymptomatic or causes a mild illness of short duration. Persistent illness has been reported; however, its prevalence and characteristics are unclear. We aimed to determine illness duration and characteristics in symptomatic UK school-aged children tested for SARS-CoV-2 using data from the COVID Symptom Study, one of the largest UK citizen participatory epidemiological studies to date. Methods In this prospective cohort study, data from UK school-aged children (age 5–17 years) were reported by an adult proxy. Participants were voluntary, and used a mobile application (app) launched jointly by Zoe Limited and King's College London. Illness duration and symptom prevalence, duration, and burden were analysed for children testing positive for SARS-CoV-2 for whom illness duration could be determined, and were assessed overall and for younger (age 5–11 years) and older (age 12–17 years) groups. Children with longer than 1 week between symptomatic reports on the app were excluded from analysis. Data from symptomatic children testing negative for SARS-CoV-2, matched 1:1 for age, gender, and week of testing, were also assessed. Findings 258 790 children aged 5–17 years were reported by an adult proxy between March 24, 2020, and Feb 22, 2021, of whom 75 529 had valid test results for SARS-CoV-2. 1734 children (588 younger and 1146 older children) had a positive SARS-CoV-2 test result and calculable illness duration within the study timeframe (illness onset between Sept 1, 2020, and Jan 24, 2021). The most common symptoms were headache (1079 [62·2%] of 1734 children), and fatigue (954 [55·0%] of 1734 children). Median illness duration was 6 days (IQR 3–11) versus 3 days (2–7) in children testing negative, and was positively associated with age (Spearman's rank-order r s 0·19, p<0·0001). Median illness duration was longer for older children (7 days, IQR 3–12) than younger children (5 days, 2–9). 77 (4·4%) of 1734 children

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Pronunciation Error Detection in Voice Input for Correct Word Suggestion 21

Pronunciation Error Detection in Voice Input for Correct Wor...

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21st International Electronics Symposium, IES 2019

作者： Kamimura, Kohei Takano, Kosuke Kanagawa Institute of Technology Course of Information and Computer Sciences Graduate School of Engineering Atsugi Kanagawa Japan Kanagawa Institute of Technology Department of Information and Computer Sciences Atsugi Kanagawa Japan

ISBN: (纸本)9781728144498

In this paper, we present a method of pronunciation error detection in voice input for suggesting correct word. With the progress of the speech recognition technology, voice typing has been widely spread in many software and information services. However, to the best of our knowledge, there is no method to correct pronunciation errors for voice input by a user, although the speech recognition function can show only the words whose pronunciations are like the ones that the user input in voice. In this study, we propose a method for correcting pronunciation mistake in voice input by extending an existing method of spell mistake correction based on edit distance. By applying pronunciation score for phoneme sequences, which is proposed in our previous study, it is expected that our method can reduce the number of candidate pronunciation to be suggested in the process of pronunciation error detection. © 2019 IEEE.

关键词： Speech recognition

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Learning Attitude Analysis based on Operation Logs of Fill-in Workbook System

Learning Attitude Analysis based on Operation Logs of Fill-i...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Kousuke Abe Tetsuo Tanaka Kazunori Matsumoto Course of Information and Computer Sciences Graduate School of Engineering Kanagawa Institute of Technology Atsugi Japan Faculy of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (纸本)9781728173986

The authors are developing a fill-in workbook system that allows faculty members to ascertain the attitude of all students to classes including those who are not active, and improve lectures through well-timed and appropriate actions. To support the actions of teachers to improve lessons and teaching materials, we analyzed the operation logs of students using our Fill-in Workbook System in multiple lessons. The transition of learning attitudes for each lesson and for each time / each slide in the lesson for the entire class is extracted. In addition, to support the student's retrospective review of the lessons, the transition in the attitude of each student to the class is extracted. The results confirmed that lessons in which the class attitude became worse could be detected by analyzing the system focus time and the degree of synchronization between the page explained by the teacher and the page referenced by the student, and that the difference in learning attitudes increased when there were few blanks. In addition, we analyzed the degree of asynchrony (jumping ahead, falling behind) with the teacher's explanation and clarified the relationship between the time taken to explain one page and learning attitude. Furthermore, it was confirmed that the student's individual learning state could be divided into three separate conditions.

关键词： Education Synchronization Task analysis Informatics

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A Method for Extracting Influential People for the Improvement of Contents 33rd

A Method for Extracting Influential People for the Improveme...

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33rd International Conference on Advanced Information Networking and Applications, AINA 2019

作者： Tsukiji, Hayato Takano, Kosuke Course of Information and Computer Sciences Graduate School of Engineering Kanagawa Institute of Technology AtsugiKanagawa Japan Department of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology AtsugiKanagawa Japan

ISBN: (纸本)9783030150341

It is very important to find influential users who have useful information as well as extract helpful knowledge that affect the efficiency of user’s intellectual work;however, it is difficult to evaluate how such helpful comments and conversations from influential users can affect works of other users. In this study, we propose a method for extracting user comments that influenced other users to improve their contents such as a presentation slide by analyzing correlation between user’s comments and improved parts of contents. Our method allows us to make actual evaluation to influential users for some specific users. In the experiment, we evaluate the feasibility of the proposed method using the actual communication history that is collected from a communication service, Slack. © 2019, Springer Nature Switzerland AG.

关键词： computers

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3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

arXiv

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arXiv 2022年

作者： Meng, Yanda Chen, Xu Gao, Dongxu Zhao, Yitian Yang, Xiaoyun Qiao, Yihong Huang, Xiaowei Zheng, Yalin The Institute of Life Course and Medical Sciences University of Liverpool LiverpoolL7 8TX United Kingdom The Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China The Department of Computer Science University of Liverpool LiverpoolL7 8TX United Kingdom The China Science IntelliCloud Technology Co. Ltd Shanghai China The Remark AI UK Limited LondonSE1 9PD United Kingdom

In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is achieved by seamlessly combining standard convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By iteratively and hierarchically fusing the features across different layers and stages of the CNNs and GCNs, our approach can provide a dense face alignment and 3D face reconstruction simultaneously for the benefit of direct feature learning of 3D face mesh. Experiments on several challenging datasets demonstrate that our method outperforms state-of-the-art approaches on both 2D and 3D face alignment tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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CNN-GCN Aggregation Enabled Boundary Regression for Biomedical Image Segmentation 23rd

CNN-GCN Aggregation Enabled Boundary Regression for Biomedic...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Meng, Yanda Wei, Meng Gao, Dongxu Zhao, Yitian Yang, Xiaoyun Huang, Xiaowei Zheng, Yalin Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool United Kingdom Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China China Science IntelliCloud Technology Co. Ltd. Shanghai China Department of Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9783030597184

Accurate segmentation of anatomic structure is an essential task for biomedical image analysis. Recent popular object contours regression based segmentation methods have increasingly attained researchers attentions. They made a new starting point to tackle segmentation tasks instead of commonly used dense pixels classification methods. However, because of the nature of CNN based network (lack of spatial information) and the difficulty of this methodology itself (need of more spatial information), these methods needed extra process to maintain more spatial features, which may cause longer inference time or tedious design and inference process. To address the issue, this paper proposes a simple, intuitive deep learning based contour regression model. We develop a novel multi-level, multi-stage aggregated network to regress the coordinates of the contour of instances directly in an end-to-end manner. The proposed network seamlessly links convolution neural network (CNN) with Attention Refinement module (AR) and Graph Convolution Network (GCN). By hierarchically and iteratively combining features over different layers of the CNN, the proposed model obtains sufficient low-level features and high-level semantic information from the input image. Besides, our model pays distinct attention to the objects contours with the help of AR and GCN. Primarily, thanks to the proposed aggregated GCN and vertices sampling method, our model benefits from direct feature learning of the objects contour locations from sparse to dense and the spatial information propagation across the whole input image. Experiments on the segmentation of fetal head (FH) in ultrasound images and of the optic disc (OD) and optic cup (OC) in color fundus images demonstrate that our method outperforms state-of-the-art methods in terms of effectiveness and efficiency. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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