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2021 IEEE Conference on Norbert Wiener in the 21st Century: Being Human in a Global Village, 21CW 2021 2021年

作者： Love, Heather Adamson, Greg Gopal, T.V. Department of English Language and Literature University of Waterloo Canada Computing and Information Systems University of Melbourne MelbourneVIC Australia Dept. of Computer Science Engineering CEG Campus Anna University Chennai India

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Stages Classification on Cervical Cell Images: A Comparative Study

Stages Classification on Cervical Cell Images: A Comparative...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Wan Azani Mustafa Mohamad Irfan Noor Hiam Alquran Miharaini Md Ghani Hafizul Fahri Hanafi Noor Hidayah Che Lah Myasar Mundher adnan Abbas Hameed Abdul Hussein Faculty of Electrical Engineering Technology Universiti Malaysia Perlis UniCITI Alam Campus Sungai Chuchuh Padang Besar Perlis Malaysia Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan Department of Communication Faculty of Modern Language and Communication Universiti Putra Malaysia Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjong Malim Perak Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

The cancer of the cervix is called cervical cancer. An element of a woman's womb is the cervix. Among other diseases that affect women, it came in at number four on the list. According to the World Health Organization's cancer report, there are currently roughly 10 million new cases of cancer recorded year, and by 2020, that number will have doubled to 20 million. With the right screening and awareness campaign, this number can be cut in half. A quarter of cancers are said to be brought on by infections, including hepatitis B, which is connected to liver cancer, and the human papillomavirus, which is connected to cervix cancer. Deep learning techniques have been successfully applied to a wide range of image classification tasks, and have the potential to be highly effective for cervical cell image classification as well. In this project, we propose to use a deep learning-based approach to classify cervical cell images into different categories, such as normal cells, abnormal cells, or cancerous cells. To achieve this goal, we will first pre-process the images to prepare them for analysis, and then extract relevant features. These features will be used to train a deep learning model, which will be fine-tuned and optimized for the specific task of cervical cell classification. In this project, transfer learning method will be by using pre-trained classifier such as ResNet-50, GoogLeNet and EfficientNet-b0. We will evaluate the performance of the model using metrics such as accuracy and compare our results to those obtained using traditional machine learning approaches. From this project, the highest accuracy achieved are 51.49%. The goal to develop a pre-trained classifier transfer learning can be used to accurately and reliably classify cervical cell images in a clinical setting are achieved.

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The topology of synergy: linking topological and information-theoretic approaches to higher-order interactions in complex systems

arXiv

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arXiv 2025年

作者： Varley, Thomas F. Mediano, Pedro A.M. Patania, Alice Bongard, Josh Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Department of Computing Imperial College London London United Kingdom Division of Psychology and Language Sciences University College London London United Kingdom Department of Mathematics University of Vermont BurlingtonVT United States

The study of irreducible higher-order interactions has become a core topic of study in complex systems, as they provide a formal scaffold around which to build a quantitative understanding of emergence and emergent properties. Two of the most well-developed frameworks, topological data analysis and multivariate information theory, aim to provide formal tools for identifying higher-order interactions in empirical data. Despite similar aims, however, these two approaches are built on markedly different mathematical foundations and have been developed largely in parallel - with limited interdisciplinary cross-talk between them. In this study, we present a head-to-head comparison of topological data analysis and information-theoretic approaches to describing higher-order interactions in multivariate data;with the aim of assessing the similarities and differences between how the frameworks define "higher-order structures." We begin with toy examples with known topologies (spheres, toroids, planes, and knots), before turning to more complex, naturalistic data: fMRI signals collected from the human brain. We find that intrinsic, higher-order synergistic information is associated with three-dimensional cavities in an embedded point cloud: shapes such as spheres and hollow toroids are synergy-dominated, regardless of how the data is rotated. In fMRI data, we find strong correlations between synergistic information and both the number and size of three-dimensional cavities. Furthermore, we find that dimensionality reduction techniques such as PCA preferentially represent higher-order redundancies, and largely fail to preserve both higher-order information and topological structure, suggesting that common manifold-based approaches to studying high-dimensional data are systematically failing to identify important features of the data. These results point towards the possibility of developing a rich theory of higher-order interactions that spans topological and information-theor

关键词： Topology

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Neural quality estimation with multiple hypotheses for grammatical error correction

arXiv

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arXiv 2021年

作者： Liu, Zhenghao Yi, Xiaoyuan Sun, Maosong Yang, Liner Chua, Tat-Seng Department of Computer Science and Technology Tsinghua University Beijing China Institute for Artificial Intelligence Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Beijing Academy of Artificial Intelligence Beijing Language and Culture University Beijing China School of Computing National University of Singapore Singapore

Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available at https://***/thunlp/VERNet. Copyright © 2021, The Authors. All rights reserved.

关键词： Error correction

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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Web-based conversation quest for enhancing English speaking skills 26

Web-based conversation quest for enhancing English speaking ...

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26th International Conference on Computers in Education, ICCE 2018

作者： Chen, Cheng-Ting Chang, Maiga Wu, Kuan-Hsing Yu, Pei-Shan Department of Applied Linguistics and Language Studies Chung Yuan Christian University Taiwan School of Computing Information and Systems Athabasca University Canada and Smart Informatics Ltd. Canada Department of Information Management Chung Yuan Christian University Taiwan

ISBN: (纸本)9789869721424

In this paper, a Conversation Quest in the educational game platform MEGA World v2.1, for English learners to practice their speaking skill is introduced. The system is designed specifically for assisting English learners to cultivate their speaking proficiency. Providing a non-threating environment with goal-oriented tasks, learners are invited to join a game with scaffold speaking tasks. Based on Vygotsky’s socio-cultural theory, social interaction is a key element for improving language acquisition and speech fluency. It is believed that English learners would improve their communicating skills of using the target language while interacting with native English speakers because they can provide support or authentic language usage examples for learners through scaffolding structures like modeling, repetition, or linguistic simplification. In reality, however, a native English speaker may not be available for all English learners, so the Conversation Quest in MEGA World v2.1 is designed and developed to provide learners with simulated conversation scenarios. © 2018 Asia-Pacific Society for Computers in Education..All Rights Reserved.

关键词： Scaffolds

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Author Correction: Spatial communication systems across languages reflect universal action constraints

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Nature human behaviour 2024年第1期8卷 181页

作者： Kenny R Coventry Harmen B Gudde Holger Diessel Jacqueline Collier Pedro Guijarro-Fuentes Mila Vulchanova Valentin Vulchanov Emanuela Todisco Maria Reile Merlijn Breunesse Helen Plado Juergen Bohnemeyer Raed Bsili Michela Caldano Rositsa Dekova Katharine Donelson Diana Forker Yesol Park Lekhnath Sharma Pathak David Peeters Gabriella Pizzuto Baris Serhan Linda Apse Florian Hesse Linh Hoang Phuong Hoang Yoko Igari Keerthana Kapiley Tamar Haupt-Khutsishvili Sara Kolding Katri Priiki Ieva Mačiukaitytė Vaisnavi Mohite Tiina Nahkola Sum Yi Tsoi Stefan Williams Shunei Yasuda Angelo Cangelosi Jon Andoni Duñabeitia Ramesh Kumar Mishra Roberta Rocca Jurģis Šķilters Mikkel Wallentin Eglė Žilinskaitė-Šinkūnienė Ozlem Durmaz Incel School of Psychology University of East Anglia Norwich UK. k.coventry@uea.ac.uk. School of Psychology University of East Anglia Norwich UK. Helmholtz Institute Department of Experimental Psychology Utrecht University Utrecht the Netherlands. Hanse-Wissenschaftskolleg Delmenhorst Germany. Department of English Friedrich-Schiller-Universität Jena Jena Germany. Department of Spanish Modern and Classic Philology University of the Balearic Islands Palma Spain. Language Acquisition and Language Processing Lab Department of Language and Literature Norwegian University of Science and Technology Trondheim Norway. Department of Spanish Language Linguistics and Literature Theory University of Seville Seville Spain. Institute of Estonian and General Linguistics University of Tartu Tartu Estonia. Centre for the Arts in Society Leiden University Leiden the Netherlands. Võro Institute Võru Estonia. Department of Linguistics University at Buffalo Buffalo NY USA. Danieli Telerobot Srl Genoa Italy. Istituto Italiano di Tecnologia (IIT) Genoa Italy. Paisii Hilendarski University of Plovdiv Plovdiv Bulgaria. Department of English University of Nevada Reno NV USA. Department of Slavonic Languages and Caucasus Studies Friedrich-Schiller-Universität Jena Jena Germany. Cognitive Science Department of Humanities Social and Political Sciences ETH Zürich Zürich Switzerland. Cognitive Science and Psycholinguistics Lab Central Department of Linguistics Tribhuvan University Kathmandu Nepal. Centre for Neural and Cognitive Sciences School of Medical Sciences University of Hyderabad Hyderabad India. Department of Communication and Cognition TiCC Tilburg University Tilburg the Netherlands. Max Planck Institute for Psycholinguistics Nijmegen the Netherlands. Department of Chemistry University of Liverpool Liverpool UK. Department of Computer Science University of Manchester Manchester UK. Laboratory for Perceptual and Cognitive Systems Faculty of Computing University o

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Machine Learning-Driven Prediction of Optical and Physical Properties in Lanthanum and Gold-Doped Zinc Borotellurite Glasses for Optoelectronic Applications

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Ceramics International 2025年

作者： S.N. Nazrin Camellia Doroody Liyana Adilla Burhanuddin Neesha Jothi N. Ibrahim Halimah Badioze Zaman M.H.M. Tahir Manzoore Elahi M. Soudagar S. Ramesh Sagar Shelare Shubham Sharma Institute of Informatics and Computing in Energy (IICE) College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional (UNITEN @ The Energy University) Putrajaya Campus Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura-140401 Punjab India Zhejiang Xingyu Mechanical and Electrical Technology Company Ltd. Tongqin Wuyi County Jinhua Zhejiang 311400 China Department of Electronic Systems Engineering (ESE) Malaysia–Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia Dept. of Digital Economy Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman 31900 Kampar Perak Department of English Language and Literature Faculty of Languages and Communication Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Center of Advanced Manufacturing and Materials Processing (AMMP) Faculty of Engineering University of Malaya Kuala Lumpur Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Phagwara Punjab 144411 India Department of Applied Chemistry Priyadarshini College of Engineering Nagpur Maharashtra 440019 India University Centre for Research and Development Chandigarh University Mohali Punjab 140413 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Jadara University Research Center Jadara University Jordan

Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimization of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The melt-quenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass's amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO 3 , BO 4 , and BO 3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical performance for photonic and optoelectronic applications. The enhanced optical properties of the synthesized glass system make it a promising candidate for photonic and optoelectronic applications, including optical fibers, solid-state lasers, and planar waveguides , where its tunable refractive index and improved transparency can optimize signal transmission and device efficiency. Additionally, the presence of gold oxide nanoparticles suggests potential use in nonl

关键词： Artificial neural networks (ANN) Energy Gold oxide Gradient boosting (GB) Random forest (RF) XGBoost (XGB)

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Automatic language identification in texts: A survey

arXiv

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arXiv 2018年

作者： Jauhiainen, Tommi Lui, Marco Zampieri, Marcos Baldwin, Timothy Lindén, Krister Department of Digital Humanities University of Helsinki School of Computing and Information Systems University of Melbourne Research Institute in Information and Language Processing University of Wolverhampton

language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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