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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Sanaz Karimijafarbigloo Reza Azad Amirhossein Kazerouni Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany School of Electrical Engineering Iran University of Science and Technology Iran Department of Radiology Northwestern University Chicago USA Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical image segmentation encounter significant challenges due to their heavy dependence on extensive labeled training data. To tackle this issue, we propose a novel self-supervised algorithm, S 3 -Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. This architectural enhancement makes it possible to comprehensively capture contextual information while preserving local intricacies, thereby enabling precise semantic segmentation. Furthermore, considering that lesions in medical images often exhibit deformations, we leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Additionally, our self-supervised strategy emphasizes the acquisition of invariance to affine transformations, which is commonly encountered in medical scenarios. This emphasis on robustness with respect to geometric distortions significantly enhances the model’s ability to accurately model and handle such distortions. To enforce spatial consistency and promote the grouping of spatially connected image pixels with similar feature representations, we introduce a spatial consistency loss term. This aids the network in effectively capturing the relationships among neighboring pixels and enhancing the overall segmentation quality. The S 3 -Net approach iteratively learns pixel-level feature representations for image content clustering in an end-to-end manner. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches. Github.

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Unsupervised extraction, labelling and clustering of segments from clinical notes

Unsupervised extraction, labelling and clustering of segment...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Petr Zelina Jana Halámková Vít Nováček Faculty of Informatics Masaryk University Brno Czechia Masaryk Memorial Cancer Institute Brno Czechia Data Science Institute NUI Galway Galway Ireland

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising in oncology). Our goal was to extract, classify (i.e. label) and cluster segments of the free-text notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities). The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.

关键词： Toxicology Buildings Semantics Pipelines Feature extraction Oncology Breast cancer

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GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning

arXiv

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arXiv 2022年

作者： Kamar, Doğay Üre, Nazım Kemal Ünal, Gözde Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Artificial Intelligence and Data Science Research Center Istanbul Technical University Istanbul Turkey

In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approaches do not work well in environments with sparse or no rewards. We propose Generative Adversarial Network-based Intrinsic Reward Module that learns the distribution of the observed states and sends an intrinsic reward that is computed as high for states that are out of distribution, in order to lead agent to unexplored states. We evaluate our approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge for a sparse reward setting and show that our approach is indeed capable of exploring efficiently. We discuss a few weaknesses and conclude by discussing future *** Codes 68T20 (Primary) 68T05, 68T07 (Secondary) © 2022, CC BY.

关键词： Generative adversarial networks

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Gamification using Octalysis Framework in Knowledge Management System for Vocational High Schools during the Covid-19 Pandemic

Gamification using Octalysis Framework in Knowledge Manageme...

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International Conference on informatics and Computing (ICIC)

作者： Mgs. Afriyan Firdaus Dwi Rosa Indah Yoppy Sazaki Eka Prasetyo Ariefin Muhammad Fachri Nuriza Muhammad Rafly Information System Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Data Structure and Accounting Information System Laboratory Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Informatics Management Department Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9798350345728

Knowledge management systems enable teachers and students to interact directly in the knowledge transfer process during the Covid-19 pandemic. In addition, applying the gamification concept in vocational disciplines could enhance the quality of teaching to increase motivation and student learning outcomes which tend to decline. Octalysis Framework, which supports the gamification concept, consists of eight cores that focus on creativity, self-expression, social dynamics, logic, thinking, and ownership depicted in an octagon making an application more fun and helpful to motivate users to enjoy and be involved in an activity. This condition is crucial and urgent in the Covid-19 pandemic that requires online and student-centered learning. This study discussed the application of Octalysis Framework-based Gamification in a Computer Learning Knowledge Management system in Vocational High Schools during the Covid-19 Pandemic. The Gamification concept with the Octalysis Framework was implemented in the Knowledge Management System. Some game mechanics in a non-game context consisting of four phases, namely the Discovery, Onboarding, Scaffolding, and Endgame was applied to improve student performance and motivation in learning. Meanwhile, The Knowledge Management System software achieved an overall quality score of 5.14 out of 6 (85.6 percent of quality achieved) covering functionality, reliability, usability, efficiency, maintainability, and portability characteristics of the ISO 9126 quality indicator.

关键词： COVID-19 Pandemics ISO Standards Games Knowledge management System software Software reliability

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction

A Comparative Analysis of Machine Learning Techniques for He...

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Student Conference on Research and Development (SCOReD)

作者： Alawi Alqushaibi Mohammed Gamal Ragab Ebrahim Hamid Sumiea Safwan Mahmood Al-Selwi Mohd Hilmi Hasan Said Jadid Abdulkadir Hitham Alhussian Mahmoud Mustafa Al-Asbahi Computer & Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Centre for Research in Data Science Universiti Teknologi PETRONAS Perak Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository’s Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare.

关键词： Heart Accuracy Machine learning algorithms Machine learning Predictive models Prediction algorithms Vectors

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Trade execution games in a Markovian environment

arXiv

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arXiv 2024年

作者： Ohnishi, Masamitsu Shimoshimizu, Makoto Faculty of Informatics Yamato University Osaka Suita City564-0082 Japan Center for Mathematical Modeling and Data Science Osaka University Toyonaka City Osaka560-8531 Japan Department of Industrial and Systems Engineering Tokyo University of Science 2641 Yamazaki Noda City Chiba278-8510 Japan

This paper examines a trade execution game for two large traders in a generalized price impact model. We incorporate a stochastic and sequentially dependent factor that exogenously affects the market price into financial markets. Our model accounts for how strategic and environmental uncertainties affect the large traders' execution strategies. We formulate an expected utility maximization problem for two large traders as a Markov game model. Applying the backward induction method of dynamic programming, we provide an explicit closed-form execution strategy at a Markov perfect equilibrium. Our theoretical results reveal that the execution strategy generally lies in a dynamic and non-randomized class;it becomes deterministic if the Markovian environment is also deterministic. In addition, our simulation-based numerical experiments suggest that the execution strategy captures various features observed in financial markets. Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamic programming

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DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

arXiv

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arXiv 2023年

作者： Bozorgpour, Afshin Sadegheih, Yousef Kazerouni, Amirhossein Azad, Reza Merhof, Dorit Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Regensburg Germany School of Electrical Engineering Iran University of Science and Technology Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained attention for their exceptional image-generation capabilities. Building on these advancements, we propose DermoSegDiff, a novel framework for skin lesion segmentation that incorporates boundary information during the learning process. Our approach introduces a novel loss function that prioritizes the boundaries during training, gradually reducing the significance of other regions. We also introduce a novel U-Net-based denoising network that proficiently integrates noise and semantic information inside the network. Experimental results on multiple skin segmentation datasets demonstrate the superiority of DermoSegDiff over existing CNN, transformer, and diffusion-based approaches, showcasing its effectiveness and generalization in various scenarios. The implementation is publicly accessible on GitHub. © 2023, CC BY.

关键词： Diffusion

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Self-supervised Semantic Segmentation: Consistency over Transformation

arXiv

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arXiv 2023年

作者： Karimijafarbigloo, Sanaz Azad, Reza Kazerouni, Amirhossein Velichko, Yury Bagci, Ulas Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany School of Electrical Engineering Iran University of Science and Technology Iran Department of Radiology Northwestern University Chicago United States Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures. However, prevailing supervised deep learning approaches for medical image segmentation encounter significant challenges due to their heavy dependence on extensive labeled training data. To tackle this issue, we propose a novel self-supervised algorithm, S3-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. This architectural enhancement makes it possible to comprehensively capture contextual information while preserving local intricacies, thereby enabling precise semantic segmentation. Furthermore, considering that lesions in medical images often exhibit deformations, we leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Additionally, our self-supervised strategy emphasizes the acquisition of invariance to affine transformations, which is commonly encountered in medical scenarios. This emphasis on robustness with respect to geometric distortions significantly enhances the model’s ability to accurately model and handle such distortions. To enforce spatial consistency and promote the grouping of spatially connected image pixels with similar feature representations, we introduce a spatial consistency loss term. This aids the network in effectively capturing the relationships among neighboring pixels and enhancing the overall segmentation quality. The S3-Net approach iteratively learns pixel-level feature representations for image content clustering in an end-to-end manner. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches. Github. © 2023, CC BY.

关键词： Semantic Segmentation

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Utilizing Automatic Speech Recognition for English Pronunciation Practice and Analyzing its Impact

Utilizing Automatic Speech Recognition for English Pronuncia...

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International Conference on Engineering Education (ICEED)

作者： Katsuyuki Umezawa Makoto Nakazawa Michiko Nakano Shigeichi Hirasawa Department of Informatics Shonan Institute of Technology Kanagawa Japan Department of Industrial Information Science Junior College of Aizu Fukushima Japan Faculty of Education and Integrated Arts and Sciences Waseda University Tokyo Japan Data Science Center Waseda University Tokyo Japan

The advancement of AI in recent years has been remarkable, along with the widespread use of speech recognition functions. In addition, an increasing number of people are self-studying in their fields of interest. In a world considered a global society, many people in countries where English is not their first language are learning English. Therefore, in this study, the authors focus on self-study English learning. The pronunciation is assumed to be correct if the speech recognition feature correctly identifies the pronounced English words and sentences. It has been recognized that feedback plays a crucial role in English pronunciation practice. If the speech recognition function can be employed to provide feedback, it would alleviate the burden on teachers and enable students to practice pronunciation independently. In this research, the authors propose practicing pronunciation using each browser's built-in speech recognition features, including Google Chrome, iOS, and Microsoft Edge, and evaluating if one's English pronunciation is correctly recognized. Through a 7-day experiment, the authors clarify the speech recognition function suitable for self-studying English pronunciation. Through this experiment, it was observed that the speech recognition function on iOS (version 15.0) outperforms compared to Google Chrome (version 107.0.5304.63) and Microsoft Edge (version 107.0.1418.24) in accurately understanding speech, even when the pronunciation is incorrect. However, it became apparent that such highly accurate speech recognition capabilities may not be suitable for self-study pronunciation practice.

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