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International Conference on Inventive Computation Technologies (ICICT)

作者： Manika Manwal Ahmad Murtaza Alvi Nagendra Kumar Turaga Amit Mittal Richard Rivera Sheshang Degadwala CSE Graphic Era Hill University Dehradun India CSE Graphic Era deemed to be University Dehradun India Department of Business Administration College of Finance Saudi Electronic University Riyadh Saudi Arabia Department of Management Studies Vignan's Foundation for Science Technology and Research (Deemed to be University) Department of Allied Science Graphic Era Hill University Bhimtal Department of Informatics and Computer Science Escuela Politecnica Nacional. Quito Ecuador Sigma Institute of Engineering Engineering Block Sigma Group of Institutes Bakrol Vadodara Gujarat

People have conducted decentralized transfer transactions through Bitcoin addresses ever since the Bitcoin system launched online, drastically improving the convenience of transactions. Simultaneously, peer-to-peer transaction logs have become a subject of study. However, it requires a significant amount of time and computing capacity to examine the entire network directly, and it is not conducive to observing the transaction mode within the entity. Consequently, it is possible to construct and analyses the transaction network based on the entity service community and further investigate entity behavior and Bitcoin entities within the service community. A central node-based label propagation algorithm is proposed by enhancing the conventional label propagation algorithm, which divides the Bitcoin entity transaction network into districts and analyses the core communities, including exchanges and mining pools. The transaction method is realistic and straightforward to comprehend. The enhanced label propagation algorithm can converge more quickly and reduce the randomness of the results of community division. The experimental results demonstrate that the internal transaction modalities of various services vary. The simplified display makes the Bitcoin transaction network more readable.

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Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

arXiv

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arXiv 2022年

作者： Nan, Yang Del Ser, Javier Tang, Zeyu Tang, Peng Xing, Xiaodan Fang, Yingying Herrera, Francisco Pedrycz, Witold Walsh, Simon Yang, Guang National Heart and Lung Institute Imperial College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao Spain Derio Spain Department of Informatics Technical University of Munich Germany University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abgudulaziz University Jeddah Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw Poland Royal Brompton Hospital Sydney Street London United Kingdom

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis. © 2022, CC BY-NC-ND.

关键词： COVID-19

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Model-based testing of Apache ZooKeeper: Fundamental API usage and watchers

Model-based testing of Apache ZooKeeper: Fundamental API usa...

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作者： Artho, Cyrille Banzai, Kazuaki Gros, Quentin Rousset, Guillaume Ma, Lei Kitamura, Takashi Hagiya, Masami Tanabe, Yoshinori Yamamoto, Mitsuharu School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Institute of Information Technology Research National Institute of Advanced Industrial Science and Technology Osaka Japan Department of Computer Science The University of Tokyo Tokyo Japan Polytech Nantes University of Nantes Nantes France Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Library Archival and Information Studies Tsurumi University Yokohama Japan Department of Mathematics and Informatics Chiba University Chiba Japan

In this paper, we extend work on model-based testing for Apache ZooKeeper, to handle watchers (triggers) and improve scalability. In a distributed asynchronous shared storage like ZooKeeper, watchers deliver notifications on state changes. They are difficult to test because watcher notifications involve an initial action that sets the watcher, followed by another action that changes the previously seen state. We show how to generate test cases for concurrent client sessions executing against ZooKeeper with the tool Modbat. The tests are verified against an oracle that takes into account all possible timings of network communication. The oracle has to verify that there exists a chain of events that triggers both the initial callback and the subsequent watcher notification. We show in detail how the oracle computes whether watch triggers are correct and how the model was adapted and improved to handle these features. Together with a new search improvement that increases both speed and accuracy, we are able to verify large test setups and confirm several defects with our model. Copyright © 2019 John Wiley & Sons, Ltd.

关键词： Model checking

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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided Radiotherapy 1st

Comparative Analysis of nnUNet and MedNeXt for Head and Neck...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Moradi, Nikoo Ferreira, Andre Puladi, Behrus Kleesiek, Jens Fatemizadeh, Emad Luijten, Gijs Alves, Victor Egger, Jan Department of Electrical Engineering Sharif University of Technology Tehran Iran University Medicine Essen Girardetstraße 2 Essen45131 Germany Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Essen Germany Partner Site Essen Essen Germany Department of Physics TU Dortmund University Dortmund Germany University Medicine Essen Essen Germany Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031832734

Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and there- fore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid- RT MRI images. We utilized the HNTS-MRG2024 dataset, which con- sists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we com- bined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository. © The Author(s) 2025.

关键词： Image segmentation

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Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Predicting Neurological Recovery from Coma After Cardiac Arr...

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computers in Cardiology (CinC)

作者： Matthew A Reyna Edilberto Amorim Reza Sameni James Weigle Andoni Elola Ali Bahrami Rad Salman Seyedi Hyeokhyen Kwon Wei-Long Zheng Mohammad M Ghassemi Michel JAM van Putten Jeannette Hofmeijer Nicolas Gaspard Adithya Sivaraju Susan T Herman Jong Woo Lee M Brandon Westover Gari D Clifford Department of Biomedical Informatics Emory University USA Department of Neurology Weill Institute for Neurosciences University of California San Francisco USA Department of Neurology Massachusetts General Hospital USA Department of Electronic Technology University of the Basque Country UPV/EHU Spain Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Michigan State University USA Clinical Neurophysiology Group University of Twente The Netherlands Department of Neurology and Clinical Neurophysiology Medisch Spectrum Twente The Netherlands Department of Neurology Rijnstate Hospital The Netherlands Department of Neurology Yale School of Medicine USA Service de Neurologie Hôpital Universitaire de Bruxelles and Université Libre de Bruxelles Belgium Department of Neurology Barrow Neurological Institute Comprehensive Epilepsy Center USA Department of Neurology Brigham and Women's Hospital USA Department of Neurology Beth Israel Deaconess Medical Center USA McCance Center for Brain Health Massachusetts General Hospital USA Harvard Medical School USA Department of Biomedical Engineering Georgia Institute of Technology USA

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach

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Scientific Reports 2025年第1期15卷 1-33页

作者： Hassan, Osama F. Ibrahim, Ahmed F. Gomaa, Ahmed Makhlouf, M.A. Hafiz, B. Information Systems Department Faculty of Computers and Informatics Suez Canal University Ismailia 41522 Egypt Artificial Intelligence Department Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai 46511 Egypt Computer Science and Engineering Department Egypt Japan University of Science and Technology (E-JUST) Alexandria 21934 Egypt Computer Science and Engineering Department National Research Institute of Astronomy and Geophysics (NRIAG) Helwan 11731 Egypt

Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging state-of-the-art transformer architectures and transfer learning models to achieve unprecedented accuracy and reliability. The proposed methodology addresses key challenges in drowsiness detection by integrating advanced data preprocessing techniques, including image normalization, augmentation, and region-of-interest selection using Haar Cascade classifiers. We employ the MRL Eye Dataset to classify eye states into “Open-Eyes” and “Close-Eyes,” evaluating a range of models, including Vision Transformer (ViT), Swin Transformer, and fine-tuned transfer learning models such as VGG19, DenseNet169, ResNet50V2, InceptionResNetV2, InceptionV3, and MobileNet. The ViT and Swin Transformer models achieved groundbreaking accuracy rates of 99.15% and 99.03%, respectively, outperforming all other models in precision, recall, and F1-score. To ensure the generalization and robustness of the proposed models, we also evaluate their performance on the NTHU-DDD and CEW datasets, which provide diverse real-world scenarios and challenging conditions. This represents a significant advancement over existing methods, demonstrating the effectiveness of transformer-based architectures in capturing complex spatial dependencies and extracting relevant features for drowsiness detection. The proposed system also incorporates a real-time drowsiness scoring mechanism, which triggers alarms when prolonged eye closure is detected, ensuring timely intervention to prevent accidents. A key novelty of this work lies in the integration of Class Activation Mapping (CAM) for enhanced model interpretability, allowing the system to focus on critical eye regions and improve decision-making transparency. The system was rigorously tested under varying lighting condit

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Preface

Preface

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作者： Dehuri, Satchidananda Mishra, Bhabani Shankar Prasad Mallick, Pradeep Kumar Cho, Sung-Bae Favorskaya, Margarita N. Department of Information and Communication Technology Fakir Mohan University Odisha Balasore India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Computer Science Yonsei University Seoul Korea Republic of

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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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A monotonic optimization approach for solving strictly quasiconvex multiobjective programming problems

A monotonic optimization approach for solving strictly quasi...

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作者： Thang, Tran Ngoc Solanki, Vijender Kumar Dao, Tuan Anh Thi Ngoc Anh, Nguyen Van Hai, Pham School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology Hyderabad TS India Department of Information Technology Uppsala University Uppsala Sweden School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam

In this article, we use a monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems which include many classes of captivating problems, for example when the criterion functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, an approximation of the weakly efficient solution set is obtained containing the whole weakly efficient solution set of the problem. The algorithm is proved to be convergent and it is suitable to be implemented in parallel using convex programming tools. Some computational experiments are reported to show the accuracy and efficiency of the algorithm. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Multiobjective optimization

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