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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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Classification of Pulmonary Embolism on Computed Tomography Angiography Using Artificial Intelligence

Classification of Pulmonary Embolism on Computed Tomography ...

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IEEE International Symposium on Biomedical Imaging

作者： Luan Silva Maria Carolina Guilherme Ribeiro Thiago Ortiz Paulo Victor Giovanna Mendes Joselisa De Paiva Wesley Calixto Leticia Rittner Rafael Loureiro Marcio Reis Anderson Soares Hospital Israelita Albert Einstein Sao Paulo Brazil Institute of Informatics (INF) Federal University of Goias Goiania Brazil Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Brazil Department of Computer Engineering and Industrial Automation State University of Campinas Campinas Sao Paulo Brazil

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Pulmonary embolism is a serious condition in which a blood clot (thrombus) forms in a deep vein, usually in the legs (deep vein thrombosis), and then travels to the lungs and blocks the pulmonary artery. The diagnosis of pulmonary embolism is a complex process as there is a variety of clinical presentations and the symptoms overlap with other medical conditions. This study aims to fill a gap by proposing a classification model for pulmonary embolism, trained on limited annotated public data and validated on a comprehensive in-house dataset. The results showed that the classification model is promising in detecting negative cases of pulmonary embolism and has satisfactory precision (95%) and recall (96%). Its efficiency in avoiding false-positive diagnoses indicates its reliability in identifying patients without pulmonary embolism, which plays an important role in clinical practice.

关键词： Pulmonary diseases Veins Computational modeling Biological system modeling Sociology Lung Data models

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Characterizing pedestrian contact interaction trajectories to understand spreading risk in human crowds

arXiv

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arXiv 2024年

作者： Kwak, Jaeyoung Lees, Michael H. Cai, Wentong School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098XH Netherlands

A spreading process can be observed when particular information, substances, or diseases spread through a population over time in social and biological systems. It is widely believed that contact interactions among individual entities play an essential role in the spreading process. Although contact interactions are often influenced by geometrical conditions, little attention has been paid to understand their effects, especially on contact duration among pedestrians. To examine how the pedestrian flow setups affect contact duration distribution, we have analyzed trajectories of pedestrians in contact interactions collected from pedestrian flow experiments of uni-, bi- and multi-directional setups. Based on turning angle entropy and efficiency, we have classified the type of motion observed in the contact interactions. We have found that the majority of contact interactions in the unidirectional flow setup can be categorized as confined motion, hinting at the possibility of long-lived contact duration. However, ballistic motion is more frequently observed in the other flow conditions, yielding frequent, brief contact interactions. Our results demonstrate that observing more confined motions is likely associated with the increase of parallel contact interactions regardless of pedestrian flow setups. This study highlights that the confined motions tend to yield longer contact duration, suggesting that the infectious disease transmission risk would be considerable even for low transmissibility. These results have important implications for crowd management in the context of minimizing spreading risk. This work is an extended version of Kwak et al. (2023) presented at the 2023 International Conference on Computational Science (ICCS). © 2024, CC BY.

关键词： Ballistics

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A Dual-Band MIMO Monopole Antenna with Enhanced Isolation for Sub 6 GHz 5G Applications

A Dual-Band MIMO Monopole Antenna with Enhanced Isolation fo...

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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Hamrioui, Fatma Zohra Bouaza, Abdelmaoula Touhami, Rachida Yagoub, Mustapha C.E. Azrar, Arab Electronic Department 10 Rue des Frères OUDEK El Harrach Algiers16200 Algeria Usthb university Bp32 Instrumentation laboratory Electronics and Informatics faculty El Alia Bab Ezzouar Algiers Algeria University of Ottawa Element laboratory School of Electrical Engineering and Computer Science Ottawa Canada Institute of Electrical and Electronic Engineering University M'Hamed BOUGARA-Boumerdes Signals and Systems Laboratory Electronics Department Boumerdes Algeria

ISBN: (纸本)9798350353754

In this paper, a novel dual-band MIMO monopole antenna for 5G applications is presented. The proposed MIMO antenna is made up of two rings that are T-shaped, generating two distinguished bands: the N77 band and the 6 GHz 5G band. A Tshaped ground branch with a bottom slit is integrated into the ground plane to mitigate the mutual coupling at lower and higher operating bands by redistributing the current, thus improving the isolation enhancement. A reversed T-shaped grounded parasitic element with extra grounded parasitic elements on the partial ground is inserted on top of the substrate, between the MIMO elements. The grounded process is introduced by inserting two metallic vias in each branch of the parasitic element. These parasitic elements effectively reduce the coupling between the MIMO antennas by restricting the current to the respective antenna, thus improving the isolation at the higher band. To verify the design principle of the suggested MIMO antenna, this latter is manufactured and tested. It exhibits excellent impedance matching and high isolation characteristics, and a compact footprint of 29 mm × 42.5 mm. Thus, it is attractive for 5G MIMO systems. © 2024 IEEE.

关键词： Monopole antennas

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Multi-Criteria Decision Making for Machine Learning Algorithms Using AHP-VIKOR techniques: Case Study Adult Autism Diagnosis 5

Multi-Criteria Decision Making for Machine Learning Algorith...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Jassim, Mustafa Mohammed Ali, Manar Hassan Elamer, Amer S. Jaber, Mustafa Musa Mohammed, Mohammed Q. Alkhayyat, Ahmed Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Informatics Baghdad10011 Iraq Dijlah University Collage Department of Computer Science Baghdad10021 Iraq Al-Esraa University College Department of Information Technology Baghdad10021 Iraq The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9781665472159

autism spectrum disorder is known as a neurodevelopmental disorder with sensory problems. Many infected adults die frequently due to the psychological conditions they live in. Machine learning helps doctors detect cases of autism spectrum disorders in adults. It has proven very successful in this field. In this study, after pre-processing and solving the data class imbalance problem the data we proposed to use MCDM methods to know prioritization and the best method to diagnose autism between four algorithms (SVM, Random Forest, Neural Network, and KNN), after obtaining the classification results we utilized AHP method to assign weights to each classification performance metrics based on expert assessment then he used both the obtained weights and the classification results to enter them into the VIKOR method to make a decision based on the weighted table where we would get the ranking of the algorithms. The random Forest method scores the best in this classification for diagnosing autism, with an accuracy rate of 0.9957 and sensitivity 0.9841. © 2022 IEEE.

关键词： Decision trees

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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When Sign Language Meets Semantic Communications 35

When Sign Language Meets Semantic Communications

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Singh, Keshav Qi, Nan InnoCube P.C. Department of Research and Development 17th Noemvriou 79 Thessaloniki55535 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China

ISBN: (纸本)9798350362244

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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Perceptual Judgments of Simulated Low Temperatures in LCD based Vehicle Displays

Perceptual Judgments of Simulated Low Temperatures in LCD ba...

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SID International Symposium Digest of Technical Papers, 2023

作者： Göreke, Hande D. Djupsjöbacka, Anders Schenkman, Bo N. Andrén, Börje Hermann, David S. Brunnström, Kjell Industrial Systems RISE Research Institutes of Sweden AB Stockholm Sweden Informatics and Media Uppsala University Uppsala Sweden Speech Music and Hearing KTH Royal Institute of Technology Stockholm Sweden Display Systems Volvo Car Corporation Göteborg Sweden Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden

A well-known drawback with LCD-displays in cold is a slow pixel response leading to poor picture quality. Low temperatures can constitute a hazard in viewing important displays in cars. Perceptual experiments with 20 test-persons were conducted to find clear and acceptable ranges on screens simulating distortions in low temperatures. The results showed perception over clear and acceptable image quality was impaired beyond -20°C for the LCD-screen in the experiments. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Liquid crystal displays

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