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Robustness modularity in complex networks

Physical Review E 2022年第5期105卷 054308-054308页

作者： Filipi N. Silva Aiiad Albeshri Vijey Thayananthan Wadee Alhalabi Santo Fortunato Indiana University Network Science Institute (IUNI) Bloomington Indiana 47408 USA Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Kingdom of Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Bloomington Indiana 47408 USA

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.

关键词： Community structure Complex systems Robustness

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Integrating educational theories with virtual reality: Enhancing engineering education and VR laboratories

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Social Sciences and Humanities Open 2024年 10卷

作者： Abbas Shah, Syed Faisal Mazhar, Tehseen Shahzad, Tariq khan, Muhammad Amir Ghadi, Yazeed Yasin Hamam, Habib Department of Computer Science & Information Technology Virtual University of Pakistan Lahore 54000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 12555 United Arab Emirates Faculty of Engineering University de Moncton Moncton E1A3E9 NB Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles BP Libreville 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Immersive technologies, including virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR), create realistic digital experiences by overlaying digital elements. This study aims to highlight the benefits of utilizing VR technology in education, particularly in universities and higher education institutions, by integrating VR with educational theories to create engaging and interactive learning experiences that enhance understanding and retention. This review includes a comprehensive analysis of 103 works, synthesizing existing literature to evaluate the suitability of various educational approaches and theories for implementation in VR-based educational applications. The findings emphasize VR's potential to transform learning by providing immersive experiences that bridge different domains and encourage engagement. By integrating learning theory into VR development, meaningful learning environments can be created, improving student understanding and retention across diverse learning settings. The study concludes that the combination of technology and educational theories provides a novel approach to motivate students and foster deep learning in VR laboratories. © 2024 The Authors

关键词： Augmented reality Educational theories engineering education Immersive technology Interactive learning VR laboratories

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WiGRUNT: Wifi-enabled gesture recognition using dual-attention network

TechRxiv

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TechRxiv 2021年

作者： Gu, Yu Zhang, Xiang Wang, Yantong Wang, Meng Liu, Zhi Li, Jianhua Dong, Mianxiong School of Computer and Information Hefei University of Technology China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Dept. of Sciences and Informatics Muroran Institute of Technology Japan

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know "where to look" and "when to look". To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain. © 2021, CC BY.

关键词： Channel state information

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Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights

arXiv

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arXiv 2024年

作者： Heidari, Moein Azad, Reza Kolahi, Sina Ghorbani Arimond, René Niggemeier, Leon Sulaiman, Alaa Bozorgpour, Afshin Aghdam, Ehsan Khodapanah Kazerouni, Amirhossein Hacihaliloglu, Ilker Merhof, Dorit School of Biomedical Engineering University of British Columbia BC Canada Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Information Science and Technology Universiti Kebangsaan Bangi Malaysia Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Electrical Engineering Shahid Beheshti University Tehran Iran Department of Computer Science University of Toronto Toronto Canada Department of Radiology Department of Medicine University of British Columbia BC Canada Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various computer Vision (CV) tasks. Building upon this paradigm, Vision Transformer (ViT) networks exploit attention mechanisms for improved efficiency. This review navigates the landscape of redesigned attention mechanisms within ViTs, aiming to enhance their performance. This paper provides a comprehensive exploration of techniques and insights for designing attention mechanisms, systematically reviewing recent literature in the field of CV. This survey begins with an introduction to the theoretical foundations and fundamental concepts underlying attention mechanisms. We then present a systematic taxonomy of various attention mechanisms within ViTs, employing redesigned approaches. Amulti-perspective categorization is proposed based on their application, objectives, and the type of attention applied. The analysis includes an exploration of the novelty, strengths, weaknesses, and an in-depth evaluation of the different proposed strategies. This culminates in the development of taxonomies that highlight key properties and contributions. Finally, we gather the reviewed studies along with their available open-source implementations at our *** aim to regularly update it with the most recent relevant papers. © 2024, CC BY-NC-SA.

关键词： computer vision

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AlGaAs soliton microcombs at room temperature

AlGaAs soliton microcombs at room temperature

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Conference on Lasers and Electro-Optics (CLEO)

作者： Lue Wu Weiqiang Xie Chao Xiang Lin Chang Yan Yu Hao-Jing Chen Yoshihisa Yamamoto John E. Bowers Kerry J. Vahala Myoung-Gyun Suh T. J. Watson Laboratory of Applied Physics California Institute of Technology Pasadena CA USA Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA Physics & Informatics Laboratories NTT Research Inc. Sunnyvale CA USA

Soliton generation in AlGaAs microresonators at room temperation is reported for the first time. The destabilizing thermo-optic effect is shown to instead provide stability for high soliton repetition rates. The optic... 详细信息

ISBN: (纸本)9781957171258

关键词： Dielectric microcavities Frequency combs Microresonators Mode locking Q factor Thermo optic effects

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SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth 3

SmartCHANGE: AI-based long-term health risk evaluation for d...

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3rd International Conference on Applied Mathematics and computer Science, ICAMCS 2023

作者： Rescic, Nina Alberts, Janna Altenburg, Teatske M. Chinapaw, Mai J. M. De Nigro, Antonio Fenoglio, Dario Gjoreski, Martin Gradisek, Anton Jurak, Gregor Kiourtis, Athanasios Kyriazis, Dimosthenis Langheinrich, Marc Mancuso, Elena Mavrogiorgou, Argyro Pechenizkiy, Mykola Pratola, Roberto Ribeiro, Jose Soric, Maroje Taj, Fawad Tammelin, Tuija H. Vastenburg, Martijn Vilanova, Anna Vrijkotte, Tanja G. M. Lustrek, Mitja Jožef Stefan Institute Department of Intelligent Systems Ljubljana Slovenia ConnectedCare Research & Development Nijmegen Netherlands Vrije Universiteit Amsterdam Amsterdam Public Health Research Institute Department of Public and Occupational Health Amsterdam Umc Amsterdam Netherlands Informatica S.p.A. Engineering Ingegneria Rome Italy Università della Svizzera Italiana Faculty of Informatics Lugano Switzerland University of Ljubljana Faculty of Sports Ljubljana Slovenia University of Piraeus Department of Digital Systems Piraeus Greece Eindhoven University of Technology Department of Mathematics and Computer Science Eindhoven Netherlands University of Porto Faculty of Sport Porto Portugal Jamk University of Applied Sciences Likes School of Health and Social Studies Jyväskylä Finland

ISBN: (纸本)9798350324266

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project's proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes. © 2023 IEEE.

关键词： Health risks

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Digital Twins for Dynamic Management of Blockchain Systems

Digital Twins for Dynamic Management of Blockchain Systems

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Simulation Winter Conference

作者： Georgios Diamantopoulos Nikos Tziritas rami Bahsoon Georgios Theodoropoulos University of Birmingham Edgbaston Birmingham UNITED KINGDOM Southern University of Science and Technology Shenzhen CHINA Department of Informatics and Telecommunications University of Thessaly Lamia GREECE School of Computer Science University of Birmingham Edgbaston Birmingham UNITED KINGDOM Department of Computer Science and Engineering Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen CHINA

ISBN: (纸本)9781665476621

Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability, and security. Infrastructure and node configuration, choice of the Consensus Protocol, and complexity of the application transactions are cited among the factors that affect the tradeoff balance. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This paper introduces the utilization of DigitalTwins for this purpose. The novel contribution of the paper is the design of a framework and conceptual architecture of a Digital Twin that can assist in maintaining the Trilemma tradeoffs of time-critical systems. The proposed Digital Twin is illustrated via an innovative approach to the dynamic selection of Consensus Protocols. Simulation results show that the proposed framework can effectively support the dynamic adaptation and management of the Blockchain.

关键词： Analytical models Simulation computer architecture Quality of service Digital twins Consensus protocol Complexity theory

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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raFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed raFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of raFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying raFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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