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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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Ultra-compact Implantable MIMO Antenna with Radiation Pattern Diversity for Scalp-based Implants

Ultra-compact Implantable MIMO Antenna with Radiation Patter...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Amine Essa Eqab Almajali Rony E. Amaya Muhammad. Ikram Soliman Mahmoud Electrical Engineering Department University of Sharjah Sharjah United Arab Emirates. XPANCEO Emerging Technologies Research Center Dubai United Arab Emirates. School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada Department of Electronics Carleton University Ottawa Canada College of Engineering and Applied Sciences American University of Kuwait Kuwait University of Khorfakkan Sharjah United Arab Emirates

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

This work presents the development of a new ultra-compact implantable two-element MIMO antenna with radiation diversity for bio-telemetry communication in scalp-based implants. The design consists of two separated mirrored semicircular slotted patches that radiate in dissimilar directions to enable radiation diversity. The proposed antenna occupies a very compact volume of 4.085 mm 3 and is designed to resonate at 2.4 GHz. Two different numerical techniques were used to validate the antenna's performance in a scalp-based environment. Due to symmetry, both antenna's radiating elements achieved a fractional bandwidth of 25.13% and a peak gain of -19.8 dBi at the resonance frequency. The antenna showed an ability to radiate in two dissimilar directions with a radiation coverage angle of ± 60°. Specific absorption rate and link budget analyses were conducted and demonstrated the antenna's ability to perform bio-telemetry communication at high data rates of up to 100 Mbps while satisfying power safety regulations.

关键词： Transmitting antennas Resonant frequency Implants Bandwidth Directive antennas Specific absorption rate Regulation Safety Telecommunications Antenna radiation patterns

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Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems

arXiv

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arXiv 2025年

作者： Bouke, Mohamed Aly Abdullah, Azizol Cengiz, Korhan Ivković, Nikola Mihaljević, Ivan Mohamud, Mudathir Ahmed Kowrina, Ahmed Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Department of Electrical-Electronics Engineering Biruni University Istanbul Turkey Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Computing SIMAD University Mogadishu252 Somalia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia Département d’Informatique et Mathématiques Faculté des Sciences et Techniques Université de Nouakchott Al Aasriya Nouakchott880 Mauritania

In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures. © 2025, CC BY.

关键词： Queueing theory

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

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Procedia computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian 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 Chennai 600119 Tamil Nadu 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.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Ritesh Rastogi T. Shanthi Pramod Kumar Naik M. Sahaya Sheela A.K Mishra Nandhini T J Department of IT and CS M.Tech(Int) Noida Institute of Engineering and Technology Delhi India Department of ECE Paavai Engineering College Chennai India Departmeny of Computer Science Engineering Dayananda Sagar University Bangalore India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Math Unit Foundation Program Dhofar University Salalah Oman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - (SIMATS) Chennai India

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the “channel capacity” in a 5G ultradense network, when the phrase “channel capacity” is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes; both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process.

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Multi-Photon Subtraction from Squeezed Light toward Full Quantum State engineering

Multi-Photon Subtraction from Squeezed Light toward Full Qua...

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Quantum 2.0, QUANTUM 2024 in Quantum 2.0 Conference and Exhibition

作者： Endo, Mamoru Nomura, Takefumi Sonoyama, Tatsuki Takahashi, Kazuma Fukui, Kosuke Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Nehra, Rajveer Marek, Petr Filip, Radim Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan NTT Device Technology Labs. NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan Department of Electrical and Computer Engineering University of Massachusetts-Amherst AmherstMA01003 United States Department of Physics University of Massachusetts-Amherst AmherstMA01003 United States Department of Optics Palacky University 17. listopadu 1192/12 Olomouc77146 Czech Republic

We have applied up to four annihilation operations to squeezed light by photon subtraction. The presence of negative values in the Wigner function for all states indicates the successful implementation of quantum mani... 详细信息

ISBN: (纸本)9781557525185

关键词： Squeezed light

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'Permanent' algorithm for the meeting shedule problem 16

'Permanent' algorithm for the meeting shedule problem

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16th IEEE International Conference on computer science and Information Technologies, CSIT 2021

作者： Turbal, Yuriy Babych, Sergii Kunanets, Nataliia Melnyk, Leonid Pasichnyk, Volodymyr National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine College of National University of Life and Environmental Sciences of Ukraine Software Engineering Rivne Ukraine National University 'Lviv Polytechnic' Department of Information Systems and Networks Lviv Ukraine National University of Water and Environmental Engineering Department of Finance and Economic Security Rivne Ukraine

ISBN: (纸本)9781665442572

An algorithm for solving the problem of scheduling meetings in the process of working on a project is proposed in the paper. The problem of project managers meetings with working groups is considered under certain restrictions., the possibility of a simultaneous meeting of the project manager with one or several project groups is considered. The proposed procedure for finding the permanent allows to build all possible combinations of the meeting schedule at a given time. The specificity of the proposed approach is the ability to take into account any number of additional conditions. © 2021 IEEE.

关键词： Managers

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Bamboo-Inspired Metamaterials Design: Microstructure Dielectrophoretic Orientation for Enhancing Insulation Performance of Advanced Power Modules

SSRN

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SSRN 2024年

作者： Yao, Huanmin Mu, Haibao Shen, Maoqun Cavallini, Andrea Li, He Zhao, Haoxiang Li, Wendong Zhang, Daning Zhang, Guanjun State Key Laboratory of Electrical Insulation and Power Equipment Xi’an Jiaotong University Shaanxi 710049 China The Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" University of Bologna Italy Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600MB Netherlands Department of Applied Physics Eindhoven University of Technology Eindhoven5600MB Netherlands

The application of nonlinear conductivity coatings in high-voltage power module is a promising strategy for significantly improving electrical insulation performance. The core of the improvement principle is that the nonlinear conductivity realizes a dynamic optimization of the discharge risk inside the material. The realization of this nonlinear conductivity requires sufficient microscopic arrangement of fillers that align with the macroscopic insulation structure. However, conventional methods fail at precise arrangement control so that typically require high filler concentration, which markedly increases dielectric loss and leakage current. Drawing inspiration from the bamboo structure, metamaterials offer an efficient tuning of nonlinear characteristics with lower filler concentrations through thoughtful design. In this context, we introduce a novel approach to fabricate SiC whisker / silicon elastomer metamaterial coatings leveraging the dielectrophoretic effect. Comprehensive testing of the physical and chemical properties of the composites confirmed the successful preparation of the material. And the metamaterial exhibits excellent nonlinear conductivity and low dielectric loss across various temperatures. Notably, after applying the metamaterial coating, the maximum electric field stress within the power module was reduced by nearly ten times, and the partial discharge inception voltage (PDIV) increased by 58.9%, 74.7%, and 32.8% at 30°C, 60°C, and 90°C, respectively. From solid physical mechanism to exquisite material design and practical device-level implementation, this study offers valuable insights into enhancing the insulation performance of power module and demonstrates substantial potential for further industrial applications. © 2024, The Authors. All rights reserved.

关键词： Dielectric losses

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Multimedia processing using deep learning technologies, high-performance computing cloud resources, and Big Data volumes

Multimedia processing using deep learning technologies, high...

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Concurrency Computation

作者： Mahmoudi, Sidi Ahmed Belarbi, Mohammed Amin Mahmoudi, Saïd Belalem, Ghalem Manneback, Pierre Computer Science Unit Faculty of Engineering UMONS Mons Belgium Computer Science Department Faculty of Exact and Applied Science University of Oran 1 Oran Algeria

The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large-scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large-scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications. © 2020 John Wiley & Sons, Ltd.

关键词： Deep learning Application programs Big data Cloud computing computer hardware computer vision Information management Multimedia systems Video signal processing computer vision applications High performance computing High performance computing resources Image and video processing Images classification images indexation and retrieval Multimedia processing Scientific contributions

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