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13th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2023

作者： Chang, Han-Yuan Hsieh, Hsiang-Yu Lin, Yih-Chuan National Formosa University Department of Computer Science and Information Engineering Taiwan Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324150

In this paper, an improved super-resolution method is proposed for compressed videos, which integrates the multi-image super-resolution (MISR) method into standard video decoders such as H.26x and AV1 to employ the reference frame (RF) and motion vector (MV) information from video decoder to gain better super-resolution efficiency and performance. The proposed motion vector-based multi-image super-resolution (MVSR) is trained by the convolutional neural networks (CNN) model, which consists of a series of convolution layers and is learned to identify the relationship of image features between the ground truth high-resolution (HR) and super-resolution (SR), and generates the SR frames that could improve the quality of the final output frame. The experimental results have shown that the proposed MVSR is capable of enhancing the SR quality over 1dB in terms of peak-to-noise ratio (PSNR) when compared to other similar approaches. © 2023 IEEE.

关键词： Motion estimation

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Challenges and future directions of secure federated learning:a survey

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Frontiers of computer science 2022年第5期16卷 173-180页

作者： Kaiyue ZHANG Xuan SONG Chenhan ZHANG Shui YU Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Faculty of Engineering and Information Technology University of Technology SydneySydney 2007Australia SUSTech-UTokyo Joint Research Center on Super Smart City Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Federated learning came into being with the increasing concern of privacy security,as people’s sensitive information is being exposed under the era of big *** is an algorithm that does not collect users’raw data,but aggregates model parameters from each client and therefore protects user’s ***,due to the inherent distributed nature of federated learning,it is more vulnerable under attacks since users may upload malicious data to break down the federated learning *** addition,some recent studies have shown that attackers can recover information merely from ***,there is still lots of room to improve the current federated learning *** this survey,we give a brief review of the state-of-the-art federated learning techniques and detailedly discuss the improvement of federated *** open issues and existing solutions in federated learning are *** also point out the future research directions of federated learning.

关键词： federated learning privacy protection security

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E-voting system using cloud-based hybrid blockchain technology

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Journal of Safety science and Resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba Maya Eapen Jani Anbarasi Vinayakumar Ravi A.Suganya Malathy Jawahar Department of Information Technology Tagore Engineering College Chennai India School of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram ChennaiIndia Department of Information Technology Jerusalem College of Engineering ChennaiIndia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process Technology Division CSIR-Central Leather Research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Antoniadis, Christos Katsalis, Kostas Tyrovolas, Dimitrios Tegos, Sotiris A. Ioannidis, Sotiris Diamantoulakis, Panagiotis D. Karagiannidis, George K. Liaskos, Christos DOCOMO Euro-Labs Munich Germany Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanon University of Ioannina Computer Science Engineering Department Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (纸本)9798331507428

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future. © 2024 IEEE.

关键词： Resource allocation

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Vaccine Distribution Modelling in Pandemics through Multi-Agent Systems: COVID-19 Case 13

Vaccine Distribution Modelling in Pandemics through Multi-Ag...

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13th International Conference on computer and Knowledge Engineering, ICCKE 2023

作者： Yarahmadi, Hossein Shiri, Mohammad Ebrahim Navidi, Hamidreza Sharifi, Arash Piriaei, Hasan Challenger, Moharram University of Antwerp Department of Computer Science Belgium Flanders Make Strategic Research Center Belgium Islamic Azad University Borujerd Branch Department of Computer Engineering Borujerd Iran Amirkabir University of Technology Department of Mathematics and Computer Science Tehran Iran Shahed University Department of Mathematics and Computer Science Iran Islamic Azad University Science and Research Branch Department of Computer Engineering Tehran Iran Islamic Azad University Borujerd Branch Department of Mathematics Borujerd Iran

ISBN: (纸本)9798350330151

Multi-agent systems (MAS) have become increasingly used in addressing complex problems that require collaboration and coordination between multiple entities. In the context of infectious disease management, MAS can enable a more nuanced understanding of the complex social dynamics at play, including the behavior and interactions of individuals that determine the rate of infection and *** paper proposes two vaccine distribution methods based on MAS and the concepts of bankruptcy and the Susceptible-Exposed-Infectious-Recovered (SEIR) model, which take into account the prioritization of individuals based on their behavior and their impact on the infection rate of the disease. To propose these methods first, we propose a base algorithm named Vaccine Allocation Problem (VAP) that prioritizes individuals based on their impact on community *** proposed methods were evaluated in comparison with classic bankruptcy methods such as Proportional (P), Talmud, and Pinile methods. In addition, we use the Covid-19 data set provided by "Belgian Institute for Health"as a case study. Simulation results indicated the better performance of the proposed method in terms of infection, hospitalization, Intensive Care Unit (ICU), new-In, and mortality rates. © 2023 IEEE.

关键词： COVID-19

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Scalable Quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

Scalable Quantum Photonic Neural Network Processor via Cavit...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Basani, Jasvith Raj Niu, Murphy Yuezhen Waks, Edo Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics Joint Quantum Institute University of Maryland College ParkMD20742 United States Department of Computer Science Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States

We present an integrated photonic architecture that uses a single atom trapped in a cavity for deterministic high-fidelity quantum operations. Our design is unique in providing a photon-number-selective nonlinearity, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Photonic integrated circuits

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Object Counting Using a Refinement Network

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Tsinghua science and Technology 2022年第5期27卷 869-879页

作者： Lehan Sun Junjie Ma Liping Jing School of Science Beijing Jiaotong UniversityBeijing 100044China Department of Computer Science and Technology and Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China

To address the scale variance and uneven distribution of objects in scenarios of object-counting tasks,an algorithm called Refinement Network(RefNet) is *** proposed top-down scheme sequentially aggregates multiscale features,which are laterally connected with low-level *** by a multiresolution density regression loss,a set of intermediate-density maps are estimated on each scale in a multiscale feature pyramid,and the detailed information of the density map is gradually added through coarse-to-fine granular refinement progress to predict the final density *** evaluate our RefNet on three crowd-counting benchmark datasets,namely,ShanghaiTech,UCFC0,and UCSD,and our method achieves competitive performances on the mean absolute error and root mean squared error compared to the state-of-the-art *** further extend our RefNet to cell counting,illustrating its effectiveness on relative counting tasks.

关键词： object counting Refinement Network(RefNet) scale variation uneven distribution

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Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review 6

Pap Smear Medical Image Classification Using Deep Learning: ...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Selvano, Elvan Elwiehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability. © 2023 IEEE.

关键词： cervical cancer deep learning machine learning pap test

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Evaluation of Expressing without asserting approaches in RDF. The case of Conjectures 20

Evaluation of Expressing without asserting approaches in RDF...

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20th Conference on Information and research science Connecting to Digital and Library science, IRCDL 2024

作者： Pasqual, Valentina Manzano, Gerald Uzeir, Eduart Tomasi, Francesca Vitali, Fabio Digital Humanities Advanced Research Center Department of Classica Philology and Italian studies University of Bologna Italy Department of Computer Science University of Bologna Italy

In this paper, we evaluate the existing reification approaches for expressing without asserting (EWA) statements in RDF along with their related contextual information and compare them with a new method, called Conjectures. Conjectures express RDF statements with three states of knowledge: undisputed claims, disputed claims, and settled claims. Conjectures extend the semantics of RDF Named graphs and introduce a new syntactical form to represent both conjectural and asserted information. Our evaluation tests were performed on a large sample of Wikidata entities about artworks interspersed with additional dummy statements to simulate alternative or abandoned claims and enrich the set of non-asserted claims. Our study evaluates metrics such as the total number of triples, loading time, dataset weight, and in particular query execution time for many different and meaningful types of queries. Results show that Conjectures is competitive with existing methods and outperforms other methods in terms of efficiency when retrieving debated statements, thereby demonstrating its potential as an effective tool for expressing nuanced RDF statements. © 2021 Copyright for this paper by its authors.

关键词： Semantics

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