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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Bamansoor, Samer Pande, Bishwajeet Al Moaiad, Yazeed Pathmanathan, P. Ravindran El-Ebiary, Yousef A. Baker Latiff, Nurul Adilah Abdul Aseh, Khairi Aledinat, Lowai Saleh Yusoff, Fakhrul Hazman MEDIU Faculty of Computer and Information Technology Malaysia Faculty of Informatics and Computing UniSZA Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India MEDIU Faculty of Computer and Information Technology Malaysia International Institute of Applied Science Swiss School of Management Netherlands UniSZA Faculty of Informatics and Computing Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Universiti Teknologi MARA Malaysia

ISBN: (纸本)9781665432221

With the emergence of current developments and their devices that allow correspondence between organizations with each other, just as it is between organizations and service providers, just as with their customers, for some reasons, including advertising and a return to what is acceptable to them with the organization. Items, deals, etc., therefore, this examination provides an internal and external investigation of the current web-based business phase with a focus on promising conditions, risks, qualities, and shortcomings of FashionValet's electronic stage, whose administrations offer more than the Internet. Also, FashionValet is a stage organizer for online shopping and style in Southeast Asia other than Poplook, Zalora, and others, and the bulk of them are important competitors to FashionValet and they implement their own systems. The FashionValet experience, for example, is the history of the creator just as the other company's foundation has been pointed out to characterize how organizations, especially FashionValet, have moved from their primary stage to where they are today as the most aggressive design project. In addition, the technologies and innovations used by FashionValet are shown to provide a comprehensive detailed blueprint. To understand the improvement in style organizations in Malaysia, it was examined further. The paper outlines the critical elements to FashionValet's thriving as an end. © 2021 IEEE.

关键词： Online systems

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Concurrent Pipeline Rendering Scheme based on GPU multi-queue and partitioning images

Concurrent Pipeline Rendering Scheme based on GPU multi-queu...

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2023 International Conference on Optics and Machine Vision, ICOMV 2023

作者： Qiu, Zixia Ma, Junchao Zhang, Haitang Sibahee, Mustafa A.Al. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Applied Technology Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61001 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518063 China Faculty of Biological & Physical Sciences Tom Mboya University Homabay40300 Kenya

ISBN: (纸本)9781510664777

Compared with rasterization rendering, ray tracing rendering can improve the image’s visual effect and make the image look more realistic. Real-time ray tracing requires very high computing power of graphics processing unit (GPU). When the number of GPUs is limited and the performance of a single GPU cannot be fully utilized, high rendering latency will occur. In this paper, we propose Multi-Queue Concurrent Pipeline Rendering (MQCPR), a novel ray-tracing parallel rendering scheme based on GPU multi-queue. This scheme divides the image area into multiple parts and uses multi-queue of GPU to enable the computation and transmission tasks in the rendering process to be executed simultaneously, which can maximize the performance of a single GPU and improve the graphics rendering speed. MQCPR may keep the GPU busy to make full use of the GPU resources. Experiments illustrate that in the case of a single GPU, compared with the single queue serial rendering scheme, the number of frames per second (FPS) is increased by 1.5 times after using MQCPR. © 2023 SPIE. All rights reserved.

关键词： Graphics processing unit

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer 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 Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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LLCNN-based Image Enhancement with Squeeze and Excitation Network for Improved Visual Quality

LLCNN-based Image Enhancement with Squeeze and Excitation Ne...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Maheswari Subburaj Thirumurugan Shanmugam Arun Kumar Sivaraman Janakiraman Nithiyanantham Priya Ravindran Babu Sivam School of CSE Vellore Institute of Technology Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of ECE K L N College of Engineering Tamilnadu India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Image enhancement, particularly in low-light conditions, has long been a research focus to improve visual quality. Low-light settings often cause visibility issues and detail loss, which recent deep-learning techniques have effectively addressed. This study explores the combination of a low-light convolutional neural network (LLCNN), a convolutional neural network designed explicitly for low-light image enhancement, with a Squeeze and Excitation Network (SEN), which recalibrate features to emphasise critical details and suppress noise. Integrating SEN into LLCNN enhances image details while reducing noise and artefacts typically present in low-light conditions. Experimental results show that this combined approach outperforms existing methods in objective metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), as well as visual quality.

关键词： Measurement Visualization PSNR Surveillance Noise Superresolution Merging Convolutional neural networks Reliability Image enhancement

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Quantum Speedups for Dynamic Programming on n-Dimensional Lattice Graphs 46

Quantum Speedups for Dynamic Programming on n-Dimensional La...

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46th International Symposium on Mathematical Foundations of Computer Science, MFCS 2021

作者： Glos, Adam Kokainis, Martins Mori, Ryuhei Vihrovs, Jevgenijs Institute of Theoretical and Applied Informatics Polish Academy of Sciences Warsaw Poland Centre for Quantum Computer Science University of Latvia Riga Latvia Tokyo Institute of Technology School of Computing Japan

ISBN: (纸本)9783959772013

Motivated by the quantum speedup for dynamic programming on the Boolean hypercube by Ambainis et al. (2019), we investigate which graphs admit a similar quantum advantage. In this paper, we examine a generalization of the Boolean hypercube graph, the n-dimensional lattice graph Q(D, n) with vertices in {0, 1, . . . ,D}n. We study the complexity of the following problem: given a subgraph G of Q(D, n) via query access to the edges, determine whether there is a path from 0n to Dn. While the classical query complexity is eΘ((D+1)n), we show a quantum algorithm with complexity eO(TnD ), where TD © Adam Glos, Martins Kokainis, Ryuhei Mori, and Jevg¯enijs Vihrovs;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of Computer Science (MFCS 2021).

关键词： Dynamic programming

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Research and Design Trends for Exam Room computing to Enhance Patient Centeredness 65th

Research and Design Trends for Exam Room Computing to Enhanc...

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65th Human Factors and Ergonomics Society Annual Meeting, HFES 2021

作者： Saleem, Jason J. Militello, Laura G. Asan, Onur Read, Jacob M. Montague, Enid Department of Industrial Engineering University of Louisville LouisvilleKY United States Applied Decision Science LLC DaytonOH United States School of Systems & Enterprises Stevens Institute of Technology HobokenNJ United States College of Computing and Digital Media DePaul University ChicagoIL United States

ISBN: (纸本)9781713844129

While there is an extensive and established history of research that demonstrates the unfortunate capacity of exam room computing and electronic health records (EHRs) to negatively impact provider-patient communication and interaction, recent trends in exam room computing are promising in that there may be an opportunity for the EHR to improve patient engagement. The logical evolution of this research is to flip the narrative to understand strategies for using exam room computing as a mediator or facilitator of provider-patient communication and interaction, rather than simply establishing ways to mitigate the documented barriers. Panelists will present and discuss their latest research and experiences that may contribute to the evolution of using exam room computing as a tool to enhance provider-patient communication and interaction. We will also discuss how a thoughtful exam room design with patient-centered exam room computing technologies and practices may positively impact specific human factors, safety, and bias outcomes. © 2021 by Human Factors and Ergonoics Society. All rights reserved.

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, computing and Intelligent technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of Engineering & technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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A Dynamic Pyramidal Relaxation Method of AC OPF 9

A Dynamic Pyramidal Relaxation Method of AC OPF

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9th Asia Conference on Power and Electrical Engineering, ACPEE 2024

作者： Tang, Yu-Yang Chen, Liang Du, Hongbo Chen, Sheng-Jie Kou, Caixia Ai, Xiaomeng Zhou, Bo Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Beijing University of Posts and Telecommunications College of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huazhong University of Science and Technology School of Electrical and Electronic Engineering Wuhan China University of Michigan Department of Industrial and Operations Engineering Ann Arbor United States

ISBN: (纸本)9798350309638

The Alternating Current Optimal Power Flow (AC OPF) is crucial for power system analysis, yet existing algorithms face challenges in meeting the diverse requirements of practical applications. This paper presents a Pyramidal Relaxation (PR) formulation of the Branch Flow Model (BFM) for AC OPF, where surface conical constraints are approximated through a union of tetrahedrons in a Mixed Integer Linear Programming (MILP) relaxation. To reduce problem size and to enhance computational efficiency, we proposed a Dynamic Pyramidal Relaxation (DPR) method, where the cuts (inner approximation and outer approximation) are adaptively added. In practice, the DPR method is embedded into a MILP commercial solver based on the branch-and-cut framework. Case studies in different test systems validate the effectiveness in terms of feasibility, accuracy, and efficiency of our proposed method. © 2024 IEEE.

关键词： Computational efficiency

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Learning with Noisy Labels: the Exploration of Error Bounds in Classification

arXiv

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arXiv 2025年

作者： Liu, Haixia Li, Boxiao Yang, Can Wang, Yang School of Mathematics and Statistics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan China School of Mathematics and Statistics Huazhong University of Science and Technology Hubei Wuhan China Department of Mathematics The Hong Kong University of Science and Technology Clear Water Bay China

Numerous studies have shown that label noise can lead to poor generalization performance, negatively affecting classification accuracy. Therefore, understanding the effectiveness of classifiers trained using deep neural networks in the presence of noisy labels is of considerable practical significance. In this paper, we focus on the error bounds of excess risks for classification problems with noisy labels within deep learning frameworks. We begin by exploring loss functions with noise-tolerant properties, ensuring that the empirical minimizer on noisy data aligns with that on the true data. Next, we estimate the error bounds of the excess risks, expressed as a sum of statistical error and approximation error. We estimate the statistical error on a dependent (mixing) sequence, bounding it with the help of the associated independent block sequence. For the approximation error, we first express the classifiers as the composition of the softmax function and a continuous function from [0, 1]d to RK. The main task is then to estimate the approximation error for the continuous function from [0, 1]d to RK. Finally, we focus on the curse of dimensionality based on the low-dimensional manifold assumption. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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A Critical Review of Detection and Prediction of Infectious Disease using IOT Sensors 2

A Critical Review of Detection and Prediction of Infectious ...

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2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Meraj, Mohammad Alvi, Syed Ahad Murtaza Quasim, Mohammad Tabrez Haidar, Sk Wasim College of Applied Computer Sciences King Saud University Al-Muzahimiya Saudi Arabia College of Computing Information Technology University of Bisha Bisha Saudi Arabia Sultanate of Oman Oman

ISBN: (纸本)9781665428675

Detection and prediction of infectious disease is a very challenging task due to the lack of substantial evidence of the disease and its behaviours. The effective infection prevention mechanisms through IoT sensors have been explored in the various researches in recent times. Various researchers use the Internet of Things (IoT) to collect real-time sensory information data for the detection and prediction of Infectious Disease. A group of sensors distributed in the workplace to detect or gathered data related to Infectious Disease is one of the major explorations of this paper. Sensors could collect the real-time data gathered in the cloud storage unit and further the user has to be intimated with the real scenario of data suggested. Behind this filtering and analytics process to execute on the gathered data and extract the data in form of user information. This paper explores the Flu, COVID-19, Zika, and H1N1, especially focus on COVID-19 as the recent pandemic. The technique as the Remote Excess of Experts thought IoT data is also the research investigation of this paper. © 2021 IEEE.

关键词： Diseases

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