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21st International Conference on Computational Science, ICCS 2021

作者： Antczak, Tomasz Skorupa, Bartosz Szurlej, Mikolaj Weron, Rafal Zabawa, Jacek Faculty of Computer Science and Management Wroclaw University of Science and Technology Wroclaw50-370 Poland Faculty of Architecture Wroclaw University of Science and Technology Wroclaw50-370 Poland

ISBN: (纸本)9783030779603

We build an agent-based model for evaluating the spatial and functional design of supermarket checkout zones and the effectiveness of safety regulations related to distancing that have been introduced after the COVID-19 outbreak. The model is implemented in the NetLogo simulation platform and calibrated to actual point of sale data from one of major European retail chains. It enables realistic modeling of the checkout operations as well as of the airborne diffusion of SARS-CoV-2 particles. We find that opening checkouts in a specific order can reduce epidemic risk, but only under low and moderate traffic conditions. Hence, redesigning supermarket layouts to increase distances between the queues can reduce risk only if the number of open checkouts is sufficient to serve customers during peak hours. © 2021, Springer Nature Switzerland AG.

关键词： COVID-19

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AHARNet: Adaptive Human Activity Recognition Model for Multimodal Consumer Electronics With Different Computation Resources

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Lei Yan, Yicheng Huang, Dongsheng Pan, Yuanyuan Cheang, Chak Fong Luo, Kunming Li, Jianqing Nanchang University Architecture and Design College Nanchang330031 China Macau University of Science and Technology Faculty of Innovation Engineering 999078 China Jiangxi Touerism and Commerce Vocational College College of Art Media and Computer Science Nanchang330100 China

With the development of consumer electronics, enhancing smart terminal devices to better serve humans has received increasing attention. Human Activity Recognition (HAR) plays a crucial role in enhancing and improving human life on smart terminal devices, and has become a popular research topic. HAR models based on deep learning have demonstrated impressive performance due to their powerful feature extraction capabilities. As HAR has rich application scenarios, HAR models typically need to be deployed on devices with different computation resources. However, the existing HAR models lack the adaptability to different computing resources. To address this issue, this paper proposes an adaptive HAR model with resource-aware capability, AHARNet. Our model has multiple output layers and achieves adaptive inference through an early-exiting mechanism. AHARNet first generates early-exiting decisions based on the available computation resources of the device through a computation resource perceptron. Then during inference, AHARNet only performs partial layers to meet the device's resource constraints. To validate the effectiveness of our model, experiments are performed on three public datasets. The experimental results show that compared to the baseline architecture, AHARNet can dynamically reduce over 61% of computational overhead while demonstrating comparable accuracy. © 1975-2011 IEEE.

关键词： Feature extraction

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Research on Practical Byzantine Fault Tolerant Algorithm Based on Trust Mechanism

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Journal of computers (Taiwan) 2022年第2期33卷 11-23页

作者： Pang, Hui Li, Yanan Wen, Xiumei Mu, Yingxue Department of Computer Science and Technology Hebei University of Architecture Big Data Technology Innovation Center of Zhangjiakou Hebei Zhangjiakou China

The consensus algorithm is one of the core technologies of the blockchain, which determines how the nodes in the blockchain network reach a consensus and enable them to jointly maintain a piece of data. This paper focuses on the Practical Byzantine Fault Tolerant Algorithm (PBFT Algorithm), and proposes a Practical Byzantine Fault Tolerant Algorithm based on Trust Mechanism (TM-PBFT Algorithm) to solve the problems of its low fault-tolerant rate and high communication cost. This algorithm supports voting for trusted nodes before the consensus cycle starts. In order to make nodes actively vote and vote for reliable nodes, this paper models the trust degree of nodes. In addition, the time factor is added to the Shapley value to design a new reward and punishment scheme, which makes the distribution of income of each node more reasonable and further encourages nodes to vote for reliable nodes. As the system runs for a long time, the probability of malicious nodes becoming master nodes decreases. Finally, a comparison experiment between the proposed algorithm and the PBFT algorithm shows that the fault tolerance and throughput of the TM-PBFT algorithm are higher than that of the PBFT algorithm, and the communication bandwidth overhead is lower than that of the PBFT algorithm. © 2022 computer Society of the Republic of China. All rights reserved.

关键词： Blockchain

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Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Calibration and Performance Evaluation of a Superconducting ...

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ISC High Performance 2024 Research Paper Proceedings (39th International Conference)

作者： Xiaolong Deng Stefan Pogorzalek Florian Vigneau Ping Yang Martin Schulz Laura Schulz QCT Department Leibniz Supercomputing Centre Garching Germany Technology Department IQM Quantum Computers Munich Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Munich Germany

As quantum computers mature, they migrate from laboratory environments to HPC centers. This movement enables large-scale deployments, greater access to the technology, and deep integration into HPC in the form of quantum acceleration. In laboratory environments, specialists directly control the systems' environments and operations at any time with hands-on access, while HPC centers require remote and autonomous operations with minimal physical contact. The requirement for automation of the calibration process needed by all current quantum systems relies on maximizing their coherence times and fidelities and, with that, their best performance. It is, therefore, of great significance to establish a standardized and automatic calibration process alongside unified evaluation standards for quantum computing performance to evaluate the success of the calibration and operation of the system. In this work, we characterize our in-house superconducting quantum computer, establish an automatic calibration process, and evaluate its performance through quantum volume and an application-specific algorithm. We also analyze readout errors and improve the readout fidelity, leaning on error mitigation.

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ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specification

arXiv

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arXiv 2022年

作者： Cychnerski, Jan Dziubich, Tomasz Brzeski, Adam Department of Computer Architecture Gdańsk University of Technology Narutowicza 11/12 Gdańsk Poland

The article presents a new multi-label comprehensive image dataset from flexible endoscopy, colonoscopy and capsule endoscopy, named ERS. The collection has been labeled according to the full medical specification of'Minimum Standard Terminology 3.0' (MST 3.0), describing all possible findings in the gastrointestinal tract (104 possible labels), extended with an additional 19 labels useful in common machine learning applications. The dataset contains around 6000 precisely and 115,000 approximately labeled frames from endoscopy videos, 3600 precise and 22,600 approximate segmentation masks, and 1.23 million unlabeled frames from flexible and capsule endoscopy videos. The labeled data cover almost entirely the MST 3.0 standard. The data came from 1520 videos of 1135 patients. Additionally, this paper proposes and describes four exemplary experiments in gastrointestinal image classification task performed using the created dataset. The obtained results indicate the high usefulness and flexibility of the dataset in training and testing machine learning algorithms in the field of endoscopic data analysis. © 2022, CC BY-SA.

关键词： Image recognition

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Influence of 3D laparoscopic surgery on surgeon’s visual pattern and mental workload

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Journal of Medical Engineering and technology 2021年第5期45卷 375-379页

作者： Zhang, Jian-Yang Shen, Zhi-Hao Wang, Bao-Ping Liu, Feng Li, Juan School of Computer and Information Technology Nanyang Normal University Nanyang China Department of Medical Engineering Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China School of Civil Engineering and Architecture Nanyang Normal University Nanyang China

Previous studies have found that surgeons perform better in three-dimensional (3D) surgery than in two-dimensional (2D) surgery. However, no comparative studies have revealed the impact of 3D laparoscopic surgery on the surgeon’s vision. To explore the effect of laparoscopic surgeons’ depth perception during 3D laparoscopic surgery, 10 participants were recruited and performed 4 sets comparative simulated laparoscopic procedures in a virtual simulator, and eye movement signals were acquired, which were used to characteristics the visual differences. Fixation rate and saccade speed were used to characterise the influence of moderating variables for visual characteristics. The results from the data showed significant differences in eye movement behaviour. Compared with 2D laparoscopic surgery, surgeons have more average fixation rate (p-values = 0.001, 0.000, 0.003 and 0.015, respectively) and faster saccade speed (p-values = 0.037, 0.003, 0.073 and 0.105, respectively) in 3D laparoscopic surgery. The results of this study showed that surgeons had more efficient visual search in 3D laparoscopic surgery. At the same time, the results also indicated that surgeon’s mental workload in 3D laparoscopic surgery was low. The relevant conclusions of this paper revealed the advantages of 3D laparoscopic surgery through visual efficiency. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Eye movements

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A Simulation Study on the Reentrant Waves in the Pacing Ventricular Tissues 5

A Simulation Study on the Reentrant Waves in the Pacing Vent...

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2020 5th International Seminar on computer technology, Mechanical and Electrical Engineering, ISCME 2020

作者： Zhang, Yue Shi, Xibo Sui, Dong College of Computer Science and Technology Harbin Engineering University China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China

The aim of the study is to investigate the relationship between the reentrant waves and I K1, and probe the effect of I K1 on the reentrant waves. Firstly, based on the TNNP06 model, the single pacing cell is derived by depressing I K1. And then, a 400 cells ×400 cells 2D tissue is developed by coupling the pacing cells together. Thirdly, the S1-S2 protocol is applied to inspire the reentrant waves. Next, the processes of the reentrant waves in the tissue corresponding to different I K1 are analysed. In addition, the action potentials of the cells in special locations are recorded and discussed. With the decrease of the I K1, the reentrant waves spin slowly and the period of reentrant waves increases. Meanwhile, the range of the action potential of the cells becomes larger when I K1 becomes smaller. The results suggest that with the decrease of I K1, the reentrant wave is weakened, and the systolic and diastolic functions of the tissue are enhanced at the same time. © Published under licence by IOP Publishing Ltd.

关键词： Tissue

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Brain Cortical Surface Can Predict Fiber Trajectories

Brain Cortical Surface Can Predict Fiber Trajectories

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IEEE International Symposium on Biomedical Imaging

作者： Tianle Han Yifan Lv Mengshen He Yiheng Liu Tianming Liu Bao Ge School of Physics & Information Technology Shaanxi Normal University Xi'an China Department of Computer Science and Bioimaging Research Center Cortical Architecture Imaging and Discovery Lab The University of Georgia GA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gray matter and white matter are fundamental constituents of the brain, and exploring the relationship between them is for gaining insights into the intricate architecture of the brain. This study utilizes information extracted from the gray matter surface of the brain to predict the trajectories of white matter fibers. Specifically, it predicts the coordinates of fiber trajectories by utilizing both fiber endpoints and reference points on the cortical surface. Both qualitative analysis and extensive quantitative experiments confirm that information derived from the brain's cortical surface can be employed to predict fiber trajectories. Furthermore, this conclusion remains robust across diverse fiber tracking and surface reconstruction techniques. The study introduces a novel perspective, elucidating the fundamental principles of fiber connectivity and the intricate relationship between gray matter and white matter.

关键词： Surface reconstruction Brain Pathology Nerve fibers Magnetic resonance imaging Grey matter Streaming media

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A novel model for data-driven smart sustainable cities of the future: the institutional transformations required for balancing and advancing the three goals of sustainability

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Energy Informatics 2021年第1期4卷 1-37页

作者： Bibri, Simon Elias Department of Computer Science The Norwegian University of Science and Technology Sem Saelands veie 9 TrondheimNO 7491 Norway Department of Architecture and Planning The Norwegian University of Science and Technology Alfred Getz vei 3 Sentralbygg 1 5th floor TrondheimNO 7491 Norway

In recent years, it has become increasingly feasible to achieve important improvements of sustainability by integrating sustainable urbanism with smart urbanism thanks to the proven role and synergic potential of data-driven technologies. Indeed, the processes and practices of both of these approaches to urban planning and development are becoming highly responsive to a form of data-driven urbanism, giving rise to a new phenomenon known as "data-driven smart sustainable urbanism." Underlying this emerging approach is the idea of combining and integrating the strengths of sustainable cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable sustainable cities to optimize, enhance, and maintain their performance on the basis of the innovative data-driven technologies offered by smart cities. These strengths and synergies can be clearly demonstrated by combining the advantages of sustainable urbanism and smart urbanism. To enable such combination, major institutional transformations are required in terms of enhanced and new practices and competences. Based on case study research, this paper identifies, distills, and enumerates the key benefits, potentials, and opportunities of sustainable cities and smart cities with respect to the three dimensions of sustainability, as well as the key institutional transformations needed to support the balancing of these dimensions and to enable the introduction of data-driven technology and the adoption of applied data-driven solutions in city operational management and development planning. This paper is an integral part of a futures study that aims to analyze, investigate, and develop a novel model for data-driven smart sustainable cities of the future. I argue that the emerging data-driven technologies for sustainability as innovative niches are reconfiguring the socio-technical landscape of institutions, as well as providing insights to policymakers into pathways for strengthening

关键词： Sustainable development

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Short-Term Speed Forecasting of Large-Scale Urban Road Network Based on Transformer 21

Short-Term Speed Forecasting of Large-Scale Urban Road Netwo...

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21st COTA International Conference of Transportation Professionals: Advanced Transportation, Enhanced Connection, CICTP 2021

作者： Zhang, Jiawei Geng, Maosi Gu, Jiangsa Chen, Xiqun Michael College of Civil Engineering and Architecture Zhejiang Univ. Hangzhou China College of Computer Science and Technology Zhejiang Univ. Hangzhou China

ISBN: (纸本)9780784483565

Intelligent transportation systems (ITS) have developed rapidly for urban road networks in recent years. Accurate and efficient short-term traffic flow speed prediction is key to the realization of ITS. Traffic flow data usually perform stochastic and nonlinear characteristics, making short-term forecasting of large-scale urban road networks challenging. To extract the spatial and temporal correlations between traffic flows, we propose a novel short-term speed forecasting of large-scale urban road network based on the deep learning algorithm Transformer used in the field of natural language processing. We test the model using a real floating car dataset collected on a large-scale urban road network. It is found that the Transformer model shows high prediction accuracy and efficiency performance and outperforms the benchmark models. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.

关键词： Benchmarking

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