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3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021

作者： Osman, Mohd Shafarudin Azizan, Azizul Hassan, Khairul Nizam Ghani, Hadhrami Ab. Hassan, Noor Hafizah Yakub, Fitri Daud, Salwani Mohd Latiff, Liza Abdul Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Advanced Informatic Department Kuala Lumpur Malaysia Hospital Putrajaya Emergency and Trauma Department Putrajaya Malaysia Universiti Malaysia Kelantan Institute for Artificial Intelligence Big Data Department of Data Science Kelantan Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Engineering Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665438971

Emergency and Trauma department (ETD) in Malaysia's public hospitals receives millions of patients than other clinics due to cheap charges and reliable services, however most of them are Green Zone non-critical patients. Long waiting time for patient treatment results into congestion and affects the quality of service provided in the ETD. To date only 20 public hospitals in Malaysia implements Hospital Information System (HIS) and patient waiting time is captured from the system manually, thus lacking the functionality to track patient in realtime. Despite studies conducted worldwide, studies on design, development and implementation of RTLS in public hospital in Malaysia have not been conducted yet. This paper examines a Bluetooth Low Energy (BLE) based Real-time Location System (RTLS) implementation in ETD with HIS integration to reduce patient waiting time. We develop a Bluetooth Low Energy (BLE) based RTLS prototype that is integrated with existing HIS at ETD, Hospital Putrajaya and augment data visualization interventions to reduce patient waiting time in ETD. Preliminary results show substantial benefits of patient waiting time reduction leading to improvement of service quality. Our preliminary findings show when RTLS is introduced in the ETD workflow, the mean ATC time was significantly reduced to 10.47% in comparison to HIS without RTLS. © 2021 IEEE.

关键词： Bluetooth

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Research on data Asset Management System of Graph database Based on Internet of Things 7

Research on Data Asset Management System of Graph Database B...

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2020 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020

作者： Gao, Lingchao Yang, Qifan Zou, Baoping Liu, Qing Wang, Chuanjiang Big Data Center of State Grid Corporation of China Beijing100052 China State Grid Info-Telecom Great Power Science and Technology Co. LTD Fuzhou350003 China State Grid Fujian Electric Power CO. LTD Fuzhou350003 China

With the development of the times and the progress of society, the popularization rate of computer network technology and information technology in China is accelerating, and the Internet of things technology also appears in people's vision, and is gradually known by people. In the context of this era of big data, it has brought great challenges to all walks of life. The development of everything must conform to the development theme of the times. In order to meet the challenge of the research and development of the data asset management system of the graph database in the new era, this paper puts forward the method of applying the Internet of things technology to the research and development of the data asset management system of the graph database. Combining with the foreign research and development plans of the data asset management system of the graph database, the data resources of the graph database are carried out from the platform system, the management structure and the organization arrangement Based on the research and analysis of production management system, a research scheme of data asset management system of graph database which can meet the development requirements of the new era is worked out. Through long-term research and analysis, we can find that the Internet of things technology analysis method proposed in this paper can effectively provide new development ideas for the research and development of data asset management system based on graph database under the Internet of things technology. © Published under licence by IOP Publishing Ltd.

关键词： Information management

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The Class B Heat Shock Factor Rchsf17 from Rosa Chinensis Enhances Basal Thermotolerance in Rosa Rugosa

SSRN

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SSRN 2024年

作者： Ding, Minghai Xing, Wen Li, Zeqing Jin, Xi Yu, Qiuxiu Sun, Jiahui College of Landscape Architecture Central South University of Forestry and Technology Changsha410004 China National Forestry and Grassland Administration Engineering Research Center for Osmanthus Fragrans Hubei University of Science and Technology Xianning437100 China Hunan Big Data Engineering Technology Research Center of Natural Protected Areas Landscape Resources Central South University of Forestry and Technology Changsha410004 China Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center Changsha China

Rosa rugosa, a species with considerable economic value, encounters cultivation impediments due to its susceptibility to heat stress. This study delineates the cloning and functional characterization of RcHsf17, a member of the B2 group Heat shock transcription factor (Hsf), isolated from the stress-tolerant Chinese rose cultivar, R. chinensis ‘Slater's crimson China’. RcHsf17 functions as a nuclear transcription factor and demonstrates reactivity to heat stress and a spectrum of stress-related treatments, including abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), and hydrogen peroxide (H2O2). The overexpression of RcHsf17 in transgenic Arabidopsis thaliana and R. rugosa enhanced their heat tolerance. Specifically, in R. rugosa, RcHsf17 overexpression resulted in the thickening of the cell wall, maintenance of photosynthetic efficiency, and a reduction in the levels of reactive oxygen species (ROS) and malondialdehyde (MDA). In addition, transcriptomic analysis indicated that under ambient conditions, RcHsf17 elevates the expression of HsfA3, HsfB1, small heat shock protein genes (sHSPs), and genes associated with cell wall formation. Under heat stress, there is a notable upregulation of HsfA6B, genes related to photosynthesis, and antioxidant genes. Collectively, these findings highlighted the significant function of RcHsf17 in enhancing heat resistance in R. rugosa by facilitating cell wall biosynthesis, reducing heat-induced impairment to photosynthesis, and improving ROS scavenging mechanisms. © 2024, The Authors. All rights reserved.

关键词： Photosynthesis

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An improved FOO voting scheme using blockchain

An improved FOO voting scheme using blockchain

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作者： Zhou, Yuanjian Liu, Yining Jiang, Chengshun Wang, Shulan College of Computer Science and Technology Henyang Normal University Henyang421002 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing408100 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

The traditional FOO e-voting protocols adopt centralized and non-transparent count center, which leads to distrust to the center and doubts the fairness and correctness of the vote. However, blockchain is the most innovative technology in the current era and promises to solve the trust problem in the system with one center. In this paper, an improved FOO e-voting protocol is proposed using blockchain, which tries to address the limitation or weakness in existing systems. The traditional trusted third party is replaced by smart contract;specifically, our scheme is deployed using hyperledger fabric. The implementation is enforced by the consensus mechanism, which ensures the security of the blockchain. Through the analysis, the proposed scheme is proved to satisfy the necessary requirements for an e-voting protocol;meantime the trust assumption is reduced significantly. Therefore, the proposed protocol is more versatile and practical. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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ViGT: Proposal-free Video Grounding with Learnable Token in Transformer

arXiv

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arXiv 2023年

作者： Li, Kun Guo, Dan Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

The video grounding (VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely Video Grounding Transformer (ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows. (1) The token is unrelated to the video or the query and avoids data bias toward the original video and query. (2) The token simultaneously performs global context aggregation from video and query features. First, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention (i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets: ANet Captions, TACoS and YouCookII. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT. Copyright © 2023, The Authors. All rights reserved.

关键词： Regression analysis

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Mean-Square Stability of Stochastic System with Impulse and Unbounded Delay

Mean-Square Stability of Stochastic System with Impulse and ...

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International Conference on Applied Mathematics, Modeling and Computational science, AMMCS 2019

作者： Li, Mengling Deng, Feiqi Liu, Xinzhi School of Mathematics and Big Data Foshan University Foshan528000 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9783030635909

This paper investigates mean-square stability of stochastic systems with time-varying parameters, impulses and unbounded delay. Applying the characteristics of stabilizing and destabilizing impulses, stochastic analysis techniques and mathematical deduction, the two conclusions on mean-square stability analysis of the considered systems are obtained. © 2021, Springer Nature Switzerland AG.

关键词： Stochastic systems

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TriTransNet: RGB-D salient object detection with a triplet transformer embedding network

arXiv

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arXiv 2021年

作者： Liu, Zhengyi Wang, Yuan Tu, Zhengzheng Xiao, Yun Tang, Bin School of Computer Science and Technology Anhui University Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China

Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers. It is the first to use three transformer encoders with shared weights to enhance multi-level features. By further designing scale adjustment module to process the input, devising three-stream decoder to process the output and attaching depth features to color features for the multi-modal fusion, the proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection, and pushes the performance to a new level. Experimental results demonstrate the effectiveness of the proposed modules and the competition of TriTransNet.1 Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Ppdrm: Privacy-Preserving Drm Training and Classification on the Cloud

SSRN

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SSRN 2023年

作者： Xu, Lida Tian, Chengliang Zhang, Guoyan Li, Leibo Tian, Weizhong Zhang, Yan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Cyber Science and Technology Shandong University Shandong Ji’nan250101 China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong Ji’nan250101 China Shandong Institute of Blockchain Shandong Ji’nan250101 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao266061 China

The prevalence of cloud computing greatly promotes the development of artificial intelligence(AI). Accurately and efficiently classify data on the cloud is a classic AI task. The recent Discriminative Ridge Machine(DRM) is a new type of supervised classifier which outperforms many other classic classifiers in accuracy and efficiency, especially for high-dimensional data or unbalanced ***, the DRM training and classification task is tedious for resource-constrained data owners. Motivated by this, in this paper, we study the outsourcing of DRM training and classification within a cloud environment and propose a quantum secure and efficient solution for this problem. Specifically, based on the two non-colluding servers model, we adopt the quantum-immune CKKS cryptosystem to design a series of homomorphic security protocols for matrix and argmin operations and realize the correct model training and data classification without leaking the privacy of training dataset, the DRM model, the query data and the classification results. We argue the $\mathcal{CPA}$ secure of our design and evaluate the accuracy and efficiency from theory and experiment perspectives. Our theoretical and experimental analysis demonstrates that our design can achieve the same classification accuracy as the origin plaintext-based DRM classifier and the query-response time is reasonable and acceptable. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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A Novel Multi-Linear Polarization Reconfigurable Antenna Array

A Novel Multi-Linear Polarization Reconfigurable Antenna Arr...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Dingzhao Chen Ming Li Pan Guo Yanhui Liu Institute of Electromagnetics and Acoustics Xiamen University Xiamen China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China Global Big Data Technologies Centre University of Technology Sydney (UTS) NSW Australia School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

A novel multi-linear polarization (MLP) reconfigurable antenna array is introduced in this paper. By choosing an appropriate element polarization state distribution, the array can work in arbitrary desired linear polarization with side-lobe level (SLL) reduced pattern. In order to obtain the patterns of the array element in each polarization state, we use an approximation method. And a new optimization strategy is adopted to improve the accuracy of the synthesized patterns. Two numerical examples are given to verify the validity and correctness of the introduced scheme.

关键词： Power dividers Feeds Approximation methods Optimization Antenna arrays

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Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

arXiv

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arXiv 2024年

作者： Xu, Yanqing Larsson, Erik G. Jorswieck, Eduard A. Li, Xiao Jin, Shi Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen518172 China Linköping University Linköping581 83 Sweden Institute for Communications Technology Technical University of Braunschweig Braunschweig38106 Germany National Mobile Communication Research Laboratory Southeast University Nanjing210096 China

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Antenna arrays

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