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Erratum to: An improved block-based matching algorithm of copy-move forgery detection

Multimedia Tools and Applications 2017年第12期77卷 15111-15111页

作者： Lai, Yuecong Huang, Tianqiang Lin, Jing Lu, Henan School of Mathematics and Computer Science Fujian Normal University Fuzhou China Faculty of Software Fujian Normal University Fuzhou China Fujian Engineering Research Center of Public Service Big Data Mining and Application Fujian Normal University Fuzhou China

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The mechanism of bone healing after traumatic brain injury

Brain‐X

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Brain‐X 2023年第3期1卷

作者： Yuan Xiong Wenbin Zhong Bobin Mi School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore Singapore Department of Orthopedics Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei China Contribution: Data curation Resources Software Supervision Writing - original draft Contribution: Data curation Software Supervision Writing - original draft Contribution: Conceptualization Investigation Supervision Validation Visualization Writing - review & editing

A growing body of evidence suggests that patients who experience traumatic brain injuries (TBIs) exhibit significantly shorter healing periods compared to those with isolated fractures. However, the precise underlying mechanism behind this phenomenon remains unclear. Recent studies have shed light on the potential role of hormonal signals and neural circuits originating in the hypothalamus, which play vital roles in regulating the skeletal system. Despite these advances, there is a lack of comprehensive research summarizing the crucial role of bone healing in TBIs and the underlying mechanisms. This review aimed to explore the underlying mechanisms responsible for the accelerated bone healing observed in TBI patients, with a specific focus on the intricate crosstalk between TBI and bone remodeling. Additionally, we comprehensively discuss and summarize the beneficial effects of TBI on the skeletal system and examine the TBI-induced signaling pathways that result in accelerated fracture healing and bone remodeling. By dissecting these pathways, we aim to identify potential targets for intervention and bone repair promotion.

关键词： bone crosstalk mechanisms TBI

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A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

arXiv

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： Computerized tomography

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Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge Computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

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Cheetah: Accelerating Dynamic Graph Mining with Grouping Updates

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ACM Transactions on Architecture and Code Optimization 1000年

作者： Yi Zhang Xiaomeng Yi Yu Huang Jingrui Yuan Chuangyi Gui Dan Chen Long Zheng Jianhui Yue Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Software Engineering Huazhong University of Science and Technology Wuhan China Eastern Institute of Technology Ningbo China National University of Singapore Singapore Singapore Michigan Technological University Houghton United States School of Computer Science and Engieering UNSW Sydney Kensington Australia

Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.

关键词： Graph mining system dynamic graph incremental computing

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Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition

arXiv

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arXiv 2022年

作者： Qin, Rong Huangfu, Luwen Hood, Devon Ma, James Huang, Sheng School of Big Data & Software Engineering Chongqing University Chongqing400044 China Fowler College of Business San Diego State University San DiegoCA92182 United States Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States College of Science San Diego State University San DiegoCA92182 United States Department of Business Analysis University of Colorado Colorado SpringsCO80918 United States Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing400044 China

Pavement Distress Recognition (PDR) is an important step in pavement inspection and can be powered by image-based automation to expedite the process and reduce labor costs. Pavement images are often in high-resolution with a low ratio of distressed to non-distressed areas. Advanced approaches leverage these properties via dividing images into patches and explore discriminative features in the scale space. However, these approaches usually suffer from information loss during image resizing and low efficiency due to complex learning frameworks. In this paper, we propose a novel and efficient method for PDR. A light network named the Kernel Inversed Pyramidal Resizing Network (KIPRN) is introduced for image resizing, and can be flexibly plugged into the image classification network as a pre-network to exploit resolution and scale information. In KIPRN, pyramidal convolution and kernel inversed convolution are specifically designed to mine discriminative information across different feature granularities and scales. The mined information is passed along to the resized images to yield an informative image pyramid to assist the image classification network for PDR. We applied our method to three well-known Convolutional Neural Networks (CNNs), and conducted an evaluation on a large-scale pavement image dataset named CQU-BPDD. Extensive results demonstrate that KIPRN can generally improve the pavement distress recognition of these CNN models and show that the simple combination of KIPRN and EfficientNetB3 significantly outperforms the state-of-the-art patch-based method in both performance and efficiency. © 2022, CC BY-NC-SA.

关键词： Convolution

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Cover Image, Volume 84, Issue 7

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Proteins: Structure, Function, and Bioinformatics 2016年第7期84卷

作者： Wei Wang Juan Liu Lin Sun Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Xinxiang 453007 China Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining Henan Province China Institute of Computer Software School of Computer Wuhan University Wuhan 430072 China

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2021年 1174卷 v-vi页

作者： Sharma, Neha Balas, Valentina Emilia Chakrabarti, Amlan Martinovic, Jan Society for Data Science Pune Maharashtra India Department of Automatics and Applied Software Faculty of Engineering University of Arad Arad Romania A.K. Choudhury School of Information Technology West Bengal India Faculty of Engineering University of Calcutta Kolkata India IT4Innovations VSB-Technical University of Ostrava Ostrava Czech Republic

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Privacy-Preserving Three-Factor Authentication Protocol for Wireless Sensor Networks Deployed in Agricultural Field

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ACM Transactions on Sensor Networks 1000年

作者： Mah Noor Fatima Mohammad S. Obaidat Khalid Mahmood Salman Shamshad Muhammad Asad Saleem Muhammad Faizan Ayub Department of Computer Science COMSATS University Islamabad Sahiwal Campus Pakistan Life Fellow of IEEE Computer Science Department University of Texas-Permian Basin USA with the King Abdullah II School of Information Technology University of Jordan 11942 Jordan and with School of Computer and Communication Engineering University of Science and Technology Beijing 100083 China Honorary Distinguished Professor Amity University Noida 201301 India Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Taiwan R.O.C Department of Software Engineering The University of Lahore Lahore Pakistan

The agriculture is the backbone of economic system, and it plays an essential part in the survival of a nation’s prosperity. In addition to providing raw materials and food, it also offers numerous employment opportunities. Consequently, agriculture necessitates modern technology to increase productivity. In these circumstances, Wireless Sensor Networks (WSNs) are used to detect climatic parameters like light, humidity, carbon dioxide, acidity, soil moisture, and temperature in an agricultural field. However, the research that is being done at the moment is unable to circumvent the issue that safety and effectiveness cannot coexist. Several studies employ time-consuming cryptographic security structures, whereas the majority of lightweight systems are designed without reviewing certain security aspects like resistance to ephemeral secret leakage (ESL) attacks, perfect forward secrecy, and so on. According to our opinion, this issue may be overcome through the use of lightweight cryptographic primitives, paying particular attention to protocol weaknesses, and keeping in mind the ever-changing security needs of individuals. We present an extensive lightweight three-factor authentication protocol with diverse security criteria, along with the adaptive privacy preservation, that is suited for user-friendly situation in the WSN enviroment. This is accomplished by removing all extraneous cryptographic structures. It has been illustrated that proposed protocol is much better in terms of the privacy and security aspects via the usage of security aspects, proof of the real-or-random (ROR) model, protocols of internet security, and applications that are subjected to experimental validation utilizing automated validation. and comparison with other protocols’ security aspects. The performance analysis shows how superior our proposed protocol is to other competing protocols in terms of communication and computational overheads, with respective efficiencies of 53% and 39%. Moreov

关键词： Authentication Mutual Authentication Privacy-Preserving Key Agreement Wireless Sensor Networks (WSNs)

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Preface

Advances in Intelligent Systems and Computing

引用

Advances in Intelligent Systems and Computing 2021年 1175卷 iv-vi页

作者： Sharma, Neha Chakrabarti, Amlan Balas, Valentina Emilia Martinovic, Jan Society for Data Science PuneMaharashtra India Department of Automatics and Applied Software Faculty of Engineering University of Arad Arad Romania A.K. Choudhury School of Information Technology University of Calcutta KolkataWest Bengal India IT4Innovations VSB-Technical University of Ostrava Ostrava Czech Republic

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