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Construction of Influenza Early Warning Model Based on Combinatorial Judgment Classifier:A Case Study of Seasonal Influenza in Hong Kong

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Current Medical science 2022年第1期42卷 226-236页

作者： Zi-xiao WANG James NTAMBARA Yan LU Wei DAI Rui-jun MENG Dan-min QIAN Department of Medical Informatics School of MedicineNantong UniversityNantong 226001China Department of Computer Science College of Engineering and Computing SciencesNew York Institute of TechnologyNew York 10023USA Department of Computer Science College of Overseas EducationNanjing University of Posts and TelecommunicationsNanjing 210023China Department of Epidemiology School of Public HealthNantong UniversityNantong 226019China Artificial Intelligence Laboratory Center De Montfort University of LeicesterLeicester LE1 9BHUnited Kingdom

Objective:The annual influenza epidemic is a heavy burden on the health care system,and has increasingly become a major public health problem in some areas,such as Hong Kong(China).Therefore,based on a variety of machine learning methods,and considering the seasonal influenza in Hong Kong,the study aims to establish a Combinatorial Judgment Classifier(CJC)model to classify the epidemic trend and improve the accuracy of influenza epidemic early warning.

关键词： influenza prediction data-driven Support Vector Machine Discriminant Analysis Ensemble Classifier

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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International Symposium on Multiple-Valued Logic

作者： Sefatul Wasi Saadia Binte Alam Rashedur Rahman M Ashraful Amin Syoji Kobashi Department of Computer Science and Engineering Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Graduate School of Engineering University of Hyogo Himeji Hyogo Japan

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively.

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The development and application of CMOS image sensor

The development and application of CMOS image sensor

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作者： Gongchen Li Fangji Zhao Zicheng Wang Zhe Chen Department of electrical engineering and automation Zhongyuan University of Technology Jinan Foreign Language School International Center Department of computer science and engineering Chinese University of Hong Kong

This paper focuses on the development of Complementary metal-oxide semiconductor(CMOS) image sensor and its applications in aerospace,medical and automotive ***,the representative events in history and the contributions of some companies to CMOS image sensor are ***,some characteristics of CMOS image sensor are analyzed in the image field *** order to evaluate the performance of CMOS image sensor,single even effect and electronic endoscope structures are analyzed and active and passive range finder experiments are carried *** results show that the imaging based on CMOS sensor can fully meet the requirements of imaging applications in many fields.

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Behavior Replication of Cascaded Dynamic Systems Using Machine Learning

Behavior Replication of Cascaded Dynamic Systems Using Machi...

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International Workshop on Robot Sensing (ROSE)

作者： Mohammed Abouheaf Wail Gueaieb Mohammad Mayyas Robotics Engineering Bowling Green State University Bowling Green OH 43403 USA School of Electrical Engineering and Computer Science Ottawa University Ottawa Ontario K1N 6N5 Canada

This paper introduces an innovative data-driven approach for replicating behaviors in interconnected and heterogeneous dynamic systems. The core concept involves real-time control of dynamic systems to closely mimic reference-model trajectories using model-free techniques. Within this coupled framework, one component possesses complete information about reference-trajectories, although not necessarily their dynamics. In contrast, follower systems, with limited connectivity to reference-model trajectories, exclusively replicate the behavior of the primary process, which retains insight into model-reference dynamics. The adopted strategies are causal, integrating higher-order error dynamics to ensure precise tracking of reference-trajectories. Furthermore, these strategies incorporate variations in reference-model dynamics via a pseudo partial derivative, akin to sensitivity derivatives in model-reference adaptive strategies. To optimize the dynamic behavior of the follower process, the solution employs a reinforcement learning mechanism through adaptive critics. This mechanism approximates the optimal strategy and the associated value function. The actor and critic weights of the adaptive critic structure are tuned using a projection technique to ensure convergence of the adapted strategy. The validation of this solution is demonstrated on a dynamic system with delays, simulating an underwater vehicle scenario. The developed methodology is rigorously compared with another high-order model-free adaptive control approach. The presented approach showcases its capability to effectively replicate behaviors, resulting in improved tracking accuracy.

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A Novel Quantum Byzantine Consensus Protocol Based on Malicious Node Prevention Mechanism

A Novel Quantum Byzantine Consensus Protocol Based on Malici...

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2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022

作者： Gao, Xinxin Xu, Juan Fan, Jihao College of Computer Science & Technology Nanjing University of Aeronautics & Astronautics Jiangsu Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Jiangsu Nanjing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

ISBN: (数字)9781665496919

ISBN: (纸本)9781665496919

Blockchain is a decentralized distributed ledger database. Consensus protocol is the core protocol of blockchain to solve Byzantine agreement problem. To let all blockchain nodes reach an agreement, the most commonly used Byzantine consensus protocol are Practical Byzantine Fault Tolerance (PBFT) protocols. PBFT requires that the number of malicious nodes t is less than n/3 (n is the total number of nodes), while quantum ones can raise the upper bound from n/3 to n/2. However, there is a problem with quantum Byzantine consensus protocols, that is, the lack of malicious-degree evaluation. When the primary and reporting nodes are malicious, the number of agreements rounds and communication overhead will be significantly increased. Thus, a prevention mechanism for quantum consensus protocols is proposed in this paper, which includes two measures to prevent (or constrain) the malicious behaviors: (1) The trust-value calculation method to judge the honesty of nodes, which adjust the value according to nodes' behaviors using our trust-value evaluation formula;(2) The dual signature method to verify the legitimacy of reporting messages, which requires two nodes to agree and sign a message. The security analysis shows that our prevention mechanism can effectively prevent malicious nodes from being the primary node and substantially reduce the false messages from dishonest nodes. © 2022 IEEE.

关键词： Blockchain

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A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-hand manipulation

A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-han...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Zhou, Jianshu Li, Yunquan Yang, Yang Cao, Hanwen Huang, Junda Liu, Yunhui Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Center of Logistics Robotics Mechanical Engineering University of Hong Kong School of Automation Nanjing University of Information Science and Technology

ISBN: (纸本)9781665436786

Bio-inspired soft robots present distinctive superiorities in safety issues working in a human-centered environment. Soft robotic hands are of prominent popularity for soft robots to be applied in real applications. With continuous efforts being devoted, researchers have been developing various soft hands performing excellent grasping capability towards daily objects with salient safety insurance. However, the functional dexterity of soft robotic hands is still limited, especially because lacking in-hand object manipulation capabilities, which inevitably limits the application potential of soft robotic hands. This restriction is induced by the limited dexterity of soft actuators usually with a one-DOF per actuator providing a predefined motion trajectory. To tackle this challenge, a novel bio-inspired multi-DOF soft actuator based on V-joint is proposed, which achieves human finger-joint-like motion. By connecting the V-joint in a desired order, it is possible to build multi-DOF soft robots like traditional rigid counterparts. A 22-DOFs anthropomorphic soft hand (S-22) is fabricated out based on the V-joint, which is capable of realizing dexterous hand gestures and 6 kinds of in-hand manipulation. The presented soft robotic approach is a promising solution for dexterous soft robotic hand design and broden the potential application of soft hands. © 2021 IEEE.

关键词： Robotic arms

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An Online Tool for Understanding and Monitoring COVID-19 Trends and Spread Based on Self-Reporting Tweets 1

An Online Tool for Understanding and Monitoring COVID-19 Tre...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xie, Jiacheng Zhang, Ziyang Hilliard, Joel An, Guanghui Tang, Xiaoting Yu, Yang Wan, Xiu-Feng Xu, Dong Bond Life Sciences Center University of Missouri Department of Electrical Engineering and Computer Science Columbia United States School of Acupuncture and Tuina Shanghai University of Traditional Chinese Medicine Shanghai China Wanggang Community Health Service Center Shanghai Pudong New Area Shanghai China Bond Life Sciences Center University of Missouri Department of Molecular Microbiology and Immunology Columbia United States

ISBN: (纸本)9798350358780

After three years of multiple waves, COVID-19 has become epidemic, causing recurrent outbreaks. Many of COVID-19 cases have mild symptoms self-assessed at home, making it difficult to acquire formal laboratory data. This makes it challenging to correctly track the daily total of confirmed cases and comprehend the disease pattern. To solve this problem, we developed a COVID-19 analytic platform https://***/ based on self-reported tweets to track the pandemic's progression and trends. This study extends traditional infectious disease monitoring and holds crucial practical significance for understanding and controlling COVID-19. © 2023 IEEE.

关键词： COVID-19

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A Novel Estimate Fitness Method by Measuring A large number of Sub-event Logs Conformance

A Novel Estimate Fitness Method by Measuring A large number ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Wang, Mimi He, Xudong Zhao, Peihai College of Information Science and Technology Donghua University Shanghai201620 China Engineering Research Center of Digitized Textile and Fashion Technology Ministry of Education Donghua University Shanghai201620 China School of Computer Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9781665480451

Conformance checking compares a process model to its corresponding execution log, to detect inconsistencies and improve compliance with business processes. Nowadays, driven by trends such as big data and process automation, business processes are becoming more complex and integrated. Unfortunately, a major downside of the current standard-de-facto technique of alignment is that alignment suffers from poor performance (worst-case exponential time complexity) on an unstructured event log or a spaghetti-like process model. Therefore, the main purpose of this paper is to provide an approach to estimating the fitness. Since the fitness of different traces could be extremely close, we first propose that an event log contains a tiny number of traces with new information in the context of token-based replay. Then, we can estimate the fitness through a sub-event log composed of at most 6 % traces. Experiments on real-life event logs demonstrate that the fitness of the complete log can be estimated using only 1% traces. © 2022 IEEE.

关键词： Compliance control

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Advances on the investigation of landslides by space-borne synthetic aperture radar interferometry

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地球空间信息科学学报（英文版） 2024年第3期27卷 602-623页

作者： Roberto Tomás Qiming Zeng Juan M.Lopez-Sanchez Chaoying Zhao Zhenhong Li Xiaojie Liu María I.Navarro-Hernández Liuru Hu Jiayin Luo Esteban Díaz William T.Szeibert José Luis Pastor Adrián Riquelme Chen Yu Miguel Cano Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencePeking UniversityBeijingChina Institute for Computer Research(IUII) University of AlicanteAlicanteSpain College of Geological Engineering and Geomatics Chang'an UniversityXi'anChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain School of Civil Engineering Lanzhou University of TechnologyLanzhouChina Departamento de Ingeniería Civil University of AlicanteAlicanteSpain Land Satellite Remote Sensing Application Center(LASAC) Ministry of Natural Resources of P.R.ChinaBeijingChina The First Topographic Surveying Brigade of Ministry of Natural Resources of the People's Republic of China Xi'anChina Sixense Departamento SatéliteL'Hospitalet de LlobregatBarcelonaSpain

Landslides are destructive geohazards to people and infrastructure,resulting in hundreds of deaths and billions of dollars of damage every ***,mapping the rate of deforma-tion of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated *** this paper,the main outcomes relevant to the joint European Space Agency(ESA)and the Chinese Ministry of science and Technology(MOST)Dragon-5 initiative cooperation project ID 59,339"Earth observation for seismic hazard assessment and landslide early warning system"are *** primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks,detect potential landslides in wide regions,and demonstrate EO-based landslide early warning system over selected *** work only focuses on the landslide hazard content of the project,and thus,in order to achieve these objectives,the following tasks were developed up to now:a)a procedure for phase unwrap-ping errors and tropospheric delay correction;b)an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements;c)the application of polarimetric SAR interferometry(PolInSAR)to increase the number and quality of monitoring points in landslide-prone areas;d)the semiautomatic mapping and preliminary classification of active displacement areas on wide regions;e)the modeling and identification of landslides in order to identify triggering factors or predict future displacements;and f)the application of an InSAR-based landslide early warning system on a selected *** achieved results,which mainly focus on specific sensitive regions,provide essential assets for planning present and future scientific activities devoted to identifying,mapping,characterizing,monitoring and predicting landslides,as well as for the implementation of early warning systems.

关键词： InSAR SAR offset tracking PolInSAR landslide mapping modeling early warning monitoring

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Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video

Mutual Information-Based Temporal Difference Learning for Hu...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Runyang Feng Yixing Gao Xueqing Ma Tze Ho Elden Tse Hyung Jin Chang School of Artificial Intelligence Jilin University Ministry of Education Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China School of Computer Science University of Birmingham

Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods directly employ optical flow or deformable convolution to predict full-spectrum motion fields, which might incur numerous irrelevant cues, such as a nearby person or background. Without further efforts to excavate meaningful motion priors, their results are suboptimal, especially in complicated spatio-temporal interactions. On the other hand, the temporal difference has the ability to encode representative motion information which can potentially be valuable for pose estimation but has not been fully exploited. In this paper, we present a novel multi-frame human pose estimation framework, which employs temporal differences across frames to model dynamic contexts and engages mutual information objectively to facilitate useful motion information disentanglement. To be specific, we design a multi-stage Temporal Difference Encoder that performs incremental cascaded learning conditioned on multi-stage feature difference sequences to derive informative motion representation. We further propose a Representation Disentanglement module from the mutual information perspective, which can grasp discriminative task-relevant motion signals by explicitly defining useful and noisy constituents of the raw motion features and minimizing their mutual information. These place us to rank No.1 in the Crowd Pose Estimation in Complex Events Challenge on benchmark dataset HiEve, and achieve state-of-the-art performance on three benchmarks PoseTrack2017, PoseTrack2018, and PoseTrack21.

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