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International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery

作者： Min Dai Danyang Meng Feng Jia Miaoling Ma Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781538680988;9781538680971

An electrocardiogram (ECG) identification method is proposed based on PRT triangle gravity tunnel of continuous electrocardiogram. The PRT center of gravity is defined by the P, R, T fiducial points in ECG signal, and the gravity tunnel of continuous PRT points is described as a matrix. The European distance is used to determine the similarity between two matrices. The performance of the proposed method is tested on MIT-BIH Database and the ECG data collected in practical environment from 25 healthy college students. Several group experiments were carried out and the average recognition accuracy is 99.87%. Comparing with the traditional correlation coefficient method, experimental results show that the average recognition accuracy is increased 1.66% when using the proposed method.

关键词： ECG identification Feature selection PRT gravity ECG Tunnel

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Real-time multi-target path prediction and planning for autonomous driving aided by FCN

arXiv

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arXiv 2019年

作者： Zhou, Hongtu Yang, Xinneng Zhang, Enwei Zhao, Junqiao Cai, Lewen Ye, Chen Wu, Yan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai Institute of Intelligent Vehicle Tongji University Shanghai Department of Computer Science Nanjing University Nanjing China

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms). Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Dietary intake of nuts in midlife and risk of cognitive impairment in old age: the Singapore Chinese Health Study

Dietary intake of nuts in midlife and risk of cognitive impa...

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营养研究与临床实践——第十四届全国营养科学大会暨第十一届亚太临床营养大会、第二届全球华人营养科学家大会

作者： Jiang,Yiwen Sheng,Liting Pan,An Koh,Woonpuay Department of Epidemiology and Biostatistics Ministry of Education Key Laboratory of Environment and Health and State Key Laboratory of Environmental Health (incubating) School of Public Health Tongji Medical College Huazhong University of Science and Technology Health Services and Systems Research Duke-NUS Medical School Saw Swee Hock School of Public Health National University of Singapore and National University Health System

Objective Prospective studies exploring the association between dietary intake of nuts and risk of cognitive impairment among Chinese are limited. Therefore, we examined this association using data from the Singapore Chinese Health Study(SCHS). Methods The SCHS included 63,257 Singapore Chinese adults aged 45-74 years at baseline(1993-1998). Dietary information was measured by a validated 165-item food-frequency questionnaire. Cognitive impairment was identified using Singapore modified Mini-Mental State Examination during follow-up 3 visits(2014-2016). After 19.7 years' follow-up, 16948 surviving participants was included in the final analysis. The association was examined using logistic regression models with adjustment for potential confounders. Results We documented 2443 participants with cognitive impairment in the follow-up 3 visits. In the multivariable-adjusted logistic regression model, nuts intake was inversely related to the risk of cognitive impairment, and the ORs(95% CIs) for participants consuming 1-3 servings/mo, 1 serving/wk, and ≥2 servings/wk of nuts was 1.01(0.89-1.13), 0.97(0.81-1.17), and 0.83(0.71-0.96) compared with individuals who consumed <1 serving/mo(P for trend=0.01)(1 serving=28 g). When we used nuts intake as continuous variable, we found that per 1 serving/wk increment of nuts intake was related to 7% lower risk of cognitive impairment(95% CI: 0.89-0.99). Further adjustment of polyunsaturated fatty acids slightly attenuated the inverse association(P for trend=0.049). Conclusions In this large population-based cohort of Chinese adults, we found that higher intake of nuts in midlife was related to a lower risk of cognitive impairment in old age and the association was partly explained by polyunsaturated fatty acids.

关键词： nuts intake cognitive impairment cohort Singapore Chinese Health Study

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Composite behavioral modeling for identity theft detection in online social networks

arXiv

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arXiv 2018年

作者： Wang, Cheng Yang, Bo Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values 0:956 in Foursquare and 0:947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to 65:3% in Foursquare and 72:2% in Yelp with the corresponding disturbance rate (False Positive Rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

Vision-based Semantic Mapping and Localization for Autonomou...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

ISBN: (纸本)9781538644522

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. © 2018 IEEE.

关键词： Autonomous vehicles

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A situation-aware road emergency navigation mechanism based on GPS and WSNS 13th

A situation-aware road emergency navigation mechanism based ...

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13th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2017

作者： Ma, Ruixin Li, Qirui Qiu, Tie Chen, Chen Sangaiah, Arun Kumar School of Software Dalian University of Technology Dalian116620 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China School of Computing Science and Engineering VIT University Vellore India School of Computer Science and Technology Tianjin University Tianjin300350 China

ISBN: (纸本)9783319780771

Traffic congestion happens when emergencies occur. Traditional congestion algorithms evaluate traffic congestion only according to real-time vehicle speed, instead of comprehensive aspects. To address this shortcoming, we provide a new algorithm for congestion evaluation based on WSNs and GPS, which provide many sensor nodes to monitor and transmit traffic message in time. This paper takes more aspects for traffic into consideration, including congestion situation, danger condition and sudden road peak flow, and turns them into weights, which help to measure congestion intensity. According to congestion intensity, congestion field is established to navigate for the vehicles. Furthermore, we propose future prediction mechanism for vehicles. Finally, we do simulation with Matlab to evaluate the performance of the prediction mechanism, and results show that the performance of prediction mechanism is better than greedy algorithm. Moreover, a route will be recommended after a comprehensive evaluation about the distance, time, congestion and traffic lights number. In a word, the prediction mechanism for traffic can not only ensure the effectiveness of the navigation, but also protect drivers from the sudden peak flow, which brings convenience and comfortableness to drivers. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.

关键词： Traffic congestion

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Object measurement system based on Surveillance video-images 1

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6th International Conference on Frontier computing, FC 2017

作者： Duan, Hui-Xian Wang, Jun Song, Lei Liu, Na Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai201204 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Shanghai International Technology and Trade United Co. Ltd Shanghai200031 China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Object measurement based on surveillance video-images is a critical task in the field of intelligent video surveillance. In this paper, we proposed an object measurement system based on surveillance video-images. Firstly, considering the scene of the urban video surveillance, a novel calibration tool is designed for such scene, which is portable, collapsible and operational. Next, based on the portable calibration tool, the intrinsic and extrinsic parameters of the surveillance camera can be estimated. Then, an object measurement system based on surveillance video-images is developed, which include the acquisition module of calibration data, the computation module of camera parameters, the selection module of measurement endpoint and the computation module of object. Finally, in the traffic checkpoint, experiments are carried out to verify the effectiveness of the object measurement system. The results show that as long as the surveillance camera is one-time calibrated based on the portable calibration tool, this system can measure the object on the surveillance video-images captured by the calibrated camera. What’s more, experimental results have shown that the object measurement system can be used to estimate the vehicle speed and measure the body height within the acceptable error range, and then have demonstrated the effectiveness of the object measurement system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Calibration

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arXiv

引用

arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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Gut microbiota and diabetes: From correlation to causality and mechanism

引用

World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social Networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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