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SSRN 2022年

作者： Zhao, Zhiyuan Yao, Wei Wang, Pengzhou Wu, Sheng Wu, Qunyong Fang, Lina Fang, Zhixiang Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China

Ride-hailing services (RHS) and traditional taxi services (TTS) play important roles in satisfying people’s daily travel demands. In recent years, RHS have experienced rapid growth, making daily travel more convenient and fundamentally changing the vehicle-hailing service market once dominated by traditional taxis. However, the rapid growth of RHS has also raised the general number of empty ride-hailing vehicles, potentially resulting in increasing traffic congestion and pollutant emissions. Finding the collaborative areas of RHS and TTS can effectively improve the efficiency of travel services. Accordingly, we propose a method to identify the collaborative areas of RHS and TTS based on sparsely sampled trajectory data. We first recover sparse trajectories based on a shortest path algorithm. Then, the indicators describing empty vehicles and the travel demand are defined and calculated. Finally, to identify these collaborative areas, rules are adopted to finding the areas with high unloaded vehicles of one vehicle type with a low travel demand to supplement the other vehicle type in high demand. A data set of Xiamen Island is used to validate the effectiveness of the proposed method. We find that (1) the proposed method can improve the usability of sparsely sampled trajectory data and effectively identify the collaborative areas of RHS and TTS. (2) At facilities such as office buildings and Software Park, ride-hailing vehicles are dispatched to supplement taxis on weekday mornings, while the reverse relationship holds on weekend mornings. The collaborative areas where RHS are dispatched to assist TTS are concentrated near large shopping malls on weekdays and weekends at 18:00;additionally, residential areas near scenic spots demand supplementary ride-hailing vehicles at 18:00 on holidays, while supplemental taxis are dispatched at 09:00. © 2022, The Authors. All rights reserved.

关键词： Taxicabs

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Machine learning in diagnosis of coronary artery disease

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Chinese Medical Journal 2021年第4期134卷 401-403页

作者： Hao Ling Zi-Yuan Guo Un-Lin Tan Ren-Chu Guan Jing-Bo Chen Chun-Li Department of Cardiology the Second Hospital of Jilin UniversityChangchunJilin 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchunJilin 130012China

Cardiovascular diseases continue to be the foremost cause of death among both males and females worldwide.[1]Coronary artery disease(CAD)represents a major subtype of cardiovascular *** evidence has highlighted a worldwide increase in incidence of CAD clinical manifestations regardless of geographical region.[2]Despite a steady decline in CAD mortality in western countries over the past few decades,approximately one-third of the population suffering from CAD will succumb to the *** and accurate diagnostic decisions are crucial for patients with CAD and aid in guidance for effective treatment.

关键词： CAD diagnosis continue

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Excess mortality associated with high ozone exposure:A national cohort study in China

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Environmental Science and Ecotechnology 2023年第3期15卷 98-105页

作者： Yang Yuan Kai Wang Haitong Zhe Sun Yu Zhan Zhiming Yang Kejia Hu Yunquan Zhang Institute of Social Development and Health Management Hubei Province Key Laboratory of Occupational Hazard Identification and ControlSchool of Public HealthWuhan University of Science and TechnologyWuhan430065China Centre for Atmospheric Science Yusuf Hamied Department of ChemistryUniversity of CambridgeCambridgeCB21EWUK Department of Earth Sciences University of CambridgeCambridgeCB23EQUK Department of Environmental Science and Engineering Sichuan UniversityChengduSichuan610065China School of Economics and Management University of Science and Technology BeijingBeijing100083China Department of Big Data in Health Science School of Public HealthZhejiang UniversityHangzhou310058China

Emerging epidemiological studies suggest that long-term ozone(O_(3))exposure may increase the risk of mortality,while pre-existing evidence is mixed and has been generated predominantly in North America and *** this study,we investigated the impact of long-term O_(3)exposure on all-cause mortality in a national cohort in China.A dynamic cohort of 20882 participants aged40 years was recruited between 2011 and 2018 from four waves of the China Health and Retirement Longitudinal Study.A Cox proportional hazard regression model with time-varying exposures on an annual scale was used to estimate the mortality risk associated with warm-season(Aprile September)O_(3)*** annual average level of participant exposure to warm-season O_(3)concentrations was 100 mg m^(-3)(range:61 e142 mg m^(-3)).An increase of 10 mg m^(-3)in O_(3)was associated with a hazard ratio(HR)of 1.18(95%confidence interval[CI]:1.13e1.23)for all-cause *** with the first exposure quartile of O_(3),HRs of mortality associated with the second,third,and highest exposure quartiles were 1.09(95%CI:0.95e1.25),1.02(95%CI:0.88e1.19),and 1.56(95%CI:1.34e1.82),respectively.A J-shaped concentration eresponse associationwas observed,revealing a non-significant increase in risk below a concentration of approximately 110 mg m^(-3).Low-temperature-exposure residents had a higher risk of mortality associated with long-term O_(3)*** study expands current epidemiological evidence from China and reveals that high-concentration O_(3)exposure curtails the long-term survival of middle-aged and older adults.

关键词： Ozone Long-term exposure All-cause mortality Cohort study

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Solving Multitrip Pickup and Delivery Problem With Time Windows and Manpower Planning Using Multiobjective Algorithms

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

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NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation

arXiv

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arXiv 2023年

作者： Li, Shaofei Dong, Feng Xiao, Xusheng Wang, Haoyu Shao, Fei Chen, Jiedong Guo, Yao Chen, Xiangqun Li, Ding School of Computer Science Peking University China Arizona State University United States Case Western Reserve University United States Sangfor Technologies Inc. China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Advanced Persistent Threats (APT) attacks have plagued modern enterprises, causing significant financial losses. To counter these attacks, researchers propose techniques that capture the complex and stealthy scenarios of APT attacks by using provenance graphs to model system entities and their dependencies. Particularly, to accelerate attack detection and reduce financial losses, online provenance-based detection systems that detect and investigate APT attacks under the constraints of timeliness and limited resources are in dire need. Unfortunately, existing online systems usually sacrifice detection granularity to reduce computational complexity and produce provenance graphs with more than 100,000 nodes, posing challenges for security admins to interpret the detection results. In this paper, we design and implement NODLINK, the first online detection system that maintains high detection accuracy without sacrificing detection granularity. Our insight is that the APT attack detection process in online provenance-based detection systems can be modeled as a Steiner Tree Problem (STP), which has efficient online approximation algorithms that recover concise attack-related provenance graphs with a theoretically bounded error. To utilize the frameworks of the STP approximation algorithm for APT attack detection, we propose a novel design of in-memory cache, an efficient attack screening method, and a new STP approximation algorithm that is more efficient than the conventional one in APT attack detection while maintaining the same complexity. We evaluate NODLINK in a production environment. The open-world experiment shows that NODLINK outperforms two state-of-the-art (SOTA) online provenance analysis systems by achieving magnitudes higher detection and investigation accuracy while having the same or higher throughput. Copyright © 2023, The Authors. All rights reserved.

关键词： Approximation algorithms

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Intersection-policy private mutual authentication from authorized private set intersection

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Science China(Information Sciences) 2020年第2期63卷 116-130页

作者： Yamin WEN Fangguo ZHANG Huaxiong WANG Yinbin MIAO Zheng GONG School of Statistics and Mathematics Guangdong University of Finance and Economics State Key Laboratory of Cryptology School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Sun Yat-sen University School of Physical and Mathematical Sciences Nanyang Technological University Department of Cyber Engineering Xidian University School of Computer South China Normal University

Private mutual authentication(PMA) enables two-way anonymous authentication between two users certified by the same trusted group authority. Most existing PMA schemes focus on acquiring a relatively onefold authentication policy that ensures affiliation-hiding or designated single-attribute ***, in practice, users are typically provided with multiple attributes. In addition to the affiliation-hiding requirement, how to effectively achieve a more flexible authentication policy for multi-attribute applications remains a challenging issue. The intersection policy for authentication is also required when the attribute intersection is not an empty set or its cardinality is no less than a threshold value. To solve the above problems, we first propose an optimal authorized private set intersection protocol with forward security based on identity-based encryption and then design a new PMA protocol with intersection-policy called IP-PMA, which provides a simple solution for secret handshakes between two members(holding multiple attributes) from the same organization. Formal security analyses proved that our two proposed protocols are secure in the random oracle model. Empirical tests demonstrated that the IP-PMA protocol is optimized with linear complexity and may be more suitable for resource-constrained applications.

关键词： private mutual authentication secret handshake intersection policy authorized private set intersection multi-attribute matching

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A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Chu Li Xinyue Peng Haotian Guan Zefan Zhang Guishen Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China School of Nursing Jilin University Changchun China School of Life Sciences Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy.

关键词：

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Experimental Study on the Zebra Crossing Behavior of Mixed Bicycles and Pedestrians

SSRN

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SSRN 2022年

作者： Ma, Jian Wang, Qiao Chen, Juan Jiang, Rui Song, Weiguo Li, Ruoyu Lian, Liping School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Chengdu610031 China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China State Key Laboratory of Fire Science University of Science and Technology of China Hefei230027 China School of Urban Planning and Design Peking University Shenzhen Graduate School School of Architectural Engineering Shenzhen Polytechnic Shenzhen518055 China

Slow transportation mode, consisting of walking and cycling, plays an important role in urban traffic system. Based on the fact that bicycles and pedestrians often share the common road facility, we conducted bicycle-pedestrian mixed flow experiments to investigate the interaction effect among individuals in a 10 m-long, 3 m-wide passageway, mimicking the zebra crossing area. The goal is to explore the influence of bicycles proportion ( p ) on traffic efficiency and investigate the spatial distribution of agents in the mixed flow. Our analyses showed that the increase of bicycle proportion results in longer passing time and less travel time because of large personal space requirements of cyclists. When the bicycle proportion is small or large, the majority pedestrians (bicycles) were divided into several parts by minor bicycles (pedestrians) along the motion direction. We also recorded the following- and lateral- distance between two nearest individuals to calculate the theoretical formula of the passing time for unidirectional bicycle-pedestrian mixed flow based on the existing formula. Comparing the passing time between numerical calculation and experimental data, the effect coefficients for bicycle and pedestrian were obtained. Focusing on the overtaking bicycles, the through gap, lateral and headway distance that decide the overtaking behavior were analyzed. These findings are the first step toward the development of bicycle-pedestrian mixed flow on zebra crossing, which is contribute to provide practical implications for the planning and design of bicycle and pedestrian shared infrastructures. © 2022, The Authors. All rights reserved.

关键词： Travel time

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TraffNet: Learning Causality of Traffic Generation for What-if Prediction

arXiv

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arXiv 2023年

作者： Xu, Ming Ai, Qiang Li, Ruimin Ma, Yunyi Qi, Geqi Meng, Xiangfu Jin, Haibo The Software College Liaoning Technical University Liaoning Huludao125100 China The Department of Civil Engineering Tsinghua University Beijing100084 China The Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100091 China The School of Electronic and Information Engineering Liaoning Technical University Liaoning Huludao125100 China

Real-time what-if traffic prediction is crucial for decision making in intelligent traffic management and control. Although current deep learning methods demonstrate significant advantages in traffic prediction, they are powerless in what-if traffic prediction due to their nature of correla-tion-based. Here, we present a simple deep learning framework called TraffNet that learns the mechanisms of traffic generation for what-if pre-diction from vehicle trajectory data. First, we use a heterogeneous graph to represent the road network, allowing the model to incorporate causal features of traffic flows, such as Origin-Destination (OD) demands and routes. Next, we propose a method for learning segment representations, which models the process of assigning OD demands onto the road network. The learned segment represen-tations effectively encapsulate the intricate causes of traffic generation, facilitating downstream what-if traffic prediction. Finally, we conduct experiments on synthetic datasets to evaluate the effectiveness of TraffNet. The code and datasets of TraffNet is available at https://***/iCityLab/TraffNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Shuai Xu, Yanqing You, Chaoqun Shao, Mingjie Quek, Tony Q.S. University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Science and Engineering Shenzhen518172 China Fudan University School of Computer Science and Technology 200433 China Beijing100149 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance. © 2025 IEEE.

关键词： Federated learning

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