| 1 | Virtual Knowledge Graphs:An Overview of Systems and Use Cases显示文摘In this paper,we present the virtual knowledge graph(VKG)paradigm for data integration and access,also known in the literature as Ontology-based Data Access.Instead of structuring the integration layer as a collection of relational tables,the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge.We explain the main notions of this paradigm,its tooling ecosystem and significant use cases in a wide range of applications.Finally,we discuss future research directions. | Guohui Xiao Linfang Ding Benjamin Cogrel Diego Calvanese | 2019 | Data Intelligence2019，1，3： | 3 |
| 2 | MIA-UNet:Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation显示文摘As an important part of the new generation of information technology,the Internet of Things(IoT)has been widely concerned and regarded as an enabling technology of the next generation of health care system.The fundus photography equipment is connected to the cloud platform through the IoT,so as to realize the realtime uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence.At the same time,important security and privacy issues have emerged.The data uploaded to the cloud platform involves more personal attributes,health status and medical application data of patients.Once leaked,abused or improperly disclosed,personal information security will be violated.Therefore,it is important to address the security and privacy issues of massive medical and healthcare equipment connecting to the infrastructure of IoT healthcare and health systems.To meet this challenge,we propose MIA-UNet,a multi-scale iterative aggregation U-network,which aims to achieve accurate and efficient retinal vessel segmentation for ophthalmic auxiliary diagnosis while ensuring that the network has low computational complexity to adapt to mobile terminals.In this way,users do not need to upload the data to the cloud platform,and can analyze and process the fundus images on their own mobile terminals,thus eliminating the leakage of personal information.Specifically,the interconnection between encoder and decoder,as well as the internal connection between decoder subnetworks in classic U-Net are redefined and redesigned.Furthermore,we propose a hybrid loss function to smooth the gradient and deal with the imbalance between foreground and background.Compared with the UNet,the segmentation performance of the proposed network is significantly improved on the premise that the number of parameters is only increased by 2%.When applied to three publicly available datasets:DRIVE,STARE and CHASE DB1,the proposed network achieves the accuracy/F1-score of 96.33%/84.34%,97.12%/83.17%and 97.06%/84.10%,respectively.The experimental results show that the MIA-UNet is superior to the state-of-the-art methods. | Linfang Yu Zhen Qin Yi Ding Zhiguang Qin | 2021 | Computer Modeling in Engineering & Sciences2021，，11： | 0 |
| 3 | Extracting commuter-specific destination hotspots from trip destination data-comparing the boro taxi service with Citi Bike in NYC显示文摘Taxi trajectories from urban environments allow inferring various information about the transport service qualities and commuter dynamics.It is possible to associate starting and end points of taxi trips with requirements of individual groups of people and even social inequalities.Previous research shows that due to service restrictions,boro taxis have typical customer destination locations on selected Saturdays:many drop-off clusters appear near the restricted zone,where it is not allowed to pick up customers and only few drop-off clusters appear at complicated crossing.Detected crossings imply recent infrastructural modifications.We want to follow up on these results and add one additional group of commuters:Citi Bike users.For selected Saturdays in June 2015,we want to compare the destinations of boro taxi and Citi Bike users.This is challenging due to manifold differences between active mobility and motorized road users,and,due to the fact that station-based bike sharing services are restricted to stations.Start and end points of trips,as well as the volumes in between rely on specific numbers of bike sharing stations.Therefore,we introduce a novel spatiotemporal assigning procedure for areas of influence around static bike sharing stations for extending available computational methods. | Andreas Keler Jukka M.Krisp Linfang Ding | 2020 | Geo-Spatial Information Science2020，23，2： | 0 |
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