Data-driven approaches have been applied to many problems in urban computing.
We mainly make three efforts:(i) we develop new taxonomy about both context features and context modeling techniques based on extensive investigations in prevailing STCFP research; (ii) we conduct extensive experiments on seven datasets with hundreds of millions of records to quantitatively evaluate the generalization ability of both distinct context features and context modeling techniques; (iii) we summarize some guidelines for researchers to conveniently utilize context in diverse applications.
In SPSSOT, we first extract the same clinical indicators from the source domain (e. g., hospital with rich labeled data) and the target domain (e. g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance.
As an innovative solution for privacy-preserving machine learning (ML), federated learning (FL) is attracting much attention from research and industry areas.
In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.
To fill in this gap, this paper makes two efforts: (i) we propose an analytic framework, called STAnalytic, to qualitatively investigate STCFP approaches regarding their design considerations on various spatial and temporal factors, aiming to make different application-driven approaches comparable; (ii) we construct an extensively large-scale STCFP benchmark datasets with four different scenarios (including ridesharing, bikesharing, metro, and electrical vehicle charging) with up to hundreds of millions of flow records, to quantitatively measure the generalizability of STCFP approaches.
However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.
To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.
We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.
Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.
RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.