Zhipeng Gui

TL;DR: A novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction that captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner.

TL;DR: This study found that: no significant performance differences in central processing unit (CPU), memory and I/O of virtual machines created and managed by different solutions, OpenNebula has the fastest internal network while both Eucalyptus and CloudStack have better virtual machine isolation and security strategies.

TL;DR: The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects and offers rational recommendations based on user preferences and practical cloud provisioning.

TL;DR: A search engine framework for efficient geospatial resource discovery is reported, which reduces integration costs by leveraging existing Geospatial Cyberinfrastructure (GCI) components and helps both scientists and general users search for more accurate results with enhanced performance and user experience through a user-friendly interface.

TL;DR: A new Quad-tree-based Fast and Adaptive KDE (QFA-KDE) algorithm for heat-map generation that captures the aggregation patterns of input point data through a quad-Tree-based spatial segmentation function and density is estimated using the calculated adaptive bandwidths.