Tao-Yang Fu

TL;DR: Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8%, in link prediction.

TL;DR: In this article, a method for capturing and managing training data collected online includes: receiving a first dataset from one or more online sources; sampling the first dataset and generating a second dataset, the second dataset including the data sampled from the first datasets; receiving an annotated second dataset with predefined labels.

TL;DR: A Parallel Concentric-circle Itinerary-based KNN query processing technique that derives different itineraries by optimizing either query latency or energy consumption is proposed and Experimental results show that PCIKNN outperforms the state-of-the-art techniques.

TL;DR: A method for capturing and organizing intelligence data using an organic data model includes: receiving one or more webpages containing social intelligence data, identifying named entities in the segmented content of the webpages, identifying topics in the web pages, identifying opinions in the Web pages, and integrating the identified named entities, topics, and opinions to construct an organic object data model.

TL;DR: This paper investigates in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks and evaluates the performance of these algorithms under several sensor network scenarios and application requirements.