Seminar 1


Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications

Author: Tsz Nam Chan (Hong Kong Baptist University)*; Leong Hou U (University of Macau); Byron Choi (Hong Kong Baptist University); Jianliang Xu (Hong Kong Baptist University); Reynold Cheng ("The University of Hong Kong, China")

Abstract

The use of Kernel Density Visualization (KDV) has become widespread in a number of disciplines, including geography, crime science, transportation science, and ecology, for analyzing geospatial data. However, the growing scale of massive geospatial data has rendered many commonly used software tools unable of generating high-resolution KDVs, leading to concerns about the inefficiency of KDV. This 90-minute tutorial aims to raise awareness among database researchers about this important, emerging, database-related, and interdisciplinary topic. It is structured into four parts: a thorough discussion of the background of KDV, a review of state-of-the-art methods for generating KDVs, a discussion of key variants of KDV, including network kernel density visualization (NKDV) and spatiotemporal kernel density visualization (STKDV), and an outline of future directions for this topic.

Seminar 2


Recent Trends in Sensor-based Activity Recognition

Author: Takuya Maekawa (Osaka University)*; Qingxin Xia (Graduate School of Information Science and Technology, Osaka university); Ryoma Otsuka (Osaka University); Naoya Yoshimura (Osaka University); Kei Tanigaki (Osaka University)

Abstract

This seminar will introduce recent trends in sensor-based activity recognition technology. Technology to recognize human activities using sensors has been a hot topic in the field of mobile and ubiquitous computing for many years. Recent developments in deep learning and sensor technology have expanded the application of activity recognition to various domains such as industrial and natural science fields. However, because activity recognition in the new domains suffers from various real problems such as the lack of sufficient training data and complexity of target activities, new solutions have been proposed for the practical problems in applying activity recognition to real-world applications in the new domains. In this seminar, we will introduce recent topics in activity recognition from the viewpoints of (1) recent trends in state-of-the-art machine learning methods for practical activity recognition, (2) recently focused domains for human activity recognition such as industrial and medical domains and their public datasets, and (3) applications of activity recognition to the natural science field, especially in animal behavior understanding.