Many mission-critical applications demand high accuracy to position people indoor, enabling novel smart location-based applications such as indoor navigation, location-based personalized recommendation, crowd analysis and people flow control. Innovative techniques are required to fuse signals from different sources and combine their strength for higher location accuracy.

Unlike traditional brick-and-mortar classrooms, there often exist large spatial and temporal gaps between teachers and learners in typical e-learning environments where learning occurs in virtual classrooms without walls. This is one of the major challenges faced by today's e-learning platforms, particularly massive open online course (MOOC) platforms such as Coursera and edX which have students from all over the world.

There are a lot of records in the era of big data, but it is difficult for users to find their favorite record. There exist many different ways of helping a user to solve this problem. One is an interactive approach which asks the user a number of questions and then learns the user's preference automatically. Another is a log-oriented approach which analyzes the user's log data recorded by the system and learns the user's preferences via some data mining and deep learning approaches. After a user's preference is found, his/her favorite record can be easily determined.

Code analysis, testing and learning play an important role to dependable software in the industry. It pursues the state-of-the-art technologies in automated test generation, fault localization program repair, and program synthesis. Software being studied includes applications for Android, artificial intelligent solutions and block chains. Technologies developed are supported by automated tools that can be applied by users on large-scale software applications.