The 2016 International Conference on Computer Science, Technology and Application (CSTA2016) were held in Changsha, China on March 18-20, 2016. The main objective of the joint conference is to provide a platform for researchers, academics and industrial professionals to present their research findings in the fields of computer science and technology.The CSTA2016 received more than 150 submissions, but only 67 articles were selected to be included in this proceedings, which are organized into 6 chapters; covering Image and Signal Processing, Computer Network, Algorithm and Simulation, Data Mining and Cloud Computing, Computer Systems and Application, Mathematics and Management.

This book constitutes the refereed proceedings of the 13th National Conference on Embedded System Technology, ESTC 2015, held in Beijing, China, in October 2015. The 18 revised full papers presented were carefully reviewed and selected from 63 papers. The topics cover a broad range of fields focusing on research about embedded system technologies, such as smart hardware, system and network, applications and algorithm.

This book proposes a linguistically motivated approach to terminological processing.Specifically, it investigates how syntactic functions can be utilized for enhanced term extraction. The rationale of this work stems from the understanding that terms tend to perform particular syntactic functions more prominently and their syntactic behaviour can be captured and represented as termhood by computing term ratios in different syntactic paths. In essence, this method is a weighting scheme that measures the probabilistic relations between term occurring patterns and syntactic paths. The studies presented in this book are aimed at answering several research questions: whether syntactic information is useful for recognizing candidate terms in medical text, how this syntactically oriented metric can be used to improve the ranking of multi-word terms, and whether this metric can be used effectively for novel term recognition as an effective feature in a machine learning paradigm. This research has led to an implemented system which demonstrates superior performance when compared with some major publically accessible systems for term extraction.