Deep Neural Network Structure to Improve Individual Performance based Author Classification

This paper proposed an improved method for author name disambiguation problem, both homonym and synonym. The data prepared is the distance data of each pair of author’s attributes, Levenshtein distance are used. Using Deep Neural Networks, we found large gains on performance. The result shows that level of accuracy is 99.6% with a low number of hidden layer

An Effective End-User Development Approach Through Domain-Specific Mashups for Research Impact Evaluation

Over the last decade, there has been growing interest in the assessment of the performance of researchers, research groups, universities and even countries. The assessment of productivity is an instrument to select and promote personnel, assign research grants and measure the results of research projects. One particular assessment approach is bibliometrics i.e., the quantitative analysis of scientific publications through citation and content analysis. However, there is little consensus today on how research evaluation should be performed, and it is commonly acknowledged that the quantitative metrics available today are largely unsatisfactory. A number of different scientific data sources available on the Web (e.g., DBLP, Google Scholar) that are used for such analysis purposes. Taking data from these diverse sources, performing the analysis and visualizing results in different ways is not a trivial and straight forward task. Moreover, people involved in such evaluation processes are not always IT experts and hence not capable to crawl data sources, merge them and compute the needed evaluation procedures. The recent emergence of mashup tools has refueled research on end-user development, i.e., on enabling end-users without programming skills to produce their own applications. We believe that the heart of the problem is that it is impractical to design tools that are generic enough to cover a wide range of application domains, powerful enough to enable the specification of non-trivial logic, and simple enough to be actually accessible to non-programmers. This thesis presents a novel approach for an effective end-user development, specifically for non-programmers. That is, we introduce a domain-specific approach to mashups that "speaks the language of users"., i.e., that is aware of the terminology, concepts, rules, and conventions (the domain) the user is comfortable with.Comment: This PhD dissertation consists of 206 page