Patterns of Change: Can modifiable software have high coupling?

There are few aspects of modern life that remain unaffected by software, and as our day-to-day challenges change, so too must our software. Software systems are complex, and as they grow larger and more interconnected, they become more difficult to modify due to excessive change propagation. This is known as the ripple effect. The primary strategies to mitigate it are modular design, and minimization of coupling, or between-module interaction. However, analysis of complex networks has shown that many are scale-free, which means that they contain some components that are highly connected. The presence of scale-free structure implies high coupling, which suggests that software systems may be hard to modify because they suffer from the ripple effect. In this thesis, a large corpus of open-source software systems is analysed to determine whether software systems are scale-free, whether scale-free structure results in high coupling, and whether high coupling results in ripple effects that propagate change to a large proportion of classes. The results show that all systems in the corpus are scale-free and that that property results in high coupling. However, analysis of system evolution reveals that existing code is modified infrequently and that there is rarely sufficient evidence to be confident that ripple effects involving a high proportion of classes have actually occurred. This thesis concludes first that while it is desirable to avoid excessive interconnectivity, it is difficult to completely eliminate high coupling; and second, that the presence of high coupling does not necessarily imply poor system design

Capisco: Semantic Analysis of Documents from the HathiTrust Corpus

The Capisco project developed a suite of tools that analyze documents by the semantics of their content and metadata. Clustering documents by semantic similarity opens a wealth of opportunities for scholarly research.The project was designed in close collaboration with two humanities scholars, from the areas of Maori & Pacific Studies and Historical Anthropology, who provided ongoing input and feedback during the development process. This report was submitted to the Workset Creation for Scholarly Analysis: Prototyping Project, funded by the Andrew W. Mellon Foundation.Andrew W. Mellon Foundation, Grant Reference No. 21300666Ope

Synthesizing intonation for computer speech output

Bibliography: p. 72-74.As a basis for designing an improved algorithm for synthetic intonation, two existing algorithms were implemented and tested by comparison with natural speech. The problems are discussed and the improved implementation is described. Interestingly, the simple approach by Witten was found to be as natural as the more complex approach by Collier and Hart and their co-workers within the constraints of this thesis, and was chosen as the basis for and improved algorithm. The results exceeded expectations but further rounds of experimentational evaluation and improvements are necessary

Seeding strategies for semantic disambiguation

Semantic disambiguation determines the meaning of words and phrases in a text, for which we use an automatically-generated Concept-in-Context (CiC) network. Words and phrases rarely belong to a single concept; disambiguation in Capisco relies on interplay between words that are in close vicinity in the text. Starting the disambiguation is a seeding process, that identifies the first concepts, which then form the context for further disambiguation steps. This paper introduces the seeding algorithm and explores seeding strategies for identifying these initial concepts in text volumes, such as books, that are stored in a digital library