Thesis (Ph. D.)--Florida State University, 2006. / Advisors: Gary R. Heald and Laura M. Arpan, Florida State University, College of Communication, Dept. of Communication. Title and description from dissertation home page (viewed June 6, 2006). Document formatted into pages; contains ix,109 pages. Includes bibliographical references.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references. Also available on the World Wide Web.

Yang, Zhaojun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 92-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- SDS Architecture --- p.1 / Chapter 1.2 --- Dialog Model --- p.3 / Chapter 1.3 --- SDS Evaluation --- p.4 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter 2 --- Previous Work --- p.9 / Chapter 2.1 --- Approaches to Dialog Modeling --- p.9 / Chapter 2.1.1 --- Handcrafted Dialog Modeling --- p.9 / Chapter 2.1.2 --- Statistical Dialog Modeling --- p.12 / Chapter 2.2 --- Evaluation Metrics --- p.16 / Chapter 2.2.1 --- Subjective User Judgments --- p.17 / Chapter 2.2.2 --- Interaction Metrics --- p.18 / Chapter 2.3 --- The PARADISE Framework --- p.19 / Chapter 2.4 --- Chapter Summary --- p.22 / Chapter 3 --- Implementation of a Dialog System based on POMDP --- p.23 / Chapter 3.1 --- Partially Observable Markov Decision Processes (POMDPs) --- p.24 / Chapter 3.1.1 --- Formal Definition --- p.24 / Chapter 3.1.2 --- Value Iteration --- p.26 / Chapter 3.1.3 --- Point-based Value Iteration --- p.27 / Chapter 3.1.4 --- A Toy Example of POMDP: The NaiveBusInfo System --- p.27 / Chapter 3.2 --- The SDS-POMDP Model --- p.31 / Chapter 3.3 --- Composite Summary Point-based Value Iteration (CSPBVI) --- p.33 / Chapter 3.4 --- Application of SDS-POMDP Model: The Buslnfo System --- p.35 / Chapter 3.4.1 --- System Description --- p.35 / Chapter 3.4.2 --- Demonstration Description --- p.39 / Chapter 3.5 --- Chapter Summary --- p.42 / Chapter 4 --- Collecting User Judgments on Spoken Dialogs with Crowdsourcing --- p.46 / Chapter 4.1 --- Dialog Corpus and Automatic Dialog Classification --- p.47 / Chapter 4.2 --- User Judgments Collection with Crowdsourcing --- p.50 / Chapter 4.2.1 --- HITs on Dialog Evaluation --- p.51 / Chapter 4.2.2 --- HITs on Inter-rater Agreement --- p.53 / Chapter 4.2.3 --- Approval of Ratings --- p.54 / Chapter 4.3 --- Collected Results and Analysis --- p.55 / Chapter 4.3.1 --- Approval Rates and Comments from Mturk Workers --- p.55 / Chapter 4.3.2 --- Consistency between Automatic Dialog Classification and Manual Ratings --- p.57 / Chapter 4.3.3 --- Inter-rater Agreement Among Workers --- p.60 / Chapter 4.4 --- Comparing Experts to Non-experts --- p.64 / Chapter 4.4.1 --- Inter-rater Agreement on the Let's Go! System --- p.65 / Chapter 4.4.2 --- Consistency Between Expert and Non-expert Annotations on SDC Systems --- p.66 / Chapter 4.5 --- Chapter Summary --- p.68 / Chapter 5 --- Collaborative Filtering for Performance Prediction --- p.70 / Chapter 5.1 --- Item-Based Collaborative Filtering --- p.71 / Chapter 5.2 --- CF Model for User Satisfaction Prediction --- p.72 / Chapter 5.2.1 --- ICFM for User Satisfaction Prediction --- p.72 / Chapter 5.2.2 --- Extended ICFM for User Satisfaction Prediction --- p.73 / Chapter 5.3 --- Extraction of Interaction Features --- p.74 / Chapter 5.4 --- Experimental Results and Analysis --- p.76 / Chapter 5.4.1 --- Prediction of User Satisfaction --- p.76 / Chapter 5.4.2 --- Analysis of Prediction Results --- p.79 / Chapter 5.5 --- Verifying the Generalibility of CF Model --- p.81 / Chapter 5.6 --- Evaluation of The Buslnfo System --- p.86 / Chapter 5.7 --- Chapter Summary --- p.87 / Chapter 6 --- Conclusions and Future Work --- p.89 / Chapter 6.1 --- Thesis Summary --- p.89 / Chapter 6.2 --- Future Work --- p.90 / Bibliography --- p.92

No description available.

published_or_final_version / Education / Doctoral / Doctor of Philosophy

Earth Mover's Distance (EMD), as a similarity measure, has received a lot of attention in the fields of multimedia and probabilistic databases, computer vision, image retrieval, machine learning, etc. EMD on multidimensional histograms provides better distinguishability between the objects approximated by the histograms (e.g., images), compared to classic measures like Euclidean distance. Despite its usefulness, EMD has a high computational cost; therefore, a number of effective filtering methods have been proposed, to reduce the pairs of histograms for which the exact EMD has to be computed, during similarity search. Still, EMD calculations in the refinement step remain the bottleneck of the whole similarity search process. In this thesis, we focus on optimizing the refinement phase of EMD-based similarity search by (i) adapting an efficient min-cost flow algorithm (SIA) for the EMD computation, (ii) proposing a dynamic distance bound, which is progressively updated and tightened during the refinement process and can be used to terminate an EMD refinement early, and (iii) proposing a dynamic refinement order for the candidates which, paired with a concurrent EMD refinement strategy, reduces the amount of needless computations. Our proposed techniques are orthogonal to and can be easily integrated with the state-of-the-art filtering techniques, reducing the cost of EMD-based similarity queries by orders of magnitude. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

Thesis (Ph.D.)--University of Melbourne, Dept. of Computer Science and Software Engineering, 2004. / Typescript (photocopy). Includes bibliographical references (leaves 247-263).

Thesis (B.A.)--Haverford College, Dept. of Computer Science, 2009. / Includes bibliographical references.

Thesis (Ph. D.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (p. 287-318). Also available via the World Wide Web.