Eliciting User Requirements Using Appreciative Inquiry,
2010
Claremont Graduate University
Eliciting User Requirements Using Appreciative Inquiry, Carol Kernitzki Gonzales
CGU Theses & Dissertations
Many software development projects fail because they do not meet the needs of users, are over-budget, and abandoned. To address this problem, the user requirements elicitation process was modified based on principles of Appreciative Inquiry. Appreciative Inquiry, commonly used in organizational development, aims to build organizations, processes, or systems based on success stories using a hopeful vision for an ideal future. Spanning five studies, Appreciative Inquiry was evaluated for its effectiveness with eliciting user requirements. In the first two cases, it was compared with traditional approaches with end-users and proxy-users. The third study was a quasi-experiment comparing the use of …
Scalable Multi-Modal Avatar Interface For Multi-User Environments,
2010
Technological University Dublin
Scalable Multi-Modal Avatar Interface For Multi-User Environments, Brian Mac Namee, Mark Dunne, John D. Kelleher
Conference papers
This research outlines an Intelligent Virtual Agent (IVA) interface, where multiple users will be able to interact with 3D avatars. This will take place in a distributed multi-modal environ- ment where the LOK8 Avatar System (AS) will need to locate it’s users from a crowd, using face tracking and novel 3D animation techniques.
Inside The Selection Box: Visualising Active Learning Selection Strategies,
2010
Technological University Dublin
Inside The Selection Box: Visualising Active Learning Selection Strategies, Brian Mac Namee, Rong Hu, Sarah Jane Delany
Conference papers
Visualisations can be used to provide developers with insights into the inner workings of interactive machine learning techniques. In active learning, an inherently interactive machine learning technique, the design of selection strategies is the key research question and this paper demonstrates how spring model based visualisations can be used to provide insight into the precise operation of various selection strategies. Using sample datasets, this paper provides detailed examples of the differences between a range of selection strategies.
Representations Of Keypoint-Based Semantic Concept Detection: A Comprehensive Study,
2010
Singapore Management University
Representations Of Keypoint-Based Semantic Concept Detection: A Comprehensive Study, Yu-Gang Jiang, Jun Yang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Based on the local keypoints extracted as salient image patches, an image can be described as a "bag-of-visual-words (BoW)" and this representation has appeared promising for object and scene classification. The performance of BoW features in semantic concept detection for large-scale multimedia databases is subject to various representation choices. In this paper, we conduct a comprehensive study on the representation choices of BoW, including vocabulary size, weighting scheme, stop word removal, feature selection, spatial information, and visual bi-gram. We offer practical insights in how to optimize the performance of BoW by choosing appropriate representation choices. For the weighting scheme, we …
