Advances in Teaching & Learning Day Abstracts 2004

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004

Mouse Behavior Recognition with The Wisdom of Crowd

In this thesis, we designed and implemented a crowdsourcing system to annotatemouse behaviors in videos; this involves the development of a novel clip-based video labeling tools, that is more efficient than traditional labeling tools in crowdsourcing platform, as well as the design of probabilistic inference algorithms that predict the true labels and the workers' expertise from multiple workers' responses. Our algorithms are shown to perform better than majority vote heuristic. We also carried out extensive experiments to determine the effectiveness of our labeling tool, inference algorithms and the overall system

Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems

Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions

VisionBlocks: A Social Computer Vision Framework

Vision Blocks (http://visionblocks.org) is an on demand, in-browser, customizable computer vision application publishing platform for masses. It empowers end-users (consumers)to create novel solutions for themselves that they would not easily obtain off-the-shelf. By transferring design capability to the consumers, we enable creation and dissemination of custom products and algorithms. We adapt a visual programming paradigm to codify vision algorithms for general use. As a proof of-concept, we implement computer vision algorithms such as motion tracking, face detection, change detection and others. We demonstrate their applications on real-time video. Our studies show that end users (non programmers) only need 50% more time to build such systems when compared to the most experienced researchers. We made progress towards closing the gap between researchers and consumers by finding that users rate the intuitiveness of the approach in a level 6% less than researchers. We discuss different application scenarios where such study will be useful and argue its benefit for computer vision research community. We believe that enabling users with ability to create application will be first step towards creating social computer vision applications and platform.Alfred P. Sloan Foundation (Research Fellowship

Of mice and mates:Automated classification and modelling of mouse behaviour in groups using a single model across cages

Behavioural experiments often happen in specialised arenas, but this may confound the analysis. To address this issue, we provide tools to study mice in the home-cage environment, equipping biologists with the possibility to capture the temporal aspect of the individual’s behaviour and model the interaction and interdependence between cage-mates with minimal human intervention. Our main contribution is the novel Global Behaviour Model (GBM) which summarises the joint behaviour of groups of mice across cages, using a permutation matrix to match the mouse identities in each cage to the model. In support of the above, we also (a) developed the Activity Labelling Module (ALM) to automatically classify mouse behaviour from video, and (b) released two datasets, ABODe for training behaviour classifiers and IMADGE for modelling behaviour

Complete 2017 Program

Program and schedule of events for the 28th Annual John Wesley Powell Student Research Conference