Matthew Martin

Matthew Martin

This study explores how Bayesian networks (BNs) can improve forecast accuracy compared to logistic regression and recalibration and aggregation methods, using data from the Good Judgment Project. Regularized logistic regression models and a baseline recalibrated aggregate were compared to two types of BNs: structure-learned BNs with arcs between predictors, and naive BNs. Four predictor variables were examined: absolute difference from the aggregate, forecast value, days prior to question close, and mean standardized Brier score. Results indicated the recalibrated aggregate achieved the highest accuracy (AUC = 0.985), followed by both types of BNs, then the logistic regression models. Performance of the BNs was likely harmed by reduced information from the discretization process and violation of the assumption of linearity likely harmed the logistic regression models. Future research should explore hybrid approaches combining BNs with logistic regression, examine additional predictor variables, and account for hierarchical data dependencies.

Mitch Rees-Jones, Matthew Martin, Tim Menzies

Predicting issue lifetime can help software developers, managers, and stakeholders effectively prioritize work, allocate development resources, and better understand project timelines. Progress had been made on this prediction problem, but prior work has reported low precision and high false alarms. The latest results also use complex models such as random forests that detract from their readability. We solve both issues by using small, readable decision trees (under 20 lines long) and correlation feature selection to predict issue lifetime, achieving high precision and low false alarms (medians of 71% and 13% respectively). We also address the problem of high class imbalance within issue datasets - when local data fails to train a good model, we show that cross-project data can be used in place of the local data. In fact, cross-project data works so well that we argue it should be the default approach for learning predictors for issue lifetime.

Matthew Martin, James Charlton, Andy M. Connor

This paper investigates the player's body as a system capable of unfamiliar interactive movement achieved through digital mediation in a playful environment. Body interactions in both digital and non-digital environments can be considered as a perceptually manipulative exploration of self. This implies a player may alter how they perceive their body and its operations in order to create a new playful and original experience. This paper therefore questions how player interaction can change as their perception of their body changes using augmentative technology.

Matthew Martin, James Charlton, Andy M. Connor

The range of video annotation software currently available is set within commercially specialized professions, distributed via outdated sources or through online video hosting services. As video content becomes an increasingly significant tool for analysis, there is a demand for appropriate digital annotation techniques that offer equivalent functionality to tools used for annotation of text based literature sources. This paper argues for the importance of video annotating as an effective method for research that is as accessible as literature annotation is. Video annotation has been shown to trigger higher learning and engagement but research struggles to explain the absence of video annotation in contemporary structures of education practice. In both academic and informal settings the use of video playback as a meaningful tool of analysis is apparent, yet the availability of supplementary annotation software is not within obvious grasp or even prevalent in standardized computer software. Practical software tools produced by the researcher have demonstrated effective video annotation in a short development time. With software design programs available for rapid application creation, this paper also highlights the absence of a development community. This paper argues that video annotation is an accessible tool, not just for academic contexts, but also for wider practical video analysis applications, potentially becoming a mainstream learning tool. This paper thus presents a practical multimodal public approach to video research that potentially affords a deeper analysis of media content. This is supported by an in-depth consideration of the motivation for undertaking video annotation and a critical analysis of currently available tools.