Knowledge discovery from tree databases using balanced optimal search

This research is a step forward in discovering knowledge from databases of complex structure like tree or graph. Several data mining algorithms are developed based on a novel representation called Balanced Optimal Search for extracting implicit, unknown and potentially useful information like patterns, similarities and various relationships from tree data, which are also proved to be advantageous in analysing big data. This thesis focuses on analysing unordered tree data, which is robust to data inconsistency, irregularity and swift information changes, hence, in the era of big data it becomes a popular and widely used data model.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Unpacking the impact of attachment to project teams on boundary-spanning behaviors

As business environments become even more competitive, project teams are required to make an effort to operate external linkages from within an organization or across organizational boundaries. Nevertheless, some members boundary-span less extensively, isolating themselves and their project teams from external environments. Our study examines why some members boundary-span more or less through the framework of group attachment theory. Data from 521 project-team members in construction and engineering industries revealed that the more individuals worry about their project team’s acceptance (group attachment anxiety), the more likely they are to perceive intergroup competition, and thus put more efforts into operating external linkages and resources to help their own teams outperform competitors. In contrast, a tendency to distrust their project teams (group attachment avoidance) generates members’ negative construal of their team’s external image, and thus fewer efforts are made at operating external linkages. Thus, project leaders and members with high group-attachment-anxiety may be best qualified for external tasks.

Comment on “Whole-genome analyses resolve early branches in the tree of life of modern birds”

Jarvis et al. (Research Articles, 12 December 2014, p. 1320) presented molecular clock analyses that suggested that most modern bird orders diverged just after the mass extinction event at the Cretaceous-Paleogene boundary (about 66 million years ago). We demonstrate that this conclusion results from the use of a single inappropriate maximum bound, which effectively precludes the Cretaceous diversification overwhelmingly supported by previous molecular studies.