Detection of Translator Stylometry using Pair-wise Comparative Classification and Network Motif Mining

Stylometry is the study of the unique linguistic styles and writing behaviours of individuals. The identification of translator stylometry has many contributions in fields such as intellectual-property, education, and forensic linguistics. Despite the research proliferation on the wider research field of authorship attribution using computational linguistics techniques, the translator stylometry problem is more challenging and there is no sufficient machine learning literature on the topic. Some authors even claimed that detecting who translated a piece of text is a problem with no solution; a claim we will challenge in this thesis.In this thesis, we evaluated the use of existing lexical measures for the translator stylometry problem. It was found that vocabulary richness could not identify translator stylometry. This encouraged us to look for non-traditional representations to discover new features to unfold translator stylometry. Network motifs are small sub-graphs that aim at capturing the local structure of a real network. We designed an approach that transforms the text into a network then identifies the distinctive patterns of a translator by employing network motif mining.During our investigations, we redefined the problem of translator stylometry identification as a new type of classification problems that we call Comparative Classification Problem (CCP). In the pair-wise CCP (PWCCP), data are collected on two subjects. The classification problem is to decide given a piece of evidence, which of the two subjects is responsible for it. The key difference between PWCCP and traditional binary problems is that hidden patterns can only be unmasked by comparing the instances as pairs. A modified C4.5 decision tree classifier, we call PWC4.5, is then proposed for PWCCP.A comparison between the two cases of detecting the translator using traditional classification and PWCCP demonstrated a remarkable ability for PWCCP to discriminate between translators.The contributions of the thesis are: (1) providing an empirical study to evaluate the use of stylistic based features for the problem of translator stylometry identification; (2) introducing network motif mining as an effective approach to detect translator stylometry; (3) proposing a modified C4.5 methodology for pair-wise comparative classification

Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.</p