Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP

A multi-tier framework for dynamic data collection, analysis, and visualization

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 52-53).This thesis describes a framework for collecting, analyzing, and visualizing dynamic data, particularly data gathered through Web questionnaires. The framework addresses challenges such as promoting user participation, handling missing or invalid data, and streamlining the data interpretation process. Tools in the framework provide an intuitive way to build robust questionnaires on the Web and perform on-the-fly analysis and visualization of results. A novel 2.5-dimensional dynamic response-distribution visualization allows subjects to compare their results against others immediately after they have submitted their response, thereby encouraging active participation in ongoing research studies. Other modules offer the capability to quickly gain insight and discover patterns in user data. The framework has been implemented in a multi-tier architecture within an open-source, Java-based platform. It is incorporated into Risk Psychology Network, a research and educational project at MIT's Laboratory for Financial Engineering.by Xian Ke.M.Eng

Let There Be Dragons! Towards Designing an Engaging Quest that Enhances Curiosity and Learning About Genetics

This study implemented a convergent parallel mixed methods approach to investigate game-based learning within an educational game compared to a modified entertainment game. Participants (N=31) were recruited from public middle and high schools as well as home school groups. Comparative data of participants’ perceptions, preferences and learning outcomes were investigated to inform better educational game design. This study also considers player personality to determine how dispositional curiosity influences an individual’s approach, acceptance, and interaction with novel learning environments, specifically games. Findings show a statistically significant gain in genetics academic knowledge after the game-based learning intervention. The difference in knowledge gained for the two games was not statistically significant. All dimensions of engagement, motivation and curiosity were statistically significantly higher for the modified entertainment game. Increases in scientific curiosity was statistically significantly higher for the modified entertainment game while scientific curiosity statistically significantly decreased after playing the educational game. Qualitative analysis revealed five themes and provided deeper understanding of game design features that enhance learning, curiosity and engagement from the player’s perception. Integration of quantitative and qualitative results suggest overall convergence and enhanced understanding of theoretical and practical implications of this research and identifies key relationships between game design, player perceptions and learning outcomes to inform better educational game design and implementation

Putting willpower into decision theory: the person as a team over time and intrapersonal team reasoning

In decision-theory, problems of self-control can be modelled as problems of intrapersonal cooperation, between a series of transient agents who each make choices at particular times. Early agents in the series can try to influence the actions of later agents, but there is no rational way to exert willpower. I show how willpower can be introduced into decision theory by applying the theory of team reasoning, which was originally developed to understand cooperation between individuals in groups and allows that there can be multiple levels of agency, the individual and the team. In the case of intertemporal choice, the levels are the transient agent and the person over time. Intra-personal team reasoning, understood as a psychological process of identifying with the person over time, can generate a plausible theory of rational control if the intertemporal problem is structured as a threshold public goods game. In this framework, willpower is the ability to align one’s present self with one’s extended interests by identifying with the person over time. I show how intra-personal team reasoning creates a space for resolutions in decision theory and how it resolves a puzzle that exists in accounts that understand willpower as making and then not reconsidering resolutions

Behavioural effects of caffeine: the specificity hypothesis

This thesis argues that caffeine use offered a survival advantage to our ancestors and that moderate use continues to offer modern humans benefits. Caffeine ingestion, through the blocking of adenosine receptors, elicits broad elements of the mammalian threat response, specifically from the ‘flight or fight’ and ‘tend and befriend’ repertoires of behaviour: in effect, caffeine hijacks elements of the stress response. If the effects of caffeine had been discovered recently, rather than being available to Homo sapiens since Neolithic hunter gatherer times, it is likely that caffeine would be considered a ‘smart’ drug. More caffeine is being ingested today than ever previously recorded. Caffeine use is found across all age groups, all socio-economic strata, most ethnic groups, and is being used increasingly by the medical and pharmaceutical industries and by the armed forces. Yet despite this wide usage and a substantial body of research literature, there is at present no clear pattern or plausible model for the way caffeine achieves its effects. There is much contradiction in the literature and ambiguity as to why caffeine use should improve performance on some tasks, impair it on others and have no effect on other tasks, for some but not all of the time. The present work argues, through an examination of the specificity of caffeine’s operation, that these effects are not arbitrary but elicited by the nature of the tasks, in particular that caffeine ingestion affects those processes and behaviours which improve the probability of survival under perceived threat or stress. This is argued through the perspective of evolutionary psychology and relies theoretically on Polyvagal Theory. The argument generates testable hypotheses and empirical support for the thesis is garnered from nine experiments on card-sorting, verbal and numerical processing, local and global categorization, field dependence-independence, the Stroop task, tests of visuo-spatial ability, and from a correlational study of caffeine use and personality traits. It is concluded that moderate caffeine use in healthy adults promotes behaviours likely to be adaptive under perceived threat or stress. Limitations of both theory and empirical work and are discussed, together with potential practical applications and suggestions for further work

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

Third-wave missionary leaders in contemporary Yakland: an analysis of six malfeasance and leadership formation cases using a maturity-support approach

The thesis presents my Maturity Quotient Model (MQM) for refining J. Robert Clinton’s Leadership Emergence Theory through microscopically analysing the function of ‘leaders response’ during malfeasance. My central argument is that it is possible to analyse, pre-empt, and remedy missionary leaders’ malfeasance in their leadership formation. The first part of the thesis presents the research’s raison d'être, clarifies the meaning of ‘Yakland’, and argues that missionaries there have to inherit the unfavourable historical biases left by their predecessors, and that their leaders’ malfeasance aggravates such negative views of them. The second part (1) presents my research findings and methodology for focusing on ‘maturity’ and ‘support levels’ to examine missionary leaders’ malfeasance, and (2) shows the identifiability and analysability of holistic maturity using the works of Frankl, Samra, Erikson, Kao, the Via Triplex, and mathematical formulation. The third part demonstrates that the lack of a suitable leadership model to examine the missionary malfeasance has necessitated the development of the MQM as the theoretical and practical framework for this research. The last part presents my research findings. Of a sample of 76 active missionaries surveyed in Yakland in 2015, 76% reported malfeasance. MQ score to some degree predicted which of these missionaries were malfeasant. Support Level was not a significant predictor of malfeasance. While the MQ score gave an indication of who was at risk of malfeasance, its predictive power was inadequate for it to be used as a tool for reliably identifying malfeasance either on its own or in combination with support level. Missionaries in the Immature Phase, not the hypothesised Maturing Phase, are more vulnerable to malfeasance; and malfeasance becomes markedly less likely at the watershed phase of fdMg (MQ>0.5971). Thus there is an argument that churches and mission agencies should be less concerned about the alpha and beta missionary leaders in the field and keep a closer watch on delta and gamma leaders who are more likely to jeopardise the mission enterprise, themselves and others. Stronger conclusions cannot be drawn because of the limited predictive power of the MQM

Early identification of individuals who may fail to reach employment

This paper compiles possible predictors of youth joblessness from an extensive review of the literature, and tests each of them to establish whether any idiosyncratic effect is evident on a range of operationalisations of NEET outcomes. A constructed counterfactual analysis is developed which has wider application in the identification of multicollinearity within empirical research

Predicting Head Pose From Speech

Speech animation, the process of animating a human-like model to give the impression it is talking, most commonly relies on the work of skilled animators, or performance capture. These approaches are time consuming, expensive, and lack the ability to scale. This thesis develops algorithms for content driven speech animation; models that learn visual actions from data without semantic labelling, to predict realistic speech animation from recorded audio. We achieve these goals by _rst forming a multi-modal corpus that represents the style of speech we want to model; speech that is natural, expressive and prosodic. This allows us to train deep recurrent neural networks to predict compelling animation. We _rst develop methods to predict the rigid head pose of a speaker. Predicting the head pose of a speaker from speech is not wholly deterministic, so our methods provide a large variety of plausible head pose trajectories from a single utterance. We then apply our methods to learn how to predict the head pose of the listener while in conversation, using only the voice of the speaker. Finally, we show how to predict the lip sync, facial expression, and rigid head pose of the speaker, simultaneously, solely from speec