[Truncated thesis] This thesis is concerned with executive functioning in two different but related ways. The first is as an information processing construct in cognitive psychology. There are many different conceptualisations of the information processing basis of executive functioning but this thesis will pursue the notion that executive functioning is best thought of as adaptation to novelty. In the thesis, this will be operationalised using performance indices (principally reaction time) from a number of information processing tasks. These tasks have typically been used in the literature to index either executive functioning or speed of information processing. Both kinds of tasks are used to tackle the second concern of this thesis, namely, how executive functioning is measured. The data analytic techniques developed in this thesis are based on the hypothesis that executive functioning is the process or processes involved in resolving task novelty and consequently measurement will be enhanced through an analysis of performance changes within tasks as the task changes from novel to familiar. The analysis methods will be based largely on the computation of coefficient of variation of reaction time in successive performance windows across the information processing tasks. An elderly sample was chosen for this thesis because of a history of research that has attempted to determine whether cognitive deficits in the elderly are the consequence of the slowing of information processing speed or to impairment in executive functioning. ... The analysis was driven by the hypothesis that a significant shift in the coefficient of variation would mark a transition from novelty to familiarity in task performance and hence from executive to non-executive phases. Three methods were applied to individual performance curves to determine the point at which for each task this transition occurred. Using criterion measures of variability to separate the task data into two stages, analyses showed, contrary to the hypothesis, that later task performance was more highly associated with executive functioning than in initial task performance. The fourth stage of analysis (Chapter 7) applied confirmatory factor analysis to the newly-formed pre- and post transition data. Evidence was found that the magnitude of the contributions of EF across the pre- and post-criterion phases was stable, failing to support the hypothesis. Finally, structural equation modelling was used to examine how age and intelligence in this elderly sample exerts its influence on task performance and whether EF or IPS was the primary cause of age-related cognitive decline. The results showed that the age and intelligence effects on performance were mediated by the requirement to adapt to novelty. Although there was limited evidence to claim that EF is the primary cause of age-related cognitive decline, ageing effects were only apparent when the participants were adapting to novelty. The thesis concludes that there is some support for the hypothesis that executive functioning is best thought of as the processes underpinning adaptation to novelty. While not a panacea, the analytic techniques developed show promise for future research.

<p>Autonomous systems are often deployed in dynamic environments and are challenged with unexpected changes (novelties) in the environments where they receive novel data that was not seen during training. Given the uncertainty, they should be able to operate without (or with limited) human intervention and they are expected to (1) Adapt to such changes while still being effective and efficient in performing their multiple tasks. The system should be able to provide continuous availability of its critical functionalities. (2) Make informed decisions independently from any central authority. (3) Be Cognitive: learns the new context, its possible actions, and be rich in knowledge discovery through mining and pattern recognition. (4) Be Reflexive: reacts to novel unknown data as well as to security threats without terminating on-going critical missions. These characteristics combine to create the workflow of autonomous decision-making process in multi-agent environments (i.e.,) any action taken by the system must go through these characteristic models to autonomously make an ideal decision based on the situation. </p> <p><br></p> <p>In this dissertation, we propose novel learning-based models to enhance the decision-making process in autonomous multi-agent systems where agents are able to detect novelties (i.e., unexpected changes in the environment), and adapt to it in a timely manner. For this purpose, we explore two complex and highly dynamic domains </p> <p>(1) Transportation Networks (e.g., Ridesharing application): where we develop AdaPool: a novel distributed diurnal-adaptive decision-making framework for multi-agent autonomous vehicles using model-free deep reinforcement learning and change point detection. (2) Multi-agent games (e.g., Monopoly): for which we propose a hybrid approach that combines deep reinforcement learning (for frequent but complex decisions) with a fixed-policy approach (for infrequent but straightforward decisions) to facilitate decision-making and it is also adaptive to novelties. (3) Further, we present a domain agnostic approach for decision making without prior knowledge in dynamic environments using Bootstrapped DQN. Finally, to enhance security of autonomous multi-agent systems, (4) we develop a machine learning based resilience testing of address randomization moving target defense. Additionally, to further  improve the decision-making process, we present (5) a novel framework for multi-agent deep covering option discovery that is designed to accelerate exploration (which is the first step of decision-making for autonomous agents), by identifying potential collaborative agents and encouraging visiting the under-represented states in their joint observation space. </p>

<p>Mental health significantly impacts issues like gun violence, school shootings, and suicide. There is a strong connection between mental health and emotional states. By monitoring emotional changes over time, we can identify triggering events, detect early signs of instability, and take preventive measures. This thesis focuses on the development of a generalized and modular system for human emotion recognition and explanation based on visual information. The aim is to address the challenges of effectively utilizing different cues (modalities) available in the data for a reliable and trustworthy emotion recognition system. Our face is one of the most important medium through which we can express our emotion. Therefore We first propose SAFER, A novel facial emotion recognition system with background and place features. We provide a detailed evaluation framework to prove the high accuracy and generalizability. However, relying solely on facial expressions for emotion recognition can be unreliable, as faces can be covered or deceptive.  To enhance the system's reliability, we introduce EMERSK, a multimodal emotion recognition system that integrates various modalities, including facial expressions, posture, gait, and scene background, in a flexible and modular manner. It employs convolutional neural networks (CNNs), Long Short-term Memory (LSTM), and denoising auto-encoders to extract features from facial images, posture, gait, and scene background. In addition to multimodal feature fusion, the system utilizes situational knowledge derived from place type and adjective-noun pairs (ANP) extracted from the scene, as well as the spatio-temporal average distribution of emotions, to generate comprehensive explanations for the recognition outcomes. Extensive experiments on different benchmark datasets demonstrate the superiority of our approach over existing state-of-the-art methods. The system achieves improved performance in accurately recognizing and explaining human emotions. Moreover, we investigate the impact of novelty, such as face masks during the Covid-19 pandemic, on the emotion recognition. The study critically examines the limitations of mainstream facial expression datasets and proposes a novel dataset specifically tailored for facial emotion recognition with masked subjects. Additionally, we propose a continuous learning-based approach that incorporates a novelty detector working in parallel with the classifier to detect and properly handle instances of novelty. This approach ensures robustness and adaptability in the automatic emotion recognition task, even in the presence of novel factors such as face masks. This thesis contributes to the field of automatic emotion recognition by providing a generalized and modular approach that effectively combines multiple modalities, ensuring reliable and highly accurate recognition. Moreover, it generates situational knowledge that is valuable for mission-critical applications and provides comprehensive explanations of the output. The findings and insights from this research have the potential to enhance the understanding and utilization of multimodal emotion recognition systems in various real-world applications.</p> <p><br></p>