The ability to sense the internal state of one's body is the process of interoception and is a marker for positive emotional outcomes. The last five years have seen burgeoning research interest in interoception, including a call for more integrative and predictive biomarkers for interoceptive ability. While there is a robust literature purporting to measure interoception, there is also significant research challenging the content validity of the current methodology. Beyond these published challenges, I offer a broader critique that suggests that the current reductionist approach fails to capture the integrative nature of interoception. I introduce an alternate methodology to assess interoceptive ability that leverages the integrative nature of the autonomic nervous system. Thirty-four undergraduates provided real-time feedback about their subjective state of arousal while watching three videos of varying intensity. Across subjects, arousal feedback did not positively correlate with physiological indices of sympathetic arousal including electrodermal activity and the inverse of pre-ejection period. However, each subject appeared to have an idiographic pattern of physiological variables that correlated strongly, although often negatively, with the subjective slider feedback. These physiological patterns provide the foundation for investigating a new biomarker for interoception that relies on the autonomic nervous system to surmise interoceptive states. / Doctor of Philosophy / A person's sense of their body is called interoception. Research has shown that people who are good at interoception tend to live happier and more fulfilling lives. But current research techniques don't do a great job measuring whether someone is good at interoception. These techniques have faced a lot of criticism for the errors they are known to make. I add my concerns that the current techniques don't reflect how we naturally sense into our bodies when we're not in a lab. I explain why I think we need a new way of measuring interoception that captures how holistic this process is. I introduce a new measure that is based on the energy in our body. I believe that people use this energy system naturally and will also be better able to reflect this is a lab setting. The people in this study used a slider dial to indicate how much energy they felt while they watched videos with different excitement levels. While they watched the videos and moved the dial, we measured their bodily readings from their heart, breathing, and sweat to see if these readings matched their dial ratings. We were surprised that the typical body readings for excitement were not directly related to the slider movements when we averaged across people, but we did find that each person had their own unique way of responding that was similar in both mind and body. This research is the basis for a new way to understand how people read their own body, and how accurate this reading is.

Teaching simulated biped robots to walk is a popular problem in machine learning. However, until this thesis, evolving a biped controller has not been attempted through an indirect encoding, i.e. a compressed representation of the solution, despite the fact that natural bipeds such as humans evolved through such an indirect encoding (i.e. DNA). Thus the promise for indirect encoding is to evolve gaits that rival those seen in nature. In this thesis, an indirect encoding called HyperNEAT evolves a controller for a biped robot in a computer simulation. To most effectively explore the deceptive behavior space of biped walkers, novelty search is applied as a fitness metric. The result is that although the indirect encoding can evolve a stable bipedal gait, the overall neural architecture is brittle to small mutations. This result suggests that some capabilities might be necessary to include beyond indirect encoding, such as lifetime adaptation. Thus this thesis provides fresh insight into the requisite ingredients for the eventual achievement of fluid bipedal walking through artificial evolution.

No / This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the under- standing of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.

摘要 本研究從新奇追求動機探討遊客對旅遊產品的態度會被什麼因素所影響。許多學者提到，遊客對於新奇追求的差異，會影響他們選擇不同觀光活動及旅遊目的地的決策，因此旅遊行程有否提供遊客新奇的體驗（亦即新奇屬性）或是基於旅遊產品本身的新奇性，應能吸引不同新奇追求程度的遊客前往旅遊。然而以往關於新奇屬性的研究多針對實體產品，鮮少從服務性產品的面向做探討，文獻中對旅遊產品新奇性的描述亦甚少著墨，因此本研究藉由操弄新奇屬性和產品新奇性，從旅遊產品的角度觀察對於不同新奇追求程度的遊客之態度會產生什麼影響。 研究結果發現，旅遊行程加入新奇屬性或是產品新奇性較高的旅遊產品並不一定能讓遊客對有較好的印象。當面對無新奇屬性的旅遊行程或低新奇性產品（團體旅遊）時，低新奇追求的遊客相較於高新奇追求的遊客有更好的態度，但其同時對有新奇屬性的旅遊行程及高新奇性產品（自由行）也抱持同樣的好感；而面對有新奇屬性的旅遊行程或高新奇產品（自由行）時，高新奇追求的遊客則相對較有提升好感的趨勢。值得注意的是，當旅遊產品為高新奇性（自由行）並同時搭配具有新奇屬性的旅遊行程後，反而會使消費者發生資訊過載的情形進而提高學習成本，導致消費者對產品的評價混淆甚至產生負面態度。 因此，如果廠商想要在市場上推出新產品，以適度的新奇性做為產品設計的主軸是較為理想的選擇，例如自由行對於高、低新奇追求程度的消費者都具有一定的吸引力，或是團體旅遊搭配具有新奇屬性的旅遊行程亦是保險的組合方式。要注意的是，過多的新奇資訊同時也會增加消費者的學習成本，一旦造成消費者的資訊超載，反而會造成負面的效果。 / This research focuses on what factors based on novelty seeking will be influencing tourists’ attitude toward the tourism product. Many scholars have already mentioned that different degree of novelty seeking among tourists may influence their choices of different sightseeing activities and trip destination. Therefore, whether the travel programs provide novel experiences to the tourists(namely novel attributes) or any novelty of the product itself should be able to attract tourists with different degree of novelty seeking. However, researches on novel attributes in the past mostly put emphasis on substantial products and seldom discussed on service products. In addition, there is a lack of literature regarding product novelty as well. For these reasons, the purpose of this research is to observe the factors which influence attitudes of tourists with different degree of novelty seeking by means of manipulating novel attributes and product novelty. The result of this research shows that travel journey with novel attributes or just a tourism product with high degree of novelty don’t necessarily make better impressions on tourists. When facing travel journey with no novel attributes or just a product with low degree of novelty (package tour), tourists with low degree of novelty seeking show more favor towards the product than those with high degree of novelty seeking, but they as well have positive attitude when there is travel journey with novel attributes or just a product with high degree of novelty (semi-independent travel). On the other hand, the tourists with high degree of novelty seeking comparatively tend to show preference for the travel journey with novel attributes or just a product with high degree of novelty (semi-independent travel). Here what calls our attention is that, when the tourism product is with high degree of novelty (semi-independent travel) and is combined with the travel journey with novel attributes at the same time, it may cause information overload for consumers thus raise their learning cost instead. This even causes consumers to produce negative or confused attitude when evaluating the product. Hence, for the (tourism product) suppliers who want to release new products to the market, I suggest that product design should be emphasize on adding appropriate novelty. For example, semi-independent travel attracts consumers with both high and low degree of novelty seeking; tour package with novel attributes is also safe for tourists. What we should be aware of is that too much novel information increases consumer’s learning cost, and once it causes consumer’s information to overload, it may cause negative effect instead.

Abstract To be able to acquire, produce, and comprehend language, precise central auditory processing (CAP), neural processes utilized for managing auditory input, is essential. However, the auditory environments are not always optimal for CAP because noise levels in children’s daily environments can be surprisingly high. In young children, CAP and its developmental trajectory as well as the influence of noise on it have scarcely been investigated. Event-related potentials (ERPs) offer promising means to study different stages of CAP in small children. Sound processing, preattentive auditory discrimination, and attention shifting processes can be addressed with obligatory responses, mismatch negativity (MMN), and novelty P3 of ERPs, respectively. In this thesis the developmental trajectory of CAP from 2 to 4 years of age as well as noise-induced changes on it, were investigated. In addition, the feasibility of the multi-feature paradigm with syllable stimuli and novel sounds in children was evaluated. To this end, obligatory responses (P1, N2, and N4) and MMNs for consonant, frequency, intensity, vowel, and vowel duration changes, as well as novelty P3 responses, were recorded in a silent condition and with babble noise using the multi-feature paradigm. The participants were voluntary, typically developing children. Significant P1, N2, N4, and MMN responses were elicited at both ages. Also a significant novelty P3, studied at the age of 2 years, was found. From 2 to 4 years, the P1 and N2 latencies shortened. The amplitudes of N2, N4, and MMNs increased and the increment was the largest at frontal electrode locations. During noise, P1 decreased, N2 increased, and the latency of N4 diminished as well as MMNs degraded. The noise-induced changes were largely similar at both ages. In conclusion, the multi-feature paradigm with five syllable deviant types and novel sounds was found to be an appropriate measure of CAP in toddlers. The changes in ERP morphology from 2 to 4 years of age suggest maturational changes in CAP. Noise degraded sound encoding, representation forming, and auditory discrimination. The children were similarly vulnerable to hampering effects of noise at both ages. Thus, noise might potentially harmfully influence language processing and thereby its acquisition in childhood. / Tiivistelmä Kielen omaksumiselle, tuottamiselle sekä ymmärtämiselle on tärkeää tarkka keskushermostollinen kuulotiedon käsittely eli ne hermostolliset prosessit, joita käytetään kuullun aineksen käsittelyyn. Kuunteluympäristöt eivät kuitenkaan aina ole optimaalisia kuulotiedon käsittelylle, sillä melutasot lasten elinympäristöissä voivat olla hyvinkin korkeita. Pienten lasten kuulotiedon käsittelyä, sen kehittymistä ja melun vaikutusta siihen on tutkittu vähän. Kuuloherätevasteet ovat toimiva tapa tarkastella pienten lasten kuulotiedon käsittelyä eri näkökulmista. Äänen käsittelyä, esitietoista kuuloerottelua ja tarkkaavuuden siirtymistä voidaan tarkastella obligatoristen vasteiden, poikkeavuusnegatiivisuuden ja novelty P3 -vasteiden avulla. Tässä väitöskirjassa tarkastellaan kuulotiedon käsittelyn kehittymistä kahden vuoden iästä neljän vuoden ikään sekä melun vaikutusta siihen. Lisäksi arvioidaan tavuärsykkeitä ja poikkeavia ääniä sisältävän monipiirreparadigman soveltuvuutta lapsitutkimuksiin. Tutkimuksissa rekisteröitiin monipiirreparadigman avulla obligatorisia vasteita (P1, N2 ja N4); konsonantin, taajuuden, intensiteetin, vokaalin ja vokaalin keston muutokselle syntyneitä MMN-vasteita sekä novelty P3 -vasteita hiljaisuudessa ja taustamelussa. Tutkimuksen osallistujat olivat vapaaehtoisia tyypillisesti kehittyviä lapsia. Molemmilla tutkimuskerroilla P1, N2, N4 ja MMN poikkesivat merkitsevästi nollatasosta samoin kuin kaksivuotiailta tutkittu novelty P3. Kahden vuoden iästä neljään vuoteen P1- ja N2-vasteiden latenssi lyheni sekä N2, N4 ja MMN vahvistuivat, muutoksen ollessa suurinta frontaalisilla elektrodeilla. Melun aikana P1 heikkeni, N2 vahvistui ja N4-vasteen latenssi lyhentyi. Lisäksi MMN-vaste heikkeni. Melun aiheuttamat muutokset olivat samankaltaisia sekä kahden että neljän vuoden iässä. Johtopäätöksenä voidaan todeta viittä eri tavuärsyketyyppiä ja yllättäviä ääniä sisältävän monipiirreparadigman olevan toimiva menetelmä taaperoiden kuulotiedon käsittelyn tutkimiseen. Kahden ja neljän ikävuoden välillä tapahtuvat muutokset vasteissa kuvastavat kehityksellisiä muutoksia kuulotiedon käsittelyssä. Melu heikentää äänitiedon peruskäsittelyä, edustumien muodostumista ja esitietoista kuuloerottelua. Lapset olivat lähes yhtä alttiita melun vaikutuksille sekä kahden että neljän vuoden iässä. Melu voi siis haitata kielen prosessointia ja sen omaksumista.

<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>

The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution because evolutionary algorithms that can consistently achieve established benchmarks are lauded as successful, thus reinforcing this paradigm. A significant problem is that such objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives may not discover them. This problem with objectives is demonstrated through an experiment in this dissertation that compares how images discovered serendipitously during interactive evolution in an online system called Picbreeder cannot be rediscovered when they become the final objective of the very same algorithm that originally evolved them. This negative result demonstrates that pursuing an objective limits evolution by selecting offspring only based on the final objective. Furthermore, even when high fitness is achieved, the experimental results suggest that the resulting solutions are typically brittle, piecewise representations that only perform well by exploiting idiosyncratic features in the target. In response to this problem, the dissertation next highlights the importance of leveraging human insight during search as an alternative to articulating explicit objectives. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with a method called novelty search for the first time to facilitate the serendipitous discovery of agent behaviors. iii In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviors. However, unlike in typical IEC, the user can then request that the next generation be filled with novel descendants, as opposed to only the direct descendants of typical IEC. The result of such an approach, unconstrained by a priori objectives, is that it traverses key stepping stones that ultimately accumulate meaningful domain knowledge. To establishes this new evolutionary approach based on the serendipitous discovery of key stepping stones during evolution, this dissertation consists of four key contributions: (1) The first contribution establishes the deleterious effects of a priori objectives on evolution. The second (2) introduces the NA-IEC approach as an alternative to traditional objective-based approaches. The third (3) is a proof-of-concept that demonstrates how combining human insight with novelty search finds solutions significantly faster and at lower genomic complexities than fully-automated processes, including pure novelty search, suggesting an important role for human users in the search for solutions. Finally, (4) the NA-IEC approach is applied in a challenge domain wherein leveraging human intuition and domain knowledge accelerates the evolution of solutions for the nontrivial octopus-arm control task. The culmination of these contributions demonstrates the importance of incorporating human insights into simulated evolution as a means to discovering better solutions more rapidly than traditional approaches.

<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>

<p dir="ltr">Current artificial intelligence (AI) systems are proficient at tasks in a closed-world setting where the rules are often rigid. However, in real-world applications, the environment is usually open and dynamic. In this work, we investigate the effects of such dynamic environments on AI systems and develop ways to mitigate those effects. Central to our exploration is the concept of \textit{novelties}. Novelties encompass structural changes, unanticipated events, and environmental shifts that can confound traditional AI systems. We categorize novelties based on their representation, anticipation, and impact on agents, laying the groundwork for systematic detection and adaptation strategies. We explore novelties in the context of stochastic games. Decision-making in stochastic games exercises many aspects of the same reasoning capabilities needed by AI agents acting in the real world. A multi-agent stochastic game allows for infinitely many ways to introduce novelty. We propose an extension of the deep reinforcement learning (DRL) paradigm to develop agents that can detect and adapt to novelties in these environments. To address the sample efficiency challenge in DRL, we introduce a hybrid approach that combines fixed-policy methods with traditional DRL techniques, offering enhanced performance in complex decision-making tasks. We present a novel method for detecting anticipated novelties in multi-agent games, leveraging information theory to discern patterns indicative of collusion among players. Finally, we introduce DABLER, a pioneering deep reinforcement learning architecture that dynamically adapts to changing environmental conditions through broad learning approaches and environment recognition. Our findings underscore the importance of developing AI systems equipped to navigate the uncertainties of the open world, offering promising pathways for advancing AI research and application in real-world settings.</p>

This dissertation attempts to expand our understanding of the deficient self-regulation (DSR) of Internet use and media habit development. Drawing from a social cognitive perspective, DSR is described as lapses in effective self-control that are self-corrected over time. A shortcoming in this area of research is that factors relevant to the technology that may encourage the development of DSR or media habits are rarely, if ever, discussed. A large focus of existing research is instead narrowly placed on individual factors that motivate DSR and media habits. An extension is proposed to theory on DSR in this dissertation by examining the role played by novelty perceptions of technology. In the initial stages of technology use, when perceptions of novelty generally grow, perceived novelty is hypothesized to elicit a state of flow, which in turn diminishes the subfunctions of self-regulation and provokes DSR. The relationship between perceived novelty and flow is moderated by psychosocial problems, boredom proneness, and self-reactive outcome expectation. As perceived novelty of a technology decreases, it is presumed that self-control is restored given that flow no longer inhibits self-regulation. However, DSR and media habits are hypothesized to persist in later technology use if individuals experience psychosocial problems, boredom proneness, or high self-reactive outcome expectations. The manifestation of DSR in later stages of technology use increases the likelihood of forming media habits. The influence of novelty perceptions was evaluated on flow, DSR, and media habits at initial and later stages of technology use. The pretest demonstrated that a novelty frame successfully manipulated novelty perceptions of Second Life, the technology used in this experiment, in anticipated directions. In the main study, perceived novelty resulted in flow, which in turn predicted growth of DSR during initial stages of Second Life use. In the familiar stages of use, DSR led to the development of media habits over time; however, the relationship between novelty perceptions and DSR was not moderated by psychosocial problems, boredom proneness, or self-reactive outcome expectation. The findings of this investigation are discussed aside their implications for research, theory, and practice.