<p>With the advents in hardware techniques and mobile computing powers, Augmented Reality (AR) has been promising in various areas of our everyday life and work. By superimposing virtual assets onto the real world, the boundary between the digital and physical spaces has been significantly blurred, which bridges a large amount of digital augmentation and intelligence with the surroundings of the physical reality. Meanwhile, thanks to the increasing developments of Artificial Intelligence (AI) perception algorithms such as object detection, scene reconstruction, and human tracking, the dynamic behaviors of digital AR content have extensively been associated with the physical contexts of both humans and environments. This context-awareness enabled by the emerging techniques enriches the potential interaction modalities of AR experiences and improves the intuitiveness and effectiveness of the digital augmentation delivered to the consumers. Therefore, researchers are gradually motivated to include more contextual information in the AR domain to create novel AR experiences used for augmenting their activities in the physical world.</p> <p>    </p> <p>On a broader level, our work in this thesis focuses on novel designs and modalities that combine contextual information with AR content behaviors in context-aware AR experiences. In particular, we design the AR experiences by inspecting different types of contexts from the real world, namely 1) human actions, 2) physical entities, and 3) interactions between humans and physical environments. To this end, we explore 1) software and hardware modules, and conceptual models that perceive and interpret the contexts required by the AR experiences, and 2) supportive authoring tools and interfaces that enable users and designers to define the associations of the AR contents and the interaction modalities leveraging the contextual information. In this thesis, we mainly study the following workflows: 1) designing adaptive AR tutoring systems for human-machine-interactions, 2) customizing human-involved context-aware AR applications, 3) authoring shareable semantic-aware AR experiences, and 4) enabling hand-object-interaction datasets collection for scalable context-aware AR application deployment. We further develop the enabling techniques and algorithms including 1) an adaptation model that adaptively vary the AR tutoring elements based on the real-time learner's interactions with the physical machines, 2) a customized video-see-through AR headset for pervasive human-activity detecting, 3) a semantic adaptation model that adjusts the spatial relationships of the AR contents according to the semantic understanding of different physical entities and environments, and 4) an AR-based interface that empowers novice users to collect high-quality datasets used for training user- and cite-specific networks in hand-object-interaction-aware AR applications.</p> <p><br></p> <p>Takeaways from the research series include 1) the usage of the modern AI modules effectively enlarges both the spatial and contextual scalability of AR experiences, and 2) the design of the authoring systems and interfaces lowers the barrier for end-users and domain experts to leverage AI outputs in the creation of AR experiences that are tailored for target users. We conclude that involving AI techniques in both the creation and implementation stages of AR applications is crucial to building an intelligent, adaptive, and scalable ecosystem of context-aware AR applications.</p>

<p dir="ltr">Data is collected everywhere in our world; however, it often is noisy and incomplete. Different sources of data may have different characteristics, quality levels, or come from dynamic and diverse environments. This poses challenges for both humans who want to gain insights from data and machines which are learning patterns from data. How can we leverage the diversity of data across domains to enhance our understanding and decision-making? In this thesis, we address this question by proposing novel methods and applications that use multiple domains as more holistic sources of information for both human and machine learning tasks. For example, to help human operators understand environmental dynamics, we show the detection and localization of distribution shifts to problematic features, as well as how interpretable distributional mappings can be used to explain the differences between shifted distributions. For robustifying machine learning, we propose a causal-inspired method to find latent factors that are robust to environmental changes and can be used for counterfactual generation or domain-independent training; we propose a domain generalization framework that allows for fast and scalable models that are robust to distribution shift; and we introduce a new dataset based on human matches in StarCraft II that exhibits complex and shifting multi-agent behaviors. We showcase our methods across various domains such as healthcare, natural language processing (NLP), computer vision (CV), etc. to demonstrate that learning from data across domains can lead to more faithful representations of data and its generating environments for both humans and machines.</p>

<p dir="ltr">In the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory.</p><p dir="ltr">A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security.</p><p dir="ltr">The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with exploring quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms.</p><p dir="ltr">In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.</p>