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Unknown-unknowns are operational scenarios in systems that are not accounted for in the design and test phase. In such scenarios, the operational behavior of the Human-in-loop (HIL) Human-in-Plant (HIP) systems is not guaranteed to meet requirements such as safety and efficacy. We propose a novel framework for analyzing the operational output characteristics of safety-critical HIL-HIP systems that can discover unknown-unknown scenarios and evaluate potential safety hazards. We propose dynamics-induced hybrid recurrent neural networks (DiH-RNN) to mine a physics-guided surrogate model (PGSM) that checks for deviation of the cyber-physical system (CPS) from safety-certified operational characteristics. The PGSM enables early detection of unknown-unknowns based on the physical laws governing the system. We demonstrate the detection of operational changes in an Artificial Pancreas(AP) due to unknown insulin cartridge errors.

We present a computational modelling approach which targets at capturing the specifics on how to virtually augment a Metaverse user's available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, the user can virtually delegate on the avatar socializing time required for maintaining the existing contacts, so as to eventually maintain spare non-avatar-mediated socializing time which can be potentially invested in additional socialization activities. We model the setting and identify the characteristic variables via using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user's non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions.

Post hoc explanations have emerged as a way to improve user trust in machine learning models by providing insight into model decision-making. However, explanations tend to be evaluated based on their alignment with prior knowledge while the faithfulness of an explanation with respect to the model, a fundamental criterion, is often overlooked. Furthermore, the effect of explanation faithfulness and alignment on user trust and whether this effect differs among laypeople and domain experts is unclear. To investigate these questions, we conduct a user study with computer science students and doctors in three domain areas, controlling the laypeople and domain expert groups in each setting. The results indicate that laypeople base their trust in explanations on explanation faithfulness while domain experts base theirs on explanation alignment. To our knowledge, this work is the first to show that (1) different factors affect laypeople and domain experts' trust in post hoc explanations and (2) domain experts are subject to specific biases due to their expertise when interpreting post hoc explanations. By uncovering this phenomenon and exposing this cognitive bias, this work motivates the need to educate end users about how to properly interpret explanations and overcome their own cognitive biases, and motivates the development of simple and interpretable faithfulness metrics for end users. This research is particularly important and timely as post hoc explanations are increasingly being used in high-stakes, real-world settings such as medicine.

In the context of avatar-mediated communication, it is crucial for the face-to-face interlocutor to sense the operator's presence and emotions via the avatar. Although androids resembling humans have been developed to convey presence through appearance and movement, few studies have prioritized deepening the communication experience for both operator and interlocutor using android robot as an avatar. Addressing this gap, we introduce the ``Cybernetic Avatar `Yui','' featuring a human-like head unit with 28 degrees of freedom, capable of expressing gaze, facial emotions, and speech-related mouth movements. Through an eye-tracking unit in a Head-Mounted Display (HMD) and degrees of freedom on both eyes of Yui, operators can control the avatar's gaze naturally. Additionally, microphones embedded in Yui's ears allow operators to hear surrounding sounds in three dimensions, enabling them to discern the direction of calls based solely on auditory information. An HMD's face-tracking unit synchronizes the avatar's facial movements with those of the operator. This immersive interface, coupled with Yui's human-like appearance, enables real-time emotion transmission and communication, enhancing the sense of presence for both parties. Our experiments demonstrate Yui's facial expression capabilities, and validate the system's efficacy through teleoperation trials, suggesting potential advancements in avatar technology.

Post hoc explanations have emerged as a way to improve user trust in machine learning models by providing insight into model decision-making. However, explanations tend to be evaluated based on their alignment with prior knowledge while the faithfulness of an explanation with respect to the model, a fundamental criterion, is often overlooked. Furthermore, the effect of explanation faithfulness and alignment on user trust and whether this effect differs among laypeople and domain experts is unclear. To investigate these questions, we conduct a user study with computer science students and doctors in three domain areas, controlling the laypeople and domain expert groups in each setting. The results indicate that laypeople base their trust in explanations on explanation faithfulness while domain experts base theirs on explanation alignment. To our knowledge, this work is the first to show that (1) different factors affect laypeople and domain experts' trust in post hoc explanations and (2) domain experts are subject to specific biases due to their expertise when interpreting post hoc explanations. By uncovering this phenomenon and exposing this cognitive bias, this work motivates the need to educate end users about how to properly interpret explanations and overcome their own cognitive biases, and motivates the development of simple and interpretable faithfulness metrics for end users. This research is particularly important and timely as post hoc explanations are increasingly being used in high-stakes, real-world settings such as medicine.

Grasp force synthesis is a non-convex optimization problem involving constraints that are bilinear. Traditional approaches to this problem involve general-purpose gradient-based nonlinear optimization and semi-definite programming. With a view towards dealing with postural synergies and non-smooth but convex positive semidefinite constraints, we look beyond gradient-based optimization. The focus of this paper is to undertake a grasp analysis of biomimetic grasping in multi-fingered robotic hands as a bilinear matrix inequality (BMI) problem. Our analysis is to solve it using a deep learning approach to make the algorithm efficiently generate force closure grasps with optimal grasp quality on untrained/unseen objects.

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.

We address the task of automatically scoring the competency of candidates based on textual features, from the automatic speech recognition (ASR) transcriptions in the asynchronous video job interview (AVI). The key challenge is how to construct the dependency relation between questions and answers, and conduct the semantic level interaction for each question-answer (QA) pair. However, most of the recent studies in AVI focus on how to represent questions and answers better, but ignore the dependency information and interaction between them, which is critical for QA evaluation. In this work, we propose a Hierarchical Reasoning Graph Neural Network (HRGNN) for the automatic assessment of question-answer pairs. Specifically, we construct a sentence-level relational graph neural network to capture the dependency information of sentences in or between the question and the answer. Based on these graphs, we employ a semantic-level reasoning graph attention network to model the interaction states of the current QA session. Finally, we propose a gated recurrent unit encoder to represent the temporal question-answer pairs for the final prediction. Empirical results conducted on CHNAT (a real-world dataset) validate that our proposed model significantly outperforms text-matching based benchmark models. Ablation studies and experimental results with 10 random seeds also show the effectiveness and stability of our models.

Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task. However, the proposed models do not fully exploit the information in the usually large and noisy KGs, and it is not clear how it can be effectively encoded to be useful for entailment. We present an approach that complements text-based entailment models with information from KGs by (1) using Personalized PageR- ank to generate contextual subgraphs with reduced noise and (2) encoding these subgraphs using graph convolutional networks to capture KG structure. Our technique extends the capability of text models exploiting structural and semantic information found in KGs. We evaluate our approach on multiple textual entailment datasets and show that the use of external knowledge helps improve prediction accuracy. This is particularly evident in the challenging BreakingNLI dataset, where we see an absolute improvement of 5-20% over multiple text-based entailment models.

Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch will lead to a significant performance drop. In this work, we aim to improve the cross-domain robustness of object detection. We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc. We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy. The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner. The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model. We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc. The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.