Human Interactor Research Articles

Understanding pre-service teachers' experiences of a mixed reality simulation environment: An analysis of pre-service teachers’ perspectives on communicating with a simulated parent avatar


 
 
 The integration of simulation platforms in the Initial Teacher Education (ITE) programs enables pre-service teachers (PSTs) to experience a range of teaching scenarios that they might not otherwise encounter in real-world practicum contexts. Understanding PSTs’ perspectives of their experiences of these new learning spaces is critical in determining the role played by simulation platforms in the preparation for placements, their specific affordances for learning and how these platforms can be effectively integrated to support PSTs’ practices. This paper reports on a pilot study of PSTs’ participation in a simulated parent conference session with a parent avatar in SIMLabTM, a mixed reality simulation environment. SIMLabTM allows the facilitation of synchronous responses by a human interactor playing a virtual reality avatar to enhance the authenticity of the experience for PSTs. Based on data drawn from a cross- sectional survey design, the paper provides an analysis of PSTs’ perspectives on six possible affordances of SIMLabTM and describes emergent themes gleaned from PSTs’ comments on their learning experiences within and in relation to SIMLabTM. The findings inform the design and implementation of the simulation experience in ITE programs.
 
 

Viewpoints AI: Demonstration

This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.

Viewpoints AI

This article describes a technical approach to movement-based interactions between a human interactor and an intelligent agent based on the theatrical Viewpoints movement framework. The Viewpoints AI system features procedural gesture interpretation using shallow semantics and deep aesthetics analysis from the Viewpoints framework. The installation creates a liminal virtual / real space for the human and AI to interact by the use of digital projection for the AI visualization and shadow play to represent the human. Observations from a recent public demonstration of the system and future directions of work are also discussed.

Interactive imitation learning for spacecraft path-planning in binary asteroid systems

Exploration of small body systems poses the problem of designing path planning strategies for possibly uncharted environments. Traditional methods aimed at developing rigorous trajectory baselines may suffer inefficiencies, or turn infeasible when confronted with unknown dynamics. In strongly non-linear dynamics, mapping point design solutions from one dynamical regime to another may be hindered by underlying chaotic behavior. Rather than relying on baseline driven approaches, more generalized strategies may be found by observing human pilots controlling spacecraft motion within varying dynamical environments; the resultant data can then be utilized to initialize machine learning agents to provide more autonomous solutions. A previous numerical experiment resulted in a technical dataset comprising of human-based path planning strategies across a range of binary asteroid systems. This dataset is now used to train various imitation learning agents, and initiate the creation of a framework that integrates human–machine cooperation into the early training phases of artificial intelligent agents; the current application is for spacecraft guidance in binary asteroid systems, as a prototype of complex, potentially unknown, orbit dynamics. An interactive training architecture, based on the DAgger algorithm, is designed and employed to train both original and interactively coached agents, the latter stemming from both corrective and evaluative feedback by a real time human interactor. All agents were interactively trained for a predefined time period. The results from this investigation may provide the first, empirical observations of behavioral cloning within multi-body dynamics with largely randomized parameters, with some notable contributions including early characterization of training time, initial evidence of an autonomous agent learning meaningful policy features via imitation, and early identification of challenges in training fully autonomous agents for a multi-body dynamics path planning problem of this complexity and high dimensional state space.

Trait Based Trustworthiness Assessment in Human-Agent Collaboration Using Multi-Layer Fuzzy Inference Approach

Trust is an essential requirement for effective Human-Agent interaction as artificial agents are becoming part of human society in a social context. To blend into our society and maximize their acceptability and reliability, artificial agents need to adapt to the complexity of their surroundings, like humans. This adaptation should come through knowing whom to trust by evaluating the trustworthiness of its human mate. It is therefore required to build cognitive agents with trust models that may allow them to trust humans the same way a human trusts other humans keeping under consideration all factors influencing the human agent trust mechanism. Several antecedents within the cognitive system itself and the surroundings dynamically influence the trust mechanism. Personality, as a trusted antecedent has been found to have a substantial impact in predicting human interactor’s trustworthiness that critically assists trust decision making. Current research, therefore, aims to infuse characteristics of respective humans as the antecedent of the human agent trust process. This is accomplished by incorporating into the trust model the agent’s capability to perceive the personality traits of the human interactor. The current work is focused on introducing a trustworthiness assessment model (TAMFIS) based on fuzzy inference to assess human’s trustworthiness towards artificial agents by exploring the human’s personality traits that predict trustworthiness. The artificial agent could develop its character towards its human collaborators that will help it in effective interactions. The testing of the proposed architecture is carried out using Dempster Shafer Theory of belief and estimation. It is anticipated that the proposed trust model will effectively evaluate the trustworthiness of human collaborators and develop a more reliable human–agent trust relationship.

Human cytomegalovirus UL23 inhibits transcription of interferon-γ stimulated genes and blocks antiviral interferon-γ responses by interacting with human N-myc interactor protein.

Interferon-γ (IFN-γ) represents one of the most important innate immunity responses in a host to combat infections of many human viruses including human herpesviruses. Human N-myc interactor (Nmi) protein, which has been shown to interact with signal transducer and activator of transcription (STAT) proteins including STAT1, is important for the activation of IFN-γ induced STAT1-dependent transcription of many genes responsible for IFN-γ immune responses. However, no proteins encoded by herpesviruses have been reported to interact with Nmi and inhibit Nmi-mediated activation of IFN-γ immune responses to achieve immune evasion from IFN-γ responses. In this study, we show strong evidence that the UL23 protein of human cytomegalovirus (HCMV), a human herpesvirus, specifically interacts with Nmi. This interaction was identified through a yeast two-hybrid screen and co-immunoprecipitation in human cells. We observed that Nmi, when bound to UL23, was not associated with STAT1, suggesting that UL23 binding of Nmi disrupts the interaction of Nmi with STAT1. In cells overexpressing UL23, we observed (a) significantly reduced levels of Nmi and STAT1 in the nuclei, the sites where these proteins act to induce transcription of IFN-γ stimulated genes, and (b) decreased levels of the induction of the transcription of IFN-γ stimulated genes. UL23-deficient HCMV mutants induced higher transcription of IFN-γ stimulated genes and exhibited lower titers than parental and control revertant viruses expressing functional UL23 in IFN-γ treated cells. Thus, UL23 appears to interact directly with Nmi and inhibit nuclear translocation of Nmi and its associated protein STAT1, leading to a decrease of IFN-γ induced responses and an increase of viral resistance to IFN-γ. Our results further highlight the roles of UL23-Nmi interactions in facilitating viral immune escape from IFN-γ responses and enhancing viral resistance to IFN antiviral effects.

Here's looking at you, baby: What gaze and movement reveal about minimal pair word-object association at 14 months

AbstractThe ability, or lack thereof, of 14-month-old infants to associate novel, minimal pair word-forms with novel objects in a variety of experimental settings has been a crucial research window into how infants go about the quintessential linguistic task of learning words. Here we ask whether the presence of a human interactor in the experimental setting facilitates minimal pair word-object association at this age. In addition to standard looking time measures to test this question, we also introduce the use of measures of infant movement derived by the application of an efficient algorithm that measures motion from 2D video. Infant gaze patterns across the experimental session identified two groups of infants, those engaging in more and those engaging in less mutual gaze with the Experimenter; both groups demonstrated success in the task by both looking time and movement measures. Infants did not succeed in the task by either measure when a videotaped Experimenter presented the labels. We suggest that infants at this age are in transition from being good “information consumers” to becoming good “information seekers,” and that the presence of the live Experimenter plays a crucial role in making it possible for infants to demonstrate their nascent word learning abilities. Further, we explore insights into the looking time results provided by the movement measures as well as novel contributions to our understanding of language acquisition afforded by the examination of infant movement.

Gesture and language, gesture as language, language as gesture: Comments on Loehr and Fais et al.

1.� Gestureandlanguage As Loehr notes in his paper in this issue (Loehr 2012), there has been a growing interest among gesture researchers and linguists in relationship between ges- ture and language. Loehr expands upon work in this area by examining interaction between intonation and gesture, noting temporal, structural, and prag- matic synchronies. Loehr reports a number of intriguing findings. First, his research shows that in- tonational and gestural events occur near each other, with tones clustering tightly around gestures. He also notes a somewhat looser correlation between gestural phrases and intermediate phrases. The most interesting finding, however, is that gestural and intonational meanings correlate. His data reveal a number of prag- matic functions that are performed by intonation and gesture in tight synchrony. Based on these findings, and primarily temporal alignment of gesture phrases with intermediate phrases, Loehr offers a possible explanation for this wide- spread synchrony between intonation and gesture. It reflects, he suggests, the size of cognitive which can be expressed through a single surge of bodily and vocal action. That is, both intermediate intonational phrases and gesture phrases act to package unfolding cognitive content, and this, Loehr suggests, provides further support for hypothesis that gesture shares a common origin with speech. Fais et al. (2012) report on a series of experiments designed to examine abil- ity of infants to associate novel, minimal pair word-forms with novel objects. They also introduce variable of whether presence of a human interactor facili- tates this word-object association. In previous studies, experimental method by which this might have been studied was standard looking time measures. One sig- nificant aspect of Fais et al.'s work - both in terms of specific language acquisi- tion questions addressed but also for a large view of nature of language and gesture - is use of an optical flow algorithm permitting sophisticated analysis of kinematics of infant motion during these tasks. The results show that infants in their study benefitted from presence of a live experimenter in demonstrating success in associating novel, minimal pair

InterActor: Speech-Driven Embodied Interactive Actor

A speech-driven embodied interactive actor called InterActor, with functions of both speaker and listener, was developed for activating human interaction and communication by generating expressive actions and motions coherently related to speech input. InterActor is the electronic media version of a physical interaction robot called InterRobot for robot-mediated communication support, which is free of the hardware restrictions for human interface of advanced graphical user interface based network communication. By using InterActor, the concept of speech-driven embodied interaction system is proposed for human interaction sharing by the entrainment between human speech and InterActor motions in remote communication. The prototype of the system is developed, and the sensory evaluation and behavioral analysis in human communication through InterActor demonstrates the effectiveness of the system. Actual applications of InterActor to human interface are also demonstrated. The system is a practical communication support system, which activates human interaction and communication on the basis of only speech input.

Identification of ubiquilin, a novel presenilin interactor that increases presenilin protein accumulation.

Mutations in the highly homologous presenilin genes encoding presenilin-1 and presenilin-2 (PS1 and PS2) are linked to early-onset Alzheimer's disease (AD). However, apart from a role in early development, neither the normal function of the presenilins nor the mechanisms by which mutant proteins cause AD are well understood. We describe here the properties of a novel human interactor of the presenilins named ubiquilin. Yeast two-hybrid (Y2H) interaction, glutathione S-transferase pull-down experiments, and colocalization of the proteins expressed in vivo, together with coimmunoprecipitation and cell fractionation studies, provide compelling evidence that ubiquilin interacts with both PS1 and PS2. Ubiquilin is noteworthy since it contains multiple ubiquitin-related domains typically thought to be involved in targeting proteins for degradation. However, we show that ubiquilin promotes presenilin protein accumulation. Pulse-labeling experiments indicate that ubiquilin facilitates increased presenilin synthesis without substantially changing presenilin protein half-life. Immunohistochemistry of human brain tissue with ubiquilin-specific antibodies revealed prominent staining of neurons. Moreover, the anti-ubiquilin antibodies robustly stained neurofibrillary tangles and Lewy bodies in AD and Parkinson's disease affected brains, respectively. Our results indicate that ubiquilin may be an important modulator of presenilin protein accumulation and that ubiquilin protein is associated with neuropathological neurofibrillary tangles and Lewy body inclusions in diseased brain.