Spencer Bell, Nobody Knows My Name

Spencer Bell, Nobody Knows My Name is an audiovisual essay about the racist depiction of an African American actor, Spencer Bell, in the first feature length film of The Wizard of Oz (Larry Semon, 1925). The audiovisual essay showcases Bell’s performance, by only selecting and using sequences that he is in. I decided to not only reverse the order of the sequences but also to reverse the footage within the clips themselves. Through reversing the footage from the film, we see Bell’s representation unfold, reanimating his performance. By focussing solely on Bell, the audiovisual essay draws attention to him as an actor and celebrates his talent whilst also illustrating the constraints in which he was working. It does so to ask questions about representation in cinema and more critically to unpick the racist imagery evident onscreen. The audiovisual essay argues that it is important to watch such depictions in order to challenge them, and to confront racist imagery. In focussing in on Bell, it is hoped it will prompt audiences to seek out his work and watch his performances in full and, in turn, understand the institutional racism he was working under. 

Is a Wizard-of-Oz Required for Robot-Led Conversation Practice in a Second Language?

The large majority of previous work on human-robot conversations in a second language has been performed with a human wizard-of-Oz. The reasons are that automatic speech recognition of non-native conversational speech is considered to be unreliable and that the dialogue management task of selecting robot utterances that are adequate at a given turn is complex in social conversations. This study therefore investigates if robot-led conversation practice in a second language with pairs of adult learners could potentially be managed by an autonomous robot. We first investigate how correct and understandable transcriptions of second language learner utterances are when made by a state-of-the-art speech recogniser. We find both a relatively high word error rate (41%) and that a substantial share (42%) of the utterances are judged to be incomprehensible or only partially understandable by a human reader. We then evaluate how adequate the robot utterance selection is, when performed manually based on the speech recognition transcriptions or autonomously using (a) predefined sequences of robot utterances, (b) a general state-of-the-art language model that selects utterances based on learner input or the preceding robot utterance, or (c) a custom-made statistical method that is trained on observations of the wizard’s choices in previous conversations. It is shown that adequate or at least acceptable robot utterances are selected by the human wizard in most cases (96%), even though the ASR transcriptions have a high word error rate. Further, the custom-made statistical method performs as well as manual selection of robot utterances based on ASR transcriptions. It was also found that the interaction strategy that the robot employed, which differed regarding how much the robot maintained the initiative in the conversation and if the focus of the conversation was on the robot or the learners, had marginal effects on the word error rate and understandability of the transcriptions but larger effects on the adequateness of the utterance selection. Autonomous robot-led conversations may hence work better with some robot interaction strategies.

Effect of Sign-recognition Performance on the Usability of Sign-language Dictionary Search

Advances in sign-language recognition technology have enabled researchers to investigate various methods that can assist users in searching for an unfamiliar sign in ASL using sign-recognition technology. Users can generate a query by submitting a video of themselves performing the sign they believe they encountered somewhere and obtain a list of possible matches. However, there is disagreement among developers of such technology on how to report the performance of their systems, and prior research has not examined the relationship between the performance of search technology and users’ subjective judgements for this task. We conducted three studies using a Wizard-of-Oz prototype of a webcam-based ASL dictionary search system to investigate the relationship between the performance of such a system and user judgements. We found that, in addition to the position of the desired word in a list of results, the placement of the desired word above or below the fold and the similarity of the other words in the results list affected users’ judgements of the system. We also found that metrics that incorporate the precision of the overall list correlated better with users’ judgements than did metrics currently reported in prior ASL dictionary research.

LEADOR: A Method for End-To-End Participatory Design of Autonomous Social Robots

Participatory design (PD) has been used to good success in human-robot interaction (HRI) but typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. In this article, we present LEADOR (Led-by-Experts Automation and Design Of Robots), an end-to-end PD methodology for domain expert co-design, automation, and evaluation of social robot behaviour. This method starts with typical PD, working with the domain expert(s) to co-design the interaction specifications and state and action space of the robot. It then replaces the traditional offline programming or WoZ phase by an in situ and online teaching phase where the domain expert can live-program or teach the robot how to behave whilst being embedded in the interaction context. We point out that this live teaching phase can be best achieved by adding a learning component to a WoZ setup, which captures implicit knowledge of experts, as they intuitively respond to the dynamics of the situation. The robot then progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in situ development also means that LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived to demonstrate the feasibility and worth of this approach, provide concrete examples in its application, and identify limitations and opportunities when applying this framework in new environments.

A Spatial Informance Design Method to Elicit Early Interface Prototypes for Augmented Reality

Designing for augmented reality (AR) applications is difficult and expensive. A rapid system for the early design process of spatial interfaces is required. Previous research has used video for mobile AR design, but this is not extensible to head-mounted AR. AR is an emergent technology with no prior design precedent, requiring designers to allow free speculation or risk the pitfalls of ‘path dependence’. In this paper, a participatory elicitation method we call ‘spatial informance design’ is presented. We found combining ‘informance design’, ‘Wizard of Oz’, improvisation, and ‘paper prototyping’, to be a fast and lightweight solution for ideation of rich designs for spatial interfaces. A study using our method with 11 participants, produced similar and wildly different interface configurations and interactions for an augmented reality email application. Based on our findings we propose design implications and an evaluation of our method using spatial informance for the design of head-mounted AR applications.

Conflictual sociability? A paradoxical approach to politicized street theatre

In Agonistics (2013), Chantal Mouffe highlights sociability and notes its potential for artists in devising agonistic counter-hegemonic performances. However, sociability as an isolated factor is unlikely to produce politicized dissent. Instead, therefore, a politicized form of conflictual sociability is created by applying Mouffe’s notion of a ‘conflictual consensus’ (an agreement between opponents to disagree) to art practice. By applying paradoxical thinking to the performance of dissent in the public realm, the article argues for sociability in service of politicized critique. The potential of conflictual sociability is examined through guerrilla street theatre performances, an artform with the capacity to generate unauthorized and participatory incursions into the urban public realm. Firstly, via autoethnographic reflections upon a practice-based research project, The Wizard of Oz (2015) performed in London, United Kingdom; and secondly, in analysis of Dread Scott’s Money to Burn (2010) performance in Wall Street, New York, United States. Conflictual sociability offers a novel methods-led process of engaging agonistically with passers-by (publics) and transforming them into activated participants. Because it is engaging, conflictual sociability creates spaces of public dialogue that antagonistic conflict potentially shuts down. This reveals an effective pedagogy for facilitating agonistic politicized dissent through performative practices in the public realm.

Koordination von Übernahmemanövern beim hochautomatisierten Fahren unter Berücksichtigung der Fahrerverfügbarkeit

ZusammenfassungEine der zentralen Problemstellungen beim bedingt- und hochautomatisierten Fahren liegt in der Gestaltung einer sicheren und komfortablen Aufgabenübertragung zwischen dem automatisierten System und dem menschlichen Fahrer und vice versa. Dieser Beitrag stellt ein holistisches Modell zur Übergabe und Übernahme von Fahraufgaben vor, welches über eine umfassende Fahrerbeobachtung anhand von verschiedenen Sensoren und Referenzsensoren eine an den Fahrerzustand angepasste Übernahme ermöglichen soll. Konfliktsituationen zwischen Fahrer und automatisiertem System sollen unter Berücksichtigung des Fahrer- und Systemzustands über einen technisch implementierten Koordinator detektiert und gelöst werden. In einem Wizard-of-Oz Fahrversuch wird die Veränderung des sensorischen, motorischen und emotionalen Fahrerzustands, welche zentrale Komponenten des Übergabemodells bilden, anhand von zwei Fahrszenarien in Übernahmesituationen detailliert analysiert. Beobachtet werden konnten dabei leicht langsamere Reaktionen der Probanden nach Nebentätigkeiten und eine deutlich steigendes Stresslevel nach Übernahmen.

Multi-Head Attention-Based Long Short-Term Memory for Depression Detection From Speech

Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the inherent information from speech. To focus on more emotionally salient regions of depression speech, in this research, we propose a multi-head time-dimension attention-based long short-term memory (LSTM) model. We first extract frame-level features to store the original temporal relationship of a speech sequence and then analyze their difference between speeches of depression and those of health status. Then, we study the performance of various features and use a modified feature set as the input of the LSTM layer. Instead of using the output of the traditional LSTM, multi-head time-dimension attention is employed to obtain more key time information related to depression detection by projecting the output into different subspaces. The experimental results show the proposed model leads to improvements of 2.3 and 10.3% over the LSTM model on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) corpus, respectively.