Miracle-dz/UniFAR-FTUs huggingface dataset:
The Shape of Our Bias: Perceived Age and Gender in the Humanoid Robots of the ABOT Database

The present study was aimed at determining the age and gender distribution of the humanoid robots in the ABOT dataset, and providing a systematic data-driven formalization of the process of age and gender categorization of humanoid robots. We involved 153 participants in an online study and asked them to rate the humanoid robots in the ABOT dataset in terms of perceived age, femininity, masculinity, and gender neutrality. Our analyses disclosed that most of the robots in the ABOT dataset were perceived as young adults, and the vast majority of them were attributed a neutral or masculine gender. By merging our data with the data in the ABOT dataset, we discovered that humanlikeness is crucial to elicit social categorization. Moreover, we found out that body manipulators (e.g., legs, torso) guide the attribution of masculinity, surface look features (e.g., eyelashes, apparel) the attribution of femininity, and that robots without facial features (e.g., head, eyes) are perceived as older. Finally, yet importantly, we unveiled that men tend to attribute lower age scores and higher femininity ratings to humanoid robots than women. Our work provides evidence of an existing underlying bias in the design of humanoid robots that needs to be addressed: the under-representation of feminine robots and lack of representation of androgynous ones. We make the results of this study publicly available to the HRI community by attaching the dataset we collected to the present paper and creating a dedicated website.
Affective Responses of Older Adults to the Anthropomorphic GenieConnect Companion Robot During Lockdown of the COVID19 Pandemic

Anthropomorphic robots may reduce loneliness in older people, however, acceptance is requisite for adoption. We collected the experiences of 10 people aged 80–92 who used a pre-market social robot, GenieConnect, for between 2 to 35 days during the COVID19 pandemic restrictions. GenieConnect is a table-top robot with a large face and animated eyes, designed for support and companionship. The robot asked ‘how are you feeling, Name’ each day and delivered lifestyle prompts such as medication reminders. We observed conflicting responses from participants - five expressed positive responses, three negative (two of these withdrew) and two neutral. Positive comments included ‘feeling not alone’; ‘having someone to talk to’; and enjoying being asked ‘how are you feeling’. Negative comments were mainly related to not liking the eyes. Design adaptations were made to increase acceptance. We conclude that robots like GenieConnect could reduce loneliness when a user-centred design approach is taken.
Adaptive Robot Discourse for Language Acquisition in Adulthood

Acquiring a second language in adulthood differs considerably from the approach taken at younger ages. Learning rates tend to decrease during adolescence, and socio-emotional characteristics, like motivation and expectations, take a different perspective for adults. In particular, acquiring communicative competence is a stronger objective for older learners, as an appropriate use of language in social contexts ensures a better community immersion and well-being. This skill is best attained through interactions with proficient speakers, but if this option is not available, social robots present a good alternative for this purpose. However, to obtain optimal results, a robot companion should adapt to the learner's proficiency level and motivation continuously to encourage speech production and increase flu-ency. Our work attempts to achieve this goal by developing an adaptive robot that modifies its spoken dialogue strategy, and visual feedback, to reflect a student's knowledge, proficiency and engagement levels in situated interactions for longterm learning.
It Will Not Take Long! Longitudinal Effects of Robot Conflict Resolution Strategies on Compliance, Acceptance and Trust

Domestic service robots become increasingly prevalent and autonomous, which will make task priority conflicts more likely. The robot must be able to effectively and appropriately negotiate to gain priority if necessary. In previous human-robot interaction (HRI) studies, imitating human negotiation behavior was effective but long-term effects have not been studied. Filling this research gap, an interactive online study ($N=103$) with two sessions and six trials was conducted. In a conflict scenario, participants repeatedly interacted with a domestic service robot that applied three different conflict resolution strategies: appeal, command, diminution of request. The second manipulation was reinforcement (thanking) of compliance behavior (yes/no). This led to a 3×2×6 mixed-subject design. User acceptance, trust, user compliance to the robot, and self-reported compliance to a household member were assessed. The diminution of a request combined with positive reinforcement was the most effective strategy and perceived trustworthiness increased significantly over time. For this strategy only, self-reported compliance rates to the human and the robot were similar. Therefore, applying this strategy potentially seems to make a robot equally effective as a human requester. This paper contributes to the design of acceptable and effective robot conflict resolution strategies for long-term use.
An Analysis of Metrics and Methods in Research from Human-Robot Interaction Conferences, 2015–2021

Standardized metrics and methods are critical to-wards wider adoption of HRI technologies in real-world applications. However, the interdisciplinary nature of HRI creates an inherently decentralized research paradigm that limits the use of standardized metrics for baseline comparisons among studies. This limitation restricts both the real-world adoption and academic replicability of HRI solutions developed by the research community. To identify specific opportunities for reuse of metrics and methods in HRI, this paper presents a comprehensive survey of 1464 papers from the ACM/IEEE International Conference on Human-Robot Interaction (HRI) and the IEEE International Conference on Robot and Human Interactive Communication (Ro-Man) over seven years. By providing a holistic perspective of the metrological tools leveraged in the current state-of-practice of HRI research, we find that a significant portion of HRI studies use custom surveys, thus limiting baseline comparison. Hence, the analysis in this work aims to advance the field of HRI by identifying specific barriers to adoption of HRI technologies in addition to proposing solutions to overcome existing limitations in the context of metrics and methodologies.
Robot, Pass Me the Tool: Handle Visibility Facilitates Task-oriented Handovers

A human handing over an object modulates their grasp and movements to accommodate their partner's capa-bilities, which greatly increases the likelihood of a successful transfer. State-of-the-art robot behavior lacks this level of user understanding, resulting in interactions that force the human partner to shoulder the burden of adaptation. This paper investigates how visual occlusion of the object being passed affects the subjective perception and quantitative performance of the human receiver. We performed an experiment in virtual reality where seventeen participants were tasked with repeatedly reaching to take a tool from the hand of a robot; each of the three tested objects (hammer, screwdriver, scissors) was presented in a wide variety of poses. We carefully analysed the user's hand and head motions, the time to grasp the object, and the chosen grasp location, as well as participants' ratings of the grasp they just performed. Results show that initial visibility of the handle significantly increases the reported holdability and immediate usability of a tool. Furthermore, a robot that offers objects so that their handles are more occluded forces the receiver to spend more time in planning and executing the grasp and also lowers the probability that the tool will be grasped by the handle. Together these findings indicate that robots can more effectively support their human work partners by increasing the visibility of the intended grasp location of objects being passed.
First Attempt of Gender-free Speech Style Transfer for Genderless Robot

Some robots for human-robot interaction are designed with female or male physical appearance. Other robots are endowed with no gender characteristics, namely genderless robots, such as Pepper and NAO robot. A robot with male or female physical appearance should possess the mapped speech gender style during a natural human-robot interaction, which can be learned from humans' male or female speech. In this paper, we make a new trial to synthesis gender-free speeches for physically genderless robots, which is promising in order to improve a more natural human-robot interaction with genderless robots. Our gender style-controlled speech synthesizer takes the speech text and gender style embedding as inputs to generate speech audio. A speech gender encoder network is used to extract the embedding of the speech gender style with female and male speeches as input. Based on the distribution of the female and male gender style embedding, we explore the gender-free speech style embedding space where we sample some gender-free embedding vectors to generate genderless speech audio. This is a preliminary work where we show how the genderless speech audio wave will be synthesized from text.
Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti

We present ∼0. ″4 resolution images of CO(3–2) and associated continuum emission from the gas-bearing debris disk around the nearby A star 49 Ceti, observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We analyze the ALMA visibilities in tandem with the broadband spectral energy distribution to measure the radial surface density profiles of dust and gas emission from the system. The dust surface density decreases with radius between ∼100 and 310 au, with a marginally significant enhancement of surface density at a radius of ∼110 au. The SED requires an inner disk of small grains in addition to the outer disk of larger grains resolved by ALMA. The gas disk exhibits a surface density profile that increases with radius, contrary to most previous spatially resolved observations of circumstellar gas disks. While ∼80% of the CO flux is well described by an axisymmetric power-law disk in Keplerian rotation about the central star, residuals at ∼20% of the peak flux exhibit a departure from axisymmetry suggestive of spiral arms or a warp in the gas disk. The radial extent of the gas disk (∼220 au) is smaller than that of the dust disk (∼300 au), consistent with recent observations of other gas-bearing debris disks. While there are so far only three broad debris disks with well characterized radial dust profiles at millimeter wavelengths, 49 Ceti’s disk shows a markedly different structure from two radially resolved gas-poor debris disks, implying that the physical processes generating and sculpting the gas and dust are fundamentally different.
“And then what happens?” Promoting Children's Verbal Creativity Using a Robot

While creativity has been previously studied in Child-Robot Interaction (cHRI), the effect of regulatory focus on creativity skills has not been investigated. This paper presents an exploratory study that, for the first time, uses the Regulatory Focus Theory (RFT) to assess children's creativity skills in an educational context with a social robot. We investigated whether two key emotional regulation techniques, promotion (approach) and prevention (avoidance), stimulate creativity during a story-telling activity between a child and a robot. We conducted a between-subjects field study with 69 children between the ages of 7 and 9 years old, divided between two study conditions: (1) promotion, where a social robot primes children for action by eliciting positive emotional states, and (2) prevention, where a social robot primes children for avoidance by evoking a states related to security and safety associated with blockage-oriented behaviors. To assess changes in creativity as a response to the priming interaction, children were asked to tell stories to the robot before (pre-test) and after (post-test) the priming interaction. We measured creativity levels by analyzing the verbal content of the stories. We coded verbal expressions related to creativity variables, including fluency, flexibility, elaboration, and originality. Our results show that children in the promotion condition generated significantly more ideas, and their ideas were on average more original in the stories they created in the post-test rather than in the pre-test. We also modeled the process of creativity that emerges during storytelling in response to the robot's verbal behavior. This paper enriches the scientific understanding of creativity emergence in child-robot collaborative interactions.
Cognitively Assistive Robots at Home: HRI Design Patterns for Translational Science

Much research in healthcare robotics explores extending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.
A Conversational Framework for Semantic Question Answering in Customer Services with Machine Learning on Knowledge Graph

Despite the recent advances in Natural Language Processing (NLP) techniques many issues and ineﬃciencies arise when it comes to creating a system capable of interacting with the users by means of text conversations. The current techniques rely on the development of chatbots that, however, require designing the conversation ﬂow, deﬁning training questions and associating the expected responses. Even though this process allows the creation of eﬀective question-answering systems, this methodology is not scalable, especially when the answers are to be found in documents. Other approaches, instead, rely on graph embedding techniques and graph neural networks to deﬁne the best answer given a question. These methods, however, require set-up training routines and to dispose of the ground truth that, in general, is diﬃcult to retrieve or create for real industrial applications. In this paper we introduce a conversational framework for semantic question answering. Our work relies on knowledge graphs and the use of machine learning for determining the best answer given a question associated with the content of the knowledge graph. In addition, by leveraging text mining techniques we are able to identify the best set of answers that suit the question that are further ﬁltered by means of deep learning algorithms.
Perceptions of Social Robots as Motivating Learning Companions for Online Learning

The shift for online learning, accelerated during the COVID-19 pandemic, has highlighted the importance of self-regulated learning skills and the challenges many students face while practicing online learning. Could social robots help by serving as motivating learning companions in online learning settings? In this study, we explore the perceived potential of motivating learning companion robots designed based on Self-Determination Theory. One hundred and eighty-five participants watched one of five videos displaying a simulated student-robot interaction and rated their perceptions of the intrinsic motivation of the student in the videos and the type of support provided by the social robot. Overall, the motivating learning companion affected the perceived sense of relatedness and perceived level of anxiety of the student in the video, as well as the social robot's perceived support. Differences between the conditions are discussed.
CHALLENGES FOR ENERGY-HARVESTING WIRELESS SENSOR NETWORKS

With the recent introduction of Energy-Harvesting nodes, security is gaining more and more importance in sensor networks. By exploiting the ability of scavenging energy from the surrounding environment, the lifespan of a node has drastically increased. This is one of the reason why security needs a new take in this topic. Traditional solutions may not work in this new field. Brand new challenges and threats may arise and new solutions have to be designed. In this paper we present a taxonomy of attacks, focusing on how they change in the energy harvesting scenario compared to regular sensor networks. Finally, we present and discuss existing security solutions for EH-WSNs.
RNomics and genomic comparative analysis reveal a large group of species-specific small non-message RNAs in the silkworm Bombyx mori

Accumulating evidences show that small nonprotein coding RNAs (ncRNAs) play important roles in development, stress response and other cellular processes. The silkworm is an important model for studies on insect genetics and control of lepidopterous pests. Here, we have performed the first systematic identification and analysis of intermediate size ncRNAs (50–500 nt) in the silkworm. We identified 189 novel ncRNAs, including 141 snoRNAs, six snRNAs, three tRNAs, one SRP and 38 unclassified ncRNAs. Forty ncRNAs showed significantly altered expression during silkworm development or across specific stage transitions. Genomic comparisons revealed that 123 of these ncRNAs are potentially silkworm-specific. Analysis of the genomic organization of the ncRNA loci showed that 32.62% of the novel snoRNA loci are intergenic, and that all the intronic snoRNAs follow the pattern of one-snoRNA-per-intron. Target site analysis predicted a total of 95 20-O-methylation and pseudouridylation modification sites of rRNAs, snRNAs and tRNAs. Together, these findings provide new clues for future functional study of ncRNA during insect development and evolution.
Recurrent Neural Networks for Reinforcement Learning : an Investigation of Relevant Design Choices

When solving real-world problems like traffic control with reinforcement learning, the observation of the environment is often noisy or simply incomplete. Traditional forms of Q-learning are ill-equipped to handle this kind of problems called Partially Observable Markov Decision Problems (POMDPs). When aiming to solve a POMDP with function approximation a lot of research has made use of recurrent neural networks. With the recent successes of Deep Q-Networks it was inevitable that Deep Recurrent Q-Networks would be introduced. Even though the results of this model are very promising, most researchers make arbitrary design choices when it comes to the recurrent networks. This thesis aims to better investigate such design choices in the context of a traffic control problem. The second contribution of this thesis is a new architecture called Action Motivation DRQN (AMDRQN). This model appends the probability of the previously chosen action to the observation input. This enables an agent to make decisions based on sequences of observation-action pairs but is also creates a new path of gradients over which the parameters can be updated using information about the action probabilities.
The influence of emotional confidence on brand attitude: using brand belief as mediating variable

Abstract The direct influence of emotional responses on thinking is firmly established in the literature. The assumption that emotion has primacy over cognition challenges conventional wisdom that both attitude and confidence are primarily cognitive in nature. In order to examine this particular assumption, an alternative conceptualisation has been introduced, called ‘emotional confidence’ (EC). This study is the first attempt to measure EC as a separate construct and to test whether EC can be a determinant of brand attitude (BA) and this relationship can be mediated by brand beliefs. The data was collected from Swansea University Students in context of car buying (N = 197) and analysed through Structural Equation Modelling (SEM). The results indicated that EC can be primary determinant of BA and the relationship between the two is mediated by brand beliefs. Theoretical and managerial implications of the findings are discussed in the light of the study’s limitations and areas for further research indicated.
Chapter 4 Perception and Synthesis of Sound-Generating Materials

The mechanical properties of sound-generating objects and events in our environment determine lawfully the acoustical structure of the signals they radiate (e.g. Fletcher and Rossing 1991). The ability of listeners to estimate the mechanical properties of everyday non-vocal, non-music sound sources based on acoustical information alone has been the object of empirical research for more than three decades (Vanderveer 1979). Given the lawful specification of the mechanics of the sound source in the acoustical structure, and the adaptive tendency to interpret sensory information in terms of the properties of objects and events in the environment, it is thus not surprising that source-perception abilities are often remarkably accurate (see Lutfi 2007, for a review). The concept of material is central to the study of source perception from both a theoretical and empirical point of view. The theoretical relevance of this concept originates from the work of Gaver, who outlined a widely influential taxonomy of everyday sound events (Gaver 1993). Accordingly, non-vocal sound sources offer perceptual systems with information about “materials in interaction”, and, at the most general level of the taxonomy, can be classified into three categories depending on the state of matter of the vibrating sound-generating substance: (i) solid sound sources (e.g. clapping); (ii) liquid sound sources (e.g. pouring coffee); (iii) aerodynamic/gaseous sound sources (e.g. wind; explosions). For a variety of reasons (e.g. easiness in manipulation of source mechanics; ecological pervasiveness), the