An android architecture for bio-inspired honest signalling in Human-Humanoid Interaction

This paper outlines an augmented robotic architecture to study the conditions of successful Human-Humanoid Interaction (HHI). The architecture is designed as a testable model generator for interaction centred on the ability to emit, display and detect honest signals. First we overview the biological theory in which the concept of honest signals has been put forward in order to assess its explanatory power. We reconstruct the application of the concept of honest signalling in accounting for interaction in strategic contexts and in laying bare the foundation for an automated social metrics. We describe the modules of the architecture, which is intended to implement the concept of honest signalling in connection with a refinement provided by delivering the sense of co-presence in a shared environment. Finally, an analysis of Honest Signals, in term of body postures, exhibited by participants during the preliminary experiment with the Geminoid Hi-1 is provided

Molecular scale contact line hydrodynamics of immiscible flows

From extensive molecular dynamics simulations on immiscible two-phase flows, we find the relative slipping between the fluids and the solid wall everywhere to follow the generalized Navier boundary condition, in which the amount of slipping is proportional to the sum of tangential viscous stress and the uncompensated Young stress. The latter arises from the deviation of the fluid-fluid interface from its static configuration. We give a continuum formulation of the immiscible flow hydrodynamics, comprising the generalized Navier boundary condition, the Navier-Stokes equation, and the Cahn-Hilliard interfacial free energy. Our hydrodynamic model yields interfacial and velocity profiles matching those from the molecular dynamics simulations at the molecular-scale vicinity of the contact line. In particular, the behavior at high capillary numbers, leading to the breakup of the fluid-fluid interface, is accurately predicted.Comment: 33 pages for text in preprint format, 10 pages for 10 figures with captions, content changed in this resubmissio

Autonomous Acquisition of Natural Situated Communication

An important part of human intelligence, both historically and operationally, is our ability to communicate. We learn how to communicate, and maintain our communicative skills, in a society of communicators – a highly effective way to reach and maintain proficiency in this complex skill. Principles that might allow artificial agents to learn language this way are in completely known at present – the multi-dimensional nature of socio-communicative skills are beyond every machine learning framework so far proposed. Our work begins to address the challenge of proposing a way for observation-based machine learning of natural language and communication. Our framework can learn complex communicative skills with minimal up-front knowledge. The system learns by incrementally producing predictive models of causal relationships in observed data, guided by goal-inference and reasoning using forward-inverse models. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime TV-style interview, using multimodal communicative gesture and situated language to talk about recycling of various materials and objects. S1 can learn multimodal complex language and multimodal communicative acts, a vocabulary of 100 words forming natural sentences with relatively complex sentence structure, including manual deictic reference and anaphora. S1 is seeded only with high-level information about goals of the interviewer and interviewee, and a small ontology; no grammar or other information is provided to S1 a priori. The agent learns the pragmatics, semantics, and syntax of complex utterances spoken and gestures from scratch, by observing the humans compare and contrast the cost and pollution related to recycling aluminum cans, glass bottles, newspaper, plastic, and wood. After 20 hours of observation S1 can perform an unscripted TV interview with a human, in the same style, without making mistakes

Autonomous Acquisition of Natural Language

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora

Modelling concept prototype competencies using a developmental memory model

The use of concepts is fundamental to human-level cognition, but there remain a number of open questions as to the structures supporting this competence. Specifically, it has been shown that humans use concept prototypes, a flexible means of representing concepts such that it can be used both for categorisation and for similarity judgements. In the context of autonomous robotic agents, the processes by which such concept functionality could be acquired would be particularly useful, enabling flexible knowledge representation and application. This paper seeks to explore this issue of autonomous concept acquisition. By applying a set of structural and operational principles, that support a wide range of cognitive competencies, within a developmental framework, the intention is to explicitly embed the development of concepts into a wider framework of cognitive processing. Comparison with a benchmark concept modelling system shows that the proposed approach can account for a number of features, namely concept-based classification, and its extension to prototype-like functionality

Lipoxin A4 and interleukin-8 levels in cystic fibrosis sputum after antibiotherapy

AbstractAntibiotics are largely prescribed for cystic fibrosis (CF) respiratory exacerbations. Effects of antibiotics on the inflammatory profile of the patients have been shown but remain controversial. Lipoxin A4 (LXA4) is a lipid mediator, reported to play a central role in resolving airway inflammation. The aim of the study was to investigate the consequences of antibiotherapy on LXA4 and IL-8 levels in CF patients' airways.MethodsEighteen CF patients (7 females, median age 20, range 8 to 47 years) consecutively admitted at the CF center of Montpellier for antibiotics during pulmonary exacerbation, were enrolled. Before and after antibiotics, all patients underwent spirometry (FEV1 and FVC), bacterial cultures and cell counts in sputa. IL-8 and LXA4 concentrations were determined in sputum samples by the median of immunometric assays.ResultsAs previously reported, after antibiotics therapy, FEV1 and FVC significantly improved. While neutrophil cell counts and IL-8 levels decreased, the LXA4 levels significantly increased after antibiotics therapy and were inversely correlated with IL-8 levels.In conclusion, we reported a correlation between antibiotics treatments and inflammatory markers in CF sputum. Our data provide evidences for a novel effect of antibiotics increasing the concentration of the anti-inflammatory lipid mediator LXA4

Study of various gynaecological problems and reproductive health awareness amongst adolescents at a rural setup in central India

Background: Adolescent problems are increasing over the years and need special consideration. We, as health care providers, need to focus on young people as investing in their health today will reap rich rewards tomorrow.Methods: All the adolescents presenting with various gynaecological problems were evaluated by detailed history taking and thorough clinical examination after taking an informed consent and explaining them our objective. After examination, adolescent girls were given education by means of slide show about menstruation, care during menses, nutritional diet and prevention of anaemia.Results: Majority of adolescents had regular menstrual pattern. Oligomenorrhoea was the most prevalent abnormality of menstrual pattern in these adolescents. 15.58% adolescents had presented with breast problems like mastalgia, lump in breast, etc. Majority of adolescents studied were aware of the physical signs of puberty (77.25%) and HIV (74.14%). But very few adolescents were aware of the physiology involved in menstruation. Around half of all the adolescents were seen to follow proper menstrual hygiene.Conclusions: Healthy adolescence, the need of the hour

Prevalence of gestational diabetes mellitus, its associated risk factors and pregnancy outcomes at a rural setup in Central India

Background: Gestational diabetes mellitus (GDM) is characterised by carbohydrate intolerance of varying severity with onset or first recognition during pregnancy. GDM is an important public health problem in India.Method: The present study was carried out in 300 antenatal women. Fasting blood glucose was measured after which they were given 75 g oral glucose and plasma glucose was estimated at 2 h. Patients with plasma glucose >140 mg/dl were labelled as GDM. Thus WHO criteria were used for diagnosing GDM. Data was collected from all subjects on demographic characteristics, socioeconomic status, education level, parity, family history of diabetes and/or hypertension, BMI, etc. and pregnancy outcome was studied.Results: Prevalence of GDM was found to be 8.33%. Gestational diabetes mellitus was found to be significantly associated with age, parity, BMI, socioeconomic status, education level and was also found to be associated with adverse pregnancy outcomes.Conclusion: GDM adversely affects maternal and fetal outcomes and its prevalence is steadily rising. Appropriate interventions are required for its control

Affective brain–computer music interfacing

We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach. An aBCMI is constructed to detect a userʼs current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a casebased reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results. The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p < 0.01) and modulate its userʼs affective states significantly above chance level (p < 0.05). Significance. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to userʼs affective states. Possible applications include use in music therapy and entertainmen

Telenoid android robot as an embodied perceptual social regulation medium engaging natural human–humanoid interaction

The present paper aims to validate our research on human–humanoid interaction (HHI) using the minimalist humanoid robot Telenoid. We conducted the human–robot interaction test with 142 young people who had no prior interaction experience with this robot. The main goal is the analysis of the two social dimensions (‘‘Perception’’ and ‘‘Believability’’) useful for increasing the natural behaviour between users and Telenoid.Weadministered our custom questionnaire to human subjects in association with a well defined experimental setting (‘‘ordinary and goal-guided task’’). A thorough analysis of the questionnaires has been carried out and reliability and internal consistency in correlation between the multiple items has been calculated. Our experimental results show that the perceptual behaviour and believability, as implicit social competences, could improve the meaningfulness and the natural-like sense of human–humanoid interaction in everyday life task-driven activities. Telenoid is perceived as an autonomous cooperative agent for a shared environment by human beings