An emotion and memory model for social robots : a long-term interaction

In this thesis, we investigate the role of emotions and memory in social robotic companions. In particular, our aim is to study the effect of an emotion and memory model towards sustaining engagement and promoting learning in a long-term interaction. Our Emotion and Memory model was based on how humans create memory under various emotional events/states. The model enabled the robot to create a memory account of user's emotional events during a long-term child-robot interaction. The robot later adapted its behaviour through employing the developed memory in the following interactions with the users. The model also had an autonomous decision-making mechanism based on reinforcement learning to select behaviour according to the user preference measured through user's engagement and learning during the task. The model was implemented on the NAO robot in two different educational setups. Firstly, to promote user's vocabulary learning and secondly, to inform how to calculate area and perimeter of regular and irregular shapes. We also conducted multiple long-term evaluations of our model with children at the primary schools to verify its impact on their social engagement and learning. Our results showed that the behaviour generated based on our model was able to sustain social engagement. Additionally, it also helped children to improve their learning. Overall, the results highlighted the benefits of incorporating memory during child-Robot Interaction for extended periods of time. It promoted personalisation and reflected towards creating a child-robot social relationship in a long-term interaction

Induced Model Matching: How Restricted Models Can Help Larger Ones

We consider scenarios where a very accurate predictive model using restricted features is available at the time of training of a larger, full-featured, model. This restricted model may be thought of as "side-information", derived either from an auxiliary exhaustive dataset or on the same dataset, by forcing the restriction. How can the restricted model be useful to the full model? We propose an approach for transferring the knowledge of the restricted model to the full model, by aligning the full model's context-restricted performance with that of the restricted model's. We call this methodology Induced Model Matching (IMM) and first illustrate its general applicability by using logistic regression as a toy example. We then explore IMM's use in language modeling, the application that initially inspired it, and where it offers an explicit foundation in contrast to the implicit use of restricted models in techniques such as noising. We demonstrate the methodology on both LSTM and transformer full models, using $N$-grams as restricted models. To further illustrate the potential of the principle whenever it is much cheaper to collect restricted rather than full information, we conclude with a simple RL example where POMDP policies can improve learned MDP policies via IMM

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

Heterogeneous integration of KY(WO4)2-on-glass : a bonding study

Rare-earth ion doped potassium yttrium double tungstate, RE: KY(WO4)(2), is a promising candidate for small, power-efficient, on-chip lasers and amplifiers. There are two major bottlenecks that complicate the realization of such devices. Firstly, the anisotropic thermal expansion coefficient of KY(WO4)(2) makes it challenging to integrate the crystal on glass substrates. Secondly, the crystal layer has to be, for example, < 1 mu m to obtain single mode, high refractive index contrast waveguides operating at 1550 nm. In this work, different adhesives and bonding techniques in combination with several types of glass substrates are investigated. An optimal bonding process will enable further processing towards the manufacturing of integrated active optical KY(WO4)(2) devices. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

The Paradigm of OS and OCB: The Influence of Person-environment Fit in Pakistani Banking Firms

Successful organizational socialization and organizational citizenship behavior are regarded as an important consequence to accomplish organizational performance. This study contributes to human resource management (HRM) by offering a contextualized model of OS, OCB and person-environment fit and its effectiveness in banking firms of Pakistan. The present research is to find the mediating effect of person-environment fit on the relationship between organizational socialization and organizational citizenship behavior

Biosynthesis and Degradation of Carotenoids in Ornamental Crops with specific reference to Chrysanthemum

Carotenoids are lipophilic secondary metabolites derived from the isoprenoid pathway, accumulated in most plant organs and widely used as an antioxidant. Carotenoids synthesized in chloroplasts are essential for protecting tissues against photo-oxidative damage in the green tissues of higher plants. The importance of carotenoids for plant growth and development is evident since at least two major phytohormones, strigolactones and abscisic acid, are derived from carotenoid precursors. In flowers, carotenoids synthesized in the chromoplasts provide colour to the petals, ranging from yellow to red, in order to attract pollinators and determines the commercial value of ornamental plants. On analysis in chrysanthemum, β, ɛ-carotenoids, lutein and its derivatives, reflecting the high expression levels of lycopene ɛ-cyclase (LCYE) were found in yellow petals compared to the ratio of β, β-carotenoids to total carotenoids found in leaves reflecting the high expression levels of lycopene β-cyclase (LCYB). Petals of the yellow-flowered cultivar Yellow Paragon showed increased accumulation and drastic componential changes of carotenoids as they mature, compared to petals of the white-flowered cultivar Paragon that showed drastically decreased carotenoid content during petal development.The white petals of chrysanthemum (Chrysanthemum morifolium Ramat.) contain a factor that inhibits the accumulation of carotenoids. All the white-flowered chrysanthemum cultivars tested showed high levels of CmCCD4a transcript in their petals, whereas most of the yellow flowered cultivars showed extremely low levels indicating that in white petals of chrysanthemums, carotenoids are synthesized but subsequently degraded into colourless compounds, which results in the white colour. Studying the regulatory mechanisms underlying carotenoid accumulation in ornamental plants at the molecular level will help in producing novel coloured cultivars by plant transformation

Towards Robust Place Recognition for Robot Localization

Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of higher flexibility and association of semantics to the model. Ideally, a place recognition algorithm should be robust to dynamic changes and it should perform consistently when recognizing a room (for instance a corridor) in different geographical locations. Also, it should be able to categorize places, a crucial capability for transfer of knowledge and continuous learning. In order to test the suitability of visual recognition algorithms for these tasks, this paper presents a new database, acquired in three different labs across Europe. It contains image sequences of several rooms under dynamic changes, acquired at the same time with a perspective and omnidirectional camera, mounted on a socket. We assess this new database with an appearance based algorithm that combines local features with support vector machines through an ad-hoc kernel. Results show the effectiveness of the approach and the value of the databas

Applying adaptive social mobile agent to facilitate learning

In this paper, we present our idea about applying an adaptive social mobile agent in a game based scenario to support foreign language vocabulary learning. We hypothesize that through implementing an adaptive agent, we may mitigate the problem of a loss in child engagement or may also prolong the time a child takes to lose interest. We then present details on architecture and implementation of an adaptive social mobile agent