None But "We Heathen": Shaku Soen at the World's Parliament of Religions

The aftermath of the performance by the Japanese delegation at the World's Parliament of Religions in Chicago in 1893 has been well documented—it marked the beginning of the West's introduction to Japanese Buddhism. What has been less well documented is the intellectual background and influences that went into producing that performance, in particular the performance of the man who would eventually emerge as the delegation's most historically prominent member, Shaku Soen (1859-1919). This paper attempts to use Soen as a case study to examine the intellectual and political milieu which Japanese Buddhism helped to inform, and was informed by, during the Meiji Era (1868-1912). It draws upon established research, as well as primary sources (including Soen's own Parliament addresses, writings, and journals) in order to support this examination

A Narrative Approach to Human-Robot Interaction Prototyping for Companion Robots

© 2020 Kheng Lee Koay et al., published by De Gruyter This work is licensed under the Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/This paper presents a proof of concept prototype study for domestic home robot companions, using a narrative-based methodology based on the principles of immersive engagement and fictional enquiry, creating scenarios which are inter-connected through a coherent narrative arc, to encourage participant immersion within a realistic setting. The aim was to ground human interactions with this technology in a coherent, meaningful experience. Nine participants interacted with a robotic agent in a smart home environment twice a week over a month, with each interaction framed within a greater narrative arc. Participant responses, both to the scenarios and the robotic agents used within them are discussed, suggesting that the prototyping methodology was successful in conveying a meaningful interaction experience.Peer reviewe

The impact of peoples' personal dispositions and personalities on their trust of robots in an emergency scenario

Humans should be able to trust that they can safely interact with their home companion robot. However, robots can exhibit occasional mechanical, programming or functional errors. We hypothesise that the severity of the consequences and the timing of a robot's different types of erroneous behaviours during an interaction may have different impacts on users' attitudes towards a domestic robot. First, we investigated human users' perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. Second, we used an interactive storyboard to evaluate participants' degree of trust in the robot after it performed tasks either correctly, or with 'small' or 'big' errors. Finally, we analysed the correlation between participants' responses regarding their personality, predisposition to trust other humans, their perceptions of robots, and their interaction with the robot. We conclude that there is correlation between the magnitude of an error performed by a robot and the corresponding loss of trust by the human towards the robot. Moreover we observed that some traits of participants' personalities (conscientiousness and agreeableness) and their disposition of trusting other humans (benevolence) significantly increased their tendency to trust a robot more during an emergency scenario.Peer reviewe

Application of support vector machines to detect hand and wrist gestures using a myoelectric armband

Farshid Amirabdollahian, Michael Walters, ‘Application of support vector machines to detect hand and wrist gestures using a myoelectric armband’, paper presented at the International conference on rehabilitation robotics: ICORR2017, London, UK, 17-21 July, 2017.The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab. Background: With the technological advances in sensing human motion, and its potential to drive and control mechanical interfaces remotely or to be used as input interfaces, a multitude of input mechanisms are used to link actions between the human and the robot. In this study we explored the feasibility of using human arm’s myoelectric signals with the aim of identifying a number of gestures automatically. Material and methods: Participants (n = 26) took part in a study with the aim to assess the gesture detection accuracy using myoelectric signals. The Myo armband was used worn on the forearm. The session was divided into three phases, familiarisation: where participants learned how to use the armband, training: when participants reproduced a number of random gestures presented on screen to train our machine learning algorithm; and recognition: when gestures presented on screen were reproduced by participants, and simultaneously recognised using the machine learning routines. Support vector machines were used to train a model using participant training values, and to recognise gestures produced by the same participants. Different Kernel functions and electrode combinations were studied. Also we contrasted different lengths of training values versus different lengths for the recognition samples. Results: One participant did not complete the study due to technical errors during the session. The remaining (n = 25) participants completed the study allowing to calculate individual accuracy for grasp detection. The overall accuracy was 94.9% with data from 8 electrodes , and 72% where only four of the electrodes were used. The linear kernel outperformed the polynomial, and radial basis function. Exploring the number of training samples versus the achieved recognition accuracy, results identified acceptable accuracies (> 90%) for training around 3.5s, and recognising grasp episodes of around 0.2s long. The best recognised grasp was the hand closed (97.6%), followed by cylindrical grasp (96.8%), the lateral grasp (94%) and tripod (92%). Discussions: The recognition accuracy for the grasp performed is similar to our earlier work where a mechatronic device was used to perform, record and recognise these grasps. This is an interesting observation, as our previous effort in aligning the kinematic and biological signals had not found statistically significant links between the two. However, when the outcome of both is used as a label for identification, in this case gesture, it appears that machine learning is able to identify both kinematic and electrophysiological events with similar accuracy. Future work: The current study considers use of support vector machines for identifying human grasps based on myoelectric signals acquired from an off the shelf device. Due to the length of sessions in the experiment, we were only able to gather 5 seconds of training data and at a 50Hz sampling frequency. This provided us with limited amount of training data so we were not able to test shorter training times (< 2.5s). The device is capable of faster sampling, up to 200Hz and our future studies will benefit from this sampling rate and longer training sessions to explore if we can identify gestures using smaller amount of training data. These results allows us to progress to the next stage of work where the Myo armband is used in the context of robot-mediated stroke rehabilitation.Peer reviewedFinal Accepted Versio

The Marginal Cost of Public Funds and Tax Reform in Africa

In this paper we propose estimates of the marginal cost of public\ud funds (MCF) in 38 African countries. We develop a simple general equilibrium model that can handle taxes on five major tax classes, and can be calibrated with little more than national accounts data. Our base case estimate of the average MCF from marginal increases in all five tax instruments is 1.2. Focusing on the lowest cost tax instruments in each country, commonly the VAT but not always, the average MCF is 1.1. A key feature of our model is explicit recognition of the informal economy. The larger the informal economy, the higher MCFs tend to be. Extending the tax base to include sections of the informal economy by removing some tax exemptions offers the potential for a low MCF source of public funds, and a lowering of MCFs on other tax instruments

Repurposing the Learning Environment: Using Robots to Engage and Support Students in Collaborative Learning through Assessment Design

This document is the Accepted Manuscript of the following paper: Martina Doolan and Michael Walters, 'Repurposing the Learning Environment: Using Robots to Engage and Support Students in Collaborative Learning through Assessment Design', in Proceedings of the 15th European Conference on e-Learning. Prague, Czech Republic, 27-28 October 2016. Antonín Janaík and Jarmila Novotná, eds., ISBN 978-1-911218-18-0, e-ISBN 978-1-911218-17-3.This paper presents a case study related to the setting up and the implementation of a multi-mode blended learning environment driven by an assessment design. The technological blend comprised access to robots and an online group space. The pedagogical blend included the assessment design and teaching and learning practice informed by current research taking place in the School of Computer Science at the University of Hertfordshire. Learners were provided with access to the research centre and the robotics house to help progress and complete the group based assessment and this was supplemented by class-based learning. The overall aim was to repurpose the learning environment and shift the emphasis from teacher-centric to learner-centric practices in order to motivate and engage learners in authentic group based assessment. Additionally, emphasis was placed on learners’ sharing work on their assessment as it progressed using a mini-project approach. This constructively aligned with the assessment and the subject delivery. In this learner-centric environment learners alongside the teacher administered feedback to students on their work as it progressed. This was intended to provide an opportunity for learners to develop their understanding and skills and take the necessary corrective action. Learner attitude was captured quantitatively by means of a questionnaire. Qualitative data was obtained using learners’ own reflections of their experience. This was provided in the form of students’ explanations of their answers to questions posed on the questionnaire. Overall learning was measured using the learner’s performance on the assessment. There are some interesting findings including learner views on the assessment design, how access to the robots and the research centre supported their learning and the learners’ overall perceptions of learning in the multi-modal blended learning environment. These findings will add to the debate on how we engage with and support learners who are growing up in a digital world and provides an example of how we can do this by taking a research-informed teaching approach to the practice of learning driven by an assessment design using robots. Keywords: Robots-in-learning, Research-Informed-Teaching, Research-Informed-Learning, Collaborative Learning, Assessment DesignFinal Accepted Versio

How does peoples’ perception of control depend on the criticality of a task performed by a robot Paladyn

© 2019 Adeline Chanseau et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License.Robot companions are starting to become more common and people are becoming more familiar with devices such as Google Home, Alexa or Pepper,one must wonder what is the optimum way for people to control their devices? This paper provides presents an investigation into how much direct control people want to have of their robot companion and how dependent this is on the criticality of the tasks the robot performs. A live experiment was conducted in the University of Hertfordshire Robot House, with a robot companion performing four different type of tasks. The four tasks were: booking a doctor’s appointment, helping the user to build a Lego character, doing a dance with the user, and carrying biscuits for the user. The selection of these tasks was based on our previous research to define tasks which were relatively high and low in criticality. The main goal of the study was to find what level of direct control over their robot participants and if this was dependent on the criticality of the task performed by the robot. Fifty people took part in the study, and each experienced every task in a random order. Overall,it was found that participants’ perception of control was higher when the robot was performing a task in a semi-autonomous mode. However, for the task "carrying biscuits", although participants perceived to be more in control with the robot performing the task in a semi autonomous mode, they actually preferred to have the robot performing the task automatically (where they felt less in control). The results also show that, for the task "booking a doctor’s appointment", considered to be the most critical of all four tasks, participants did not prefer that the robot chose the date of the appointment as they felt infantilised.Peer reviewe