Robot Comedy Lab: experimenting with the social dynamics of live performance

Copyright © 2015 Katevas, Healey and Harris. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.This document is protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.This work was funded by EPSRC (EP/G03723X/1) through the Media and Arts Technology Program, an RCUK Center for Doctoral Training

Walking in Sync: Two is Company, Three's a Crowd.

Eventual gait synchronization between two individuals while walking and talking with each other has been shown to be an indicator of agreeableness and companionship. The inferred physical signal from this subconscious phenomenon can po-tentially be an indicator of cooperation or relation between two individuals. In this paper we investigate this effect, and whether having a third person actively engaging in the same act or conversation can reduce this synchronization level. Using high frequency accelerometer data from a ded-icated smartphone app, we perform a number of controlled experiments on a number of individuals in different group configuration. Our results bring an interesting insight: it is the non-verbal social signals such as the gaze, head orienta-tion and gestures that is the key factor in synchronization, not necessarily the number or configuration of the walkers. These early results can lead us on detecting relationships between individuals or detecting the group formation and numbers for crowd-sensing applications when only partial data is available. Categories and Subject Descriptors Human-centered computing [Ubiquitous and mobile com-puting]: Empirical studies in ubiquitous and mobile com-putin

Effective patient–clinician interaction to improve treatment outcomes for patients with psychosis: a mixed-methods design

BACKGROUND:At least 100,000 patients with schizophrenia receive care from community mental health teams (CMHTs) in England. These patients have regular meetings with clinicians, who assess them, engage them in treatment and co-ordinate care. As these routine meetings are not commonly guided by research evidence, a new intervention, DIALOG, was previously designed to structure consultations. Using a hand-held computer, clinicians asked patients to rate their satisfaction with eight life domains and three treatment aspects, and to indicate whether or not additional help was needed in each area, with responses being graphically displayed and compared with previous ratings. In a European multicentre trial, the intervention improved patients’ quality of life over a 1-year period. The current programme builds on this research by further developing DIALOG in the UK. RESEARCH QUESTIONS:(1) How can the practical procedure of the intervention be improved, including the software used and the design of the user interface? (2) How can elements of resource-oriented interventions be incorporated into a clinician manual and training programme for a new, more extensive ‘DIALOG+’ intervention? (3) How effective and cost-effective is the new DIALOG+ intervention in improving treatment outcomes for patients with schizophrenia or a related disorder? (4) What are the views of patients and clinicians regarding the new DIALOG+ intervention? METHODS:We produced new software on a tablet computer for CMHTs in the NHS, informed by analysis of videos of DIALOG sessions from the original trial and six focus groups with 18 patients with psychosis. We developed the new ‘DIALOG+’ intervention in consultation with experts, incorporating principles of solution-focused therapy when responding to patients’ ratings and specifying the procedure in a manual and training programme for clinicians. We conducted an exploratory cluster randomised controlled trial with 49 clinicians and 179 patients with psychosis in East London NHS Foundation Trust, comparing DIALOG+ with an active control. Clinicians working as care co-ordinators in CMHTs (along with their patients) were cluster randomised 1 : 1 to either DIALOG+ or treatment as usual plus an active control, to prevent contamination. Intervention and control were to be administered monthly for 6 months, with data collected at baseline and at 3, 6 and 12 months following randomisation. The primary outcome was subjective quality of life as measured on the Manchester Short Assessment of Quality of Life; secondary outcomes were also measured. We also established the cost-effectiveness of the DIALOG intervention using data from the Client Service Receipt Inventory, which records patients’ retrospective reports of using health- and social-care services, including hospital services, outpatient services and medication, in the 3 months prior to each time point. Data were supplemented by the clinical notes in patients’ medical records to improve accuracy. We conducted an exploratory thematic analysis of 16 video-recorded DIALOG+ sessions and measured adherence in these videos using a specially developed adherence scale. We conducted focus groups with patients (n = 19) and clinicians (n = 19) about their experiences of the intervention, and conducted thematic analyses. We disseminated the findings and made the application (app), manual and training freely available, as well as producing a protocol for a definitive trial. RESULTS:Patients receiving the new intervention showed more favourable quality of life in the DIALOG+ group after 3 months (effect size: Cohen’s d = 0.34), after 6 months (Cohen’s d = 0.29) and after 12 months (Cohen’s d = 0.34). An analysis of video-recorded DIALOG+ sessions showed inconsistent implementation, with adherence to the intervention being a little over half of the possible score. Patients and clinicians from the DIALOG+ arm of the trial reported many positive experiences with the intervention, including better self-expression and improved efficiency of meetings. Difficulties reported with the intervention were addressed by further refining the DIALOG+ manual and training. Cost-effectiveness analyses found a 72% likelihood that the intervention both improved outcomes and saved costs. LIMITATIONS:The research was conducted solely in urban east London, meaning that the results may not be broadly generalisable to other settings. CONCLUSIONS:(1) Although services might consider adopting DIALOG+ based on the existing evidence, a definitive trial appears warranted; (2) applying DIALOG+ to patient groups with other mental disorders may be considered, and to groups with physical health problems; (3) a more flexible use with variable intervals might help to make the intervention even more acceptable and effective; (4) more process evaluation is required to identify what mechanisms precisely are involved in the improvements seen in the intervention group in the trial; and (5) what appears to make DIALOG+ effective is that it is not a separate treatment and not a technology that is administered by a specialist; rather, it changes and utilises the existing therapeutic relationship between patients and clinicians in CMHTs to initiate positive change, helping the patients to improve their quality of life. FUTURE RESEARCH:Future studies should include a definitive trial on DIALOG+ and test the effectiveness of the intervention with other populations, such as people with depression. TRIAL REGISTRATION:Current Controlled Trials ISRCTN34757603. FUNDING:The National Institute for Health Research Programme Grants for Applied Research programme

PPFL: privacy-preserving federated learning with trusted execution environments

We propose and implement a Privacy-preserving Federated Learning (PPFL) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54x) and a similar amount of network traffic (1.002x) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side

SensingKit: Evaluating the Sensor Power Consumption in iOS devices

4 pages, 2 figures, 3 tables. To be published in the 12th International Conference on Intelligent Environments (IE'16)4 pages, 2 figures, 3 tables. To be published in the 12th International Conference on Intelligent Environments (IE'16

Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing

21 pages, 6 figures, conference paper21 pages, 6 figures, conference pape

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

To appear in IEEE Internet of Things JournalTo appear in IEEE Internet of Things JournalTo appear in IEEE Internet of Things JournalTo appear in IEEE Internet of Things JournalInternet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance