The ABCD of usability testing

We introduce a methodology for tracking and auditing feedback, errors and suggestions for software packages. This short paper describes how we innovate on the evaluation mechanism, introducing an (Antecedent, Barrier, Consequence and Development) ABCD form, embedded within an eParticipation platform to enable end users to easily report on any usability issues. This methodology will be utilised to improve the STEP cloud eParticipation platform (part of the current STEP Horizon2020 project http://step4youth.eu. The platform is currently being piloted in real life contexts, with the participation of public authorities that are integrating the eParticipation platform into their regular decision-making practices. The project is involving young people, through engagement and motivation strategies and giving them a voice in Environmental decision making at the local level. The pilot evaluation aims to demonstrate how open engagement needs to be embedded within public sector processes and the usability methodology reported here will help to identify the key barriers for wide scale deployment of the platform

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

Stress Strengthens Memory of First Impressions of Others' Positive Personality Traits

Encounters with strangers bear potential for social conflict and stress, but also allow the formation of alliances. First impressions of other people play a critical role in the formation of alliances, since they provide a learned base to infer the other's future social attitude. Stress can facilitate emotional memories but it is unknown whether stress strengthens our memory for newly acquired impressions of other people's personality traits. To answer this question, we subjected 60 students (37 females, 23 males) to an impression-formation task, viewing portraits together with brief positive vs. negative behavior descriptions, followed by a 3-min cold pressor stress test or a non-stressful control procedure. The next day, novel and old portraits were paired with single trait adjectives, the old portraits with a trait adjective matching the previous day's behavior description. After a filler task, portraits were presented again and subjects were asked to recall the trait adjective. Cued recall was higher for old (previously implied) than the novel portraits' trait adjectives, indicating validity of the applied test procedures. Overall, recall rate of implied trait adjectives did not differ between the stress and the control group. However, while the control group showed a better memory performance for others' implied negative personality traits, the stress group showed enhanced recall for others' implied positive personality traits. This result indicates that post-learning stress affects consolidation of first impressions in a valence-specific manner. We propose that the stress-induced strengthening of memory of others' positive traits forms an important cue for the formation of alliances in stressful conditions

Facial expressions depicting compassionate and critical emotions: the development and validation of a new emotional face stimulus set

Attachment with altruistic others requires the ability to appropriately process affiliative and kind facial cues. Yet there is no stimulus set available to investigate such processes. Here, we developed a stimulus set depicting compassionate and critical facial expressions, and validated its effectiveness using well-established visual-probe methodology. In Study 1, 62 participants rated photographs of actors displaying compassionate/kind and critical faces on strength of emotion type. This produced a new stimulus set based on N = 31 actors, whose facial expressions were reliably distinguished as compassionate, critical and neutral. In Study 2, 70 participants completed a visual-probe task measuring attentional orientation to critical and compassionate/kind faces. This revealed that participants lower in self-criticism demonstrated enhanced attention to compassionate/kind faces whereas those higher in self-criticism showed no bias. To sum, the new stimulus set produced interpretable findings using visual-probe methodology and is the first to include higher order, complex positive affect displays

Energy Linearity and Resolution of the ATLAS Electromagnetic Barrel Calorimeter in an Electron Test-Beam

A module of the ATLAS electromagnetic barrel liquid argon calorimeter was exposed to the CERN electron test-beam at the H8 beam line upgraded for precision momentum measurement. The available energies of the electron beam ranged from 10 to 245 GeV. The electron beam impinged at one point corresponding to a pseudo-rapidity of eta=0.687 and an azimuthal angle of phi=0.28 in the ATLAS coordinate system. A detailed study of several effects biasing the electron energy measurement allowed an energy reconstruction procedure to be developed that ensures a good linearity and a good resolution. Use is made of detailed Monte Carlo simulations based on Geant which describe the longitudinal and transverse shower profiles as well as the energy distributions. For electron energies between 15 GeV and 180 GeV the deviation of the measured incident electron energy over the beam energy is within 0.1%. The systematic uncertainty of the measurement is about 0.1% at low energies and negligible at high energies. The energy resolution is found to be about 10% sqrt(E) for the sampling term and about 0.2% for the local constant term

Position resolution and particle identification with the ATLAS EM calorimeter

In the years between 2000 and 2002 several pre-series and series modules of the ATLAS EM barrel and end-cap calorimeter were exposed to electron, photon and pion beams. The performance of the calorimeter with respect to its finely segmented first sampling has been studied. The polar angle resolution has been found to be in the range 50-60 mrad/sqrt(E (GeV)). The neutral pion rejection has been measured to be about 3.5 for 90% photon selection efficiency at pT=50 GeV/c. Electron-pion separation studies have indicated that a pion fake rate of (0.07-0.5)% can be achieved while maintaining 90% electron identification efficiency for energies up to 40 GeV.Comment: 32 pages, 22 figures, to be published in NIM