Multimodal field data entry:performance and usability issues

Mobile technologies have yet to be widely adopted by the Architectural, Engineering, and Construction (AEC) industry despite being one of the major growth areas in computing in recent years. This lack of uptake in the AEC industry is likely due, in large part, to the combination of small screen size and inappropriate interaction demands of current mobile technologies. This paper discusses the scope for multimodal interaction design with a specific focus on speech-based interaction to enhance the suitability of mobile technology use within the AEC industry by broadening the field data input capabilities of such technologies. To investigate the appropriateness of using multimodal technology for field data collection in the AEC industry, we have developed a prototype Multimodal Field Data Entry (MFDE) application. This application, which allows concrete testing technicians to record quality control data in the field, has been designed to support two different modalities of data input speech-based data entry and stylus-based data entry. To compare the effectiveness or usability of, and user preference for, the different input options, we have designed a comprehensive lab-based evaluation of the application. To appropriately reflect the anticipated context of use within the study design, careful consideration had to be given to the key elements of a construction site that would potentially influence a test technician's ability to use the input techniques. These considerations and the resultant evaluation design are discussed in detail in this paper

Approaches to automated detection of cyberbullying:A Survey

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely; supervised learning, lexicon based, rule based and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rules-based approaches match text to predefined rules to identify bullying and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of quality representative labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field

SUIT: a methodology and framework for Selection of User Interface development Tools

This thesis describes the findings of an industrial survey that identified the context of use for software development projects. This context of use is parameterised and combined with a categorisation of UIDT functionality to produce an extensible and tailorable reference model or framework for UIDT evaluation and selection. An accompanying methodology - which together with the framework is known as SUIT (Selection of User Interface Development Tools) - guides the use of the framework such that project-specific context of use can be modelled and thereafter systematically considered during UIDT selection. This thesis proposes that such focussed and documented consideration of context of use during UIDT selection increases the quality of a selection decision and therefore facilitates reuse of UIDT evaluation and selection results. An evaluative study is described which demonstrates the effectiveness and viability of the SUIT framework and methodology as a paper-based UIDT evaluation facility. The same study also identifies the need for a computer-based tool to support the management of UIDT evaluation data and to assist its comparison and analysis. Experiences with this study, the results of the industrial study, and the structure of the framework and methodology provided input into a set of requirements for a computer-based visualisation environment that supports the comparison and analysis of UIDT data. The SUIT data visualisation environment and its qualitative evaluation are described. The evaluation results identify the usefulness and practicability of the SUIT approach when supported by the visualisation environment. They also suggest a number of refinements and extensions to the tool. The results provide an initial corpus of knowledge regarding practical strategies used by evaluators to compare and analyse UIDT evaluation data. These strategies are modelled using a novel purpose-built graphical notation that focuses on sequencing, flexibility, and patterns of activity

ALEX®: a mobile Adult Literacy Experiential Learning application

An alarmingly high number of adults in the world's most developed countries are linguistically functionally illiterate. The research presented in this paper describes ALEX©, an ongoing attempt to successfully develop an innovative assistive, mobile, experiential language-learning application to support the daily literacy education and needs of such adults, anywhere, anytime. We introduce a set of guidelines we have collated to inform the design of mobile assistive technologies, introduce our application and describe the design activities to date that have led to the development of our current application. We present this overview in the hope that it is useful to others working in the fledgling domains of mobile assistive technology design and/or mobile experiential language-learning technologies

GEO Label Web Services for Dynamic and Effective Communication of Geospatial Metadata Quality

We present demonstrations of the GEO label Web services and their integration into a prototype extension of the GEOSS portal (http://scgeoviqua.sapienzaconsulting.com/web/guest/geo_home), the GMU portal (http://gis.csiss.gmu.edu/GADMFS/) and a GeoNetwork catalog application (http://uncertdata.aston.ac.uk:8080/geonetwork/srv/eng/main.home). The GEO label is designed to communicate, and facilitate interrogation of, geospatial quality information with a view to supporting efficient and effective dataset selection on the basis of quality, trustworthiness and fitness for use. The GEO label which we propose was developed and evaluated according to a user-centred design (UCD) approach in order to maximise the likelihood of user acceptance once deployed. The resulting label is dynamically generated from producer metadata in ISO or FDGC format, and incorporates user feedback on dataset usage, ratings and discovered issues, in order to supply a highly informative summary of metadata completeness and quality. The label was easily incorporated into a community portal as part of the GEO Architecture Implementation Programme (AIP-6) and has been successfully integrated into a prototype extension of the GEOSS portal, as well as the popular metadata catalog and editor, GeoNetwork. The design of the GEO label was based on 4 user studies conducted to: (1) elicit initial user requirements; (2) investigate initial user views on the concept of a GEO label and its potential role; (3) evaluate prototype label visualizations; and (4) evaluate and validate physical GEO label prototypes. The results of these studies indicated that users and producers support the concept of a label with drill-down interrogation facility, combining eight geospatial data informational aspects, namely: producer profile, producer comments, lineage information, standards compliance, quality information, user feedback, expert reviews, and citations information. These are delivered as eight facets of a wheel-like label, which are coloured according to metadata availability and are clickable to allow a user to engage with the original metadata and explore specific aspects in more detail. To support this graphical representation and allow for wider deployment architectures we have implemented two Web services, a PHP and a Java implementation, that generate GEO label representations by combining producer metadata (from standard catalogues or other published locations) with structured user feedback. Both services accept encoded URLs of publicly available metadata documents or metadata XML files as HTTP POST and GET requests and apply XPath and XSLT mappings to transform producer and feedback XML documents into clickable SVG GEO label representations. The label and services are underpinned by two XML-based quality models. The first is a producer model that extends ISO 19115 and 19157 to allow fuller citation of reference data, presentation of pixel- and dataset- level statistical quality information, and encoding of 'traceability' information on the lineage of an actual quality assessment. The second is a user quality model (realised as a feedback server and client) which allows reporting and query of ratings, usage reports, citations, comments and other domain knowledge. Both services are Open Source and are available on GitHub at https://github.com/lushv/geolabel-service and https://github.com/52North/GEO-label-java. The functionality of these services can be tested using our GEO label generation demos, available online at http://www.geolabel.net/demo.html and http://geoviqua.dev.52north.org/glbservice/index.jsf

Comparison between first and second wave of COVID-19 outbreak in older people. The COPE multicentre European observational cohort study

Background: Effective shielding measures and virus mutations have progressively modified the disease between the waves, likewise health care systems have adapted to the outbreak. Our aim was to compare clinical outcomes for older people with COVID-19 in Wave 1 (W1) and 2 (W2). Methods: All data, including the Clinical Frailty Scale (CFS), were collected for COVID-19 consecutive patients, aged ≥65, from thirteen hospitals, in W1 (February-June 2020) and W2 (October 2020-March 2021). The primary outcome was mortality (time to mortality and 28-day mortality). Data were analysed with multilevel Cox proportional hazards, linear and logistic regression models, adjusted for wave baseline demographic and clinical characteristics. Results: Data from 611 people admitted in W2 were added to and compared with data collected during W1 (N = 1340). Patients admitted in W2 were of similar age, median [IQR], W2 = 79 [73-84]; W1 = 80 [74-86]; had a greater proportion of men (59.4% vs 53.0%); had lower 28-day mortality (29.1% vs 40.0%), compared to W1. For combined W1-W2 sample, W2 was independently associated with improved survival: time-to-mortality aHR= 0.78 (95%CI 0.65-0.93), 28-day mortality aOR = 0.80 (95%CI 0.62-1.03). W2 was associated with increased length of hospital stay aHR = 0.69 (95%CI 0.59-0.81). Patients in W2 were less frail, CFS (adjusted mean difference [aMD]=-0.50, 95%CI -0.81, -0.18), as well as presented with lower CRP (aMD=-22.52, 95%CI -32.00, -13.04). Conclusions: COVID-19 older adults in W2 were less likely to die than during W1. Patients presented to hospital during W2 were less frail and with lower disease severity and less likely to have renal decline