A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective

Gamification of Mobile Experience Sampling Improves Data Quality and Quantity

The Experience Sampling Method is used to capture high-quality in situ data from study participants. This method has become popular in studies involving smartphones, where it is often adapted to motivate participation through the use of gamification techniques. However, no work to date has evaluated whether gamification actually affects the quality and quantity of data collected through Experience Sampling. Our study systematically investigates the effect of gamification on the quantity and quality of experience sampling responses on smartphones. In a field study, we combine event contingent and interval contingent triggers to ask participants to describe their location. Subsequently, participants rate the quality of these entries by playing a game with a purpose. Our results indicate that participants using the gamified version of our ESM software provided significantly higher quality responses, slightly increased their response rate, and provided significantly more data on their own accord. Our findings suggest that gamifying experience sampling can improve data collection and quality in mobile settings

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Designing for quality in real-world mobile crowdsourcing systems

PhD ThesisCrowdsourcing has emerged as a popular means to collect and analyse data on a scale for problems that require human intelligence to resolve. Its prompt response and low cost have made it attractive to businesses and academic institutions. In response, various online crowdsourcing platforms, such as Amazon MTurk, Figure Eight and Prolific have successfully emerged to facilitate the entire crowdsourcing process. However, the quality of results has been a major concern in crowdsourcing literature. Previous work has identified various key factors that contribute to issues of quality and need to be addressed in order to produce high quality results. Crowd tasks design, in particular, is a major key factor that impacts the efficiency and effectiveness of crowd workers as well as the entire crowdsourcing process. This research investigates crowdsourcing task designs to collect and analyse two distinct types of data, and examines the value of creating high-quality crowdwork activities on new crowdsource enabled systems for end-users. The main contribution of this research includes 1) a set of guidelines for designing crowdsourcing tasks that support quality collection, analysis and translation of speech and eye tracking data in real-world scenarios; and 2) Crowdsourcing applications that capture real-world data and coordinate the entire crowdsourcing process to analyse and feed quality results back. Furthermore, this research proposes a new quality control method based on workers trust and self-verification. To achieve this, the research follows the case study approach with a focus on two real-world data collection and analysis case studies. The first case study, Speeching, explores real-world speech data collection, analysis, and feedback for people with speech disorder, particularly with Parkinson’s. The second case study, CrowdEyes, examines the development and use of a hybrid system combined of crowdsourcing and low-cost DIY mobile eye trackers for real-world visual data collection, analysis, and feedback. Both case studies have established the capability of crowdsourcing to obtain high quality responses comparable to that of an expert. The Speeching app, and the provision of feedback in particular were well perceived by the participants. This opens up new opportunities in digital health and wellbeing. Besides, the proposed crowd-powered eye tracker is fully functional under real-world settings. The results showed how this approach outperforms all current state-of-the-art algorithms under all conditions, which opens up the technology for wide variety of eye tracking applications in real-world settings

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

Principles for Designing Context-Aware Applications for Physical Activity Promotion

Mobile devices with embedded sensors have become commonplace, carried by billions of people worldwide. Their potential to influence positive health behaviors such as physical activity in people is just starting to be realized. Two critical ingredients, an accurate understanding of human behavior and use of that knowledge for building computational models, underpin all emerging behavior change applications. Early research prototypes suggest that such applications would facilitate people to make difficult decisions to manage their complex behaviors. However, the progress towards building real-world systems that support behavior change has been much slower than expected. The extreme diversity in real-world contextual conditions and user characteristics has prevented the conception of systems that scale and support end-users’ goals. We believe that solutions to the many challenges of designing context-aware systems for behavior change exist in three areas: building behavior models amenable to computational reasoning, designing better tools to improve our understanding of human behavior, and developing new applications that scale existing ways of achieving behavior change. With physical activity as its focus, this thesis addresses some crucial challenges that can move the field forward. Specifically, this thesis provides the notion of sweet spots, a phenomenological account of how people make and execute their physical activity plans. The key contribution of this concept is in its potential to improve the predictability of computational models supporting physical activity planning. To further improve our understanding of the dynamic nature of human behavior, we designed and built Heed, a low-cost, distributed and situated self-reporting device. Heed’s single-purpose and situated nature proved its use as the preferred device for self-reporting in many contexts. We finally present a crowdsourcing system that leverages expert knowledge to write personalized behavior change messages for large-scale context-aware applications.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144089/1/gparuthi_1.pd

Social networking, social media and complex emergencies: an annotated bibliography

Andrew Skuse and Tait Brimacomb

Social networking, social media and complex emergencies: an annotated bibliography

Andrew Skuse and Tait Brimacomb