Infrastructure Engineering - Research Publications
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ItemTarget Word Masking for Location Metonymy Resolution(International Committee on Computational Linguistics, 2020)
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ItemPlace questions and human-generated answers: A data analysis approach(Springer, Cham, 2020-01-01)This paper investigates place-related questions submitted to search systems and their human-generated answers. Place-based search is motivated by the need to identify places matching some criteria, to identify them in space or relative to other places, or to characterize the qualities of such places. Human place-related questions have thus far been insufficiently studied and differ strongly from typical keyword queries. They thus challenge today’s search engines providing only rudimentary geographic information retrieval support. We undertake an analysis of the patterns in place-based questions using a large-scale dataset of questions/answers, MS MARCO V2.1. The results of this study reveal patterns that can inform the design of conversational search systems and in-situ assistance systems, such as autonomous vehicles.
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ItemSmartphone usability for emergency evacuation applications(LIPIcs, 2019-09-01)Mobile phone ubiquity has allowed the implementation of a number of emergency-related evacuation aids. Yet, these applications still face a number of challenges in human-mobile interaction, namely: (1) lack of widely accepted mobile usability guidelines, (2) people's limited cognitive capacity when using mobile phones under stress, and (3) difficulty recreating emergency scenarios as experiments for usability testing. This study is intended as an initial view into smartphone usability under emergency evacuations by compiling a list of experimental observations and setting the ground for future research in cognitively-informed spatial algorithms and app design.
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ItemThe grass is greener on the other side: understanding the effects of green spaces on Twitter user sentiments(ACM Press, 2018)Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.