SAT-Geo: A social sensing based content-only approach to geolocating abnormal traffic events using syntax-based probabilistic learning

2022 ◽  
Vol 59 (2) ◽  
pp. 102807
Author(s):  
Lanyu Shang ◽  
Yang Zhang ◽  
Christina Youn ◽  
Dong Wang
2021 ◽  
Vol 88 ◽  
pp. 101629
Author(s):  
Nengcheng Chen ◽  
Yan Zhang ◽  
Wenying Du ◽  
Yingbing Li ◽  
Min Chen ◽  
...  

2021 ◽  
Vol 35 (2) ◽  
pp. 621-659
Author(s):  
Lewis Hammond ◽  
Vaishak Belle

AbstractMoral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3717
Author(s):  
James C. Young ◽  
Rudy Arthur ◽  
Michelle Spruce ◽  
Hywel T. P. Williams

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.


2016 ◽  
Vol 88 ◽  
pp. 107-114 ◽  
Author(s):  
Giovanni Pilato ◽  
Umberto Maniscalco

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