A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

A tool to aid redesign of flexible transport services to increase efficiency in rural transport service provision

This research was supported by the Research Councils UK Digital Economy programme award (reference: EP/G066051/1) to the dot.rural Digital Economy Hub, at the University of Aberdeen.Peer reviewedPublisher PD

Reputation Agent: Prompting Fair Reviews in Gig Markets

Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets. Unfair reviews, created when requesters consider factors outside of a worker's control, are known to plague gig workers and can result in lost job opportunities and even termination from the marketplace. Our tool leverages machine learning to implement an intelligent interface that: (1) uses deep learning to automatically detect when an individual has included unfair factors into her review (factors outside the worker's control per the policies of the market); and (2) prompts the individual to reconsider her review if she has incorporated unfair factors. To study the effectiveness of Reputation Agent, we conducted a controlled experiment over different gig markets. Our experiment illustrates that across markets, Reputation Agent, in contrast with traditional approaches, motivates requesters to review gig workers' performance more fairly. We discuss how tools that bring more transparency to employers about the policies of a gig market can help build empathy thus resulting in reasoned discussions around potential injustices towards workers generated by these interfaces. Our vision is that with tools that promote truth and transparency we can bring fairer treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202

Methodology for profiling literature in healthcare simulation

The publications that relate to the application of simulation to healthcare have steadily increased over the years. These publications are scattered amongst various journals that belong to several subject categories, including Operational Research, Health Economics and Pharmacokinetics. The simulation techniques that are applied to the study of healthcare problems are also varied. The aim of this study is to present a methodology for profiling literature in healthcare simulation. In our methodology, we have considered papers on healthcare that have been published between 1970 and 2007 in journals with impact factors that belonging to various subject categories reporting on the application of four simulation techniques, namely, Monte Carlo Simulation, Discrete-Event Simulation, System Dynamics and Agent-Based Simulation. The methodology has the following objectives: (a) to categorise the papers under the different simulation techniques and identify the healthcare problems that each technique is employed to investigate; (b) to profile, within our dataset, variables such as authors, article citations, etc.; (c) to identify turning point (strategically important) papers and authors through co-citation analysis of references cited by the papers in our dataset. The focus of the paper is on the literature profiling methodology, and not the results that have been derived through the application of this methodology. The authors hope that the methodology presented here will be used to conduct similar work in not only healthcare but also other research domains