Introducing the ambivalent socialiser

Social interaction can be a powerful strategy for persuasive technology interventions, yet many users are reluctant to engage with others online because they fear pressure, failure and shame. We introduce the 'ambivalent socialiser', a person who is simultaneously keen but also reluctant to engage with others via social media. Our contribution is to identify four approaches to introducing sociality to ambivalent socialisers: structured socialising, incidental socialising, eavesdropping and trace sensing. We discuss the rationale for these approaches and show how they address recent critiques of persuasive technology. Furthermore, we provide actionable insights for designers of persuasive technology by showing how these approaches can be implemented in a social media application.

Cues in the environment: A design principle for ambient intelligence

The aim of this paper is to propose design principles for ambient intelligence (AmI) environments. The question we are investigating is how these environments can be designed to support a group to be able to carry out common goal-oriented activities. The approach we are taking in answering this question is informed by the concept of collective intelligence (CI). We are applying the concept of CI to AmI as we have found it works well in biological and social systems. Examples from nature demonstrate the power of CI stimulated by implicit cues in the environment. We use these examples to derive design principles for AmI environments. By applying these design principles to a concrete scenario, we are able to propose ways to help decrease environmental pollution within urban areas.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

Cooperative game play with avatars and agents: Differences in brain activity and the experience of play

The current study sought to identify the impact of whether teammates in a cooperative videogame were controlled by other humans (avatars) or by the game (agents). The impact on player experience was explored through both subjective questionnaire measures and brain wave activity measurement (electroencephalography). Play with human teammates was associated with a greater sense of relatedness, but less competence and flow than play with other computer-controlled teammates. In terms of brain activity, play with human teammates was associated with greater activity in the alpha, theta and beta power bands than play with computer-controlled teammates. Overall, the results suggest that play with human teammates involves greater cognitive activity in terms of 'mentalising' than play with computer-controlled teammates. Additionally, the associations between subjective measures of player experience and brain activity are described. Limitations of the current study are identified and key directions for future research are discussed.

Modelling, calibrating, and validating car following and lane changing behaviour

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety; this realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. Thus, this special issue will focus on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC.

When and why threats go undetected: Impacts of event rate and shift length on threat detection accuracy during airport baggage screening

Objective: We aimed to assess the impact of task demands and individual characteristics on threat detection in baggage screeners. Background: Airport security staff work under time constraints to ensure optimal threat detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate threat detection. Method: We examined threat detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Results: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse threat detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Conclusion: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that threats go undetected. Application: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal threat detection and passenger safety.

Participation in Urban Interaction Design for Civic Engagement

The future of civic engagement is characterised by both technological innovation as well as new technological user practices that are fuelled by trends towards mobile, personal devices; broadband connectivity; open data; urban interfaces; and cloud computing. These technology trends are progressing at a rapid pace, and have led global technology vendors to package and sell the “Smart City” as a centralised service delivery platform predicted to optimise and enhance cities’ key performance indicators – and generate a profitable market. The top-down deployment of these large and proprietary technology platforms have helped sectors such as energy, transport, and healthcare to increase efficiencies. However, an increasing number of scholars and commentators warn of another “IT bubble” emerging. Along with some city leaders, they argue that the top-down approach does not fit the governance dynamics and values of a liberal democracy when applied across sectors. A thorough understanding is required, of the socio-cultural nuances of how people work, live, play across different environments, and how they employ social media and mobile devices to interact with, engage in, and constitute public realms. Although the term “slacktivism” is sometimes used to denote a watered down version of civic engagement and activism that is reduced to clicking a “Like” button and signing online petitions, we believe that we are far from witnessing another Biedermeier period that saw people focus on the domestic and the non-political. There is plenty of evidence to the contrary, such as post-election violence in Kenya in 2008, the Occupy movements in New York, Hong Kong and elsewhere, the Arab Spring, Stuttgart 21, Fukushima, the Taksim Gezi Park in Istanbul, and the Vinegar Movement in Brazil in 2013. These examples of civic action shape the dynamics of governments, and in turn, call for new processes to be incorporated into governance structures. Participatory research into these new processes across the triad of people, place and technology is a significant and timely investment to foster productive, sustainable, and liveable human habitats. With this article, we want to reframe the current debates in academia and priorities in industry and government to allow citizens and civic actors to take their rightful centrepiece place in civic movements. This calls for new participatory approaches for co-inquiry and co-design. It is an evolving process with an explicit agenda to facilitate change, and we propose participatory action research (PAR) as an indispensable component in the journey to develop new governance infrastructures and practices for civic engagement. We do not limit our definition of civic technologies to tools specifically designed to simply enhance government and governance, such as renewing your car registration online or casting your vote electronically on election day. Rather, we are interested in civic media and technologies that foster citizen engagement in the widest sense, and particularly the participatory design of such civic technologies that strive to involve citizens in political debate and action as well as question conventional approaches to political issues. The rationale for this approach is an alternative to smart cities in a “perpetual tomorrow,” based on many weak and strong signals of civic actions revolving around technology seen today. It seeks to emphasise and direct attention to active citizenry over passive consumerism, human actors over human factors, culture over infrastructure, and prosperity over efficiency. First, we will have a look at some fundamental issues arising from applying simplistic smart city visions to the kind of a problem a city poses. We focus on the touch points between “the city” and its civic body, the citizens. In order to provide for meaningful civic engagement, the city must provide appropriate interfaces.

Professional Driving : An Emphasis on Organizational Safety Climate

According to the literature and statistical figures, professional drivers constitute a high-risk group in traffic and should be investigated in connection with the factors related to safe driving. However, safety-related behaviours and outcomes among professional drivers have attracted very little attention from safety researchers. In addition, comparing different professional and non-professional driver groups in terms of critical on-the-road characteristics and outcomes has been indicated in the literature as being necessary for a more comprehensive understanding of driver groups and the nature of driving itself. The aim of the present study was to investigate professional driving from a safety climate stand point in relation to predominant driving-related factors and by considering the differences between driver groups. Hence, four Sub-studies were conducted according to a framework emphasizing the relationships between safety climate, driver groups, driver stress, human factors (i.e., driver behaviour and performance) and accidents. Demographic information, as well as data for driver behaviour, performance, and driver stress was collected by questionnaire. The data was analysed using factor analysis, analysis of covariance as well as hierarchical and logistic regression analysis. The results revealed multi-dimensional factor structures for the safety climate measures. Considering the relationships between variables, differences were evidenced regarding on-the-road stress reactions, risky driver behaviours and penalties, between the various professional and non-professional driver groups. Driver stress was found to be related to accidents. The results also indicated that the safety climate has positive relationships with both driver behaviour and performance, and as well as involvement in accidents. The present study has a number of critical implications resulting from the fact that the way in which the effects of safety climate on professional driving were investigated, as well as the differences between professional and non-professional driver groups, was unique. Additionally, for the first time, a safety climate scale was developed specifically for professional drivers. According to the results of the study and to previous literature, a tentative model was proposed representing a possible route for the relationships between safety climate, human factors, driver stress, driver groups and accidents, by emphasizing the effects of safety climate.

Assessment of driver vision functions in relation to their crash involvement in India

Among the human factors that influence safe driving, visual skills of the driver can be considered fundamental. This study mainly focuses on investigating the effect of visual functions of drivers in India on their road crash involvement. Experiments were conducted to assess vision functions of Indian licensed drivers belonging to various organizations, age groups and driving experience. The test results were further related to the crash involvement histories of drivers through statistical tools. A generalized linear model was developed to ascertain the influence of these traits on propensity of crash involvement. Among the sampled drivers, colour vision, vertical field of vision, depth perception, contrast sensitivity, acuity and phoria were found to influence their crash involvement rates. In India, there are no efficient standards and testing methods to assess the visual capabilities of drivers during their licensing process and this study highlights the need for the same.

Computational modeling and psychophysics in low- and mid-level vision

This thesis addresses a series of topics related to the question of how people find the foreground objects from complex scenes. With both computer vision modeling, as well as psychophysical analyses, we explore the computational principles for low- and mid-level vision.

We first explore the computational methods of generating saliency maps from images and image sequences. We propose an extremely fast algorithm called Image Signature that detects the locations in the image that attract human eye gazes. With a series of experimental validations based on human behavioral data collected from various psychophysical experiments, we conclude that the Image Signature and its spatial-temporal extension, the Phase Discrepancy, are among the most accurate algorithms for saliency detection under various conditions.

In the second part, we bridge the gap between fixation prediction and salient object segmentation with two efforts. First, we propose a new dataset that contains both fixation and object segmentation information. By simultaneously presenting the two types of human data in the same dataset, we are able to analyze their intrinsic connection, as well as understanding the drawbacks of today’s “standard” but inappropriately labeled salient object segmentation dataset. Second, we also propose an algorithm of salient object segmentation. Based on our novel discoveries on the connections of fixation data and salient object segmentation data, our model significantly outperforms all existing models on all 3 datasets with large margins.

In the third part of the thesis, we discuss topics around the human factors of boundary analysis. Closely related to salient object segmentation, boundary analysis focuses on delimiting the local contours of an object. We identify the potential pitfalls of algorithm evaluation for the problem of boundary detection. Our analysis indicates that today’s popular boundary detection datasets contain significant level of noise, which may severely influence the benchmarking results. To give further insights on the labeling process, we propose a model to characterize the principles of the human factors during the labeling process.

The analyses reported in this thesis offer new perspectives to a series of interrelating issues in low- and mid-level vision. It gives warning signs to some of today’s “standard” procedures, while proposing new directions to encourage future research.