Purpose This study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context. Design/methodology/approach In a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior (N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy. Findings The results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack. Practical implications This study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments. Originality/value The authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.

Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this article, we propose a framework for objective personality detection that leverages humans’ physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using non-invasive commercial-grade eye-tracking and skin conductivity sensors. These responses are then processed and used to build a machine learning classifier capable of accurately predicting a wide range of personality traits. We investigate and discuss the performance of various machine learning methods, the most and least accurately predicted traits, and also assess the importance of the different stimuli, features, and physiological signals. Our work demonstrates that personality traits can be accurately detected, suggesting the applicability of the proposed framework for robust personality detection and use by psychology practitioners and researchers, as well as designers of personalised interactive systems.

Personality is an established domain of research in psychology, and individual differences in various traits are linked to a variety of real-life outcomes and behaviours. Personality detection is an intricate task that typically requires humans to fill out lengthy questionnaires assessing specific personality traits. The outcomes of this, however, may be unreliable or biased if the respondents do not fully understand or are not willing to honestly answer the questions. To this end, we propose a framework for objective personality detection that leverages humans' physiological responses to external stimuli. We exemplify and evaluate the framework in a case study, where we expose subjects to affective image and video stimuli, and capture their physiological responses using a commercial-grade eye-tracking sensor. These responses are then processed and fed into a classifier capable of accurately predicting a range of personality traits. Our work yields notably high predictive accuracy, suggesting the applicability of the proposed framework for robust personality detection.

Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.

In the human-machine collaboration context, understanding the reason behind each human decision is critical for interpreting the performance of the human-machine team. Via an experimental study of a system with varied levels of accuracy, we describe how human trust interplays with system performance, human perception and decisions. It is revealed that humans are able to perceive the performance of automatic systems and themselves, and adjust their trust levels according to the accuracy of systems. The 70% system accuracy suggests to be a threshold between increasing and decreasing human trust and system usage. We have also shown that trust can be derived from a series of users' decisions rather than from a single one, and relates to the perceptions of users. A general framework depicting how trust and perception affect human decision making is proposed, which can be used as future guidelines for human-machine collaboration design.

User system trust is critical to the uptake of recommendations, and several factors of trust have been identified and compared. In this paper we present a cross-cultural, crowdsourced study examining user perceptions of nine factors of trust and link the observed differences to trust development processes and cultural dimensions. While some factors consistently instil trust, others are preferred only in certain countries. Our findings and the discovered links are important for design of trusted recommender systems.

How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces and content selection strategies on user trust. It evaluates the subjective ranking of nine key factors of trust grouped into three dimensions and examines the differences observed with respect to users' personality traits.

Trust is a key factor affecting the way people rely on automated systems. On the other hand, system performance has comprehensive implications on a user's trust variations. This paper examines systems of varied levels of accuracy, in order to reveal the relationship between system performance, a user's trust and reliance on the system. In particular, it is identified that system failures have a stronger effect on trust than system successes. We also describe how patterns of trust change according to a number of consecutive system failures or successes. Importantly, we show that increasing user familiarity with the system decreases the rate of trust change, which provides new insights on the development of user trust. Finally, our analysis established a correlation between a user's reliance on a system and their trust level. Combining all these findings can have important implications in general system design and implementation, by predicting how trust builds and when it stabilizes, as well as allowing for indirectly reading a user's trust in real time based on system reliance.

Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system's output serves as one of the inputs for the users' decision making processes. In this work, we study the dynamics of explicit and implicit user trust in a simulated automated quality monitoring system, as a function of the system accuracy. We establish that users correctly perceive the accuracy of the system and adjust their trust accordingly.

This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.

Accurate and noise robust multimodal activity and mental state monitoring can be achieved by combining physiological, behavioural and environmental signals. This is especially promising in assistive driving technologies, because vehicles now ship with sensors ranging from wheel and pedal activity, to voice and eye tracking. In practice, however, multimodal user studies are confronted with challenging data collection and synchronisation issues, due to the diversity of sensing, acquisition and storage systems. Referencing current research on cognitive load measurement in a driving simulator, this paper describes the steps we take to consistently collect and synchronise signals, using the Orbit Measurement Library (OML) framework, combined with a multimodal version of a cinema clapperboard. The resulting data is automatically stored in a networked database, in a structured format, including metadata about the data and experiment. Moreover, fine-grained synchronisation between all signals is provided without additional hardware, and clock drift can be corrected post-hoc.

Physiological sensors are widely used in user studies, often by practitioners with limited expertise in networking. However, large data volumes, and processing times often prevent the use of a single computer to collect the readings in real time. With multiple collection machines appear the problems of data aggregation and, more importantly, synchronisation. This paper describes how the OML reporting library allows solving the aggregation problem at low cost by introducing a lightweight instrumentation reporting to a centralised database. However, with unknown delays in network paths during aggregation and unreliable clocks on acquisition machines, synchronisation is hard to attain. We present a preliminary study of the theoretical feasibility of post hoc synchronisation corrections, supported by an experiment applying correction techniques to artificially impaired clocks and network transmissions. Based on the results of this experiment this paper highlights potential improvements.

It has been previously established that high cognitive load influences driving performance, but is the driver's perception of their experience while driving also influenced by cognitive load? To our knowledge, little evaluation has taken place regarding the investigation of such an effect of cognitive load on user experience, especially in the automotive domain. This paper introduces motivation and background of our current research on real-time monitoring of drivers' mental states aiming to explore the previously mentioned relations between mental states and UX in a driving simulator environment. Furthermore, the paper presents initial ideas of task designs that are targeted to elicit and induce mental states like cognitive load and selected user experience factors we consider being interesting in the automotive domain and that we aim to discuss with other researchers and practitioners in the workshop.

Driver distraction is an important risk factor for road traffic injuries, and has been the focus of a number of empirical studies aiming to raise awareness about the risks of distracted driving and to promote countermeasures. While some of the recorded road incidents in these studies have their roots in distracting events (such as mobile phone usage) a large proportion of recorded road incidents can be attributed to more elusive driver inattention factors not linked to specific trigger events. These distraction categories are especially challenging and currently not in focus of current research as they are difficult to detect and address by suitable prognostic measures, in order to improve road safety. To contribute to this issue, this paper presents research into monitoring drivers' mental states in real-time, using objective measurements. We propose an iterative research methodology where specific mental states are elicited, user response captured experimentally, and interaction models built using advanced machine learning techniques. Behavioral measures such as speech, eye activity or posture, and physiological measures such as galvanic skin response or heart rate provide input features for the models. This driver-centric approach addresses the complex issue of driver inattention, and can help improve road safety through active monitoring of road users, customized decision support in the vehicle, and objective training feedback. Low-fidelity simulators we have built allowed us to roll out some preliminary tasks prompting encouraging feedback from subjects during informal testing.

High cognitive load arises from complex time and safety-critical tasks, for example, mapping out flight paths, monitoring traffic, or even managing nuclear reactors, causing stress, errors, and lowered performance. Over the last five years, our research has focused on using the multimodal interaction paradigm to detect fluctuations in cognitive load in user behavior during system interaction. Cognitive load variations have been found to impact interactive behavior: by monitoring variations in specific modal input features executed in tasks of varying complexity, we gain an understanding of the communicative changes that occur when cognitive load is high. So far, we have identified specific changes in: speech, namely acoustic, prosodic, and linguistic changes; interactive gesture; and digital pen input, both interactive and freeform. As ground-truth measurements, galvanic skin response, subjective, and performance ratings have been used to verify task complexity. The data suggest that it is feasible to use features extracted from behavioral changes in multiple modal inputs as indices of cognitive load. The speech-based indicators of load, based on data collected from user studies in a variety of domains, have shown considerable promise. Scenarios include single-user and team-based tasks; think-aloud and interactive speech; and single-word, reading, and conversational speech, among others. Pen-based cognitive load indices have also been tested with some success, specifically with pen-gesture, handwriting, and freeform pen input, including diagraming. After examining some of the properties of these measurements, we present a multimodal fusion model, which is illustrated with quantitative examples from a case study. The feasibility of employing user input and behavior patterns as indices of cognitive load is supported by experimental evidence. Moreover, symptomatic cues of cognitive load derived from user behavior such as acoustic speech signals, transcribed text, digital pen trajectories of handwriting, and shapes pen, can be supported by well-established theoretical frameworks, including O'Donnell and Eggemeier's workload measurement [1986] Sweller's Cognitive Load Theory [Chandler and Sweller 1991], and Baddeley's model of modal working memory [1992] as well as McKinstry et al.'s [2008] and Rosenbaum's [2005] action dynamics work. The benefit of using this approach to determine the user's cognitive load in real time is that the data can be collected implicitly that is, during day-to-day use of intelligent interactive systems, thus overcomes problems of intrusiveness and increases applicability in real-world environments, while adapting information selection and presentation in a dynamic computer interface with reference to load.

This paper presents a longitudinal study that explores the combined effect of cognitive load and expertise on the use of a scratchpad. Our results confirm that such cognitive support benefits users under high cognitive load through visual aid, perceptual motor use and helps to improve meaningful learning and successful problem solving. Indeed, we found significant changes in stroke frequency affected by cognitive load, which we believe are caused by the scratchpad essentially augmenting or extending working memory capacity. However, the discrepancy between stroke frequencies under low and high load is reduced with expertise. These results indicate that pen stroke frequency, which can be automated with electronic devices, could be used as an indicator of cognitive load, or conversely, of expertise level.

Multimodal interfaces are known to be useful in map-based applications, and in complex, time-pressure based tasks. Cognitive load variations in such tasks have been found to impact multimodal behaviour. For example, users become more multimodal and tend towards semantic complementarity as cognitive load increases. The richness of multimodal data means that systems could monitor particular input features to detect experienced load variations. In this paper, we present our attempt to induce controlled levels of load and solicit natural speech and pen-gesture inputs. In particular, we analyse for these features in the pen gesture modality. Our experimental design relies on a map-based Wizard of Oz, using a tablet PC. This paper details analysis of pen-gesture interaction across subjects, and presents suggestive trends of increases in the degree of degeneration of pen-gestures in some subjects, and possible trends in gesture kinematics, when cognitive load increases.

Smart camera, or an intelligent camera, is an embedded vision system which captures and processes image to extract application-specific information in real time. Smart cameras are used in many applications such as automatic control systems, machine vision systems, automatic video surveillance systems and human computer interfaces. What makes a smart camera 'smart' is a special processing unit inside the camera which performs application specific information processing, for example, skin color detection and motion detection for surveillance purpose. The design of smart camera as an embedded system is challenging because on one hand video processing has insatiable demand for performance and power, and on the other hand embedded systems place considerable constraints on the design. We present our work in progress to build GestureCam, an FPGA-based smart camera that can recognize simple pre-defined head and hand gestures. As an application of the GestureCam, we present the design of a GestureBrowser, an extension to the Mozilla Firefox browser which uses the GestureCam to capture and recognize a user's head and hand gesture commands to control Web navigation. The GestureBrowser idea is a step further toward next generation natural human computer interaction. Also presented is a proof of concept work for the GestureBrowser idea, using a Webcam as image capture device

We explore the concept of interaction styles used to navigate through hypermedia systems. A demonstrator was built to conduct a user study with the objective of detecting whether any interaction pattern exists in relation to input modality choices. Our lightweight server-side web demonstrator is able to adapt output modalities as a function of input received from the user. The interface and content displayed are built from predefined presentation schemes that attempt to optimize the user's experience and website's functionality. The results suggest that some levels of entrenchment do occur with reference to modality choices, with 45% of participants deviating from their preferred pattern in one or less interaction turns.

Operators of traffic control rooms are often required to quickly respond to critical incidents using a complex array of multiple keyboards, mice, very large screen monitors and other peripheral equipment. To support the aim of finding more natural interfaces for this challenging application, this paper presents PEMMI (Perceptually Effective Multimodal Interface), a transport management system control prototype taking video-based manual gesture and speech recognition as inputs. A specific theme within this research is determining the optimum strategy for gesture input in terms of both single-point input selection and suitable multimodal feedback for selection. It has been found that users tend to prefer larger selection areas for targets in gesture interfaces, and tend to select within 44% of this selection radius. The minimum effective size for targets when using 'device-free' gesture interfaces was found to be 80 pixels (on a 1280x1024 screen). This paper also shows that feedback on gesture input via large screens is enhanced by the use of both audio and visual cues to guide the user's multimodal input. Audio feedback in particular was found to improve user response time by an average of 20% over existing gesture selection strategies for multimodal tasks.