Behavioural studies revealed that the dog–human relationship resembles the human mother–child bond, but the underlying mechanisms remain unclear. Here, we report the results of a multi-method approach combining fMRI (N = 17), eye-tracking (N = 15), and behavioural preference tests (N = 24) to explore the engagement of an attachment-like system in dogs seeing human faces. We presented morph videos of the caregiver, a familiar person, and a stranger showing either happy or angry facial expressions. Regardless of emotion, viewing the caregiver activated brain regions associated with emotion and attachment processing in humans. In contrast, the stranger elicited activation mainly in brain regions related to visual and motor processing, and the familiar person relatively weak activations overall. While the majority of happy stimuli led to increased activation of the caudate nucleus associated with reward processing, angry stimuli led to activations in limbic regions. Both the eye-tracking and preference test data supported the superior role of the caregiver’s face and were in line with the findings from the fMRI experiment. While preliminary, these findings indicate that cutting across different levels, from brain to behaviour, can provide novel and converging insights into the engagement of the putative attachment system when dogs interact with humans.

The Physics of Notations [9] (PoN) is a design theory presenting nine principles that can be used to evaluate and improve the cognitive effectiveness of a visual notation. The PoN has been used to analyze existing standard visual notations (such as BPMN, UML, etc.), and is commonly used for evaluating newly introduced visual notations and their extensions. However, due to the rather vague and abstract formulation of the PoN's principles, they have received different interpretations in their operationalization. To address this problem, there have been attempts to formalize the principles, however only a very limited number of principles was covered. This research-in-progress paper aims to better understand the difficulties inherent in operationalizing the PoN, and better separate aspects of PoN, which can potentially be formulated in mathematical terms from those grounded in user-specific considerations.

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The billion dollars' worth pet industry is catching up on the wearables market, as pet activity and location trackers are increasingly worn by our furry friends. Despite the growing body of work on user perceptions of human wearables, very few works have addressed canine activity trackers and their impact on pet owners' lifestyles and the human-animal bond. In this paper we report on an empirical study investigating perceptions of 81 users of a popular dog activity tracker. The results show that dog activity trackers are perceived to have positive impact on owners' motivation to increase their mutual physical activities with their dogs. The human-dog bond is perceived to be further reinforced by the use of activity trackers, increasing human awareness to animals' needs by giving them a "digital voice," and potentially improving the quality of human caregiving.

With the advance of modern technologies, computerbased systems for animals are gaining popularity. In particular, there is an explosion of products and gadgets for pets: wellness monitoring applications (e.g., FitBark and PetPace), automatic food dispensers, cognitive enrichment apps, and many more. Furthermore, the discipline of Animal-Computer Interaction has emerged, focusing on a user-centric development of technologies for animals, making them stakeholders in the development process. Animal-centric technologies have already been developed to support activities of rescue and assistance dogs, to provide environmental and cognitive enrichment for animals in captivity, and to support conservation and animal behavior research. Going beyond human stakeholders poses new exciting challenges for requirement engineering and can be used to significantly expand its boundaries under broader theoretical and methodological frameworks. This paper highlights these challenges and proposes a research agenda for developing methodologies for requirement elicitation and analysis for a user-centric development of computerized systems for non-human users.

Despite the availability of various methods and tools to facilitate secure coding, developers continue to write code that contains common vulnerabilities. It is important to understand why technological advances do not sufficiently facilitate developers in writing secure code. To widen our understanding of developers' behaviour, we considered the complexity of the security decision space of developers using theory from cognitive and social psychology. Our interdisciplinary study reported in this article (1) draws on the psychology literature to provide conceptual underpinnings for three categories of impediments to achieving security goals, (2) reports on an in-depth meta-analysis of existing software security literature that identified a catalogue of factors that influence developers' security decisions, and (3) characterises the landscape of existing security interventions that are available to the developer during coding and identifies gaps. Collectively, these show that different forms of impediments to achieving security goals arise from different contributing factors. Interventions will be more effective where they reflect psychological factors more sensitively and marry technical sophistication, psychological frameworks, and usability. Our analysis suggests “adaptive security interventions” as a solution that responds to the changing security needs of individual developers and a present a proof-of-concept tool to substantiate our suggestion.

We report on a mixed-method, comparative study investigating whether there is a difference between privacy concerns expressed about pet wearables as opposed to human wearables – and more importantly, why. We extracted the privacy concerns found in product reviews (N=8,038) of pet wearables (activity, location, and dual-function trackers), contrasting the (lack of) concerns and misuse to a curated set of reviews for similar human-oriented wearables (N=20,431). Our findings indicate that, while overall very few privacy concerns are expressed in product reviews, for pet wearables they are expressed even less, even though consumers use these devices in a manner which impacts both personal and bystander privacy. An additional survey of pet owners (N=201) eliciting what factors would cause them to not purchase (or stop using) pet wearables indicated comparably few privacy concerns, strengthening the representativeness of our findings. A thematic analysis reveals that the lack of privacy concerns may be explained by, among other factors, emotional drivers to purchase the device, and prioritization of (desired) functionality to support those emotional drivers over privacy requirements. Moreover, we found that pet wearables are used in different ways than originally intended, which raise novel privacy implications to be dealt with. We propose that in order to move towards more privacy-conscious use of pet wearables, a combination of understanding consumer rationale and behavior as well as ensuring data protection legislation is adequate to real-world use is needed.

Computational approaches were called for to address the challenges of more objective behavior assessment which would be less reliant on owner reports. This study aims to use computational analysis for investigating a hypothesis that dogs with ADHD-like (attention deficit hyperactivity disorder) behavior exhibit characteristic movement patterns directly observable during veterinary consultation. Behavioral consultations of 12 dogs medically treated due to ADHD-like behavior were recorded, as well as of a control group of 12 dogs with no reported behavioral problems. Computational analysis with a self-developed tool based on computer vision and machine learning was performed, analyzing 12 movement parameters that can be extracted from automatic dog tracking data. Significant differences in seven movement parameters were found, which led to the identification of three dimensions of movement patterns which may be instrumental for more objective assessment of ADHD-like behavior by clinicians, while being directly observable during consultation. These include (i) high speed, (ii) large coverage of space, and (iii) constant re-orientation in space. Computational tools used on video data collected during consultation have the potential to support quantifiable assessment of ADHD-like behavior informed by the identified dimensions.