Search

The field of animal affective computing is rapidly emerging, and analysis of facial expressions is a crucial aspect. One of the most significant challenges that researchers in the field currently face is the scarcity of high-quality, comprehensive datasets that allow the development of models for facial expressions analysis. One of the possible approaches is the utilisation of facial landmarks, which has been shown for humans and animals. In this paper we present a novel dataset of cat facial images annotated with bounding boxes and 48 facial landmarks grounded in cat facial anatomy. We also introduce a landmark detection convolution neural network-based model which uses a magnifying ensemble method. Our model shows excellent performance on cat faces and is generalizable to human and other animals facial landmark detection. [ABSTRACT FROM AUTHOR]

Shelters are stressful environments for domestic dogs which are known to negatively impact their welfare. The introduction of outside stimuli for dogs in this environment can improve their welfare and life conditions. However, our current understanding of the influence of different stimuli on shelter dogs’ welfare is limited and the data is still insufficient to draw conclusions. In this study, we collected 28 days (four weeks) of telemetry data from eight male dogs housed in an Italian shelter for a long period of time. During this period, three types of enrichment were introduced into the dogs’ pens for one week each: entertaining objects, intraspecific, and interspecific social enrichment, by means of the presence of female conspecifics and the presence of a human. To quantify their impact, we introduce novel metrics as indicators of sheltered dogs’ welfare based on telemetry data: the variation of heart rate, muscle activity, and body temperature from an average baseline day, quality of sleep, and the regularity for cyclicity of the aforementioned parameters, based on the day-night cycle. Using these metrics, we show that while all three stimuli statistically improve the dogs’ welfare, the variance between individual dogs is large. Moreover, our findings indicate that the presence of female conspecific is the best stimulus among the three explored options which improves both the quality of sleep and the parameters’ cyclicity. Our results are consistent with previous research findings while providing novel data-driven welfare indicators that promote objectivity. Thus, this research provides some useful guidelines for managing shelters and improving dogs’ welfare. [ABSTRACT FROM AUTHOR]

Behavioral traits in dogs are assessed for a wide range of purposes such as determining selection for breeding, chance of being adopted or prediction of working aptitude. Most methods for assessing behavioral traits are questionnaire or observation-based, requiring significant amounts of time, effort and expertise. In addition, these methods might be also susceptible to subjectivity and bias, negatively impacting their reliability. In this study, we proposed an automated computational approach that may provide a more objective, robust and resource-efficient alternative to current solutions. Using part of a 'Stranger Test' protocol, we tested n = 53 dogs for their response to the presence and neutral actions of a stranger. Dog coping styles were scored by three dog behavior experts. Moreover, data were collected from their owners/trainers using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ). An unsupervised clustering of the dogs' trajectories revealed two main clusters showing a significant difference in the stranger-directed fear C-BARQ category, as well as a good separation between (sufficiently) relaxed dogs and dogs with excessive behaviors towards strangers based on expert scoring. Based on the clustering, we obtained a machine learning classifier for expert scoring of coping styles towards strangers, which reached an accuracy of 78%. We also obtained a regression model predicting C-BARQ scores with varying performance, the best being Owner-Directed Aggression (with a mean average error of 0.108) and Excitability (with a mean square error of 0.032). This case study demonstrates a novel paradigm of 'machine-based' dog behavioral assessment, highlighting the value and great promise of AI in this context. [ABSTRACT FROM AUTHOR]

The early and accurate diagnosis of brachycephalic obstructive airway syndrome (BOAS) in dogs is pivotal for effective treatment and enhanced canine well-being. Owners often do underestimate the severity of BOAS in their dogs. In addition, traditional diagnostic methods, which include pharyngolaryngeal auscultation, are often compromised by subjectivity, are time-intensive and depend on the veterinary surgeon's experience. Hence, new fast, reliable assessment methods for BOAS are required. The aim of the current study was to use machine learning techniques to bridge this scientific gap. In this study, machine learning models were employed to objectively analyze 366 audio samples from 69 Pugs and 79 other brachycephalic breeds, recorded with an electronic stethoscope during a 15-min standardized exercise test. In classifying the BOAS test results as to whether the dog is affected or not, our models achieved a peak accuracy of 0.85, using subsets from the Pugs dataset. For predictions of the BOAS results from recordings at rest in Pugs and various brachycephalic breeds, accuracies of 0.68 and 0.65 were observed, respectively. Notably, the detection of laryngeal sounds achieved an F1 score of 0.80. These results highlight the potential of machine learning models to significantly streamline the examination process, offering a more objective assessment than traditional methods. This research indicates a turning point towards a data-driven, objective, and efficient approach in canine health assessment, fostering standardized and objective BOAS diagnostics. [ABSTRACT FROM AUTHOR]

Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data. [ABSTRACT FROM AUTHOR]

Manual tools for pain assessment from facial expressions have been suggested and validated for several animal species. However, facial expression analysis performed by humans is prone to subjectivity and bias, and in many cases also requires special expertise and training. This has led to an increasing body of work on automated pain recognition, which has been addressed for several species, including cats. Even for experts, cats are a notoriously challenging species for pain assessment. A previous study compared two approaches to automated 'pain'/'no pain' classification from cat facial images: a deep learning approach, and an approach based on manually annotated geometric landmarks, reaching comparable accuracy results. However, the study included a very homogeneous dataset of cats and thus further research to study generalizability of pain recognition to more realistic settings is required. This study addresses the question of whether AI models can classify 'pain'/'no pain' in cats in a more realistic (multi-breed, multi-sex) setting using a more heterogeneous and thus potentially 'noisy' dataset of 84 client-owned cats. Cats were a convenience sample presented to the Department of Small Animal Medicine and Surgery of the University of Veterinary Medicine Hannover and included individuals of different breeds, ages, sex, and with varying medical conditions/medical histories. Cats were scored by veterinary experts using the Glasgow composite measure pain scale in combination with the well-documented and comprehensive clinical history of those patients; the scoring was then used for training AI models using two different approaches. We show that in this context the landmark-based approach performs better, reaching accuracy above 77% in pain detection as opposed to only above 65% reached by the deep learning approach. Furthermore, we investigated the explainability of such machine recognition in terms of identifying facial features that are important for the machine, revealing that the region of nose and mouth seems more important for machine pain classification, while the region of ears is less important, with these findings being consistent across the models and techniques studied here. [ABSTRACT FROM AUTHOR]

Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of pain and emotional states in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic—classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research. [ABSTRACT FROM AUTHOR]

In animal research, automation of affective states recognition has so far mainly addressed pain in a few species. Emotional states remain uncharted territories, especially in dogs, due to the complexity of their facial morphology and expressions. This study contributes to fill this gap in two aspects. First, it is the first to address dog emotional states using a dataset obtained in a controlled experimental setting, including videos from (n = 29) Labrador Retrievers assumed to be in two experimentally induced emotional states: negative (frustration) and positive (anticipation). The dogs' facial expressions were measured using the Dogs Facial Action Coding System (DogFACS). Two different approaches are compared in relation to our aim: (1) a DogFACS-based approach with a two-step pipeline consisting of (i) a DogFACS variable detector and (ii) a positive/negative state Decision Tree classifier; (2) An approach using deep learning techniques with no intermediate representation. The approaches reach accuracy of above 71% and 89%, respectively, with the deep learning approach performing better. Secondly, this study is also the first to study explainability of AI models in the context of emotion in animals. The DogFACS-based approach provides decision trees, that is a mathematical representation which reflects previous findings by human experts in relation to certain facial expressions (DogFACS variables) being correlates of specific emotional states. The deep learning approach offers a different, visual form of explainability in the form of heatmaps reflecting regions of focus of the network's attention, which in some cases show focus clearly related to the nature of particular DogFACS variables. These heatmaps may hold the key to novel insights on the sensitivity of the network to nuanced pixel patterns reflecting information invisible to the human eye. [ABSTRACT FROM AUTHOR]

Facial expressions in non-human animals are closely linked to their internal affective states, with the majority of empirical work focusing on facial shape changes associated with pain. However, existing tools for facial expression analysis are prone to human subjectivity and bias, and in many cases also require special expertise and training. This paper presents the first comparative study of two different paths towards automatizing pain recognition in facial images of domestic short haired cats (n = 29), captured during ovariohysterectomy at different time points corresponding to varying intensities of pain. One approach is based on convolutional neural networks (ResNet50), while the other—on machine learning models based on geometric landmarks analysis inspired by species specific Facial Action Coding Systems (i.e. catFACS). Both types of approaches reach comparable accuracy of above 72%, indicating their potential usefulness as a basis for automating cat pain detection from images. [ABSTRACT FROM AUTHOR]

Problem definition: Data errors in business processes can be a source for exceptions and hamper business outcomes. Relevance: The paper proposes a method for analyzing data inaccuracy issues already at process design time, in order to support process designers by identifying process parts where data errors might remain unrecognized, so decisions could be taken based on inaccurate data. Methodology: The paper follows design science, developing a method as an artifact. The conceptual basis is the notion of data inaccuracy awareness – the ability to tell whether potential discrepancies between real and IS values may exist. Results: The method was implemented on top of a Petri net modeling tool and validated in a case study performed in a large manufacturing company of safety–critical systems. Managerial implications: Anticipating consequences of data inaccuracy already during process design can help avoiding them at runtime. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The problem of inconsistency in requirements engineering has been in the spotlight of the RE community from the early 1990s. In the early years, inconsistency was perceived in the literature as a problem that needs to be eliminated on sight. More recently, it has become recognized that maintaining consistency at all times is not only infeasible, but also even counterproductive. Based on this recognition, paradigms and tools have been proposed in the RE literature for managing inconsistency. However, over the same period, inconsistency as perceived and managed in practice has not received much attention. Our research aims to better understand the phenomenon of inconsistency and the strategies to address it in RE practice. This paper describes an empirical study investigating practitioners' perceptions of inconsistency manifestations in RE, their attitudes towards these manifestations, and strategies they apply to address them. The findings of this research led to the two contributions: (a) an explanation of how the ideas of the RE field about managing RE inconsistency are reflected in practitioners' perceptions of the inconsistency that they encounter in their daily work, and (b) the identification of some barriers that appear to be hindering practitioners' adoption of the RE field's inconsistency management strategies, together with possible reasons underlying these barriers. [ABSTRACT FROM AUTHOR]

This research was aimed at eliciting the requirements of practitioners who use conceptual modeling in their professional work for the visual notations of modeling languages. While the use of conceptual modeling in practice has been addressed, what practitioners in fact require of the visual notation of the modeling languages they use has received little attention. This work was thus motivated by the need to understand to what extent practitioners' requirements are acknowledged and accommodated by visual notation research efforts. A mixed-method study was conducted, with a survey being offered over the course of several months to LinkedIn professional groups. The requirements included in the survey were based on a leading design theory for visual notations, the Physics of Notations. After preprocessing, 104 participant responses were analyzed. Data analysis included descriptive coding and qualitative analysis of purposes for modeling and additional requirements beyond the scope of visual design. Statistical and factorial analysis was used to explore potential correlations between the importance of different requirements as perceived by practitioners and the demographic factors (e.g., domain, purpose, topics). The results indicate several correlations between demographic factors and the perceived importance of visual notation requirements, as well as differences in the perceived relative importance of different requirements for models used to communicate with modeling experts as compared to non-experts. Furthermore, the results show an evolution from trends identified in studies conducted in the previous decade. The identified correlations with practitioners' demographics reveal several research challenges that should be addressed, as well as the potential benefits of more purpose-specific tailoring of visual notation design. Furthermore, the shift in practitioner demographics as compared to those found in earlier work indicates that the research and development of conceptual modeling efforts needs to stay up-to-date with the way practitioners employ conceptual modeling. [ABSTRACT FROM AUTHOR]

Many commonly used logics, including classical logic and intuitionistic logic, are trivialized in the presence of inconsistency, in the sense that inconsistent premises cause the derivation of any formula. It is thus often useful to define inconsistency-tolerant variants of such logics, which are faithful to the original logic with respect to consistent theories but also allow for nontrivial inconsistent theories. A common way of doing so is by incorporating distance-based considerations for concrete logics. So far this has been done mostly in the context of two-valued semantics. Our purpose in this paper is to show that inconsistency-tolerance can be achieved for any logic that is based on a denotational semantics. For this, we need to trade distances for the more general notion of dissimilarities. We then examine the basic properties of the entailment relations that are obtained and exemplify dissimilarity-based reasoning in various forms of denotational semantics, including multi-valued semantics, non-deterministic semantics, and possible-worlds (Kripke-style) semantics. Moreover, we show that our approach can be viewed as an extension of several well-studied forms of reasoning in the context of belief revision, database integration, consistent query answering, and inconsistency maintenance in knowledge-based systems. [ABSTRACT FROM AUTHOR]

We define a general family of canonical labelled calculi, of which many previously studied sequent and labelled calculi are particular instances. We then provide a uniform and modular method to obtain finite-valued semantics for every canonical labelled calculus by introducing the notion of partial non-deterministic matrices. The semantics is applied to provide simple decidable semantic criteria for two crucial syntactic properties of these calculi: (strong) analyticity and cut-admissibility. Finally, we demonstrate an application of this framework for a large family of paraconsistent logics. [ABSTRACT FROM AUTHOR]

Maximality is a desirable property of paraconsistent logics, motivated by the aspiration to tolerate inconsistencies, but at the same time retain from classical logic as much as possible. In this paper we introduce the strongest possible notion of maximal paraconsistency, and investigate it in the context of logics that are based on deterministic or non-deterministic three-valued matrices. We show that all reasonable paraconsistent logics based on three-valued deterministic matrices are maximal in our strong sense. This applies to practically all three-valued paraconsistent logics that have been considered in the literature, including a large family of logics which were developed by da Costa's school. Then we show that in contrast, paraconsistent logics based on three-valued properly nondeterministic matrices are not maximal, except for a few special cases (which are fully characterized). However, these non-deterministic matrices are useful for representing in a clear and concise way the vast variety of the (deterministic) three-valued maximally paraconsistent matrices. The corresponding weaker notion of maximality, called premaximal paraconsistency, captures the 'core' of maximal paraconsistency of all possible paraconsistent determinizations of a non-deterministic matrix, thus representing what is really essential for their maximal paraconsistency. [ABSTRACT FROM AUTHOR]