3,408 research outputs found
An artificial intelligence approach to predicting personality types in dogs
Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles
Taking Politics at Face Value: How Features Expose Ideology
Previous studies using computer vision neural networks to analyze facial images have uncovered patterns in the feature extracted output that are indicative of individual dispositions. For example, Wang and Kosinski (2018) were able to predict the sexual orientation of a target from his or her facial image with surprising accuracy, while Kosinski (2021) was able to do the same in regards to political orientation. These studies suggest that computer vision neural networks can be used to classify people into categories using only their facial images.However, there is some ambiguity in regards to the degree to which these features extracted from facial images incorporate facial morphology when used to make predictions. Critics have suggested that a subject’s transient facial features, such as using makeup, having a tan, donning a beard, or wearing glasses, might be subtly indicative of group belonging (Agüera y Arcas et al., 2018). Further, previous research in this domain has found that accurate image categorization can occur without utilizing facial morphology at all, instead relying upon image brightness, color dominance, or the background of the image to make successful classifications (Leuner, 2019; Wang, 2022).
This dissertation seeks to bring some clarity to this domain. Using an application programming interface (API) for the popular social networking site Twitter, a sample of nearly a quarter million images of ideological organization followers was created. These images were followers of organizations supportive of, or oppositional to, the polarizing political issues of gun control and immigration. Through a series of strong comparisons, this research tests for the influence of facial morphology in image categorization. Facial images were converted into point and mesh coordinate representations of the subjects’ faces, thus eliminating the influence of transient facial features. Images were able to be classified using facial morphology alone at rates well above chance (64% accuracy across all models utilizing only facial points, 62% using facial mesh). These results provide the strongest evidence to date that images can be categorized into social categories by their facial morphology alone
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Adjustable robust optimization with nonlinear recourses
Over the last century, mathematical optimization has become a prominent tool for decision making. Its systematic application in practical fields such as economics, logistics or defense led to the development of algorithmic methods with ever increasing efficiency. Indeed, for a variety of real-world problems, finding an optimal decision among a set of (implicitly or explicitly) predefined alternatives has become conceivable in reasonable time. In the last decades, however, the research community raised more and more attention to the role of uncertainty in the optimization process. In particular, one may question the notion of optimality, and even feasibility, when studying decision problems with unknown or imprecise input parameters. This concern is even more critical in a world becoming more and more complex —by which we intend, interconnected —where each individual variation inside a system inevitably causes other variations in the system itself.
In this dissertation, we study a class of optimization problems which suffer from imprecise input data and feature a two-stage decision process, i.e., where decisions are made in a sequential order —called stages —and where unknown parameters are revealed throughout the stages. The applications of such problems are plethora in practical fields such as, e.g., facility location problems with uncertain demands, transportation problems with uncertain costs or scheduling under uncertain processing times. The uncertainty is dealt with a robust optimization (RO) viewpoint (also known as "worst-case perspective") and we present original contributions to the RO literature on both the theoretical and practical side
Understanding Agreement and Disagreement in Listeners’ Perceived Emotion in Live Music Performance
Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications
"Le present est plein de l’avenir, et chargé du passé" : Vorträge des XI. Internationalen Leibniz-Kongresses, 31. Juli – 4. August 2023, Leibniz Universität Hannover, Deutschland. Band 2
[No abstract available]Deutschen Forschungsgemeinschaft (DFG)/Projektnr. 517991912VGH VersicherungNiedersächsisches Ministerium für Wissenschaft und Kultur (MWK
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