1. Title: Exploring the dynamics of Emotions in the space of colours through the Viable Systems Approach (vSa) perspective.
ABSTRACT
We explore the positioning of emotions in the colour space and the relationship linking a particular emotion
to a colour hue rather than another. By shifting the focus on Computer Science Perspective (CSP) through the lens of the Viable Systems Approach (vSa), we want to draw attention on the dynamism of the algorithm used to calculate the Emotional Distance (ED), which, once the emotions have been positioned in space, allows us to measure the distance between two different hues in the space of colours quickly and intuitively. We propose a literature review to better understand both trends and gaps.
Keywords: colours, Emotions, AI, CSP, VSA
2. Title: Finding solutions of the Main Problems of Waste Management in the Touristic Areas and Hospitality Industry.
ABSTRACT
The touristic areas and hospitality industry generate significant amounts of waste, which poses significant challenges in terms of management and disposal. This research paper aims to identify the main problems associated with waste management in these sectors and propose possible solutions. The results will help identify the best practices and strategies that can be implemented to ensure proper waste management in these areas. The scope of this extended abstract is to provide an overview of the benefits of smart waste bins and their role in promoting sustainable waste management in large events. The objective is to highlight the advantages of using smart waste bins, including reduced waste generation, improved hygiene, and enhanced convenience, and to demonstrate how they can contribute to a cleaner, more sustainable, and more organised event environment. Additionally, the objective is to emphasise the importance of using technology in waste management and to encourage event organisers to adopt smart waste bins in their events.
Key words: sustainability, waste management, smart waste bins
3. Statistical and Machine Learning Tools for Consonance Detection
Abstract
The study of emotions through Artificial Intelligence (AI) is a hot topic, as technological development increasingly focuses on understanding and emulating human beings. However, emotion detection risks being an ineffective investigative tool if not underpinned by solid theoretical foundations that reinterpret AI-detected emotions from a broader perspective. The Viable Systems Approach (VSA) may be the missing link between studying the emotional sphere and understanding the social dynamics that characterise human interactions. The concepts of consonance and resonance, typical of VSA, enable the description of information acquisition patterns and understanding the dynamics underlying individual decision-making. The purpose of our study is to demonstrate the possibility of automatically detecting consonance within groups using statistical analysis techniques and Facial Expression Recognition (FER) algorithms through field experimentation. The trial was conducted in two phases: In the first, a structured questionnaire categorised participants into distinct value clusters based on their strong beliefs. In the second phase, FER techniques gauged the change in the initial level of consonance within these clusters during a shared contextual experience. Results showed the feasibility of detecting initial consonance levels and their context-driven variation (resonance), both qualitatively and partially quantitatively. These findings verify the practicability of measuring consonance, paving the way for new applications of emotion detection tools in AI.
Index Terms—Viable Systems Approach (VSA), emotion detection, Facial Expression Recognition (FER), consonance, resonance.
4. Support Vector Machines Models for Human Decision-Making Understanding: A Different Perspective On Emotion Detection
ABSTRACT
The high integration of artificial intelligence (AI) into our daily lives has led to much research on the technology's potential, particularly with its ability to understand human emotions during complex decision-making processes. Our study mainly focuses on the potential of Support Vector Machine (SVM) models in facial emotion recognition (FER), an important aspect of human-computer interaction (HCI) and examines this potential through the Viable System Approach (VSA) perspectives.
The importance of human-computer interaction (HCI) is highlighted in this study, which recognises the significant effects of AI and the Internet of Things (IoT) on several aspects of human beings. The understanding of emotions as an important difference between humans and machines is an ongoing issue, underscoring the necessity for AI to incorporate emotional intelligence. The main objective of the project is to fill the knowledge gap existing between AI and human surroundings, ethics, and social factors. To achieve this, it focuses on two main objectives:
1. Theoretical Value: Exploring the relationship between human emotions and the processes involved in making decisions in different social circumstances through the lens of VSA.
2. Practical/Experimental Value: Developing and testing various SVM models for the automatic recognition and classification of human emotions, with the aim of understanding which parameters most affect the classification accuracy.
Such a multidisciplinary methodology allows to bring together different ideas from computer science, machine learning, marketing, psychology, sociology, and business economics, providing a comprehensive understanding of AI's role in complex systems, especially in emotional perception and decision-making.
From an experimental point of view, we realised three different SVM models based on the most widely used kernel functions (linear, polynomial, and radial). Then, we used the "Japanese Female Facial Expression (JAFFE)" dataset to test the models on three different configurations of the initial data, to understand which parameters are most influential for the performance of the classifiers and to investigate the limitations and potential of SVMs for emotion recognition.
The paper's originality lies in its multidisciplinary nature, integrating computer science with the Viable Systems Approach (VSA), providing a fresh perspective on FER. This approach is not just about developing a framework for human-computer interaction (HCI) but delves deeper into understanding the social dynamics underlying decision-making. In addition, our study exhibits good experimental novelty, offering new insights into the impact of different parameters on SVM performances.
In conclusion, our paper emphasises the significance of emotional aspects in HCI and the potential of AI in understanding human emotions. By employing the VSA, it extends the discussion on AI’s capabilities in complex decision-making processes, highlighting the necessity for AI systems to resonate cognitively with human users in increasingly digital environments.
5. Title: Emotion Detection in AI and its Impact on Business and Society: A Viable Systems Approach (VSA) Perspective in the Human+ Era.
ABSTRACT
The study of emotions through artificial intelligence (AI) is a hot topic, as technological development increasingly focuses on emulating and recreating human behaviours. However, the effectiveness of emotion detection as an investigative tool is at risk without solid theoretical foundations supporting it and reinterpreting emotions from a broader perspective. Emotion detection is one of the most important components of current AI research that is being explored nowadays, as it bridges the gap between business, technology, and societal dynamics in a quickly changing world. Our paper provides a new perspective on emotion detection based on Viable Systems Approach (VSA), with special emphasis on its significance in the Human+ age we live in. It also examines the effects of AI’s ability to recognise and immediately respond to human emotions on business practices and wider community relationships. VSA may be considered the missing link between studying the emotional sphere and understanding the social dynamics characterising human interactions: "consonance" and "resonance", for instance, allow understanding the acquisition patterns and dynamics underlying individual decision-making processes. Our work is classified as a conceptual paper, aimed at providing an overview of VSA theory and offering a novel interpretive key for emotion detection that enables new application possibilities for AI in the field of studying human dynamics.
Key words: VSA (Viable Systems Approach), AI, Human-Machine Interaction, Emotion Detection, Consonance, Resonance.