Abstract

Abstract
In this study we explore the impact of multisensory stimuli in virtual reality on users' emotional responses, addressing a knowledge gap in this rapidly evolving field. Utilizing a range of sensory inputs, including taste, haptics, and smell, in addition to audiovisual cues, this study aims to understand how different combinations of these stimuli affect the users' emotional experience. Two immersive virtual experiences have been developed for this purpose. One included a scenario to evoke positive emotions through selectively chosen pleasant multisensory stimuli, validated in a focus group. The other sought the contrary: to trigger negative emotions by integrating selected combinations of unpleasant multisensory stimuli, also validated in the same focus group. Through a comparative analysis, our findings revealed significant differences in emotional responses between the groups exposed to positive and negative stimuli combinations. Results indicated that combinations involving haptics and taste were particularly effective in eliciting intense emotions using positive stimuli, but their impact was less significant with negative stimuli. This investigation suggests that a fully multisensory virtual environment integrating positive stimuli might lead to cognitive overload, reducing overall emotional responses. In contrast, environments with negative stimuli could enhance emotional engagement and be more likely to avoid cognitive overload. These findings have important implications for designing emotionally resonant and compelling virtual reality experiences. This research enhances the understanding of sensory integration in virtual reality and its effects on emotional engagement, offering valuable insights for developing more impactful virtual experiences. © 2013 IEEE.

Abstract

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.

Abstract
Software testing is an important part of the software development lifecycle. As it is a highly sought-after skill in the industry, it is not surprising that there has been a great deal of research into the teaching of software testing in higher education. Most of this research proposes or evaluates pedagogical approaches or software testing tools to assist teachers in educating the next generation of software engineers. These evaluations are often limited to measuring teachers' opinions about the use of a novel pedagogical approach or an educational tool and students' acceptance and performance in terms of desired software testing skills. While tools and pedagogical approaches address specific aspects of a course, to date, little attention has been paid to the opinions of the students about all the individual aspects of a software testing course. This paper aims to address this missing student perspective by taking a holistic view of software testing course designs. To address this gap, an exploratory study was performed by distributing a questionnaire to 103 students from ten different courses to gauge their opinions on a software testing course they are enrolled in. The results show that students generally have a positive perception of the different aspects of their software testing course. However, several areas for improvement were suggested based on the gathered data.

Abstract
Climate change is causing the emergence of new pest species and diseases, threatening economies, public health, and food security. In Europe, olive groves are crucial for producing olive oil and table olives; however, the presence of the olive fruit fly (Bactrocera Oleae) poses a significant threat, causing crop losses and financial hardship. Early disease and pest detection methods are crucial for addressing this issue. This work presents a pioneering comparative performance study between two state-of-the-art object detection models, YOLOv5 and YOLOv8, for the detection of the olive fruit fly from trap images, marking the first-ever application of these models in this context. The dataset was obtained by merging two existing datasets: the DIRT dataset, collected in Greece, and the CIMO-IPB dataset, collected in Portugal. To increase its diversity and size, the dataset was augmented, and then both models were fine-tuned. A set of metrics were calculated, to assess both models performance. Early detection techniques like these can be incorporated in electronic traps, to effectively safeguard crops from the adverse impacts caused by climate change, ultimately ensuring food security and sustainable agriculture. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.