The simulator sickness questionnaire, and the erroneous zero baseline assumption

Cybersickness assessment is predominantly conducted via the Simulator Sickness Questionnaire (SSQ). Literature has highlighted that assumptions which are made concerning baseline assessment may be incorrect, especially the assumption that healthy participants enter with no or minimal associated symptoms. An online survey study was conducted to explore further this assumption amongst a general population sample (N = 93). Results for this study suggest that the current baseline assumption may be inherently incorrect

Player Modeling

Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player\u27s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model\u27s input and a proposed classification for the model\u27s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model

A Python Tool for Selecting Domain-Specific Data in Machine Translation

As the volume of data for Machine Translation (MT) grows, the need for models that can perform well in specific use cases, like patent and medical translations, becomes increasingly important. Unfortunately, generic models do not work well in such cases, as they often fail to handle domain-specific style and terminology. Only using datasets that cover domains similar to the target domain to train MT systems can effectively lead to high translation quality (for a domain-specific use-case) (Wang et al., 2017; Pourmostafa Roshan Sharami et al., 2021; Pourmostafa Roshan Sharami et al., 2022). This highlights the limitation of data-driven MT when trained on general domain data, regardless of dataset size. To address this challenge, researchers have implemented various strategies to improve domain-specific translation using Domain Adaptation (DA) methods (Saunders, 2022; Sharami et al., 2023). The DA process involves initially training a generic model, which is then fine-tuned using a domain-specific dataset (Chu and Wang, 2018). One approach to generating a domain-specific dataset is to select similar data from generic corpora for a specific language pair and then utilize both general (to train) and domain-specific (to fine-tune) parallel corpora for MT. In line with this approach, we developed a language-agnostic Python tool implementing the methodology proposed by Sharami et al. (2022). This tool uses monolingual domain-specific corpora to generate a parallel in-domain corpus, facilitating data selection for DA

A Python Tool for Selecting Domain-Specific Data in Machine Translation

As the volume of data for Machine Translation (MT) grows, the need for models that can perform well in specific use cases, like patent and medical translations, becomes increasingly important. Unfortunately, generic models do not work well in such cases, as they often fail to handle domain-specific style and terminology. Only using datasets that cover domains similar to the target domain to train MT systems can effectively lead to high translation quality (for a domain-specific use-case) (Wang et al., 2017; Pourmostafa Roshan Sharami et al., 2021; Pourmostafa Roshan Sharami et al., 2022). This highlights the limitation of data-driven MT when trained on general domain data, regardless of dataset size. To address this challenge, researchers have implemented various strategies to improve domain-specific translation using Domain Adaptation (DA) methods (Saunders, 2022; Sharami et al., 2023). The DA process involves initially training a generic model, which is then fine-tuned using a domain-specific dataset (Chu and Wang, 2018). One approach to generating a domain-specific dataset is to select similar data from generic corpora for a specific language pair and then utilize both general (to train) and domain-specific (to fine-tune) parallel corpora for MT. In line with this approach, we developed a language-agnostic Python tool implementing the methodology proposed by Sharami et al. (2022). This tool uses monolingual domain-specific corpora to generate a parallel in-domain corpus, facilitating data selection for DA

Correlating Facial Expressions and Subjective Player Experiences in Competitive Hearthstone

In this study, we used recordings of players’ facial expressions that are captured during competitive Hearthstone games to analyse the correlation between in-game player affective responses and subjective post-game self-reports. With this, we aimed to examine whether eye gaze, head pose and emotions gathered as objective data from face recordings would be associated with subjective experiences of players which were collected in the form of a post-game survey. Data was collected during a live offline Hearthstone competition, which involved a total of 17 players and 31 matches played. Correlation analyses between in-game and post-game variables show that players’ facial expressions and eye gaze measurements are associated with both players’ attention to the opponent and their mood influenced by the opponent. In future research, these results may be used to implement predictive player models

Applications of Artificial Intelligence in Live Action Role-Playing Games (LARP)

Live Action Role-Playing (LARP) games and similar experiences are becoming a popular game genre. Here, we discuss how artificial intelligence techniques, particularly those commonly used in AI for Games, could be applied to LARP. We discuss the specific properties of LARP that make it a surprisingly suitable application field, and provide a brief overview of some existing approaches. We then outline several directions where utilizing AI seems beneficial, by both making LARPs easier to organize, and by enhancing the player experience with elements not possible without AI.Comment: 8 pages, 2 figures. Published at IEEE Conference on Games, 202