In 2023, the Hidden Heroes game jam allowed game developers to share untold stories of individuals who had a significant impact on their lives. As a result, 33 games were created embracing the stories of real people. Interviews were conducted with 12 of these developers to understand their experiences of creating games about authentic personal stories. Thematic analysis revealed eight themes and three categories that captured the experiences and challenges of making such personal narrative games. Based on these findings, we reflected on the future of authentic personal narrative games.

Cardistry is an application that enables users to create their own playing cards for use in evocative storytelling games. It is driven by OpenAI’s Generative Pre-trained Transformer (GPT) models that generate unique card titles, cards suits, imagery, and poetry based on the user’s input. It allows the user to preserve their digital cards in an online repository and print them for tabletop game play use. Cardistry was designed to begin exploring the question of whether widely available GPT models could be used to adapt the process of gaminiscing to make it more accessible to designers and players alike. This short paper details the concept, design, and implementation of Cardistry as a first step in exploring this research question. It explains how the adapted gaminiscing process is different from the original process, discusses the limitations of the implementation, and expresses what future research would be required to answer the research question.

High-fidelity game development could be advantageous for improving player experience. However, it often comes at a price—labor costs, monetary investments, and lengthy development processes and it is challenging to balance between game fidelity and development cost. This work focuses on a specific genre—gaminiscing games, that are designed in a narrative way to archive and recreate personal oral history. Our study aims to explore the potential possibility of low-fidelity game design but without significantly, or even at all, sacrificing the player experience. Concretely, this paper explores whether a game design with higher fidelity and a specific type of scene would always correlate with a better player experience. An experiment with 42 participants was conducted using a commercial gaminiscing game called Brukel. Results show that it is not the case that both fidelity and scene would always significantly affect the overall experience of the game. This finding could shed light on practical game design, where game designers can choose the level of production that best aligns with their game's objectives.

Bug-a-Tug is a game without tangible gameplay – it omits any means of player input, and therefore, cannot give feedback. Bug-a-Tug accomplishes this by creating the illusion of player input, feedback, and impact on the game system. This unique design questions the importance of player perception and belief in constructing meaning and gameplay. It also questions whether gameplay and interactivity can be achieved even when a game subverts our traditional understandings of such terms.

This paper explores the concept of meaningful play in the context of a game that subverts the traditional input-output understanding of the term. This paper delves into the factors that contribute to the understanding of meaningful play. Using this understanding, a non-meaningful game was constructed that challenges the conventional notion of meaningful play by creating the illusion of player input and impact on the game system without any actual player input or game feedback. By analyzing the non-meaningful game, this paper contributes to the ongoing conversation around meaningful play, indicating a need for a new definition. Lastly, through the creation of a game void of actual player input, this paper illustrates the importance of player perception and belief in constructing meaning within games. This questions the modern understanding of video games and opens new avenues for further research in the field of game studies.

Sparse support vector machine (SVM) is a robust predictive model that can effectively remove noise and preserve signals. Like Lasso, it can efficiently learn a solution path based on a set of predefined parameters and therefore provides strong support for model selection. Sparse SVM has been successfully applied in a variety of data mining applications including text mining, bioinformatics, and image processing. The emergence of big-data analysis poses new challenges for model selection with large-scale data that consist of tens of millions samples and features. In this paper, a novel screening technique is proposed to accelerate model selection for l1-regularized l2-SVM and effectively improve its scalability. This technique can precisely identify inactive features in the optimal solution of a l1-regularized l2-SVM model and remove them before training. The technique makes use of the variational inequality and provides a closed-form solution for screening inactive features in different situations. Every feature that is removed by the screening technique is guaranteed to be inactive in the optimal solution. Therefore, when l1-regularized l2-SVM uses the features selected by the technique, it achieves exactly the same result as when it uses the full feature set. Because the technique can remove a large number of inactive features, it can greatly increase the efficiency of model selection for l1-regularized l2-SVM. Experimental results on five high-dimensional benchmark data sets demonstrate the power of the proposed technique.

The purpose of this paper is to examine the use of text mining to investigate coded information. Inventory codes are usually nominal rather than ordinal. However, codes are not completely devoid of meaning; generally items in the inventory that are similar are assigned base numbers with added digits to distinguish between individual items. The standard approach to using such data in statistical models that require at least ordinal data is to create dummy variables, ie the presence or absence of an item. Instead, it is possible to treat the code as a text word, and to use text analysis to examine the data. In this paper, two examples from the healthcare industry are examined. The first examines ICD-9 codes that are used in Medicare billing to list patient risk factors and complications. The second examines medications routinely prescribed to patients in open heart surgery.