Game Representation Research Articles

The Evolution of Female Character Representations in Video Games from a Feminism Perspective

The Evolution of Female Character Representations in Video Games from a Feminism Perspective

Theoretical and Methodological Framework for Studying Video Games and Orientalism

AbstractThis article proposes a theoretical and methodological framework for studying orientalism and representation in video games, grounded in information science, communication studies, game studies, and cultural studies. Utilizing the approaches of critical transculturalism, game production studies, and a newly refined concept of gamevironments, the proposed framework understands video games as hybrid cultural artifacts existing within interconnected technical, socio‐cultural, political, economic, and religious spheres. It examines games on three levels: as computational systems encompassing procedural, narrative, and audiovisual layers; as designed by authors encoding worldviews and ideas; and as dynamically interpreted by players. Encompassing the contexts influencing design and reception, this framework enables nuanced analysis of how games perpetuate or challenge orientalist discourses. Methodologically, it integrates techniques from across disciplines to study games' components, creators, audiences, and wider environments. Using Arab and Iranian gaming cultures as an example, this article highlights applying the framework to local productions negotiating global media flows and political constraints. Overall, it aims to offer an expansive and multifaceted toolkit for researching orientalism, representation, and identity construction in video games.

Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.

Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the "chaos game" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences. We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.

Playing Various Strategies in Dominion with Deep Reinforcement Learning

Deck-building games, like Dominion, present an unsolved challenge for game AI research. The complexity arising from card interactions and the relative strength of strategies depending on the game configuration result in computer agents being limited to simple strategies. This paper describes the first application of recent advances in Geometric Deep Learning to deck-building games. We utilize a comprehensive multiset-based game representation and train the policy using a Soft Actor-Critic algorithm adapted to support variable-size sets of actions. The proposed model is the first successful learning-based agent that makes all decisions without relying on heuristics and supports a broader set of game configurations. It exceeds the performance of all previous learning-based approaches and is only outperformed by search-based approaches in certain game configurations. In addition, the paper presents modifications that induce agents to exhibit novel human-like play strategies. Finally, we show that learning strong strategies based on card combinations requires a reinforcement learning algorithm capable of discovering and executing a precise strategy while ignoring simpler suboptimal policies with higher immediate rewards.

Motivationally Appealing Computer Science e-Learning Games: An Inclusive Design Approach

Research has shown that e-learning games do not have the same level of appeal to girls, as they do to boys; particularly in the crucial 11-14 age group. In the United Kingdom, this is typically when they start to make subject choices that impact their future studies and careers. Given the shortage of females who choose computer science as a career, this study explores how e-learning games can be designed to be motivationally appealing to young learners. It further explores the role of game representations and its appeal to this age group. This empirical study addresses the research question: “Can we develop e-learning games which appeal and motivate girls of age 11-14 to study computer science concepts?” Two e-learning games were developed: one included game representations such as game colour, graphics, character, age appropriateness, storyline, number of players, violence, identified as appealing to young females and the other game included antithetical or neutral representations. The two developed prototypes were used to explore key e-learning game representations as used to teach computer science concepts. A total of 304 participants, comprising of 152 girls and 152 boys from a combination of same sex and mixed secondary schools in Southeast England, engaged with both experimental games. The experiment also elicited information on how learners interact with these games and the resulting game appeal, motivation and learning. The insight gained from the analysis of data captured during the experiments, provide the evidence to demonstrate that inclusive e-learning games which motivate and appeal to girls of age 11-14 can be developed. This can have a positive influence on their willingness to use such games to learn computer science concepts. This implies that the study found positive outcomes related to e-learning game appeal, motivation and the learning of girls of this age group. A follow-up longitudinal study could investigate the impact of significant e-learning game representations that appeal to the target group. This could provide additional evidence on the changes in the appeal of the investigated significant game representations over time, due to the influence of other factors such as socio-economic and socio-cultural differences. This understanding can further enhance inclusive e-learning strategies to improve diversity in computer science education and consequently the career pipeline.

Representation of mental illness in video games beyond stigmatization

The representation of mental illness is part of many video games; however, it is often accompanied by stigmatization of and discrimination against those who are affected. Recently, a more positive approach has been found, including a dimensional representation of mental disorders such as depression or anxiety, especially in games developed by independent developers. The study examined the most popular video games of 2018 and 2019 based on their representation of mental illness in a mixed-methods approach. A quantitative coding process examining general aspects of the games, the characters affected, and the illness representation is followed by a qualitative video game analysis of the games in the sample with a dimensional representation of mental illness. It was found that 16 of the 74 games examined included characters who were affected by mental illnesses. For the most part, mental illness is an essential aspect of the games analyzed in the sample represented by the main characters. However, the depiction of mental illness often lacks depth and dimensionality. Two examples in the study offer a dimensional representation that includes mental illness as characters, as part of the environment and atmosphere, and provides illness-related advice as part of the gameplay. These findings can be helpful for developing games with the potential to reduce discrimination and stigmatization of mental illness and those affected in the future by pointing out aspects leading to a more dimensional and empathetic portrayal of mental illness in existing games. Additionally, the category system used in this study is adaptable to future qualitative research on mental illness representation in video games.

Gene Teams are on the Field: Evaluation of Variants in Gene-Networks Using High Dimensional Modelling.

In medical genetics, each genetic variant is evaluated as an independent entity regarding its clinical importance. However, in most complex diseases, variant combinations in specific gene networks, rather than the presence of a particular single variant, predominates. In the case of complex diseases, disease status can be evaluated by considering the success level of a team of specific variants. We propose a high dimensional modelling based method to analyse all the variants in a gene network together, which we name "Computational Gene Network Analysis" (CoGNA).To evaluate our method, we selected two gene networks, mTOR and TGF- β. For each pathway, we generated 400 control and 400 patient group samples. mTOR and TGF- β pathways contain 31 and 93 genes of varying sizes, respectively. We produced Chaos Game Representation images for each gene sequence to obtain 2-D binary patterns. These patterns were arranged in succession, and a 3-D tensor structure was achieved for each gene network. Features for each data sample were acquired by exploiting Enhanced Multivariance Products Representation to 3-D data. Features were split as training and testing vectors. Training vectors were employed to train a Support Vector Machines classification model. We achieved more than 96% and 99% classification accuracies for mTOR and TGF- β networks, respectively, using a limited amount of training samples.

Spike2CGR: an efficient method for spike sequence classification using chaos game representation

Spike2CGR: an efficient method for spike sequence classification using chaos game representation

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.

Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each other in the new representation space. Specifically, we rephrase whole-genome sequences with frequency chaos game representation (FCGR) and learn latent embeddings that 'encapsulate' phages and host relationships through contrastive learning. The contrastive learning method works well on the imbalanced dataset. Based on the learned embeddings, a proposed pipeline named CL4PHI can predict known hosts and unseen hosts in training. We compare our method with two recently proposed state-of-the-art learning-based methods on their benchmark datasets. The experiment results demonstrate that the proposed method using contrastive learning improves the prediction accuracy on known hosts and demonstrates a zero-shot prediction capability on unseen hosts. In terms of potential applications, the rapid pace of genome sequencing across different species has resulted in a vast amount of whole-genome sequencing data that require efficient computational methods for identifying phage-host interactions. The proposed approach is expected to address this need by efficiently processing whole-genome sequences of phages and prokaryotic hosts and capturing features related to phage-host relationships for genome sequence representation. This approach can be used to accelerate the discovery of phage-host interactions and aid in the development of phage-based therapies for infectious diseases.

Cancer disease multinomial classification using transfer learning and SVM on the genes’ sequences

INTRODUCTION: Early disease detection plays an important role in medical field especially for cancer disease, which helps doctors in diagnosing and identifying the therapeutic process. Aiming to provide assistance, many biological techniques other than machine and deep learning models were proposed. They were applied on a different type of data such as medical images and clinical data. Despite the efficiency of those techniques, they remain costly and need a lot of execution and preparation time, and resources.OBJECTIVES: In this paper, we present a novel method of disease detection analyzing the genes sequences composition.METHODS: We start by extracting k-mer nucleotides as features from gene sequences with the Frequency Chaos Game Representation (FCGR) technique. Since extracted data are huge, we use a DeepInsight model to extract the most representative k-mers.A combination of a transfer learning model, which is Residual neural Network (ResNet), and a support vector machine (SVM) algorithm is then used then to classify samples into 18 cancer disease types.RESULTS: We achieved an accuracy of 0.98 while choosing FCGR6 in feature extraction, and a combination of ResNet50 and SVM in the multinomial classification step, against an accuracy of 0.97 while using ResNet50 with a fully connected layer and FCGR5.CONCLUSION: Defining the gene sequence alterations helps in the disease detection at early stage. Here, we adopt the FCGR method (that gives the frequency of each k-mer) in defining features of the gene sequences. Then, we use deep learning models to deal with the big number of characteristics and predicting different cancer diseases.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.

As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification. In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins. The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Seismic trends in Indian plate: A study on epicentral characteristics by using a driven iterated function system

This paper studies the trends exhibited by earthquake epicenters around the Indian tectonic Plate with respect to time. For this study, a novel method based on a modified Iterated Function System has been used. The modified Chaos Game Representation plots generated for the study gave characteristic images implying that the occurrence of earthquakes in some specific combinations showed higher preference around the study area. The statistical data on the number of different combinations of earthquake occurrences were identified from these images. The seismicity trends over different periods are compared to the current seismic trend, and their difference is quantified by using a statistical tool called proximity index. The study proves that the patterns exhibited by earthquakes are independent of the number of earthquakes, and the current seismic trends exhibited by the Indian Plate show a high resemblance with the seismic trend during the years 1975–1988.

Integrating Computational Thinking and Data Science: The Case of Modding Classification Games

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games’ content, leading them to develop a critical stance towards the game data model and representations.

Using Chaos-Game-Representation for Analysing the SARS-CoV-2 Lineages, Newly Emerging Strains and Recombinants.

Viruses have high mutation rates, facilitating rapid evolution and the emergence of new species, subspecies, strains and recombinant forms. Accurate classification of these forms is crucial for understanding viral evolution and developing therapeutic applications. Phylogenetic classification is typically performed by analyzing molecular differences at the genomic and sub-genomic levels. This involves aligning homologous proteins or genes. However, there is growing interest in developing alignment-free methods for whole-genome comparisons that are computationally efficient. Here we elaborate on the Chaos Game Representation (CGR) method, based on concepts of statistical physics and free of sequence alignment assumptions. We adopt the CGR method for classification of the closely related clades/lineages A and B of the SARS-Corona virus 2019 (SARS-CoV-2), which is one of the fastest evolving viruses. Our study shows that the CGR approach can easily yield the SARS-CoV-2 phylogeny from the available whole genomes of lineage A and lineage B sequences. It also shows an accurate classification of eight different strains and the newly evolved XBB variant from its parental strains. Compared to alignment-based methods (Neighbour-Joining and Maximum Likelihood), the CGR method requires low computational resources, is fast and accurate for long sequences, and, being a K-mer based approach, allows simultaneous comparison of a large number of closely-related sequences of different sizes. Further, we developed an R pipeline CGRphylo, available on GitHub, which integrates the CGR module with various other R packages to create phylogenetic trees and visualize them. Our findings demonstrate the efficacy of the CGR method for accurate classification and tracking of rapidly evolving viruses, offering valuable insights into the evolution and emergence of new SARS-CoV-2 strains and recombinants.

“A Checkbox on a Character Sheet”

Gender representation is an oft-discussed topic in game studies literature, but “gender representation” tends to mean “female representation”. Many discussions of gender in game studies lack the nuance of queer gender identities that lay beyond a binary notion of gender. This literature review seeks to analyze the state of nonbinary gender representation in video games, looking at both characters and avatar creation screens, to find scholarship about queer gender identities, as well as games that are gender-inclusive. Ultimately, the video game industry has a long way to go to break beyond the binary of gender stereotypesthat it has become known for.

A Game Representation for a Finite Horizon State Constrained Continuous Time Linear Regulator Problem

A supremum-of-quadratics representation for a class of extended real valued barrier functions is developed and applied in the context of solving a continuous time linear regulator problem subject to a single state constraint of bounded norm. It is shown that this very simple state constrained regulator problem can be equivalently formulated as an unconstrained two-player game. By demonstrating equivalence of the upper and lower values, and exploiting existence and uniqueness of the optimal actions for both players, state feedback characterizations for the corresponding optimal policies for both players are developed. These characterizations are illustrated by a simple example.

Chromatic Differentiation of Functional Mappings of the Composition of Nucleic Acids

Color visualization of the DNA of diverse living beings can help in the exploration of the issue of chromatic differentiation of functional mappings of the nucleotide composition of DNA molecules. By “chromatic differentiation”, we mean the coloring of these mappings. Algorithms for coloring genetic representations improve the perception of complex genetic information using color. Methodologically, to build the chromatic differentiation of functional mappings of the nucleotide composition of DNA, we employed the system of nucleotide Walsh functions and the Chaos Game Representation (CGR) algorithm. The authors compared these two approaches and proposed a modified CGR algorithm. The work presents various algorithms of chromatic differentiation based on the nucleotide Walsh functions at a specific location of the fragment in the nucleotide chain and on the frequencies of those fragments. The results of the analysis provide examples of chromatic differentiation in a variety of parametric spaces. The paper describes various approaches to coloring and video animation of DNA molecules in their chromatically differentiated spans of physicochemical parameters.

Composing games into complex institutions.

Game theory is used by all behavioral sciences, but its development has long centered around the economic interpretation of equilibrium outcomes in relatively simple games and toy systems. But game theory has another potential use: the high-level design of large game compositions that express complex architectures and represent real-world institutions faithfully. Compositional game theory, grounded in the mathematics underlying programming languages, and introduced here as a general computational framework, increases the parsimony of game representations with abstraction and modularity, accelerates search and design, and helps theorists across disciplines express real-world institutional complexity in well-defined ways. Relative to existing approaches in game theory, compositional game theory is especially promising for solving game systems with long-range dependencies, for comparing large numbers of structurally related games, and for nesting games into the larger logical or strategic flows typical of real world policy or institutional systems.

Representation of the XXIV Olympic Winter Games in Beijing in the Memosphere of the PRC: Conflictological Analysis

The paper claims that the 2022 Winter Olympics in Beijing, held in country, known for it`s socio-cultural peculiarities, reveals wide opportunities for this sport mega-event representation. The context is often unavailable for the audience beyond China, which is caused by Chinese media discourse ethno-cultural features. Such media discourse component as memes expresses online sociocultural identity, the way of thinking, behavior and values of the audience. The study examined 2022 Winter Olympics in Beijing Chinese memes using the methodology tested before. The aim of the research was to study the Chinese Olympic memes-sphere. The sample deals with 450 memes, selected in Chinese Internet sector. The monitoring was held by Chinese masters of the journalism faculty. The memes were sampled from the students` social media news feeds and special platforms, dealing with Olympic memes. The main criteria were meme's relevance (accordance to the Beijing Olympic themes) and the type (only creolized memes used to be included). The 1000-memes sample was originally formed (B №=1000), then 450 memes were selected for the second research stage (B1 №=450). The research found that Chinese meme sphere has vivid features, which influenced Olympic discourse.

A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques

The coronavirus disease 2019 (COVID-19) pandemic has been spreading quickly, threatening the public health system. Consequently, positive COVID-19 cases must be rapidly detected and treated. Automatic detection systems are essential for controlling the COVID-19 pandemic. Molecular techniques and medical imaging scans are among the most effective approaches for detecting COVID-19. Although these approaches are crucial for controlling the COVID-19 pandemic, they have certain limitations. This study proposes an effective hybrid approach based on genomic image processing (GIP) techniques to rapidly detect COVID-19 while avoiding the limitations of traditional detection techniques, using whole and partial genome sequences of human coronavirus (HCoV) diseases. In this work, the GIP techniques convert the genome sequences of HCoVs into genomic grayscale images using a genomic image mapping technique known as the frequency chaos game representation. Then, the pre-trained convolution neural network, AlexNet, is used to extract deep features from these images using the last convolution (conv5) and second fully-connected (fc7) layers. The most significant features were obtained by removing the redundant ones using the ReliefF and least absolute shrinkage and selection operator (LASSO) algorithms. These features are then passed to two classifiers: decision trees and k-nearest neighbors (KNN). Results showed that extracting deep features from the fc7 layer, selecting the most significant features using the LASSO algorithm, and executing the classification process using the KNN classifier is the best hybrid approach. The proposed hybrid deep learning approach detected COVID-19, among other HCoV diseases, with 99.71% accuracy, 99.78% specificity, and 99.62% sensitivity.