Search Results (18)

This scoping review investigates the use of fuzzy logic in serious games. Articles were searched in nine databases: ACM Digital Library, IEEE Xplore, IOPscience, MDPI, PubMed, ScienceDirect, Springer, Wiley, and Web of Science. The search retrieved 494 articles published between January 2020 and February 2025, of which 28 met the inclusion criteria. Specifically, four research questions were addressed, focusing on the taxonomy of serious games that use fuzzy logic, the characteristics of game design, the purpose and implementation of the fuzzy logic system within the game, and the experiments conducted in the studies. Results reported that 80% of the studies focused on educational serious games, while 20% addressed health applications. Mouse, keyboard, and smartphone touch screen were the most widely used interaction methods. The adventure genre was the most widely implemented in the studies (35.71%). Fuzzy logic was mainly used for adjusting game difficulty, followed by providing tailored feedback in the game. Mamdani inference was the most widely used inference method in the studies. Although 79% of the studies involved human participants in their experiments, 57% did not perform any statistical analysis of their results. Full article

This systematic review analyzes the representation of older adults in the media to determine whether news coverage contributes to reinforcing or combating ageism. For societies undergoing population ageing, it is essential to understand the image of old age conveyed by the media, as they play a significant role in shaping public perception. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed. In total, 21 articles addressing the media representation of old age were selected from 1435 search results across three databases: Scopus, Web of Science, and PubMed. The results show that the media do not sufficiently make older adults visible, often present negative narratives about old age, and use stigmatizing terms to refer to this group. Most of the research comes from the field of sociology, mainly employs discourse analysis, and does not examine journalistic aspects such as genres, information sources, or the images accompanying news stories. In conclusion, the reviewed literature provides a valuable diagnosis of media ageism but highlights the need to broaden the disciplinary perspective and incorporate analyses and proposals aimed at transforming journalistic routines, in order to move toward a more plural, realistic, and stigma-free representation of older adults in the media. Full article

This paper proposes a formal descriptive model for understanding the evolution of characters in detective fiction from the 19th to the 20th century, using methodologies and technologies from the Semantic Web. The integration of Digital Humanities within the theory of comparative literature opens new paths of study that allow for a digital approach to the understanding of intertextuality through close reading techniques and ontological modelling. In this research area, the variety of possible textual relationships, the levels of analysis required to classify these connections, and the inherently referential nature of certain literary genres demand a structured taxonomy. This taxonomy should account for stylistic elements, narrative structures, and cultural recursiveness that are unique to literary texts. The detective figure, central to modern literature, provides an ideal lens for examining narrative intertextuality across the 19th and 20th centuries. The analysis concentrates on character traits and narrative functions, addressing various methods of rewriting within the evolving cultural and creative context of authorship. Through a comparative examination of a representative sample of detective fiction from the period under scrutiny, the research identifies mechanisms of (meta)narrative recurrence, transformation, and reworking within the canon. The outcome is a formal model for describing narrative structures and techniques, with a specific focus on character development, aimed at uncovering patterns of continuity and variation in diegetic content over time and across different works, adaptable to analogous cases of traditional reworking and narrative fluidity. Full article

Reclassification of massive datasets acquired through different approaches, such as web scraping, is a big challenge to demonstrate the effectiveness of a machine learning model. Notably, there is a strong influence of the quality of the dataset used for training those models. Thus, we propose a threshold algorithm as an efficient method to remove stopwords. This method employs an unsupervised classification technique, such as K-means, to accurately categorize user reviews from the IMDb dataset into their most suitable categories, generating a well-balanced dataset. Analysis of the performance of the algorithm revealed a notable influence of the text vectorization method used concerning the generation of clusters when assessing various preprocessing approaches. Moreover, the algorithm demonstrated that the word embedding technique and the removal of stopwords to retrieve the clustered text significantly impacted the categorization. The proposed method involves confirming the presence of a suggested stopword within each review across various genres. Upon satisfying this condition, the method assesses if the word’s frequency exceeds a predefined threshold. The threshold algorithm yielded a mapping genre success above 80% compared to precompiled lists and a Zipf’s law-based method. In addition, we employed the mini-batch K-means method for the clustering formation of each differently preprocessed dataset. This approach enabled us to reclassify reviews more coherently. Summing up, our methodology categorizes sparsely labeled data into meaningful clusters, in particular, by using a combination of the proposed stopword removal method and TF-IDF. The reclassified and balanced datasets showed a significant improvement, achieving 94% accuracy compared to the original dataset. Full article

Massive text collections are the backbone of large language models, the main ingredient of the current significant progress in artificial intelligence. However, as these collections are mostly collected using automatic methods, researchers have few insights into what types of texts they consist of. Automatic genre identification is a text classification task that enriches texts with genre labels, such as promotional and legal, providing meaningful insights into the composition of these large text collections. In this paper, we evaluate machine learning approaches for the genre identification task based on their generalizability across different datasets to assess which model is the most suitable for the downstream task of enriching large web corpora with genre information. We train and test multiple fine-tuned BERT-like Transformer-based models and show that merging different genre-annotated datasets yields superior results. Moreover, we explore the zero-shot capabilities of large GPT Transformer models in this task and discuss the advantages and disadvantages of the zero-shot approach. We also publish the best-performing fine-tuned model that enables automatic genre annotation in multiple languages. In addition, to promote further research in this area, we plan to share, upon request, a new benchmark for automatic genre annotation, ensuring the non-exposure of the latest large language models. Full article

Drawing on new media scholarship, the article suggests that Percival Everett’s poetry can be understood through the lens of hypergraphical knowledge. In this context, Everett’s poetry operates as a synchronic and diachronic exploration of poetic movements, genres, forms, and inheritances, embodying network-temporal relations similar to the hypernarrator(s) of his fiction. Ultimately, this analysis observes the expansive and cohesive nature of Everett’s work, inviting readers to refocus their attention on the indeterminate surface of, and the intricate web of meaning in his poetry. Full article

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems. Full article

Traditional methods of student assessment (SA) include self-reported surveys, standardized tests, etc. These methods are widely regarded by researchers as inducing test anxiety. They also ignore students’ thinking processes and are not applicable to the assessment of higher-order skills. Digital game-based assessment (DGBA) is thought to address the shortcomings of traditional assessment methods. Given the advantages of DGBA, an increasing number of empirical studies are working to apply digital games for SA. However, there is a lack of any systematic review of DGBA studies. In particular, very little is known about the characteristics of the games, the content of the assessment, the methods of implementation, and the distribution of the results. This study examined the characteristics of DGBA studies, and the adopted games on SA in the past decade from different perspectives. A rigorous systematic review process was adopted in this study. First, the Web of Science (WOS) database was used to search the literature on DGBA published over the last decade. Then, 50 studies on SA were selected for subsequent analysis according to the inclusion and exclusion criteria. The results of this study found that DGBA has attracted the attention of researchers around the world. The participants of the DGBA studies were distributed across different educational levels, but the number of participants was small. Among all game genres, educational games were the most frequently used. Disciplinary knowledge is the most popular SA research content. Formative assessment modeling with process data and summative assessment using final scores were the most popular assessment methods. Correlation analysis was the most popular analysis method to verify the effectiveness of games on SA. However, many DGBA studies have reported unsatisfactory data analysis results. For the above findings, this study further discussed the reasons, as well as the meanings. In conclusion, this review showed the current status and gaps of DGBA in the SA application; directional references for future research of researchers and game designers are also provided. Full article

Personality Disorders (PDs) are psychiatric conditions involving maladaptive personality traits and behaviours. Previous research has shown that musical preferences and the use of music may be related to personality traits. Additionally, music therapy is increasingly being used as a treatment option for people with PDs. Using the PRISMA guidelines, a systematic literature search was undertaken using three databases: PubMed, Web of Science, and PsycInfo. The following search terms were used: PubMed: “personality disorder” AND (music OR “music therapy”); Web of Science (advanced search): TS = (personality disorder) AND TS = (music or “music therapy”); PsycInfo: “personality disorder” AND (music OR “music therapy”). A total of 24 studies were included in this review and summarised into four categories: music preference, music therapy, music performance, and music imagery, all in relation to PDs or traits associated with PDs. The analysis found that individuals with personality traits associated with PDs may prefer different types or genres of music or interact with music differently than those without these traits. Additionally, music therapy (MT) was found to offer a potentially useful treatment option for PDs. The power of these findings was limited by the small number of included studies. This review offers a useful foundation upon which further research looking at MT as a potential treatment option for PDs can be built. Full article

Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use real data to support immediate decision making. Web scraping technologies support supplying sufficient relevant and up-to-date well-structured data from unstructured data sources like websites. Machine learning applications generate models for in-depth data analysis and decision making. The Internet Movie Database (IMDB) is one of the largest movie databases on the internet. IMDB movie information is applied for statistical analysis, sentiment classification, genre-based clustering, and rating-based clustering with respect to movie release year, budget, etc., for repository dataset. This paper presents a novel clustering model with respect to two different rating systems of IMDB movie data. This work contributes to the three areas: (i) the “grey area” of web scraping to extract data for research purposes; (ii) statistical analysis to correlate required data fields and understanding purposes of implementation machine learning, (iii) k-means clustering is applied for movie critics rank (Metascore) and users’ star rank (Rating). Different python libraries are used for web data scraping, data analysis, data visualization, and k-means clustering application. Only 42.4% of records were accepted from the extracted dataset for research purposes after cleaning. Statistical analysis showed that votes, ratings, Metascore have a linear relationship, while random characteristics are observed for income of the movie. On the other hand, experts’ feedback (Metascore) and customers’ feedback (Rating) are negatively correlated (−0.0384) due to the biasness of additional features like genre, actors, budget, etc. Both rankings have a nonlinear relationship with the income of the movies. Six optimal clusters were selected by elbow technique and the calculated silhouette score is 0.4926 for the proposed k-means clustering model and we found that only one cluster is in the logical relationship of two rankings systems. Full article

This essay will focus on the central position that Jane Austen holds in the growing culture of memes in the Social Web and examine how these present-day cameo artefacts are both transforming the way Austen is perceived and appropriated today, and exploiting her work as a source of inspiration for contemporary debates on genre, gender, and sexuality. It will first trace the origins of memes, these cultural replicators that discharge mini portions of irony, in Northanger Abbey—a novel depending on the reader’s active participation—and argue that the literary landscape of the 1790s popular culture (as reflected in Austen) is a foreshadowing of post-millennial memes. Furthermore, through a close reading of a plethora of memes based on stills from screen adaptations of Pride and Prejudice, the essay will study how Austen’s renowned Mr. Darcy—filtered through the famous impersonations by Collin Firth and Matthew Macfadyen—has activated new re-imaginings of masculinity and heterosexuality in the post-#MeToo epoch. As some memes suggest, Mr. Darcy, a reformed hero who has learned how to match hegemony with sensibility, is the perfect antidote to the anathema of toxic masculinity and the perfect catch to the crowds of female Janeites. At the same time, however, a large number of memes indicate that, to an expanding male fandom that steers away from a nostalgic reactionary return to Austen, Mr. Darcy is celebrated for the queer potential of his conflicting features. Full article

The purpose of this study is to assess the learning effect of a multilingual web-based argumentative writing instruction model called the Ensayo Científico Multilingüe (ECM, Multilingual Scientific Essay) adapting the didactic model called Genre-based Writing Instruction (GBWI) in an experiment conducted over three months. For this purpose, a quasi-experimental research model was applied to 150 students in the experimental group and 150 in the control group, with two measurements, pre and post-test, for three dependent variables: (a) writing metacognition and its dimensions; (b) written argumentative self-efficacy; and (c) rhetorical moves and steps of an argumentative essay. The latter variable was measured by the content analysis method. Variables (a) and (b) were both measured with instruments validated in a population of 518 university students using structural equations. The findings demonstrate the positive effect of the ECM, which combines WBWI and GBWI in argumentative written learning in the students’ mother tongue in all variables measured, applying statistics such as the Shapiro–Wilk statistic, parametric contrast, and the Wilcoxon signed-rank test. In relation to the findings, with respect to the evaluated variables, it was discovered, specifically, that the rhetorical steps in which the students showed a significant improvement were innovations, quotes/research, definitions of concepts, refutations, definitive reasons, and bibliographical references. Likewise, the rhetorical steps that did not present significant differences following the application of the ECM were discovered, and they were: reason summary, formulation of premise, and reasons for. Furthermore, it can be stated that for the ECM there was an increase, above all, in awareness of the following metacognitive dimensions: (a) writing self-regulation; (b) writing planning; and (c) writing revision, as well as argumentative self-efficacy. The novelties of this research with respect to the precedents reside in that it offers valid and concrete results on the effect of a multilingual web design integrated into a well-defined didactic model of argumentative writing on writing metacognition and its dimensions, argumentative structuring and its rhetorical steps, and argumentative self-efficacy. The related studies consider only some of these variables, but not all of them together or their complexity. These results have allowed us to establish specific didactic–technological proposals for improving the ECM that are transferable to didactic designs to guide written argumentation at higher academic levels using multilingual web technologies and integrating the metacognitive, behavioral, and motivational dimensions of writing. Full article

The video game industry has evolved significantly, with different genres becoming popular over time, but how to visualize such information by curating data into a form that makes it easier to identify and understand the trends is quite an interesting research topic. This research focuses on producing an animation of aesthetically pleasing two-dimensional (2D) undirected graphs based on PC video game datasets. The data are further analyzed for developing a web-based application giving users the ability to control and create the animation of a graph. To make it easier to understand the animation of a graph, the changes between the displays of the previous and the following periods are set as small as possible, allowing a user to grasp the differences of the graph’s structure faster. A genetic algorithm-based undirected graph drawing that minimizes both the aesthetic criteria and mental map cost are proposed in this research to tackle this problem. Furthermore, based on our experiments, we could find the best period to start with, so we do not necessarily need to start from the first period to calculate the animation result. Our experiment results proved that a smoother animation could be achieved, and information is better preserved throughout the animation. Full article

This paper casts light on cultural heritage storytelling in the context of interactive documentary, a hybrid media genre that employs a full range of multimedia tools to document reality, provide sustainability of the production and successful engagement of the audience. The main research hypotheses are enclosed in the statements: (a) the interactive documentary is considered a valuable tool for the sustainability of cultural heritage and (b) digital approaches to documentary storytelling can provide a sustainable form of viewing during the years. Using the Greek interactive documentary (i-doc) NEW LIFE (2013) as a case study, the users’ engagement is evaluated by analyzing items from a seven-year database of web metrics. Specifically, we explore the adopted ways of the interactive documentary users to engage with the storytelling, the depth to which they were involved along with the most popular sections/traffic sources and finally, the differences between the first launch period and latest years were investigated. We concluded that interactivity affordances of this genre enhance the social dimension of cultural, while the key factors for sustainability are mainly (a) constant promotion with transmedia approach; (b) data-driven evaluation and reform; and (c) a good story that gathers relevant niches, with specific interest to the story. Full article

This study aims at discovering social desires and conflicts from subculture narrative multimedia. Since one of the primary purposes in the subculture consumption is vicarious satisfaction, the subculture works straightforwardly describe what their readers want to achieve and break down. The latent desires and conflicts are useful for understanding our society and realizing smart governance. To discover the social issues, we concentrate on that each subculture genre has a unique imaginary world that consists of inventive subjects. We suppose that the subjects correspond to individual social issues. For example, game fiction, one of the popular genres, describes a world like video games. Under game systems, everyone gets the same results for the same efforts, and it can be interpreted as critics for the social inequality issue. Therefore, we first extract subjects of genres and measure the membership degrees of subculture works for each genre. Using the subjects and membership degrees, we build a genealogy tree of subculture genres by tracing their evolution and differentiation. Then, we extract social issues by searching for the subjects that come from the real world, not imaginary. If a subculture work criticizes authoritarianism, it might include subjects such as government officials and bureaucrats. A combination of the social issues and genre genealogy tree will show diachronic changes in our society. We have evaluated the proposed methods by extracting social issues reflected in Korean web novels. Full article