Leveraging Human Thinking Style for User Attribution in Digital Forensic Process

User attribution, the process of identifying a human in a digital medium, is a research area that has receive significant attention in information security research areas, with a little research focus on digital forensics. This study explored the probability of the existence of a digital fingerprint based on human thinking style, which can be used to identify an online user. To achieve this, the study utilized Server-side web data of 43-respondents were collected for 10-months as well as a self-report thinking style measurement instrument. Cluster dichotomies from five thinking styles were extracted. Supervised machine-learning techniques were then applied to distinguish individuals on each dichotomy. The result showed that thinking styles of individuals on different dichotomies could be reliably distinguished on the Internet using a Meta classifier of Logistic model tree with bagging technique. The study further modeled how the observed signature can be adopted for a digital forensic process, using high-level universal modeling language modeling process- specifically, the behavioral state-model and use-case modeling process. In addition to the application of this result in forensics process, this result finds relevance and application in human-centered graphical user interface design for recommender system as well as in e-commerce services. It also finds application in online profiling processes, especially in e-learning system

Encouraging Gameful Experience in Digital Game-Based Learning: A Double-Mediation Model of Perceived Instructional Support, Group Engagement, and Flow

In the current digitalization era, digital game-based learning (DGBL) is used in education to engage and motivate students. Gameful experience (GE) is a crucial precondition to determine the effectiveness of these games. However, previous research focused solely on the effects of games on inter alia student engagement, and empirical research regarding GE is lacking. This study investigates the factors that encourage students’ GE. Grounded in the theory of experience, a double-mediation model is developed that considers the direct relationship between instructional support and students’ GE and examines how group engagement and flow mediate this relationship. Data from 336 students participating in a team-based business simulation game revealed that group engagement and flow sequentially double-mediated the positive relationship between instruc-tional support and GE. Thus, to encourage students’ GE, educators and education institutions should provide appropriate instructional support that promotes group engagement as well as flow among students

Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

Popularity of MultiplayerOnlineBattle Arena (MOBA)video gameshas grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables(predictors)to train the models. For this reason, this paper proposes a listof variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames.In order to get the mentioned list,the consulted workswere groupedbythe used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed

Rational Agent Architecture to Recommend which Item to Buy in MOBA Videogames

Los videojuegos multijugador de arena de batalla en línea (MOBA), es un genero de videojuegos que durante la última década han ganado popularidad en la escena competitiva de los E-Sports. Este incremento en su popularidad y la complejidad propia de los mismos han llamado la atención de investigadores en todas las áreas del conocimiento, incluyendo la Inteligencia Artificial. Dichos investigadores han utilizado una amplia variedad de técnicas de Aprendizaje de Maquina buscando mejorar la experiencia de diversos usuarios -jugadores novatos, jugadores expertos, espectadores, entre otros- a través de modelos de predicción, sistemas de recomendación y, aunque se han utilizado técnicas de optimización; estas últimas han sido las menos utilizadas en los videojuegos tipo MOBA. Por ello, el presente trabajo de investigación propone la arquitectura de un agente racional capaz de recomendar a un jugador que objeto comprar para aumentar sus probabilidades de ganar una partida, utilizando una técnica de optimización para la generación de recomendaciones. En la arquitectura propuesta, el agente percibe su ambiente con la información disponible en el API del videojuego League of Legends -uno de los MOBA mas populares actualmente-. Tal información es interpretada por una Regresión Logística que durante las etapas tempranas del juego demostró tener una precisión alrededor de 0.975. A su vez, la técnica de optimización seleccionada para generar la sugerencia fue GRASP; en promedio cada sugerencia es generada en 0.36 segundos, estas sugerencias durante la experimentación lograron aumentar la probabilidad de ganar una partida en promedio 5.2x.Multiplayer online battle arena (MOBA) video games are a genre of video games that during the last decade have gained popularity in the competitive E-Sports scene. This increase in popularity and MOBA’s complexity have attracted the attention of researchers in all areas of knowledge, including Artificial Intelligence (AI). AI researchers have used a wide variety of Machine Learning techniques seeking to improve the experience of various users - novice players, expert players, spectators, among others - through prediction models, recommendation systems and optimization algorithms. However, optimization algorithms have been the least used in MOBA videogames. For that reason, this research proposes the architecture of a rational agent capable of recommending to a player what item to buy to increase his probabilities of winning a game, using an optimization technique for generating recommendations. In the proposed architecture, the agent perceives his environment with the information available in the API of League of Legends -currently, one of the most popular MOBA videogames -. Such information is interpreted by a Logistic Regression that during the early stages of the game was shown to have an accuracy around 0.975. Additionally, the optimization technique selected to generate the suggestion was GRASP. On average each suggestion is generated in 0.36 seconds. During experimentation, these suggestions increase the probability of winning a game on average 5.2x.Magíster en Inteligencia ArtificialMaestrí