Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Exploring player experience and social networks in MOBA Games: The case of League of Legends

A pesar de la popularidad de los juegos de arena de combate multijugador en línea (MOBA en inglés) como League of Legends (LoL), tanto la experiencia de jugador (PE) que proporciona este género relativamente reciente como las redes sociales que se generan a su alrededor siguen, en gran medida, inexplorados. Con el incremento del tiempo que los jugadores dedican a este tipo de juegos competitivos en línea, los impactos positivos y negativos de hacerlo cobran relevancia; es, por lo tanto, importante entender cómo se estructura dicha experiencia para abordar de forma sistemática los mecanismos que desencadenan respuestas de los jugadores. El presente trabajo empieza obteniendo y caracterizando una muestra de jugadores de League of Legends y sigue con el uso de las variables resultantes y de la estructura de las relaciones sociales como entradas para explorar su relación con la experiencia de los jugadores. Al fin y al cabo, la PE es básica para involucrar al jugador y, por lo tanto, es clave para el éxito de cualquier juego digital. Los resultados muestran, entre otros, cómo los jugadores de League of Legends perciben el juego como “justo” para su nivel de competencia en cualquier rango, mientras que su afinidad respecto a los compañeros se ve afectada por la estructura social. La empatía y los sentimientos negativos, no obstante, no parecen verse afectados por la composición del equipo. Entender la experiencia del jugador en League of Legends puede no tan sólo ser útil para mejorar el propio LoL o los juegos de tipo MOBA, sino también para desarrollar juegos más inmersivos a la vez que se mejora su calidad. A medida que los juegos competitivos online se convierten rápidamente en una de las mayores actividades colectivas humanas a nivel global, la investigación sobre la experiencia del jugador adquiere también una importancia crucial

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Smartphone User Privacy Preserving through Crowdsourcing

In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary private data breach. However, the majority of regular users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose a permission control framework based on crowdsourcing. At its core, our framework runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or not the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using a transitional Bayesian inference model. The recommendation is based on aggregated expert responses and their confidence level. As a complete framework design of the system, this thesis also includes a solution for Android app risks estimation based on behaviour analysis. To eliminate the negative impact from dishonest app owners, we also proposed a bot user detection to make it harder to utilize false recommendations through bot users to impact the overall recommendations. This work also covers a multi-view permission notification design to customize the app safety notification interface based on users\u27 need and an app recommendation method to suggest safe and usable alternative apps to users

What did I do Wrong in my MOBA Game?: Mining Patterns Discriminating Deviant Behaviours

International audienceThe success of electronic sports (eSports), where professional gamers participate in competitive leagues and tournaments , brings new challenges for the video game industry. Other than fun, games must be difficult and challenging for eSports professionals but still easy and enjoyable for amateurs. In this article, we consider Multi-player Online Battle Arena games (MOBA) and particularly, " Defense of the Ancients 2 " , commonly known simply as DOTA2. In this context, a challenge is to propose data analysis methods and metrics that help players to improve their skills. We design a data mining-based method that discovers strategic patterns from historical behavioral traces: Given a model encoding an expected way of playing (the norm), we are interested in patterns deviating from the norm that may explain a game outcome from which player can learn more efficient ways of playing. The method is formally introduced and shown to be adaptable to different scenarios. Finally, we provide an experimental evaluation over a dataset of 10, 000 behavioral game traces

Everything You Never Wanted to Know about Trolls:An Interdisplinary Exploration of the Who's, What's, and Why's of Trolling in Online Games

Summary Within the world of online gaming, trolling has become a regular menace. While gamers try to connect and socialize with one another, or even simply play the game, there are other gamers – trolls – on the prowl for an entirely different kind of good time, one in which they are enjoying themselves at the expense of everyone else (Chapters 2 and 3). Although trolling is common, and mass-media has latched onto it as a hot topic, it is only recently that the academic community has begun to take a serious look at how trolling occurs in and affects the gaming community at large. However, a lot of this literature is either descriptive in nature (see Thacker & Griffiths, 2012), or jumps ahead to prevention (see Cheng et al., 2017) without taking a deeper look at more than a single underlying motivation at a time. In short, there is a complex and prolific phenomenon happening online, but the research on it is only emerging. This dissertation’s goal is to take a deeper look at trolling as a phenomenon, beyond what has been done so far. More specifically, I aim to figure out a) what trolling is, b) why people do it, and c) who helps and who hinders trolling in online games. To do this, I took four different perspectives: the troll’s (Chapter 2), the researcher’s (Chapter 3), the victim’s (Chapter 4), and the bystander’s (Chapter 5). The purpose of Chapter 2 is to give the troll’s perspective on trolling, something that researchers had yet to do at the time. To do this, I interviewed 22 people who said that they had a history of trolling in online games. More specifically, I asked them about times they witnessed, were victims of, or perpetrated trolling, as well as what they thought about how the gaming community dealt with and felt about trolls and trolling. My goal with these interviews was threefold: I wanted to figure out a) what trolls consider trolling, b) what motivates them to do it, and c) the role of everyone else in game when it comes to encouraging or discouraging more trolling. What I found was that although trolling was almost universally considered a negative part of online gaming culture, and all the trolls in our group of participants started as victims of trolls before becoming trolls themselves, the online community neither encourages nor discourages it, making it an asocial activity. The next chapter allowed me to look at an archive of trolling incidents to find patterns in the way that different people involved in real-life trolling incidents communicate with one another. This public online archive consisted of 10,000 reported incidents of trolling in the popular online game League of Legends, and it included game data like player statistics, as well as everything all the players involved said during the game. Once the data was properly cleaned and prepared, myself and my co-author, Dr. Rianne Conijn, analysed the chat logs in two different ways: structural topic modelling (STM), and a traditional dictionary-based content analysis. In this way, we were able to see what characterized all the different actors – the troll, their victim(s), and the bystanders – and what was similar when it came to their messages. All this information was then compared to what existed already in literature used to describe trolls and trolling and complement what I had learned about trolls from Chapter 2. The key finding was that trolls and their teammates actually share a lot of the negative speech patterns (e.g., profanity, negative emotional content) normally associated with only trolls. Practically, this means that we have to be extremely careful as researchers when labelling trolls for the purpose of study, as we could very easily be falsely labelling victims. After speaking to trolls and looking at trolling interactions broadly, Chapter 4 focuses intently on the victim and their personal experience in a trolling simulation, taking into account their cultural background and values. It is also the first study to directly compare and contrast two different types of trolling: verbal (flaming) and behavioural (ostracism). They are both really common online occurrences, so the participants could easily relate, but they are extremely different in how they are executed, with flaming being vicious insults and ostracism being totally ignoring a person. Our participants were either Dutch, Pakistani, or Taiwanese, so that we could also look at how people from vastly different cultural backgrounds would react to – behaviourally and emotionally – the different kinds of trolling in the study. We simulated a trolling experience by putting our participants in a virtual game of catch with two computerized co-players, who they were led to believe were real people of either the same nationality or a minority member (e.g., a Moroccan immigrant in the Netherlands), who I had programmed to either troll them or silently watch the trolling happen. We found that there are indeed cultural differences when it comes to reactions, as well as differences between reactions to the two trolling types, but the core take-away is that future trolling interventions have to take into account the cultures of the target population as well as the specific type of trolling they are trying to fix or prevent in order to be effective. In the penultimate chapter, I shift the focus one last time to bystanders by putting participants in a game of League of Legends with two confederates who would troll one another throughout the game. This study’s goal was to see what motivated gamers to report trolls to an authority figure (the game developer) using the game’s built-in reporting functions, as the results of Chapter 2’s study suggested that this was an effective trolling deterrent. It is also, according to the results of the same study, the least-used recourse by bystanders faced with trolls in the proverbial wild. We found that how warm and friendly the troll was perceived to be and how competent the victim was perceived to be were what determined whether the participant reported our fake troll or not. A more competent victim and a less warm troll lead to more reports. To conclude, there is still a lot more to learn about trolls and trolling, but the field is farther along now than when this project started in 2015. There is a broad definition developed that encompasses most of the descriptive literature on trolling in games thus far. We also now know that there is the indication of a trolling cycle that requires further exploration. This is particularly important to know when it comes to the world of game development, as knowing the cycle exists allows for multiple points of intervention in order to protect their customers. Finally, this dissertation has shown the complexity of not just trolls – who are often portrayed in the media as one-dimensional antagonists – but also of everyone else involved in trolling interactions. Trolls, victims, and bystanders are all multi-faceted humans, and trolling, like all interactions, is an intricate social dance that deserves to be studied in even further depth in the future than what I have done here

Computational model of negotiation skills in virtual artificial agents

Negotiation skills represent crucial abilities for engaging in effective social interactions in formal and informal settings. Serious games, intelligent systems and virtual agents can provide solid tools upon which one-to-one training and assessment can be reliably made available. The aim of the present work is to fill the gap between the recent growing interest towards soft skills, and the lack of a robust and modern methodology for supporting their investigation. A computational model for the development of Enact, a 3D virtual intelligent platform for training and testing negotiation skills, will be presented. The serious game allows users to interact with simulated peers in scenarios depicting daily life situations and receive a psychological assessment and adaptive training reflecting their negotiation abilities. To pursue this goal, this work has gone through different research stages, each with a unique methodology, results and discussion described in its specific section. In the first phase, the platform was designed to operationalize the examined negotiation theory, developed and assessed. The negotiation styles considered, consistently with previous findings, have been found not to correlate with personality traits, coping strategies and perceived self-efficacy. The serious game has been widely tested for its usability and underwent two development and release stages aimed at improving its accuracy, usability and likeability. The variables measured by the platform have been found to predict in all cases at least two of the negotiation styles considered. Concerning the user feedback, the game has been judged as useful, more pleasant than the traditional test, and the perceived time spent on the game resulted significantly lower than the real time spent. In the second stage of this research, the game scenarios were used to collect a dataset of documents containing natural language negotiations between users and the virtual agents. The dataset was used to assess the correlations between the personal pronouns' use and the negotiation styles. Results showed that more engaged styles generally used pronouns with a significantly higher frequency than less engaged styles. Styles with a high concern for self showed a higher frequency of singular personal pronouns while styles with a high concern for others used significantly more relational pronouns. The corpus of documents was also used to perform multiclass classification on the negotiation styles using machine learning. Both linear (SVM) and non-linear models (MNB, CNN) performed reliably with a state-of-the-art accuracy

3D Sensing Character Simulation using Game Engine Physics

Creating visual 3D sensing characters that interact with AI peers and the virtual envi- ronment can be a difficult task for those with less experience in using learning algorithms or creating visual environments to execute an agent-based simulation. In this thesis, the use of game engines was studied as a tool to create and execute vi- sual simulations with 3D sensing characters, and train game ready bots. The idea was to make use of the game engine’s available tools to create highly visual simulations without requiring much knowledge in modeling or animation, as well as integrating exterior agent simulation libraries to create sensing characters without needing expertise in learning algorithms. These sensing characters, were be 3D humanoid characters that can perform the basic functions of a game character such as moving, jumping, and interacting, but also have simulated different senses in them. The senses that these characters can have include: touch using collision detection, vision using ray casts, directional sound, smell, and other imaginable senses. These senses are obtained using different game develop- ment techniques available in the game engine and can be used as input for the learning algorithm to help the character learn. This allows the simulation of agents using off-the- shelf algorithms and using the game engine’s motor for the visualizations of these agents. We explored the use of these tools to create visual bots for games, and teach them how to play the game until they reach a level where they can serve as adversaries for real-life players in interactive games. This solution was tested using both reinforcement learning and imitation learning algorithms in an attempt to compare how efficient both learning methods can be when used to teach sensing game bots in different game scenarios. These scenarios varied in both objective and environment complexity as well as the number of bots to access how each solution behaves in different scenarios. In this document is presented a related work on the agent simulation and game engine areas, followed by a more detailed solution and its implementation ending with practical tests and its results.Criar visualizações de personagens 3D com sentidos que interagem com colegas de IA e com o ambiente virtual pode ser uma tarefa difícil para programadores com menos experiência no uso de algoritmos de aprendizagem automática ou na criação de ambientes visuais para executar simulações baseadas em agentes. Nesta tese foi estudado o uso de motores de jogos como ferramenta para criar e execu- tar simulações visuais com personagens 3D, e treinar bots para jogos. A ideia foi usar as ferramentas disponíveis do motor de jogos para criar simulações visuais sem exigir muito conhecimento em modelação ou animação, para além de integrar bibliotecas de simulação de agentes externas para criar personagens com sentidos sem precisar de conhecimentos em algoritmos de aprendizagem automática. Estas personagens 3D são humanoides que podem desempenhar as funções básicas de uma personagem de um jogo como mover, saltar e interagir, mas também terão simulados neles diferentes sentidos. Os sentidos que estas personagens podem ter inclui: o tato, colisões, visão, som direcional, olfato e outros sentidos imagináveis. Estes sentidos são obtidos usando diferentes técnicas de desenvol- vimento de jogos disponíveis no motor de jogos, e podem ser usados como inputs para os algoritmos de aprendizagem automática para ajudar as personagens a aprender. Esta solução foi testada usando algoritmos de Reinforcement Learning e Imitation Le- arning, com o intuito de comparar a eficiência de ambos os métodos de aprendizagem quando usados para ensinar bots de jogos em diferentes cenários. Estes cenários variaram em complexidade de objetivo e ambiente, e também no número de bots para que se possa visualizar como cada algoritmo se comporta em diferentes cenários. Neste documento será apresentado um estado da arte nas áreas de simulação de agentes e motores de jogos, seguido de uma proposta de solução mais detalhada para este problema