Injustice and Balance in Pervasive Video Games

Sendo um meio artistico intimamente relacionado com o avanço da tecnologia, alguns dos video-jogos mais interessantes são feitos através da expansão dos limites técnicos do que é considerado um jogo. Tal como o salto de gráficos em 2D para 3D, o impacto da internet em jogos multi-player e a inter-conectividade entre jogadores, ou a forma como a realidade virtual e a realidade aumentada têm mudado o que é possível dentro de um mundo virtual, há um outro género que tem aumentado em popularidade com a chegada de tecnologia nova - jogos pervasivos. O género de jogos pervasivos engloba jogos que misturam o mundo virtual do jogo e o mundo real através dos dados contextuais e de localização do jogador, servindo-se dos novos avanços tecnológicos acerca de tecnologia móvel. O fenómeno de Pokémon GO liderou a quebra do género para o mainstream, permitindo a que muitos outros jogos semelhantes tenham sucesso e obtendo para si uma audiência mundial considerável. Ao contrário de jogos de realidade aumentada - que esperam que o jogador veja o mundo através de uma câmara e a ela aplica os elementos do mundo virtual - os jogos pervasivos usam dados do mundo real como a sua base de construção. Existe bastante junção entre estes dois géneros de jogo, como vendo os dinossauros do Jurassic Park Alive através do telemóvel após os encontrar num parque local. Um grande problema presente nos jogos pervasivos é central ao aspeto que os torna únicos: devido a serem tão dependentes do contexto e localização do jogador, jogos pervasivos têm maior tendência para perconceitos que tornam alguns contextos melhores que outros. Um exemplo relevante é como no Pokémon GO, jogadores em zonas rurais têm uma quantidade reduzida ou nula de geração de Pokémons, enquanto jogadores em grandes cidades têm uma enchente constante de novos monstros para apanhar. Para resolver este problema, esta dissertação tem como objetivo desenvolver uma plataforma de análise de dados para jogos pervasivos que permita aos desenvolvedores obter um parecer relativamente ao balanço do seu jogo. Este objetivo será alcançado fazendo um cruzamento entre os dados do jogador e os contextuais, servindo-se de técnicas de aprendizagem automática para entender o que está a funcionar no jogo e o que não está. Esta tese não tem apenas a finalidade de oferecer aos desenvolvedores uma ferramenta que permita melhorar o balanço de jogos pré-existentes, mas sim resolver um problema significativo que tem estado a impedir o avanço e a experimentação de jogos pervasivos com experiências mais complexas e profundas.Being an art medium closely tied with the advancement of technology, some of the most interesting video games get made by expanding the technical limits of play in many new ways. Just like the jump from 2D graphics to 3D, how the internet shaped multiplayer games and inter-connectivity between players, and the way that now Virtual Reality and Augmented Reality are changing what is possible in a virtual world, another game genre has grown in popularity with the advent of new, exciting technology - pervasive games. The genre of pervasive games encompasses games that merge the game's virtual world and the real world together by taking advantage of the player's location data and contextual information, using the new leaps in technology regarding mobile internet. The phenomenon of Pokémon GO spearheaded the genre's break into the mainstream, allowing many other similar games to thrive and carving for itself a really large audience worldwide. Unlike augmented reality games - that expect you to see the world through a camera, that it then applies elements of the virtual world to - pervasive games uses the data from the real world as its base. There is a lot of cross-over between these two genres, like seeing the dinosaurs in Jurassic Park Alive through your phone after finding them in a local park. A big issue with pervasive games is central to its claim-to-fame: due to being so context and location-dependent, pervasive games are bound to have biases that make some contexts much better than others. A prime example of this is how in Pokémon GO, players in rural areas have nearly no Pokémon spawns, while players in cities have a constant stream of new monsters to catch. To solve this, this dissertation aims to develop an analytics platform for pervasive games that allows developers to obtain feedback on their game's balance. This goal will be achieved by cross-referencing player data and context data, using machine learning techniques to discover what works and what doesn't. This work's goal isn't simply to offer a tool to developers that help balance their games but also to help solve a significant issue holding pervasive games back from experimenting with more complex and deep experiences

A Web API ecosystem through feature-based reuse

The current Web API landscape does not scale well: every API requires its own hardcoded clients in an unusually short-lived, tightly coupled relationship of highly subjective quality. This directly leads to inflated development costs, and prevents the design of a more intelligent generation of clients that provide cross-API compatibility. We introduce 5 principles to establish an ecosystem in which Web APIs consist of modular interface features with shared semantics, whose implementations can be reused by clients and servers across domains and over time. Web APIs and their features should be measured for effectiveness in a task-driven way. This enables an objective and quantifiable discourse on the appropriateness of a certain interface design for certain scenarios, and shifts the focus from creating interfaces for the short term to empowering clients in the long term

The Promise & Perils of Open Finance

We are at the dawn of a new age of Open Finance. Open Finance seeks to harness the potential of new platform technology to enhance customer data access, sharing, portability, and interoperability—thereby leveling the informational playing field and fostering greater competition between incumbent financial institutions and a new breed of financial technology (fintech) disruptors. According to its proponents, this competition will yield a radical restructuring of the financial services industry, offering more and better choices for consumers looking to make fast payments, borrow money, invest their savings, manage household budgets, and compare financial products and services. The promise of Open Finance is very real. Yet its proponents have largely ignored the economics driving the development of the key players at the heart of this new infrastructure: data aggregators. Data aggregators are the connective tissue of Open Finance—the pipes through which most of this valuable data flow. Like other types of infrastructure, these pipes are characterized by economies of scale and network effects that erect substantial barriers to entry, undercut competition, and propel the market toward monopoly. In the United States, these dynamics are compounded by the highly fragmented structure of both the conventional financial services industry and the emerging fintech ecosystem. The result is an embryonic market structure in which a small handful of data aggregators have a massive head start, and where one company in particular—Plaid—already enjoys a dominant market position. This Article describes the promise and perils of Open Finance and explains how policymakers can tap into its potential while simultaneously preventing the abuse of monopoly power and avoiding the creation of a new strain of too-big-to-fail institutions

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Esports Analytics Through Encounter Detection

Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed

Mearka - Architecting and evaluation of a Sports Video Tagging Software Toolkit

In the past decade, substantial advancements have been achieved in effectively utilizing video surveillance and associated analysis technologies within the realm of sports. This progress has been particularly noteworthy in elite sports, where the exploitation of athletes’ digital footprints for sports analytics has emerged as a catalytic factor, ushering in a paradigm shift in comprehending and formulating strategic approaches to the game. The architecture of sports video analytics systems can be broadly categorized into (1) tagging and (2) analysis. Tagging involves annotating metadata to specific video sequences and events, and this tagged metadata is subsequently utilized in the causal analysis process. Multiple enterprise solutions are available today for recording videos, and positions and producing tagged data for the top teams. The issue is that they are often expensive, time-delayed metadata, and the sports organizations do not control where the data is stored or how the analytics company uses it. The alternative to enterprise solutions is manually generating the soccer metadata, which is time-consuming and possibly impossible if, for example, one wants to tag every player’s position throughout a game. This thesis presents Mearka, a distributed soccer tagging system based on cheap common-off-the-shelf components. It allows for tagging events LIVE during a soccer game through the Mearka-app, as well as generating player position metadata with time offsets into a user-uploaded video through the Mearka web-interface, automatically detected using machine learning. After detection, it is possible to download the soccer metadata as a JSON file through the web-interface. The experiment results demonstrate that Mearka can complete the detection of players’ positions from a 90 minutes soccer game within 12 hours after detection is started, with a video resolution of 1920x1080 at 25FPS. Expanding Mearka to only detect on every 10th frame could potentially make Mearka a viable real-time tagging option, as it is able to detect on ≈3 frames per second, and a turnaround of 12 hours detects on every single video frame

Gaining a competitive advantage through data analytics and business intelligence

The banking industry is at the brink of a digital revolution with start-up companies pushing technology into this sector. This paper aims to research exact methods that Fintech use to disrupt the field and explain how established Banks could make an impact by implementing the min to daily business. To give clear recommendations on trending techniques, a quantitative study was conducted with a preceding quantitative research stating the importance of the matter. The results are clear, with Artificial intelligence being, by far the most common, state of the art technique used to disrupt the way people use and perceive banking

Esports – Video Game Data Analysis

Tese de Mestrado, Engenharia Informática, 2023, Universidade de Lisboa, Faculdade de CiênciasThe phenomenon of Esports has been growing and, with it, the interest in online video games by players and spectators. With technological evolution, it has become increasingly easier to use data collection techniques about the events that take place during a match, generating large volumes of data that can be used to analyze the performance of players and teams. This analysis is of great importance in both personal and professional contexts. Casual players look for methods to understand what mistakes they are making and the optimal way to play certain characters, while in a professional context, the focus is mostly on understanding what strategies are used by other teams and how to counter them. For the analysis of this volume of data to be effective, it is fundamental to explore data analysis mechanisms combined with visualization techniques (visual analytics) applied to spatio-temporal data and the various types of events during a match that are of interest to players, coaches, and analysts. These events can range from a player’s position (space) at a given instant (time) to more game-specific events, such as where the player died. The goal of this project is to explore and apply machine learning algorithms to spatio-temporal data to discover patterns in player behaviors while continuing the work on visual analytics of video game data. The investigation extends to exploring datasets from new games, ultimately leading to the selection of League of Legends (LoL) as the focal point for in-depth analysis. One significant challenge in this pursuit is the scarcity of readily available datasets featuring spatio-temporal data. To overcome this obstacle, the research project involves the creation of spatio-temporal datasets from the selected game, LoL, through data collection facilitated by the Riot API. In summary, this research project not only builds on previous work but also introduces new data analysis techniques, notably the clustering of spatio-temporal data, to uncover possibly hidden patterns of player behaviors in the world of League of Legends. The results obtained provide valuable insights on the players, particularly focusing on the jungler role. They provide information regarding potential death patterns and the most frequently visited locations on the map as the game progresses. Additionally, these results make it possible to observe differences in spatio-temporal data across various game patches. The culmination of these efforts promises valuable insights into the gaming ecosystem, with potential applications in game design, player engagement, and beyond