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Com o crescimento da indústria dos jogos eletrônicos, surgem novos mercados e tecnologias. Os jogos eletrônicos da última geração exigem cada vez mais processamento e placas de vídeo mais poderosas. Uma solução que vem ganhando cada vez mais destaque é o Cloud Gaming, no qual o jogador realiza um comando e a informação é enviada e processada remotamente em uma nuvem, localizada na Internet, e que retorna com as imagens como uma sequência de vídeo para o jogador. Para melhorar a qualidade de experiência (QoE) é proposto um modelo que diminui o tempo de resposta entre o jogador e a nuvem, através de um arcabouço chamado Cloud Manager que utiliza a técnica de implementação de camadas, na camada do plano de fundo e predição de movimentos, utilizando uma matriz de predição, na camada do personagem. Para validar os resultados é utilizado um jogo de ação com ponto de vista onipresente dentro do sistema de Cloud Gaming Uniquitous. / With the growing video games industry, new markets and technologies are emerging. Electronic games of the new generation are increasingly requiring more processing and powerful video cards. The solution that is gaining more prominence is Cloud Gaming, which the player performs a command, the information is sent and processed remotely on a cloud, then the images return as a video stream back to the player using the Internet. To improve the Quality of Experience (QoE), it is proposed a model that reduces the response time between the player command and the stream of the resulting game scenes through a framework called Cloud Manager that use layer caching techniques, in the background, and future state prediction using a prediction matrix, in the character layer. To validate the results, a action game with god-view as point of view is used in a Cloud Gaming system called Uniquitous.

Com o crescimento da indústria dos jogos eletrônicos, surgem novos mercados e tecnologias. Os jogos eletrônicos da última geração exigem cada vez mais processamento e placas de vídeo mais poderosas. Uma solução que vem ganhando cada vez mais destaque é o Cloud Gaming, no qual o jogador realiza um comando e a informação é enviada e processada remotamente em uma nuvem, localizada na Internet, e que retorna com as imagens como uma sequência de vídeo para o jogador. Para melhorar a qualidade de experiência (QoE) é proposto um modelo que diminui o tempo de resposta entre o jogador e a nuvem, através de um arcabouço chamado Cloud Manager que utiliza a técnica de implementação de camadas, na camada do plano de fundo e predição de movimentos, utilizando uma matriz de predição, na camada do personagem. Para validar os resultados é utilizado um jogo de ação com ponto de vista onipresente dentro do sistema de Cloud Gaming Uniquitous. / With the growing video games industry, new markets and technologies are emerging. Electronic games of the new generation are increasingly requiring more processing and powerful video cards. The solution that is gaining more prominence is Cloud Gaming, which the player performs a command, the information is sent and processed remotely on a cloud, then the images return as a video stream back to the player using the Internet. To improve the Quality of Experience (QoE), it is proposed a model that reduces the response time between the player command and the stream of the resulting game scenes through a framework called Cloud Manager that use layer caching techniques, in the background, and future state prediction using a prediction matrix, in the character layer. To validate the results, a action game with god-view as point of view is used in a Cloud Gaming system called Uniquitous.

By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human eye tracking) using different evaluation methods over three generated dataset of synthetic patterns and natural images. Our results showed that the objective model is partially valid and highly center-biased.By using empirical data obtained from subjective experiments we propose a special function, the Probability of Characteristic Radially Dependency Function, to model the lateral distribution of visual attention process.

What is the difference between intention and action? To start answering this complex question, we have created a serious game that allows us to capture a large quantity of experimental data and study human behavior. In the game, users catch flies, presented to the left or to the right of the screen, by dragging the tongue of a frog across a touchscreen monitor. The movement of interest has a predefined starting point (the frog) and necessarily transits through a via-point (a narrow corridor) before it proceeds to the chosen left/right direction. Meanwhile, the game collects data about the movement performed by the player. This work is focused on the analysis of such movements. We try to find criteria that will allow us to predict (as early as possible) the direction (left/right) chosen by the player. This is done by analyzing kinematic information (e.g. trajectory, velocity profile, etc.). Also, processing such data according to the dynamical movement primitives approach, allows us to find further criteria that support a classification of human movement. Our preliminary results show that individually considered, participants tend to create and use stereotypical behaviors that can be used to formulate predictions about the subjects’ intention to reach in one direction or the other, early after the onset of the movement.

Context. In multiplayer games, player information takes time to reach other players because of network latency. This can cause inconsistencies because the actions from the other players are delayed. To increase consistency, movement prediction can be used to display other players closer to their actual position. Objectives. The goal was to compare different prediction methods and see how well they do in a 2D racing game. Methods. A testing framework was made to easily implement new methods and to get test results. Experiments were conducted to gather racing data from participants and was then used to analyze the performance of the methods offline. The distance error between the predicted position and the real position was used to measure the performance. Results. Out of the implemented algorithms, Input Prediction had the lowest average distance error at all latency. All methods tested did better than Dead Reckoning when above 600ms. Stored data algorithms did not do worse when predicting on a curvy part of the track unlike the other algorithms tested. Conclusions. Different methods are supported by different games and applications. Movement prediction should be tailored to its environment for best accuracy. Due to Input Predictions simple nature and its results here, it is a worthy contender as the go-to algorithm for games.

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Stock price prediction is of strong interest but a challenging task to both researchers and investors. Recently, sentiment analysis and machine learning have been adopted in stock price movement prediction. In particular, retail investors’ sentiment from online forums has shown their power to influence the stock market. In this paper, a novel system was built to predict stock price movement for the following trading day. The system includes a web scraper, an enhanced sentiment analyzer, a machine learning engine, an evaluation module, and a recommendation module. The system can automatically select the best prediction model from four state-of-the-art machine learning models (Long Short-Term Memory, Support Vector Machine, Random Forest, and Extreme Boost Gradient Tree) based on the acquired data and the models’ performance. Moreover, stock market lexicons were created using large-scale text mining on the Yahoo Finance Conversation boards and natural language processing. Experiments using the top 30 stocks on the Yahoo users’ watchlists and a randomly selected stock from NASDAQ were performed to examine the system performance and proposed methods. The experimental results show that incorporating sentiment analysis can improve the prediction for stocks with a large daily discussion volume. Long Short-Term Memory model outperformed other machine learning models when using both price and sentiment analysis as inputs. In addition, the Extreme Boost Gradient Tree (XGBoost) model achieved the highest accuracy using the price-only feature on low-volume stocks. Last but not least, the models using the enhanced sentiment analyzer outperformed the VADER sentiment analyzer by 1.96%.

The assessment of ageing tunnels requires a deeper understanding of the long-term behaviour of twin tunnels, whilst lack of permeability data limits the accuracy of long-term predictions. This thesis therefore investigates long-term twin-tunnel behaviour through finite-element parametric analyses, and provides additional pereability data through laboratory studies. Permeability tests are performed on fissured London Clay, exploring the effect of isotropic stress cycles on the permeability of fissures. A model explaining the permeability-stress relationship is proposed to explain irrecoverable changes observed in fissure permeability, and is formulated mathematically for numerical implementation. Laboratory investigations are performed on grout from the London Underground tunnels, investigating permeability, porosity, microstructure and composition. A deterioration process is proposed to explain observations, consisting of acid attack and leaching. The deterioration had appeared to transform the grout from impermeable to permeable relative to the soil. The change in grout permeability with time would strongly influence long-term movements. The long-term behaviour of single tunnels is investigated in a finite-element parametric study. A new method is formulated to predict long-term horizontal and vertical surface displacements after excavation of a single tunnel, and incorporates an improved measure of relative soil-lining permeability. The study also predicts significant surface movements during the consolidation period, contradicting the lack of further building damage observed in the field. A further parametric study also investigates the long-term behaviour of twin tunnels. Key interaction mechanisms are identified, leading to the postulation of the long-term interaction behaviour under different tunnelling conditions. Long-term interaction is found to be complex and significant, and should be accounted for in numerical simulations.

The problem of location or movement prediction can be described as the task of predicting the future location of an item using the past locations of that item. It is a problem of increasing interest with the arrival of location-based services and autonomous vehicles. Even if short term prediction is more commonly studied, especially in the case of vehicles, long-term prediction can be useful in many applications like scheduling, resource managing or traffic prediction. In this master thesis project, I present a feature representation of movement that can be used for learning of long-term movement patterns and for long-term movement prediction both in space and time. The representation relies on periodicity in data and is based on weighted n-grams of windowed trajectories. The algorithm is evaluated on heavy transport vehicles movement data to assess its ability to from a search index retrieve vehicles that with high probability will move along a route that matches a desired transport mission. Experimental results show the algorithm is able to achieve a consistent low prediction distance error rate across different transport lengths in a limited geographical area under business operation conditions. The results also indicate that the total population of vehicles in the index is a critical factor in the algorithm performance and therefore in its real-world applicability. / Lokaliserings- eller rörelseprognosering kan beskrivas som uppgiften att förutsäga ett objekts framtida placering med hjälp av de tidigare platserna för objektet. Intresset för problemet ökar i och med införandet av platsbaserade tjänster och autonoma fordon. Även om det är vanligare att studera kortsiktiga förutsägelser, särskilt när det gäller fordon, kan långsiktiga förutsägelser vara användbara i många applikationer som schemaläggning, resurshantering eller trafikprognoser. I detta masterprojekt presenterar jag en feature-representation av rörelse som kan användas för att lära in långsiktiga rörelsemönster och för långsiktig rörelseprediktion både i rymden och tiden. Representationen bygger på periodicitet i data och är baserad på att dela upp banan i fönster och sedan beräkna viktade n-grams av banorna från de olika fönstren. Algoritmen utvärderas på transportdata för tunga transportfordon för att bedöma dess förmåga att från ett sökindex hämta fordon som med stor sannolikhet kommer att röra sig längs en rutt som matchar ett önskat transportuppdrag. Experimentella resultat visar att algoritmen kan uppnå ett konsekvent lågt fel i relativt predikterat avstånd över olika transportlängder i ett begränsat geografiskt område under verkliga förhållanden. Resultaten indikerar även att den totala populationen av fordon i indexet är en kritisk faktor för algoritmens prestanda och därmed även för dess applicerbarhet för verklig användning.