This article consists of presenting Astroroll, a Virtual Reality (VR) game in which the player assumes the role of the first wheelchairbound astronaut at the Lunar Station.This game was designed to be played only using an eye gaze to interact and a wheelchair to move, using a technique called Redirect Walk (RW).RW is a technique that aims to modify movement in VR in relation to the real world, making the path taken between the real world and the virtual world different.Using this technique correctly is possible to enlarge the virtual space and keep the gameplay in a short area in the real world.It was developed to be exhibited at Casa da Descoberta, the public science museum of the Fluminense Federal University (UFF), in the city of Niterói, in a space that demands a compact play area to accommodate all visitors.

With the popularization of virtual reality (VR) devices, there is a growing demand for new forms of interaction in immersive environments, especially in the educational context.External accessories, widely used in electronic entertainment devices, have shown promise in improving the user experience in VR.In this project, we conducted an innovative experiment in which the user uses a bicycle as an interface to explore the surfaces of celestial bodies in a space-themed virtual environment.

Virtual Reality (VR) provides immersive and interactive experiences that support learning and skill acquisition.This paper introduces WindSurf VR, a simulation-based VR game set in a protected natural reserve in Camboinhas, Niterói.The game merges realistic windsurfing mechanics with an environmental conservation mission, challenging players to collect floating debris.By integrating physical engagement with ecological consciousness, WindSurf VR seeks to foster sustainability education through an engaging and competitive experience.

Star Rocks is an educational virtual reality (VR) game that invites players to explore the differences in gravity across celestial bodies through an immersive experience.Created as part of a public outreach initiative at Casa da Descoberta, the science museum of Universidade Federal Fluminense, the game lets users experience how gravity changes on Earth, the Moon, and Mars.Through the mechanics of hitting targets while adjusting to different gravitational pulls, the game introduces fundamental physics concepts without needing formal instruction to play.Featuring diegetic user interfaces and realistic terrain models sourced, Star Rocks balances authenticity with playability, making scientific concepts accessible and enjoyable.

Summary Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far.

Unmanned vehicles keep growing attention as they facilitate innovative commercial and civil applications within the Internet of Things (IoT) realm. In this context, autonomous sailing boats are becoming important marine platforms for performing different tasks, such as surveillance, water, and environmental monitoring. Most of these tasks heavily depend on artificial intelligence (AI) technologies, such as visual navigation and path planning, and comprise the so-called AI of Things (AIoT). In this article, we propose 1) the OpenBoat, an automating system architecture for AIoT-enabled sailboats with application-agnostic autonomous environment monitoring capability and 2) the F-Boat, a fully functional prototype of OpenBoat built with commercial off-the-shelf (COTS) components on a real sailboat. F-Boat includes low-level control strategies for autonomous path following, communication infrastructure for remote operation and cooperation with other systems, edge computing with AI accelerator, modular support for application-specific monitoring systems, and navigation aspects. F-Boat is also designed and built for robustness situations to guarantee its operation under extreme events, such as high temperatures and bad weather, through extended periods of time. We show the results of field experiments running in Guanabara Bay, an important aquatic ecosystem in Brazil, that demonstrate the functionalities of the prototype and demonstrate the AIoT capability of the proposed architecture.

Virtual reality (VR) is gaining increasing popularity across various domains, but the current state of technology imposes limitations on the level of realism and complexity achievable in computer graphics when displayed through VR head-mounted devices (HMDs). To improve the user experience in HMDs, optimization techniques are needed to enhance performance without sacrificing quality. One such technique is Foveated Rendering (FR), which leverages the human visual system to optimize resource usage. FR degrades the image quality at the periphery of the human vision, where visual acuity is lower, to save resources. This paper aims to investigate if the perception of the peripheral area is affected whenever users are in movement in a VR environment. Our findings show a significant correlation between speed movement and Foveated rendering parameters in both scenarios. The least amount of degradation was observed in the idle state and the most in the high-speed state, indicating that users perceive less degradation at higher speeds. These results are particularly relevant for path-tracing-based algorithms, due to the possibility of reducing the number of rays required for the rendering whenever there is movement.

Recently, new Neural Language Models pre-trained on a massive corpus of texts are available. These models encode statistical features of the languages through their parameters, creating better word vector representations that allow the training of neural networks with smaller sample sets. In this context, we investigate the application of these models to predict Item Response Theory parameters in multiple choice questions. More specifically, we apply our models for the Brazilian National High School Exam (ENEM) questions using the text of their statements and propose a novel optimization target for regression: Item Characteristic Curve. The architecture employed could predict the difficulty parameter b of the ENEM 2020 and 2021 items with a mean absolute error of 70 points. Calculating the IRT score in each knowledge area of the exam for a sample of 100,000 students, we obtained a mean absolute below 40 points for all knowledge areas. Considering only the top quartile, the exam’s main target of interest, the average error was less than 30 points for all areas, being the majority lower than 15 points. Such performance allows predicting parameters on newly created questions, composing mock tests for student training, and analyzing their performance with excellent precision, dispensing with the need for costly item calibration pre-test step.

Real time Path-tracing is becoming an important approach for the future of games, digital entertainment, and virtual reality applications that require realism and immersive environments. Among different possible optimizations, denoising Monte Carlo rendered images is necessary in low sampling densities. When dealing with Virtual Reality devices, other possibilities can also be considered, such as foveated rendering techniques. Hence, this work proposes a novel and promising rendering pipeline for denoising a real-time path-traced application in a dual-screen system such as head-mounted display (HMD) devices. Therefore, we leverage characteristics of the foveal vision by computing G-Buffers with the features of the scene and a buffer with the foveated distribution for both left and right screens. Later, we path trace the image within the coordinates buffer generating only a few initial rays per selected pixel, and reconstruct the noisy image output with a novel non-homogeneous denoiser that accounts for the pixel distribution. Our experiments showed that this proposed rendering pipeline could achieve a speedup factor up to 1.35 compared to one without our optimizations.

Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing properly cropped images of entire bodies. Recently, the live Re-ID setting was introduced to represent the practical application context of Re-ID better. It consists in searching for the query in short videos, containing whole scene frames. The initial live Re-ID baseline used a pedestrian detector to build a large search gallery and a classic Re-ID model to find the query in the gallery. However, the galleries generated were too large and contained low-quality images, which decreased the live Re-ID performance. Here, we present a new live Re-ID approach called TrADe, to generate lower high-quality galleries. TrADe first uses a Tracking algorithm to identify sequences of images of the same individual in the gallery. Following, an Anomaly Detection model is used to select a single good representative of each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011 dataset and shows significant improvements over the baseline.

Abstract The computational tool known as Power Flow is essential for planning and operating electrical networks, especially considering the interconnected ones, which leads to tackle more complex, large‐size modeling problems and perform extensive analyses during decision‐making processes. This paper proposes a practical approach to solve the power flow problem using parallel computation of the Newton–Raphson method. To this aim, CPU and GPU implementations are tested and a hybrid CPU‐GPU approach proved to be more effective to solve the problem in hand. Numerical results obtained on benchmark power networks are presented and discussed.

Project comprehension questions, such as “which modified artifacts can affect my work?” and “how can I identify the developers who should be assigned to a given task?” are difficult to answer, require an analysis of the project and its data, are context specific, and cannot always be pre-defined. Current research approaches are restricted to post hoc analyses over software repositories. Very few interactive exploratory tools exist since the large amount of data that need to be analyzed prohibits its exploration at interactive rates. Moreover, such analyses typically require the user to create complex scripts or queries to extract the desired information from data. Here we present Dominoes, a tool for interactive data exploration aimed at end users (i.e., project managers or developers). Dominoes allows users to interact with different types and units of data to investigate project relationships and view intermediate results as charts, tables, and graphs. Additionally, it allows users to save the derived data as well as their exploration paths for later use. In a scenario-based evaluation study, participants achieved a success rate of 86 percent in their explorations, with a mean time of 7.25 minutes for answering a set of (project) exploration questions.

Arguably, player behavior profiling is one of the most relevant tasks of Game Analytics. However, to fulfill the needs of this task, gameplay data should be handled so that the player behavior can be profiled and even understood. Usually, gameplay data is stored as raw log-like files, from which gameplay metrics are computed. However, gameplay metrics have been commonly used as input to classify player behavior with two drawbacks: (1) gameplay metrics are mostly handcrafted and (2) they might not be adequate for fine-grain analysis as they are just computed after key events, such as stage or game completion. In this paper, we present a novel approach for player profiling based on provenance graphs, an alternative to log-like files that model causal relationships between entities in game. Our approach leverages recent advances in deep learning over graph representation of player states and its neighboring contexts, requiring no handcrafted features. We perform clustering on learned nodes representations to profile at a fine-grain the player behavior in provenance data collected from a multiplayer battle game and assess the obtained profiles through statistical analysis and data visualization.

Medical images are important elements for the diagnosis of diseases. Computer Aided Diagnostic has evolved in recent years along with the processing capacity of computers as well as the emergence of new computational techniques. Segmentation is a valuable approach for identifying a specific area in human body images, such as the lungs and heart. This work proposes an algorithm to segment anatomical structures using parallel 3D region growing. Experiments using different Computer Tomography scans show that the proposed approach can run 150 times faster than the typical sequential region growing algorithm while providing good results in the identification of the target region.

Simulations of cardiac electrophysiological models in tissue, particularly in 3D require the solutions of billions of differential equations even for just a couple of milliseconds, thus highly demanding in computational resources. In fact, even studies in small domains with very complex models may take several hours to reproduce seconds of electrical cardiac behavior. Today's Graphics Processor Units (GPUs) are becoming a way to accelerate such simulations, and give the added possibilities to run them locally without the need for supercomputers. Nevertheless, when using GPUs, bottlenecks related to global memory access caused by the spatial discretization of the large tissue domains being simulated, become a big challenge. For simulations in a single GPU, we propose a strategy to accelerate the computation of the diffusion term through a data-structure and memory access pattern designed to maximize coalescent memory transactions and minimize branch divergence, achieving results approximately 1.4 times faster than a standard GPU method. We also combine this data structure with a designed communication strategy to take advantage in the case of simulations in multi-GPU platforms. We demonstrate that, in the multi-GPU approach performs, simulations in 3D tissue can be just 4× slower than real time.

Summary The increasing amount of resources available on current GPUs sparked new interest in the problem of sharing its resources by different kernels. While new generations of GPUs support concurrent kernel execution, their scheduling decisions are taken by the hardware at runtime. The hardware decisions, however, heavily depend on the order at which the kernels are submitted to execution. In this work, we propose a novel optimization approach to reorder the kernels invocation focusing on maximizing the resources utilization, improving the average turnaround time. We model the kernel assignments to the hardware resources as a series of knapsack problems and use a dynamic programming approach to solve them. We evaluate our method using kernels with different sizes and resource requirements. Our results show significant gains in the average turnaround time and system throughput compared to the kernels submission implemented in modern GPUs.

The player should enjoy games through the game controller of their preference and keep the best possible game experience regardless their option. This article proposes a novel classification model for game interaction techniques from the player perspective and with direct control of a single humanoid avatar based on three characteristics: Effort, Duration, and Context. Combining the three characteristics, we classify the game interaction techniques into nine isomorphic categories. We conducted three experiments. The first (N = 57) and second (N = 45) were a paired quantitative comparison evaluation method, allowing the volunteer to play the same game level twice, changing only the interaction technique for one action. This aimed at validating our initial proposal with users. The third (N = 100) was also a comparative test. The volunteer played the same stage twice using the button-interaction based version as a reference against one of another four device-based versions. The goal was to extend the proposed classification model to other interaction devices to check if the interaction metaphor was kept coherent. In all three experiments, we chose the 202 participants as a convenience sample. The users’ experiments support all the research hypotheses. We conclude that, if our proposed guideline with our interaction technique classification model is applied, it may expect a positive impact on the players’ experiences.

Automated story generation is a desired feature in games and interactive media because it can control how a virtual world evolves so that it can be adapted to the players' choices. In order to have variety and quality in the generated stories, previous works have relied on simulation‐based storytelling, in which a story is generated as their characters, represented as agents, try to achieve their goals. One challenge of this approach is to make the agents act more like human characters and less like omniscient intelligent beings. In this article, we present a perception model for simulation‐based story generation that introduces errors into characters' knowledge, (mis)leading them to non‐optimal, but still coherent, believable actions. The perception is executed using a description of the virtual world's elements using physical characteristics, and a pattern matching process that associates combinations of physical characteristics with predefined combinations of attributes, which are allowed to be wrong, and consequently may result in non‐perfect interpretations of the world. We developed a story generation system from the proposed model and tested it with a version of the Little Red Riding Hood story, famous for its perception failure. Our results show interesting variations for the traditional known ending.

Version control is considered to be a vital component for supporting professional software development. While it has been widely used for textual artifacts, such as source code or documentation, little attention has been given to binary artifacts. This omission can place huge restrictions on projects in the game and media industries as they contain large amounts of binary data, such as images, videos, three-dimensional models, and animations, along with their source code. For these kinds of artifacts, existing strategies such as storing the file as a whole for each revision or saving conventional binary deltas consume significant storage space with duplicate data and, even worse, do not provide any understandable information on which modifications were made. As a response to this problem, this paper introduces a change-set model infrastructure to support version control of image artifacts using a specialized data structure. Additionally, our approach can deal with the maintenance of duplicate nearly identical images through a merge operation. Because of the amount of data that has to be processed, we designed our solution based on a parallel architecture, which permits a massively parallel approach to version control. The paper also compares our approach with some popular open-source version control systems, showing their repository growth in relation to ours as well as the time required to process image artifacts. Finally, we demonstrate that our architecture requires less storage space and runs much faster than current methods. Copyright © 2015 John Wiley & Sons, Ltd.

Advances in cloud computing have enabled cloud gaming systems. In those systems, game logic and rendering is processed remotely in a cloud server and audio and video outputs are streamed to a thin client with limited computing capabilities, such as mobile phones, low-powered computers or even smart TVs. There are still many challenges involved in the task of providing cloud games, especially when trying to reduce server encoding time and video bitrate. In this work, we present a novel optimization technique based on image layers caching. Our technique allows that previously encoded layers are reused reducing encoding workload. In addition, only newly encoded layers are streamed, reducing the overall bitrate sent from server to client. We achieved around 23% stream size reduction with about 5% encoding time increase for cases where background cache usage is maximized.

Identifying expertise in a project is essential for task allocation, knowledge dissemination, and risk management, among other activities. However, keeping a detailed record of such expertise at class and method levels is cumbersome due to project size, evolution, and team turnover. Existing approaches that automate this task have limitations in terms of the number and granularity of elements that can be analyzed and the analysis timeframe. In this paper, we introduce a novel technique to identify expertise for a given project, package, file, class, or method by considering not only the total number of edits that a developer has made, but also the spread of their changes in an artifact over time, and thereby the breadth of their expertise. We use Dominoes - our GPU-based approach for exploratory repository analysis - for expertise identification over any given granularity and time period with a short processing time. We evaluated our approach through Apache Derby and observed that granularity and time can have significant influence on expertise identification.

In this paper, we present Dominoes, an approach for analyzing software repositories with thousands of artifacts by considering multiple perspectives of the software development data. In order to achieve computational power we model the data and its relationships as matrices, making possible to efficiently process them with a GPUs (Graphics Processing Unit) based architectures. Dominoes can support automated exploration of different relationships among project artifacts, where users have the flexibility to interactively combine and compose them. Our solution organizes data extracted from software repositories into multiple matrices that can be treated as domino pieces (e.g. [commit|method]). The connection of such pieces corresponds to a set of matrices operations, which derive additional domino pieces. These derived domino pieces represent specific project entity relationships (e.g. number of commits in which two methods co-occurred) and can be used for further explorations. As an evaluation of the Dominoes framework we present two exploratory case studies based on Apache Derby. First, we use Dominoes to show how dependencies among artifacts can be derived. Then, we identify expertise of developers by considering the commits that developers make to artifacts. We show that identifying relationships among 34,335 elements along 7,578 commits takes about 0.2 minutes in GPU, while the same processing in CPU takes about 413 minutes. Besides, identifying expertise of developer on a set of 34,335 files and 36 developers takes about 0.1 minute in GPU, whereas in CPU it takes 324 minutes.

We introduce an interactive method to assess cataracts in the human eye by crafting an optical solution that measures the perceptual impact of forward scattering on the foveal region. Current solutions rely on highly-trained clinicians to check the back scattering in the crystallin lens and test their predictions on visual acuity tests. Close-range parallax barriers create collimated beams of light to scan through sub-apertures, scattering light as it strikes a cataract. User feedback generates maps for opacity, attenuation, contrast and sub-aperture point-spread functions. The goal is to allow a general audience to operate a portable high-contrast light-field display to gain a meaningful understanding of their own visual conditions. User evaluations and validation with modified camera optics are performed. Compiled data is used to reconstruct the individual's cataract-affected view, offering a novel approach for capturing information for screening, diagnostic, and clinical analysis.

Fluid simulation is very important for the study of natural phenomena. Most of these phenomena could not be simulated computationally some years ago and for its study the research only had available the manual calculation of its govern equation or a small physical simulation of the phenomena to be studied. With the technology advance, most of these phenomena could be simulate even in real time coming out a new research field called Computational Fluid Dynamic (DFC) only to deal with it. Many researches in DFC use Graphics Processor Unity (GPU) to simulate fluids, forgiving the existence of the CPU, which became idle most time during the simulation. Due the presented fact, we propose a method for using GPU and CPU together during the fluid simulation. Fluid simulation in this work is based on Smoothed Particle Hydrodynamics (SPH), a mesh free Newtonian method for fluid simulation. Also, the rigid body and fluid interaction is dealt at triangle level, archiving more precise results.

In computer graphics, aliasing is a problem which is always present when discrete elements are mapped to continuous functions or vice-versa. Although there is no general solution for this kind of problem, there are many techniques that aim at reducing the effects of aliasing. The article first discusses how interpolation methods are usually applied in order to correct this problem and shows the limitations of those techniques. It then presents another solution for this problem, that can be used together with the interpolation. It increases the texture details, making use of procedural functions.