The game designers craft is very young if compared to filmmaking and software development. The knowledge base and formal techniques of these areas is far more comprehensive. Even after decades of evolution of the games production software, the range of design centered techniques and tools is still limited, as observed by many authors. Thereby, efforts have been made towards the establishment of game design formal methods. This paper presents a sys-tematization over the contributions of researchers and designers towards conceptual and concrete tools. These efforts converge to two approaches: the build of a shared design vocabulary and a game design modeling language. While valuable, the existing implementations of these approaches are not mature enough to gain industry adepts, serving only as reference to future works. Moreover, it is needed to discover the designers particular methods, which may contribute towards the constitution of a unified design toolbox.
Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is declining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.
Purpose This paper aims to identify and to understand how current data portals comply with open government data (OGD) principles in the context of Brazilian local government. Design/methodology/approach In this paper, we assessed a sample of 561 municipalities from a universe of interest of 3,052 ones expected to disclose information using the internet. As part of our methodology, the authors analyzed the required items for active disclosure and the technical requirements, all enforced by Brazilian law and close to OGD principles which are the focus of analysis of the authors. Findings The findings generally show the vast majority of assessed data portals did not comply with the basic requirements stated by national law, consequently not complying with OGD principles, and prevent society from benefiting from government data openness. The authors also found arguments that the national law should explicitly reproduce OGD principles, as they demonstrate clearer understanding about the global context of open data. Originality/value The contributions of this work can be used to plan public data openness actions over the internet and envision effective accountability and public participation with clearer legislation and with the effective implementation of OGD principles in data portals.
Binary Image Analysis problems can be solved by set operators implemented as programs for a Binary Morphological Machine (BMM). This is a veiy general and powerful approach to solve this type of problems. However, the design of these programs is not a task manageable by non experts on Mathematical Morphology. In order to overcome this difficulty we have worked on tools that help users describe their goals at higher levels of abstraction and to translate them into BMM programs. Some ofthese tools are based on the representation of the goals of the user as a collection of input-output pairs of images and the estimation of the target operator from these data. PAC learning is a well suited methodology for this task, since in this theory "concepts" are represented as Boolean functions that are equivalent to set operators. In order to apply this technique in practice we must have efficient learning algorithms. In this paper we introduce two PAC learning algorithms, both are based on the minimal representation of Boolean functions, which has a straightforward translation to the canonical decomposition of set operators. The first algorithm is based on the classical Quine-McCluskey algorithm for the simplification of Boolean functions, and the second one is based on a new idea for the construction of Bollean functions: the incremental splitting of intervals. We also present a comparative complexity analysis of the two algorithms. Finally, we give some application examples.
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