![]() ![]() In these modes, players "blend"Įxisting images into new images under varying constraints. It also includes a credits system, which can be used to purchase items from the store. It allows users to create their own AI art, view featured community art, and queue up artwork. Through three different play modes in crea.blender, each aligned withĮstablished creativity assessment methods. is a platform that uses cutting-edge Artificial Intelligence technology to create artwork from text input. We expand on, and exploreĪspects of these questions in this pilot study. Human creativity and involvement in the process. Human-computer collaboration raises questions about the relevance and level of (often with Machine Learning) collaborate on a creative task. Co-creative systems are systems in which humans and computers For validating the developed network, test measurements are carried out and compared with the state of the art.Download a PDF of the paper titled crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity, by Janet Rafner and 11 other authors Download PDF Abstract: We present a pilot study on crea.blender, a novel co-creative game designedįor large-scale, systematic assessment of distinct constructs of humanĬreativity. Finally, both networks are combined into one and the performance is compared. Moreover, a neural network for human activity classification has to be designed in order to compare the classification task with and without possible modifications in the input data due to the developed network. Furthermore, a neural network for replacing the processing steps is trained and evaluated. For this purpose, a realistic 3D model of a room with moving people has to be created (e.g. In order to generate sufficient radar training data, we work with artificially generated data using ray-tracing software. The task of the master´s thesis would therefore initially be to map the radar processing steps in a neural network. This would make it possible to carry out the entire chain from raw data to classification in an AI network. However, another option is to directly replace the pre-processing steps with an AI network. This enables the recognition of gestures and specific motions of humans. Based on this pre-processed data, human activities are classified using machine learning techniques by feeding the data into a classifier network (neural networks / deep learning). In state-of-the-art radar signal processing, several consecutive Fourier transforms are applied to the radar raw data to identify the features of the targets. They can be used, for example, to detect people in the building, their general movements and even to measure vital parameters. For both private and public buildings, radar sensors therefore offer many attractive possibilities for performing tasks, which involve human behavior, even with high data protection requirements. Since the data do not have any immediately recognizable personal reference. Radar sensors offer a decisive advantage in this respect. However, the audiovisual sensor technology is a relatively strong intrusion into people’s privacy. Audio and camera systems have already established voice and gesture control in some households. Smart home applications are increasingly finding their way into the everyday lives of many people. ![]()
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