Co-creativity in music refers to two or more musicians or musical agents interacting with one another by composing or improvising music. However, this is a very subjective process and each musician has their own preference as to which improvisation is better for some context. In this paper, we aim to create a measure based on total information flow to quantitatively evaluate the co-creativity process in music. In other words, our measure is an indication of how "good" a creative musical process is. Our main hypothesis is that a good musical creation would maximize information flow between the participants captured by music voices recorded in separate tracks. We propose a method to compute the information flow using pre-trained generative models as entropy estimators. We demonstrate how our method matches with human perception using a qualitative study.

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are automatically learned by the framework, hence providing greater control over the different kinds of images produced. Due to the high model complexity of InfoGAN, the generative distribution tends to be concentrated around the training data points. This is a critical problem as the models may inadvertently expose the sensitive and private information present in the dataset. To address this problem, we propose a differentially private version of InfoGAN (DP-InfoGAN). We also extend our framework to a distributed setting (DPD-InfoGAN) to allow clients to learn different attributes present in other clients' datasets in a privacypreserving manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can synthesize high-quality images with flexible control over image attributes while preserving privacy.CCS Concepts: • Security and privacy → Domain-specific security and privacy architectures; • Computing methodologies → Machine learning; Computer vision.

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In this paper, the first deep reinforcement learning model for home automation systems is presented. Home automation has been one of the most important applications in the field of Artificial Intelligence. The system should learn the pattern and behaviour of the user automatically from experience and take future actions accordingly. The system proposed here makes use only of images to learn the user's needs using Deep Q-Learning, thus minimizing the use of any sensors and other hardware. The model makes use of a Convolutional Neural Network that takes as input, the image and outputs the future reward for each action. The system was tested with images of a house and describes the methods and results in the paper.

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