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Newly emerging and evolving technologies such as Cloud, Fog and Edge Computing, as well as Internet of Things, Cyber-Physical Systems and Distributed Ledger Technology (such as blockchain) together with advances in Artificial Intelligence (AI) research are increasingly becoming a common and pervasive phenomenon in our everyday lives. Their co-evolution with society is driving the emergence of future socio-technical systems, which further promote ubiquitous entanglement between humans and machines. Fog, Edge and Dew computing as post-Cloud computing paradigms aim to relocate computing resources closer to end users in order to mitigate cloud-specific issues of highly centralized computation. Dew computing as the youngest of the post-cloud paradigms promotes human centered independence and collaboration between devices within scalable distributed computing infrastructures. Meanwhile, the field of artificial intelligence is adapting to recent challenges posed by user data privacy regulations as well as opportunities for applications on mobile devices based on their growing computational abilities. The usage of artificial intelligence in pervasive and scalable distributed computing systems is a natural step towards ubiquitous intelligent infrastructures and collaborative human and machine environments. Federated learning is an artificial intelligence technique enabling collaborative learning in distributed devices environment without sharing the training data sets, which are often private. This paper provides the overview of the federated learning paradigm showing that it inherently leverages both independence and collaboration, thus exemplifying implementation of dew intelligence within scalable distributed computing hierarchy.

Virtual drug screening is one of the most widely used approaches for finding new drugs candidates. The process consists in selecting one or more chemical compounds with the highest binding free energy to target proteins. Given that the empirical space of chemical compounds is extremely large and estimated to has over 50 millions of them, finding the most effective drug is computationally challenging. Furthermore, the vast majority of proteins still lack the experimentally obtained 3D structures, making it hard to accurately calculate their binding free energies with chemical compounds. With this in mind, the aim of our study was to investigate the accuracy of the Autodock Vina program in a virtual drug screening on a set of proteins that do not have experimentally determined structures. To do this, we performed a virtual drug screening with the Autodock Vina on a large set of drug-kinase pairs taken from the IDG-Dream Drug-Kinase Binding Prediction Challenge. The results obtained show that the Autodock Vina can be used effectively in such unstructured environments.

Inspired by the successful performance of Convolutional Neural Networks (CNN) in automatically predicting complex image properties such as memorability, in this work we explore their transferability to the domain of art images. We employ a CNN model trained to predict memorability scores of natural images to explore the memorability of artworks belonging to different genres and styles. Our experiments show that nude painting and portrait are the most memorable genres, while landscape and marine painting are the least memorable. Regarding image style, we show that abstract styles tend to be more memorable than figurative. Additionally, on the subset of abstract images, we explore the relation between memorability and other features related to composition and color, as well as the sentiment evoked by the image. We show that there is no correlation between symmetry and memorability, however memorability positively correlates with the image’s probability of evoking positive sentiment.

With recent technological developments, modern computing systems are becoming more and more powerful. Common computers are capable of calculating more than 50, 000 million instructions per second. Currently the biggest obstacle in computer usage is slow communication between the human and the computer. With innovation and development of novel (natural based) sensors capable of capturing positions and natural motion of human body a new, natural, way of interacting with computer is enabled. This paper gives an overview of eye tracking technology, and presents its application in nature based human- computer interaction (HCI) systems by extending standard controllers, such as the keyboard and the mouse. Novel approaches enable HCI closer to the communication patterns of human beings. In addition, the usage of those concepts in applications for eye diseases and disorders diagnostics and rehabilitation is discussed.