Abstract
We extend dynamic logic with binders (for state variables) by distinguishing between observable and silent transitions. This differentiation gives rise to two kinds of observational interpretations of the logic: abstractor and behavioural specifications. Abstractor specifications relax the standard model class semantics of a specification by considering its closure under weak bisimulation. Behavioural specifications, however, rely on a behavioural satisfaction relation which relaxes the interpretation of state variables and the satisfaction of modal formulas phi and [alpha]phi by abstracting from silent transitions. A formal relation between abstractor and behavioural specifications is provided which shows that both coincide semantically under mild conditions. For the proof we instantiate the previously introduced concept of a behaviour-abstractor framework to the case of dynamic logic with binders and silent transitions.

Abstract
This paper intends to contribute with a new fuzzy modal logic to model and reason about transition systems involving uncertainty in behaviours. Our formalism supports fuzziness at transitions and on the proposition symbols assignment levels. Against of other approaches in the literature, our bisimulation and bisimilarity notions generalise the analogous standard notions of classic modal logic and of process algebras. Moreover, the outcome of our logic is also fuzzy, with the semantic interpretation of connectives supported by the Godel algebra.

Abstract

Abstract
Robotics is very appealing and is long recognized as a great way to teach programming, while drawing inspiring connections to other branches of engineering and science such as maths, physics or electronics. Although this symbiotic relationship between robotics and programming is perceived as largely beneficial, educational approaches often feel the need to hide the underlying complexity of the robotic system, but as a result fail to transmit the reactive essence of robot programming to the roboticists and programmers of the future. This paper presents ROSY, a novel language for teaching novice programmers through robotics. Its functional style is both familiar with a high-school algebra background and a materialization of the inherent reactive nature of robotic programming. Working at a higher-level of abstraction also teaches valuable design principles of decomposition of robotics software into collections of interacting controllers. Despite its simplicity, ROSY is completely valid Haskell code compatible with the ROS ecosystem. We make a convincing case for our language by demonstrating how non-trivial applications can be expressed with ease and clarity, exposing its sound functional programming foundations, and developing a web-enabled robot programming environment. © 2020 IEEE.

Abstract
Data is everywhere and in everything we do. Most of the time, usable information is hidden in raw data and because of that, there is an increasing demand for people capable of working creatively with it. To fully understand how we can assist data science workers to become more productive in their jobs, we first need to understand who they are, how they work, what are the skills they hold and lack, and which tools they need. In this paper, we present the results of the analysis of several interviews conducted with data scientists. Our research allowed us to conclude that the heterogeneity between these professionals is still understudied, which makes the development of methodologies and tools more challenging and error prone. The results of this research are particularly useful for both the scientific community and industry to propose adequate solutions for these professionals.

Abstract
Data science has started to become one of the most important skills one can have in the modern world, due to data taking an increasingly meaningful role in our lives. The accessibility of data science is however limited, requiring complicated software or programming knowledge. Both can be challenging and hard to master, even for the simple tasks. With this in mind, we have approached this issue by providing a new data science platform, termed DS4All.Curation, that attempts to reduce the necessary knowledge to perform data science tasks, in particular for data cleaning and curation. By combining HCI concepts, this platform is: simple to use through direct manipulation and showing transformation previews; allows users to save time by eliminate repetitive tasks and automatically calculating many of the common analyses data scientists must perform; and suggests data transformations based on the contents of the data, allowing for a smarter environment. © 2020, Springer Nature Switzerland AG.