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.

Abstract
In Wireless Sensor Networks (WSN), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network lifetime and, consequently, the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Particularly, balancing the energy required to transport data efficiently across such dynamic environments poses significant challenges to routing protocol design and operation, being the trade-off of reducing data redundancy while achieving an acceptable delivery rate a fundamental research topic. In this way, this work proposes a new energy-aware epidemic protocol that uses the current state of the network energy to create a dynamic distribution topology by self-adjusting each node forwarding behavior as eager or lazy according to the local residual battery. Simulated evaluations demonstrate its efficiency in energy consumption, delivery rate, and reduced computational burden when compared with classical gossip protocols as well as with a directional protocol.

Abstract

Abstract
Reactive programming (RP) languages and Synchronous Coordination (SC) languages share the goal of orchestrating the execution of computational tasks, by imposing dependencies on their execution order and controlling how they share data. RP is often implemented as libraries for existing programming languages, lifting operations over values to operations over streams of values, and providing efficient solutions to manage how updates to such streams trigger reactions, i.e., the execution of dependent tasks. SC is often implemented as a standalone formalism to specify existing component-based architectures, used to analyse, verify, transform, or generate code. These two approaches target different audiences, and it is non-trivial to combine the programming style of RP with the expressive power of synchronous languages. This paper proposes a lightweight programming language to describe component-based Architectures for Reactive systems, dubbed ARx, which blends concepts from RP and SC, mainly inspired to the Reo coordination language and its composition operation, and with tailored constructs for reactive programs such as the ones found in ReScala. ARx is enriched with a type system and with algebraic data types, and has a reactive semantics inspired in RP. We provide typical examples from both the RP and SC literature, illustrate how these can be captured by the proposed language, and describe a web-based prototype tool to edit, parse, and type check programs, and to animate their semantics. © IFIP International Federation for Information Processing 2020.