Abstract
The widespread adoption of digital technology tied with the 4th industrial revolution means the complete reinvention of how business is done. Digital Twin (DT) technologies are now a key technology trend that is already being developed and commercialized to optimize numerous manufacturing processes. In this paper, and from an Information Systems (IS) discipline viewpoint, we take stock of the different technological visions of the DT in manufacturing. We leverage this summary as a stepping stone for discussing the DT's sociotechnical design implications by pointing how this approach is essential for the design of DT software that is specific to its environment and users and co-evolves with it. Furthermore, we present our vision for a DT-based Digital Platform that can support product design and life-cycle management while generating value through an ecosystem of twin-driven product-service systems. Lastly, we show how the Transformer 4.0 project will demonstrate the main principle of our vision by placing the DT of the power transformer with the dual role of virtual counterpart of the physical product and as the architectural framework for (i) managing and processing the historical data collected from the multiple working instances of DTs, and (ii) managing and integrating design information (models, specifications, design data, among others). Copyright (C) 2021 The Authors.

Abstract
There are significant difficulties in deploying and reusing application code within the robotics community. Container technology proves to be a viable solution for such problems, as containers isolate application code and all its dependencies from the surrounding computational environment. They are also light, fast and performant. Manual generation of configuration files required by orchestration tools such as Docker Compose is very time-consuming, especially for more complex scenarios. In this paper a solution is presented to ease the development and deployment of Robot Operating System (ROS) packages using containers, by automatically generating all files used by Docker Compose to both containerize and orchestrate multiple ROS workspaces, supporting multiple ROS distributions and multi-robot scenarios. The proposed solution also generates Dockerfiles and is capable of building new Docker images at run-time, given a list of desired ROS packages to be containerized. Integration with existing Docker images is supported, even if non-ROS-related. After an analysis of existing solutions and techniques for containerizing ROS nodes, the multi-stage pipeline adopted by the proposed solution for file generation is detailed. Then, a real usage example of the proposed tool is presented, showcasing how it an aid both the development and deployment of new ROS packages and features. © 2021 IEEE.

Abstract
Programming courses are needed for an increasing number of students in the Higher Education Institutions of today. Of all the programming languages covered in typical courses, the C and Assembly languages are among the most critical. As they are very low level languages, their knowledge helps the students to understand the inner workings of a computer. At the same time, their differences from other programming languages, demands from the learner a serious adjustment of the mental model. As the programming tools and environments are also different, there is the need of supporting the students in their learning, using a minimum of infrastructure, due to financial restrictions, and to support the maximum number of students, with the existing resources. The use of a Virtual Machine based on a Live Linux distribution, together with an enhanced set of software tests can provide students with an easy to install development platform, providing a good amount feedback, with very limited network usage. The methods described in this paper have been applied with good results, and can be used to support live or online classes.

Abstract

Abstract
The evolution of technology and the increasing connectivity between devices lead to an increased risk of cyberattacks. Good protection systems, such as Intrusion Detection System (IDS) and Intrusion Prevention System (IPS), are essential in trying to prevent, detect and counter most of the attacks. However, the increasing creativity and type of attacks raise the need for more resources and processing power for the protection systems which, in turn, requires horizontal scalability to keep up with the massive companies' network infrastructure and with the complexity of attacks. Technologies like machine learning, show promising results and can be of added value in the detection and prevention of attacks in real-time. But good algorithms and tools are not enough. They require reliable and solid datasets to be able to effectively train the protection systems. The development of a good dataset requires horizontal-scalable, robust, modular and fault-tolerance systems, so that the analyses may be done also in real-time. This paper describes an architecture for horizontal-scaling capture architecture, able to collect packets from multiple sources and prepared for real-time analysis. It depends on multiple modular nodes with specific roles to support different algorithms and tools.

Abstract
Internet-of-Things has reshaped the way people interact with their surroundings and automatize the once manual actions. In a smart home, controlling the Internet-connected lights is as simple as speaking to a nearby conversational assistant. However, specifying interaction rules, such as making the lamp turn on at specific times or when someone enters the space is not a straightforward task. The complexity of doing such increases as the number and variety of devices increases, along with the number of household members. Thus, managing such systems becomes a problem, including finding out why something has happened. This issue lead to the birth of several low-code development solutions that allow users to define rules to their systems, at the cost of discarding the easiness and accessibility of voice interaction. In this paper we extend the previous published work on Jarvis [1], a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept is presented, detailing its architecture and natural language processing capabilities. A feasibility experiment was carried with mostly non-technical participants, providing evidence that Jarvis is intuitive enough to be used by common end-users, with participants showcasing an overall preference by conversational assistants over visual low-code solutions.