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Citizens of the digital world are threatened. The digital systems that surround them are increasingly able to make autonomous decisions over and above them and on their behalf. They feel that their moral rights, as well as the social, economic, and political spheres, can be affected by the behavior of such systems. Although unavoidable, the digital world is becoming uncomfortable and potentially hostile to its users as human beings and as citizens. Notwithstanding the introduction of the GDPR and of initiatives to establish criteria on software transparency and accountability, users feel vulnerable and unprotected. In this paper, we present EXOSOUL, an overarching research framework that aims at building a software a personalized exoskeleton that enhances and protects users by mediating their interactions with the digital world according to their own ethics of actions and privacy of data. The exoskeleton disallows or adapts the interactions that would result in unacceptable or morally wrong behaviors according to the ethics and privacy preferences of the users. With their software shield, users will feel empowered and in control, and more in the balance of forces with the other actors of the digital world. To reach the breakthrough result of automatically building a personalized exoskeleton, EXOSOUL identifies multidisciplinary challenges never touched before: 1) defining the scope for and inferring citizen's ethical preferences; 2) treating privacy as an ethical dimension managed through the disruptive notion of active data; and 3) automatically synthesizing ethical actuators, i.e., connector components that mediate the interaction between the user and the digital world to enforce her ethical preferences. In this paper, we discuss the research challenges of EXOSOUL in terms of their feasibility and risks.

Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task. Recently some machine learning approaches have been proposed to predict metamorphic relations. These approaches predicting single label metamorphic relation can alleviate this problem to some extent. However, many applications involve multi-group metamorphic relations, and these approaches are clearly inefficient. To address this problem, in this paper we propose a Multi-Label Metamorphic Relations prediction approach based on an improved radial basis function (RBF) neural network named RBF-MLMR. First, RBF-MLMR uses state-of-the-art soot analysis tool to generate control flow graph and corresponds labels from the source codes of programs. Second, the extracted nodes and the path properties constitute multi-label data sets for the control flow graph. Finally, a multi-label RBF neural network prediction model is established to predict whether the program satisfies multiple metamorphic relations. In order to improve the prediction results, affinity propagation and k-means clustering algorithms are used to optimize the RBF neural network structure of RBF-MLMR. A set of dedicated experiments based on public programs is conducted to validate RBF-MLMR. The experimental results show that RBF-MLMR can achieve accuracy of around 80% for predicting two and three metamorphic relations.

Robots are meant to replace humans for a broad variety of everyday tasks, such as environmental monitoring or patrolling large public areas for security assurance. The main focus of researchers and practitioners has been on providing tailored software and hardware solutions for very specific and often complex tasks. On one hand, these solutions show great potential and provide advanced capabilities for solving the specific task. On the other hand, the polarized attention to task-specific solutions makes them hard to reuse, customize, and combine. In this paper we propose a family of domain-specific modeling languages for the specification of civilian missions of mobile multi-robot systems. These missions are meant to be described in terms of models that are: 1) closer to the general problem domain; 2) independent from the underlying technologies; 3) ready to be analyzed, simulated, and executed; and 4) extensible to new application domains, thus opening up the use of robots to even non-technical operators. Moreover, we show the applicability of the proposed family of languages in two real-world application domains: unmanned multicopters and autonomous underwater vehicles.