Many components of Cyber-Physical Systems (CPS) are designed based on models that represent the assumed behavior of the CPS at the time of deployment. However, significant or continuous small changes in the CPS, as well as wear and tear reduce the effectiveness of the CPS and its model and may lead to a total failure of the overall system. In this paper, we propose a novel lifecycle-based view of CPS models. First, we define the model's lifespan as the period from the initial conception of the model until it is no longer fit to represent the system behavior. For better differentiation, a lifespan is divided into the initial, operation, and adaptation phases. In the initial phase, a known-good baseline performance metric is established for the model's suitability to reflect the system behavior. In the operation phase, the model is used for CPS analysis, data smoothing, and fault location while its suitability is monitored. The adaptation phase is intended for necessary adaptations to the model and to the CPS itself, which lead to new iterations. To implement these lifecycle augmentations of the CPS, we use formal modeling in the form of Hidden Markov Models extended by unobservable transitions (Є-HMMT) to represent the assumed system behavior and compare the data of the observed system behavior with this modeling. In addition, we are testing our proposed formalism by designing a CPS model based on smart home systems and running a simulation for validation. The simulation covers unforeseen system changes and corrupted data.

In this paper, we investigate the human ability to distinguish political social bots from humans on Twitter. Following motivated reasoning theory from social and cognitive psychology, our central hypothesis is that especially those accounts which are opinion-incongruent are perceived as social bot accounts when the account is ambiguous about its nature. We also hypothesize that credibility ratings mediate this relationship. We asked N = 151 participants to evaluate 24 Twitter accounts and decide whether the accounts were humans or social bots. Findings support our motivated reasoning hypothesis for a sub-group of Twitter users (those who are more familiar with Twitter): Accounts that are opinion-incongruent are evaluated as relatively more bot-like than accounts that are opinion-congruent. Moreover, it does not matter whether the account is clearly social bot or human or ambiguous about its nature. This was mediated by perceived credibility in the sense that congruent profiles were evaluated to be more credible resulting in lower perceptions as bots.

This paper exploits extended Bayesian networks for uncertainty reasoning on Petri nets, where firing of transitions is probabilistic. In particular, Bayesian networks are used as symbolic representations of probability distributions, modelling the observer's knowledge about the tokens in the net. The observer can study the net by monitoring successful and failed steps. An update mechanism for Bayesian nets is enabled by relaxing some of their restrictions, leading to modular Bayesian nets that can conveniently be represented and modified. As for every symbolic representation, the question is how to derive information - in this case marginal probability distributions - from a modular Bayesian net. We show how to do this by generalizing the known method of variable elimination. The approach is illustrated by examples about the spreading of diseases (SIR model) and information diffusion in social networks. We have implemented our approach and provide runtime results.