We propose a software architectural model, which uses reactive programming for collecting and filtering live tweets and interpreting their potential correlation to software vulnerabilities and exploits. We aim to investigate if we could discover the existence of exploits for disclosed vulnerabilities in Twitter data streams. Reactive programming is used for performing filtering and querying of tweets to find potential exploits. The result of processing Twitter data streams with reactive programming could be broadcast, by pointing towards potential exploits, which might create a cyber-attack.

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Research on an Artificial Pancreas has gained its momentum and focused on the processing of clinical data for continuous insulin administration. However, the overall research is rather sketchy, fragmented and not very well coordinated. In this paper, we propose an architectural model for creating software intensive environments, which address deficiencies of current solutions for insulin infusion. A new way of defining which data should be collected and which types of computations should be performed with the data is important if we wish to come close to the functioning of a natural pancreas. An excerpt of the proposed software architecture has been deployed using Watson Analytics and performed upon a selection of data collected from sensors, individual patient's input and persistent patient records.