Many quality aspects of software systems are addressed in the existing literature on software architecture patterns. But the aspect of system administration seems to be a bit overlooked, even though it is an important aspect too. In this work we present three software architecture patterns that, when applied by software architects, support the work of system administrators: PROVIDE AN ADMINISTRATION API, SINGLE FILE LOCATION, and CENTRALIZED SYSTEM LOGGING. PROVIDE AN ADMINISTRATION API should solve problems encountered when trying to automate administration tasks. The SINGLE FILE LOCATION pattern should help system administrators to find the files of an application in one (hierarchical) place. CENTRALIZED SYSTEM LOGGING is useful to prevent coming up with several logging formats and locations. Abstract provided by the authors. Published in PLoP '13: Proceedings of the 20th Conference on Pattern Languages of Programs ACM.
Particle verbs (e.g., look up) are lexical items for which particle and verb share a single lexical entry. Using event-related brain potentials, we examined working memory and long-term memory involvement in particle-verb processing. Dutch participants read sentences with head verbs that allow zero, two, or more than five particles to occur downstream. Additionally, sentences were presented for which the encountered particle was semantically plausible, semantically implausible, or forming a non-existing particle verb. An anterior negativity was observed at the verbs that potentially allow for a particle downstream relative to verbs that do not, possibly indexing storage of the verb until the dependency with its particle can be closed. Moreover, a graded N400 was found at the particle (smallest amplitude for plausible particles and largest for particles forming non-existing particle verbs), suggesting that lexical access to a shared lexical entry occurred at two separate time points.
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This research demonstrates the power and robustness of the vocabulary method by Hernández-Rubio et al. (2019) for aspect extraction from online review data. We showcase that this algorithm not only works on the English language based on the CoreNLP toolkit, but also extend it on the Dutch language, specifically with aid of the Frog toolkit. Results on sampled datasets for three different retailers show that it can be used to extract fine-grained aspects that are relevant to acquire corporate reputation insights.
