In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Distributed controllers are oftentimes used in largescale SDN deployments where they run a myriad of network applications simultaneously. Such applications could have different consistency and availability preferences. These controllers need to communicate via east/west interfaces in order to synchronize their state information. The consistency and the availability of the distributed state information are governed by an underlying consistency model. Earlier, we suggested [1] the use of adaptivelyconsistent controllers that can autonomously tune their consistency parameters in order to meet the performance requirements of a certain application. In this paper, we examine the feasibility of employing adaptive controllers that are built on-top of tunable consistency models similar to that of Apache Cassandra. We present an adaptation strategy that uses clustering techniques (sequential k-means and incremental k-means) in order to map a given application performance indicator (χ) into a feasible consistency level (Φ) that can be used with the underlying tunable consistency model. In the cases that we modeled and tested, our results show that in the case of sequential k-means, with a reasonable number of clusters (≥ 50), a plausible mapping (low RMSE) could be estimated between the application performance indicators (χ) and the consistency level indicator (Φ). In the case of incremental k-means, the results also showed that a plausible mapping (low RMSE) could be estimated using a similar number of clusters (≥ 50) by using a small threshold (≃ 0.01).
A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers

The ability to monitor the progress of students’ academic performance is a critical issue to the academic community of higher learning. A system for analyzing students’ results based on cluster analysis and uses standard statistical algorithms to arrange their scores data according to the level of their performance is described. In this paper, we also implemented k-mean clustering algorithm for analyzing students’ result data. The model was combined with the deterministic model to analyze the students’ results of a private Institution in %igeria which is a good benchmark to monitor the progression of academic performance of students in higher Institution for the purpose of making an effective decision by the academic planners. Keywordsk – mean, clustering, academic performance, algorithm.
Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance

Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natural language processing, it is unclear whether they accurately encode all necessary facets of conceptual meaning. In this paper, we evaluate how well these representations can predict perceptual and conceptual features of concrete concepts, drawing on two semantic norm datasets sourced from human participants. We find that several standard word representations fail to encode many salient perceptual features of concepts, and show that these deficits correlate with word-word similarity prediction errors. Our analyses provide motivation for grounded and embodied language learning approaches, which may help to remedy these deficits.

Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning