Editor's Summary The prolific commentary disseminated via Twitter on the riots in London and other British cities in August 2011 has given rise to the question of whether their reflection in such social media forums may have added to the unrest. Investigators analyzed 600,000 tweets and retweets about the riots for evidence that Twitter was used as a central organizational tool to promote illegal group action. Results indicated that irrelevant tweets died out and that Twitter users retweeted to show support for their beliefs in others' commentaries. Tweets offered by well‐known and popular individuals were more likely to be retweeted. In the case of the British riots, there is little overt evidence that Twitter was used to promote illegal activities at the time, though it was useful for spreading word about subsequent events.

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Abstract. Knowledge representation (KR) is used to store and retrieve meaningful data. This data is saved using dynamic data structures that are suitable for the style of KR being implemented. The KR allows the system to manipulate the knowledge in the data by using reasoning operations. The data structure, together with the contents of the transformed knowledge, is known as the knowledge base (KB). An algorithm and the associated data structures make up the reasoning operation, and the performance of this operation is dependent on the KB.In this paper, the basic reasoning operation for a query-answer system, projection, is explored using different theoretical algorithms. Within this discussion, the associated algorithms will be using different KBs for their Conceptual Graph (CG) knowledge representation. The basic projection algorithm defined using the CG representation is looking for a graph morphism of a query graph onto a graph from the KB.The overall running time for the projection operation is known to be a NP class problem; however, by modifying the algorithm, taking into account the associated KB, the actual time needed for discovering and creating the projection/s can be improved. In fact, a new projection algorithm will be defined that, given a typical query onto a carefully defined KB, presents a running time for the actual projection that only grows with the number of projections present.

The goal of logic synthesis is to obtain high-quality designs from specifications. Current approaches to logic synthesis often trade off design quality for technology independence. In this paper, we present a model of logic synthesis that uses technology-specific design rules and extends rule-based search to functional decomposition and technology mapping. While this model improves design quality by taking advantage of the target technology, it is not robust to technology changes. To improve robustness, we augment the model with two learning components: one for acquiring rules that make use of physical cells in a technology library, and another for acquiring rules that make use of appropriate design styles. These components are related to work in the learning of macro-operators and explanation-based learning.