Towards a Context Knowledge Taxonomy. Combined Methodologies to Improve a Fast-Search Concept Extraction for an Ontology Population

Context in Architectural Design can be defined-related-comparable to hypothesis and boundary conditions in mathematics. An eco-system that influences it by means of natural and artificial events, space and time dimension. The research has the aim to analyze the critical issues related to Context by providing a contribution to the study of interactions between Context Knowledge and Architectural Design and how it can be used to improve the performance of the buildings and reducing design mistakes. The research focusing on formal ontologies, has developed a model that enables a semantic approach to design application programs, to manage information, to answer design questions and to have a clear relation between the formal representation of the context domain and its meanings. This context model provides an advancement on the state of the art in simplified design assumptions, in term of ontology ambiguity and complexity reduction, by using algorithms to extract and optimize branches of the graph. The extraction does not limit the number of relations, that can be extended and improve context taxonomy coherency and accuracy

Interacting Attention-gated Recurrent Networks for Recommendation

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge Management (CIKM), 201

A Lightweight, Non-intrusive Approach for Orchestrating Autonomously-managed Network Elements

Software-Defined Networking enables the centralized orchestration of data traffic within a network. However, proposed solutions require a high degree of architectural penetration. The present study targets the orchestration of network elements that do not wish to yield much of their internal operations to an external controller. Backpressure routing principles are used for deriving flow routing rules that optimally stabilize a network, while maximizing its throughput. The elements can then accept in full, partially or reject the proposed routing rule-set. The proposed scheme requires minimal, relatively infrequent interaction with a controller, limiting its imposed workload, promoting scalability. The proposed scheme exhibits attracting network performance gains, as demonstrated by extensive simulations and proven via mathematical analysis.Comment: 6 pages 7, figures, IEEE ISCC'1