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.

Input: Consider Input: In many applications spanning from sensor to social networks, transportation systems, gene regulatory networks or big data, the signals of interest are defined over the vertices of a graph. The aim of this paper is to propose a least mean square (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of observations over a subset of vertices. A detailed mean square analysis provides the performance of the proposed method, and leads to several insights for designing useful sampling strategies for graph signals. Numerical results validate our theoretical findings, and illustrate the performance of the proposed method. Furthermore, to cope with the case where the bandwidth is not known beforehand, we propose a method that performs a sparse online estimation of the signal support in the (graph) frequency domain, which enables online adaptation of the graph sampling strategy. Finally, we apply the proposed method to build the power spatial density cartography of a given operational region in a cognitive network environment.

Output: Least Mean Squares Estimation of Graph Signals


Input: Consider Input: A new approach called SKYSET (Synthetic Knowledge Yield Social Entities Translation) is proposed to validate completeness and to reduce ambiguity from written instructional documentation. SKYSET utilizes a quintuple set of standardized categories, which differs from traditional approaches that typically use triples. The SKYSET System defines the categories required to form a standard template for representing information that is portable across different domains. It provides a standardized framework that enables sentences from written instructions to be translated into sets of category typed entities on a table or database. The SKYSET entities contain conceptual units or phrases that represent information from the original source documentation. SKYSET enables information concatenation where multiple documents from different domains can be translated and combined into a single common filterable and searchable table of entities. Index Terms – Cognitive Linguistics, Dual Intentional Semantic Decomposition and Reconstruction (DISDR), Frame Semantics, Information Concatenation, Information Integration, Information Summarization, Knowledge Architecture, Knowledge Management Internal External (KMIE), Metalinguistic Awareness, Synthetic Knowledge Yield Social Entities Translation (SKYSET).

Output: Introducing SKYSET - a Quintuple Approach for Improving Instructions


Input: Consider Input: Being able to automatically and quickly under-<lb>stand the user context during a session is a main issue for<lb>recommender systems. As a first step toward achieving that<lb>goal, we propose a model that observes in real time the<lb>diversity brought by each item relatively to a short sequence<lb>of consultations, corresponding to the recent user history. Our<lb>model has a complexity in constant time, and is generic since<lb>it can apply to any type of items within an online service (e.g.<lb>profiles, products, music tracks) and any application domain (e-<lb>commerce, social network, music streaming), as long as we have<lb>partial item descriptions. The observation of the diversity level<lb>over time allows us to detect implicit changes. In the long term,<lb>we plan to characterize the context, i.e. to find common features<lb>among a contiguous sub-sequence of items between two changes<lb>of context determined by our model. This will allow us to<lb>make context-aware and privacy-preserving recommendations,<lb>to explain them to users. As this is an on-going research, the<lb>first step consists here in studying the robustness of our model<lb>while detecting changes of context. In order to do so, we use a<lb>music corpus of 100 users and more than 210,000 consultations<lb>(number of songs played in the global history). We validate<lb>the relevancy of our detections by finding connections between<lb>changes of context and events, such as ends of session. Of<lb>course, these events are a subset of the possible changes of<lb>context, since there might be several contexts within a session.<lb>We altered the quality of our corpus in several manners, so as<lb>to test the performances of our model when confronted with<lb>sparsity and different types of items. The results show that our<lb>model is robust and constitutes a promising approach. Keywords-User Modeling; Diversity; Context; Real-Time<lb>Analysis of Navigation Path; Recommender Systems


Output: Toward a Robust Diversity-Based Model to Detect Changes of Context