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.
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Question: 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).

Answer: Introducing SKYSET - a Quintuple Approach for Improving Instructions

Question: Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.

Answer: Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments

Question: Semi-supervised learning based on the low-density separation principle such as<lb>the cluster and manifold assumptions has been extensively studied in the last<lb>decades. However, such semi-supervised learning methods do not always per-<lb>form well due to violation of the cluster and manifold assumptions. In this paper,<lb>we propose a novel approach to semi-supervised learning that does not require<lb>such restrictive assumptions. Our key idea is to combine learning from positive<lb>and negative data (standard supervised learning) and learning from positive and<lb>unlabeled data (PU learning), the latter is guaranteed to be able to utilize unla-<lb>beled data without the cluster and manifold assumptions. We theoretically and<lb>experimentally show the usefulness of our approach.

Answer:
Beyond the Low-density Separation Principle: A Novel Approach to Semi-supervised Learning