Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Context Extraction"”.
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Crowd-sensing for smart city applications: towards solving crowd-sensing data challenges by introducing edge and cloud services
… with two reduction techniques: optimization and context extraction. The trust service calculates trust using different factors. Then, if the trust value is above a predefined threshold, data are trusted; otherwise, they are discarded. The scheduler removes redundant data and schedules sending …
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Entity-relation search: context pattern driven extraction and indexing
… on searching relations between entities with context constraints. In particular, we are interested in efficiently searching for the relations among medical entities (e.g. diseases, chemicals, species, genes, or mutations) in a professional medical corpus. Existing relation extraction systems, …
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Scalable information extraction with large language models
Information extraction (IE) transforms unstructured text into structured knowledge such as entities and relations, and is fundamental to applications including knowledge graph construction, information retrieval, question answering, and domain-specific document understanding. Although large …
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Context-Awareness for Adversarial and Defensive Machine Learning Methods in Cybersecurity
… and produces great results when combined with contextual properties. In the world of the Internet of Things, the extraction of information regarding context, or contextual information, is increasingly prominent with scientific advances. Combining such advancements with artificial intelligence …
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Context Awareness in the Internet of Things and its Applications
In the last decade the role of context awareness, traditionally focused on human to machine interaction, has broadened its perspectives to the machine to machine paradigma. The main goal of this dissertation is both to understand how to apply context awareness to situations , perceived by smart …
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Deep Learning Models for Context-Aware Object Detection
In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to …