Search results for "semantic"
showing 10 items of 941 documents
Multiscale Attention-Based Prototypical Network For Few-Shot Semantic Segmentation
2021
International audience; Deep learning-based image understanding techniques require a large number of labeled images for training. Few-shot semantic segmentation, on the contrary, aims at generalizing the segmentation ability of the model to new categories given only a few labeled samples. To tackle this problem, we propose a novel prototypical network (MAPnet) with multiscale feature attention. To fully exploit the representative features of target classes, we firstly extract rich contextual information of labeled support images via a multiscale feature enhancement module. The learned prototypes from support features provide further semantic guidance on the query image. Then we adaptively i…
An Innovative Similarity Measure for Sentence Plagiarism Detection
2016
We propose and experimentally assess Semantic Word Error Rate (SWER), an innovative similarity measure for sentence plagiarism detection. SWER introduces a complex approach based on latent semantic analysis, which is capable of outperforming the accuracy of competitor methods in plagiarism detection. We provide principles and functionalities of SWER, and we complement our analytical contribution by means of a significant preliminary experimental analysis. Derived results are promising, and confirm to use the goodness of our proposal.
Conceptual Ontological Object Knowledge Base and Language
2008
This paper deals with AI in aspect of knowledge acquisition and ontology base structure. The core of the system was designed in an object model to optimize it for further processing. Direct concept linking was used to assure fast semantic network processing. Predefined attributes used in the core minimize the number of basic connections within the ontology and help in inference. The system is assumed to generate questions and to specify the knowledge. The AI system defined in this way opens a possibility for better understanding of such basic human mind mechanisms as learning or analyzing.
Validation of Semantic Analyses of Unstructured Medical Data for Research Purposes
2019
BACKGROUND: In secondary data there are often unstructured free texts. The aim of this study was to validate a text mining system to extract unstructured medical data for research purposes. METHODS: From a radiological department, 1,000 out of 7,102 CT findings were randomly selected. These were manually divided into defined groups by 2 physicians. For automated tagging and reporting, the text analysis software Averbis Extraction Platform (AEP) was used. Special features of the system are a morphological analysis for the decomposition of compound words as well as the recognition of noun phrases, abbreviations and negated statements. Based on the extracted standardized keywords, findings rep…
An Integrated Framework for Meta Modeling
2006
Meta modeling is an essential means to systematize, formalize, standardize, integrate, analyze and compare models, techniques, methods and tools. Numerous fields, such as databases, software engineering, software architectures, semantic web, computer-aided tools and method engineering, have benefited from it. The importance of meta modeling is ever increasing along with the emergence of novel approaches, architectures, techniques and languages based on UML and MDA. This paper presents a framework to integrate and compare divergent conceptions of meta modeling in databases, software engineering, and information systems development. The framework is applied to analyze and compare conceptions …
WiseNET - smart camera network interacting with a semantic model
2016
This paper presents an innovative concept for a distributed system that combines a smart camera network with semantic reasoning. The proposed system is context sensitive and combines the information extracted by the smart camera with logic rules and knowledge of what the camera observes, building information and events that may occurred. The proposed system is a justification for the use of smart cameras, and it can improve the classical visual sensor networks (VSN) and enhance the standard computer vision approach. The main application of our system is smart building management, where we specifically focus on increasing the services of the building users.
Latent Semantic Description of Iconic Scenes
2005
It is proposed an approach for the automatic description of scenes using a LSA–like technique. The described scenes are composed by a set of elements that can be geometric forms or iconic representation of objects. Every icon is characterized by a set of attributes like shape, colour and position. Each scene is related to a set of sentences describing their content. The proposed approach builds a data driven vector semantic space where the scenes and the sentences are mapped. A new scene can be mapped in this created space accordingly to a suitable metric. Preliminary experimental results show the effectiveness of the procedure.
A Conversational Agent Based on a Conceptual Interpretation of a Data Driven Semantic Space
2005
In this work we propose an interpretation of the LSA framework which leads to a data-driven “conceptual” space creation suitable for an “intuitive” conversational agent. The proposed approach allows overcoming the limitations of traditional, rule-based, chat-bots, leading to a more natural dialogue.
A word prediction methodology for automatic sentence completion
2015
Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network lang…
Proactive Future Internet: Smart Semantic Middleware for Overlay Architecture
2009
Some initiatives towards Future Internet, e.g., GENI, DARPA's Active Networks, argue the need for programmability of the network components. Some other initiatives extend this with argumentation for declarative networking, where the behavior of a network component is specified using some high-level declarative language, with a software-based engine implementing the behavior based on that specification. Our Proactive Future Internet (PROFI) vision follows these initiatives targeting also the following two problems: interoperability of the network elements programmed by different organizations, and the need for flexible cooperation among network elements, including coordination, conflict reso…