scholarly journals Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 494-505
Author(s):  
Radu-Casian Mihailescu ◽  
Georgios Kyriakou ◽  
Angelos Papangelis
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Natural Language ◽  
Learning Approach ◽  
Language Understanding ◽  
Virtual Sensor ◽  
Virtual Sensors ◽  
Multi Level ◽  
High Level ◽  
User Friendly

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.

Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation

2021 ◽  
Author(s):  
K.G.C.M Kooragama ◽  
L.R.W.D. Jayashanka ◽  
J.A. Munasinghe ◽  
K.W. Jayawardana ◽  
Muditha Tissera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Approach ◽  
Speech Evaluation

scholarly journals Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

2018 ◽  
Author(s):  
D. Kuhner ◽  
L.D.J. Fiederer ◽  
J. Aldinger ◽  
F. Burget ◽  
M. Völker ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Referring Expressions ◽  
Non Invasive ◽  
Neuronal Signal ◽  
High Level ◽  
User Friendly ◽  
Robotic Tasks

AbstractAs autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

scholarly journals Graph Convolutional Networks for Cross-Modal Information Retrieval

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xianben Yang ◽  
Wei Zhang
Keyword(s):  
Computer Vision ◽  
Information Retrieval ◽  
Deep Learning ◽  
Retrieval System ◽  
Retrieval Model ◽  
Language Understanding ◽  
Convolutional Networks ◽  
Image Retrieval System ◽  
High Level ◽  
Research Hotspots

In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.

FindThatQuote: A Question-Answering Web-based System to Locate Quotes using Deep Learning and Natural-Language Processing

2021 ◽  
Author(s):  
Nathan Ji ◽  
Yu Sun
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Success Rate ◽  
Language Processing ◽  
Question Answering ◽  
Specific Information ◽  
Web Based ◽  
User Friendly ◽  
Web Based System

The digital age gives us access to a multitude of both information and mediums in which we can interpret information. A majority of the time, many people find interpreting such information difficult as the medium may not be as user friendly as possible. This project has examined the inquiry of how one can identify specific information in a given text based on a question. This inquiry is intended to streamline one's ability to determine the relevance of a given text relative to his objective. The project has an overall 80% success rate given 10 articles with three questions asked per article. This success rate indicates that this project is likely applicable to those who are asking for content level questions within an article.

scholarly journals Natural Language Processing and Fuzzy Tools for Business Processes in a Geolocation Context

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Isis Truck ◽  
Mohammed-Amine Abchir
Keyword(s):  
Natural Language Processing ◽  
User Interface ◽  
Natural Language ◽  
Language Processing ◽  
Business Processes ◽  
Software Agent ◽  
Natural Language Interfaces ◽  
Low Level ◽  
High Level ◽  
Fuzzy Linguistic

In the geolocation field where high-level programs and low-level devices coexist, it is often difficult to find a friendly user interface to configure all the parameters. The challenge addressed in this paper is to propose intuitive and simple, thus natural language interfaces to interact with low-level devices. Such interfaces contain natural language processing (NLP) and fuzzy representations of words that facilitate the elicitation of business-level objectives in our context. A complete methodology is proposed, from the lexicon construction to a dialogue software agent including a fuzzy linguistic representation, based on synonymy.

scholarly journals THE PREPARATION OF FUTURE TEACHERS: MODERN APPROACHES TO LEARNING

2020 ◽  
pp. 20-30
Author(s):  
Iryna Khalymon ◽  
Svitlana Shevchenko
Keyword(s):  
Deep Learning ◽  
Prospective Teachers ◽  
Approaches To Learning ◽  
Assessment System ◽  
Survey Study ◽  
Learning Approach ◽  
Deep Approach ◽  
Surface Approach ◽  
Teacher Trainers ◽  
High Level

The article is devoted to the issue of approaches to learning used by prospective teachers. The key difference between two main approaches to learning — deep and surface — lies in the intention or absence of intention to understand the material. Deep approach to learning is likely to result in a high level of understanding and better learning outcomes while surface approach leads to ineffective learning. Understanding students’ approaches to learning helps adjust teaching strategies for more effective learning. The purpose of the article is to identify the approaches to learning used by prospective teachers and consider the implications for teacher trainers. The article reports findings of a survey study into the approaches to learning used by prospective teachers of the Ukrainian language and literature mastering English as a specialism at a Ukrainian university. The main research tool was a questionnaire consisting of twenty statements. Ten statements concerning deep approach include such aspects as deep approach per se, relating ideas, use of evidence, intrinsic motivation. The other ten statements relate to surface approach and include such subscales as surface approach proper, syllabus boundness, fear of failure, extrinsic motivation. The respondents were asked to express how much they agreed with the statements using Likert scale. The data were analysed to find out mean scores and standard deviation. The findings demonstrate that students employ both approaches almost equally intensely: the mean scores of the Deep Learning Approach and the Surface Learning Approach scales are 3.64 and 3.34 correspondingly. However, according to the evaluation criteria the figures fall into different categories: deep approach has a high level of prevalence while surface approach has a moderate one. Basing on the obtained results, the authors suggest that teacher trainers should pay more attention to promoting deep learning approach through applying effective instructional strategies which improve the quality of learning; fostering students’ personal interest in learning; changing the focus of the assessment system from reproducing to understanding

scholarly journals Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers

2019 ◽  
pp. 307-319 ◽  
Author(s):  
Serafín Alonso ◽  
Antonio Morán ◽  
Daniel Pérez ◽  
Perfecto Reguera ◽  
Ignacio Díaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Virtual Sensor
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