Dialog construction in a collaborative project management environment

Ramos, Milton Pires; Tacla, César Augusto; Sato, Gilson Yukio; Paraíso, Emerson Cabrera; Barthès, Jean-Paul

doi:10.1109/cscwd.2010.5471963

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The VUIs research using a personal assistant focus on the improvement of their use, and how to use it in collaborative environments and for a specific purpose like a financial adviser, government services, operators, receptionist, travel assistant, for sales, web services discovery, just to mention a few [25][22] [27]. There are assistive technology applications for sign language, visual impairments, deaf or hard of hearing, disorder speech, motor impairment, and limited physical mobility [6][9].…”

Section: Virtual Environmentmentioning

confidence: 99%

The state of voice user interfaces in Latin America

et al. 2020

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This paper describes and analyzes the state of research in Voice User Interfaces (VUIs) in Latin America based on the review of scientific documents published in SCOPUS from 1999 to June 2020, through a bibliometric analysis. We analyzed 419 academic papers. Although a gradual increase is observed over the years, the number of published documents has increased considerably since 2014. Brazil (44%) and Mexico (28%) are the countries with more documents published. Co-authorship occurs between Latin American countries (Brazil, Argentina, Mexico, Ecuador, and Costa Rica). However, the mayor collaboration from Latin American countries occurs with the United States, France, Germany, Spain, Portugal, the United Kingdom, and Japan. The main researched topics are studies of automatic speech recognition, artificial intelligence, speech processing, and human-computer interaction, which have grown over the past few years. Natural language processing, conversational agents, user experience, and chatbots are keywords related to more recent studies. Our analysis reveals that the primary active research developed in the short-term future are personal assistants and assistive technology using voice user interfaces.

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Section: Virtual Environmentmentioning

confidence: 99%

The state of voice user interfaces in Latin America

et al. 2020

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Query Building in a Distributed Semantic Indexing System

Moulin

Lai

2011

Studies in Computational Intelligence

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Application of machine learning to limited datasets: prediction of project success

Bang¹,

Aarvold²,

Hartvig³

et al. 2022

ITcon

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Much research is conducted on the importance of success factors. This study contributes to the body of knowledge by using artificial intelligence (AI), specifically machine learning (ML), to analyse success factors through data from construction projects. Previously conducted studies have explored the use of AI to predict project success and identify important success factors in projects; however, to the extent of the authors’ knowledge, no studies have implemented the same method as this study. This study conducts quantitative analysis on a sample of 160 Norwegian construction projects, with data obtained from a detailed questionnaire delivered to relevant project team members. The method utilises ML through a Random Forest Classifier (RFC). The findings obtained from the analysis show that it is possible to use AI and ML on a limited dataset. Furthermore, the findings show that it is possible to identify the most important success factors for the projects in question with the developed model. The findings suggest that a group of selected processes is more important than others to achieve success. The identified success factors support the theoretically acknowledged importance of thorough and early planning and analysis, complexity throughout the project, leadership involvement, and processes supporting project success.

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Dialog construction in a collaborative project management environment

Cited by 3 publications

References 11 publications

The state of voice user interfaces in Latin America

The state of voice user interfaces in Latin America

Query Building in a Distributed Semantic Indexing System

Application of machine learning to limited datasets: prediction of project success

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