Learning Chinese Formulaic Expressions in a Scenario-Based Interactive Environment

2017 ◽  
Vol 50 (4) ◽  
pp. 641-660 ◽  
Author(s):  
Naoko Taguchi ◽  
Qiong Li ◽  
Xiaofei Tang
Keyword(s):  
Interactive Environment

A Comprehensive Overview of AI-Enabled Music Classification and Its Influence in Games

2021 ◽  
Author(s):  
Tiancheng Yang ◽  
Shah Nazir
Keyword(s):  
Artificial Intelligence ◽  
Computer Assisted ◽  
Comprehensive Overview ◽  
Effective Learning ◽  
Music Classification ◽  
Interactive Tools ◽  
Visually Impaired People ◽  
Interactive Environment ◽  
Assisted Technologies

Abstract With the development and advancement of information technology, artificial intelligence (AI) and machine learning (ML) are applied in every sector of life. Among these applications, music is one of them which has gained attention in the last couple of years. The music industry is revolutionized with AIbased innovative and intelligent techniques. It is very convenient for composers to compose music of high quality using these technologies. Artificial intelligence and Music (AIM) is one of the emerging fields used to generate and manage sounds for different media like the Internet, games, etc. Sounds in the games are very effective and can be made more attractive by implementing AI approaches. The quality of sounds in the game directly impacts the productivity and experience of the player. With computer-assisted technologies, the game designers can create sounds for different scenarios or situations like horror and suspense and provide gamer information. The practical and productive audio of a game can guide visually impaired people during other events in the game. For the better creation and composition of music, good quality of knowledge about musicology is essential. Due to AIM, there are a lot of intelligent and interactive tools available for the efficiency and effective learning of music. The learners can be provided with a very reliable and interactive environment based on artificial intelligence. The current study has considered presenting a detailed overview of the literature available in the area of research. The study has demonstrated literature analysis from various perspectives, which will become evidence for researchers to devise novel solutions in the field.

scholarly journals VOLARE: Visual analysis of disease-associated microbiome-immune system interplay

2018 ◽  
Author(s):  
Janet C. Siebert ◽  
Charles Preston Neff ◽  
Jennifer M. Schneider ◽  
EmiLie H. Regner ◽  
Neha Ohri ◽  
...  
Keyword(s):  
Immune System ◽  
Web Application ◽  
Visual Analysis ◽  
System Development ◽  
Patient Specific ◽  
Domain Experts ◽  
Immune System Development ◽  
Interactive Environment ◽  
System Logs ◽  
Linear Regressions

AbstractBackgroundRelationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. High-dimensional “omic” methodologies such as 16S ribosomal RNA (rRNA) sequencing and Time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features is one approach for quantifying relationships between “omes”, and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts need to vet candidate results for follow-up experiments.ResultsTo support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies—microbiome:cytokine data from fecal samples in HIV, microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that, in using VOLARE, domain experts frequently generated detailed regression plots, suggesting that this detail aids the vetting of results.ConclusionsSystematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Interactive environment for affective visual analysis of large data within virtual reality

2014 ◽  
Author(s):  
Sergey V. Kovalchuk ◽  
Aleksey A. Bezgodov ◽  
Denis M. Terekhov ◽  
Alexander V. Boukhanovsky
Keyword(s):  
Virtual Reality ◽  
Visual Analysis ◽  
Large Data ◽  
Interactive Environment
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