scholarly journals Special Issue of INFORMS Journal on Applied Analytics—Analytics, Artificial Intelligence, and Data Science for Technical Infrastructure

2020 ◽  
Vol 50 (1) ◽  
pp. 97-98
Author(s):  
Martin Valdez-Vivas ◽  
Julie Drew ◽  
Alexander Gilgur
Keyword(s):  
Artificial Intelligence ◽  
Data Science ◽  
Special Issue

scholarly journals J.UCS special issue on Challenges for Smart Environments – Human-Centered Computing, Data Science, and Ambient Intelligence. Smart Human-Centered Computing (volume 2)

2021 ◽  
Vol 27 (12) ◽  
pp. 1272-1274
Author(s):  
Ashot Harutyunyan ◽  
Gregor Schiele
Keyword(s):  
Artificial Intelligence ◽  
Conference Proceedings ◽  
Smart City ◽  
Ambient Intelligence ◽  
Data Science ◽  
Smart Environments ◽  
Special Issue ◽  
American University ◽  
Collaborative Technologies ◽  
The University

Based on a successful funded collaboration between the American University of Armenia, the University of Duisburg-Essen and the University of Chile, in previous years a network was built, and in September 2020 a group of researchers gathered (although virtually) for the 2nd CODASSCA workshop on “Collaborative Technologies and Data Science in Smart City Applications”. This event has attracted 25 paper submissions which deal with the problems and challenges mentioned above. The studies are in specialized areas and disclose novel solutions and approaches based on existing theories suitably applied. The authors of the best papers published in the conference proceedings on Collaborative Technologies and Data Science in Artificial Intelligence Applications by Logos edition Berlin were invited to submit significantly extended and improved versions of their contributions to be considered for a journal special issue of J.UCS. There was also a J.UCS open call so that any author could submit papers on the highlighted subject. For this volume, we selected those devoted mainly to human-computer interaction problematics, which were rigorously reviewed in three rounds and 6 papers nominated to be published.

How We Became Instrumentalists (Again)

2018 ◽  
Vol 48 (5) ◽  
pp. 673-684 ◽  
Author(s):  
Matthew L. Jones
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Data Science ◽  
Predictive Analytics ◽  
The United States ◽  
Applied Statistics ◽  
Computational Statistics ◽  
Special Issue ◽  
Epistemic Values

In the last two decades, a highly instrumentalist form of statistical and machine learning has achieved an extraordinary success as the computational heart of the phenomenon glossed as “predictive analytics,” “data mining,” or “data science.” This instrumentalist culture of prediction emerged from subfields within applied statistics, artificial intelligence, and database management. This essay looks at representative developments within computational statistics and pattern recognition from the 1950s onward, in the United States and beyond, central to the explosion of algorithms, techniques, and epistemic values that ultimately came together in the data sciences of today. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

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