After two successful versions of the Workshop on Collaborative Technologies and
Data Science in Smart City Applications (CODASSCA 2018 and 2020) we are glad to
present proceedings of the third version held at the American University of
Armenia (AUA), August 23-25, 2022 in Yerevan, Armenia. This book presents 15
selected and carefully revised papers, which were originally made available at
the start of the workshop in the Open Access Proceedings, also entitled Data
Science, Human-Centered Computing, and Intelligent Technologies. Society,
technology, and science are undergoing a rapid and revolutionary transformation
towards incorporating Artificial Intelligence in every system humans use in
everyday life for creating Smart Environments (SmE) through Ambient
Intelligence (AmI) in highly interconnected and collaborative scenarios. The
main source and asset for making smart systems is rich data, which is produced
today in extraordinarily large quantities thanks to recent advances in sensors
and sensor networks and is carefully processed for pervasive and embedded
computing. Rich data enhances the capabilities of everyday objects and eases
collaboration among people. Mobile systems could enhance the possibilities
available for designers and practitioners. Effective analysis, quality
assessment, and utilization of big data are key factors for success in many
business and service domains, including smart systems. Major industrial domains
are on the way to performing this tectonic shift based on Big Data, Artificial
Intelligence, Collaborative Technologies, Smart Environments supporting Virtual
and Mixed Reality Applications, Multimodal Interaction, and Reliable Visual and
Cognitive Analytics. However, before we can effectively and efficiently turn
the huge amount of generated data into information and knowledge, a number of
requirements must be fulfilled and international standards for the quality of
and access to the data developed and applied. The first requirement is to
ensure that data quality--which includes the accuracy and integrity of the
obtained data, timely delivery, suitable quantity, integrity, privacy and
security requirements, and Digital Rights Management--complement realization
and deployment of modern design, implementation, and evaluation tools. The
second is to develop models, which can turn the data into valuable information
and then into knowledge. Two important characteristics are desirable for
regression and classification models: accuracy and interpretability. While
accuracy deals with the ability of the model to predict a certain outcome,
interpretability deals with the ability of the model to explain the reasons for
producing a certain outcome. The aim of this workshop is to bring together
researchers and practitioners working on both theoretical and practical aspects
of data generation, data processing, and knowledge creation. These aspects
include social issues that arise when using AI-powered systems in collaborative
scenarios and smart cities applications.