6 research outputs found
Trustworthiness in Mobile Cyber Physical Systems
Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS
Data Science and Knowledge Discovery
Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining
Cognitive Models and Computational Approaches for improving Situation Awareness Systems
2016 - 2017The world of Internet of Things is pervaded by complex environments
with smart services available every time and everywhere. In
such a context, a serious open issue is the capability of information
systems to support adaptive and collaborative decision processes
in perceiving and elaborating huge amounts of data. This requires
the design and realization of novel socio-technical systems based on
the “human-in-the-loop” paradigm. The presence of both humans
and software in such systems demands for adequate levels of Situation
Awareness (SA). To achieve and maintain proper levels of
SA is a daunting task due to the intrinsic technical characteristics
of systems and the limitations of human cognitive mechanisms.
In the scientific literature, such issues hindering the SA formation
process are defined as SA demons.
The objective of this research is to contribute to the resolution
of the SA demons by means of the identification of information
processing paradigms for an original support to the SA and the
definition of new theoretical and practical approaches based on
cognitive models and computational techniques.
The research work starts with an in-depth analysis and some
preliminary verifications of methods, techniques, and systems of
SA. A major outcome of this analysis is that there is only a limited
use of the Granular Computing paradigm (GrC) in the SA
field, despite the fact that SA and GrC share many concepts and
principles. The research work continues with the definition of contributions
and original results for the resolution of significant SA
demons, exploiting some of the approaches identified in the analysis
phase (i.e., ontologies, data mining, and GrC). The first contribution addresses the issues related to the bad perception of data
by users. We propose a semantic approach for the quality-aware
sensor data management which uses a data imputation technique
based on association rule mining. The second contribution proposes
an original ontological approach to situation management,
namely the Adaptive Goal-driven Situation Management. The approach
uses the ontological modeling of goals and situations and
a mechanism that suggests the most relevant goals to the users at
a given moment. Lastly, the adoption of the GrC paradigm allows
the definition of a novel model for representing and reasoning
on situations based on a set theoretical framework. This model
has been instantiated using the rough sets theory. The proposed
approaches and models have been implemented in prototypical systems.
Their capabilities in improving SA in real applications have
been evaluated with typical methodologies used for SA systems. [edited by Author]XXX cicl
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems