scholarly journals Innovative Technologies for Cultural Heritage. Tattoo Sensors and AI: The New Life of Cultural Assets

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1909
Author(s):  
Maurizio Talamo ◽  
Federica Valentini ◽  
Andrea Dimitri ◽  
Ivo Allegrini
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Information Technology ◽  
Cultural Heritage ◽  
Smart Sensors ◽  
Simple Process ◽  
Cultural Assets ◽  
The Social ◽  
New Generation ◽  
New Skin

Conservation and restoration of cultural heritage is something more than a simple process of maintaining the existing. It is an integral part of the improvement of the cultural asset. The social context around the restoration shapes the specific actions. Today, preservation, restoration, enhancement of cultural heritage are increasingly a multidisciplinary science, meeting point of researchers coming from heterogeneous study areas. Data scientists and Information technology (IT) specialists are increasingly important. In this context, networks of a new generation of smart sensors integrated with data mining and artificial intelligence play a crucial role and aim to become the new skin of cultural assets.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Defining Knowledge Constituents and Contents

2016 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Sead Spuzic ◽  
Ramadas Narayanan ◽  
Megat Aiman Alif ◽  
Nor Aishah M.N.
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Information Technology ◽  
Knowledge Extraction ◽  
Information Environment ◽  
Electronic Records ◽  
Software Complexity ◽  
New Information ◽  
Key Terms

While it appears that a consensus is crystalising with regard to the hierarchy of concepts such as “knowledge”, “definition” and “information”, there is an increasing urgency for improving definitions of these terms. Strategies such as “knowledge extraction” or “data mining” rely on the increasing availability of digital (electronic) records addressing almost any aspect of socio-economic realm. Information processors are invaluable in the capacity of turning large amount of data into information. However, a new problem emerged on the surface in this new information environment: numerous concepts and terms are blurred by ambiguous definitions (including the concept of 'definition' itself). This triggered a need for mitigating hindrances such as homonymy and synonymy, leading further to demands on the decoding software complexity of which equals the artificial intelligence applications. Information technology presumably copes with this diversity by providing the information decoding 'tools'. This opens a never-ending opportunity for further permutations of tasks and service abilities. The solution, however, is to address the causes rather than indulge in multiplying the superficial remedies. Clearly, the multiplicity of definitions for the same concepts, false synonyms and so forth show that there is a need for introducing definitions of sufficient dimensionality. In this article, a number of examples of important concepts are presented first to point at the ambiguities associated with them, and then to propose their disambiguation. The minimum intent is to demonstrate how these key terms can be defined to avoid ambiguities such as pleonasm, homonymy, synonymy and circularity.

Organizational Data Mining (ODM)

2008 ◽  
pp. 2289-2295 ◽  
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Introduction

2012 ◽  
Author(s):  
John O. McGinnis
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Information Age ◽  
Modern Technology ◽  
Weapons Of Mass Destruction ◽  
Mass Destruction ◽  
Information Markets ◽  
The Social ◽  
Political Problem

This introductory chapter analyzes the central political problem of our time, namely how to adapt democracy to the acceleration of the information age. Modern technology creates a supply of new tools for improved governance, but it also creates an urgent demand for putting these tools to use. We need better policies to obtain the benefits of innovation as quickly as possible and to manage the social problems that speedier innovation will inevitably create—from pollution to weapons of mass destruction. Our task is to place politics progressively within the domain of information technology—to use its new or enhanced tools, such as empiricism, information markets, dispersed media, and artificial intelligence, to reinvent governance. An overview of the subsequent chapters is also presented.

Mapping the Preservation Landscape for the Twenty-First Century

2013 ◽  
Vol 42 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Ross Harvey ◽  
Martha Mahard
Keyword(s):  
Information Technology ◽  
Cultural Heritage ◽  
Digital Technologies ◽  
First Century ◽  
The Core ◽  
Cultural Assets ◽  
Twenty First Century ◽  
Heritage Collections ◽  
Digital Or

AbstractInformation technology has had a profound effect on the preservation landscape at the beginning of the twenty-first century, blurring the traditional boundaries separating cultural heritage institutions and demanding new skills and approaches to the management of cultural assets, whether digital or analog. Concepts around which the core principles of preservation were built have been challenged and are shifting to accommodate new practices and standards. Changes in our approach to longevity, choice, quality, integrity, and access are being driven by digital technologies. A new set of principles, applicable to all materials, whether digital or not, are proposed. In the context and aims of preservation as we understand it today, these principles are a framework for the management of our cultural heritage collections.

The Ever Expanding Domain of Computation

2012 ◽  
Author(s):  
John O. McGinnis
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Social World ◽  
The Internet ◽  
Human History ◽  
Empirical Methods ◽  
The Social ◽  
The World

This chapter makes the case that because of computational advances, the world is changing fast, perhaps faster than at any other time in human history. The increasing pace of change could potentially generate social turbulence and instability. However, computational advances are also driving advances in information technology, from the growth and deepening of the Internet, to the burgeoning power of empirical methods, to the increasing capability of artificial intelligence. The key to improving governance is to bring politics within the domain of such information technology. Only a politics that exploits the latest fruits of the computational revolution can manage the disruption that this revolution is bringing to the social world.

Linking Knowledge

2021 ◽  
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
Semantic Web ◽  
Cultural Heritage ◽  
Open Data ◽  
Linked Open Data ◽  
Social Sciences And Humanities ◽  
The Social ◽  
Knowledge Organization Systems ◽  
Heterogeneous Knowledge

The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.

scholarly journals How Should Chemistry Educators Respond to the Next Generation of Technology Change?

2020 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Harry E. Pence
Keyword(s):  
Artificial Intelligence ◽  
Chemistry Education ◽  
New Technologies ◽  
Classroom Teaching ◽  
Technology Change ◽  
Similar Argument ◽  
Instructional Technologies ◽  
Individualized Learning ◽  
The Social ◽  
New Generation

Chemical educators are facing a new generation of instructional technologies that impact classroom teaching. New technologies, like smartphones, cloud computing and artificial intelligence take learning beyond the classroom; 3D printing, virtual reality, and augmented reality provide new ways to teach the virtualization skills that are important for chemists. These technologies cause students to become more isolated, so students may not develop the social skills that they will need for today’s workplace. Individualized learning may be beneficial to many students, but it will create challenges for faculty. Although this article focuses on chemistry education, it should be apparent that a similar argument could be made for other sciences, like physics and biology.

Data Mining for the Social Sciences

2015 ◽  
Author(s):  
Paul Attewell ◽  
David Monaghan
Keyword(s):  
Social Sciences ◽  
Data Mining ◽  
The Social
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