Emmanuel Sardis

National Technical University of Athens

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Theodora Varvarigou

National Technical University of Athens

Eftychios Protopapadakis

National Technical University of Athens

Nikolaos Doulamis

National Technical University of Athens

Anastasios Doulamis

National Technical University of Athens

Anastasios Doulamis

National Technical University of Athens

Ioannis Kavouras

National Technical University of Athens

Ioannis Rallis

National Technical University of Athens

Vasileios Anagnostopoulos

University of Central Florida

Magdalini Kardara

National Technical University of Athens

Athanasios Voulodimos

National Technical University of Athens

Cooperative Institutions

National Technical University of Athens

National and Kapodistrian University of Athens

Institute of Communication and Computer Systems

Technical University of Crete

University of Patras

Hellenic Mediterranean University

University of Crete

University of West Attica

Centre for Technological Research of Crete

Space Hellas (Greece)

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Biopolis, Long Term Preservation of Digital User Content

Emmanuel Sardis Vasileios Anagnostopoulos Anastasios Doulamis Theodora Varvarigou

The Biopolis takes a radical approach to the long term preservation of digital user content. The system will allow Biopolis enriched User Content to be distributed and redistributed through a network of commercial and non-commercial file (Preservation Services), while ensuring a lasting relationship, between the holder and the owner of the intellectual content. Based on web and mobile technologies and using a cloud based storage system will allow the end user to maintain the time and geolocation parameter on its digital content, providing new advanced business models for e-business end users.

Geolocation

Digital Preservation

Digital content

Web content

Content management

10.1109/icebe.2013.75

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Assessing the Lockdown Effects on Air Quality During COVID-19 Era

Frontiers in artificial intelligence and applications (2021)

Ioannis Kavouras Eftychios Protopapadakis Maria Kaselimi Emmanuel Sardis Nikolaos Doulamis

In this work we investigate the short-term variations in air quality emissions, attributed to the prevention measures, applied in different cities, to mitigate the COVID-19 spread. In particular, we emphasize on the concentration effects regarding specific pollutant gases, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2). The assessment of the impact of lockdown on air quality focused on four European Cities (Athens, Gladsaxe, Lodz and Rome). Available data on pollutant factors were obtained using global satellite observations. The level of the employed prevention measures is employed using the Oxford COVID-19 Government Response Tracker. The second part of the analysis employed a variety of machine learning tools, utilized for estimating the concentration of each pollutant, two days ahead. The results showed that a weak to moderate correlation exists between the corresponding measures and the pollutant factors and that it is possible to create models which can predict the behaviour of the pollutant gases under daily human activities.

Nitrogen dioxide

10.3233/faia210095

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Multi-agent Based Surveillance of Workflows.

Web Intelligence/IAT Workshops (2010)

Emmanuel Sardis Vasilios Anagnostopoulos Theodora Varvarigou

Workflow recognition through processing of humans and objects in a camera sensor network, presents a significant challenge recently. Human action recognition and sequence of actions manipulation, that construct a workflow situation/rule, has many practical applications in many different real human application environments. This article presents a multi agent based real time infrastructure, for recognizing humans workflows, by evaluating and processing computer vision signals, from multiple cameras sensors.

Action Recognition

Activity Recognition

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Multi-agent Based Surveillance of Workflows

Emmanuel Sardis Vasileios Anagnostopoulos Theodora Varvarigou

Action Recognition

10.1109/wi-iat.2010.172

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Secure Enterprise Interoperability Ontology for Semantic Integration of Business to Business Applications

2013 Eighth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2013)

Emmanuel Sardis Spyridon V. Gogouvitis Thanassis Bouras Panagiotis Gouvas Theodora Varvarigou

With the ever growing number of products that are targeted at solving a particular problem, rather than addressing all the requirements, which might otherwise be met with a multipurpose product, that need to be integrated in an enterprise business workflow, it is critical that data is able to easily flow from one application to another, in order to securely integrate a set of enterprise computer applications. Automation in Business to-Business (B2B) Enterprise Application Integration (EAI) scenarios confronts challenges related to the resolution of data heterogeneities, service discovery and process composition that should be integrated under a secure framework. In this paper, we propose the Secure Enterprise Interoperability Ontology (SENIO) that provides a secure, shared, common understanding of data, services and processes within B2B integration scenarios. SENIO is based on security-related ontologies that are designed to describe security properties of web services, which can be specific, by stating the particular standards/protocols supported, or more general, such as the security mechanisms used and the credentials required. The security properties associated with registered web services as well as requests for web services are security requirements and capabilities. Finally, we present the utilization of SENIO in a real-world B2B scenario, which describes a network of Small and Medium Enterprises (SMEs) cooperating with each other in order to provide secure human resource services.

Enterprise application integration

10.1109/3pgcic.2013.17

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On surveillance and workflow control system design

IEEE International Conference on Progress in Informatics and Computing (2010)

Emmanuel Sardis Vassileios Anagnostopoulos Athanasios Voulodimos Theodora Varvarigou

Workflow control performance is a cornerstone of operational excellence in refining and calculating either the human resources or the materials that belong to a sequence of events constituting a workflow. Controlling events in real life industrial environments and monitoring them through surveillance applications has become mainstream in the refining industry and is changing the maintenance methodology surrounding control assets from predictive to condition based. This has made monitoring and diagnosing control and alarming problems challenging. Identifying specific events in a process and raising an alarm for security and safety reasons is the main point of this article. In brief we present the general system architecture of our proposed solution and an application that uses surveillance video inputs and raises appropriate alarms in case of normal or abnormal workflows. The paper also presents main issues that have been considered during our research on alarming systems and explains the main problems of applying those in industrial environments.

Cornerstone

10.1109/pic.2010.5687452

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Sensor Networks and Multi-Agents inIndustrial Workflows

International Journal of Machine Learning and Computing (2011)

Emmanuel Sardis Anastasios Doulamis Nikolaos Matsatsinis

Production lines need fast technologies for critical online decisions.Orchestration of different signals and processes from a number of different sensors around the industry, into related workflows are the key signals on workflows tasks recognition.The advantages of sensor networks' technologies outputs, that their outputs are controlled by multi agents systems, in order to orchestrate and execute related production workflows, is the main point of this research work.In specific, the aim of this manuscript is to present a system that manipulates and controls these workflows executions.System architecture and sub modules are presented in more detail through the manuscript, plus a production scenario implementation.

10.7763/ijmlc.2011.v1.30

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PERIKLIS - Electronic Democracy in the 21st Century

Communications in computer and information science (2014)

Emmanuel Sardis Panagiotis Kokkinos Magdalini Kardara

10.1007/978-3-319-11710-2_19

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PERIKLIS - Electronic Democracy in the 21st Century Using Mobile and Social Network Technologies.

Panagiotis Kokkinos Kostas Koumoutsos Nikolaos Doulamis Emmanouel Varvarigos Dimitris Petrantonakis

PERIKLIS is a Greek National project for the development of an innovative e-Government platform for public services, using mobile and social network technologies. PERIKLIS consists of a content management system for publishing, editing and modifying content in both desktop and mobile and a middleware framework containing core functionalities and intelligence. The main services of the platform include: identity management, social relations analysis, degree of reliability calculation, interoperability with various social networking sites and e-Government services and provision of geo-located services. PERIKLIS e-government platform will be evaluated in two major national pilots. This work presents the core modules and system architecture plus their innovative research solutions for its construction and implementation.

Social network (sociolinguistics)

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A Dilemma-Based Learning-to-Rank Approach for Generative Design in Urban Architectural Regeneration

Algorithms (2024)

Ioannis Kavouras Ioannis Rallis Dimitra Zouli Emmanuel Sardis Eftychios Protopapadakis

Continuous urbanization and climate change degrade urban living conditions. Nature-based solutions in architectural and urban design offer promising remedies but are often hindered by time, cost, and early design phase challenges. To address this, we present a Generative Design System framework utilizing AI-generated images and learning-to-rank algorithms. This system generates numerous image solutions to inspire architects and urban planners, significantly accelerating early design stages. To manage the overwhelming volume of images, we introduce a dilemma-based learning approach that employs learning-to-rank and smart bubble sorting algorithms to prioritize images based on user preference. A case study demonstrates the framework’s potential, providing valuable insights into its application, benefits, and limitations in urban design.

Generative Design

Rank (graph theory)

Generative model

10.3390/a17120538

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