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4. FAME: Federated Decentralized Trusted Data Marketplace for Embedded Finance

Author

Mavrogiorgou, Argyro, primary, Kiourtis, Athanasios, additional, Makridis, Georgios, additional, Kotios, Dimitrios, additional, Koukos, Vasileios, additional, Kyriazis, Dimosthenis, additional, Soldatos, John, additional, Fatouros, Georgios, additional, Drakoulis, Dimitrios, additional, Maló, Pedro, additional, Serrano, Martin, additional, Isaja, Mauro, additional, Lazcano, Raquel, additional, Vera, Juan Manuel, additional, Fournier, Fabiana, additional, Limonad, Lior, additional, Perakis, Konstantinos, additional, Miltiadou, Dimitrios, additional, Kranas, Pavlos, additional, Sen, Alper, additional, Zattarin, Irene, additional, and Troiano, Ernesto, additional

Published
2023
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9. AI4Gov: Trusted AI for Transparent Public Governance Fostering Democratic Values

Author

Manias, George, primary, Apostolopoulos, Dimitris, additional, Athanassopoulos, Sotiris, additional, Borotis, Spiros, additional, Chatzimallis, Charalampos, additional, Chatzipantelis, Theodoros, additional, Compagnucci, Marcelo Corrales, additional, Draksler, Tanja Zdolsek, additional, Fournier, Fabiana, additional, Goralczyk, Magdalena, additional, Gucek, Alenka, additional, Karabetian, Andreas, additional, Kefala, Stavroula, additional, Moumtzi, Vasiliki, additional, Kotios, Dimitris, additional, Kovacic, Matei, additional, Kyrkou, Danai, additional, Limonad, Lior, additional, Magopoulou, Sofia, additional, Mavrogiorgos, Konstantinos, additional, Nechifor, Septimiu, additional, Ntalaperas, Dimitris, additional, Panagiotidou, Georgia, additional, Papadopoulou, Martha, additional, Papageorgiou, Xanthi S., additional, Papageorgopoulos, Nikos, additional, Pavlovic, Dusan, additional, Politi, Elena, additional, Stroumpou, Vicky, additional, Vontas, Apostolos, additional, and Kyriazis, Dimosthenis, additional

Published
2023
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20. Automated Rule-Based Data Cleaning Using NLP

Author

Mavrogiorgos, Konstantinos, primary, Mavrogiorgou, Argyro, additional, Kiourtis, Athanasios, additional, Zafeiropoulos, Nikolaos, additional, Kleftakis, Spyridon, additional, and Kyriazis, Dimosthenis, additional

Published
2022
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33. iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records

Author

Manias, George, Akker, Harm Op Den, Azqueta, Ainhoa, Burgos, Diego, Capocchiano, Nikola Dino, Crespo, Borja Llobell, Dalianis, Athanasios, Damiani, Andrea, Filipov, Krasimir, Giorgos Giotis, Maritini Kalogerini, Kostadinov, Rostislav, Kranas, Pavlos, Kyriazis, Dimosthenis, Artitaya Lophatananon, Malwade, Shwetambara, Marinos, George, Melillo, Fabio, Mas, Vicent Moncho, Muir, Kenneth, Nieroda, Marzena, De Nigro, Antonio, Pandolfo, Claudia, Patiño-Martinez, Marta, Picioroaga, Florin, Pnevmatikakis, Aristodemos, Syed-Abdul, Shabbir, Tomson, Tanja, Vicheva, Dilyana, and Wajid, Usman

Subjects
Pancreatic Cancer, Disease prevention, Artificial Intelligence, Personalisation, Pancreatic cancer, Holistic Health Records, Holistic Health Records (HHRs)
Abstract

—Scientific and clinical research have advanced the ability of healthcare professionals to more precisely define diseases and classify patients into different groups based on their likelihood of responding to a given treatment, and on their future risks. However, a significant gap remains between the delivery of stratified healthcare and personalization. The latter implies solutions that seek to treat each citizen as a truly unique individual, as opposed to a member of a group with whom they share common risks or health-related characteristics. Personalisation also implies an approach that takes into accountpersonal characteristics and conditions of individuals. This paper investigates how these desirable attributes can be developed and introduces a holistic environment, the iHELP, that incorporates big data management and ArtificialIntelligence (AI) approaches to enable the realization of datadriven pathways where awareness, care and decision support is provided based on person-centric early risk prediction, prevention and intervention measures.

Published
2021
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34. Prioritization of IoT Devices Healthcare Data based on Attribute Scoring and Metadata Annotation

Author

Argyro Mavrogiorgou, Athanasios Kiourtis, and Dimosthenis Kyriazis

Subjects
Metadata, Information privacy, Focus (computing), Data visualization, Ranking, Generalization, Computer science, business.industry, Volume (computing), business, Data science, Variety (cybernetics)
Abstract

Current era is surrounded by data deriving from a plethora of platforms, sensors, and systems with Internet of Things (IoT) devices being among the top candidates that contribute towards this data explosion. As data evolves in variety, volume, and importance, additional focus should be provided on the most valuable data, since not all data are equally important and able to lead to valuable insights. Consequently, specifically designed techniques are implemented aiming to identify and take advantage of the subset of data that can provide meaningful and positive impact. Nevertheless, these techniques lack of generalization and applicability in multiple domains since they are aiming towards specifically designed scenarios. Towards this goal, this paper proposes a data prioritization technique through which incoming data of IoT devices can be filtered, offering the most critical slices of data to their requestors. The proposed approach is designed to prioritize the data to be sent from an IoT device of a citizen to a Healthcare Practitioner, where the first step involves the ingestion of these data, in order to be split, scored for their importance - based on already pre-trained classifiers, and annotated with metadata describing their nature. Following the attribute scoring and metadata annotation stages, these data are filtered and sliced based on specific requirements, being finally provided to the requesting Healthcare Practitioner. The latter approach has been experimented and evaluated with multiple datasets, efficiently managing and identifying IoT devices healthcare data of high and low priority.

Published
2021
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35. H-GAN: the power of GANs in your Hands

Author

Aggeliki Tsoli, Giorgos Karvounas, Sergiu Oprea, Nikolaos Kyriazis, Sergio Orts-Escolano, Antonis A. Argyros, Iason Oikonomidis, Jose Garcia-Rodriguez, Alberto Garcia-Garcia, and Pablo Martinez-Gonzalez

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Basis (linear algebra), Computer Science - Artificial Intelligence, business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), media_common.quotation_subject, Computer Science - Computer Vision and Pattern Recognition, Tone mapping, Real image, Translation (geometry), Machine Learning (cs.LG), Task (project management), Domain (software engineering), Artificial Intelligence (cs.AI), Software, Perception, Computer vision, Artificial intelligence, business, media_common
Abstract

We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach implementing multi-scale perceptual discriminators. It is designed to translate synthetic images of hands to the real domain. Synthetic hands provide complete ground-truth annotations, yet they are not representative of the target distribution of real-world data. We strive to provide the perfect blend of a realistic hand appearance with synthetic annotations. Relying on image-to-image translation, we improve the appearance of synthetic hands to approximate the statistical distribution underlying a collection of real images of hands. H-GAN tackles not only the cross-domain tone mapping but also structural differences in localized areas such as shading discontinuities. Results are evaluated on a qualitative and quantitative basis improving previous works. Furthermore, we relied on the hand classification task to claim our generated hands are statistically similar to the real domain of hands., Comment: Paper accepted at The International Joint Conference on Neural Networks (IJCNN) 2021

Published
2021
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41. beHEALTHIER: A Microservices Platform for Analyzing and Exploiting Healthcare Data

Author

Argyro Mavrogiorgou, Nikolaos Zafeiropoulos, Athanasios Kiourtis, Konstantinos Mavrogiorgos, Andreas Menychtas, Spyridon Kleftakis, Ilias Maglogiannis, and Dimosthenis Kyriazis

Subjects
computer.internet_protocol, Computer science, business.industry, Data management, Big data, Collective intelligence, Service-oriented architecture, Construct (python library), Microservices, Data science, Data quality, Software architecture, business, computer
Abstract

The era of big data is surrounded by plenty of challenges, concerning aspects related to data quality, data management, and data analysis. Plenty of these challenges are met in several domains, such as the healthcare domain, where the corresponding healthcare platforms not only have to deal with managing and/or analyzing a tremendous quantity of health data, but also have to accomplish these actions in the most efficient and secure way possible. Towards this direction, medical institutions are paying attention to the replacement of traditional approaches such as the Monolithic and Service Oriented Architecture (SOA), which deal with many difficulties for handling the increasing amount of healthcare data. This paper presents a platform for overcoming these issues, by adopting the Microservice Architecture (MSA), being able to efficiently manage and analyze these vast amounts of data. More specifically, the proposed platform, namely beHEALTHIER, offers the ability to construct health policies out of data of collective knowledge, by utilizing a newly proposed kind of electronic health records (i.e., eXtended Health Records (XHRs)) and their corresponding networks, through the efficient analysis and management of ingested healthcare data. In order to achieve that, beHEALTHIER is architected based upon four (4) discrete and interacting pillars, namely the Data, the Information, the Knowledge and the Actions pillars. Since the proposed platform is based on MSA, it fully utilizes MSA's benefits, achieving fast response times and efficient mechanisms for healthcare data collection, processing, and analysis.

Published
2021
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43. iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records

Author

Manias, George, primary, Op Den Akker, Harm, additional, Azqueta, Ainhoa, additional, Burgos, Diego, additional, Capocchiano, Nikola Dino, additional, Crespo, Borja Llobell, additional, Dalianis, Athanasios, additional, Damiani, Andrea, additional, Filipov, Krasimir, additional, Giotis, Giorgos, additional, Kalogerini, Maritini, additional, Kostadinov, Rostislav, additional, Kranas, Pavlos, additional, Kyriazis, Dimosthenis, additional, Lophatananon, Artitaya, additional, Malwade, Shwetambara, additional, Marinos, George, additional, Melillo, Fabio, additional, Mas, Vicent Moncho, additional, Muir, Kenneth, additional, Nieroda, Marzena, additional, De Nigro, Antonio, additional, Pandolfo, Claudia, additional, Patino-Martinez, Marta, additional, Picioroaga, Florin, additional, Pnevmatikakis, Aristodemos, additional, Syed-Abdul, Shabbir, additional, Tomson, Tanja, additional, Vicheva, Dilyana, additional, and Wajid, Usman, additional

Published
2021
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45. H-GAN: the power of GANs in your Hands

Author

Oprea, Sergiu, primary, Karvounas, Giorgos, additional, Martinez-Gonzalez, Pablo, additional, Kyriazis, Nikolaos, additional, Orts-Escolano, Sergio, additional, Oikonomidis, Iason, additional, Garcia-Garcia, Alberto, additional, Tsoli, Aggeliki, additional, Garcia-Rodriguez, Jose, additional, and Argyros, Antonis, additional

Published
2021
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46. Leveraging the serverless paradigm for realizing machine learning pipelines across the edge-cloud continuum

Author

Efterpi Paraskevoulakou and Dimosthenis Kyriazis

Subjects
Service (systems architecture), Computer science, business.industry, Cloud computing, Machine learning, computer.software_genre, Pipeline (software), Data modeling, Workflow, Analytics, Scalability, Business logic, Artificial intelligence, business, computer
Abstract

The exceedingly exponential-growing data rate highlighted numerous requirements and several approaches have been released to maximize the added-value of cloud and edge resources. Whereas data scientists utilize algorithmic models in order to transform datasets and extract actionable knowledge, a key challenge is oriented towards abstracting the underline layers: the ones enabling the management of infrastructure resources and the ones responsible to provide frameworks and components as services. In this sense, the serverless approach features as the novel paradigm of new cloud-related technology, enabling the agile implementation of applications and services. The concept of Function as a Service (FaaS) is introduced as a revolutionary model that offers the means to exploit serverless offerings. Developers have the potential to design their applications with the necessary scalability in the form of nanoservices without addressing themselves the way the infrastructure resources should be deployed and managed. By abstracting away the underlying hardware allocations, the data scientist concentrates on the business logic and critical problems of Machine Learning (ML) algorithms. This paper introduces an approach to realize the provision of ML Functions as a Service (i.e., ML-FaaS), by exploiting the Apache OpenWhisk event-driven, distributed serverless platform. The presented approach tackles also composite services that consist of single ones i.e., workflows of ML tasks including processes such as aggregation, cleaning, feature extraction, and analytics; thus, reflecting the complete data path. We also illustrate the operation of the approach mentioned above and assess its performance and effectiveness exploiting a holistic, end-toend anti-fraud detection machine learning pipeline.

Published
2021
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47. Indexing of Cloud Stored Electronic Health Records for Consented Third Party Accessing

Author

Dimosthenis Kyriazis, Chrysostomos Symvoulidis, Argyro Mavrogiorgou, Athanasios Kiourtis, and Charalampos Tsigkounis

Subjects
health information exchange, Computer science, business.industry, health record indexing, Medical record, Search engine indexing, Interoperability, Context (language use), Health information exchange, Cloud computing, lcsh:Telecommunication, World Wide Web, Index (publishing), lcsh:TK5101-6720, Health care, business, electronic health
Abstract

Electronic Health Record (EHR) data is being required to be shaped into a standardized and interoperable format for easy and secure accessing from anywhere, towards the vision of a borderless Health Information Exchange (HIE). Towards this vision, EHR data is being securely stored in different cloud repositories, making it however difficult and time-consuming to access it in emergency cases from healthcare practitioners, or even from the owners. In order to access the EHR data, it is needed specific credentials and real-time approved consent, which cannot be feasible in cases where this data must be urgently accessed and furtherly processed. In this paper, a health record indexing methodology is being proposed (i.e. HR index) that facilitates the real-time accessing of non-privileged users, to the cloud stored EHR data of citizens who are facing emergency situations. In this context, the research that has been conducted prior to specifying and designing the proposed HR index is being provided, including a detailed study of data and health data indexing techniques and methodologies. The overall vision, scope and usage of the HR index is provided in the context of an Emergency scenario, concluding to the design and evaluation of the HR index, under specific circumstances and requirements.

Published
2021
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50. An Evaluation of Neural Machine Translation and Pre-trained Word Embeddings in Multilingual Neural Sentiment Analysis

Author

Dimosthenis Kyriazis, George Manias, Argyro Mavrogiorgou, and Athanasios Kiourtis

Subjects
Interpretation (logic), Machine translation, Artificial neural network, business.industry, Computer science, Sentiment analysis, computer.software_genre, Data modeling, Task analysis, Multilingualism, Artificial intelligence, business, computer, Word (computer architecture), Natural language processing
Abstract

One of the main elements in several application domains, such as policy making, addresses the scope of public opinion analysis. The latter is recently realized through sentiment analysis and Natural Language Processing, for identifying and extracting subjective information from raw texts. An additional challenge refers to the exploitation and correlation of data from different languages, in order to analyse sentiments by considering all available information in a holistic way. This paper investigates the impact of Neural Machine Translation in sentiment analysis, based on the translation of text for which neural sentiment analysis in English has been already applied and is applied again on the translated German and Greek texts. The latter is performed through the same Neural Network models that were used in the original language, in order to analyse the differences between the performed neural sentiment analysis on the source and the target languages. The outcomes of the proposed approach have a twofold interpretation. While, it is concluded that modern Neural Machine Translation tools are mature enough to provide high accuracy translations and have minor impact on multilingual sentiment analysis, on the other hand it is shown that additional research should be performed on the multilingualism that pre-trained Word Embeddings can provide.

Published
2020
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