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Artificial Intelligence Applications and Innovations

Ilias Maglogiannis, John Macintyre, Lazaros Iliadis

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Ilias Maglogiannis, John Macintyre, Lazaros Iliadis. Artificial Intelligence Applications and Innovations: 17th IFIP WG 12.5 International Conference, AIAI 2021, Hersonissos, Crete, Greece, June 25–27, 2021, Proceedings. Springer International Publishing, AICT-627, 2021, IFIP Advances in Information and Communication Technology, 978-3-030-79149-0. 10.1007/978-3-030-79150-6 . hal-03287655

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Distributed under a Creative Commons Attribution 4.0 International License
Editor-in-Chief

Kai Rannenberg, Goethe University Frankfurt, Germany

Editorial Board Members

TC 1 – Foundations of Computer Science
Luís Soares Barbosa*†, University of Minho, Braga, Portugal

TC 2 – Software: Theory and Practice
Michael Goedicke, University of Duisburg-Essen, Germany

TC 3 – Education
Arthur Tatnall*‡, Victoria University, Melbourne, Australia

TC 5 – Information Technology Applications
Erich J. Neuhold, University of Vienna, Austria

TC 6 – Communication Systems
Burkhard Stiller, University of Zurich, Zürich, Switzerland

TC 7 – System Modeling and Optimization
Fredi Tröltzsch, TU Berlin, Germany

TC 8 – Information Systems
Jan Pries-Heje, Roskilde University, Denmark

TC 9 – ICT and Society
David Kreps*§, National University of Ireland, Galway, Ireland

TC 10 – Computer Systems Technology
Ricardo Reis*¶, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

TC 11 – Security and Privacy Protection in Information Processing Systems
Steven Furnell*∥, Plymouth University, UK

TC 12 – Artificial Intelligence
Eunika Mercier-Laurent*∥, University of Reims Champagne-Ardenne, Reims, France

TC 13 – Human-Computer Interaction
Marco Winckler*∥, University of Nice Sophia Antipolis, France

TC 14 – Entertainment Computing
Rainer Malaka, University of Bremen, Germany
# IFIP – The International Federation for Information Processing

IFIP was founded in 1960 under the auspices of UNESCO, following the first World Computer Congress held in Paris the previous year. A federation for societies working in information processing, IFIP's aim is two-fold: to support information processing in the countries of its members and to encourage technology transfer to developing nations. As its mission statement clearly states:

> IFIP is the global non-profit federation of societies of ICT professionals that aims at achieving a worldwide professional and socially responsible development and application of information and communication technologies.

IFIP is a non-profit-making organization, run almost solely by 2500 volunteers. It operates through a number of technical committees and working groups, which organize events and publications. IFIP's events range from large international open conferences to working conferences and local seminars.

The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high.

As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed.

The working conferences are structured differently. They are usually run by a working group and attendance is generally smaller and occasionally by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is also rigorous and papers are subjected to extensive group discussion.

Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers.

IFIP distinguishes three types of institutional membership: Country Representative Members, Members at Large, and Associate Members. The type of organization that can apply for membership is a wide variety and includes national or international societies of individual computer scientists/ICT professionals, associations or federations of such societies, government institutions/government related organizations, national or international research institutes or consortia, universities, academies of sciences, companies, national or international associations or federations of companies.

More information about this series at http://www.springer.com/series/6102
Ilias Maglogiannis · John Macintyre ·
Lazaros Iliadis (Eds.)

Artificial Intelligence
Applications
and Innovations

17th IFIP WG 12.5 International Conference, AIAI 2021
Hersonissos, Crete, Greece, June 25–27, 2021
Proceedings

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Springer
Editors

Ilias Maglogiannis
University of Piraeus
Piraeus, Greece

John Macintyre
University of Sunderland
Sunderland, UK

Lazaros Iliadis

Democritus University of Thrace
Xanthi, Greece

ISSN 1868-4238

ISSN 1868-422X (electronic)

IFIP Advances in Information and Communication Technology

ISBN 978-3-030-79149-0

ISBN 978-3-030-79150-6 (eBook)

https://doi.org/10.1007/978-3-030-79150-6

© IFIP International Federation for Information Processing 2021

Chapter "Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-Based Approaches: Challenges and Perspectives" is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter.

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface

Artificial Intelligence (AI) continues to advance, following extreme development
rhythms in the new era of the 21st century. It has already made its way into our daily
lives in various forms. It is estimated that more than 80 billion USD have been invested
by car industries for the design and development of autonomous self-driving vehicles.
AI technologies like *Google Duplex* are accomplishing real-world conversations and
arrangements with humans, using *Deep Neural Networks* (e.g., Google voice search,
Wavenet). It is estimated by the International Data Corporation, a global provider of
market intelligence, that investments in AI business globally will reach up to 110
billion USD by 2024.

AI is a major part of the Fourth Industrial Revolution, together with other tech-
nologies like the *Internet of Things*, *Genetic Engineering*, *Quantum Computing*, and its
impact in the evolution of our post-modern societies in various domains is huge and
growing. On the other hand, there are major areas of ethical concern for our societies,
namely privacy, surveillance, bias-discrimination, and elimination of entire job cate-
gories. Moreover, serious questions arise on the superiority and indispensability of
human judgment on important aspect of life. In other words, “Can smart machines
outthink our human judgment?”.

The 17th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2021) offered insight into all timely challenges related to technical, legal, and ethical aspects of intelligent systems and their applications. New algorithms and potential prototypes employed in diverse domains were introduced.

AIAI is a mature international scientific conference series that has been held all over
the world and it is well established in the scientific area of AI. Its history is long and
very successful, following and propagating the evolution of intelligent systems.

The first event was organized in Toulouse, France, in 2004. Since then, it has had a
continuous and dynamic presence as a major global, but mainly European, scientific
event. It has been organized in China, Greece, Cyprus, Australia, and France. It has
always been technically supported by the International Federation for Information
Processing (IFIP) and more specifically by Working Group 12.5, which is interested in
AI applications.

Following a long-standing tradition, this Springer volume belongs to the IFIP AICT
series and it contains the papers that were accepted to be presented orally at the AIAI
2021 conference. An additional volume comprises the papers that were accepted and
presented at the workshops and were held as parallel events. The event was held during
June 25–27, 2021, in Greece (virtually). The diverse nature of papers presented
demonstrates the vitality of AI algorithms and approaches. It certainly proves the very
wide range of AI applications as well.

The response of the international scientific community to the AIAI 2021 call for
papers was more than satisfactory, with 113 papers initially submitted by authors in 21
different countries from all over the globe, namely Australia, Austria, Belgium, Czech
Republic, Egypt, France, Germany, Greece, Lebanon, Netherlands, India, Italy, China, Poland, Portugal, Romania, Sweden, Taiwan, Turkey, UK, and USA.

All papers were peer reviewed by at least two independent academic referees. Where needed, a third referee was consulted to resolve any potential conflicts. A total of 54 papers (47.79% of the submitted manuscripts) were accepted to be published as full papers (12 pages long) in the proceedings. Owing to the high quality of the submissions, the Program Committee decided that it should accept 7 more papers to be published as short papers (10 pages long).

The accepted papers cover the following thematic topics:

Activity Recognition

Adaptive Learning

Adversarial Neural Networks

AI for Media

AI for Music Composition

Anomaly Detection and AI

Anxiety Recognition and AI

Autoencoders

Autonomous Driving

Bioinformatics and AI

Classification in Pattern Recognition

Clustering

Computer Vision

Convolutional and Recurrent ANN

Cybersecurity and AI

Cyber Supply-Chain and AI

Data Mining

Deep Learning ANN

Dialogue Act Recognition

Embedded Machine Learning

Fake News Detection and AI

Feature Selection

Financial Applications of AI

Fuzzy Modeling

Genetic Algorithms and Optimization

Hybrid Intelligent Models

Interoperability and AI

Image Analysis for Face Recognition

Machine Learning

Meta-Learning and AutoML

Multi Agent Systems

Natural Language

Recommendation Systems

Robotics

Social Media Intelligent Modeling

SOM
Swarm Intelligence

Text Mining and Machine Translation

Time Series

Emotion recognition

Eight keynote speakers were invited to give lectures on timely aspects of AI. We wish to thank all keynote speakers for enlightening our conference with their state-of-the-art lectures.

In addition to the main conference, the following eight scientific workshops on timely AI subjects were organized under the framework of AIAI 2021:

* The 6th Workshop on 5G-Putting Intelligence to the Network Edge (5G-PINE 2021)

* The 1st Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AI-BIO 2021)

* The 1st Workshop on Defense Applications of AI (DAAI 2021)

* The 1st Workshop on Distributed AI for REsource-Constrained Platforms (DARE 2021)

* The 1st Workshop on Energy Efficiency and Artificial Intelligence (EEAI 2021)

* The 10th Mining Humanistic Data Workshop (MHDW 2021)

* The 1st Workshop on AI and Ethics (AIETH 2021)

* Designing a Novel Adaptive Cybersecurity Solution for Internet-of-Vehicle Workshop (nIoVe)

We are grateful to everyone who made AIAI 2021 such a success, and we hope that we can meet again in person at the next event.

June 2021

Ilias Maglogiannis

Lazaros Iliadis

John MacIntyre
# Organization

## Executive Committee

### General Chairs

<table><tr><td>Ilias Maglogiannis</td><td>University of Piraeus, Greece</td></tr><tr><td>John Macintyre</td><td>University of Sunderland, UK</td></tr></table>

### Program Co-chairs

<table><tr><td>Lazaros Iliadis</td><td>Democritus University of Thrace, Greece</td></tr><tr><td>Xiao-Jun Wu</td><td>Jiangnan University, China</td></tr><tr><td>Petia Koprinkova</td><td>Bulgarian Academy of Sciences, Bulgaria</td></tr></table>

### Honorary Co-chairs

<table><tr><td>Plamen Angelov</td><td>Lancaster University, UK</td></tr><tr><td>Vera Kurkova</td><td>Czech Academy of Sciences, Czech Republic</td></tr></table>

### Advisory Chairs

<table><tr><td>Pietro Lio</td><td>University of Cambridge, UK</td></tr><tr><td>Barbara Hammer</td><td>University of Bielfeld, Germany</td></tr><tr><td>Costas Iliopoulos</td><td>King's College London, UK</td></tr></table>

### Publication and Publicity Chair

<table><tr><td>Antonis Papaleonidas</td><td>Democritus University of Thrace, Greece</td></tr></table>

### Liaison Co-chairs

<table><tr><td>Ioannis Chochliouros</td><td>Hellenic Telecommunication Organization (OTE), Greece</td></tr><tr><td>Panagiotis Kikiras</td><td>European Defence Agency, Belgium</td></tr></table>

### Special Sessions/Tutorials Co-chairs

<table><tr><td>Dimitrios Kalles</td><td>Hellenic Open University, Greece</td></tr><tr><td>Dimitrios Kosmopoulos</td><td>University of Patras, Greece</td></tr><tr><td>Kostantinos Delibasis</td><td>University of Thessaly, Greece</td></tr></table>

### Steering Committee

<table><tr><td>Ilias Maglogiannis</td><td>University of Piraeus, Greece</td></tr><tr><td>Plamen Angelov</td><td>Lancaster University, UK</td></tr><tr><td>Lazaros Iliadis</td><td>Democritus University of Thrace, Greece</td></tr></table>
Workshops Co-chairs

Christos Makris

Katia Kermanidis

Phivos Mylonas

Spyros Sioutas

University of Patras, Greece

Ionian University, Greece

Ionian University, Greece

University of Patras, Greece

Program Committee

Georgios Alexandridis

Serafín Alonso Castro

Ioannis Anagnostopoulos

Costin Badica

Giacomo Boracchi

Francisco Carvalho

Diego Casado-Mansilla

Ioannis Chamodrakas

Kostantinos Delibasis

Konstantinos Demertzis

Georgios Drakopoulos

Mauro Gaggero

Eleonora Giunchiglia

Peter Hajek

Giannis Haralabopoulos

Lazaros Iliadis Democritus

Andreas Kanavos

Nikos Karacapilidis

Petros Kefalas

Katia Kermanidis

Petia Koprinkova-Hristova

Stelios Krinidis

Florin Leon

Aristidis Likas

Pietro Liò

Ioannis Livieris

Doina Logofătu

Ilias Maglogiannis

Goerge Magoulas

Christos Makris

Mario Malcangi

Andreas Menychtas

Vangelis Metsis

Nikolaos Mitianoudis

Antonio Moran

University of the Aegean, Greece

University of Leon, Spain

University of Thessaly, Greece

University of Craiova, Romania

Politecnico di Milano, Italy

Polytechnic Institute of Tomar, Portugal

Polytechnic School of the University of Alcalá, Spain

National and Kapodistrian University of Athens, Greece

University of Thessaly, Greece

Democritus University of Thrace, Greece

Ionian University, Greece

National Research Council of Italy, Italy

University of Oxford, UK

University of Pardubice, Czech Republic

University of Nottingham, UK

University of Thrace, Greece

Ionian University, Greece

University of Patras, Greece

CITY College, International Faculty of the University of Sheffield, Greece

Ionian University, Greece

Bulgarian Academy of Sciences, Bulgaria

Centre for Research and Technology Hellas, Greece

Technical University of Iasi, Romania

University of Ioannina, Greece

University of Cambridge, UK

University of Patras, Greece

Frankfurt University of Applied Sciences, Germany

University of Piraeus, Greece

Birkbeck, University of London, UK

University of Patras, Greece

University of Milan, Italy

University of Piraeus, Greece

Texas State University, USA

Democritus University of Thrace, Greece

University of Leon, Spain
Konstantinos Moutselos

Phivos Mylonas

Stefanos Nikiforos

Stavros Ntalampiras

Vladimir Olej

Mihaela Oprea

Ioannis P. Chochliouros

Basil Papadopoulos

Antonis Papaleonidas

Pavlidis George

Isidoros Perikos

Elias Pimenidis

Panagiotis Pintelas

Bernardete Ribeiro

Antonio Staiano

Christos Timplalexis

Nicolas Tsapatsoulis

Tsolakis Apostolos

Petra Vidnerová

Paulo Vitor de Campos

Souza

Gerasimos Vonitsanos

University of Piraeus, Greece

Ionian University, Greece

Ionian University, Greece

University of Milan, Italy

University of Pardubice, Czech Republic

University Petroleum-Gas of Ploiesti, Romania

Hellenic Telecommunications Organization (OTE), Greece

Democritus University of Thrace, Greece

Democritus University of Thrace, Greece

Centre for Research and Technology Hellas, Greece

University of Patras, Greece

University of the West of England, UK

University of Patras, Greece

University of Coimbra, Portugal

University of Naples Parthenope, Italy

Centre for Research and Technology Hellas, Greece

Cyprus University of Technology, Cyprus

Centre for Research and Technology Hellas, Greece

Czech Academy of Sciences, Czech Republic

CEFET-MG, Brazil

University of Patras, Greece
Abstracts of Keynotes
# Is “Big Tech” Becoming the “Big Tobacco” of Artificial Intelligence?

John Macintyre

Pro Vice Chancellor at the University of Sunderland, UK gave a Keynote Lecture on a very hot topic related to AI and Ethics.

**Abstract.** Recent developments in research, development, implementation and use of AI include worrying trends which ask big questions about the future direction of the whole field. As part of this, the role of “Big Tech” – the huge corporate entities who now dominate the development of AI technologies and products – is crucial, both in terms of the technology they develop, and the researchers they employ. Their dominance places them at the apex of the R&D and product development activity in AI, which in turn means they have a great responsibility to ensure that this activity leads to fair, transparent, accountable, and ethical AI systems and products. They also have a great responsibility to support and nurture their staff. This talk will examine recent developments in AI and the role of Big Tech, and ask whether they are stepping up to these responsibilities.
# Machine Learning: A Key Ubiquitous Technology in the 21st Century

Hojjat Adeli

Ohio State University, Columbus, USA, Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Honorary Professor, Southeast University, Nanjing, China, Member, Polish and Lithuanian Academy of Sciences, Elected corresponding member of the Spanish Royal Academy of Engineering.

**Abstract.** Machine learning (ML) is a key and increasingly pervasive technology in the 21st century. It is going to impact the way people live and work in a significant way. In general, machine learning algorithms simulate the way brain learns and solves an estimation/recognition problem. They usually require a learning phase to discover the patterns among the available data, similar to the humans. An expanded definition of ML is advanced as algorithms that can learn from examples and data and solve seemingly interactable learning and unteachable problems, referred to as ingenious artificial intelligence (AI). Recent and innovative applications of ML in various fields and projects currently being pursued by leading high-tech and industrial companies such as Boeing, Google, IBM, Uber, Baidu, Facebook, and Tesla are reviewed. Then, machine learning algorithms developed by the author and his associates are briefly described. Finally, examples are presented in different areas from health monitoring of smart highrise building structures to automated EEG-based diagnosis of various neurological and psychiatric disorders such as epilepsy, the Alzheimer's disease, Parkinson's disease, and autism spectrum disorder.
# Human-Centered Computer Vision:
## Core Components and Applications

Antonis Argyros<fnref n="1" />,<fnref n="2" />

**Abstract.** Computer vision is an area of artificial intelligence aimed at developing technical systems capable of perceiving the environment through image and video processing and analysis. In this talk, we mainly focus on human-centered computer vision, that is, computer vision for capturing aspects of human presence such as the geometry and motion of the human body, as well as for recognizing human actions, behavior, intentions and emotional states. Such technologies may constitute a fundamental building block for the development of a variety of applications in almost all aspects of human life (health, security, work, education, transportation, entertainment, etc.). In this special area, we give specific examples of our research activity and highlight the significant boost achieved due to the exploitation of state-of-the-art machine learning techniques and deep neural networks. We also give examples of applications developed based on these technologies in the field of robotics and ambient intelligence environments.

<footnote n="1">¹ Computer Science Department, University of Crete, Greece</footnote>

<footnote n="2">² Researcher, Foundation for Research and Technology – Hellas (FORTH)</footnote>
# Unveiling Recurrent Neural Networks - What Do They Actually Learn and How?

Peter Tino

School of Computer Science, University of Birmingham, UK

**Abstract.** When learning from "dynamic" data where the order in which the data is presented does matter, the key issue is how such temporal structures get represented within the learning machine. In the case of artificial neural networks, an often-adopted strategy is to introduce feedback-connections with time delays. This enables the neurons to form their activation patterns based on the past, as well as the current neural activations. Neural networks of this kind became known as Recurrent Neural Networks (RNN). Many diverse architectures fall under this umbrella, with a wide variety of application domains. We will briefly review past attempts to understand the way RNNs learn to represent the past in order to perform the tasks they are trained on.

To that end, we will adopt the general view of RNNs as parameterized state space models and input driven non-autonomous dynamical systems. We will then present some new results connecting RNNs to a widely known class of models in machine learning - kernel machines. In particular, we will show that RNNs can be viewed as "temporal feature spaces". This framework will enable us to understand how high-dimensional RNNs constructed with very few degrees of freedom in their parameterization can still achieve competitive performances. Such observations can be viewed as "dynamical analogs" to classical "static" kernel machines that often achieve excellent performance using rich feature spaces constructed with very few degrees of freedom (e.g. single scale parameter in Gaussian kernels).
# Deep Learning and Kernel Machines

Johan Suykens

KU Leuven, ESAT-Stadius and Leuven AI Institute, Belgium

**Abstract.** Over the last decades, with neural networks and deep learning, several powerful architectures have been proposed, including e.g., convolutional neural networks (CNN), stacked autoencoders, deep Boltzmann machines (DBM), deep generative models and generative adversarial networks (GAN). On the other hand, with support vector machines (SVM) and kernel machines, solid foundations in learning theory and optimization have been achieved. Within this talk, we outline a unifying picture and show several new synergies, for which model representations and duality principles play an important role. A recent example is restricted kernel machines (RKM), which connects least squares support vector machines (LS-SVM) to restricted Boltzmann machines (RBM). New developments on this will be shown for deep learning, generative models, multi-view and tensor-based models, latent space exploration, robustness and explainability.
# How Can Artificial Intelligence Efficiently Support Sustainable Development?

Eunika Mercier-Laurent

Université de Reims Champagne-Ardenne, CReSTIC/MODECO, France

**Abstract.** This talk considers the multiple role AI may play in sustainability. Actually, sustainable development is among the greatest challenges for humanity. Sustainability and development are apparently opposite. The current efforts to face the Planet Crisis by separate actions generate less impact than expected. Artificial Intelligence approaches and capacity of available technologies are underexplored. Eco-innovation actions focus mainly on smart transportation, smart use of energy and water and waste recycling but do not consider the necessary evolution of behaviors and focus. The trendy Digital transformation follows mostly traditional approaches. The concepts such as Smart, Intelligent, Innovative, Green or Wise City invented to promote existing technology transform the IT market. Most of offers consist in data processing with statistical/optimization methods. But AI can do better – the AI approaches and techniques combined with adequate thinking may help innovating the way of facing Planet Crisis.
Backpropagation Free Deep Learning

Jose C. Principe

University of Florida, USA

**Abstract.** This talk presents recent results that show the feasibility of training deep networks classifiers without backpropagation. We will prove that it is possible to substitute error propagation in general conditions and practically achieve the same performance as conventional algorithms. This methodology allows modularization of the algorithmic pipeline and improves explainability. We will then address some of the benefits of this technology for applications.
Brain-Inspired Data Analytics for Incremental
and Transfer Learning of Cognitive
Spatio-Temporal Data
and for Knowledge Transfer

Nikola Kasabov

Fellow IEEE, Fellow RSNZ, Fellow INNS College of Fellows
Professor of Knowledge Engineering and Founding Director KEDRI

Auckland University of Technology, Auckland, New Zealand
George Moore Chair/Professor, University of Ulster, UK

Honorary Professor Teesside University UK and the University of Auckland, NZ

**Abstract.** The talk argues and demonstrates that brain-inspired spiking neural network (SNN) architectures can be used for incremental and transfer learning, i.e. to learn new data and new classes/tasks/categories incrementally utilising some previously learned knowledge. Similarly to how the brain manifests transfer learning, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as they adopt self-organising learning principles. The new learned knowledge can be extracted in forms of graphs and symbolic fuzzy rules and its evolution traced over time. The presented approach is illustrated on an exemplar brain-inspired SNN architecture NeuCube (free software and open source available from www.kedri.aut.ac.nz/neucube and from www.neucube.io). The extraction of symbolic rules from NeuCube at each learning tasks and each subject allows for knowledge transfer between humans and machines in an adaptive, evolving, interactive way. This opens the field to build new types of open and transparent BCI and AI systems.

More details can be found in: N.Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer, 2019, https://www.springer.com/gp/book/9783662577134.
Abstracts of Tutorials
# Modern Methods and Tools for Human Biosignal Analysis

Vangelis Metsis

Texas State University, USA

**Abstract.** The term biosignal refers to any signal that can be measured from living organisms. Biosignals have been used in medicine, sports science, and psychology for diagnoses, and there have been impressive advancements in these areas. Recently, the fields of human-computer interaction and affective computing have found an interest in using biosignals as a means of understanding the human state and intention. This interest has been reinforced by the fact that acquiring information with sensors and interfacing electrically with the human body has become much easier in the past few years. Moving from large analog technologies to digital ones has led to the miniaturization of sensing devices. Wireless transmission technologies (e.g., Bluetooth low energy), which can be easily integrated with the acquisition hardware, have removed the need for bulky wiring. This tutorial will present an overview of modern applications of human biosignals and will provide practical examples of machine learning-based methods and tools for biosignal analysis. Traditional machine learning algorithms for feature extraction and classification will be compared with recent developments in deep learning and its applications to biosignal and time-series data processing in general.
# Anomaly Detection in Images

Giacomo Boracchi

Politecnico di Milano, Italy

Anomaly detection problems are ubiquitous in engineering: the prompt detection of anomalies is often a primary concern, since these might provide precious information for understanding the dynamics of a monitored process and for activating suitable countermeasures. In fact, anomalies are typically the most informative regions in an image (e.g., defects in images used for quality control). Not surprisingly, anomaly detection problems have been widely investigated in the image processing and pattern recognition communities and are key in application scenarios ranging from quality inspection to health monitoring. The tutorial presents a rigorous formulation of the anomaly-detection problem that fits with many imaging scenarios and applications. The tutorial describes, by means of illustrative examples, the most important anomaly-detection approaches in the literature, and their connection with the machine-learning perspective of semi-supervised and unsupervised learning/monitoring. Special emphasis will be given to anomaly-detection methods based on learned models, which are often adopted to handle images and signals. In particular, these will be divided into traditional models (including dictionaries yielding sparse representations) and deep learning models. The tutorial is accompanied by various examples from our research projects where we applied anomaly-detection algorithms to solve real world problems: visual quality inspection for monitoring chip and nanofiber production.
# Contents

## Adaptive Modeling/Neuroscience

‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking
Raj Bhalwankar and Jan Treur
3

A Computational Model for the Second-Order Adaptive Causal Relationships Between Anxiety, Stress and Physical Exercise
Lars Rass and Jan Treur
17

## AI in Biomedical Applications

ebioMelDB: Multi-modal Database for Melanoma and Its Application on Estimating Patient Prognosis
Aigli Korfiati, Giorgos Livanos, Christos Konstantinou, Sophia Georgiou, and George Sakellaropoulos
33

Improved Biomedical Entity Recognition via Longer Context Modeling.
Nikolaos Stylianou, Panagiotis Kosmoliaptsis, and Ioannis Vlahavas
45

Scalable NPairLoss-Based Deep-ECG for ECG Verification
Yu-Shan Tai, Yi-Ta Chen, and (Andy) An-Yeu Wu
57

Comparative Study of Embedded Feature Selection Methods on Microarray Data.
Hind Hamla and Khadoudja Ghanem
69

## AI Impacts/Big Data

The AI4Media Project: Use of Next-Generation Artificial Intelligence Technologies for Media Sector Applications.
Filareti Tsalakanidou, Symeon Papadopoulos, Vasileios Mezaris, Ioannis Kompatsiaris, Birgit Gray, Danae Tsabouraki, Maritini Kalogerini, Fulvio Negro, Maurizio Montagnuolo, Jesse de Vos, Philo van Kemenade, Daniele Gravina, Rémi Mignot, Alexey Ozerov, Francois Schnitzler, Artur Garcia-Saez, Georgios N. Yannakakis, Antonios Liapis, and Georgi Kostadinov
81

Regression Predictive Model to Analyze Big Data Analytics in Supply Chain Management
Elena Puica
94
## Automated Machine Learning

An Automated Machine Learning Approach for Predicting Chemical Laboratory Material Consumption
António João Silva and Paulo Cortez
105

An Ontology-Based Concept for Meta AutoML
Bernhard G. Humm and Alexander Zender
117

Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes
Rebekka Olsson Omslandseter, Lei Jiao, and B. John Oommen
129

## Autonomous Agents

Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19
Dimitris Tsiktsiris, Antonios Lalas, Minas Dasygenis, Konstantinos Votis, and Dimitrios Tzovaras
145

Evaluating Task-General Resilience Mechanisms in a Multi-robot Team Task
James Staley and Matthias Scheutz
155

## Clustering

A Multi-view Clustering Approach for Analysis of Streaming Data
Vishnu Manasa Devagiri, Veselka Boeva, and Shahrooz Abghari
169

Efficient Approaches for Density-Based Spatial Clustering of Applications with Noise
Pretom Kumar Saha and Doina Logofatu
184

Self-organizing Maps for Optimized Robotic Trajectory Planning Applied to Surface Coating
Maria Tzinava, Konstantinos Delibasis, and Spyros Kamnis
196

## Convolutional NN

An Autoencoder Convolutional Neural Network Framework for Sarcopenia Detection Based on Multi-frame Ultrasound Image Slices
Emmanuel Pintelas, Ioannis E. Livieris, Nikolaos Barotsis, George Panayiotakis, and Panagiotis Pintelas
209
Automatic Classification of XVI Images in Manufacturing
Bertram Sabrowsky-Hirsch, Roxana-Maria Holom,
Christian Gusenbauer, Michael Reiter, Florian Reiterer,
Ricardo Fernández Gutiérrez, and Josef Scharinger
220

Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition
Jiří Martínek, Christophe Cerisara, Pavel Král, and Ladislav Lenc
232

Just-in-Time Biomass Yield Estimation with Multi-modal Data
and Variable Patch Training Size
Patricia O'Byrne, Patrick Jackman, Damon Berry, Thomas Lee,
Michael French, and Robert J. Ross
243

Robustness Testing of AI Systems: A Case Study for Traffic
Sign Recognition
Christian Berghoff, Pavol Bielik, Matthias Neu, Petar Tsankov,
and Arndt von Twickel
256

## Data Mining/Word Counts

BIBLIOBICLUSTER: A Bicluster Algorithm for Bibliometrics
Gloria Gheno
271

Topic Identification via Human Interpretation of Word Clouds: The Case
of Instagram Hashtags
Stamatios Giannoulakis and Nicolas Tsapatsoulis
283

## Deep Learning

A Comparative Study of Deep Learning Techniques for Financial
Indices Prediction
Argyrios P. Ketsetsis, Konstantinos M. Giannoutakis, Georgios Spanos,
Nikolaos Samaras, Dimitrios Hristu-Varsakelis, Dimitrios Thomas,
and Dimitrios Tzovaras
297

An Effective Loss Function for Generating 3D Models from Single 2D
Image Without Rendering
Nikola Zubić and Pietro Liò
309

Collaborative Edge-Cloud Computing for Personalized Fall Detection
Anne H. Ngu, Shaun Coyne, Priyanka Srinivas, and Vangelis Metsis
323

Deep Dense and Convolutional Autoencoders for Machine Acoustic
Anomaly Detection
Gabriel Coelho, Pedro Pereira, Luis Matos, Alexandrine Ribeiro,
Eduardo C. Nunes, André Ferreira, Paulo Cortez, and André Pilastri
337
Neural Network Compression Through Shunt Connections and Knowledge Distillation for Semantic Segmentation Problems. 349
Bernhard Haas, Alexander Wendt, Axel Jantsch, and Matthias Wess

System-Wide Anomaly Detection of Industrial Control Systems via Deep Learning and Correlation Analysis. 362
Gordon Haylett, Zahra Jadidi, and Kien Nguyen Thanh

Verification of Size Invariance in DNN Activations Using Concept Embeddings. 374
Gesina Schwalbe

Artificial Intelligence in Music Composition. 387
Mincer Alaeddine and Anthony Tannoury

Deep Learning and AI for 5G Technology: Paradigms. 398
Mahnaz Olfati and Kiran Parmar

## Fuzzy Modeling

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices. 411
Petr Hajek, Vladimir Olej, Wojciech Froelich, and Josef Novotny

## Hyperdimensional Computing

PQ-HDC: Projection-Based Quantization Scheme for Flexible and Efficient Hyperdimensional Computing. 425
Chi-Tse Huang, Cheng-Yang Chang, Yu-Chuan Chuang, and An-Yeu (Andy) Wu

Hyperdimensional Computing with Learnable Projection for User Adaptation Framework. 436
Yu-Ren Hsiao, Yu-Chuan Chuang, Cheng-Yang Chang, and An-Yeu (Andy) Wu

## Internet of Things/Internet of Energy

"SAVE" - An Integrated Approach of Personal and Home Safety for Active Assisted Living. 451
Sorin-Aurel Moraru, Adrian Alexandru Moșoi, Dominic Mircea Kristaly, Florin Sandu, Dan Floroian, Delia Elisabeta Ungureanu, and Liviu Marian Perniu

BEMS in the Era of Internet of Energy: A Review. 465
Asimina Dimara, Christos-Nikolaos Anagnostopoulos, Konstantinos Kotis, Stelios Krinidis, and Dimitrios Tzovaras
## Machine Learning

A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data
Gentry Atkinson and Vangelis Metsis
479

An Automated Tool to Support an Intelligence Learner Management System Using Learning Analytics and Machine Learning
Shareeful Islam, Haralambos Mouratidis, and Hasan Mahmud
494

Classification of Point Clouds with Neural Networks and Continuum-Type Memories
Stefan Reitmann, Elena V. Kudryashova, Bernhard Jung, and Volker Reitmann
505

Cyber Supply Chain Threat Analysis and Prediction Using Machine Learning and Ontology
Abel Yeboah-Ofori, Haralambos Mouratidis, Umar Ismai, Shareful Islam, and Spyridon Papastergiou
518

Intelligent Techniques and Hybrid Systems Experiments Using the Acumen Modeling and Simulation Environment
Sotirios Tzamaras, Stavros Adam, and Walid Taha
531

Predicting CO₂ Emissions for Buildings Using Regression and Classification
Alexia Avramidou and Christos Tjortjis
543

Robust Pose Estimation Based on Maximum Correntropy Criterion
Qian Zhang and Badong Chen
555

The Generative Adversarial Random Neural Network
Will Serrano
567

Using Machine Learning Methods to Predict Subscriber Churn of a Web-Based Drug Information Platform
Georgios Theodoridis and Athanasios Tsadiras
581

Analysis and Prediction for House Sales Prices by Using Hybrid Machine Learning Approaches
S. M. Soliman Hossain, Jyoti Rawat, and Doina Logofatu
594

## Multi Agent Systems

Dynamic Plume Tracking Utilizing Symbiotic Heterogeneous Remote Sensing Platforms
Iakovos T. Michailidis, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos, and Yiannis Boutalis
607
<table><tr><td>Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-Based Approaches: Challenges and Perspectives. . . Vincenzo Iannino, Valentina Colla, Alessandro Maddaloni, Jens Brandenburger, Ahmad Rajabi, Andreas Wolff, Joaquin Ordieres, Miguel Gutierrez, Erwin Sirovnik, Dirk Mueller, and Christoph Schirm</td><td>619</td></tr></table>

## Natural Language

<table><tr><td>A Comparative Assessment of State-Of-The-Art Methods for Multilingual Unsupervised Keyphrase Extraction. Nikolaos Giarelis, Nikos Kanakaris, and Nikos Karacapilidis</td><td>635</td></tr></table>

<table><tr><td>An Approach Utilizing Linguistic Features for Fake News Detection. Dimitrios Panagiotis Kasseropoulos and Christos Tjortjis</td><td>646</td></tr></table>

<table><tr><td>CEA-TM: A Customer Experience Analysis Framework Based on Contextual-Aware Topic Modeling Approach. Ariona Shashaj, Davide Stirparo, and Mohammad Kazemi</td><td>659</td></tr></table>

<table><tr><td>Machine Learning Meets Natural Language Processing - The Story so Far. Nikolaos-Ioannis Galanis, Panagiotis Vafiadis, Kostas-Gkouram Mirzaev, and George A. Papakostas</td><td>673</td></tr></table>

<table><tr><td>SemAI: A Novel Approach for Achieving Enhanced Semantic Interoperability in Public Policies. George Manias, Argyro Mavrogiorgou, Athanasios Kiourtis, and Dimosthenis Kyriazis</td><td>687</td></tr></table>

## Recommendation Systems

<table><tr><td>Optimization of Multi-stakeholder Recommender Systems for Diversity and Coverage. Iordanis Koutsopoulos and Maria Halkidi</td><td>703</td></tr></table>

<table><tr><td>Recommending Database Architectures for Social Queries: A Twitter Case Study. Michael Marountas, Georgios Drakopoulos, Phivos Mylonas, and Spyros Sioutas</td><td>715</td></tr></table>

<table><tr><td>Science4Fashion: An Autonomous Recommendation System for Fashion Designers. Sotirios-Filippos Tsarouchis, Argyrios S. Vartholomaios, Ioannis-Panagiotis Bountouridis, Athanasios Karafyllis, Antonios C. Chrysopoulos, and Pericles A. Mitkas</td><td>729</td></tr></table>
## Sentiment Analysis

A Two-Step Optimised BERT-Based NLP Algorithm for Extracting Sentiment from Financial News

Rapheal Olaniyan, Daniel Stamate, and Ida Pu

745

Learning Sentiment-Aware Trading Strategies for Bitcoin Leveraging Deep Learning-Based Financial News Analysis

N. Passalis, S. Seficha, A. Tsantekidis, and A. Tefas

757

## Smart Blockchain Applications/Cybersecurity

Federated Blockchained Supply Chain Management: A CyberSecurity and Privacy Framework

Konstantinos Demertzis, Lazaros Iliadis, Elias Pimenidis, Nikolaos Tziritas, Maria Koziri, Panagiotis Kikiras, and Michael Tonkin

769

Validation and Verification of Data Marketplaces

Will Serrano

780

Author Index

793