Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Fuzzy adaptive resonance theory: Applications and extensions

Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully group data for preprocessing purposes, and improves results over the absence of quantization with statistical significance. --Abstract, page iv

Fault identification utilizing hybrid modelling based feature extraction models

Process Engineerin

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua

A Tutorial on Machine Learning for Failure Management in Optical Networks

Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task

Quantum Neural Networks with Qutrits

Οι κβαντικοί υπολογιστές, εκμεταλλευόμενοι τις αρχές της κβαντικής μηχανικής, έχουν τη δυνατότητα να μεταμορφώσουν πολλούς τεχνολογικούς τομείς, χρησιμοποιώντας κβαντικά bit (qubits) που μπορούν να υπάρχουν σε υπέρθεση και εναγκαλισμό, επιτρέποντας, μεταξύ άλλων δυνατοτήτων, την παράλληλη αναζήτηση λύσεων. Πρόσφατες εξελίξεις στο κβαντικό υλικό επέτρεψαν την υλοποίηση πολυδιάστατων κβαντικών καταστάσεων σε νέες πλατφόρμες μικροκυκλωμάτων, προτείνοντας μια ακόμη ενδιαφέρουσα προσέγγιση. Η χρήση qudits, κβαντικών συστημάτων με υψηλότερες διάστασεις, προσφέρει αυξημένο χώρο για αναπαράστη πληροφορίας, αλλά επίσης πειραματικές υλοποιήσεις έχουν επιδείξει ανθεκτικότητα έναντι θορύβου και σφαλμάτων. Αυτό επισημαίνει περαιτέρω την θέση τους στο μέλλον του κβαντικού υπολογισμού. Σε αυτήν τη πτυχιακή, εξετάζεται η δυνατότητα των qutrits για την επίλυση προβλημάτων μηχανικής μάθησης σε κβαντικό υπολογιστή. Ο επεκταμένος χώρος καταστάσεων που προσφέρουν τα qutrits επιτρέπει πλουσιότερη αναπαράσταση δεδομένων. Για το σκοπό αυτό, χρησιμοποιώντας το μαθηματικό πλαίσιο του SU(3), εισάγεται η χρήση των πινάκων Gell-Mann για την κωδικοποίηση σε έναν 8-διάστατο χώρο. Αυτό εξοπλίζει τα συστήματα κβαντικού υπολογισμού με τη δυνατότητα επεξεργασίας και αναπαράστασης περισσότερων δεδομένων σε ένα μόνο qutrit. Η έρευνα επικεντρώνεται σε προβλήματα ταξινόμησης χρησιμοποιώντας qutrits, όπου διεξάγεται μια συγκριτική ανάλυση μεταξύ του προτεινόμενου χάρτη χαρακτηριστικών Gell-Mann, κυκλώματων που χρησιμοποιούν qubits και μοντέλων κλασσικής μηχανικής μάθησης. Επιπλέον, εξερευνούνται τεχνικές βελτιστοποίησης σε χώρους Hilbert υψηλών διαστάσεων, με σκοπό την αντιμετώπιση προκλήσεων, όπως τα vanishing gradients και το πρόβλημα των barren plateaus. Τέλος, καλύπτονται πρόσφατες εξελίξεις στον κβαντικό υλικό, με ειδική έμφαση σε συστήματα βασισμένα σε qutrits. Ο κύριος στόχος αυτής της πτυχιακής εργασίας είναι να εξετάσει τη δυνατότητα κωδικοποίησης Gell-Mann για προβλήματα ταξινόμησης, να αποδείξει την εφικτότητα της επέκτασης των χώρων Hilbert για εργασίες μηχανικής μάθησης και να ορίσει μια αξιόπιστη βάση για εργασία με γεωμετρικούς χάρτες χαρακτηριστικών. Αναλύωντας τις σχεδιαστικές επιλογές και πειραματικές διατάξεις λεπτομερώς, αυτή η έρευνα στοχεύει να συμβάλει στην ευρύτερη κατανόηση των δυνατοτήτων και των περιορισμών των συστημάτων με qutrits στο πλαίσιο της κβαντικής μηχανικής μάθησης, συνεισφέροντας στην πρόοδο του κβαντικού υπολογισμού και των εφαρμογών του σε πρακτικούς τομείς.Quantum computers, leveraging the principles of quantum physics, have the potential to revolutionize various domains by utilizing quantum bits (qubits) that can exist in superpositions and entanglement, allowing for parallel exploration of solutions. Recent advancements in quantum hardware have enabled the realization of high-dimensional quantum states on a chip-scale platform, proposing another potential avenue. The utilization of qudits, quantum systems with levels exceeding 2, not only offer increased information capacity, but also exhibit improved resilience against noise and errors. Experimental implementations have successfully showcased the potential of high-dimensional quantum systems in efficiently encoding complex quantum circuits, further highlighting their promise for the future of quantum computing. In this thesis, the potential of qutrits is explored to enhance machine learning tasks in quantum computing. The expanded state space offered by qutrits enables richer data representation, capturing intricate patterns and relationships. To this end, employing the mathematical framework of SU(3), the Gell-Mann feature map is introduced to encode information within an 8-dimensional space. This empowers quantum computing systems to process and represent larger amounts of data within a single qutrit. The primary focus of this thesis centers on classification tasks utilizing qutrits, where a comparative analysis is conducted between the proposed Gell-Mann feature map, well-established qubit feature maps, and classical machine learning models. Furthermore, optimization techniques within expanded Hilbert spaces are explored, addressing challenges such as vanishing gradients and barren plateaus landscapes. This work explores foundational concepts and principles in quantum computing and machine learning to ensure a solid understanding of the subject. It also highlights recent advancements in quantum hardware, specifically focusing on qutrit-based systems. The main objective is to explore the feasibility of the Gell-Mann encoding for multiclass classification in the SU(3) space, demonstrate the viability of expanded Hilbert spaces for machine learning tasks, and establish a robust foundation for working with geometric feature maps. By delving into the design considerations and experimental setups in detail, this research aims to contribute to the broader understanding of the capabilities and limitations of qutrit-based systems in the context of quantum machine learning, contributing to the advancement of quantum computing and its applications in practical domains