PON REACT-EU Research Themes


Computer Science Curriculum 

 

Applicazione di tecniche di Intelligenza Artificiale per il dissasemblaggio selettivo di componeti elettronici da RAEE

Il riciclo dei rifiuti di apparecchiature elettriche ed elettroniche (RAEE)  consente di recuperare moltissime materie prime da riutilizzare in nuovi processi produttivi. Oggi su più di 50 materiali diversi, comprese plastiche e gomme,  presenti nel RAEE, solo una quindicina sono riciclati, tutti  gli altri vanno persi con pesanti ricadute negative per l’uomo e l’ambiente.  Tra i materiali recuperabili da RAEE, 27 sono definiti dalla Commissione Europea "materiali critici rari". Tra essi citiamo antimonio, fosforo, gallio, berillio, germanio, silicio, bismuto, tantalio, tungsteno  e cadmio. L'applicazione della visione artificiale, integrata con modelli di apprendimento automatico deep learning, al dissasemblaggio selettivo di componeti elettronici dal RAEE che sara' sviluppata in questo progetto rendera' possibile estrarre con maggiore resa i materiali in esso contenuti.   Tali materiali potranno sia essere reimmessi come materie prime nei cicli produttivi, che essere usati per costituire riserve strategiche (“miniere urbane”) che permetteranno, e in particlare nel caso   dei materiali critici rari, di ridurre la dipendenza nell'approvvigionamento da un mercato attualmente subordinato a instabilità politiche. L'approccio proposto permetta' di passare da un modello produttivo lineare ad uno circolare in accordo con l’obiettivo 12 del documento ONU “Sustainable Development Goals”.
 
Temi PNR
 
p. 143   5.6.1 Green technologies
 
p. 145 Articolazione 4: Riduzione dei rifiuti e della domanda di critical raw materials tramite approcci di
disassembling e materials recovery, remanufacturing e refurbishing
 
responsabile scientifico: Francesco Masulli
societa’ coinvolta VRLabs - Vega Research Laboratories s.r.l., Startup Innovativa
 

 
Sustainable representation learning with causal ML
 
Representation learning approaches provide remarkable results but are often based on complex architectures requiring huge datasets and unsustainable computations, with negative effects in terms of amount of energy consumption and environmental impacts. Despite this gargantuan amount of data and compute, state-of-the-art methods often generalize poorly in practice due to slight changes in between the training and test distribution. Causal machine learning offers an elegant approach to study this problem but it does not scale as well as modern deep learning. Our goal in this project is to explore how causal structure in the data distribution can inform the design of novel forms of inductive bias, making AI models easier to train, more sustainable and thus environment-friendly, and more reliable in solving real world problems.
 
The project will be co-supervised by Nicoletta Noceti and Lorenzo Rosasco (UniGe) and Francesco Locatello (Amazon Web Services). The student will be part of the prestigious ELLIS PhD program (European Laboratory for Learning and Intelligent System -- https://ellis.eu/), allowing a unique exposure to leading research environments in both academia and industry. The student will gain valuable experience in both, sharing their time between the Genova machine learning center (MaLGa) and the Amazon AWS research labs in Tuebingen, Germany. Competitive compensation applies to support the stays abroad.
 
Relevant literature:
 
Towards Causal Representation Learning https://arxiv.org/abs/2102.11107
Desiderata For Representation Learning: A Causal Perspective https://arxiv.org/abs/2109.03795 
 
Representation Learning via Invariant Causal Mechanisms https://arxiv.org/abs/2010.07922
 
Azienda: Amazon
Referenti: Noceti, Rosasco
 

 

 Digitial Solutions and Technologies for Green Manufacturing 

Digital solutions and technologies based on IoT and AI to support the efficient management of the production process,  the service to the final customer and the possibility of reuse and applicar. In particular, machine learning and AI methodologies, supported by IT infrastructures with distributed microservices architecture, for the reliable and safe management of processes; development of digital platforms for value-added services and for tracking and certification of the production chain; devices and sensors for connected products and adaptive systems.
 
Collegato a POR Efficacity, Aware
Temi PNR 2021-2027
OT3. Circular Manufacturing  (Industria per un’economia pulita e circolare)
 
5.4.6  Innovazione per l’industria manifatturiera
Articolazione 1. Industria circolare, pulita ed efficiente
Articolazione 2. Industria inclusiva
Articolazione 3. Industria intelligente
 
Referente: Delzanno
Azienda: Flairbit, Gruppo FOS
 

Systems Engineering Curriculum

 

TITOLO DEL PROGETTO

Modellistica, simulazione e controllo della mobilità pedonale in sistemi turistici con alta densità: applicazione al Parco Nazionale delle Cinque Terre.

 

 

 

DESCRIZIONE DEL PROGETTO

 

PNR 2021-2027

OT5 CLIMA, ENERGIA,MOBILITÀ SOSTENIBILE

5.5.1 Mobilità sostenibile

 

 

Il progetto ha diversi obiettivi:

·       Definire tecnologie quali bigliettazione elettronica e relativo software gestionale per ottimizzare i flussi turistici in zone che possono raggiungere alta densità pedonale

·       Utilizzare tali tecnologie per supportare il tracciamento di possibili contatti con persone positive al COVID

·       Ottimizzare i flussi pedonali per minimizzare i contatti tra persone

·       Valutare i flussi turistici in arrivo dall’esterno per minimizzare i tempi di attesa attraverso tecniche di simulazione

Gli indici di prestazione che verranno considerati sono quelli economici, l’emissione di CO2 causato dai flussi turistici, la capacità del territorio a sopportare i flussi, l’esposizione ai rischi ambientali, l’esposizione al rischio COVID.

La validazione delle tecnologie di simulazione e controllo proposte sarà effettuata sui tre comuni delle 5 Terre con il qual il DIBRIS ha una convenzione quadro. Il progetto sarà svolto con il supporto di Mypass, società leader nel campo delle tecnologie per la gestione di flussi turistici (skipass, accesso a Venezia, e 5Terre card).

La ricerca prevede anche un soggiorno all’estero presso il Politecnico di Annecy.

 

Il programma di ricerca è articolato nelle seguenti attività.

FASE 1. Definizione del network di sensori atti al monitoraggio dei flussi turistici e dell’architettura del sistema complessivo.

FASE 2. Realizzazione dei sistemi di trasmissione, acquisizione, e raccolta dei dati.

FASE 3. Definizione della metodologia per identificare key perfomance indicators e soglie di criticità in funzione della domanda turistica. Definizione di indici di pressione turistica per i diversi giorni dell’anno sui 5 borghi.

FASE 4. Definizione e realizzazione di un sistema di supporto alle decisioni WEB GIS e applicazioni per dispositivi mobili dedicate agli operatori.

FASE 5. Definizione di un simulatore basato su un modello previsionale di criticità a orizzonte temporale variabile.

 

IMPRESA (non possono essere enti pubblici o istituti di ricerca pubblici)

 

E’ previsto uno stage di 6 mesi presso MyPass srl, Genova.

Inoltre è previsto un periodo di ricerca presso il Politecnico di Annecy, Università Savoia Monte Bianco, presso il quale sono attivati diversi corsi indirizzati a sistemi tecnologici per la gestione di flussi turistici in ambiente montano.

 

TITOLO DEL PROGETTO

Monitoraggio e controllo del comportamento alla guida per migliorare la sicurezza e contenere l’impatto sull’ambiente

 

 

 

DESCRIZIONE DEL PROGETTO

 

PNR 2021-2027

OT5 CLIMA, ENERGIA,MOBILITÀ SOSTENIBILE

5.5.1 Mobilità sostenibile

 

Il progetto si colloca nell’ambito del digitale, in particolare in relazione alla transizione dei veicoli da una guida manuale ad una guida autonoma. In questa transizione, dovrà essere migliorata l’interazione tra il veicolo, l’infrastruttura ed il conducente, quest’ultimo immerso in un insieme di tecnologie in continua evoluzione e per il quale vi potrebbe essere tempo non sufficiente a fornire adeguata formazione ed informazione.

La sfida principale della ricerca proposta è di misurare il comportamento umano alla guida, fornendo un'impronta comportamentale di ogni conducente e rilevare e segnalare comportamenti emergenti in tempo reale che possano conferire rischi o che comunque siano migliorabili dal punto di vista dell’impatto ambientale. Il progetto di ricerca si propone di dimostrare la fattibilità applicativa su specifici comportamenti legati al trasporto di merci pericolose su strada.

SEBASTIAN si basa sui seguenti aspetti multidisciplinari.

a) Tecnologie e metodologie di monitoraggio per la misurazione del comportamento umano

Dati biometrici: Il progetto cercherà di definire una classificazione del comportamento e definirà i requisiti per i sensori non invasivi per monitorare il comportamento nei processi di trasporto. Nel laboratorio di automazione del DIBRIS, c'è già esperienza nell'uso di magliette intelligenti con ECG, sensori di temperatura e actigrafi, caschi e berretti intelligenti per monitorare l'EEC e algoritmi di visione artificiale che tracciano il comportamento dell'occhio umano e delle palpebre.

Dati del veicolo e dell'infrastruttura: gli stati specifici del traffico, le condizioni meteorologiche e la manutenzione del veicolo sono dati strettamente legati alla sicurezza e sicuramente influenzano il comportamento umano. Inoltre, alcuni dati come i dati del bus CAN possono anche costituire una preziosa fonte informativa, ad es. un'improvvisa ripetizione dei freni può essere correlata a un comportamento a rischio specifico quale improvvisa sonnolenza.

Dati emotivi e formativi: le competenze individuali così come alcune informazioni psicologiche ad esempio riguardanti la propensione o l’avversione al rischio del conducente sono un'altra componente che dovrebbe essere misurata per migliorare il modello comportamentale oggetto di studio e la sua interazione con gli altri sistemi.

b) Metodologie di analisi

Il progetto indagherà strumenti di progettazione e analisi basati su diversi approcci.

Tecniche di data mining e data fusion e modellazione ontologica: Il monitoraggio genererà un'enorme quantità di dati per i quali sarà necessario sviluppare tecniche adeguate per rilevare un comportamento a rischio o comunque non ottimali dai punti di vista dell’impatto ambientale.

Incertezza dei dati: un processo di trasporto viene considerato come un sistema complesso, il cui comportamento non corretto può essere causato da un'interazione errata o dal guasto di uno o più dei suoi componenti, compreso un comportamento a rischio. L'incertezza di solito influisce sulla valutazione complessiva. L'impostazione formale della tecnologia data dalle belief functions verrà adottata in quanto offre un buon compromesso tra espressività e calcolabilità, e poiché risulta più generale della teoria della probabilità.

Valutazione dell'affidabilità del conducente: non è facile quantificare le prestazioni del comportamento umano a causa di un gran numero di fattori che la influenzano e della sua variabilità. Nel progetto, proponiamo di quantificare gli aspetti multidimensionali del modello comportamentale umano e di integrarlo in un modello globale dell'analisi del rischio di incidente e di impatto ambientale.

d) controllo in tempo reale dei processi di guida umana

Gli approcci Model Predictive Control (MPC) sono alla base delle metodologie di simulazione e controllo del progetto. Dal punto di vista teorico, il progetto estenderà gli approcci MPC esistenti per ideare algoritmi distribuiti stocastici MPC (DSMPC) per il controllo in tempo reale e la diagnosi dei guasti. Lo schema DSMPC sfrutterà modelli dinamici stocastici che descrivono l'evoluzione dei parametri incerti e formuleranno un problema di controllo ottimo stocastico che minimizzi una data misura di rischio e di impatto ambientale. Confrontando l'evoluzione del sistema complessivo reale con le uscite date dal controllo MPC, è possibile identificare traiettorie potenzialmente pericolose, che potrebbero portare a un comportamento a rischio.

 

 

IMPRESA (non possono essere enti pubblici o istituti di ricerca pubblici)

 

Aitek S.p.A.

Aitek rappresenta una importante realtà nel panorama ligure per quanto riguarda l’automazione in processi di trasporto e logistici. Verrà quindi effettuato uno stage di sei mesi presso l’azienda per approfondire argomenti tecnologici del progetto, in particolare in relazione con l’interazione veicolo infrastruttura stradale.

Inoltre è previsto un periodo di stage di sei mesi presso UTC –(Francia), in particolare presso il laboratorio congiunto Renault sulla guida autonoma, per approfondire tematiche relative a decisioni in condizioni di incertezza e a sensori innovativi utilizzati dall’industria automobilistica.

 

RESPONSABILE SCIENTIFICO

Michela Robba

TITOLO DEL PROGETTO

Gestione e controllo di sistemi energetici ed ambientali

 

 

DESCRIZIONE DEL PROGETTO

 

L’attività riguarda lo sviluppo, l’applicazione e sperimentazione in campo di metodi e modelli per la gestione di sistemi energetici e ambientali. In particolare, si integreranno metodologie e tecnologie abilitanti proprie delle seguenti aree di ricerca: ottimizzazione, controllo, automazione, elaborazione di dati (machine learning) che permettano l’integrazione tra diversi agenti, la resilienza e l’utilizzo di grandi moli di dati. Gli ambiti applicativi riguardano: smart grids, comunità energetiche, sistemi agricoli sostenibili, edifici. Si utilizzerà inoltre la Smart Polygeneration Microgrid presso il campus di Savona, menzionata all’interno del PNIR (Piano Nazionale Infrastrutture di Ricerca).

I fattori innovativi proposti dal progetto possono essere così articolati:

1.     Sviluppo di algoritmi di ottimizzazione distribuita da implementare su nodi edge e reti di sensori IoT.

2.     sviluppo di metodi di controllo predittivo per incremento dell’Efficienza Energetica (EE) e di integrazione con microreti e smart grid. I metodi di controllo e gestione dell’energia sviluppati avranno l’obiettivo di massimizzare l’efficienza nell’uso delle risorse energetiche a livello di edificio, gruppo di edifici e distretto, o di un generico sistema energetico.

3.     sviluppo di nuove metodologie di gestione ed interpretazione del dato energetico. Per quanto riguarda lo sfruttamento dei dati, sarà sviluppato uno strato di servizi informativi in grado di consentire ai vari sottosistemi di interagire assicurando così l’interoperabilità semantica e la modularità del sistema. Strettamente legato a questo strato sarà lo sviluppo di servizi per il supporto decisionale a partire dai dati disponibili. Un’attenzione particolare sarà dedicata ai metodi di Machine Learning e, nel dettaglio, a metodi basati su regole, in grado di esprimere le correlazioni in una forma facilmente comprensibile dall’utente finale.

IMPRESA (non possono essere enti pubblici o istituti di ricerca pubblici)

Maps

 

RESPONSABILE SCIENTIFICO

Michela Robba

TITOLO DEL PROGETTO

Ottimizzazione e controllo per la mobilità sostenibile

 

 

DESCRIZIONE DEL PROGETTO

 

L’attività riguarderà principalmente lo sviluppo di modelli matematici per la progettazione, pianificazione e gestione di sistemi di trasporto sostenibili, con particolare riferimento ai veicoli elettrici, all’idrogeno, al traffico e alla logistica.

A livello internazionale, le politiche e la normativa nell’ambito dello sviluppo sostenibile e della riduzione delle emissioni hanno portato alla necessità di studiare ed implementare nuovi sistemi per la gestione e l’integrazione nella rete di rinnovabili, sistemi di accumulo, veicoli elettrici, politiche di riduzione dei consumi. Nel caso dei veicoli elettrici inoltre risulta necessario integrare la gestione della rete di trasporto stradale con la rete di distribuzione elettrica e le colonnine di ricarica. Infatti, la ricarica distribuita di veicoli elettrici potrebbe, da un lato, creare problemi alla rete elettrica, ma dall’altro i veicoli potrebbero essere utilizzati come sistema di accumulo (vehicle to grid V2G, vehicle to building V2B).

L’attività sarà integrata con infrastrutture energetiche e di ricerca, messe a disposizione da UNIGE e DUFERCO ENERGIA, nell’ambito della mobilità elettrica e delle smart grid:

-Smart Polygeneration Microgrid e Sustainable Energy Building presso il Campus di Savona;

-Colonnine elettriche già installate presso il Campus di Savona;

-22 colonnine di ricarica nei comuni di Genova, Cogoleto, Arenzano, Cairo Montenotte e Savona;

-   Use cases forniti da Duferco Energia come ad esempio progetto di installazione di stazioni di ricarica all’interno di un parcheggio interrato multipiano (Marina Porto Antico) e creazione di hub di ricarica per veicoli elettrici anche leggeri (moto, quadricicli e biciclette) all’interno di un progetto Europeo (ELVITEN)

- Gli asset disponibili grazie ai progetti europei aggiudicati da Duferco Energia come Muse Grids per l’implementazione di modelli per il Vehicle to Home e il Vehicle to Grid nei dimostratori di Osimo (AN) con furgoni e autovetture.

L’attività di ricerca si focalizzerà su diversi aspetti complementari tra loro:

1) Gestione di colonnine elettriche (Power management). Il power management consiste nella creazione di metodi per l’ottimizzazione dei flussi di potenza all’interno di un sistema composto da molteplici punti di ricarica per veicoli elettrici.

2)  Schedulazione di veicoli elettrici in smart grid e microgrid caratterizzate da fonti rinnovabili intermittenti, sistemi di accumulo, generatori tradizionali ad alta efficienza e colonnine di ricarica di ultima generazione abilitate per il vehicle to grid (V2G).

3) Routing di veicoli elettrici per il trasporto di merci, rifiuti e persone.

IMPRESA (non possono essere enti pubblici o istituti di ricerca pubblici)

 

Duferco

 

 

 

immaginesfondo

 

 

 Computer Science Workshop 2021
2nd Edition
October 1st, 2021, 13.45-18

Conference Hall, DIBRIS, Valletta Puggia, Department of the University of Genova

Via Dodecaneso 35, Genova, 16146 IT

con il contributo dell’Università degli studi di Genova

 


Motivation and goals

 

We are glad to announce the 2nd edition of the Computer Science Workshop at the Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS) at  Università di Genova.

What about research in Computer Science?

Computer Science is constantly increasing in complexity with many and many new fields of research emerging.  Here, at the University of Genoa, we are involved in some of these fields as Virtual and Augmented Reality, Multi-Agent systems, Data Science, Data Management, Computer Graphics, Security, Machine Learning, Programming Languages, Logic, Computer Vision, Software Engineering and many others. The main goal of the workshop is discussing the many aspects of the Computer Science research fields, to present a broad perspective of this subject and look for possible (unexpected) interconnections. We are glad to host three speakers:

The presentations are aimed at providing an international view of a wide range of research topics in Computer Science. The workshop includes a coffee break offered to the participants, during which all the PhD students in Computer Science are going to present their work. It is a nice opportunity for discussing, sharing new ideas, and more generally networking.

 


 

Registration

 

The event is completely free for the participants. A coffee break will be offered during the poster session. 

PLEASE USE THE REGISTRATION LINK ONLY FOR IN-PERSON PARTICIPATION 

 

Participants data are collected for the sole purpose of the event organization. By subscribing to the event you accept Eventbrite Terms of Service

 

Online Attendance

The event will be transmitted freely online and live via Zoom at the following link:

https://us02web.zoom.us/j/88544356441?pwd=SVRnTmlkRUZZeEozU2U3RjZWU0dJZz09

ID:    885 4435 6441
psw: 937063

 

 

 

 


 

 


 

Abstracts

TITLE: Computational Fact Checking for Textual Claims

SPEAKER: Paolo Papotti

ABSTRACT: Fact checkers and social platforms are overwhelmed by the amount of false content that is produced online every day. To support fact checkers and content moderators, several research efforts have been focusing on automatic verification methods to assess claims. These initiatives have grown and multiplied in the last year due to the "infodemic" associated with the COVID-19 pandemic. Better access to data and new algorithms are pushing computational fact checking forward, with experimental results showing that verification methods enable effective labeling of claims, both in simulations and in real world efforts such as https://coronacheck.eurecom.fr. However, while fact checkers start to adopt some of the resulting tools, the misinformation fight is far from being won. In this talk, we will cover the opportunities and limitations of computational fact checking and its role in fighting misinformation. 

 

TITLE: Positioning in 3D video games

SPEAKER: Marco Tarini

ABSTRACT: An element at the core of any 3D video game is the positioning (location and orientation) of objects, such as characters, scene elements, etc., inside a virtual world. How are these spatial relationships internally represented and updated? Which of the known mathematical models are best suited to the typical needs of a videogame (from rendering to animation, from artificial intelligence to physical simulation, from level design to 3D sound rendering)? In this short seminar, we will present and compare the main alternative mathematical approaches that can be adopted for a technical task that is so close to the base of the functioning of a videogame to be too often taken for granted, despite the important far reaching consequences.

 

TITLE: Tensor decompositions: algebra, geometry and applications

SPEAKER: Alessandro Oneto

ABSTRACT: Tensors are higher-order generalisation of matrices, namely multi-indexed arrays of numbers. They are used to store multidimensional datasets, encoding the higher-order relationships which are intrinsic within the data. Tensor decompositions are algorithmic techniques which help to interpret, visualise and store the data. They extend the idea of matrix decompositions, such as singular value decomposition, principal component analysis and non-negative matrix factorisation. The possibility to analyse multi-dimensional structures preserving the high-order nature within the data allowed tensor decompositions to find applications in several areas involving signal processing, data mining, complexity theory, dimensionality reduction and many others. At the same time, tensor decompositions attracted a broad community in pure mathematics. Indeed, there are many successful examples of tensor decompositions problems solved by using methods from multilinear algebra, commutative algebra and algebraic geometry. In this talk, I will give an introduction on tensor decompositions, in particular canonical polyadic decompositions (CPD) and high-order singular value decompositions (HOSVD). In particular, I will try to underline the interdisciplinarity of tensor decompositions by giving examples of their applications as well as describing the algebraic and geometric theory behind them. 

 


 

Speakers 

 

 

 

Paolo Papotti

Paolo Papotti is an Associate Professor at EURECOM, France since 2017. He got his PhD from Roma Tre University (Italy) in 2007 and had research positions at the Qatar Computing Research Institute (Qatar) and Arizona State University (USA). His research is focused on data integration and information quality. He has authored more than 100 publications, and his work has been recognized with two “Best of the Conference” citations (SIGMOD 2009, VLDB 2016), two best demo award (SIGMOD 2015, DBA 2020), and two Google Faculty Research Award (2016, 2020). He is associate editor for PVLDB and the ACM Journal of Data and Information Quality (JDIQ).





Marco Tarini

Marco Tarini (PhD 2003, Univ. Pisa) works as an Associate Professor at the Università degli Studi di Milano, Italy, where he teaches Computer Graphics and 3D Video game development, as well as other advanced courses on game dev and geometry processing at the same univerity and others. A prolific researcher, his interests are in Computer Graphics (geometry processing and real time rendering, and especially surface representation, remeshing, texturing, 3D  acquisition, and animation) and its applications (Video Games, Scientific Visualization, Cultural Heritage, and 3D Fabrication). In these fields, prof. Tarini authored a large number of influential articles in all the the top-tier scientific journals in the field, receiving several awards and recognitions for this activity, and collaborates with several research lab worldwide. Marie Curie Mobility Fellow, prof. Tarini has been leading several funded projects, in addition to his activity as a core developer in popular Open Source software projects and libraries. A former game developer, his hobbies include game design (including board games). 

 

 

Alessandro Oneto

I am an assistant professor at the Department of Mathematics of the University of Trento. My mathematical background is in pure commutative algebra and algebraic geometry, but I like to study geometric models that come from questions in applied sciences such as tensors and their decompositions or other statistical models coming from phylogenetic or machine learning. I did my undergraduate studies at the University of Genoa and I got a PhD in Mathematics at Stockholm University in 2016. After postdoctoral experiences at Inria Sophie-Antipolis (France) and at U. Politecnica the Catalunya (Spain), I held an A. v. Humboldt Fellowship at OVGU Magdeburg (Germany). Since April 2020, I joined the University of Trento and I am a member of the Laboratory TensorDec.





Online Poster Session

 Since the Poster Session will be host physically, we will be offering a "quasi-online" Poster Session also to virtual participants at the following link:

https://docs-dibris.github.io/CSW21

On the website, it will be possible to explore all the posters presented and interact with the authors by commenting under each poster. To comment, a GitHub account is required. Each author will give priority to the in-presence attendance, but a constant look up to her/his webpage will be granted.

 

 


 

Organization

DOCS - DOttorandi In Computer Science

 

This event is thought and fully organized by the organization of PhD students in Computer Science. 

This organization was born nearly two years ago with the aim of sharing knowledge and discuss the on-going research in our department. We organize weekly seminars and other events during the year. 

The members of the organization:

Matteo Moro, Davide Garbarino, Claudio Mancinelli, Elena Nicora, Issa Mouawad, Federico Dassereto, Francesco Dagnino, Chiara Accinelli, Daniele Traversaro 

   

A special thanks to Danilo Franco for handling the event on the Master degree side and for the help. 

A special thanks to  SUSI Illustration for the graphics and the logos.

 


 

Contacts

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October 2021

Admitted to the PhD Program

  • Computer Science (2 scholarships):

    Touijer Larbi, Marco Mochi

  • Systems Engineering (2 scholarships):

    Binjaku Kleona, Fernandez Valderrama Daniel

 

Admission to the online interviews that will take place on October 8, 2021 

  • Computer Science

    Ali Rashid, Alvi Muhammad Saad Qaisar, Ch Hassan Anwar Ul, Khan Fauzia, Marco Mochi, Rafique Hamad, Rehman Answar Ur, Touijer Larbi

  • Systems Engineering

    Binjaku Kleona, Fernandez Valderrama Daniel, Mochi Marco, Briatore Federico, Khan Sajjad


July 2021

Computer Science: Final ranking

 

 

 

PhD Program in  Computer Science and Systems Engineering 

Research Projects Proposals 2021 (XXXVII Cycle)


2 Scholarships funded by Leonardo Labs@Genoa:

The research projects are related to the following themes: 

  • Porting, optimization and parallelization of HPC and AI applications on high-end GPU nodes and clusters, targeting near real-time simulations and predictions
  • Development of a digital twin framework based on HPC and BigData technologies for Aerospace and Cyber Security applications
  • Convergence of HPC and Cloud infrastructures: study of HPC container technologies (e.g. Docker, Singularity) to deliver applications as a service on cloud with HPC nodes 

Students will share their time between the Leonardo Labs (65%) and Dibris (35%).
For more information, please contact Dr. Carlo Cavazzoni, Head of Computational R&D, and Director of the HPC Lab at Leonardo SpA
email: This email address is being protected from spambots. You need JavaScript enabled to view it.


Research line: Data Science and Engineering 

Title: Emotion Recognition for Positive Computing
Proposer: Francesco Masulli, Stefano Rovetta
Research area: Machine Learning, Computational Intelligence
Curriculum: Computer Science

Description:
 Interpersonal interaction is oftentimes intricate and nuanced, and its success is often predicated upon a variety of factors. These factors range widely and can include the context, mood, and timing of the interaction, as well as the expectations of the participants. For one to be a successful participant, her/he must perceive a counterpart’s disposition as the interaction progresses and adjust accordingly. People vary widely in their accuracy at recognizing the emotions of others. This project is aimed at exploiting multiple sensorial sources for emotion recognition, including video of subject faces, audio of speech, written texts, and physiological signals as measured by wearables. The data processing methods used include signal analysis, fuzzy logic and machine learning, with particular focus on deep neural networks. The main aim of this activity is the development of a set of tools supporting the design of positive computing systems, that is systems supporting the well-being.

Link to the group or personal webpage: https://person.dibris.unige.it/masulli-francesco/

References:

[1] Rafael A. Calvo Dorian Peters, Positive Computing: Technology for Wellbeing and Human Potential, The MIT Press,, 2017
[2]Miyakoshi, Yoshihiro, and Shohei Kato. "Facial Emotion Detection Considering Partial Occlusion Of Face Using Baysian Network". Computers and Informatics (2011): 96–101.
[3] Poria, Soujanya; Cambria, Erik; Bajpai, Rajiv; Hussain, Amir (September 2017). "A review of affective computing: From unimodal analysis to multimodal fusion". Information Fusion. 37: 98–125. doi:10.1016/j.inffus.2017.02.003. Hdl:1893/25490.
[4] B. Schuller, G. Rigoll M. Lang. "Hidden Markov model-based speech emotion recognition". ICME '03. Proceedings. 2003 International Conference on Multimedia and Expo, 2003.
[5] Stefano Rovetta, Zied Mnasri, Francesco Masulli, Alberto Cabri: Emotion Recognition from Speech: An Unsupervised Learning Approach. Int. J. Comput. Intell. Syst. 14(1): 23-35 (2021)


Title: Computational Intelligence Methods for Flow Induction
Proposer: Francesco Masulli, Stefano Rovetta
Research area: Machine Learning, Computational Intelligence
Curriculum: Computer Science

Description:
 In positive psychology, the flow is a state of consciousness in which the person is fully immersed in an activity, such as chess playing, partner dancing, education, music, sports practicing, and gaming. To induce flow the undertaken activity’s challenges and the person’s skills must increase in balanced way over time. At the cortical level the experience of flow is linked to dopamine release. The duration of the time spent in flow experiences induces positive effects, such as raising self-esteem, lowering anxiety, leading to better performance in artistic and scientific creativity, teaching, learning, sports, etc. This project is aimed at exploiting multiple sensorial sources for flow monitoring in natural and controlled (e.g., gaming) contexts. Considered biometric signals include heart rate, blood pressure, facial expression, eye movements, pupil dilation, and skin conductivity obtained from consumer's market devices. The data processing methods used include signal analysis and machine learning, with particular focus on deep neural networks. Moreover, a fuzzy control of game challenges levels will be implemented in order to optimize the flow experience and accelerate the achievement of high skills by the player.

Link to the group or personal webpage: https://person.dibris.unige.it/masulli-francesco/

References:
[1] Koepp MJ, et al Evidence for striatal dopamine release during a video game. Nature. 1998 May 21;393(6682):266-8.
[2] Jane McGonigal: Reality Is Broken: Why Games Make Us Better and How They Can Change the World, Penguin Books (2011)
[3] Nakamura, J., & Csikszentmihályi, M. (2009). The concept of flow. In Handbook of positive psychology (89-105). New York, NY: Oxford University Press


Title: Computational Intelligence Tools for Noninvasive Dysphagia Diagnosis
Proposer: Francesco Masulli, Stefano Rovetta
Research area: Machine Learning, Computational Intelligence
Curriculum: Computer Science

Description:
 The term Dysphagia means "difficulty swallowing." It is the inability of food or liquids to pass easily from the mouth, into the throat, and down into the esophagus to the stomach during the process of swallow. Dysphagia can occur in all age groups, resulting from congenital abnormalities, structural damage, and/or medical conditions. This project is aimed at exploiting Computational Intelligence methods in noninvasive tests for the diagnosis of the dysphagia and for the evaluation of its grade based on the analysis of vocal traits. The considered methods include Fuzzy Systems and Convolutionary Neural Networks, and Deep Autoencoders. Link to the group or personal webpage: https://person.dibris.unige.it/masulli-francesco/

References:

[1] Boczko F (November 2006). "Patients' awareness of symptoms of dysphagia". Journal of the American Medical Directors Association. 7 (9): 587–90. doi:10.1016/j.jamda.2006.08.002. PMID 17095424.
[2] Brady A (January 2008). "Managing the patient with dysphagia". Home Healthcare Nurse. 26 (1): 41–46, quiz 47–48. doi:10.1097/01.NHH.0000305554.40220.6d. PMID 18158492. S2CID 11420756.
[3] Smithard DG, Smeeton NC, Wolfe CD (January 2007). "Long-term outcome after stroke: does dysphagia matter?". Age and Ageing. 36 (1): 90–94. doi:10.1093/ageing/afl149. PMID 17172601.


Title: Edge AI -based Real-Time Object Detection and Tracking
Proposer: Francesco Masulli, Stefano Rovetta
Research area: Machine Learning, Computational Intelligence
Curriculum: Computer Science

Description: Many Artificial Intelligence and Computer Vision application domains, including video surveillance, industrial control, urban traffic monitoring, robotics, require the implementation of advanced machine learning algorithms on the edge, for reasons real-time reaction, communication workload, and privacy. This project is aimed at exploring the edge implementation of deep learning algorithms for real-time video object detection and tracking on low impact SWAP (Size, Weight and Power) platforms such as NVIDIA Jetson.
Link to the group or personal webpage: https://person.dibris.unige.it/masulli-francesco/

References:

[1] Ho GTS, Tsang YP, Wu CH, Wong WH, Choy KL. A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities. Sensors (Basel). 2019;19(8):1796. Published 2019 Apr 15.
[2] Mandal, V.; Mussah, A.R.; Jin, P.; Adu-Gyamfi, Y. Artificial Intelligence-Enabled Traffic Monitoring System. Sustainability 2020, 12, 9177.
[3] Okarma, K. Applications of Computer Vision in Automation and Robotics. Appl. Sci. 2020, 10, 6783.
[4] Yaacoub, JP.A., Noura, H.N., Salman, O. et al. Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations. Int. J. Inf. Secur. (2021).


Title: Exploiting causality for efficient Computer Vision

Proposers: Nicoletta Noceti, Lorenzo Rosasco, Francesco Locatello (Amazon AWS labs,  Tuebingen)
Curriculum: Computer Science
Research line: Data Science and Engineering
Topics: Computer Vision, Machine Learning

Description: Machine learning approaches for Computer Vision tasks provide remarkable results but are often based on complex architectures requiring huge datasets and massive computations. Efficiency is the challenge we wish to tackle with this project. We will explore how structure in the data distribution can inform the design of novel forms of inductive bias. In particular, we will investigate how causal structure can be used towards this end. The project will be co-supervised by Nicoletta Noceti and Lorenzo Rosasco  (UniGe) and Francesco Locatello (Amazon). The time of the student will be shared between the Genova Machine Learning center (MaLGa) and the Amazon AWS labs in Tuebingen.

Link to the research group: https://ml.unige.it


Title: Large Scale Computing Theory and Applications
Proposer: Giorgio Delzanno
Research area: Data Science and Engineering
Curriculum: Computer Science

Description: Multithreaded, parallel and distributed applications are at the core of modern software architectures and applications. For instance, reference architectures for the Internet of Things are typically based on scalable distributed engines mixing the use of local, low-power system on chips (i.e. Edge and Fog computing) together with remote computing infrastructures (e.g. the Cloud) for data ingestion, real time and batch processing and data visualisation. We can find here multiple open challenges ranging from new architectures for data processing (e.g. Spark), to seamlessly provide data integration and code migration, to enable high performance/efficient computing as services (HPCaaS),  exploiting the computational power of edge computing resources at their best (e.g. neural accelerators). 
In this general setting we are interested in the more specific following research lines:

  • large scale machine learning: deploying machine learning algorithms on distributed and cluster architectures, CPU accelerators (GPUs, neural CPUs) (in collaboration with Nicoletta Noceti and Lorenzo Rosasco)
  • tools and techniques to deploy cloud-HPC-edge architectures (in collaboration with Daniele D'Agostino CNR)
  • software and physical architectures for smart applications (e.g. smart building and communities) combining edge and cloud computing (in collaboration with other researches at Dibris)
  • large scale computing platforms for augmented and virtual reality environments (simulation, training, distance learning, computer science education, etc)
  • validation of distributed algorithms and protocols required in large scale applications using model checking, SMT solvers, etc (in collaboration with Arnaud Sangnier Paris VII)

Link to the group or personal webpage:  Giorgio-Delzanno's-webpage   DBLP

References:

[1]  L. BixioGiorgio DelzannoS. ReboraM. Rulli
A Flexible IoT Stream Processing Architecture Based on Microservices. Inf. 11(12): 565 (2020)

[2] D. AnconaL. BenvenutoG. DelzannoG. Gambari:
Flow Programming: A Flexible way to bring the Internet of Things into the Lab. 
UMAP  2020: 155-158 

[2] S. ConchonG. DelzannoA. Ferrando:
Declarative Parameterized Verification of Distributed Protocols via the Cubicle Model Checker. 
Fundam. Informaticae 178(4): 347-378 (2021)


Title: Ethic-by-Design Query Processing / Responsible Query Processing
Proposer: Barbara Catania
Research area: Data Science and Engineering
Curriculum: Computer Science

Description: Nowadays, large-scale technologies for the management and the analysis of big data have a relevant and positive impact: they can improve people’s lives, accelerate scientific discovery and innovation, and bring about positive societal change. At the same time, it becomes increasingly important to understand the nature of these impacts at the social level and to take responsibility for them, especially when they deal with human-related data.

Properties like diversity, serendipity, fairness, or coverage have been recently studied at the level of some specific data processing systems, like recommendation systems, as additional dimensions that complement basic accuracy measures with the goal of improving user satisfaction [2].

Due to the above-mentioned social relevance and to the fact that ethical need to take responsibility is also made mandatory by the recent General Data Protection Regulation of the European Union [GDPR16], nowadays the development of solutions satisfying - by design - non-discriminating requirements is currently one of the main challenges in data processing and is becoming increasingly crucial when dealing with any data processing stage, including data management stages [1, 3, 4].   

Based on our past experience in advanced query processing for both stored and streaming data, the aim of the proposed research is to design, implement, and evaluate ad hoc query processing techniques for stored and stream data to automatically enforce specific beyond-accuracy properties, with a special reference to diversity. The focus will be on compositional techniques: property satisfaction will be preserved in any more complex query workflow, possibly iteratively combining together several query processing steps.

Link to the group or personal webpage: dama.dibris.unige.it

References:

[1] S Abiteboul et Al. Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). Dagstuhl Manifestos 7(1): 1-29 (2018)

[2] M Kaminskas, D Bridge. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. TiiS 7(1): 2:1-2:42 (2017) 

[3] J Stoyanovich, B Howe, H.V. Jagadish. Special Session: A Technical Research Agenda in Data Ethics and Responsible Data Management. SIGMOD Conf. 2018: 1635-1636 (2018)

[4] J Stoyanovich, K Yang, H.V. Jagadish. Online Set Selection with Fairness and Diversity Constraints. EDBT 2018: 241-252 (2018)


Title: Assessing similarity: the role of embeddings in schema/ontology matching and in query relaxation
Proposer(s):  Giovanna Guerrini
Research area(s): Data Science and Engineering
Curriculum: Computer Science

Description:
A large number of applications need to be able to assess similarity between concepts that are represented by words, possibly bound by hierarchical structures. Word embeddings are used for many natural language processing (NLP) tasks thanks to their ability to capture the semantic relations between words by embedding them in a metric space with lower dimensionality [1]. Word embeddings have been mostly used to solve traditional NLP problems, such as question answering, textual entailment, and sentiment analysis. 

Recently, embeddings emerged as a new way of encapsulating hierarchical information [2]. Specifically, hyperbolic embeddings lie in hyperbolic spaces, which are suitable to represent the hierarchical structure and maintain distances among elements. The idea behind hyperbolic embeddings is very simple: forcing elements with semantic correlations to be closest to each other in the embedding space.

The aim of this research theme is to exploit this way to assess similarity to more efficiently and accurately establish mappings between schemas and align ontologies [3], which are crucial tasks for information integration. The ability of efficiently establish similar/corresponding terms can also be exploited in relaxed query processing [4].

Finally, the possibility of exploit embeddings also for assessing many-faceted similarity for concepts described by a word but also positioned in space, as in the case of geo-terms, can be investigated [5].

Link to the group or personal webpage:

dama.dibris.unige.it

References:

[1] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. ICLR Workshop, 2013.
[2] M. Nickel and D. Kiela. Poincaré embeddings for learning hierarchical representations. NIPS 2017.
[3] P. Shvaiko & J. Euzenat. Ontology matching: state of the art and future challenges. IEEE Transactions on knowledge and data engineering, 25(1), 158-176, 2013.
[4] Barbara Catania, Giovanna Guerrini. Adaptively Approximate Techniques in Distributed Architectures. SOFSEM 2015: 65-77
[5] K. Beard. A semantic web based gazetteer model for VGI.  ACM SIGSPATIAL Workshop on Crowdsourced and Volunteered Geographic Information, 2012.


Title: Machine learning for prognostic maintenance
Proposers: Stefano Rovetta, Francesco Masulli
Curriculum: Computer Science


Description: 
Predictive maintenance is widely acknowledged as the "killer application" of machine learning in Industry 4.0.  This research activity will develop machine learning methods for prognostic maintenance, an approach that aims not only at predicting the future maintenance necessities, but also at describing causes and effects of future evolutions of a system: "foresight," as opposed to "forecast."

The  activity will be carried on in collaboration with a software company that already markets a more traditional solution for predictive maintenance. Therefore, the work will build on an existing, substantial body of tools and know-how. The candidate is expected to develop competences that are of great technical, industrial, ans well as commercial, interest.

Link to the group or personal webpage: https://www.dibris.unige.it/rovetta-stefano

References:

[1] Vogl, G.W., Weiss, B.A. & Helu, M. "A review of diagnostic and prognostic capabilities and best practices for manufacturing." J Intell Manuf (2019) 30: 79.


Title: Smart request processing for personalised data space-user interactions through approximation and learning

Proposers: Barbara Catania, Giovanna Guerrini
Curriculum: Computer Science

Description:
The increase of data size and complexity requires a deep revisiting of user-data interactions and a reconsideration of the notion of query itself. A huge number of applications need user-data interactions that emphasize user context and interactivity with the goal of facilitating interpretation, retrieval, and assimilation of information [1]. The ability to learn from observations and interactions [2] as well as to approximate process requests [3,4] are two key ingredients in these new settings.

The aim of this research theme is to devise smart innovative approaches for exploiting, from a processing viewpoint and a focus on graph-shaped data, the role of user context (geo-location, interests, needs) and of similar requests repeated over time to inform approximation and refine knowledge on underlying data, which in turn can be used to more efficiently and effectively fulfill information needs. Preliminary approaches in this direction have been proposed in [5].

Link to the group or personal webpage: dama.dibris.unige.it

References:

[1] Georgia Koutrika. Databases & People: Time to Move on From Baby Talk. EDBT/ICDT ‘18
[2] Yongjoo Park, Ahmad Shahab Tajik, Michael Cafarella, and Barzan Mozafari. Database Learning: Toward a Database that Becomes Smarter Every Time. ACM SIGMOD ‘17
[3] Peter Haas. Some Challenges in Approximate Query Processing. EDBT/ICDT ‘2018
[4] Barbara Catania, Giovanna Guerrini. Adaptively Approximate Techniques in Distributed Architectures. SOFSEM ‘15
[5] Barbara Catania, Francesco De Fino, Giovanna Guerrini. Recurring Retrieval Needs in Diverse and Dynamic Dataspaces: Issues and Reference Framework. EDBT/ICDT Workshops 2017


 Title: Human-object interaction

Proposer: Francesca Odone, Nicoletta Noceti 

Curriculum: Computer Science

Research line: Data Science and Engineering

Topics: Computer Vision, Machine Learning, Deep Learning

Description: In many human-centered applications, it is important to study the interaction of a person with the surrounding environment. In this project we will consider smart environments as a reference application domain. Here we consider different sources of video information, including RGB and RGB-D cameras providing environmental as well as ego-centric views (wearable cameras).

Building on ongoing research, where we estimate the 2D and 3D pose of people, as well as the pose of objects in the scene, our first goal is to detect meaningful interactions and points of contact between people and objects; this activity will be carried out considering both third-view and ego-view. These interactions will be used to improve atomic action recognition performances, and to understand more complex activities.

The long-term goal of this proposal will be the anticipation of human intentions, on specific human-object interaction tasks (e.g. object grasping).

References

Dessalene, Eadom, Chinmaya Devaraj, Michael Maynord, Cornelia Fermuller, and Yiannis Aloimonos. "Forecasting action through contact representations from first person video." IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 

Zhang, M., Teck Ma, K., Hwee Lim, J., Zhao, Q. and Feng, J., 2017. Deep future gaze: Gaze anticipation on egocentric videos using adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recogniton (pp. 4372-4381).

Link to the research group: https://ml.unige.it/research/mlv/ 


 Title: Interpretable video analysis methods

Proposer: Francesca Odone, Nicoletta Noceti 

Curriculum: Computer Science

Research line: Data Science and Engineering

Topics: Computer Vision, Machine Learning, Deep Learning

Description: Interpretable models are a main research direction today, as an alternative to very efficient black box neural networks. Current research is leading to hybrid models (combining end-to-end with modular interpretable architectures) to balance the benefits of both approaches – efficiency on one side, a clearer understanding on the way the architecture works on the other. In the computer vision domain, while state-of-the art is proposing various alternatives to “explain” the meaning of black boxes outputs (explainability has not to be confused with interpretability), there is still room for interpretable models. This is true in particular in the case of video analysis, which is still struggling with the complexity of the task and the size of the data. This project will start with an exploration of the fastly evolving state of the art, and will focus primarily on interpretable features learnt from video datasets. Particular attention will be put on approaches to modify traditional convolutional neural networks (CNNs) into interpretable CNNs. Among the application scenarios, we will take into account the medical one, where interpretability is a major requirement. In particular we will relate this work with ongoing research on human motion analysis for diagnostic  and rehabilitation purposes.

 References

 Hohman, F., Kahng, M., Pienta, R. and Chau, D.H., 2018. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE transactions on visualization and computer graphics, 25(8), pp.2674-2693.

Zhang, Q., Wu, Y.N. and Zhu, S.C., 2018. Interpretable convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8827-8836).

Meng, L., Zhao, B., Chang, B., Huang, G., Sun, W., Tung, F. and Sigal, L., 2019. Interpretable spatio-temporal attention for video action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops .


Link to the research group: https://ml.unige.it/research/mlv/


Title: Machine learning for modeling networked systems

Proposer: Annalisa Barla
Curriculum: Computer Science

Description:
We are recruiting one PhD candidate, to work on machine learning methods that are suitable for:
- understanding of large amounts of textual data
- inferring of complex relational models
The methods should be designed to deal with large-scale temporal data (dynamical systems). These methods are gaining relevance in all those fields that aim at modeling underlying semantic laws of complex phenomena. 
In particular we have in mind two possible scenarios: (1) data driven web design, where the aim is to devise an optimal information architecture; (2) to the analysis of structured biomedical and clinical data, where the aim is to identify relational patterns that are predictive of a certain pathological condition.
The ideal candidate should have strong mathematical and computational skills and be interested in working on either NLP applications (topic modeling, text representation) or in modeling theory (block modeling, pattern-based community detection).   

Link to the group or personal webpage: ml.unige.it


Research line: Artificial Intelligence and Multiagent Systems 

Title: Automated Reasoning and/or Natural Language Processing for Evidence Analysis

Proposers: Viviana Mascardi

Curriculum: Computer Science

Description: Evidence analysis involves examining fragmented incomplete  knowledge and reasoning on it, in order to reconstruct plausible crime scenarios. The DigForASP COST Action involves the proposer in the role of Working Group Leader and aims at exploiting Automated Reasoning techniques and tools to analyse evidences, in a way that is explainable. In many cases, evidences are either described in textual documents written in natural language, or hidden in semi-structured data where short texts are involved (for example, telephone records including transcripts of calls and sms): their automated analysis cannot be carried out without a pre-processing phase based on Natural Language Processing.

The aim of this research is to exploit either Automated Reasoning or Natural Language Processing, or both if the candidate will possess both skills, to provide an automated support to evidence analysis. The research will be carried out within DigForASP and will take advantage of a collaboration with the "Tribunale di Genova", with which the proposer is currently collaborating for dissemination activities related with AI and Law.

Link to the group or personal webpage: https://www.dibris.unige.it/mascardi-viviana

References:
DigForASP COST Action (CA17124,https://digforasp.uca.es, funded for four years starting from 09/2018by the European Cooperation in Science and Technology)


Title: Improve Model Checking in Multi-Agent Systems via Runtime Verification

Advisors: Angelo Ferrando, Vadim Malvone (Télécom Paris) and Viviana Mascardi
 
Model Checking [1] is a well-known formal verification technique which is used to check if a mathematical model of the system satisfies a property (i.e. what we expect the system should do). Here, the properties are specified via temporal logics. Unfortunately, this kind of technique is computationally expensive and becomes easily unfeasible when applied to large systems (state space explosion). Luckily, other more lightweight formal verification techniques exist, like Runtime Verification [2], where the verification of the property is performed at runtime on the actual system (rather than on a model of the latter).
 
To solve the verification in which several systems interact with each other, the model checking problem has been extended to multi-agent systems. In this context, temporal logics have been extended to temporal logics for the strategic reasoning [3,4]. Here, the state space explosion remains an open problem and in addition, the introduction of a set of agents, each one with its visibility, makes the verification very hard and often undecidable.
 
Runtime verification might help to overcome these difficulties, but it has never been applied in the strategic scenario. The aim of this research theme is to introduce, in theory and practice, runtime verification in the model checking problem for multi-agent systems. Moreover, by enhancing Runtime Verification with strategic behaviour, very interesting new features can be exploited. For instance, the dynamic synthesis of strategies for the agents by monitoring the system execution. 
 
[1] Model Checking, Edmund M. Clarke, Jr., Orna Grumberg and Doron A. Peled, MIT Press, 1999.
[2] Martin Leucker, Christian Schallhart. A brief account of runtime verification. J. Log. Algebraic Methods Program., 2009.
[3] R. Alur, T.A. Henzinger, and O. Kupferman. Alternating-Time Temporal Logic. JACM, 49(5):672–713, 2002.
[4] F. Mogavero, A. Murano, G. Perelli, and M. Y. Vardi. Reasoning About Strategies: On the Model-Checking Problem. TOCL, 15(4):34:1--34:47, 2014.
 

Title: Hybrid deliberative/reactive robot architectures for task scheduling and joint task/motion planning 
Proposers: Marco Maratea (This email address is being protected from spambots. You need JavaScript enabled to view it.), Fulvio Mastrogiovanni (This email address is being protected from spambots. You need JavaScript enabled to view it.)
Curriculum: Computer Science

Short description: A longstanding goal of Artificial Intelligence (AI) is to design robots able to autonomously and reliably move in the environment and manipulate objects to achieve their goals. Autonomy and reliability can be considered still unsolved issues when robots operate in everyday environments, especially in the presence of humans. Traditionally, beside specific activities in perception, knowledge representation, action, and the mechanical structure of robots, an important research trend is related to the architecture robots may adopt to enforce autonomy and reliability, and specifically robustness and resilience to unexpected events, as well as uncertainty in perception and action outcomes.

This project aims at investigating, designing and prototyping robot architectures able to (i) interleave scheduling (i.e., the long-term definition of what a robot should do in the future) and task planning (i.e., which specific actions a robot should perform next), and (ii) integrate task and motion planning (i.e., what robot trajectories correspond to planned actions), in full perception-representation-reasoning-action loop.

On the one hand, the integration between scheduling and planning has not received sufficient attention in the literature, and only recently the issue has been studied, possibly relying on modeling through logical languages, e.g., PDDL or Answer Set Programming.

On the other hand, while discrete task planning has been considered mainly in the AI community, continuous motion planning has been the focus in much Robotics research. Such a separation leads to suboptimal robot behavior in real-world scenarios, especially in case of not modeled events, misperceptions or uncertainty in sensory data. However, in the past few years, a number of approaches have been discussed in the literature, which aim at integrating the discrete and the continuous planning process. The recent introduction of such planning formalisms as PDDL+ is a decisive step in this direction, and its effective use in Robotics architectures has not been fully explored yet.

The Ph.D. student will be involved in ongoing research activities in the application of advanced AI techniques to Robotics. In particular, the following topics will be considered:

  • Definition of expressive and computationally efficient knowledge representation approaches for robots.
  • Definition of innovative and efficient scheduling algorithms suitable for a robotic setting.
  • Representation of robot perceptions using logic formalisms able to ground further reasoning processes, i.e., knowledge revision, update, fusion.
  • Design and implementation of joint task/action planning strategies for robots. 

Link to the group/personal page: http://www.star.dist.unige.it/~marco/, https://www.dibris.unige.it/mastrogiovanni-fulvio


 Title: Inductive and Deductive Reasoning in Transportation

 Proposers: Davide Anguita (This email address is being protected from spambots. You need JavaScript enabled to view it.), Marco Maratea (This email address is being protected from spambots. You need JavaScript enabled to view it.)

 Macro-areas: Artificial Intelligence, Data Analysis

 Curriculum:Computer Science

Short description:Inference is defined as the act or process of deriving logical conclusions from premises known or assumed to be true. Deduction is an inference approach since it does not implies any risk. Once a rule is assumed to be true and the case is available there is no source of uncertainty in deriving, through deduction, the result. Inductive reasoning, instead, implies a certain level of uncertainty since we are inferring something that is not necessary true but probable. The inductive reasoning is the simple inference procedure that allows to increment our level of knowledge since the induction allows to infer something that is not possible to logically deduce just based on the premises. New generation of information systems collects and store a large amount of heterogeneous data which allow to induce Data Driven Models able to forecast their evolution. Deep, Multi-Task, Transfer, Semi-Supervised learning algorithms together with rigorous statistical inference procedures allow us to transform large and heterogeneous amounts of distributed and hard-to-interpret pieces of information in meaningful, easy to interpret and actionable information. Data Driven Models scale well with the amount of data available but they are not as effective if exploited for deduction purposes. On the contrary, Model Based Reasoning allows to model in an effective way complex systems based on the physical knowledge about them and deduce meaningful information by solving complex (optimization) problems. The general idea is to encode an application problem as a logical specification. This specification is subsequently evaluated by a general-purpose solver, whose answers can be mapped back to solutions of the initial application problem. The Model Based Reasoning limitation is that they may not scale well with the size of problem. The scope of this PhD proposal is to make inductive and deductive reasoning work together for the purpose of solving real world problems related to the transportation domain (e.g. Railway, Busses, and Airways). Transport of goods and people is a multifaceted problem since it involves technical constraints coming from the limited physical assets, safety constraints, social and cultural implication. In Europe the increasing volume of people and freight transported is congesting the transportation systems. The challenge of this research theme is twofold. From one side there is a necessity to exploit and further refine the state-of-the-art tools and basic research themes in the inductive and deductive fields in order to make induction and deduction work together and solve the respective limitations. From the other side there are plenty of real world problems in public transportation (e.g. multimodal transportation systems, train dispatching, combinatorial problems, and forecast problems) that needs the combination of different technological tools and techniques in order to be able to obtain satisfying results.

Link to the group/personal page: www.smartlab.ws,http://www.star.dist.unige.it/~marco/ 


Title: Planning and Scheduling for Digital Health
Proposer: Marco Maratea (This email address is being protected from spambots. You need JavaScript enabled to view it.)
Macro-areas: Artificial Intelligence
Curriculum: Computer Science

 Short description: Modern hospitals are often characterized by long surgical waiting lists, which are usually caused by inefficiencies in the ability to (optimally) schedule internal processes, and/or by the (un)availability of critical resources (e.g., ICU beds), with an obvious dissatisfaction of patients, unoptimal use of resources as well as possible increase of costs. These issues have been (are being) particularly impacting during the Covid-19 pandemic, and are likely to be even more critical in the post pandemic. Related problems can be efficiently tackled by employing automated planning and scheduling techniques based on Artificial Intelligence methodologies, in which AI languages specify the requirements/desiderata of the problem, and an automated AI solver gives as output the (optimal) plan/schedule. Moreover, by using these methodology the resulting plan/schedule can be (relatively easily) explained to an end user (Trustworthy/Explainable AI). 

Examples of problems that can be solved are:

-  Operating room scheduling in presence of (scarce) resources, e.g. ICU beds [1,3]; workforce management [4]; waiting list management; chemotherapy treatment planning [2].

- Vaccination planning.

Link to the group/personal page: http://www.star.dist.unige.it/~marco/ 

Referce

  1. Dodaro, G. Galata, M.K. Khan, M. Maratea, I. Porro – Operating Room (Re)Scheduling with Bed Management via ASP. Theory and Practice of Logic Programming journal. To appear.
  2. Dodaro, G. Galata, M. Maratea, M. Mochi, I. Porro - Chemotherapy Treatment Scheduling via Answer Set Programming.
    Proc. CILC 2020. CEUR Workshop Proceedings, Vol. 2710: 342-365, 2020.
  3. Dodaro, G. Galata, M.K. Khan, M. Maratea, I. Porro – An ASP-based Framework for Operating Room Scheduling with Beds Management
    Proc. of the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019), LNCS 11784, pages 67-81, 2019.
  4. Dodaro, M. Maratea - Nurse Scheduling via Answer Set Programming.
    Proc. LPNMR 2017, LNCS 10377, pages 301-307, 2017.

Research line: Secure and Reliable Systems

Title: Runtime monitoring and verification with RML

Proposer: Davide Ancona

Research activity: Secure and Reliable Systems (SRS) (https://www.dibris.unige.it/en/29-dibris/ricerca/343-secure-and-reliable-systems-en)

Curriculum: Computer Science

Description:
Runtime Monitoring (RM) is concerned with the runtime analysis of software and hardware system executions in order to infer properties relating to
system behavior. Example applications include telemetry, log aggregation, threshold alerting, performance monitoring and adherence to correctness properties, more commonly referred to as Runtime Verification [1,2]. RM has gained popularity as a solution to ensure software reliability, bridging the gap between formal verification [3] and testing [4]: on the one hand, the notion of event trace abstracts over system executions, thus favoring system agnosticism to better support reuse and interoperability; on the other hand, monitoring a system offers more opportunities for addressing error recovery, self-adaptation, and issues that go beyond software reliability. RML (Runtime Monitoring Language) [9,6,5,7] is an expressive Domain Specific Language for RM which favors abstraction and simplicity, to better support reusability and portability of specifications and interoperability of their generated monitors. The main aim of this research theme is to further study and advance RML; there are several research directions that deserve further investigation, depending on the interests of the prospective PhD student.

- Theory: challenging theoretical issues concern the formal semantics of RML, its decidability and expressive power.
- Language design and implementation: RML is a research DSL amenable to extensions that include
* direct support for analysis of data streams;
* rule engine implementation to allow generated monitors to react to events with specifically triggered actions;
* integration with ontologies, for a more flexible handling of events;
* better support to usability and development with error reporting, static checking, an IDEs.
- Applications: assessment on the usability and applicability of RML calls for experimentation in important research areas, such as the Internet of Things [8] and Cyber Security.

References:
[1] Y. Falcone, K. Havelund, G. Reger, A Tutorial on Runtime Verification, in: Engineering Dependable Software Systems, 141–175, 2013.

[2] E. Bartocci, Y. Falcone, A. Francalanza, G. Reger, Introduction to Runtime Verification, in: Lectures on Runtime Verification - Introductory
and Advanced Topics, 1–33, 2018.

[3] W. Ahrendt, J. M. Chimento, G. J. Pace, G. Schneider, Verifying data- and control-oriented properties combining static and runtime verification: theory and tools, Formal Methods in System Design 51 (1) (2017) 200–265.

[4] M. Leotta, D. Clerissi, L. Franceschini, D. Olianas, D. Ancona, F. Ricca, M. Ribaudo,
Comparing Testing and Runtime Verification of IoT Systems: A Preliminary Evaluation based on a Case Study. ENASE 2019: 434-441

[5] https://rmlatdibris.github.io/

[6] L. Franceschini, RML: Runtime Monitoring Language, Ph.D. thesis,
DIBRIS - University of Genova, URL http://hdl.handle.net/11567/1001856, March 2020

[7] D. Ancona, L. Franceschini, A. Ferrando, V. Mascardi, A SWI-Prolog based implementation of RML,
Workshop on Trends, Extensions, Applications and Semantics of Logic Programming, ETAPS 2020

[8] D. Ancona, L. Franceschini, G. Delzanno, M. Leotta, M. Ribaudo, F. Ricca,
Towards Runtime Monitoring of Node.js and Its Application to the Internet of Things. ALP4IoT@iFM 2017: 27-42

[9] D. Ancona, L. Franceschini, A. Ferrando, V. Mascardi, RML: Theory and practice of a domain specific language for runtime verification. Science of Computer Programming, Volume 205, 2021


Title: Novel Testing Approaches for Modern Software Systems
Proposers: Maurizio Leotta, Filippo Ricca
Curriculum: Computer Science

Short Description:
Modern software applications have a significant impact on all aspects of our society, being crucial for a multitude of economic, social, and educational activities. Indeed, a considerable slice of modern software runs on web browsers and smartphones, and a good portion of the market is occupied by IoT applications.

As a consequence, the correctness and quality of such applications is of undeniable importance. The complexity of such kind of systems combined with the ever shorter development cycles, drastically demand for novel approaches to testing. Several interesting research directions are lately emerging, ranging from the automated generation of test suites using, e.g., search based strategies to the usage of machine learning (ML) and Artificial Intelligence (AI) to even increase the effectiveness of Testing frameworks and Tools.

The PhD candidate will select one research direction, among the many available and covered by the @DIBRIS Software Testing group, and will work on defining novel approaches and solutions to improve the state of the art.

Link to the personal webpages:

https://www.disi.unige.it/person/LeottaM/

https://www.disi.unige.it/person/RiccaF/ 

References

[1] M. Polo, P. Reales, M. Piattini, C. Ebert. Test Automation. IEEE Software, 30(1), pp.84-89, 2013.

[2] M. Leotta, D. Clerissi, F. Ricca, P. Tonella. Approaches and Tools for Automated End-to-End Web Testing. Advances in Computers, 101, pp.193-237, Elsevier, 2016. 


Title: Software-Engineering the Internet of Things

Proposer(s): Gianna Reggio
Curriculum: Computer Science, Secure and Reliable Systems

Short Description: Internet of Things (IoT)[3] based systems are very recent, and pose new difficult problems to developers, as stated e.g. in [1] and [2], for which no software engineering support is available yet: "Confronted by the wildly diverse and unfamiliar systems of the IoT, many developers are finding themselves unprepared for the challenge. No consolidated set of software engineering best practices for the IoT has emerged. Too often, the landscape resembles the Wild West, with unprepared programmers putting together IoT systems in ad hoc fashion and throwing them out into the market, often poorly tested.", as stated by [2].

The thesis aims initially at assessing the state-of-the-art of IoT based systems development, surveying companies and startups, and the scarce existing literature, to identify:
- the currently used development processes, methods, and software engineering techniques, e.g. testing (if any);
- the mostly used software tools, frameworks, standards and protocols;
- the perceived problems, and unsatisfied needs.

Then, the task of capturing and specifying the requirements for an IoT-based system will be considered, with a particular emphasis in understanding which are the relevant non-functional requirements. The preliminary proposal of [4] of a method based on the UML and following the service-oriented paradigm for capturing and specifying the requirements on an IoT-based system a will be extended to cover the non-functional requirements, and validated by industrial cases studies

Finally, the work will tackle the task of designing and implementing an IoT-system starting from the requirement specifications of the previous step, proposing specific methods. The new methods can also help to understand what are the most appropriate protocols and technologies to choose.

[1] D. Spinellis. 2017. Software-Engineering the Internet of Things. IEEE Software 34, 1 (2017), 4-6. http://ieeexplore.ieee.org/document/7819398/
[2] X. Larrucea, A. Combelles, J. Favaro, and K. Taneja. 2017. Software Engineering for the Internet of Things. IEEE Software 34, 1 (2017), 24Ð28. https://doi.org/doi.ieeecomputersociety.org/10.1109/MS.2017.28
[3] IEEE Internet Initiative. 2015. Towards a definition of the Internet of Things (IoT). (2015). Available at iot.ieee.org/images/files/pdf/IEEE_IoT_Towards_Definition_Internet_of_Things_Revision1_27MAY15.pdf.
[4] Gianna Reggio. 2018. A UML-based Proposal for IoT System Requirements Specification. In MiSEÕ18: MiSEÕ18:IEEE/ACM 10th International Workshop on Modelling in Software Engineering , May 27, 2018, Gothenburg, Sweden. ACM, New York, NY, USA, Article 4, 8 pages. https://doi.org/10.1145/3193954. 3193956

Link to the group/personal webpage: http://sepl.dibris.unige.it/ 


Title: A Holistic Method for Business Process Analytics

Proposers: Gianna Reggio, Filippo Ricca
Curriculum: Computer Science, Secure and Reliable Systems

Short Description: In the last decade, the availability of massive storage systems, large amounts of data (big data) and the advances in several disciplines related to data science provided powerful tools for potentially improving the business activities of the organizations. Unfortunately, it is rather difficult to graft modern big data practices into existing infrastructures and into company cultures that are ill-prepared to embrace big data, for example [1] reports the following staggering figures about the success rate of big-data projects:" A year ago, Gartner estimated that 60 percent of big data projects fail. As bad as that sounds, the reality is actually worse. According to Gartner analyst Nick Heudecker? this week, Gartner was "too conservative" with its 60 percent estimate. The real failure rate? "closer to 85 percent." 

In other words, abandon hope all ye who enter here, especially because "the problem isn't technology," Heudecker said. It's you. "

Initially, we plan to investigate which are the reasons leading to the failure of big-data projects by surveying scientific and grey literature, and also if and how
the few existing approaches to support big-data/analytics projects (e.g. CRISP-DM [6] and DataOps [7]) can overcome them.

Then, we will consider the restrict field of "Business Process Analytics" (BPA), that refers to collecting and analysing the business process-related data to answer some process-centric questions (see, e.g. [3] and [2]).
Based on the initial investigations, the aim of the thesis is to develop a holistic method combining business process modelling and data-driven business process improvement to successfully leverage big-data. The method will help:
- connect the business processes, and the stakeholderÕs goals with the available data;
- elicit the right questions for improving the business activities, and successively selecting the right analytic techniques for answering them;
- optimize the data collection and storage with respect the useful analysis.

Some initial ideas can be found in [4].

References:
[1] M. Asay. 85% of big data projects fail, but your developers can help yours succeed. TechRepublic, CBS Interactive.
November 10, 2017. www.techrepublic.com/article/85-of-big-data-projects-fail-but-your-developers-can-help-yours-succeed/
[2] S.Beheshti,B.Benatallah,S.Sakr,D.Grigori,H.Motahari-Nezhad,M.Barukh,A.Gater, and S. Ryu. Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data. Springer, 2016.
[3] M. zur MŸhlen and R. Shapiro. Business Process Analytics, pages 137Ð157. Springer, 2010.
[4] Reggio G., Leotta M., Ricca F., Astesiano E. Towards a Holistic Method for Business Process Analytics. In: Zhang L., Ren L., Kordon F. (eds) Challenges and Opportunity with Big Data. Monterey Workshop 2016. Lecture Notes in Computer Science, vol 10228. Springer. 2017.
[6] Cross-industry standard process for data mining (CRISP-DM). https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining. Last seen March 2018.
[7] The DataOps Manifesto. http://dataopsmanifesto.org/. Last seen March 2018.

Link to the group or personal webpage: http://sepl.dibris.unige.it/index.php


Research line: Human-Computer Interaction 

Titolo: Interactive Sonification of Human Movement

Proposers: Antonio Camurri, Andrea Cera, Gualtiero Volpe 

Curriculum: Computer Science

Description.

Information visualization if a well-known field in computer science to convey information and in human-computer interfaces. Recent neuroscience research has shown the importance of sonification: our brains use all available sensory feedback, including sound, to keep track of the changing structure and position of the body in space and to adjust actions. The relation between sound and movement is supported by tight links between auditory and motor areas of the brain. For instance, listening to rhythms activates motor and premotor cortical areas, hence the use of rhythmic acoustic feedback to entrain movement. In addition, natural or artificial sounds such as tones and music have been shown to trigger emotional responses in listeners. Studies on the human brain have shown unlearned preference for certain types of sound, such as harmonic and periodic sounds of which music is a particular case. This growing body of work supports the use of sonification of movement and related processes as a powerful way to increase positive body awareness and facilitate engagement with movement. Sonification of body movement, as a means to inform, has been shown to improve motor control and possibly motor learning.

The proposed PhD research project is a part of a broader project exploring how movement qualities can be recognized by means of the auditory channel: can we perceive an expressive full-body movement quality by means of its interactive sonification? The research will investigate cross-modal correspondences (Spence 2011) to design computational models and systems implementing sonification models (based on sound signal synthesis and processing) and capable to “translate” movement qualities into the auditory channel in real-time.  The starting points for the research in this PhD  are the papers by Singh et al (2016) and Niewiadomski et al (2019).

Research activities will benefit from results from the European funded project FET PROACTIVE EnTimeMent, and short residencies at the premises of one or more partners of EnTimeMent, including UCL University College London (Prof Nadia Berthouze). The research will include experiments and the development of prototype applications in one of the EnTimeMent scenarios, including serious games for therapy and rehabilitation (in collaboration with the Gaslini Children Hospital, and with UCL), sport, or artistic and education applications.

Disciplines: Human-Computer Interaction, Sound and music computing, Sonic interaction design, affective computing, Multimodal interfaces and systems.

Link to the group/personal webpage:

www.casapaganini.org

entimement.dibris.unige.it

ariel.unige.it  (Joint Augmented Rehabilitation Lab DIBRIS – Gaslini Children Hospital)

References

  1. Singh, S. Piana, D. Pollarolo, G. Volpe, G. Varni, A. Tajadura-Jiménez, A. CdeC Williams, A. Camurri, N. Bianchi-Berthouze (2016) Go-with-the-Flow: Tracking, Analysis and Sonification of Movement and Breathing to Build Confidence in Activity Despite Chronic Pain. Human–Computer Interaction, Taylor & Francis, 31(3-4), pp.335-383.
  2. Niewiadomski, M. Mancini, A. Cera, S. Piana, C. Canepa, A. Camurri (2019) Does embodied training improve the recognition of mid-level expressive movement qualities sonification? Journal on Multimodal User Interfaces, 13(3), pp.191-203.

Spence C (2011) Crossmodal correspondences: a tutorial review. Atten Percept Psychophys 73(4):971–995. https://doi.org/10.3758/s13414-010-0073-7

EU FET PROACTIVE EnTimeMent Project (entimement.dibris.unige.it)


Titolo: Affective Motion Capture

Proposers: Antonio Camurri, Giorgio Gnecco, Marcello Sanguineti, Gualtiero Volpe 

Curriculum: Computer Science

Description. The decreasing cost of whole-body sensing technology and its increasing reliability are leading to innovative techniques and technologies capable to recognize people’s affective states. A growing interest in, and understanding of, the role played by full-body expressions as a powerful affective communication channel is consolidated in both industry and research institutions (Kleinsmith and Berthouze 2013). Motion-capture technology is moving beyond the mere tracking of low-level features (such as position and speed) and the recognition of “what” movement is performed. Rather, the automated analysis of “how” a movement is performed at different temporal scales opens a wide range of applications, from therapy and rehabilitation to several cultural and creative industry applications. In this direction, a growing amount of investment is made on novel motion-capture technologies capable to analyze and predict the expressive, affective, and social qualities in both individual and group behavior.

This proposal focuses on research in affective body expression perception, recognition, and prediction. It will benefit from the ongoing activities of the European-funded FET PROACTIVE EnTimeMent 4-year project (entimement.dibris.unige.it). EnTimeMent aims at the foundation and consolidation of radically new models and motion analysis technologies for automated prediction and analysis of human movement qualities, entrainment, and non-verbal full-body social emotions. The approach is grounded on novel neuroscientific, biomechanical, psychological, and computational evidence dynamically suited to the human time, towards time-adaptive technologies operating at multiple time scales in a multi-layered approach. The research will benefit also from the motion capture and multimodal technology infrastructure available at Casa Paganini-InfoMus of Dibris (www.casapaganini.org). Specific application testbeds to validate and evaluate research results will be identified in one of the EnTimeMent scenarios (cognitive-motor rehabilitation, performing arts, sport).

Research activities will include collaborations and short residencies at the premises of one or more partners of the EnTimeMent project, including Qualisys (motion capture industry), Euromov – University of Montpellier (Prof Benoit Bardy), UCL University College London (Prof Nadia Berthouze), with IMT - School for Advanced Studies, Lucca (Prof Giorgio Gnecco), and with the incubators of startups GDI Hub (London) and Wylab (Chiavari),

Disciplines: Human-Computer Interaction, Affective computing, Motion capture, Multimodal interfaces and systems, Operation research.

Link to the group/personal webpage: www.casapaganini.org   entimement.dibris.unige.it

References

Kleinsmith, A.,  Bianchi-Berthouze, N. (2013)" Affective Body Expression Perception and Recognition: A Survey," IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 15-33, Jan.-March 2013, doi:10.1109/T-AFFC.2012.16

EU FET PROACTIVE EnTimeMent Project (entimement.dibris.unige.it)


Titolo: Automated analysis of expressive qualities of full-body human movement

Proposers: Antonio Camurri, Giorgio Gnecco, Marcello Sanguineti, Gualtiero Volpe

Curriculum: Computer Science

 Description. The role played by full-body movements in conveying affective expressions and social signals is widely recognized by the scientific community [1], and a growing number of applications exploiting full-body expressive movement and non-verbal social signals is available. The possibility to automatically measure movement qualities is very valuable in many different interactive applications, including therapy and rehabilitation in autism, and in cognitive and motor disabilities. In [2,3], the automated analysis of the origin of movement (i.e., where in the body the movement initiates), which is an important component in understanding and modelling expressive movement, was investigated.

In the thesis, the approach proposed in [2,3] will be developed in several directions by using the same approach, based on a mathematical model called cooperative game. In general, mathematical games [4] study interactions among subjects, by modelling conflict or cooperation between intelligent entities called players. In the analysis of full-body human movement, the game model is built over a suitably-defined three-dimensional structure representing the human body. The players represent a subset of body joints. Each group of players has an associated utility, which represents their joint contribution to a common task. Using a utility constructed starting from a movement-related feature such as speed, a cooperative game index called Shapley value [4] can be exploited to analyse expressive qualities (e.g., to identify the movement origin, as done in [2,3] using the feature speed). Targets of the proposed thesis include, e.g:

- Considering different formulations of the cooperative game and/or different cooperative indices.

- Extracting and embedding into the model other features and/or sets of features calculated from movements,     such as position, acceleration, speed, jerks, and angular acceleration.

- Investigating the time series of the Shapley values to capture the dynamics of movement in finer details (e.g., the importance of different timescales in recognizing a specific movement).

- Modelling biomechanical constraints, which determine the way we move as well as the way we perceive movements.

- Analysing the automatic detection of movement qualities different from the origin of movement.

- Conceiving novel experiments, in order to build up the movement repertoire and enlarge the available motion-capture data set.

As an outcome of the thesis, a larger set of computational methods and software tools will be available for the automatic analysis of expressive qualities associated with full-body human movement.

This thesis will benefit from the ongoing activities of the European-funded FET PROACTIVE EnTimeMent 4-year project (entimement.dibris.unige.it). EnTimeMent aims at the foundation and consolidation of radically new models and motion analysis technologies for automated prediction and analysis of human movement qualities, entrainment, and non-verbal full-body social emotions. The approach is grounded on novel neuroscientific, biomechanical, psychological, and computational evidence dynamically suited to the human time, towards time-adaptive technologies operating at multiple time scales in a multi-layered approach. The research will benefit also from the motion capture and multimodal technology infrastructure available at Casa Paganini-InfoMus of Dibris (www.casapaganini.org). Specific application testbeds to validate and evaluate research results will be identified in one of the EnTimeMent scenarios (cognitive-motor rehabilitation, performing arts, sport). 

Research activities will include collaborations and short residencies at the premises of one or more partners of the EnTimeMent project, including Qualisys (motion capture industry), Euromov – University of Montpellier (Prof Benoit Bardy), UCL University College London (Prof Nadia Berthouze), with IMT - School for Advanced Studies, Lucca (Prof. Giorgio Gnecco), and with the incubators of startups GDI Hub (London) and Wylab (Chiavari),

Disciplines: Human-Computer Interaction, Affective computing, Motion capture, Multimodal interfaces and systems, Operation research.

Link to the group/personal webpage: www.casapaganini.org  entimement.dibris.unige.it

References

[1] A. Kleinsmith and N. Bianchi-Berthouze, “Affective body expression perception and recognition: A survey,” IEEE Transactions on Affective

Computing, vol. 4, no. 1, pp. 15–33, 2013.

[2] K. Kolykhalova, G. Gnecco, M. Sanguineti, A. Camurri, and G. Volpe: Graph-restricted game approach for investigating human movement qualities”. Proc. 4th Int. Conf. on Movement Computing (MOCO ’17). London, UK: ACM, 2017, article no. 30, 4 pages.

[3] K. Kolykhalova, G. Gnecco, M. Sanguineti, A. Camurri, and G. Volpe: Automated analysis of human movement qualities: An approach based on transferable-utility games on graphs,” submitted.

[4] M. Maschler, E. Solan, and S. Zamir, Game Theory. Cambridge, UK: Cambridge University Press, 2013.


Title: Self avatar and embodiment for Human Computer Interaction in Mixed Reality

Proposer: Manuela Chessa and Fabio Solari
Curriculum : Computer Science

Short Description Interaction in Virtual or Augmented Reality (Mixed Reality to indicate the coexistence of both virtual and real elements) environments is obtained by means of video-based solutions (RGBD sensors) or tracking devices (controllers, sensorized gloves). Similarly, users’ navigation in the virtual world is obtained by tracking the 6DOF position of the users’ head. Though the hands and the head 6DOF positions are tracked and the VR environment is updated accordingly, most applications only display a partial representation of the user, such as the controllers or models of the hands. In the literature, there are many works trying to understand whether a self-avatar would have a positive benefit to interaction tasks, the sense of presence and perceptual judgments [1]. Moreover, the graphic properties of the self-avatar could lead to the observation of the uncanny valley problem [2]. Finally, the manipulation of the self-avatar can be the software support for both innovative entertainment applications and for understanding self-consciousness, with potential application in the context of neuro-rehabilitation, pain treatments, and to contribute to the understanding of neurological and psychiatric disease.

The aim of this research theme is to develop novel and efficient solutions to create and manipulate self-avatar inside virtual and augmented reality environments, addressing the alignment and the spatial co-localization of both virtual and real elements, but also considering nonrealistic and “impossible” situations. Moreover, the realism and the graphics properties of the avatar should be considered by addressing computer graphics techniques. The effect of the presence of the self-avatar, also considering its realism and degree of complexity, will be examined with respect to efficacy of the interaction, embodiment, sense of presence and acceptance of the developed systems.

[1] Pan Y, Steed A (2017) The impact of self-avatars on trust and collaboration in shared virtual environments. PLoS ONE 12(12): e0189078. https://doi.org/10.1371/journal.pone.0189078

[2] Schwind, V., Wolf, K., & Henze, N. (2018). Avoiding the uncanny valley in virtual character design. Interactions, 25(5), 45-49.

Link to the group/personal webpage: 

www.dibris.unige.it/en/chessa-manuela

www.dibris.unige.it/en/solari-fabio


 Title: Multimodal interactive systems based on non-physical dimensions of touch

Proposers: Antonio Camurri, Enrico Puppo, Davide Anguita, Gualtiero Volpe

Description This PhD proposal aims at investigating computational models and developing techniques and systems for the automated measure of tactility: how non-verbal, social, affective content usually communicated and perceived by touch can be communicated and perceived without any physical contact. Can tactility be as effective as the physical one in socio-emotional communication? Scientific research (e.g., McKenzie et al., 2010) as well as artistic theories and practice (e.g., the dance) demonstrate the existence of tactility. Humans are able to perceive touch even in cases of lack of physical contact, since movement alone may induce in an observer the perception of touch. Touch conveys emotions, facilitates or enhances compliance in social interactions. Touch reduces the negative effects of several chronic disease. Illusory touch occurs when people believe they have been touched but no actual tactile stimulation has been applied. This PhD proposal focuses on computational models of tactility, that is, to study and develop systems to enable the communication and perception of non-verbal, social, affective content usually communicated and perceived by touch, but without any physical contact. Tactility is the carrier of non-verbal emotional and social communication. Research challenges include the following: how does an observer perceive tactility and its role in socio-emotional interaction? Does an observer of tactility performed on a "ghost" body perceive the same socio-affective message as on a physical body? 

Proposed work plan
- Assessment of the interdisciplinary existing state of the art: motion capture, biomechanics, crossmodal perception (Spence 2011), humanistic theories and computational models of non-verbal multimodal full-body movement analysis (Kleinsmith & Berthouze 2016), social signal processing (Vinciarelli et al 2012), analysis of 3D trajectories, machine learning. Software environments for the development of real-time multimodal systems (EyesWeb http://www.infomus.org/eyesweb_ita.php);
- Design of a dataset and of pilot experiments. The dataset consist of a pool of movements performed by a number of pairs or small groups of participants highly skilled in movement execution (e.g., dancers) as well as poorly skilled. For example, two participants in front of each other at a few steps of distance; the first slowly walks to approach and touch the other (e.g. on a shoulder); then she returns to the original position, the second leaves the scene, and the first repeats the same action and touches the “ghost” of the second participant: she touches the memory, a sort of tactile photography.

The dataset will be recorded using the Qualysis (www.qualisys.com) motion capture and other sensor systems (physiology, IMUs, audio) available at DIBRIS premises of Casa Paganini-InfoMus;
- Analysis of tactility: extraction of a collection of multimodal features from the recorded data that explain the difference of same touch gesture on a real human Vs the “ghost”;
- Assessment of the analysis outcomes by comparison with ratings of the same dataset provided by human participants;
- Development, evaluation and validation of prototypes of multimodal systems exploiting tactility.

Expected results
- A collection of an archive of MoCap and multimodal data for the analysis of tactility, to be made publicly available to the research community;
- Development of novel algorithms, techniques, and software libraries for the automated analysis of tactility;
- Scientific publications in top-level international conferences and journals;
- Development of prototypes of systems exploiting tactility in at least one of the following scenarios: therapy and rehabilitation in specific activities of the ARIEL (Augmented Rehabilitation) Joint Laboratory DIBRIS-Gaslini Children Hospital; enhanced active experience of cultural heritage in collaboration with Palazzo Reale Museum in Genoa;
- Participation to public dissemination events: e.g., European Commission events, international workshops and conferences, summer schools, science festivals;
- The research may be part of international projects, including European funded Horizon 2020 ICT projects, running at Casa Paganini-InfoMus research centre. 

Link to the group or personal Webpage:

www.casapaganini.org www.youtube.com/InfoMusLab dance.dibris.unige.it

Casa Paganini – InfoMus Research Centre publications:  http://www.infomus.org/publications_ita.php

References
- Camurri, A., & Volpe, G. (2016). The Intersection of art and technology. IEEE MultiMedia, 23(1), 10-17.
- Kleinsmith, A., Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing4(1), 15-33.
- McKenzie, K. J., Poliakoff, E., Brown, R. J., and Lloyd, D. M. (2010). Now you feel it, now you don't: how robust is the phenomenon of illusory tactile experience? Perception, 39(6), 839-850.
- Spence, C. (2011). Crossmodal correspondences: A tutorial review. Attention, Perception, & Psychophysics, 73(4), 971-995.
- Vinciarelli, A., Pantic, M., Heylen, D., Pelachaud, C., Poggi, I., D'Errico, F., & Schroeder, M. (2012). Bridging the gap between social animal and unsocial machine: A survey of social signal processing. IEEE Transactions on Affective Computing3(1), 69-87.


Title: Techniques for the design and implementation of (Spatial) Augmented Reality Environments

Proposer: Manuela Chessa
Curriculum : Computer Science

Short Description Augmented Reality (AR) allows a real-time blending of digital information (e.g. text, virtual elements, images, sounds) onto the real world. Among the different technologies to design and implement AR scenarios, we can distinguish between wearable devices (e.g. the HoloLens) and non-wearable solutions, in particular Spatial Augmented Reality (SAR). This approach allows displaying additional information, virtual objects, or even changing the appearance of the physical objects directly onto the real environment. It is worth noting that,  compared to head mounted displays AR displays or handheld devices, SAR has some advantages, e.g. it allows the interaction with physical (yet augmented) objects and it scales well to multiple users and therefore supports collaborative tasks naturally. On the other side, many issues are still open, e.g., a robust detection of the 3D structure of the environments and the registration between virtual and real contents. Moreover, the combination with handled or wearable devices can further improve the range of possible solutions and interaction systems.

The research theme aims to develop novel techniques to create AR and SAR environments in which people can interact in an ecological way. Besides virtual visual information added to the real world, also sensorized objects providing controlled force and tactile feedbacks could be used to augment reality and devise novel interaction paradigms.

Link to the group/personal webpage: 

www.dibris.unige.it/en/chessa-manuela

www.manuelachessa.it


Research line: Systems Engineering 

Title:  Sustainable planning and control of distributed power and energy systems

Proposers: R. Minciardi, M. Robba
Curriculum: Systems Engineering

Description: The increase in the use of renewable energies, the emergence of distributed generation and storage systems, and, in general, the concept of “smart grids”, have given rise to the necessity of defining new decision and control schemes for planning and management purposes. Currently, a major challenge is represented by the lack of a unified mathematical framework including robust tools for modeling, simulation, control and optimization of time critical operations in complex multicomponent and multiscaled networks, characterized by microgrids, interconnected buildings, renewables, storage systems and electric vehicles. The difficulty of defining effective real time optimal control schemes derives from the structure of a power grid, and, specifically, from the presence of several issues: renewable and traditional power production, bidirectional power flows, dynamic storage systems, demand response requirements, and stochastic aspects (such as uncertainties in renewable, prices, and demand forecasting). This results in optimization problems, which are generally intractable within a real time optimal control scheme, if all components of the whole system are represented at a full level of detail. Moreover, the new regulation related to new market entrants and schemes requires a revision and improvement of distributed energy management systems planning and management, as well as their coordination in order to optimize self-consumption and energy distribution.

The proposed PhD research activity will fall within this framework and has the objective of developing and applying tractable approaches for planning and optimal control, taking into account stochastic issues (i.e., intermittent renewables, demands, prices) and considering different possible architectures (multilevel, decentralized, distributed). In particular, the formulation of the optimization and control problems will be based on realistic models for the electrical grid and for its various sub-systems (microgrids, intelligent buildings, storage systems, renewables). Moreover, different energy distribution systems will be taken into account in relation to polygenerative systems: district heating, buildings heating and cooling, with the associated storage systems, water, and gas distribution networks. Finally, different kinds of demands will be taken into account (heat, cool, electricity), as well as electrical vehicles with charging/discharging cycles within a smart grid. 

References

F. Delfino, R. Minciardi,  F. Pampararo, M. Robba. A Multilevel Approach for the Optimal Control of Distributed Energy Resources and Storage, IEEE Transactions on Smart Grid, Special Issue on Control Theory and Technology in Smart Grid, to appear

S. Bracco, F. Delfino, F. Pampararo, M. Robba, M. Rossi. A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators, Energy, 2013

H. Dagdougui, R. Minciardi, A. Ouammi, M. Robba, R. Sacile. A dynamic decision model for the real time control of hybrid renewable energy production systems, IEEE Systems Journal, Vol. 4, No. 3, 2010, p. 323-333.


Title: Optimal routing and charging of electrical vehicles in a smart grid.
Proposers: Riccardo Minciardi, Massimo Paolucci, Michela Robba
Curriculum:  System Engineering

Description: At international level, different new policies have been developed to reduce CO2 emissions, such as the Kyoto Protocol, the European 20-20-20 strategy, and the Energy roadmap 2050. The result is an increase of green technologies for energy production and transportation. Due to the presence of intermittent and distributed production (such as renewables (RES)) and loads (such as electrical vehicles (EVs)), the actual electrical grid management has to be changed, and new control strategies are necessary for the integration of electrical and transportation networks. In fact, on one side, EVs need to be charged in the fastest time possible, and, on the other side, smart grids should afford such a request. In particular, from users’ perspective it is necessary to know electrical consumes over a specific path, and to decide where and how much to charge EVs to satisfy their own travel exigencies. Instead, from the grid perspective, it is necessary to offer adequate charging facilities based on control strategies that are able to satisfy users but guaranteeing electrical grid constraints. The proposed PhD research activity will fall within this framework. In particular, the following main objectives/activities can be listed:

  • Definition and development of a discrete event optimization model for microgrids with EVs.
  • Definition and development of power management strategies for charging stations.
  • Optimal routing and charging of EVs: development of meta- and math-heuristics.

Demonstration activities with real charging stations and case studies (in collaboration with companies) are foreseen  during the three years.

Link to personal homepage

http://www.dibris.unige.it/minciardi-riccardo

http://www.dibris.unige.it/robba-michela

http://www.dibris.unige.it/paolucci-massimo

References:

Schneider, M., Stenger, A., Hof, J., 2014, An Adaptive VNS Algorithm for Vehicle Routing Problems with Intermediate Stops, in Technical Report LPIS-01/2014.

Yagcitekin, B., Uzunoglu, M., 2016, A double-layer smart charging strategy of electric vehicles taking routing and charge scheduling into account,


Title:  Smart scheduling approaches for manufacturing industry.

Proposer: Massimo Paolucci
Curriculum:  System Engineering

Short description: Scheduling in manufacturing industry involves key decisions about how to exploit at best the available resources (e.g., machines, tool, workers, energy) in order to efficiently perform the required production activities. Scheduling decisions are at the operational level, that is, they regard a short time planning horizon (a day or shift) and must take into account detailed production conditions and requirements. In real manufacturing industries scheduling problems are at a large scale (the number of activities to be performed may be huge and workshops may include many machines and tools) so that the number of possible alternative decisions usually grows exponentially. In addition, even if scheduling problems share common features, several relevant differences exist which characterize different industrial sector (e.g., food and beverage, fashion, automotive). Therefore an effective general purpose solution approach that could represent the basis for developing scheduling systems for different sectors, avoiding to restart from scratch with a specific algorithm, seems not available. The introduction of the Industry 4.0 paradigm will allow to rely on fresh data from the field, so improving the possibility of planning, adapting and revising the scheduling decisions more effectively, even reacting to the unpredicted changes that usually characterize the real production systems. Finally, sustainability issues, as energy consumption and carbon footprint, need to be included among the scheduling objectives.

The purpose of this research project is to design a new solution approach for facing a large class of the scheduling problems emerging in manufacturing industry. Such an approach can be based on several building block and strategies (recent metaheuristics as adaptive large neighborhood search or bio-inspired algorithms, simulation-optimization as well as heuristics based on mathematical programming) that can be exploited to design a solver framework for this class of hard combinatorial problems. 

Link to the group or personal webpage:

http://www.dibris.unige.it/paolucci-massimo


Title: Strategic and tactical planning in production and logistics for manufacturing industry

Proposer(s): Massimo Paolucci
Curriculum:  System Engineering

Short description: Planning at strategic and tactical level are connected problems influencing both the design of the supply chain and the manufacturing production activities. Such decisions usually involve the activation of facilities, the allocation of the available resources, as well as the aggregate management of both production and distribution activities over a medium-long time horizon. Apparently strategic and tactical planning decisions impact not only business objectives but also environmental sustainability; as an example, in closed loop supply chains planning decisions also include the use of recovered materials and components from returned products and in general the reduction of energy consumption.

Therefore the proposed research project aims at considering the problem of planning in the supply chain for manufacturing production systems in order to define a general purpose decision support system able to operate both at strategic and tactical level. The purpose is to determine a unified model and a set of optimization approaches to support planning decisions at different levels (e.g., supply network design, inventory and lot-size planning, distribution planning), including sustainability aspects, such as remanufacturing and energy consumption and emissions. Since at least part of the considered optimization problems are computationally intractable as they belong to the NP-hard complexity class, the algorithms that need to be designed and tested can range from exact approaches, based on mathematical programming models, to heuristic, metaheuristics (from neighborhood search techniques to population and bio-inspired algorithm) or matheuristics (i.e., methods that include mathematical programming models in a heuristic solution framework).

Link to the group or personal webpage:

http://www.dibris.unige.it/paolucci-massimo


Title:  Hyper- and meta- heuristics for multi-objective optimization

Proposer: Massimo Paolucci
Curriculum:  System Engineering

Short description: Most of the decision problems in real life applications require to take into account more than one objective/criterion. Usually such objectives are non-commensurable and conflicting. These problems arise in many different fields and often express the conflict between customer satisfaction, stakeholders profit and social and environmental sustainability. As an example, in manufacturing industry and logistics, planning the activities on the supply chain should aim at timely meeting the customer demand, reducing production, inventory and transportation costs, minimizing the energy consumption and CO2 emission, favoring material recycling and so on. Multi-criteria decision making deals with this wide class of decision problems and embeds Multi-objective optimization as those methods whose purpose is to define the set of solutions which deserve to be considered by decision makers. Such solutions are the so-called efficient or Pareto optimal ones. In general the problem of determining the Pareto optimal solutions for a multi-objective optimization problem is NP-hard and the dimension of such set of solutions is exponential. For this reason, metaheuristic algorithms, such as Genetic Algorithms, Simulated Annealing, Ant Colony Optimization and Particle Swarm Optimization, have been applied to multi-objective optimization since 90s. The purpose of this research is to deep investigate the possible use of metaheuristics for multi-objective optimization, trying in particular to design general purpose self-adapting algorithms. This can be pursued by experimenting the so-called hyper-heuristics, consisting in combining higher level metaheuristics with lower level metaheuristics, where the purpose of the former is to identify the best configuration of the latter when solving a given optimization problem.


 Transportation Network Optimization Via Transferable-Utility Games

Proposer: Marcello Sanguineti
Curriculum: Systems Engineering

Short description. Network connectivity is an important aspect of any transportation network, as the role of the network is to provide the society with the ability to easily travel from point to point using various modes. Analyzing networks' connectivity can assist the decision makers with the identification of weak components, to detect and prevent failures, and to improve the connectivity in terms of reduced travel time, reduced costs, increase reliability, easy access, etc..

A basic question in network analysis is: how “important” is each node? An important node might, e.g., highly contribute to short connections between many pairs of nodes, handle a large amount of the traffic, generate relevant information, represent a bridge between two areas, etc. To quantify the relative importance of nodes, one possible approach consists in using the concept of “centrality” [1, Chapter 10]. A limitation of classical centrality measures is the fact that they evaluate nodes based on their individual contributions to the functioning of the network. For instance, the importance of a stop in a transportation network can be computed as the difference between the full network capacity and the capacity when the stop is closed. However, such an approach is inadequate when, for instance, multiple stops can be closed simultaneously. As a consequence, one needs to refine the existing centrality measures, in such a way to take into account that the network nodes do not act merely as individual entities, but as members of groups of nodes. To this end, one can exploit game theory [2], which, in general terms, provides a basis to develop a systematic study of the relationship between rules, actions, choices, and outcomes in situations that can be either competitive or non-competitive.

The idea at the roots of game-theoretic centrality measures [3] is the following: the nodes are considered as players in a cooperative game, where the value of each coalition of nodes is determined by certain graph-theoretic properties. The key advantage of this approach is that nodes are ranked not only according to their individual roles in the network, but also taking into account how they contribute to the roles of all possible groups of nodes. This is important in various applications in which a group's performance cannot be simply described as the sum of the individual performances of the group members involved. In the case of transportation networks, suppose we have at our disposal a certain budget. One possible approach consists in addressing the question of whether investing all the money in increasing the capacity and/or service of a transportation component (road section, bridge, transit route, bus stop, etc.)  substantially improves the whole network. A better way of proceeding for the network analyst/designer would probably consist in considering to simultaneously improve a (possibly small) subset of the components. In this case, to evaluate the importance of a component one has to take into account the potential gain of improving one component as a part of a group of components, not merely the potential gain of improving the component alone. This approach can be formalized in terms of cooperative game theory [2], where the nodes are players whose performances are studied in coalitions, i.e., subsets of players.

This research project, which takes the hint from the works [4,5], consists in developing methods and tools from a particular type of cooperative games, called “cooperative games with transferable utility”, for brevity “TU games”, to optimize transportation networks. Given a transportation network a TU game will be defined, which takes into account the network topology, the weights associated with the arcs, and the demand based on the origin-destination matrix (weights associated with nodes). The nodes of the network represent the players of the TU game.

We aim at exploiting game-theoretic solution concepts developed during decades of research to identify the nodes that play a major role in the network. In particular, we shall use the so-called solution concept known as Shapley value [2], which represents a criterion according to which each node is attributed a value, in such a way that the larger the value the larger the node importance.  The Shapley value enjoys mathematical properties well-suited to the proposed analysis. Computational aspects related to the evaluation of the Shapley value will be investigated, too [6], studying the possibility of polynomial-time computation with respect to the network dimension.

Depending on whether the analysis focuses on the “physical nodes” or the “physical links”, the definition of the player changes. This research project considers both. When the transportation nodes (representing, e.g., intersections, transit terminals, bus stops, major points of interest, etc.) will be analyzed, the network on which the TU game will be defined is identical to the physical network. On the other hand, when arcs (e.g., road segments, transit routes, rail lines, etc.) will be analyzed, the network will be transformed in such a way that the physical links are modeled as nodes.

Link to the group/personal webpage:

http://www.dist.unige.it/msanguineti/

References 

[1] S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications. Vol. 8. Cambridge University Press, 1994.

[2] J. González-Díaz, I. García-Jurado, and M.G. Fiestras-Janeiro, An Introductory Course on Mathematical Game Theory. AMS, 2010.

[3] T.P. Michalak, Game-Theoretic Network Centrality - New Centrality Measures Based on Cooperative Game Theory, 2016. Available from: http://game-theoretic-centrality.com/index.html.

[4] Y . Hadas and, M. Sanguineti, An Approach to Transportation Network Analysis Via Transferable-Utility Games. 96th Annual Meeting of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, DC, 8-12 gennaio 2017.

[5] Y. Hadas, G. Gnecco, M. Sanguineti, An Approach to Transportation Network Analysis Via Transferable    Utility


 

List of courses offered by our program in 2021

Dates, abstracts, contacts and references are available at the URL link

*NEW*
Open Positions in the PhD Program in Computer Science and Systems Engineering of the
 University of Genova 
funded by the REACT-EU project

The PhD Program in Computer Science and Systems Engineering (http://phd.dibris.unige.it/csse/) of the Department  of Informatics, Bioengineering,  Robotics,  and Systems Engineering (DIBRIS) at the University of Genova offers several 3-years grants  
in the context of the initiative  "Dottorati su tematiche green del nuovo Asse IV del PON Ricerca e Innovazione 2014-2020 Istruzione e ricerca per il recupero – REACT-EU”

  • 2 scholarships in the Computer Science Curriculum (15.343,28 euros gross per year)
  • 2 scholarships in the Systems Engineering Curriculum (15.343,28 euros gross per year) 
More details on the research projects are available here: 

Prospective applicants are encouraged to contact the reference person for the theme they are interested in.
One necessary requirement for admission is that you have a University degree at Master level, or you expect to obtain it by 31 October 2021. 
If you have a foreign degree (ie you did not graduate in Italy) your application your follow an ad hoc procedure to have your degree recognised as equivalent to an Italian degree.

Application Form
General Information and application forms are available at the URL:

See on-line help for instructions (you will need to fill the form with your personal data and to specify the PhD program
you are applying for (Computer Science and Systems Engineering) and the curriculum).

You will need to upload a number of documents (in pdf format):

  • A valid photo ID (both sides and all pages)
  • A CV that clearly indicates your prior research experience (this includes e.g. project work, publications, teaching experience, title and short summary of your Master’s thesis)
  • A research project consistent with the research theme that you selected (10 pages maximum). Guidelines to write a research project proposal are available Instructions and guidelines
  • The transcript of exams with grades

Each canditate should provide 1 to 3 reference letters to support the application.
You must not upload reference letters. They must be sent directly by the writer to the PhD coordinator (see below).

Important notice for non local candidates
For non-local candidates (i.e., not known to faculty members at DIBRIS) 
it is particularly important to secure reference letters from well-established researchers that can provide a reliable assessment of the candidate's abilities.

Reference Letters

The reference letters must be sent by the writer of the letter to the following email addresses, before the submission deadline 

-computer science: phd.compsci at dibris.unige.it (replace at with @)
-systems engineering: phd.syseng at dibris.unige.it (replace at with @)

A template may be found in the Template for reference letters.

Selection process 
A Selection Committee (COMMISSIONE) evaluates background knowledge and research potential of the candidates, based on the information submitted with their application (TITOLI) and based on a technical/motivational INTERVIEW. At the end the Committee produces a ranked list (GRADUATORIA) of candidates sufficiently qualified and trained to have a reasonable expectation of successfully completing the graduate program.

 The ranking is primarily based on a combination of the following criteria:

  • Quality of previous undergraduate/graduate workStrength of reference letter
  • Statements in the applicant's research proposal
  • Other evidence of graduate potential
  • Availability of an advisor in the research area selected by the applicant
  • Availability of positions in the PhD program

 


October 2020


Candidates admitted to the interviews

The lists of candidates admitted to the online interviews (July 6, 2020) are listed below.
Admitted candidates will be contacted by email with more detailed instructions.

Final rankings 


 

July 2020


Candidates admitted to the interviews

The lists of candidates admitted to the online interviews (July 6, 2020) are listed below.
Admitted candidates will be contacted by email with more detailed instructions.

Final rankings