DM 352/2022 – PNRR Missione 4
Componente 2 “Dalla Ricerca all’Impresa”
Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese”
| 1 | Digital Twin and Innovative Systems Emulator |
| Keywords: Digital twins, software engineering, embedded systems, software testing, emulator Research objectives: The world of Embedded Systems which today characterizes the most innovative and disruptive solutions has a limit due to the ability to test software before having the boards and electronic components fully defined and built. This denotes two salient aspects of product development: • Concept and design with approaches and methodologies for the best management of additions and components • Card emulators, which do not have impossible costs (e.g. because they are linked to monopoly of the Chip or Firmware manufacturer) that allow you to test virtually all the software parts that are part of the innovative project. The fact of creating continuous physical prototypes to be able to test the software, when working with software emulators could accelerate all the checks through the virtualization system, is a fundamental step for the acceleration of digital transformation processes Proposed research activity: • State of the art in software engineering for embedded systems • State of the art in testing embedded systems • Definition of a testing methodology • Definition of a framework for testing • Definition of case studies • Test of the framework Supporting research projects PNRR Missione 4, componente 2 “Dalla Ricerca all’Impresa” – Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese” MUR – DM 352/2022 Possible connections with research groups, companies, universities. ONE-SYS Srl (co-funder) Supervisor: Prof. Giacomo Cabri Co-supervisor: Ing. Cristina Nizzoli, prof. Marko Bertogna | |
| 2 | Efficient blockchain integration on IoT devices for Industry 4.0 |
| Keywords: blockchain, IoT, Industry 4.0, tracking Research objectives: Internet of Things (IoT) devices are present in a multitude of scenarios, from industry to the health sector, from transport to security. In the industrial context they are used to track goods, identify them and control the production process. Specifically for the tracking scenario, recently the blockchain have been proposed as a viable solution to provide a transparent and complete history of the goods. However, there is still much work to do pertaining the efficient integration of the blockchain technology in constrained devices, with heterogeneous architectures and with different communication technologies. In this work, the PhD candidate will have the opportunity to design and develop novel architectures to integrate the blockchain the challenging scenario of Industry 4.0. Since this is a PNRR funded grant, the candidate is also expected to work with an industry which will make it possible to test the proposed solutions in real scenarios. Proposed research activity: • State of the art on blockchain in IoT • Problem analysis and modeling • Design and development of a small scale testbed • Integration of the research findings on a real scenario • Participation to relevant international schools and conferences Supporting research projects: PNRR Missione 4, componente 2 “Dalla Ricerca all’Impresa” – Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese” MUR – DM 352/2022 Possible connections with research groups, companies, universities. Digibelt Srl (co-funder), University of New South Wales (Australia) Supervisor: Prof. Luca Bedogni Co-supervisor: Ing. Michele Fantoni | |
| 3 | Smart and semi-automatic tools for optimisation and configuration of high-performance real-time embedded systems |
| Keywords: heterogeneous SoC, operating systems, high-performance embedded systems Research objectives: In current critical applications on high-performance embedded systems, e.g. in autonomous driving or industrial robotics, software task allocation on computing hardware resources is still mainly a human process, guided by the software architect experience. Resulting configurations often suffer from both hardware underutilisation and pessimism in the analyses of real-time properties. This research project will design methodologies and (semi-)automated tools to support application designers and architects providing optimal tradeoff between performance and deterministic execution on heterogenous, multi-core systems on a chip. Proposed research activity: Study and task modelling of key features of critical, high-performance applications, e.g. with DAG models, and of hardware and system software configurations about temporal and execution requirements. Artificial intelligence tools and real time theory will form the basis upon which to build original solutions mixing the two approaches. Evaluation of the proposed solutions will be provided by prototyping and integrating the novel models and approaches in new and existing software tools and frameworks, e.g. including open source products like Amalthea; and also by validation against industrial and/or automotive software application, e.g. including open source products like Autoware.Auto. Industrial sponsor Minerva Systems https://minervasys.tech designs and delivers system software and development tools to enable future autonomous systems on high-end embedded computers Supporting research projects: PNRR Missione 4, componente 2 “Dalla Ricerca all’Impresa” – Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese” MUR – DM 352/2022 Possible connections with research groups, companies, universities. • Minerva Systems Srl (co-funder) • Prof. Marco Caccamo, TUM (Germany) • Prof. Renato Mancuso, Boston University (USA) • Prof. Rodolfo Pellizzoni, University of Waterloo (Canada) Supervisor: Prof. Andrea Marongiu Co-supervisor: Dott. Marco Solieri, CEO and Founder | |
| 4 | Data management, analytics and intelligent AI-based knowledge extraction for multilingual and multi-alphabetic heritages |
| Keywords: Digital libraries; minority languages; humanistic informatics; computer archiving; inter-cultural communication Research objectives: The linguistic and social impact of multiculturalism can no longer be neglected in any sector, bringing to the urgent need of creating systems and procedures for managing and sharing cultural heritages also in supranational and multi-literate contexts. The long-term objective of this research, that will be performed in an interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. The FSCIRE Library in Palermo is the unique and exclusive case study, and this research will face the challenge posed by its non-latin alphabets in matters of data extraction, big data management, artificial intelligence and librarianship and cataloguing at large. Proposed research activity: • State of the art on OCR, text mining, long-term preservation, big data management and interpretable machine learning • Design of algorithms for automatic text recognition, metadata and knowledge extraction • Design of the Data Management, Interactive Search and Supervised Cataloguing techniques • Test of the proposed algorithms • Participation to relevant international schools and conferences Supporting research projects: PNRR Missione 4, componente 2 “Dalla Ricerca all’Impresa” – Investimento 3.3 “Introduzione di dottorati innovativi che rispondono ai fabbisogni di innovazione delle imprese e promuovono l’assunzione dei ricercatori dalle imprese” MUR – DM 352/2022 Possible connections with research groups, companies, universities: • MIM.FSCIRE Srl (co-funder) • International research group coordinated by “Fondazione per le scienze religiose di Bologna” of the “Big Data, Artificial Intelligence and Religious Studies” research line, in collaboration with Biblioteca La Pira, Palermo, and IDEO (Cairo). Supervisor: Prof. Riccardo Martoglia Co-supervisor: Prof. Federico Ruozzi |
DM 351/2022 – PNRR Missione 4
Componente 1 “Potenziamento dell’offerta dei servizi di istruzione: dagli asili nido all’Università”
Investimento 3.4 “Didattica e competenze universitarie avanzate”
Investimento 4.1 “Estensione del numero di dottorati di ricerca e dottorati innovativi per la pubblica amministrazione e il patrimonio culturale”
| 5 | A modern and innovative learning system to enable efficient, lean and cost-effective public administration |
| Hosting public administration: Regione Emilia Romagna Research Objectives: Topics are digital transition, big data, accessibility and inclusiveness within educational systems. In particular, the research program focuses on the development of personalized learning models able to achieve two sustainability objectives specified by the United Nations General Assembly. These are SDG4 (Quality education — Ensure inclusive and equitable quality education and promote university learning opportunities for all) and SGD 10 (Reduced inequality — Reduce inequalities among on-site and on-line students and limit digital barriers to students with disabilities). The successful candidate will spend at least 6 months in the hosting public administration. Supervisor: Prof. Marco Furini Co-supervisor: Prof. Manuela Montangero | |
| 6 | Challenges of the Public Administration in the Evolving World of ICT |
| Hosting Public Administration: Comune di Modena The successful candidate will spend at least 6 months in the hosting public administration. |
Contratti di apprendistato di alta formazione
| 7 | Design e sviluppo di sistemi operativi multicore |
| Keywords: multi-cores, memory interference, caches, heterogeneous hardware Research objectives: In modern high-end embedded systems, performance is obtained through high parallelism and heterogeneity of the processing elements (PE). To enable system scalability at contained costs resource sharing is a common paradigm, one important example being the sharing of main memory (DRAM). The resulting, complex architecture of these systems poses important performance and control challenges at the operating system level. As the number of PEs increases resource contention and interference become prominent, which – in absence of control – may lead to high and unpredictable latencies. Ultimately, this may inflate in an uncontrolled way the duration of some of the tasks executed by the system. The naive approach of preventing components from accessing shared resources in parallel heavily underutilizes the bandwidth of these resources, and leads to severe performance loss. The main goal of this research area is to find OS-level solutions that allow for a high utilization of shared resources, while at the same time controlling memory access latency. Supporting company: • Evidence S.r.l. Supervisor: Prof. Paolo Valente, Prof. Andrea Marongiu Industrial Co-supervisor: Alessandro Biasci, Paolo Gai | |
| 8 | Next-generation system software layers high-performance real-time embedded systems (Software di sistema ad alte prestazioni o alta predicibilità) |
| Keywords: heterogeneous SoC, operating systems, high-performance embedded systems Research objectives: In current critical applications on high-performance embedded systems, e.g. in autonomous driving or industrial robotics, the system software is called to face unprecedented challenges in terms of complexity. Large numbers, and great heterogeneity in the safety/security profiles of the computing workloads/environments – threads, tasks, operating systems, partitions, enclaves, virtual machines – as well as of the computing engines – multi-core CPUs, (GP)GPUs, FGPAs, neural accelerators. Current technologies typically address either feature/performance richness or high determinism. This research project will design methodologies and components to innovate the state of the art in real-time operating systems, hypervisors and middlewares to support both high-performance and deterministic applications. Supporting company: • Minerva Systems https://minervasys.tech (funder) Possible connections with research groups, companies, universities: • Prof. Marco Caccamo, TUM (Germany) • Prof. Renato Mancuso, Boston University (USA) • Prof. Rodolfo Pellizzoni, University of Waterloo (Canada) Supervisor: Prof. Andrea Marongiu Industrial co-supervisor: Dr. Marco Solieri, CEO and Founder |
Borse di studio Dipartimentali su tematica vincolata
| 9 | HW/SW solutions for the acceleration of AI workloads on heterogeneous embedded systems |
| Keywords: Heterogeneous SoC, embedded AI, parallel programming, accelerators, HW/SW co-design Research objectives: The research activity aims at developing methodologies and tools to facilitate the deployment of AI workloads on autonomous systems operating with heterogeneous systems-on-a-chip (HeSoC) as a main compute medium. The researcher will work on vertical solutions involving the programming model, the low-level software layers and the underlying architecture, exploiting paradigms such as heterogeneous embedded systems and reconfigurable logic, and devising innovative solutions to enable the tight coupling of traditional processors and custom accelerators. Proposed research activity: • State of the art in heterogeneous embedded computing • State of the art in the deployment of AI workloads on embedded systems • Definition of a framework for the efficient execution of the target workloads on constrained HeSoCs • Definition of methodologies to counter the key performance blockers • Definition of relevant case studies • Evaluation of the proposed framework and methodologies • Participation to relevant international school, internships in relevant international institutions operating in the field Supporting research projects (and Department) H2020-ECSEL – AI4CSM (Department of Physics, Informatics and Mathematics) Technology Innovation Institute, Abu Dhabi, United Arab Emirates Possible connections with research groups, companies, universities. Prof. Luca Benini, Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland Dr. Daniele Palossi, Dalle Molle Institute for Artificial Intelligence in Lugano (IDSIA), Switzerland Supervisor: Prof. Andrea Marongiu | |
| 10 | Vehicle motion planning and Control in Complex Environments (Pianificazione e controllo del moto del veicolo in ambienti urbani complessi) |
| Keywords: autonomous vehicles, motion planning, predictive control, artificial intelligence, automation Research objectives: In the last decade, many algorithms have been proposed for motion planning and control of autonomous vehicles in simplified settings. A latest research trend is now focusing on more complex algorithms that are capable of operating in non-ideal environments and extreme corner cases. The proposed thesis aims at developing a motion planning and control approach for autonomous systems in challenging environments, such as bad weather conditions, sensor-occluded scenarios, and safety-critical emergency maneuvering. Besides classical methods based on Kalman filter-based prediction and optimization-based controllers, the research will look into machine learning and data-driven methods, and their interplay with more classic approaches. The solution will be developed using high-fidelity simulators, and tested in both urban and racing scenarios to validate its robustness. Proposed research activity: • State-of-the-art in motion planning and control for autonomous vehicles; • State-of-the-art in data-driven approaches for motion planning and control; • Definition of a framework for motion planning and control in safety-critical scenarios in occluded and non-occluded environments; • Integration of the proposed framework in simulation and full-scale vehicles; • Participation in relevant conferences and international challenges. Supporting research projects (and Department): Technology Innovation Institute (TII), Abu Dhabi, United Arab Emirates Possible connections with research groups, companies, universities: • HIPERT SRL • TII • Università di Pisa Supervisor: Prof. Marko Bertogna |
Tematica libera – Other funding schemes
| 11 | Autonomic computing for collective self-adaptive systems |
| Keywords: Autonomic computing, distributed systems, adaptive systems, IoT, autonomous vehicles Research objectives: Collective adaptive systems are more and more spreading in our life. Situations like sets of mobile phones or IoT devices are becoming real and can be exploited to support human activities, but this requires appropriate approaches to be managed; in the future we envision sets of autonomous vehicles that must be coordinated. Autonomic computing is a very good candidate paradigm to address these scenarios. The objective of the research is to define a framework for the development of collective self-adaptive systems, based on autonomic computing. The framework will be composed of a methodology that guides the developers in the development addressing the self-* properties, and of tools enabling the support for the developers. Some case studies will be proposed to test the framework. Proposed research activity: • State of the art in autonomic computing • State of the art in collective adaptive systems • Definition of a framework for autonomic computing in collective adaptive systems • Definition of a methodology • Definition of case studies • Test of the framework • Participation to relevant international school Supporting research projects (and Department) H2020 – FIRST (FIM) Possible connections with research groups, companies, universities. Dr. Antonio Bucchiarone, FBK Trento (I) Dr. Lai Xu, Bournemouth University (UK) Prof. Emma Hart, Edinburgh Napier University (UK) Prof. Marco Aiello, Stuttgart University (D) Supervisor: Prof. Giacomo Cabri | |
| 12 | Data Analytics and Machine Learning for P4 medicine |
| Keywords: Big data, data analytics, machine learning, health data Research objectives: Medical practice is evolving rapidly, away from the traditional but inefficient detect-and-cure approach, and towards a Preventive, Predictive, Personalised and Participative (P4) vision. This vision is increasingly data-driven, and is underpinned by many forms of “Big Health Data”. The main research objective of this PhD project is to contribute to the above ambitious goals with big data analytics and machine learning solutions for collections that include a broad variety of (longitudinal) data including genetics, electronic health records, patient reported outcomes, lifestyle indicators and wearables. The project is in collaboration with the Unit of Infection Diseases of the University Hospital Policlinico di Modena and the School of Computing at Newcastle University (UK). The PhD candidate will have the opportunity to work with retrospective and prospective health data collections mainly in the context of infection diseases, also including COVID-19 data. Proposed research activity: ● Data cleaning techniques and application to clinical and self-generated datasets; ● Study of the state of the art of data mining and machine learning techniques in the field of medicine and people wellness; ● Study of Machine Learning solutions for P4 medicine; ● Model Interpretability and P4 medicine; ● ML model evaluation and assessment; ● Standardization of new data-driven clinical scores. Supporting research projects: The PhD student will be hosted at the Department of Physics Informatics and Math. Prof. Mandreoli has active projects with Prof. Guaraldi and Prof. Missier. The project will use retrospective Electronic Health Data, mainly from Policlinico of Modena, prospective health data generated by IRB approved studies. Possible connections with research groups, companies, universities: The study will be conducted together with Prof. Giovanni Guaraldi (Dipartimento Chirurgico, Medico, Odontoiatrico e di Scienze Morfologiche con Interesse Trapiantologico, Oncologico e di Medicina Rigenerativa), physician of the Infection Disease Unit of Policlinico of Modena, and his research group. The co-tutor Prof. Paolo Missier is a reader with the School of Computing at Newcastle University. Joint research activities are also currently active with the National Innovation Centre for Data (NICD -UK). Supervisor: Prof. Federica Mandreoli | |
| 13 | Multi-level organization of complex systems |
| Keywords: Information theory, evolutionary computation, nonlinear dynamics, criticality, adaptation Research objectives: The detection of intermediate structures in complex systems is not always a trivial task, while in contrast their characterization can lead to a meaningful description of the overall properties of the system, and in this way to its understanding. A large part of these structures is characterized by groups of variables (genes, chemical species, individuals, agents, robots…) that appear to be well coordinated among themselves and have a relatively weaker interaction with the remainder of the system. This general observation is the basis of several algorithms aiming to identify intermediate level structures in different fields: notable examples are the identification of functional neuronal regions in the brain, autocatalytic systems in chemistry, or the identification of communities in socio-economic systems. We are therefore interested in the identification and study of such structures, proposing algorithms and/or analysing data coming from the world of biology and of artificial systems. Supervisor: Prof. Marco Villani | |
| 14 | Evolving artificial systems |
| Keywords: Information theory, evolutionary computation, nonlinear dynamics, criticality, adaptation Research objectives: Finding general properties of evolving systems has proven extremely difficult. A particularly interesting proposal is that evolution (either natural or artificial) drives complex systems towards “dynamically critical” states, which may have relevant advantages with respect to systems whose dynamics is ordered or disordered. According to this hypothesis, these critical systems can provide an optimal balance between stability and responsiveness. In this thesis we aim at determining under which conditions this turns out to be the case, by using abstract models. The systems described by these models will interact in an abstract “environment”, and the conditions under which critical systems have an edge will be analyzed. To achieve this ambitious goal, we will exploit the synergy among dynamical systems methods, information theory and evolutionary computation. Supervisor: Prof. Marco Villani | |
| 15 | Multi-user activity recognition |
| Keywords: activity recognition, inertial sensor, machine learning Research objectives: Human Actitivy Recognition (HAR) is a set of techniques which identifies the activity a user is performing, through the analysis of a set of sensors which describe actions and movements from users. This allows to configure a computing system based on the current scenario of the user, and provide a more tailored experience. More recently classic HAR system have also been proposed to detect and classify group actions, in which it is not only important to classify signals obtained from a single person, but also to share data among a set of devices carried by different people, which is the main topic of this thesis work. Proposed research activity: • State of the art on HAR for groups of people • Design and development of a data gathering platform • Analysis and study of techniques to realize HAR for groups of people • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: University of Stuttgart (Germany), University of Bamberg (Germany), University of New South Wales (Australia) Supervisor: Prof. Luca Bedogni | |
| 16 | Game science Data-Driven Analytics and Explainable AI for Game Features Discovery and Computational Thinking fostering |
| Keywords: Gaming-data analytics, interpretable machine learning, supervised classification, Computational Thinking, education Research objectives: This research aims at exploring the application of data analytics and (explainable) machine learning techniques to better understand games and discover new features that will possibly help in effectively exploiting them in different socially useful domains (e.g., game features that could better foster Computational Thinking in education, those better suited to be applied in social distancing contexts, and so on). Through interaction with experts in the field of gaming and education, and the application of state-of-the-art ML and analytics techniques on a large pool of gaming-related data, the foreseen results will possibly have an impact on a large and heterogenous public, including teachers, players and game designers. Proposed research activity: • State of the art on data and text analytics, explainable machine learning • Collection and design of a large dataset of gaming related data • Design and testing of an information processing pipeline for automatically discovering game features • Testing on the field (classrooms, gaming fairs, …) • Participation to relevant international schools, conferences and fairs Possible connections with research groups, companies, universities: Game Science Research Center (https://gamescience.imtlucca.it/) Supervisor: Prof. Riccardo Martoglia | |
| 17 | Qualitative and quantitative data analysis for effective diffusion, prediction and social media message composition assistance in a Cultural Heritage scenario |
| Keywords: Textual data analytics; Social network analysis; Interpretable machine learning; Data Science; Communication of Cultural Heritage Research objectives: Writing successful messages on social media is tougher than most social media managers think. Aside from knowing when to post messages for maximum exposure, it is necessary to know what subject, hashtags and content features (images, multimedia content, etc.) work best for generating likes and forwards. The goal of this research project is to support effective communication and marketing campaigns in the Cultural Heritage (CH) domain. Newly designed algorithms will guarantee a continuously updated overview of the communicative power of a social media account w.r.t. competitors, and will provide managers with effective tools for writing successful messages and predicting their impact before posting them. Proposed research activity: • State of the art on social network analysis, text analytics • Definition of an automated method for social media data extraction and data corpus creation • Definition of a reference set of CH-related KPIs, including feature-oriented KPIs, quantifying the features of messages, and goal-oriented KPIs, quantifying impact and diffusion of messages • Design and implementation of data analysis algorithms enabling “on-the-fly” computation of the KPIs • Design and implementation of interpretable machine learning algorithms for: predicting message diffusion (and explaining the reasons) and suggesting specific message composition for maximizing diffusion • Test of the proposed algorithms • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: Interdepartmental Research Center on Digital Humanities (DHMoRe), University of Bologna, … Supervisor: Prof. Riccardo Martoglia Co-supervisor: Prof. Marco Furini, Prof. Manuela Montangero | |
| 18 | Social Network analysis, Text and Graph Analytics techniques for user influence and contribution analysis in a pandemic context |
| Keywords: Text analytics; Social network / graph analysis; Interpretable machine learning; Big Data and graph databases; Centrality and community detection Research objectives: Social Networks are becoming a common place for discussion and exchange of ideas. During the pandemic, their role has become even more fundamental: people have been publishing thoughts, worries and moods, involuntarily creating a huge amount of data representing a real mine of information. The main objective of this research is to collect and analyze large datasets of users messages posted on major social network platforms during pandemic periods, discovering for instance the most influential figures, the language they used, how they contributed to the discussions and the reason for their importance, but also the role that institutions have played, temporal trends, and so on. Proposed research activity: • State of the art on graph and text analytics, social network analysis and graph databases • Extraction of text data from major social network platforms and design of graph databases from them • Definition and exploitation of ad-hoc centrality analysis, community detection and sentiment analysis techniques, also taking the temporal factor into account • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: University of Bologna Supervisor: Prof. Riccardo Martoglia Co-supervisor: Prof. Marco Furini, Prof. Manuela Montangero | |
| 19 | Bibliometric & “Touristicity” Data Analytics: Large Scale data analytics techniques for bibliometric data |
| Keywords: data and text analytics; big data management; scientific conferences; bibliometric and correlation analysis; research impact Research objectives: The importance and impact of literature (e.g., conferences papers) in many scientific areas is testified by quantitative indexes: many sources provide a huge quantity of bibliometric data, from Scopus to Google Scholar, from Microsoft Academic Graph to DBLP. The main goal of this research is to unlock the potential of these data and investigate novel research questions related to them, among them the possible correlation between the impact of scientific conferences and the venue where they took place. The obtained results will be the first attempt to focus on the relationship between venue characteristics and papers impact; the findings will open up new possibilities, such as supporting conference organizers in their organization efforts. Proposed research activity: • State of the art on data and text analytics, bibliometric data • Definition of (big) data management pipelines to obtain and manage relevant datasets • Definition of novel research / analytics questions on the data, involving the bibliometric and venue-related (“touristicity”) aspects • Large scale analysis and definition of predictive models on the extracted data • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: Istituto di Scienze e Tecnologie della Cognizione (ISTC) – Consiglio Nazionale delle Ricerche (CNR) Supervisor: Prof. Riccardo Martoglia Co-supervisor: Prof. Luca Bedogni, Prof. Giacomo Cabri | |
| 20 | Real-Time Edge-Cloud Big Data Management and Analytics techniques for Smart Cities |
| Keywords: smart city framework, big data management and analytics, edge computing, cloud data management, predictive analysis Research objectives: Exposing city information to dynamic, distributed, powerful, scalable, and user-friendly big data systems is expected to enable the implementation of a wide range of new opportunities; however, the size, heterogeneity and geographical dispersion of data often makes it difficult to combine, analyze and consume them in a single system. This research aims at an innovative framework that will facilitate the design of advanced big-data analytics workflows. The design will cover the whole compute continuum, from edge to cloud, and rely on a well-organized distributed infrastructure that will support a wide range of different applications; novel predictive approaches (e.g., for traffic data prediction) will integrate both classic and machine learning models in a streaming big data architecture. The tests will be performed on real use-cases, such as the Modena Automotive Smart Area (MASA). Proposed research activity: • State of the art on smart city applications, streaming big data management, machine learning predictive techniques • Definition of the cloud and edge data acquisition, management and analytics procedures inside a novel smart city architecture • Test of the proposed techniques on synthetic and real use cases • Participation to relevant international schools and conferences Supervisor: Prof. Riccardo Martoglia Co-supervisor: Roberto Cavicchioli | |
| 21 | Creating a Bayesian phylogenetic algorithm for evolutionarily modeling of natural language syntax |
| Keywords: quantitative phylogenetics, Bayesian analysis, syntactic parameters, natural language, reconstruction Research objectives: The goal of this project is to work out dedicated algorithms to extract historical signals from abstract natural language data (syntactic parameters), to reconstruct ancestral states, and to explain language transmission and diversification across time. The novelty of the project consists in the implementation of computational quantitative tools of Bayesian derivation on a type of language abstract structures (syntactic parameters) characterized by formal properties (for example, their multilayered deductive structure, www.parametricomparison.unimore.it) and peculiarities which are not observed in the linguistic data traditionally used in the field to automatically generate hypotheses of historical relatedness across languages: these peculiarities partially undermine the robustness of automatically-generated phylogenetic hypotheses because the quantitative tools which are traditionally used in linguistics are not able to deal with the peculiarities of syntactic characters and, consequently, to fully process the information provided by parametric data. To this end, novel/refined quantitative tools are required: this process is precisely aimed at implementing such tools. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov | |
| 22 | Modeling structural interdependencies and their effects in constraining language diversity |
| Keywords: networks, binary strings, implicational structure, machine learning, language transmission and change Research objectives: The goal of this project is to explore the multi-layered deductive structure of natural language grammars and its effects in constraining language diversity. In formal cognitive approaches to language structure and diversity, crosslinguistic structural variation is assumed to be universally constrained by a finite set of abstract points of variation (syntactic parameters) whose reciprocal interactions generate the attested surface diversity across languages, which turns then out to be predictable once the subset of variable abstract structures and their reciprocal interactions is fully defined and formalized. The purpose of this project is to explore and identify different possible types of parameter interdependencies and their effects on language acquisition and change, through the implementation of machine learning techniques and data-driven approaches able to disclose parameter dependencies which cannot be identified through manual data analysis, to formalize implicational rules among parameters, and to compare them with those produced by linguists’ expert judgement. These procedures will be aimed at: (a) discovering parameters that can be entirely dispensable as fully deducible from combinations of the others; (b) detecting further possible partial dependencies among parameters; (c) identifying the largest set of parameter values which are identically set in all the languages of a given family; (d) finding the least restrictive sets of parameter values that are sufficient to distinguish a family from all others. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov | |
| 23 | Realizing parametric phylogenetic analyses of families or subfamilies of languages |
| Keywords: language family, comparative methods, parametric comparison, computational phylogenies, data collection and analysis Research objectives: The goal of this project is to perform parametric comparison within historically established language families through the implementation of the formal and quantitative tools developed by the Parametric Comparison Method (www.parametricomparison.unimore.it), in order to explore their phylogenetic and historical structure and discover deeper historical relationships with other families. This investigation will be conducted using the comparison procedure designed and implemented by the PCM, which consists of the following toolkit: (1) a set of binary syntactic parameters which define crosslinguistic variation in nominal structures across the World’s languages: languages are represented as strings of binary values (+/-); (2) a set of implicational formulas defining parameter interdependencies: one value (though not the other) of a given parameter p1 may entail the irrelevance of another parameter p2, whose manifestations become then predictable; (3) a set of co-varying surface manifestations for each parameter; (4) a parameter setting procedure: each parameter is associated with a set of questions of the type “Does the (set of) structure(s)/interpretation(s) α occur in language L?”; only answers YES, which are based on positive evidence, set parameter values; (5) a set of distance-based and character-based quantitative algorithms and statistical procedures to extract historical signals from parameter values and generate language phylogenies. Possible connections with research groups, companies, universities: Research on this project will be conducted in collaboration with the Center for Language history and diversity, of which the Dipartimento di Comunicazione ed Economia is a member, and the Department of Linguistics at York, with whom the DCE has an academic cooperation agreement. The student who will be conducting this research will be asked to work in strict collaboration with the University of York. Supervisor: Prof. Cristina Guardiano Co-Supervisor: Prof. Giuseppe Longobardi, Dr. Dimitar Lubomirov Kazakov | |
| 24 | Privacy in Mobile Crowdsensing Systems |
| Keywords: crowdsensing, privacy, mobile computing, context-aware-computing Research objectives: Ubiquitous services gather personal data which is processed to provide tailored services to users. An example is Location Based Services (LBS), in which the location of the user is used to provide close point of interest, to track the movements and forecast future needs and alike. A specific use-case is that of Mobile CrowdSensing (MCS), in which a crowd of users collect data about a monitored phenomenon, and share it with a central entity. The data is usually geolocalized, to provide a more precise context of it. In this domain there are a number of potential issues which arise, specifically concerning the privacy of the users, which expose a lot of private information which, in case they are accessed by a malicious entity, may highlight private parts of the user routine. The proposed thesis will advance the understanding of privacy issues in MCS, by testing and analyzing available systems, while also designing state of the art algorithms which limit the information exposed, while still maintaining the service quality of the Ubiquitous application. Proposed research activity: • State of the art on MCS and on Privacy issues related to it • Test of real systems, with quantitative and qualitative analysis • Design and development of novel MCS data exchange protocols • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: IMDEA Networks (Madrid), University of New South Wales (Australia) Supervisor: Prof. Luca Bedogni | |
| 25 | Efficient, scalable and flexible device to edge offloading |
| Keywords: edge computing, machine learning, smart systems, context-aware-computing Research objectives: Edge computing refers to computing units which are placed at the edge of the network, close to the end devices. They typically serve as a lower latency computing unit to process information which devices cannot process on their own, due to battery or computational constraints. However the problem of defining how, when and what data has to be offloaded from a device to an Edge server is yet to be solved, as it encompasses a series of different parameters to take into account such as the operational characteristics of both the device and the edge server, the network, the time contraints on the results and so on. In this research these the PhD candidate will work towards a generalized model for Edge computing offloading from smart devices, which must take into account the heterogeneity of the data, the operational constraints of the device and of the edge server, the service quality to meet. Proposed research activity: • State of the art on Edge computing offloading • Design and implementation of a general purpose testbed for Device-to-edge offloading • Design, implementation and evaluation of Device-to-edge offloading protocols and techniques • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: University of California, Irvine (USA) Supervisor: Prof. Luca Bedogni | |
| 26 | Automated Cyber Operations |
| Keywords: cyber security, graph theory, planning, automation, artificial intelligence The volume and complexity of modern attacks and defenses (as testified by the size of the CVE, CWE, CAPEC, MITRE ATTACK catalogues) is making manual exploitation and hardening of systems increasingly unfeasible. In the next few years we foresee an adoption of even more automated offensive and defensive tools and decision makers. This research thesis is motivated by the need for frameworks, algorithms, tools that help the human operator to carry on offensive operations (vulnerability assessments, penetration testing) and defensive operations (systems hardening, source code auditing) in a semi-automated fashion. The main goal is to explore the potential for automation and artificial intelligence in the activities involved in classical security assessments (representation of security-related knowledge, decision making, task planning and execution, creating digital twins, monitoring of progress, reporting) in order to improve efficiency and efficacy. Supervisor: Mauro Andreolini | |
| 27 | Privacy-enhancing authentication and authorization solutions |
| Keywords: cyber security, applied cryptography, authentication and authorization, distributed systems, data structures, algorithms The emergence of novel computing systems is showing limitations of existing security solutions for authentication and authorization procedures, either due to limited capabilities of constrained devices and networks, limited usability and scalability within systems consisting of a huge amount of devices, attack surfaces including physical access to devices. Moreover, novel security paradigms require designing augmented security guarantees including increased privacy of users identities and reduced trust in identity providers. This research involves students in studying state-of-the-art authentication and authorization protocols and architectures, to improve their knowledge on applied cryptography solutions including implementations and non-standard primitives, and to get expertise in analyzing and formulating threat models for novel computer systems. Supervisor: Luca Ferretti | |
| 28 | Scoring Systems for Cyber Security |
| Keywords: cyber security, graph theory, algorithms, cyber ranges, monitoring To increase the resilience of their infrastructures, both military and civilian organizations have started to train security personnel on cyber ranges, pre-arranged virtual environments through which it is possible to effectively simulate realistic security scenarios on a system architecture closely resembling the original one. Training goals are many and diverse in nature: to discover vulnerabilities in existing systems, to harden existing systems, to evaluate the security of a soon-to-be deployed component, to teach secure programming practices, to perform incident response on a compromised system. Unfortunately, current evaluation strategies of student performance in an exercise share a severe limitation: they are focused exclusively on goal achievement (yes or no), and not on the specific path followed by the student. Therefore, giving a more precise assessment and, more generally, understanding the reasons behind success or failure, is impossible. This research is motivated by the need for techniques, algorithms and tools to model user activities in an exercise, compare the path carried out by the student with an “optimal” path devised by an instructor and suggest avenues for improvement. The goal is to propose novel cyber scores that can be used to capture the abilities of a student (speed, precision, ability to discover new vulnerabilities), highlight potential weaknesses, compare different students in the same scenario. Supervisor: Mauro Andreolini | |
| 29 | Controlling Quality of Service in Shared-Memory on-chip Systems |
| Modern commercial-off-the-shelf (COTS) systems on a chip (SoC) are highly parallel and heterogeneous computers. The main CPU is coupled to specialized accelerators (GPU, FPGA) that can maximize performance/Watt for specific tasks. While this design paradigm guarantees very high peak performance, it is also typically based on main memory and interconnect sharing, which can create high contention. Without control of such effects, the various compute units may experience high and hard-to-predict increase of shared resources access time. This may ultimately inflate the duration of some tasks uncontrollably, failing to provide the required quality of service at the system level. Many solutions have been proposed to control resource contention in such systems, which either severely underutilize the available bandwidth shared resource or struggle in keeping latencies under control when dynamic workloads are employed. The goal of this thesis is to advance the state of the art by studying solutions that allow for a high utilization of a target SoC while at the same time providing the requires quality of service levels, even when the workload changes unpredictably over time. Proposed research activity: • Study of the state of the art on heterogeneous SoC performance bottlenecks, with particular emphasis on main memory sharing • Study of methods and models that capture the behavior of the system in presence of co-scheduling of multiple compute units • Design, implementation and evaluation of frameworks that deploy such models on real systems • Participation to relevant international schools and conferences Possible connections with research groups, companies, universities: • Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland • University of Massachusetts, Lowell, MA – USA Supervisor: Andrea Marongiu, Paolo Valente | |
| 30 | Customer delight and Artificial Intelligence. How the new technological environment redefines customer experience |
| Supervisor: I. Baghi Co-supervisor: V. Gabrielli, S. Grappi | |
| 31 | Artificial Intelligence and sustainable consumption. The role of new technology in improving sustainable consumption models |
| Supervisor: I. Baghi Co-supervisor: V. Gabrielli, S. Grappi | |
| 32 | Recommendation systems to personalize entertainment systems |
| Keywords: Personalized entertainment, artificial intelligence, music/video sequencing, crossmedia promotion. Research objectives: The use of artificial intelligence (AI) to develop innovative and personalized multimedia experience. These models can personalize the music/video consumption process, help users to discover new sources, avoid the selection process, define strategies to engage users and multimedia material over social platforms, and more. Proposed research activity: • Design algorithms to understand the user’s habits in terms of multimedia material consumption • Design of AI-based algorithms to select material based on the user’s understanding obtained in step 1 • Design of recommender algorithms to personalize the multimedia experience • Design of strategies to engage users and multimedia material over social platforms • Design of sequencing strategies to meet the user’s habits and preferences in terms of multimedia material consumption Supervisor: Marco Furini Co-supervisor: Manuela Montangero | |
| 33 | IoT technologies and Edge computing to create people’s digital twins |
| Supervisor: Luca Bedogni Co-supervisor: Marco Furini | |
| 34 | Social Sensing for the cultural scenario |
| Supervisor: Luca Bedogni Co-supervisor: Marco Furini, Manuela Montangero |