Digital Skills for Sustainable Societal Transitions

The student must acquire three fundamental types of knowledge: 1) the understanding of basics concepts (theory and models) of AI and ML 2) the ability to solve concrete problems applying ML algorithms using interactive frameworks and SW libraries 3) the ability to modify and adapt template ML algorithms for a new purpose These skills will be applied to the solution of practical problems.

This course has a threefold objective: 1) to introduce to the student to the 'cultural' and technological issues related to AI and machine learning (ML) 2) to teach the use of machine learning techniques as a way to solve real problems 3) to gain practice implementing AI and ML algorithms and getting them to work for practical field of application

THEORY [40 HRS] - Hello AI and ML. We introduce the core idea of teaching a computer to learn models using data without being explicitly programmed. - Background Concepts, Probability and Linear Algebra Review - Representation of numerical and non-numerical data - Linear Regression - Neural Networks Representation and Learning - Support Vector Machines - Unsupervised Learning vs Supervised Learning - Dimensionality Reduction - Application Examples LABS [21 HRS] - Software Development Framework Tutorial - Several assignments designed to help you understand how to implement the learning algorithms in practice.

Modalita di esame: Prova orale facoltativa; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Optional oral exam; Computer-based written test in class using POLITO platform;
... The exam will consist of two parts: 1) A set of assignments addressing specific ML tasks to be developed during the course. For each assignment, instructors will provide a dataset and the students will have to develop analysis pipelines to solve the assigned problem (based on the topics presented during lectures and labs). Then, students should write a technical report detailing the employed methodology and a critical analysis of the obtained results. Both the designed pipeline and the report will be submitted by students to the instructor for evaluation. 2) A written test that aims at assessing the student's knowledge of the theoretical aspects of the course (through practical exercises and open-answer questions) and the ML design skills (through the designing of analysis pipelines that implement the solution of a practical problem for which datasets are provided). The exam will assess the developed skills of the candidate to solve specific problems by applying the knowledge acquired during the course in the design of ML pipelines of data analysis. The duration of the written test is 1.5 hours and it is a closed book test. The maximum score for the written test is 30 cum laude. During the discussion of the results of the written test, the instructor can request a supplementary oral test that can cover the whole course program and is meant to assess and elaborate the student's skills.

• Handouts of class material. - On-line material with references provided during the course.

The student must acquire two fundamental types of knowledge: 1) the ability to solve concrete problems through programs implemented using a programming language and executed on a computer 2) the understanding of the quantitative aspects of computer science such as performance, computational power, representation of information, computer architecture These skills will be applied to the solution of practical problems.

This course has a twofold objective: 1) to introduce to the student to the 'cultural' and technological issues related to computer science 2) teaching the use of a computer programming language as a way to solve real problems

PROBLEM SOLVING AND ALGORITHMS [21 HRS] - Flow charts - Pseudo-code - Design algorithm solution for real problems Python LANGUAGE [40 HRS] - Data types - Numeric constants and variables - Strings and their manipulation - Input/Output of numbers and and strings - Arithmetic operators, powers, and mathematical functions - Boolean variables and operators - Control-flow structures (iterative and conditional) - Functions and calls - Lists, Sets, and Dictionaries - Complex data structures (Dictionaries of sets and dictionaries of lists) - Text Files - Exceptions handling

Modalita di esame: Prova orale facoltativa; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Optional oral exam; Computer-based written test in class using POLITO platform;
... The exam will consist of two parts: 1) A set of assignments addressing specific programming tasks to be developed during the course. For each assignment, students will have to develop the program that implements the solution to solve the assigned problem (based on the topics presented during lectures and labs) and submit the code to the instructor for evaluation. 2) A written test that aims at assessing the student's knowledge of the theoretical aspects of the course (through numerical exercises and open-answer questions) and the programming skills (through the writing of a Python program that implements the solution of a practical problem). The main object of the exam is to evaluate the programming skills acquired by the students during the course. The duration of the written test is 1.5 hours and it is a closed book test. The maximum score for the written test is 30 cum laude. During the discussion of the results of the written test, the instructor can request a supplementary oral test that can cover the whole course program and is meant to assess and elaborate the student's skills.

• Handouts of class material. - Python For Everyone (3rd Edition), Cay S. Horstmann, Rance D. Necaise, ISBN: 978-1-119-49853-7, https://www.wiley.com/en-us/Python+For+Everyone%2C+3rd+Edition-p-9781119498537

Having completed the course, the student is expected to be able to: • Identify and ascertain key ethical issues related to the use of data and their impact on the IT industries, the users and the civil society. • Engage critically with the complex and multi-faceted regulatory framework on the use of data and the legal and ethical issues that arise. • Consider and evaluate the societal transitions in a reflective manner, including an evaluation of policy and regulatory options for maximizing the worth and minimizing risk of data-driven technologies. • Demonstrate effective dissemination of findings to professional and academic standards.

The course addresses the multi-faceted regulatory challenges of the data economy in the context of societal transitions. It aims to provide students with a critical understanding of the risks and perils of a data-driven society, as well as with awareness of the legal and regulatory options to mitigate those risks. Students will acquire knowledge of the applicable legal framework (with particular reference to informational privacy, consumer protection and data protection) and will be introduced to the challenges of applying legal principles to factual scenarios emerging from societal transitions, such as smart cities, self-driving vehicles, mobility-on-demand, e-government and digital citizenship. In this connection, the course will pay particular attention to the EU General Data Protection Regulation (GDPR) and the way in which Courts and Data Protection Authorities have interpreted legal principles in relation to Big Data and Artificial Intelligence. As an in-depth study, a section of the course will be devoted to the change in modeling formal ontologies for legal knowledge. The emergence of autonomous artificial agents is challenging the modes and the structure of the legal discourse on topics like legal personhood, contractual and tortious responsibility, and even creativeness. While waiting for new policies to be adopted by national and international institutions, the inadequacy of legal orders to govern the phenomenon of artificial agents is embarrassing policy makers and courts.

The course focuses on the many aspects of the current international and European legal framework and it discusses how it applies to factual scenarios of the societal transition. Indicative content include: PART 1 – REGULATING DATA SOCIETIES: LEGAL AND ETHICAL ISSUES (30 hours) • Digital transition and the issues of “datafication� of human societies • Data as an instrument of power • Responsible data processing and sharing • Personal data in the context of informational privacy • The principles and structure of the EU General Data Protection Regulation (GDPR) • Forms of protection of data: contract, trade secrets and “de facto� powers PART 2 – AUTONOMOUS AGENTS, AI AND THE LAW (30 hours) • Public sector information and access to data • The legal protection of databases and computer programs: the role of “open source� software • Regulation of Artificial Intelligence (AI) • Autonomous Agents and the Law: personhood and responsibility • Artificial Intelligence and copyright, the case of creativeness

Modalita di esame: Elaborato scritto individuale;
Exam: Individual essay;
... The assessment consists of an individual essay (4,000 words) on a topic chosen by each student from a list of topics made available by the teachers. In the essay, students are expected to: • Identify the relevant legal and ethical questions arising from the chosen topic • Explain the applicable legal framework and the legal issues involved • Where applicable, identify points of different judicial interpretation • Refer every issue to a point of law where possible • Provide a conclusion to the question on the basis of the argument given in the body of the essay • Demonstrate critical analysis, evaluation and judgement • Ensure the argument is logical, coherent, well presented and structured • Ensure proper referencing is applied • Include a full bibliography

Online resources: • ICO Guide to Data Protection https://ico.org.uk/for-organisations/guide-to-data-protection/ • General Data Protection Regulation https://gdpr-info.eu/ Recommended readings: • Shoshana Zuboff, The Age of Surveillance Capitalism (Profile Books, 2019) • Frank Pasquale, The Black Box Society (Harvard University Press, 2015) • Jaron Lanier, Who Owns the Future? (Penguin Books, 2015) • Ugo Pagallo, The Law of Robots. Crimes, Contracts and Torts (Springer, 2013) • Pompeu Casanovas et al., AI Approaches to the Complexity of Legal Systems (Springer, 2010) Articles, papers and other reading material will be made available to the students on the Course platform.

• Knowledge of the main characteristics of a database management system - Knowledge of the structure and properties of the relational model - Knowledge of query languages for a relational database: SQL language - Ability to write queries in the SQL languages - Knowledge of the conceptual Entity-Relationship model for data representation and of the methodology for conceptual and logical design of a relational database - Ability to design a relational database - Knowledge of the interaction techniques between SQL statements and reporting tools - Ability to design and develop an interactive dashboard - Knowledge of fundamentals behind spatial databases

The course is offered on the 2nd semester of the 1st year. The course presents database management systems and introduces the methodologies for the design, querying, and management of databases, introduction to business intelligence and the creation of interactive dashboards for data reporting. Laboratory sessions allow experimental activities on the most widespread commercial and open-source products.

• Characteristics of a database management system - Characteristics of the relational model - SQL language: statements for data definition and processing - Conceptual data model (Entity-Relationship) and methodology for the conceptual and logical design of a relational database - Introduction to Business intelligence: reporting environments for data analyses (query definition, generation of reports and interactive dashboards) - Introduction to spatial databases

Modalita di esame: Elaborato progettuale individuale; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Individual project; Computer-based written test in class using POLITO platform;
... The exam includes a written part, which lasts 90 minutes, and the evaluation of the reports on the individual practices assigned during the course. The delivery of the individual practices' reports is optional. The written part will assess the knowledge of the SQL language and the ability to write queries in the SQL language; the knowledge of the key aspects related to the design of relational databases; knowledge of some advanced aspects of databases design and query (such as spatial database, creating dashboards for reporting and for supporting decision making). The written part consists of close and open-ended questions (e.g., multiple-choice tests, open exercises). The points assigned to each exercise/question will be explicitly indicated next to the text of the exercise/question. Students are not allowed to use textbooks, notes, or additional electronic devices. The individual practice reports will assess the knowledge and ability of some course topics (such as the ability to write queries in SQL, the ability to design interactive dashboards to support decision making). The delivery of individual practices’ reports during the course is optional (max 3 points). The final exam grade is defined by considering the evaluation of the written part, and, optionally, of the individual practices. The individual practices are considered only if the grade of the written part is 18 or above.

The reference book, covering the majority of the course topics, is: Database Systems – Concepts, Languages and Architectures Paolo Atzeni, Stefano Ceri, Stefano Paraboschi and Riccardo Torlone McGraw-Hill. Copies of the slides used during the lectures, examples of written exams and exercises, and materials for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the Portal.

At the end of the course, the student will be able to demonstrate: 1) Knowledge and understanding ability The course addresses the main areas of digital government transformation (transparency and open data, simplification of administrative procedures, applications based on Artificial Intelligence, teleworking arrangements). It also reviews the theoretical approaches for the study of digital government transformation. 2) Capability to apply knowledge and understanding At the end of this course the student will be able collect and process data relating to digital government transformation. The student will also be able to recognize the key factors that affect the design and the implementation of digital government transformation projects. 3) Capability to approach the subject in a critical manner At the end of this course the student will be able to formulate a judgment on the quality of digital government transformation project. The student will also be able to address digital government transformation in terms of risks and adverse effects that may endanger its success. 4) Communication abilities At the end of the course the student will be able to use adequate language for the communication of digital government transformation initiatives. 5) Learning ability At the end of the course the student will be able to review the literature on digital government transformation and to draft a methodology for the analysis of specific case studies.

Digital government transformation has a central role to play to tackle societal challenges and make use of the emerging digital opportunities. Amongst others, the digital transformation of public administration can strengthen trust in government that is necessary for public policies to have effect: by increasing transparency, responsiveness, reliability, and integrity of public governance. Digital government transformation is also significant for the development of the data economy, for ensuring the movement of data as an enabler for digital innovation and for reducing the costs and the barriers to functioning of markets. The course explores the potential of digital technologies in the public sector. It also addresses the patterns of implementation of innovation projects in the public sector focusing on the public management rather than the technical aspects of the digital transformation. This implies analyzing the strategies that the promoters of digital innovation employ in order to define appropriate modes of public governance, shaping new technologies socially given the specific context in which they are embedded. The course aims to provide students with skills to be used in the following fields: - public administration: ensuring the success of e-government measures that public managers and civil servants are increasingly required to implement; optimizing the supply of online public services in line with the users’ needs. international organizations: formulation of standards and recommendations concerning digital transformation in the public sector; monitoring and evaluation of digital government transformation initiatives. - consulting: providing public administrations with knowledge and innovative solutions for digital transformation. - public/private research centers and networks: re-using public sector information to conduct research on trends in public administration and make data-driven decisions; data collection supporting implementation of digital government transformation. - public engagement: providing advice to facilitate participation in decision-making as well as linking public and private partners. - business: re-using public sector information to generate new services and innovative products.

Key principles of digital government transformation (trustworthiness and security, openness and inclusiveness, user centricity, interoperability by default, “once only� - 6 hours); - From New Public Management to Digital Era Governance: the role of digitalization within public management paradigms (6 hours); - Digital government transformation as a multi-level endeavor: the role of international organizations and national, regional and local governments in domestic contexts (6 hours); - Contextual factors and enabling policy steps that affect the implementation of digital government transformation (6 hours); - Transparency and Open data (6 hours); - Digitalization of administrative procedures (6 hours); - Big Data and Artificial intelligence in the public sector (12 hours); - Teleworking in the public sector (6 hours); - Smart cities programmes (6 hours).

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa; Elaborato progettuale in gruppo;
Exam: Written test; Optional oral exam; Group project;
... The exam tests: • the knowledge and the understanding of the concepts and theoretical approaches for the study of digital government transformation presented in the lectures; • the capability of using concepts and theoretical approaches to analyse, and deal with, digital government transformation. Oral presentation (1/3 of the mark): 20-minutes oral presentations will be assigned to couples of students. Each couple will cover one of the major areas of digital government transformation that will be addressed during the lectures. Oral presentations will take place during seminars for this course. They will be followed by a Q&A session with the peers. Final essay (2/3 of the mark): An assignment (3,000 words) addressing one topic of digital government transformation. The final essay requires the identification of a research question, a brief overview of the relevant literature, a proposal of data and theory to address the research question.

Anthopoulos, L., Reddick, C.G., Giannakidou, I. e Mavridis, N. [2016], Why e-government projects fail? An analysis of Healthcare.gov website, in «Government Information Quarterly», vol. 33, pp. 161-173. Di Giulio, M. e Vecchi, G. [2021], Implementing digitalization in the public sector. Technologies, agency, and governance, in «Public Policy and Administration», https://doi.org/10.1177%2F09520767211023283. Giest, S. e Raaphorst, N. [2018], Unraveling the hindering factors of digital public service delivery at street-level: the case of electronic health records, in «Policy Design and Practice», vol. 1, pp. 141-154. Giest, S. e Samuels, A. [2020], ‘For good measure’: data gaps in a big data world, in «Policy Science», vol. 53, n. , pp. 559-569. Goldfinch, S. [2007], Pessimism, Computer Failure, and Information Systems Development in the Public Sector, in «Public Administration Review», vol. 67, n. 5, pp. 917-929. Meijer, A. [2015], E-governance innovation: Barriers and strategies, in «Government Information Quarterly», vol. 32, n. 2, pp. 198-206. Wirtz, B.W. e Müller, M.M. [2019], An integrated artificial intelligence framework for public management, in «Public Management Review», vol. 21, n. 7, pp. 1076-1100. Young, M.M., Bullock, J.B. e Lecy, J.D. [2019], Artificial Discretion as a Tool of Governance: A Framework for Understanding the Impact of Artificial Intelligence on Public Administration, in «Perspectives on Public Management and Governance», vol. 2, n. 4, pp. 301-313. Further readings Bailey, D.E. e Kurland, N.B. [2002], A review of telework research: findings, new directions, and lessons from the study of modern work, in «Journal of Organizational Behavior», vol. 23, n. 4, pp. 383-400. Bullock, J.B. [2019], Artificial Intelligence, Discretion, and Bureaucracy, in «American Review of Public Administration», vol. 49, n. 7, pp. 751-761. Clarke, A. [2020], Digital government units: what are they, and what do they mean for digital era public management renewal?, in «International Public Management Journal», vol. 23, n. 3, pp. 358-379. de Vries, H., Tummers, L. e Bekkers, V. [2019], The Benefits of Teleworking in the Public Sector: Reality or Rhetoric?, in «Review of Public Personnel Administration», vol. 39, n. 4, pp. 570-593. Homburg, V. [2018] ICT, e-government and e-governance: bits & bytes for public administration, in E. Ongaro e S. van Thiel (a cura di), The Palgrave Handbook of Public Administration and Management in Europe, Basingstoke, Palgrave, pp. 347-361. Janowski, J. [2015], Digital government evolution: From transformation to contextualization, in «Government Information Quarterly», vol. 32, n. 3, pp. 221-236. Janssen, M., Charalabidis, Y. e Zuiderwijk, A. [2012], Benefits, Adoption Barriers and Myths of Open Data and Open Government, in «Information Systems Management», vol. 29, n. 4, pp. 258-268. Maciejewski, M. [2017], To do more, better, faster and more cheaply: using big data in public administration, in «International Review of Administrative Sciences», vol. 83, n. 1, pp. 120-135. Margetts, H. e Dunleavy, P. [2013], The second wave of digital-era governance: a quasi-paradigm for government on the Web, in «Philosophical Transactions of the Royal Society A», vol. 371, 20120382. Mele, V., Bellé, N. e Cucciniello, M. [2021], Thanks, but No Thanks: Preferences towards Teleworking Colleagues in Public Organizations, in «Journal of Public Administration and Theory», vol. 31, n. 4, pp. 790-805. Mergel, I., Hedelmann, N. e Haug, N. [2019], Defining digital transformation: Results from expert interviews, in «Government Information Quarterly», vol. 36, n. 4, 101385. Misuraca, G. e Van Noordt, C. [2020], AI Watch - Artificial Intelligence in public services, Lussemburgo, Joint Research Centre. Misuraca, G., Barcevičius, E. e Codagnone, C. [2020], Exploring Digital Government Transformation in the EU, Lussemburgo, Joint Research Centre. Sieber, R.E. e Johnson, P.A. [2015], Civic open data at a crossroads: Dominant models and current challenges, in «Government Information Quarterly», vol. 32, n. 3, pp. 308-315.

The main learning outcomes will relate to the following topics: - theoretical-explanatory frames relating to the transformations and impacts of digital media in contemporary society; - methodologies and tools for social media analysis aimed at improving public and private entities communication with regard to their customers, general citizens, media outlets etc.; The student must be able to apply the knowledge and the competencies acquired to achieve the following objectives: - realising social media listening analysis on different platforms such as Twitter, Instagram, Tumblr etc.; - being able to refer data to wider interpretative frames and narratives which may arise in the digital public sphere; - being able to map social media contents in relation with issues which may arise in specific contexts such as private or public companies and institutions based in the local area.

The spread of Internet and digital media transformed the processes of communication, socialization, production and consumption around the world. Recently social media platforms have increasingly modified and somehow disrupted the ways in which individuals and groups get together in urban areas, debate, participate in civic life and promote themselves and their business. Through the perspective of media sociology the course will provide students with the theoretical and analytical frameworks useful to understand the major transitions of contemporary digital societies. The main focus will be on social media platforms and their role in marketing communication, in the civic public sphere and in the processes of urban heritage valorisation. Along with the theoretical knowledge, students will learn how to analyse social media contents thanks to Digital Methods (social analytics tools) and they will learn how to develop Social listening activities related to corporate brands and to public or private institutions’ communication. Those competencies and abilities will reinforce the students professional skills in relation with the profile of Digital Expert in the field of innovative and sustainable data-based services oriented to public and private companies and institutions.

The course is articulated in two main parts: - The first part will include a number of theoretical lessons aimed at explaining the main frameworks related to the transformation and impacts of digital media in contemporary societies; - The second part will be devoted, through a project-based approach, to the methodologies (Digital Methods) and tools required for social media contents analysis. Among the two parts a number of lessons could be dedicated to case studies analysis or to the intervention of other experts and teachers or to the students’ advancements monitoring.

Modalita di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Individual essay; Group essay;
... The final evaluation consists of two parts: 1) Students will have to prepare a report (individual or group report) related to the Digital Methods application on a specific case study or project previously agreed by the teacher. The report (15 pages minimum) will describe the context, the objective, the research methodology deployed and the principal results of the analysis. The report will be evaluated both in his substantial and formal aspects (writing, spelling etc.). 2) An oral individual interview on a selection of the reading materials. During the oral exam, the student will have to answer up to four questions on the topics presented during the classes or referenced in the reading materials selected. The final vote will be given by the weighted average of the report vote (50%) and the average of the oral interview (50%). Those who do not submit the report will agree on a specific exam program with the teacher.

Boyd, D. M. and Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. Castells, M. (2013). Communication power. Oxford: OUP. Crawford, K. (2009). Following you: Disciplines of listening in social media. Continuum, 23(4), 525-535. Fuchs, C. (2017). Social media: A critical introduction. London: Sage. Fuchs, C. (2018). Propaganda 2.0: Herman and Chomsky’s propaganda model in the age of the internet, big data and social media. In Pedro-Carañana, J., Broudy, D. and Klaehn, J. (Eds.) The propaganda model today: Filtering perception and awareness. London: University of Westminster Press. Available at: 10.16997/book27. f. Gillespie, T. (2018). Custodians of the Internet: Platforms, content moderation, and the hidden decisions that shape social media. New Haven, CT: Yale University Press. Jenkins, H., Shresthova, S., Gamber-Thompson, L., Kligler-Vilenchik, N., & Zimmerman, A. (2018). By any media necessary: The new youth activism (Vol. 3). NYU Press. Jenkins, H., Ford, S., & Green, J. (2018). Spreadable media: Creating value and meaning in a networked culture (Vol. 15). NYU press. Rogers, R. (2019). Doing digital methods. SAGE Publications Limited. Sunstein, C. (2017). Hashtag Republic. Princeton University Press, Princeton (US). Tuten, T. L. (2020). Social media marketing. Sage. van der Hoeven, A. (2019). Historic urban landscapes on social media: The contributions of online narrative practices to urban heritage conservation. City, Culture and Society, 17, 61-68.

• Knowledge of the main types of geographical data and the related methods of creating, editing, processing (with hints relating to automation through scripting) and representing • Ability to manage spatial data in different reference and coordinate systems • Ability to communicate issues of spatial relevance in a clear and unambiguous way through the preparation of a report on original themes and which includes cartographic outputs

Geomatics’ techniques and tools allow to manage georeferenced spatial data and to generate added value information and can be applied in multiple application areas (i.e., decarbonization processes, industry 4.0, cultural heritage, etc.) and in response to various challenges (i.e., sustainable mobility, optimization of industrial processes, predictive maintenance, digital twins, innovative dissemination tools). In this context, Geographic Information Systems (GIS) play a key role, allowing to acquire data, to position them with respect to a reference coordinate system (georeferencing), to structure them effectively, to analyze them, to share them and to generate innovative and effective dissemination products. This course aims to: • describe GIS systems peculiarities and GIS different data types • describe how spatial data can be navigated, queried, visualized and combined into mapping products • showcase methods and tools for creating new data and analysing existing ones • demonstrate the application of these techniques in different areas through practical examples, presented during lessons, and the production of a final report by the students on a topic of their choice.

• Introduction (15 hours): o list and description of the main disciplines connected to geomatics o how a GIS works: definitions and components, main capabilities, differences in respect to CAD systems o GIS data: vector geometries (2D and 3D), raster basics, non-spatial tables metadata, storage options o spatial coordinate systems, map projections and related concepts of precision, accuracy, and nominal scale o methods for discovering existing data o instruments for acquiring new data o main types of cartographic products (static and dynamic) • Visualizing, navigating and querying GIS data (20 hours) o the layer concept o moving around a map o layer symbology, classification and labelling o attributes management (including functions for joining data) o info and query (attribute- and location-based) tools o hints on how to manage 3D data o map layout creation • Generating new data or information (20 hours) o editing and analyzing vector data o processing raster data o vector-raster conversion and combination o creating processing workflows • Managing extensions/plugins (5 hours) In the case of classroom training, lessons and exercises will be organized in an informatics laboratory where appropriate hardware and software will be made available to the students. In the case of distance learning, the teacher will provide hardware requirements and the possibility to install locally the required software.

Modalita di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Individual essay; Group essay;
... The exam will be oral and will focus on the presentation and subsequent discussion of a report in which students, individually or in small groups of maximum 3 people, will have to demonstrate the application of conceptss, methods and analyses presented during the course. The written document must be a descriptive and self-explanatory text, designed to provide a complete picture of the objectives, methods, and results. It is advisable to follow the instructions in the document Knowing how to communicate (https://didattica.polito.it/tesi/SaperComunicare.pdf), as regards the production of a technical-scientific report (section 2). The level of detail of the report must be a good compromise between the readability of the document and the need to provide the reader with the tools not only to understand the analysis, but also to be able to reproduce (and possibly improve) the proposed procedure. The subject of the paper should be agreed in advance with the teacher: in this case, the material necessary for the assessment must be delivered 1 week before the exam date. If the object is not agreed, the material needed for the assessment must be delivered within 2 weeks from the date of the exam. All the topics covered during the course and, if necessary, their application using the adopted GIS software, installed in the laboratory or on student’s PC, are an integral part of the oral exam. The judgment criteria used to compose the final grade include the evaluation of: • the ability to produce a concise, coherent and unambiguous written paper (45%) • the ability to expose the contents of the paper clearly and in accordance with the indicated time (25%) • the theoretical knowledge relating to the topics covered during the lessons (30%)

• Mario A. Gomarasca, Basics of Geomatics, Springer, 2014, EAN: 9789400789517 • Material provided by the teacher

By taking this course, students will learn how a modern computer is organized, how it works and how it can communicate to other computers and digital devices. In particular, at the end of this course, the students will: • Understand how information is represented in digital computing and communication systems. • Be able to apply problem solving methodologies which are at the basis of the art of computer programming • Understand the basic concepts, issues and solution concerning computer networking. • Have received a comprehensive overview of the most promising and widely adopted digital technologies in many domains of the real life.

ICT, or information and communications technology, is the infrastructure and components that enable modern computing. It consists of all devices, networking components, applications and systems that combined together allow people and organizations to interact in the digital world. Some ICT components are well established (e.g., computers and telephones have existed for decades), others are more recent (e.g., smartphones, digital TVs and robots). ICT has drastically changed how people work, communicate, learn and live, and it continues to revolutionize all parts of the human experience, as first computers and now robots do many of the tasks once handled by humans. Specifically, ICT underpins broad shifts in society, as individuals are moving massively from personal, face-to-face interactions to ones in the digital space. ICT permeates all aspects of life, providing newer, better, and quicker ways for people to interact, network, seek help, gain access to information, and learn. Besides its presence everywhere, ICT has an immense economic significance. The job market strongly requires that, independently from their fields of specialization, new graduates and young professionals own extensive know-how and background in ICT. This course provides an introduction to ICT. It offers the basic concepts of data representation and logic operation in computers and digital devices, the fundamental principles of computer architecture, including both hardware and software components, the background of computer networking and distributed systems; in addition, it provides a comprehensive overview of the key technologies in modern ICT, including heterogeneous digital systems, cloud, IoT, databases, data analytics, artificial intelligence and cybersecurity. The contents presented here are preparatory to the courses of the program in “Digital Skills for Sustainable Societal Transitions� that will address specific topics of the ICT domain (e.g., computer programming, databases, artificial intelligence, IoT), as well as courses in which ICT will be the enabling factor for several societal contexts.

• Course introduction • History of computers • Computer programming and operation • Data representation and logic networks • Computer organization and hardware components: o Processor o Memory o Peripherals • Operating systems and software • Data communication and networking: o Distributed systems o Internet • Advanced technologies : o Embedded platforms o Databases o Data analytics o Artificial intelligence o Cloud, fog, edge o IoT o Cybersecurity

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa;
Exam: Written test; Optional oral exam;
... Mandatory written exam, an additional oral exam is at discretion of the instructor. The exam covers all the material presented in class, it lasts 1.5 hours for 4 or 5 questions and/or short problems. The exam is “closed book� and students must pre-register for it. Students must show picture ID at classroom entrance; during the exam, no cell phone or connected device is allowed.

Lecture notes distributed by the instructor and available on the course portal. Additional reading material will be provided on some specific topics covered in the lectures. No textbook is required.

1) Knowledge and understanding ability Develop a thorough knowledge of major organizational theories and organizational behavior theories; develop an understanding of the importance of management and organization in any business sector. 2) Capability to apply knowledge and understanding The course will provide the conceptual tools to face a method and critical approach to the complex and dynamic reality of working in the business and organizational world. Several examples and cases will be discussed in class to apply theoretical concepts and tools to practical situations 3) Capability to approach the subject in a critical manner Thanks to the group’s works that will be held in each lesson, students will be able to elaborate on personal reflections on the current transformations that the working context is facing. They will also be challenged to think about the consequences of digital transformation in the workplace and reflect on social responsibility issues. 4) Communication abilities The dialogue with the professor and among them will allow students to develop interaction and communication capabilities. Cases will be discussed in class in each lesson to enhance students’ participation. 5) Learning ability The course aims to develop the students’ self-learning abilities through individual activities and study. The Professor will provide students with numerous cases and materials for individual study of the topics discussed in class.

The course aims to introduce students to the current challenges of working in business and organizations. Within the degree program in “Digital skills for Sustainable Societal Transition�, the course’s purpose is to develop the "human skills" of students and future workers in organizations. Students will be introduced to the broad theoretical apparatus derived from theories of organization and organizational behavior to understand their implications in terms of management. The course will be divided into three parts. The first part will introduce the topic of "people management," inclusive of concepts fundamental to the DISST degree program, including diversity management and motivation. The second part will focus on the group dimension ("group management") to understand the fundamentals of group behavior and leadership. In the third part, we will focus on "organizational structure," with emphasis on the topics of organizational culture, change management, conflict, and negotiation. Throughout the course, and particularly through group work, students will be challenged to think about the challenges that technological and digital development poses to work within an organizational context. They will be confronted to think about the consequences of digital transformation in the workplace and reflect on social responsibility issues.

Part 1: People management • Diversity in Organizations • Attitudes and Job Satisfaction • Personality and Values • Perception and Individual Decision Making • Emotions and Moods • Motivation Concepts • Motivation: From Concepts to Applications Part 2: Group Management • Foundations of Group Behavior • Understanding Work Teams • Power and Politics • Communication • Leadership Part 3: The Organization System • Foundations of Organization Structure • Organizational Culture • Human Resources Management - Policies and Practices • Change management and Stress Management • Conflict and Negotiation

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam tests: • the knowledge and the understanding of the concepts and theories presented in the lectures; • the capability to use models and concepts to analyse and deal with organizational and managerial issues posed by the digital transformation The exam is composed of two parts and will partially differ for attending and non-attending students: • Attending students: o Group essay and presentation (30% of final evaluation): students will be asked to choose a topic to be developed for group work. The outcome of the group work will be a written essay of maximum 5 pages including references (times new roman 12). The essay will be handed in and presented in class by all group members using PowerPoint in the last lesson of the course. Evaluation will be given to the group and not to the single student. o Individual assessment (70% of final evaluation): 20 written multiple-choice questions on the theories and topics discussed in class (1 hour) • Non-attending students: o Individual essay (30% of final evaluation): students will be asked to choose a topic to develop for individual work. The outcome of the essay will be a written report of maximum 5 pages including references (times new roman 12). The essay will be handed in on the day of the individual assessment. The evaluation will be given to the single student. o Individual assessment (70% of final evaluation): 20 written multiple-choice questions on the theories and topics discussed in class (1 hour)

Robbins, P. R, Judge, T.A. 2022. Organizational Behavior. Updated Eighteen edition. Global edition. Pearson, UK. Specific files will be provided by the Professor with cases for class discussion

At the end of the course, students will be expected to: • Know and understand the main concepts, theories, and debates that define the field of digital heritage and its relationship with cultural heritage studies. • Know and understand the opportunities, limitations, and ethical implications of digital technologies in the documentation, preservation, interpretation, management, and dissemination of cultural heritage. • Know and understand the characteristics, uses, and critical issues associated with digitised and born-digital heritage sources. • Know and understand the role of data, metadata, standards, and digital infrastructures in the creation, management, and reuse of heritage information. • Know and understand the functions and practices of GLAM institutions (Galleries, Libraries, Archives, and Museums) in the digital environment. • Apply digital methods and tools to collect, organise, analyse, interpret, and communicate heritage-related information from multiple sources. • Critically evaluate the quality, reliability, representativeness, and limitations of digital heritage data and resources. • Analyse complex heritage scenarios through the integration of historical, cultural, spatial, and digital information. • Design digital heritage projects by defining objectives, identifying appropriate digital methods, and selecting suitable data sources and tools. • Reuse, curate, and disseminate digital heritage resources from GLAM institutions in accordance with ethical, legal, and professional standards. • Communicate heritage knowledge through digital formats and platforms tailored to different audiences and purposes. • Critically reflect on the social, cultural, and political implications of digital technologies in heritage research and practice.

Digital technologies are profoundly transforming how cultural heritage is documented, researched, interpreted, managed, and communicated. The increasing availability of digitised and born-digital sources, together with advances in data management, visualization, artificial intelligence, and digital communication, is creating new opportunities as well as new challenges for cultural heritage world. In this context, digital heritage has emerged as a critical field that combines technological innovation with historical, cultural, ethical, and societal questions. The course aims to provide students with the theoretical foundations and critical perspectives necessary to understand and engage with digital heritage in contemporary practice. Particular attention is given to the role of digital technologies in the production, interpretation, representation, and dissemination of heritage knowledge across different cultural and institutional contexts. The course encourages students to critically examine digital tools and methodologies rather than considering them as neutral technologies, reflecting on issues such as data quality, representation, accessibility, inclusivity, and digital sustainability. By integrating historical inquiry, heritage studies, and digital methods, the course contributes to the development of interdisciplinary competences that are increasingly required in heritage institutions, research environments, and cultural creative industries. Through lectures, discussions, and project-based activities, students will acquire the conceptual and methodological foundations needed to design and critically evaluate digital heritage initiatives, preparing them to address the opportunities and complexities of heritage in the digital age.

1. Introduction to Digital Heritage: Concepts, Definitions and Debates (1 CFU): Digital heritage as a field of research and practice. Heritage as data, memory, narrative, and representation. From cultural heritage to digital heritage. Digitisation and born-digital heritage. International frameworks and debates, including the UNESCO Charter on the Preservation of Digital Heritage. Expanding understandings of digital heritage beyond technological approaches through critical and historical perspectives. Analysis of international case studies. 2. Digital Collections, Archives and GLAM Ecosystems (1 CFU): Digital archives and collections. The role of GLAM institutions (Galleries, Libraries, Archives, and Museums) in the digital age. Digitisation strategies, preservation, access, curation, and reuse of cultural heritage resources. Open access, copyright, and ethical considerations. Critical analysis of digital collections and archival practices through case studies. 3. Data, Metadata and Critical Digital Heritage (1 CFU): Understanding heritage as data. Data creation, documentation, metadata, and paradata. Standards, interoperability, linked data, and FAIR principles. Critical approaches to data and digital infrastructures. Data quality, representation, inclusivity, bias, and power relations in digital heritage. The role of data in shaping heritage narratives and public knowledge. 4. Spatial Humanities and Digital Approaches to Place (1 CFU): The spatial turn in the humanities and heritage studies. Historical GIS, WebGIS, spatial storytelling, and mapping practices. Digital methods for exploring cultural landscapes, cities, memory, and historical transformations across time and space. Analysis of heritage through multiple spatial scales and interconnected datasets. 5. Digital Storytelling, Visualisation and Public Engagement (1 CFU): Digital narratives and heritage interpretation. Visualisation methods including maps, timelines, multimedia environments, and digital exhibitions. Storytelling as a tool for communicating heritage significance and fostering public engagement. Opportunities and challenges in representing diverse, multicultural, and contested heritage narratives. 6. Emerging Technologies and the Future of Heritage (1 CFU): Artificial intelligence, machine learning, virtual and augmented reality, 3D modelling, digital twins, and generative technologies in heritage research and practice. Opportunities, limitations, ethical implications, and critical reflections on technological innovation in the heritage sector.

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato grafico prodotto in gruppo;
Exam: Written test; Compulsory oral exam; Group graphic design project;
... You will be working in groups on a specific case study. The final evaluation will be based on your group work, and individual assessment (based on the written test and compulsory oral exam evalutations, but also active partecipation to the course and engagement in class discussions) The final group work on the case study (50 % - group vote): Your group work will use digital heritage tools and methods to analyse, interpret, and communicate heritage in innovative and critical ways. You need to submit your final work 1 week before the exam date. On the exam date, you will be presenting your project. Final Presentation of the group work (15% - individual vote): You will be presenting your group work in the exam date. Members of the teaching committee MAY pose questions from the course contents if they see necessary. Written exam (Moodle) (15 % - individual vote): Multiple choice exam on Moodle from the course contents. It will be conducted in the last week of the courses. Process diary (10 % - individual vote): Each group is requested to keep a diary. Members should write in turn to register not only the develeopment of the process but also to include visuals as well as the group dynamics, emotions, challenges, excitements. Physical hand-written diaries are preferred, however, also digital diary is accepted. You need to submit your diary 1 week before the exam date with your group work. Peer review (10 % - individual vote): You will receive an anoynmous evaluation from your group mates.

Bibliographical references will be provided during the course according to the development of the topics and the students’ project work. The following readings provide the main theoretical, methodological, and critical references for the course. Compulsary readings will be included in the written exam and questions of the teachign committee during the final presentation can come from bibliography. COMPULSORY READINGS: • UNESCO. (2003). Charter on the Preservation of the Digital Heritage. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000179529 • Drucker, J. (2014). Graphesis: Visual Forms of Knowledge Production. Cambridge, MA: Harvard University Press. (Chapter 5 “Interface and Interpretation�.) • D’Ignazio, C. and Klein, L. F. (2020). Data Feminism. Cambridge, MA: MIT Press. (Compulsory part: Chapter 1 “The Power Chapter�.) • Terras, M. (2022). “Digital humanities and digitised cultural heritage.� In J. O’Sullivan (Ed.), The Bloomsbury Handbook to the Digital Humanities, pp. 255-266. London: Bloomsbury Academic. • Dinler, M. (2024). “Metadata and Linked Open Data in Digital Heritage for Decolonization.� In The Palgrave Encyclopedia of Cultural Heritage and Conflict. Cham: Springer Nature. https://doi.org/10.1007/978-3-030-61493-5_52-1 • Jaillant, L., Warwick, C., Gooding, P., Aske, K., Layne-Worthey, G. and Downie, J. S. (Eds.). (2025). Navigating Artificial Intelligence for Cultural Heritage Organisations. London: UCL Press. https://doi.org/10.14324/111.9781800088375\ • UNESCO. (2025). Report of the Independent Expert Group on Artificial Intelligence and Culture (CULTAI). UNESCO. https://www.unesco.org/sites/default/files/medias/fichiers/2025/09/CULTAI_Report%20of%20the%20Independent%20Expert%20Group%20on%20Artificial%20Intelligence%20and%20Culture%20%28final%20online%20version%29%201.pdf • Goudarouli, E., Sichani, A.-M., & Winters, J. (2026). Exploring Digital Cultural Heritage. Access, Use, Value and Sustainability. University of London Press. https://doi.org/10.63674/bxds6730 (Chapter 1: Introduction) FURTHER SUGGESTED READINGS: • Cameron, F. and Kenderdine, S. (Eds.). (2007). Theorizing Digital Cultural Heritage: A Critical Discourse. Cambridge, MA: MIT Press. (Read: Introduction.) • Kirschenbaum, M. G. (2008). Mechanisms: New Media and the Forensic Imagination. Cambridge, MA: MIT Press. • Denard, H. (Ed.). (2009). The London Charter for the Computer-Based Visualisation of Cultural Heritage, Version 2.1. https://www.londoncharter.org/ • Bodenhamer, D. J., Corrigan, J. and Harris, T. M. (Eds.). (2010). The Spatial Humanities: GIS and the Future of Humanities Scholarship. Bloomington: Indiana University Press. • Burdick, A., Drucker, J., Lunenfeld, P., Presner, T. and Schnapp, J. (2012). Digital_Humanities. Cambridge, MA: MIT Press. • Giaccardi, E. (Ed.). (2012). Heritage and Social Media: Understanding Heritage in a Participatory Culture. London: Routledge. • Gold, M. K. (Ed.). (2012). Debates in the Digital Humanities. Minneapolis: University of Minnesota Press. • Tamborrino, R. and Rinaudo, F. (2015). “Digital urban history as an interpretation key of cities’ cultural heritage.� In Digital Heritage 2015, vol. 2, pp. 235-242. IEEE. https://doi.org/10.1109/DigitalHeritage.2015.7419503 • Padilla, T. (2019). Responsible Operations: Data Science, Machine Learning, and AI in Libraries. Dublin, OH: OCLC Research. https://doi.org/10.25333/xk7z-9g97 • Dinler, M. (2021). “Counter-Mapping through Digital Tools as an Approach to Urban History: Investigating the Spatial Condition of Activism.� Sustainability, 13(16), 8904. https://doi.org/10.3390/su13168904 • Forte, P. (Ed.). (2026). Patrimoni culturali e intelligenza artificiale. Napoli: Editoriale Scientifica. (Open access: https://editorialescientifica.it/prodotto/patrimoni-culturali-e-intelligenza-artificiale/)

The internship is an F-type training activity (free credits) worth 7/10 Credits corresponding to a workload of 175/250 hours at the host institution. You can do your internship during the second semester of your second year. The internship allows you to carry out a professionalizing activity with a strong applicative content within public and private bodies operating in the processes of digital transitions. The activity carried out during your internship will be the starting point for the development of the subject matter that you will explore more in depth in your thesis, which requires a commitment of other 20 credits. Your academic tutor of the internship will therefore also be your thesis supervisor.

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;

The course aims to provide students with the knowledge of object oriented programming and tools for its implementation in Python. Knowledge of the protocols for implementing web service oriented communication paradigms. Knowledge of the main tools for programming environment in real-time and in the presence of sensors and actuators. Tools for data analysis using machine learning. The student will learn how to design a complete IoT system (microservice-based) and deploy it in a realistic application domain, starting data collection on IoT devices, data organization on the host, data processing, visualisation and user interaction.

Internet of Things indicates a variety of devices connected via the Internet, including wearable devices, sensors deployed on buildings, industrial plants, production and distribution energy systems and in general on cyber-physical systems. IoT technology is widely adopted in many application domains, such as smart factories, buildings and districts for energy monitoring or surveillance, environment monitoring and control of natural phenomena, within transport systems for traffic management and finally within industrial and manufacturing devices. In this context, the aim of the course is to give the student ability to use IoT devices and platforms connected to the network using programming paradigms for distributed, parallel and object oriented systems. The ultimate goal will be the ability of programming in the Internet-of-Things and Cyber-Physical-Systems world. The student will also acquire the ability to program according paradigms publish / subscribe. The student will explore the concepts and tools for interoperability between heterogeneous devices, protocols, Machine-to-Machine, operating systems-oriented smart objects and programming with real-time constraints and energy consumption. Finally, it will be handled aspects related to IoT integration in the cloud and the development of platforms for end users. During the course will be presented case studies related to monitoring systems within smart cities and smart factories and includes a series of laboratory exercises where the student will implement the concepts of programming with sensors and actuators, and allowing remote interaction with these devices.

Introduction to object oriented programming and Python Microservices and Middleware programming IoT communication protocols IoT platforms and use cases Labs

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... Written exam: • Written test on all the course topics Project report: • Oral presentation about the project developed during the course

Teaching material: • Lecture notes provided • Scientific papers • Material for labs

At the end of the course students are able to: - identify and analyze trade-offs and synergies between SDG 11, aimed at "Making cities and human settlements inclusive, safe, resilient and sustainable", and SDGs 7 “Affordable Clean Energy�. These two topics will be also connected to SDGs 12 (Circular economy) and 13 (Climate change). Thanks to system thinking approaches, students will be able to acquire capacity for viewing problems and human action as interconnected wholes and for understanding the (often lagged) feedback loops that may make complexity very difficult to address through the traditional, linear modeling processes; - connect decision-making and sustainable urban development with the paradigm of complexity and interdisciplinary. Thanks to the illustration and application of methods and approaches based on environmental economics, multicriteria analysis and systems thinking, students will be able to understand the big picture around a problem and forecast its long-term evolution rather than concentrating on specific, short-term cause-effect relationships; - understand and connect scientific knowledge with decision-making problems at urban district level, by means of a rigorous definition of sustainability challenges in terms of measurable levels and flows of common resources (such as air quality) and an ability to build mental models of the complex net of cause-effect relationships that revolve around such common resources; - analyze the pros and cons of alternatives towards sustainable communities, using the evaluation methods illustrated in this course; - apply in real case studies sustainable development actions thanks to the interactive workshops applied during the class.

The atelier focuses on the decision-making process in the context of societal transition. It introduces the challenge of sustainable communities posed by United Nations Agenda 2030 Sustainable Development Goals (SDG 11) and it illustrates from a theoretical and practical viewpoint the main assessment and management methods and tools in support of complex and 'wicked' problems related to energy transition. Specific attention is paid to systemic and multidisciplinary approaches. All theoretical topics are intertwined with workshops and interactive sessions carried out within smaller groups of students. The university campus and urban districts are used as demonstrators and virtual laboratories, to visualize the practical impact of the decision-making processes studied at the urban district level.

The Atelier includes three main themes with sub-themes as follows: ENERGY TRANSITION AND ENERGY MODELING • Energy challenges in urban context • Energy modelling of buildings at urban scale • Energy atlases and open databases • Energy efficiency measures • Renewable energy technologies • Self-consumption and energy independence communities PROJECT CYCLE ANALYSIS AND EVALUATION METHODS • Project cycle management/SWOT analysis/Stakeholders analysis • Cost Benefit Analysis, Environmental Economics • Multi attribute Utility and Multi Criteria Evaluation • Environmental rating protocols (e.g. LEED, Itaca, etc.) SYSTEM THINKING APPROACHES, DYNAMIC SPATIAL DECISION SUPPORT SYSTEMS • System thinking, Scenario workshops and Storytelling methods • Multicriteria GIS-based theories and application The three blocs will include interactive laboratories and workshops where students have the opportunity to apply the theories presented in each part.

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Written test; Compulsory oral exam; Group project;
... The assessment is expressed in 30/30 marks and the criteria guiding the evaluation are the followings: a) comprehension of the topics; b) ability to present adequately the topic; c) ability to understand the implications of real-world case studies and the connections with other topics. The exams is consisting of three mandatory parts as follows. 1. An individual written test, which will be multiple choices. The weight of this written test is 1/3 of the total. 2. An oral exam for each group presenting the workshop's results and exercises. The portfolio includes all workshop results and classroom exercises. The weight of this oral test is 2/3 of the total. The written exams will focus on the theoretical approaches and methodologies illustrated in the three modules.

• AA.VV., Solar Portal of Turin Metropolitan City, http://www.provincia.torino.gov.it/speciali/2013/portale_solare/; http://www.cittametropolitana.torino.it/cms/ambiente/risorse-energetiche/osservatorio-energia/portale-solare • Albino, V., Berardi, U., Dangelico, R.M. (2015), Smart Cities: Definitions, Dimensions, Performance, and Initiatives, Journal of Urban Technology, vol. 22, issue 1, pp. 3-21. doi: 10.1080/10630732.2014.942092. • Brandon P.S., P. Lombardi. Evaluating Sustainable Development in the Built Environment (second ed.), A John Wiley & Sons, Ltd., Publication (2011). http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0632064862.html • European Environment Agency (2013), Achieving energy efficiency through behaviour change. https://www.eea.europa.eu/publications/achieving-energy-efficiency-through-behaviour . Accessed July 2019. • JRC, Photovoltaic Geographical Information System (PVGIS), https://ec.europa.eu/jrc/en/pvgis • Lombardi P., F. Abastante, S.T. Moghadam (2017), Multicriteria spatial decision support systems for future urban energy retrofitting scenarios, Sustainability 10.3390/su9071252. • Lombardi P., S Giordano, H Farouh, W Yousef (2012), Modelling the smart city performance, Innovation: The European Journal of Social Science Research 25 (2), 137-149 • McKinsey Global Institute (2018), Smart cities: digital solutions for a more livable future. mckinsey.com/mgi. Accessed June 2019. • Mutani, G., Beltramino, S., Forte, A., A Clean Energy Atlas for Energy Communities in Piedmont Region (Italy), International Journal of Design & Nature and Ecodynamics, Vol. 15, No. 3, 2020, pp. 343-353, DOI 10.18280/ijdne.150308). • Mutani, G., Pisanello, F., Nuvoli, G., nZEB towards a nearly future. Critical issues and strengths of a technological development, IEEE CANDO EPE 2019 Conference, Budapest, November 2019, pp 191-196, ISBN: 978-1-7281-4358-3, DOI 10.1109/CANDO-EPE47959.2019.9110965. • Mutani, G., Todeschi, V., Beltramino, S., Energy consumption models at urban scale to measure energy resilience, Sustainability - Bridging the Gap: The Measure of Urban Resilience, Sustainability 2020, 12 (14), 5678, pp.1-31; DOI:10.3390/su12145678. • Mutani, G., Todeschi, V., Building Energy Modeling at Neighborhood Scale, Energy Efficiency, Volume 13, Issue 5, June 2020, Springer Ed., DOI 10.1007/s12053-020-09882-4. • Nazem, S., Bruni, V., Fabris, E., Marcus, A., Melis, B., Roccella, G. (2020), Building Communities Through Digital Data Sharing, Springer. • Serge Salat, Françoise Labbe and Caroline Nowacki 2011. Cities and Forms. On Sustainable Urbanism, Hermann. • Todeschi, V., Mutani, G., Baima, L., Nigra, M., Robiglio, M., Smart Solutions for Sustainable Cities—The Re-Coding Experience for Harnessing the Potential of Urban Rooftops, Applied Science, Special Issue Building Physics and Building Energy Systems, Vol. 10, 7112, 2020, pp. 1-27; doi:10.3390/app10207112. • Torabi Moghadam, S., Delmastro, C., Corgnati, S.P, Lombardi, P. (2017), Urban energy planning procedure for sustainable development in the built environment: A review of available spatial approaches, Journal of Cleaner Production, vol. 165, pp. 811-827. doi: 10.1016/j.jclepro.2017.07.142. • Torabi Moghadam, S., Lombardi, P. (2019), An interactive multi-criteria spatial decision support system for energy retrofitting of building stocks using CommuntiyVIZ to support urban energy planning, Building and Environment, vol. 163, pp. 106-233. doi: 10.1016/j.buildenv.2019.106233 • World Economic Forum (2019), Making Affordable Housing a Reality in Cities, WEF Report. https://www.weforum.org/whitepapers/making-affordable-housing-a-reality-in-cities. Accessed Novembre 2019.

La prova finale rappresenta un importante momento formativo del corso di laurea magistrale e consiste in una tesi che deve essere elaborata in modo originale dallo studente sotto la guida di un relatore e la eventuale collaborazione di correlatori. Le attività previste nella tesi richiedono normalmente l'applicazione di quanto appreso in più insegnamenti e atelier, l'integrazione con elementi aggiuntivi e la capacità di proporre spunti innovativi. Allo studente viene offerta la possibilità di approfondire uno specifico tema, elaborando una tesi di carattere comunque professionalizzante, all'interno della quale è previsto un periodo di tirocinio e, in alcuni casi, un approfondimento all'interno di un laboratorio. L'argomento della tesi di Laurea Magistrale è individuato dallo studente all'interno delle discipline presenti nel piano di studio del Corso di Laurea Magistrale e in riferimento all'attività di tirocinio svolta e deve essere concordato con il relatore. L'esposizione e la discussione dell'elaborato avvengono di fronte ad un'apposita Commissione. Il laureando dovrà dimostrare capacità di operare in modo autonomo, padronanza dei temi trattati e attitudine alla sintesi nel comunicarne i contenuti e nel sostenere una discussione. Le acquisizioni da valutare per l'assegnazione del punteggio di tesi riguardano: originalità e rilevanza dei risultati, approfondimento del tema e rigore metodologico, impegno e autonomia di ricerca, presentazione e rappresentazione dei risultati, capacità di argomentare il proprio pensiero. La tesi è redatta e presentata in lingua inglese. Modalità di assegnazione e dettagli sullo svolgimento della prova finale sono precisati nel regolamento didattico di Corso di Laurea Magistrale.

The knowledge acquired all along the course refers to innovation in transport systems and methods for data analytics. The collection and analysis of data (and big data) will allow obtaining useful information to provide original insights into, for example, the state of the transport market (transport demand), the travel behaviour, how to refine traveller experience strategies and so on. To this end, the students will be able to: - use tools to acquire data thanks to several channels: apps, sensors, etc. (IoT); - store data through the construction of databases; - process information thanks to advance statistical methods (e.g. multivariate statistical analysis) and data mining techniques, identifying correlations, patterns and trends in big volumes of data through neural networks, decision tree and analysis of associations; focusing on dedicated applications for transport sector, services (e.g. to customise technologies and successfully integrate them into existing systems) and infrastructural resources (computing capacity, storage, etc.). The capacity of data collection and analysis will be acquired together with the ability of ensuring data privacy and security to critically analyse the existing transport systems to make them progress towards a greater sustainability, also thanks to digital transition. The competence acquired will allow students to; • conceive an intelligent mobility system; • design and develop mobility services and solutions; • lead a "Digital Transition" project in the field of transport; or • conduct an "intelligent transport system" project in a company or a local authority; and to work in professional contexts as:  communities and transport authorities;  mobility service providers;  operators transporting people or goods;  infrastructure managers;  industrial manufacturers or automotive equipment manufacturers;  digital industry: IT services companies, software publishers, computer manufacturers, telecom operators and equipment manufacturers.

The course aims at training students to become capable of making the best and fastest use of digital technologies in the transport and mobility sector. Digital transformation of transport systems and mobility is proceeding fast and involves different aspects that will be analysed in the course, to allow students to acquire knowledge about: • infrastructures and services and their use; • traveller information, with specific attention to real time; • integrated transport, multimodal mobility and transport on demand; • new forms of mobility with attention to shared mobility; • Connected and Automated Transport; • Mobility as a Service (MaaS) and smart and integrated ticketing. Thus, the course aims to give a basic knowledge on the above topics, including economic and environmental implications, to give the necessary background to students to manage the data collection, communication and exchange to provide more sustainable and inclusive mobility services. The students will learn how using different methods, from traditional (surveys) to innovative ones (apps, sensors, etc.) that allow to connect things, to gather information, send information back, or both (IoT). Then, the course will provide the knowledge to treat and analyse data coming from several and different sources, with a particular attention to the aspect related to data security and privacy, making cybersecurity another element of the knowledge acquired in the course. The data security and privacy will be considered also under the lens of social and ethical implications. Definitely, the course aims to address the training needs of the transport sector that is evolving more and more towards digitalisation and increased connectivity, with attention to manage the digital transition in favour of a greater sustainability and inclusion. What we could synthesise in the concept of “smart mobility�. Case studies and examples of applications will allow students to transform knowledge in ability to create the competence to be spent in their professional life. To this end, students will be required to put into practice the lessons through exercises and case studies using methods of statistics, mathematics and information science as multivariate statistical analysis, probability, programming (e.g. python), data storage, data analytics, machine learning. Part of the course is devoted to the development of case studies applied to transport sector with focus on sustainability, accessibility, connectivity and inclusivity, where students are required to work as in a professional context to form their competence.

The course will be based on concept of learning by doing. Theoretical lectures (42h) will be followed by practical application with work on data (18h). INTRODUCTION TO TRANSPORT SYSTEMS Definitions and fundamentals of the discipline of transport. What are transport and mobility. Concepts of transport demand and transport supply. Characteristics of the transport demand (derived, not-derived, induced, latent) and of the transport supply (infrastructures and services). Impacts of the transport systems (economic, social, environmental). Analysis of the main trends in the mobility of people and goods and their main determinants. INTELLIGENT TRANSPORT SYSTEMS (ITS) Definition, technical and legal evolution; regulation. Fundamentals of ITS (standard and architecture) and application to innovation for transport systems, in mobility management, and in the definition of mobility patterns. Key underlying technologies of ITS for public transport management: a) Advanced Communications Systems (ACS); b) Automatic Vehicle Location (AVL) Systems; c) In-Vehicle Diagnostic Systems; d) Automatic Passenger Counter Systems; e) Traffic Counter Systems f) Electronic Payment Systems; g) Real time fleet management systems; h) Connected vehicles (V2V, V2I) and Autonomous vehicles. Communication and protocols. DATA COLLECTION AND INFORMATION PROCESSING Methods to identify and collect data from different sources will be covered. Methods ranging from traditional surveys to digital methods including: • use of smartphone apps as a collaborative tool for collecting mobility data; • data from smart cards; • sensors & IoT; • open data and real time data of transport systems and services. Standardisation of the collected data format where possible and use of data fusion techniques will also be covered. Other related topics that will be touched upon include data storage options, data privacy and security. Introduction to big data and big data analytics for transport sector. From data-driven models to decision intelligence and cognitive era. Use of big data analytics techniques to provide concrete applications for transport operators, to understand the state of the market, the transport demand, the travel behaviour and to better tailor the mobility services to improve user experience. Data analysis approach that is descriptive, predictive, prescriptive, i.e. exploiting big data analytics applications through which to generate 'insights', knowledge useful for decision-making processes (e.g. anticipating customers' needs by knowing their preferences and habits in real time). NEW MOBILITY SERVICES AND THE DIGITAL TRANSITION The new forms of mobility: shared, electric, automated. Technical and economical characteristics and general view about the transformations induced by the new mobility services carried by digital platforms. Actors, policies, legal aspects, cybersecurity. DIGITAL PLATFORMS AND MaaS (MOBILITY as a SERVICE) Digital platforms in the transport sector. Mobility as a Service: definition, ecosystem and typology. Benchmark of different MaaS across the world: limit and challenges. MaaS Stakeholders: roles and expectation. Business models of MaaS. CASE STUDIES (practical part) SHARED MOBILITY Collection of data from car sharing, bike-sharing, scooter sharing. Construction of data bases for analysing data to represent the transport demand, the travel behaviour and the effect on the territory in terms of land use, costs and impacts. CONNECTED AND AUTOMATED VEHICLES Definition of potential scenarios providing the introduction of automated vehicles, for passengers and for freight. Use of available data for studies or metadata. DIGITAL PLATFORMS AND MaaS Design of a MaaS and definition of the MaaS architecture using design techniques as service blueprinting. Definition of business model. The practical part will be held in the laboratories (LAIB) or, alternatively, the needed software will be usable also at home, being licensed to Politecnico and, in any case, mainly open source software and tools will be used.

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group essay; Group project;
... Exam: compulsory oral exam; group report for each case study. Students will have to prepare a report for each Case study (group report) whose content is indicated in the syllabus (practical part). The group reports have to be uploaded on the teaching portal in pdf format. The deadline for the upload of each report is fixed by the teacher after each Case study. It will not possible to register to the exam of not all the group reports have been uploaded at the moment of the registering for the exam. It is possible to upload multiple versions of the report, however only the last one will be considered. Each report will be corrected and the grade (maximum 30 and praise) will be proposed to the group. Once corrected, the reports can be changed/updated only if all students of the group agree to do so. The report will be valid for 2 years so that students that did not pass the oral exam do not have to prepare another report. Each student will then have an oral exam on the topics faced during the course for in-depth discussion of topics discussed in lessons and/or addressed during the case studies. During the individual oral exam, the student will have to answer up to four questions on the topics presented during the classes. During the oral exam, the student will also discuss the group reports by presenting some of the results he/she obtained and described in the report. The oral examination must be sufficient and over 18/30. The final vote will be given by the weighted average of the oral vote (80%) and the average of group's reports grade (20%). The maximum vote will be 30 cum laude.

The nature of the course and the available references do not allow to have a textbook and the attendance to the course is fundamental for an effective learning process. During the course (at each time) the teacher will upload in the teaching portal a rich range of material, formed by reports, scientific articles, slides as well as open access software and manuals. There will be a section with material for the course and a section with additional material for whom interested to deepen the topics of the course for pure sake of additional knowledge.