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The paper presents a methodology for describing in generative terms the structure of urban fabrics: the objective is to transfer conceptually the knowledge about the domain of urban space into a hierarchical and interrelated semantic structure with relevant concepts, elements and their mutual relationships, providing explicit and unambiguous definitions. The conceptual and operational instrument adopted for this purpose is the ontology, a method of knowledge representation and management coming from the Artificial Intelligence. This approach aims to create a customisable digital design tool, to support the designer in the early stages of urban design process, such as street pattern and massing definition, by generating in real time a number of design scenarios, starting from a large number of constraints and requests. This paper focuses on the knowledge formalisation aspects of the research that is the basis for the generative modelling of urban space.

Intro -- Acknowledgments -- Contents -- 1 Introduction -- 2 Ontologies and Data Models for Cross-platform Social Media Data -- 2.1 Data Models for Social Media Data Analysis -- Homophily Analysis -- Social Identity Linkage -- Personality Analysis -- 2.2 Ontologies for Social Media Data -- Ontologies for Sentiment Analysis -- Ontologies for Situational Awareness -- 2.3 Potential Future Research Topics -- Metadata -- Federated Learning -- 3 Methods for Text Generation in NLP -- 3.1 Introduction -- 3.2 Past Approaches -- 3.3 GANs in NLP -- Reinforcement learning strategies -- Operating on continuous representations instead of discrete symbols -- Gumbel-softmax -- 3.4 Large Neural Language Models (LNLMs or LLMs) -- The Transformer and BERT -- BERT variants -- Introduction to GPT-3 -- 3.5 Dangers of E ective Generative LLMs -- Marginalized Group and Gender Bias -- Generation of Hateful Content -- De-biasing Approaches -- Environmental and Financial Impacts -- Identifying Information Extraction Attacks -- Simpler Approaches -- Potential Research Direction # 1 (Large Neural Language Models) -- 3.6 Detecting Generated Text -- Overview -- Detection of Machine-Generated Text -- The Issue with Simple Detection -- Detection of Fake News Content -- Issues of Comparison and Dataset Standardization -- Content-based Approaches -- Social-response-based Approaches -- Hybrid Approaches -- Graph-based Approaches -- Multimodal Approaches: Incorporating Visual Information -- Potential Research Direction # 2 (Fake News Detection) -- 4 Topic and Sentiment Modelling for Social Media -- 4.1 Introduction -- 4.2 Introduction to Topic Modelling -- 4.3 Overview of Classical Approaches to Topic Modelling -- LDA -- 4.4 Neural Topic Modelling -- Variational Topic Modelling -- LDA2Vec -- Top2Vec -- Use of Pre-trained Embeddings for Neural Topic Modelling.

This book provides a broad overview of the state of the art of the research in generative methods for the analysis of social media data. It especially includes two important aspects that currently gain importance in mining and modelling social media: dynamics and networks. Throughout the book, open problems, highlighting potential future directions, are clearly identified. The book aims at researchers and graduate students in social media analysis, information retrieval, and machine learning applications.--

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.

The new paradigm of the complexity of modern and historic structures, which are characterised by complex forms, morphological and typological variables, is one of the greatest challenges for building information modelling (BIM). Generation of complex parametric models needs new scientific knowledge concerning new digital technologies. These elements are helpful to store a vast quantity of information during the life cycle of buildings (LCB). The latest developments of parametric applications do not provide advanced tools, resulting in time-consuming work for the generation of models. This paper presents a method capable of processing and creating complex parametric Building Information Models (BIM) with Non-Uniform to NURBS) with multiple levels of details (Mixed and ReverseLoD) based on accurate 3D photogrammetric and laser scanning surveys. Complex 3D elements are converted into parametric BIM software and finite element applications (BIM to FEA) using specific exchange formats and new modelling tools. The proposed approach has been applied to different case studies: the BIM of modern structure for the courtyard of West Block on Parliament Hill in Ottawa (Ontario) and the BIM of Masegra Castel in Sondrio (Italy), encouraging the dissemination and interaction of scientific results without losing information during the generative process.