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Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting these methods to incorporate alternative text sources like micro-blogging posts and news has proven challenging as they struggle to model open-domain entities and relations, typically found in these sources. In this paper, we propose an enhanced information extraction pipeline tailored to the extraction of a knowledge graph comprising open-domain entities from micro-blogging posts on social media platforms. Our pipeline leverages dependency parsing and classifies entity relations in an unsupervised manner through hierarchical clustering over word embeddings. We provide a use case on extracting semantic triples from a corpus of 100 thousand tweets about digital transformation and publicly release the generated knowledge graph. On the same dataset, we conduct two experimental evaluations, showing that the system produces triples with precision over 95% and outperforms similar pipelines of around 5% in terms of precision, while generating a comparatively higher number of triples.

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Industries such as construction and business companies are becoming increasingly digitized. The amount of data to be monitored and processed has increased significantly since the advent of the Internet of Things and the massive use of sensors. In addition to the data from these sensors, large amounts of data that require specific handling and processing are received. Much of this data is eventually represented in digital twins as a monitoring or decision-support tool. In this paper, we present an architecture to improve digital twin-based experiences that need to represent information from multiple sources. This architecture is demonstrated using the specific use case of a digital twin for an office of an Italian company. The implementation leverages the Matterport 3D media platform and integrates different technologies and sensors. An evaluation of the solution has also been carried out. The results show high user acceptance and the opening of multiple possibilities to enrich the virtual model with further data from different sources.

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The irruption of advanced technologies and the limited knowledge of software architectures are making it difficult for many small and medium-sized manufacturing companies to keep up with what is being called the fourth industrial revolution (Industry 4.0, Industry of the Future). Container orchestration platforms provide layers of simplification for key requirements such as interoperability, security, and privacy, and provide mechanisms that allow companies and technology providers to focus on their specific functionalities and goals, instead of investing considerable time and effort in the underlying platform on which the solution will operate. This article focuses on these platforms and the issues when developing them, and proposes a risk- and goal-oriented hybrid meta-framework for security and privacy analysis. The meta-framework uses well-known security and privacy standards and frameworks as a reference and can be used to understand assets and requirements and, in particular, to select and configure countermeasures. For practical evaluation of the meta-framework, it was applied to a real case. This case shows how the needs of the KITT4SME project platform were analyzed to support, among others, four manufacturing pilot cases and to define the key security and privacy features that should be introduced when implementing a software platform for easy uptake by small and medium enterprises.

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Both the operational phase and embodied emissions that are introduced during the construction phase through the manufacture, sourcing, and installation of the building's materials and components are significant contributors to carbon emissions from the built environment. It is essential to change the current design and (re)construction processes in order to achieve the energy-saving targets for the EU building stock and move toward a society that is net carbon neutral. This change must be made from both a technical perspective as well as from a methodological perspective. To accomplish this, the EU has suggested several regulations and legislative steps to phase out inefficient structures. The most recent of these initiatives propose the idea of a Digital Building Logbook, which serves as a central repository for all pertinent building data, including information on energy efficiency. In this work, we present a survey of the elements that have been taken into consideration for the creation of the Digital Building Logbook to give an overview of what research has been done so far.

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Music is an extremely subjective art form whose commodification via the recording industry in the 20th century has led to an increasingly subdivided set of genre labels that attempt to organize musical styles into definite categories. Music psychology has been studying the processes through which music is perceived, created, responded to, and incorporated into everyday life, and, modern artificial intelligence technology can be exploited in such a direction. Music classification and generation are emerging fields that gained much attention recently, especially with the latest discoveries within deep learning technologies. Self attention networks have in fact brought huge benefits for several tasks of classification and generation in different domains where data of different types were used (text, images, videos, sounds). In this article, we want to analyze the effectiveness of Transformers for both classification and generation tasks and study the performances of classification at different granularity and of generation using different human and automatic metrics. The input data consist of MIDI sounds that we have considered from different datasets: sounds from 397 Nintendo Entertainment System video games, classical pieces, and rock songs from different composers and bands. We have performed classification tasks within each dataset to identify the types or composers of each sample (fine-grained) and classification at a higher level. In the latter, we combined the three datasets together with the goal of identifying for each sample just NES, rock, or classical (coarse-grained) pieces. The proposed transformers-based approach outperformed competitors based on deep learning and machine learning approaches. Finally, the generation task has been carried out on each dataset and the resulting samples have been evaluated using human and automatic metrics (the local alignment).

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In recent decades, modern societies are experiencing an increasing adoption of interconnected smart devices. This revolution involves not only canonical devices such as smartphones and tablets, but also simple objects like light bulbs. Named the Internet of Things (IoT), this ever-growing scenario offers enormous opportunities in many areas of modern society, especially if joined by other emerging technologies such as, for example, the blockchain. Indeed, the latter allows users to certify transactions publicly, without relying on central authorities or intermediaries. This work aims to exploit the scenario above by proposing a novel blockchain-based distributed paradigm to secure localization services, here named the Internet of Entities (IoE). It represents a mechanism for the reliable localization of people and things, and it exploits the increasing number of existing wireless devices and blockchain-based distributed ledger technologies. Moreover, unlike most of the canonical localization approaches, it is strongly oriented towards the protection of the users' privacy. Finally, its implementation requires minimal efforts since it employs the existing infrastructures and devices, thus giving life to a new and wide data environment, exploitable in many domains, such as e-health, smart cities, and smart mobility.

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In the current age of overwhelming information and massive production of textual data on the Web, Event Detection has become an increasingly important task in various application domains. Several research branches have been developed to tackle the problem from different perspectives, including Natural Language Processing and Big Data analysis, with the goal of providing valuable resources to support decision-making in a wide variety of fields. In this paper, we propose a real-time domain-specific clustering-based event-detection approach that integrates textual information coming, on one hand, from traditional newswires and, on the other hand, from microblogging platforms. The goal of the implemented pipeline is twofold: (i) providing insights to the user about the relevant events that are reported in the press on a daily basis; (ii) alerting the user about potentially important and impactful events, referred to as hot events, for some specific tasks or domains of interest. The algorithm identifies clusters of related news stories published by globally renowned press sources, which guarantee authoritative, noise-free information about current affairs; subsequently, the content extracted from microblogs is associated to the clusters in order to gain an assessment of the relevance of the event in the public opinion. To identify the events of a day d we create the lexicon by looking at news articles and stock data of previous days up to d-1 Although the approach can be extended to a variety of domains (e.g. politics, economy, sports), we hereby present a specific implementation in the financial sector. We validated our solution through a qualitative and quantitative evaluation, performed on the Dow Jones' Data, News and Analytics dataset, on a stream of messages extracted from the microblogging platform Stocktwits, and on the Standard & Poor's 500 index time-series. The experiments demonstrate the effectiveness of our proposal in extracting meaningful information from real-world events and in spotting hot events in the financial sphere. An added value of the evaluation is given by the visual inspection of a selected number of significant real-world events, starting from the Brexit Referendum and reaching until the recent outbreak of the Covid-19 pandemic in early 2020.

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MARIO is a companion robot that aims to help people with dementia (PWD) to battle isolation and loneliness by enabling them to stay socially active by providing a number of applications focused on hobbies (music, movies, etc), staying engaged with communities (reading headlines, reading local twitter feeds etc.) and staying connected with family and friends (telephoning them, reading their news from twitter, etc.). This paper presents the results from the initial trials of MARIO interacting with PWD involving a limited set of applications. It confirms some of the challenges hypothesized at the outset of the study and provides guidelines for future development work.

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