Data science for engineering design: State of the art and future directions

Abstract Engineering design (ED) is the process of solving technical problems within requirements and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that uses computational systems to extract knowledge from structured and unstructured data. The synergies between these two fields have a long story and throughout the past decades, ED has increasingly benefited from an integration with DS. We present a literature review at the intersection between ED and DS, identifying the tools, algorithms and data sources that show the most potential in contributing to ED, and identifying a set of challenges that future data scientists and designers should tackle, to maximize the potential of DS in supporting effective and efficient designs. A rigorous scoping review approach has been supported by Natural Language Processing techniques, in order to offer a review of research across two fuzzy-confining disciplines. The paper identifies challenges related to the two fields of research and to their interfaces. The main gaps in the literature revolve around the adaptation of computational techniques to be applied in the peculiar context of design, the identification of data sources to boost design research and a proper featurization of this data. The challenges have been classified considering their impacts on ED phases and applicability of DS methods, giving a map for future research across the fields. The scoping review shows that to fully take advantage of DS tools there must be an increase in the collaboration between design practitioners and researchers in order to open new data driven opportunities

How to do research on the societal impact of research? Studies from a semantic perspective

We review some recent works of our research lab that have applied novel text mining techniques to the issue of research impact assessment. The techniques are Semantic Hypergraphs and Lexicon-based Named Entity Recognition. By using these techniques, we address two distinct and open issues in research impact assessment: the epistemological and logical status of impact assessment, and the construction of quantitative indicators. © 2021 18th International Conference on Scientometrics and Informetrics, ISSI 2021. All rights reserved

B4DS @ PRELEARN: Ensemble method for prerequisite learning

In this paper we describe the methodologies we proposed to tackle the EVALITA 2020 shared task PRELEARN. We propose both a methodology based on gated recurrent units as well as one using more classical word embeddings together with ensemble methods. Our goal in choosing these approaches, is twofold, on one side we wish to see how much of the prerequisite information is present within the pages themselves. On the other we would like to compare how much using the information from the rest of Wikipedia can help in identifying this type of relation. This second approach is particularly useful in terms of extension to new entities close to the one in the corpus provided for the task but not actually present in it. With this methodologies we reached second position in the challenge

Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media

[EN] In recent years, social media have become ubiquitous and important for social networking and content sharing. Moreover, the content generated by these websites remains largely untapped. Some researchers proved that social media have been a valuable source to predict the future outcomes of some events such as box-office movie revenues or political elections. Social media are also used by companies to measure the sentiment of customers about their brand and products. This work proposes a new social media based model to measure how users perceive new products from a technical point of view. This model relies on the analysis of advantages and drawbacks of products, which are both important aspects evaluated by consumers during the buying decision process. This model is based on a lexicon developed in a related work (Chiarello et. al, 2017) to analyse patents and detect advantages and drawbacks connected to a certain technology. The results show that when a product has a certain technological complexity and fuels a more technical debate, advantages and drawbacks analysis is more efficient than sentiment analysis in producing technical-functional judgements.Chiarello, F.; Bonaccorsi, A.; Fantoni, G.; Ossola, G.; Cimino, A.; Dell'orletta, F. (2018). Technical Sentiment Analysis: Measuring Advantages and Drawbacks of New Products Using Social Media. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 145-156. https://doi.org/10.4995/CARMA2018.2018.8336OCS14515

B4DS @ PRELEARN: Ensemble Method for Prerequisite Learning

In this paper we describe the methodologies we proposed to tackle the EVALITA 2020 shared task PRELEARN. We propose both a methodology based on gated recurrent units as well as one using more classical word embeddings together with ensemble methods. Our goal in choosing these approaches, is twofold, on one side we wish to see how much of the prerequisite information is present within the pages themselves. On the other we would like to compare how much using the information from the rest of Wikipedia can help in identifying this type of relation. This second approach is particularly useful in terms of extension to new entities close to the one in the corpus provided for the task but not actually present in it. With this methodologies we reached second position in the challenge

Agricoltura di Precisione e Industria 4.0: possibili integrazioni e sviluppi tecnologici

Il settore agricolo sta vivendo negli ultimi anni un grande processo di cambiamento per far fronte a una serie di circostanze che hanno su di esso un impatto non trascurabile sia a livello di sostenibilità ambientale, correlata all’incremento della popolazione e la diminuzione delle superfici coltivabili, sia a livello di domanda di mercato dei prodotti agricoli con consumatori sono sempre più esigenti ed informati sulle tecniche di coltivazione e allevamento. Si rendono sempre più necessari innovazioni tecnologiche in grado di fronteggiare queste criticità e l’Agricoltura di Precisione ne è l’esempio più eclatante. Infatti, se l’Industria 4.0 è considerata come la quarta rivoluzione industriale, l’Agricoltura di Precisione rappresenta di fatto la rivoluzione agricola del XXI Secolo. Sebbene le fondamenta di queste due rivoluzioni appaiono basate su concetti distanti tra loro, è possibile individuare delle affinità riguardo agli effetti che queste determinano sui processi aziendali sullo sviluppo delle innovazioni e delle applicazioni tecnologiche. Partendo da questa riflessione, il presente lavoro cerca di comprendere quanto i due domini di Industria 4.0 e Agricoltura di Precisione siano collegati tra loro analizzando le tecnologie più utilizzate in questi ambiti ed evidenziando modelli e pattern comuni che siano in grado di rappresentare le sovrapposizioni tra i due settori di attività. L’intersezione tra le tecnologie di Industria 4.0 e l’Agricoltura di Orecisione costituisce un riferimento importante per individuare le tecnologie più diffuse e maggiormente redditizie per il settore agricolo nonché utili per il miglioramento qualitativo e quantitativo dei prodotti agricoli