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https://ceur-ws.org/Vol-3102/xpreface.pdf
Preface of the 1st Italian Workshop on Artificial
Intelligence and Applications for Business and
Industries
Francesco Epifania1 and Luca Marconi1,2[0000−0002−0236−6159]
1
Social Thingum, Milan, Italy
2
Department of Informatics, Systems and Communication, University of Milano -
Bicocca, Milan, Italy francesco.epifania@socialthingum.com
l.marconi3@campus.unimib.it
luca.marconi@socialthingum.com
http://www.socialthingum.it/en/
http://www.disco.unimib.it/en
1 Background and Motivations
The 1st Italian Workshop on Artificial Intelligence and Applications for Busi-
ness and Industries (AIABI) is co-located and held within the 20th International
Conference of the Italian Association for Artificial Intelligence (AIxIA 2021) and
organized by Social Thingum, an Italian notable AI scale-up company and pri-
vate research and technology-transfer center, located in Milan, Lombardy. The
workshop is also sponsored by Assintel, the National Association of ICT Compa-
nies of Confcommercio, as well as by InnovUp, the Italian Innovation & Startup
Ecosystem. This edition is held online due to the COVID-19 pandemics and
the program of the meeting is available on the official workshop website. The
workshop is focused on the current technological scenario of Artificial Intelli-
gence (AI) for business in heterogenous fields and industries. Among the editions
of the AIxIA annual International Conferences, this is the very first workshop
specifically devoted to the many applications and potentialities of AI in business
contexts and domains. Therefore, it represented a major official opportunity for
organizations, academics, researchers and specifically firms, decision-makers and
practitioners, in the Italian and international AI landscape, to share and ana-
lyze heterogenous research works and business case studies dealing with AI in
business fields. Indeed, the main idea behind this workshop is creating a fruitful
occasion for both the academic and the industrial world to share knowledge and
experience related to how AI is actually and currently affecting business cases
and companies. According to its mission, this workshop is aimed at providing
participants with a broad set of insights on the several potential synergies of AI
and business.
Indeed, nowadays AI is becoming crucial in every business field. AI is currently
reshaping organizations and how technologies affect management and business.
AI has the power to transform business and society, in a transversal and perva-
sive way, due to its ability to extract and manage knowledge potentially in every
Francesco Epifania and Luca Marconi
industry. Researchers and scientists are aware that AI is transforming business
models of all industries, by reshaping existing organizational processes. More-
over, AI has the potential to provide higher quality, greater efficiency, and better
outcomes than human experts. In the organizational and business framework, AI
can provide assistance to decision-makers and technicians beyond the scope of
humans. Recent advances in computational power and resources, the exponen-
tial increase in data availability, and new machine-learning techniques now allow
organizations to exploit AI-based solutions also for managerial tasks. It suffices
to mention that AI-based solutions play important roles in Unilever’s talent ac-
quisition process, in Netflix’s decision-making processes regarding movie plots,
directors, and actors, and in Pfizer’s drug discovery and scientific development
activities.
Accordingly, the main motivation of the workshop is simple and straightforward:
how can we deal with the immense potential of AI for creating the maximum
possible value in business and heterogeneous industries? This question triggers
several lines, that are particularly relevant for the current research in AI. The
workshop tries to address these research lines and provides a forum for the Ital-
ian community to discuss problems, challenges and innovative approaches in the
area. The final aim of the workshop is contributing in depicting the overall sce-
nario and framework of the exploitation, advantages and current issues of AI in
business.
2 Topics of Interest
1. AI in Business
2. Application of AI in industries and market
3. AI use-cases in heterogeneous business contexts
4. AI potential in leveraging Education and training of companies stakeholders
5. Explainable, Interpretable and Trustworthy AI in business
6. Strategies to exploit the AI potential to leverage business competitive ad-
vantages
7. Theoretical aspects of AI potentialities for business
8. Evaluating AI Systems and AI impact in real business scenarios
9. Ethics for AI in companies and industries
3 Accepted Papers
We believe that the program provides a good balance between the different top-
ics related to the area of AI for Business and Industries. Moreover, the program
is further enriched through notable invited speakers: Iuri Frosio and Piero Altoe,
respectively the Principal Research Scientist and the EMEA Energy Developer
Relations Manager of NVIDIA, and Alessandro Rozza, the Chief Research Offi-
cer of Lastminute, all directly coming from the intersection between the business
world and the applied AI research scenario. Together with the invited speakers,
the event is enriched by all the other speakers, representing relevant companies
and research centers.
It is particularly worth to notice that, among the works accepted, there is a
specific focus on technology-transfer projects and positive accounts of fruitful col-
laborations between universities, research centers and companies. Such projects
are particularly fundamental especially for the growth of companies’ business,
and also for the advance of innovation in the AI landscape. Then, this focus on
the technology-transfer projects is also an appropriate and key reported result,
especially considering public concerns by the Italian government, and specifi-
cally the Italian Ministry of Economic Development and the Italian Ministry for
Technological Innovation and Digital Transition, towards valuable technology-
transfer projects to foster innovation in the country, as also confirmed by the
recent publication of the AI National Strategy. We are really confident that our
common effort is contributing to the current evolution of the Italian AI land-
scape and, in general, to improving the Italian level of innovation within Europe.
The call for papers attracted 19 submissions by 45 different authors. After the
review process, 17 of 19 papers were accepted for publication (acceptance rate:
89.5%): 14 as regular papers and 3 as short papers. The accepted papers range
from the definition of methodologies or frameworks to apply AI systems to em-
powering business processes to specific machine learning or deep learning ap-
proaches for predicting relevant features in different application domains. Going
into details, accepted papers address several topics from different perspectives.
In the following, we provide a short overview of such works, grouping them by
topics.
Many papers propose specific tools and applications related to AI in hetero-
geneous industrial processes. In particular, Ali Zaidi et al. presents a new frame-
work of acquisition and data analysis to inspect and monitor power lines via
UAVs and deep learning. Lazzarinetti et al. propose a framework for continuous
defect prediction based on machine learning algorithms trained on a publicly
available dataset. The framework is composed of a machine learning model for
detecting the presence of logical bugs in code on the basis of the available data
generated by DevOps tools and a dashboard to monitor the software projects sta-
tus. Marzullo et al. present a comparison between traditional encoding/decoding
methods for real-time video streaming and deep learning-based approaches. Mas-
sarenti et al. propose a methodology based both on deep learning algorithms and
statistical tools for the creation of a digitization system capable of managing
critical issues, like low scan quality and complex structure of documents. The
methodology is composed of 5 modules to manage the poor quality of scanned
documents, identify the template and detect tables in documents, extract and
organize the text into an easy-to-query schema and perform queries on it through
search patterns. The methodology is designed using real data coming from two
different companies and is tested by considering the companies’ real business
Francesco Epifania and Luca Marconi
needs. Tegegn et al. describes a method to estimate the percentage of a given
material in a mixture, given its near infrared spectrum in input, by the means
of deep learning, near-infrared and derivative spectroscopy. Monticelli et. al. fo-
cuses on a model-based recommender systems for e-commerce to support the
user in configuring hardware components for computer, in the context of a ICT
business company. Massarenti et al. propose a methodology based both on deep
learning algorithms and statistical tools for the creation of a digitization system
capable of managing critical issues, like low scan quality and complex structure
of documents, in the context of Robotic Process Automation. The methodology
is designed using real data coming from two different companies and is tested
by considering the companies’ real business needs. Lazzarinetti et al. define a
benchmark aiming to evaluate the performances of different machine learning
algorithms in the domain of predictive maintenance and Industry 4.0.
Other contributions propose machine learning or deep learning approaches for
customer services or customer process management. Specifically, Massarenti et
al. report a useful process for enhancing the process of creating and analyzing
text clustering algorithms and therefore improved a conversational customer ser-
vice agent. Mesenzani et al. investigate how AI can create value for companies
in Customer Management processes, exploring the current and future trends
and the most relevant issues in AI adoption, and analysing the intersection be-
tween the technological and organizational issues. Lazzarinetti et al. introduce
a conversational framework for semantic question answering. This work relies
on knowledge graphs and the use of machine learning for determining the best
answer given a question associated with the content of the knowledge graph.
In addition, by leveraging text mining techniques authors declare to be able in
identifying the best set of answers that suit the question that are further filtered
by means of deep learning algorithms. Deola et al. report a first research out-
line to improve the performances of chatbots in different training conditions, for
supporting customer management issues. Schiaffino et al. present an applied re-
search project and a software engineering approach to integrate machine learning
and AI methods with content management systems (CMS) so that their useful-
ness and effectiveness could be improved.
In addition, some contributions focus on AI-based systems in other heteroge-
neous domains, like Social Media Management and Marketing, BioTech or Ed-
ucation. In particular, Viola et al. exploit state-of-the-art Convolutional Neural
Networks to provide a methodological tool for predicting Instagram posts pop-
ularity. Lazzarinetti et al. define an algorithm for the prediction of the glycemic
index through the evaluation of two different models, evaluating and comparing
their performances. Crinieri et al. present a solution to classify skin lesions im-
ages using deep learning models and support medical decision-making processes
and management. Di Fraia et al. propose a first framework concept to develop AI
tools to support higher education school students with specific learning disorders.
4 Committee
As a final remark, the co-chairs would like to thank all the members of the
Program Committee (listed below), the organizers of the AI*IA 2021 Conference,
the Italian Association for Artificial Intelligence, the University of Milano –
Bicocca, as well as the sponsors, Assintel, the Italian National Association of ICT
Companies, and InnovUp, the Italian Italian Innovation & Startup Ecosystem.
4.1 Program Chairs
Francesco Epifania, Social Thingum (ITALY)
Luca Marconi, Social Thingum, University of Milano - Bicocca (ITALY)
4.2 Organizers
Simone Caporale, Profima srl, AI Magister (ITALY)
Giulia Cisotto, University of Padova (ITALY)
Ernesto Damiani, University of Milan (ITALY)
Francesco Epifania, Social Thingum (ITALY)
Guido di Fraia, IULM University (ITALY)
Alberto Fioravanti, Digital Magics (ITALY)
Giacinto Fiore, AI Week Italia (ITALY)
Iuri Frosio, NVIDIA (USA)
Luca Marconi, Social Thingum, University of Milano - Bicocca (ITALY)
Ricardo Anibal Matamoros Aragon, Social Thingum, University of Milano - Bic-
occa (ITALY)
Luca Nardone, Unicredit (ITALY)
Filippo Neri, Università di Napoli Federico II, ProMarket 11 (ITALY)
Alessandro Rozza, lastminute.com group (SWITZERLAND)
Alberto Schiaffino, Engitel S.p.A. (ITALY)
Pasquale Viscanti, AI Week Italia (ITALY)
4.3 Program Committee
Barbara Rita Barricelli, Università degli Studi di Brescia (ITALY)
Giulia Cisotto, University of Padova (ITALY)
Ernesto Damiani, University of Milan (ITALY)
Donato De Ieso, dilium srl, Assintel (ITALY)
Guido di Fraia, IULM University (ITALY)
Adam Elwood, lastminute.com group (SWITZERLAND)
Francesco Epifania, Social Thingum (ITALY)
Alberto Fioravanti, Digital Magics (ITALY)
Iuri Frosio, NVIDIA (USA)
Francesco Epifania and Luca Marconi
Altin Kadareja, Cardo AI (ITALY)
Eugenia Kovatcheva, University of Sofia (BULGARY)
Alberto Lucchetti, lastminute.com group (SWITZERLAND)
Sara Manzoni, University of Milano - Bicocca (ITALY)
Luca Marconi, Social Thingum, University of Milano - Bicocca (ITALY)
Giulio Massucci, Wavenure (ITALY)
Ricardo Anibal Matamoros Aragon, Social Thingum, University of Milano - Bic-
occa (ITALY)
Luca Nardone, Unicredit (ITALY)
Filippo Neri, Universita’ di Napoli Federico II, ProMarket 11 (ITALY)
Roumen Nikolov, University of Sofia (BULGARY)
Alessandro Rozza, lastminute.com group (SWITZERLAND)
Italo Zoppis, University of Milano - Bicocca (ITALY)