On the implications of big data and machine learning in the interplay between humans and machines

Big data and machine learning are profoundly shaping social, economic, and political spheres, becoming part of the collective imagination. In recent years, barriers have fallen and a wide range of products, services, and resources, that exploit Artificial Intelligence, have emerged. Hence, it becomes of fundamental importance to understand the limits and, consequently, the potentialities of predictions made by a machine that learns directly from data. Understanding the limits of machine predictions would allow dispelling false beliefs about the potentialities of machine learning algorithms, avoiding at the same time possible misuses. To tackle this problem, completely different research lines are emerging, that focus on different aspects. In this thesis, we study how the presence of big data and artificial intelligence influences the interaction between humans and computers. Such a study should produce some high-level reflections that can contribute to the framing of how the interaction between humans and computers has changed, since the presence of big data and algorithms that can make computers somehow intelligent, albeit with some limitations. In the different chapters of the thesis, various case studies that we faced during the Ph.D. are described, chosen specifically for their peculiar characteristics. Starting from the obtained results, we provide several high-level reflections on the implications of the interaction between humans and machines

An alternative approach to dimension reduction for pareto distributed data: a case study

Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis. In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87-90%, even in the presence of categorical descriptors

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

Abstract In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural network (RNN), based on the use of 15 million of readings gathered from 1 million of mechanical water meters, spread throughout Northern Italy, led to non-positive results. We learned this was due to a lack of specific attention devoted to the quality of the analyzed data. We, hence, developed a novel methodology, based on a new semantics which we enforced on the training data. This allowed us to extract only those samples which are representative of the complex phenomenon of defective water meters. Adopting such a methodology, the accuracy of our RNN exceeded the 80% threshold. We simultaneously realized that the new training dataset differed significantly, in statistical terms, from the initial dataset, leading to an apparent paradox. Thus, with our contribution, we have demonstrated how to reconcile such a paradox, showing that our classifier can help detecting defective meters, while simplifying replacement procedures

Strumenti e contesti del lavoro collaborativo

Analisi degli strumenti e dei contesti del lavoro collaborativ

Particulate Matter and COVID-19 Disease Diffusion in Emilia-Romagna (Italy). Already a Cold Case?

As we prepare to emerge from an extensive and unprecedented lockdown period, due to the COVID-19 virus infection that hit the Northern regions of Italy with the Europe’s highest death toll, it becomes clear that what has gone wrong rests upon a combination of demographic, healthcare, political, business, organizational, and climatic factors that are out of our scientific scope. Nonetheless, looking at this problem from a patient’s perspective, it is indisputable that risk factors, considered as associated with the development of the virus disease, include older age, history of smoking, hypertension and heart disease. While several studies have already shown that many of these diseases can also be favored by a protracted exposure to air pollution, there has been recently an insurgence of negative commentary against authors who have correlated the fatal consequences of COVID-19 (also) to the exposition of specific air pollutants. Well aware that understanding the real connection between the spread of this fatal virus and air pollutants would require many other investigations at a level appropriate to the scale of this phenomenon (e.g., biological, chemical, and physical), we propose the results of a study, where a series of the measures of the daily values of PM2.5, PM10, and NO2 were considered over time, while the Granger causality statistical hypothesis test was used for determining the presence of a possible correlation with the series of the new daily COVID19 infections, in the period February–April 2020, in Emilia-Romagna. Results taken both before and after the governmental lockdown decisions show a clear correlation, although strictly seen from a Granger causality perspective. Moving beyond the relevance of our results towards the real extent of such a correlation, our scientific efforts aim at reinvigorating the debate on a relevant case, that should not remain unsolved or no longer investigated

In-vehicle human machine interface: gamification e machine learning a supporto dell'eco-driving

Considerando che una delle maggiori problematiche dei veicoli elettrici è l’autonomia della batteria, questo progetto di tesi si pone l'obiettivo di realizzare un sistema che aiuti l'utente a guidare in maniera efficiente, aiutandolo a salvaguardare la carica della batteria. In particolare, si vuole realizzare un sistema che aiuti l'utente durante le frenate. Questo sistema, tramite meccanismi di Machine Learning, dovrà predire lo stato di carica della batteria, sulla base del valore di alcuni parametri che riguardano la frenata. In base al valore predetto, il sistema dovrà poi fornire al conducente delle indicazioni, in tempo reale, su come andare a modificare la frenata. Si vogliono inoltre integrare in questo sistema anche dei meccanismi di gamification. Lo scopo è di incentivare l'utente a seguire le indicazioni proposte dal sistema, fornendogli obiettivi da raggiungere, livelli in cui progredire, la competizione con altri utenti, la condivisione dei propri successi e il guadagno di ricompense

Modeling CoVid-19 Diffusion with Intelligent Computational Techniques is not Working. What Are We Doing Wrong?

As Europe is experiencing a second violent CoVid-19 storm, with the PCR-based testing system deteriorating due to the high volumes of people to be tested daily, there is a general reconsideration of the mathematical theories at the basis of our contact tracing and testing approaches. Drawing upon the concept of super spreader, we propose the use of (less sensitive) rapid tests to detect those secondary infections that do not need the use of PCRs, thus saving the most part of PCR tests currently used. This before the system fails

On improving GlovePi: Towards a many-to-many communication among deaf-blind users

The wide diffusion of mobile devices, digital technologies and telecommunication providers and infrastructures greatly supports communication and social activities among people all over the world. This (r)evolution in communication could represent a great opportunity for those people who use assistive technologies due to some kinds of disability, but it could become a digital severe barrier at the same time. Assistive technologies supporting people with disabilities can be a strategic tool to enhance their inclusion, integration, and independence, in particular for persons with disabilities that involve more senses, such as deaf-blindness, which is the combination of blindness and deafness. Deaf-blind users can communicate by mainly exploiting the sense of touch. Focusing on this kind of communication, we have designed and developed GlovePi, a low-cost wearable device, based on a glove equipped with sensors, a raspberry-pi and mobile devices. In this paper, we present an improved version of the GlovePi system, which extends the form of communication, by supporting the many-to-many one, aiming to increase the inclusion of deaf-blind people in social life and daily activities

Almaworld: Data visualization to support international research collaborations

Monitoring and evaluating research activities and their results can be intended as initiatives devoted to support universities and research institutes policymakers and governments while enhancing international relationships with colleagues from foreign affiliations. In this context, the integration of data coming from multiple and different sources, as well as the adoption of data visualization techniques can play a strategic role, letting the users browse official and reliable data by means of proper and effective user interface and interaction mechanisms. In this paper, we present AlmaWorld, a web application designed and developed to map the international co-authorships of the professors and the researchers of the University of Bologna, by exploiting two dimensions: the spatial and the temporal ones. The paper presents the approaches we have adopted in the design phase and a prototype we have developed

Modeling Patients' Online Medical Conversations: A Granger Causality Approach

Using AI-derived computerized techniques, we have modeled the large amount of online Reddit conversations exchanged among patients discussing around the prescriptions to take prenatal medical tests (both invasive and non-invasive). Our study has revealed that a patient\u2019s decision to take a specific test (thus possibly suffering medical implications) might significantly have a direct causal influence on her general everyday mood. Preliminary experimental results achieved exploiting the Granger causality analysis technique are discussed at length