Embedded Systems may have competing design objectives, such as to maximize the reliability, increase the functional safety, minimize the product cost, and minimize the energy consumption. The architectures must be therefore configured to meet varied requirements and multiple design objectives. In particular, reliability and safety are receiving increasing attention. Consequently, the configuration of fault-tolerant mechanisms is a critical design decision. This work proposes a method for automatic selection of appropriate fault-tolerant design patterns, optimizing simultaneously multiple objective functions. Firstly, we present an exact method that leverages the power of Satisfiability Modulo Theory to encode the problem with a symbolic technique. It is based on a novel assessment of reliability which is part of the evaluation of alternative designs. Afterwards, we empirically evaluate the performance of a near-optimal approximation variation that allows us to solve the problem even when the instance size makes it intractable in terms of computing resources. The efficiency and scalability of this method is validated with a series of experiments of different sizes and characteristics, and by comparing it with existing methods on a test problem that is widely used in the reliability optimization literature.

This PhD thesis explores gamification strategies concerning video games for crowdsourcing, in particular for linguistic annotation. First, a categorization of the current approaches is proposed. In doing so, a new framework is provided to analyse and understand different game design strategies and their impact on linguistic annotation tasks. Two artefacts are developed to test and validate the framework: Spacewords, a 2D space shooter game, and High School Superhero, a 3D role-playing game. In particular, research questions and hypotheses concerning so-called orthogonal mechanics are tested, which are defined by previous research as game mechanics that although being similar to those found in commercial games, can hinder the annotation process by adding a layer of challenge and unpredictability. The artefacts are employed for three tasks: synonymy, linguistic acceptability and abusive language annotation. It is found that some challenging game-like features slightly improves Precision measure in certain circumstances. Experiments also suggest that motivation to play may be improved by in-game resources or collectible elements. Finally, in High School Superhero a mismatch is observed between the judgements given by linguists and players. The approach adopted in this work is intended to pave the way to a more insightful use of gaming elements inspired by entertainment games in the context of games with a purpose for linguistic annotation.

This thesis addresses the problem of domain shift in 3D point cloud perception. In the last decades, there has been tremendous progress in within-domain training and testing. However, the performance of perception models is affected when training on a source domain and testing on a target domain sampled from different data distributions. As a result, a change in sensor or geo-location can lead to a harmful drop in model performance. While solutions exist for image perception, addressing this problem in point clouds remains unresolved. The focus of this thesis is the study and design of solutions for mitigating domain shift in 3D point cloud perception. We identify several settings differing in the level of target supervision and the availability of source data. We conduct a thorough study of each setting and introduce a new method to solve domain shift in each configuration. In particular, we study three novel settings in domain adaptation and domain generalization and propose five new methods for mitigating domain shift in 3D point cloud perception. Our methods are used by the research community, and at the time of writing, some of the proposed approaches hold the state-of-the-art. In conclusion, this thesis provides a valuable contribution to the computer vision community, setting the groundwork for the development of future works in cross-domain conditions.

Quantum computing is a form of computation that exploits quantum mechanical phenomena for information processing, with promising applications (among others) in optimization and machine learning. Indeed, quantum machine learning is currently one of the most popular directions of research in quantum computing, offering solutions with an at-least-theoretical advantage compared to the classical counterparts. Nevertheless, the quantum devices available in the current Noisy Intermediate-Scale Quantum (NISQ) era are limited in the number of qubits and significantly affected by noise. An interesting alternative to the current prototypes of general-purpose quantum devices is represented by quantum annealers, specific-purpose quantum machines implementing the heuristic search for solving optimization problems known as quantum annealing. However, despite the higher number of qubits, the current quantum annealers are characterised by very sparse topologies. These practical issues have led to the development of hybrid classical-quantum schemes, aiming at leveraging the strengths of both paradigms while circumventing some of the limitations of the available devices. In this thesis, several hybrid classical-quantum algorithms for optimization and machine learning are introduced and/or empirically assessed, as the empirical evaluation is a fundamental part of algorithmic research. The quantum computing models taken into account are both quantum annealing and circuit-based universal quantum computing. The results obtained have shown the effectiveness of most of the proposed approaches.

The challenge of implementing and executing a legal contract in a machine has been gaining significant interest recently with the advent of blockchain, smart contracts, LegalTech and IoT technologies. Popular software engineering methods, including agile ones, are unsuitable for such outcome-critical software. Instead, formal specifications are crucial for implementing smart contracts to ensure they capture the intentions of stakeholders, also that their execution is compliant with the terms and conditions of the original natural-language legal contract. This thesis concerns supporting the semi-automatic generation of formal specifications of legal contracts written in Natural Language (NL). The main contribution is a framework, named Contratto, where the transformation process from NL to a formal specification is subdivided into 5 steps: (1) identification of ambiguous terms in the contract and manual disambiguation; (2) structural and semantic annotation of the legal contract; (3) discovery of relationships among the concepts identified in step (2); (4) formalization of the terms used in the NL text into a domain model; (5) generation of formal expressions that describe what should be implemented by programmers in a smart contract. A systematic literature review on the main topic of the thesis was performed to support the definition of the framework. Requirements were derived from standard business contracts for a preliminary implementation of tools that support the transformation process, particularly concerning step (2). A prototype environment was proposed to semi-automate the transformation process although significant manual intervention is required. The preliminary evaluation confirms that the annotation tool can perform the annotation as well as human annotators, albeit novice ones.

Computing is becoming exponentially more pervasive, and so-called process of ``Digital Transformation'' is but starting. As computers become ever more relevant, our societies will need computing professionals that are well-equipped to face the many challenges their own discipline amplified. The education of computer scientists, so far, mostly focused on equipping them with technical skills. Society and academia, however, are increasingly recognising computing as a field where disciplines collide and intersect. An example that we investigate is that of Innovation and Entrepreneurship (I&E), a field that has often be used to equip computer science students with soft skills and non-technical competences. Computer science faces some unique problems, among which a lower student interest for non-technical subjects, and a constant process of epistemic and technological obsolescence. This thesis showcases some experiences that aim to address these challenges, going towards (re)connecting the Humans and Machines participating in computer science education with the needs of the World of today and tomorrow. Our work combines some theoretical reflections with pedagogical experiments, to ensure that our work has at the same time descriptive power and empirical validation. To aid teachers and learners in the change process, these experiments share a pedagogical approach rooted on Active Learning, ranging from Challenge-Based Learning to Peer Education, to custom-tailored teaching methodologies. In designing each experiment, we start by asking ourselves: how is what we want to teach practiced in the real world? Theoretically, this thesis contributes to the state of the art by conducting a horizontal exploration of how computer science education can enter an age ever more dominated by so-called ambiguity. Methodologically, we propose lightweight techniques for qualitative measurement that are rigorous, but introduce little methodological burden, emphasising our work's reflective and exploratory dimension. Our work aims to show how, using the same broad design process, courses can be flexibly adapted to fit an ever-changing world, including significant disruptions such as the transition to online education.

Low-power wireless technology is a part and parcel of our daily life, shaping the way in which we behave, interact, and more generally live. The ubiquity of cheap, tiny, battery-powered devices augmented with sensing, actuation, and wireless communication capabilities has given rise to a ``smart" society, where people, machines, and objects are seamlessly interconnected, among themselves and with the environment. Behind the scenes, low-power wireless protocols are what enables and rules all interactions, organising these embedded devices into wireless networks, and orchestrating their communications. The recent years have witnessed a persistent increase in the pervasiveness and impact of low-power wireless. After having spawned a wide spectrum of powerful applications in the consumer domain, low-power wireless solutions are extending their influence over the industrial context, where their adoption as part of feedback control loops is envisioned to revolutionise the production process, paving the way for the Fourth Industrial Revolution. However, as the scale and relevance of low-power wireless systems continue to grow, so do the challenges posed to the communication substrates, required to satisfy ever more strict requirements in terms of reliability, responsiveness, and energy consumption. Harmonising these conflicting demands is far beyond what is enabled by current network stacks and control architectures; the need to timely bridge this gap has spurred a new wave of interest in low-power wireless networking, and directly motivated our work. In this thesis, we take on this challenge with a main conceptual and technical tool: concurrent transmissions (CTX), a technique that, by enforcing nodes to transmit concurrently, has been shown to unlock unprecedented fast, reliable, and energy efficient multi-hop communications in low-power wireless networks, opening new opportunities for protocol design. We first direct our research endeavour towards industrial applications, focusing on the popular IEEE 802.15.4 narrowband PHY layer, and advance the state of the art along two different directions: interference resilience and aperiodic wireless control. We tackle radio-frequency noise by extensively analysing, for the first time, the dependability of CTX under different types, intensities, and distributions of reproducible interference patterns, and by devising techniques to push it further. Specifically, we concentrate on CRYSTAL, a recently proposed communication protocol that relies on CTX to rapidly and dependably collect aperiodic traffic. By integrating channel hopping and noise detection in the protocol operation, we provide a novel communication stack capable of supporting aperiodic transmissions with near-perfect reliability and a per-mille radio duty cycle despite harsh external interference. These results lay the ground towards the exploitation of CTX for aperiodic wireless control; we explore this research direction by co-designing the Wireless Control Bus (WCB), our second contribution. WCB is a clean-slate CTX-based communication stack tailored to event-triggered control (ETC), an aperiodic control strategy holding the capability to significantly improve the efficiency of wireless control systems, but whose real-world impact has been hampered by the lack of appropriate networking support. Operating in conjunction with ETC, WCB timely and dynamically adapts the network operation to the control demands, unlocking an order-of-magnitude reduction in energy costs w.r.t. traditional periodic approaches while retaining the same control performance, therefore unleashing and concretely demonstrating the true ETC potential for the first time. Nevertheless, low-power wireless communications are rapidly evolving, and new radios striking novel trade-offs are emerging. Among these, in the second part of the thesis we focus on ultra-wideband (UWB). By providing hitherto missing networking primitives for multi-hop dissemination and collection over UWB, we shed light on the communication potentialities opened up by the high data throughput, clock precision, and noise resilience offered by this technology. Specifically, as a third contribution, we demonstrate that CTX not only can be successfully exploited for multi-hop UWB communications but, once embodied in a full-fledged system, provide reliability and energy performance akin to narrowband. Furthermore, the higher data rate and clock resolution of UWB chips unlock up to 80% latency reduction w.r.t. narrowband CTX, along with orders-of-magnitude improvements in network-wide time synchronization. These results showcase how UWB CTX could significantly benefit a multitude of applications, notably including low-power wireless control. With WEAVER, our last contribution, we make an additional step towards this direction, by supporting the key functionality of data collection with an ultra-fast convergecast stack for UWB. Challenging the internal mechanics of CTX, WEAVER interleaves data and acknowledgements flows in a single, self-terminating network-wide flood, enabling the concurrent collection of different packets from multiple senders with unprecedented latency, reliability, and energy efficiency. Overall, this thesis pushes forward the applicability and performance of low-power wireless, by contributing techniques and protocols to enhance the dependability, timeliness, energy efficiency, and interference resilience of this technology. Our research is characterized by a strong experimental slant, where the design of the systems we propose meets the reality of testbed experiments and evaluation. Via our open-source implementations, researchers and practitioners can directly use, extend, and build upon our contributions, fostering future work and research on the topic.

Il 40 per cento degli intervistati afferma che non ha trovato informazioni esaustive sui rischi e benefici dei farmaci trovati, mentre il 52 per cento afferma che le informazioni trovate hanno aiutato a seguire le indicazioni e i consigli del medico. Tra i rispondenti che si sono sottoposti a visita medica e che hanno discusso le informazioni trovate online con il proprio medico di fiducia, l'84 per cento ha ricevuto la prescrizione di farmaci. Di questi, solo il 17 per cento riporta che il farmaco prescritto era lo stesso trovato online, il 74 per cento è stato inviato da uno specialista e l'80 per cento ha ricevuto una prescrizione per test diagnostici. Più della metà dei rispondenti ha anche riportato azioni intraprese dal medico diverse dalla prescrizione del farmaco trovato online. Il 20 per cento degli intervistati afferma che le informazioni trovate sul farmaco da prescrizione in Internet hanno ridotto il suo / la sua fiducia nel medico, mentre il 41 per cento afferma che lo ha aiutato ad avere una comunicazione migliore con il proprio medico di fiducia. Nonostante le preoccupazioni sulle conseguenze negative della comunicazione di salute online, non abbiamo riscontrato differenze in termini di effetti sulla salute tra i pazienti che hanno assunto i farmaci “menzionati” online e coloro che hanno preso altri farmaci da prescrizione. / We conducted a national online survey about health care experiences associated with digital communication of prescription drugs. 46 percent of the sample (265 adults) found information about prescription drugs during their online search in the last 12 months. 40 percent of respondents agreed they didn’t find exhaustive information about risks and benefits while 52 percent agreed it helped in following their physician’s indications and advise. Among the respondents who had a physician visit during which health information found online was discussed, 84 percent received a drug prescription with only 17 percent being the same drug found on internet, 74 percent was sent to a specialist and 80 percent received a diagnostic test prescription. More than half also reported actions taken by their physician other than prescribing the drug brand found online. 20 percent respondents states that info found on the prescription drug in Internet reduced his/her trust in the physician while 41 percent states it helped in his/her communication with physician. Despite concerns about online health communication’s negative consequences, we found no differences in health effects between patients who took “advocated”/”mentioned” drugs and those who took other prescription drugs.

In an increasingly interconnected world where English has become the lingua franca of business, culture, entertainment, and academia, learners of English as a second language (L2) have been steadily growing. This has contributed to an increasing demand for automatic spoken language assessment systems for formal settings and practice situations in Computer-Assisted Language Learning. One common misunderstanding about automated assessment is the assumption that machines should replicate the human process of assessment. Instead, computers are programmed to identify, extract, and quantify features in learners' productions, which are subsequently combined and weighted in a multidimensional space to predict a proficiency level or grade. In this regard, transferring human assessment knowledge and skills into an automatic system is a challenging task since this operation should take into account the complexity and the specificities of the proficiency construct. This PhD thesis presents research conducted on methods and techniques for the automatic assessment and feedback of L2 spoken English, mainly focusing on the application of deep learning approaches. In addition to overall proficiency grades, the main forms of feedback explored in this thesis are feedback on grammatical accuracy and assessment related to particular aspects of proficiency (e.g., grammar, pronunciation, rhythm, fluency, etc.). The first study explores the use of written data and the impact of features extracted through grammatical error detection on proficiency assessment, while the second illustrates a pipeline which starts from disfluency detection and removal, passes through grammatical error correction, and ends with proficiency assessment. Grammar, as well as rhythm, pronunciation, and lexical and semantic aspects, is also considered in the third study, which investigates whether it is possible to use systems targeting specific facets of proficiency analytically when only holistic scores are available. Finally, in the last two studies, we investigate the use of self-supervised learning speech representations for both holistic and analytic proficiency assessment. While aiming at enhancing the performance of state-of-the-art automatic systems, the present work pays particular attention to the validity and interpretability of assessment both holistically and analytically and intends to pave the way to a more profound and insightful knowledge and understanding of automatic systems for speaking assessment and feedback.

Every day, billions of mobile network log data (commonly defined as Call Detailed Records, or CDRs) are generated by cell phones operators. These data provide inspiring insights about human actions and behaviors, which are essentials in the development of context aware appli- cations and services. This potential demand has fostered major research activities in a variety of domains such as social and economic development, urban planning, and health prevention. The major challenge of this thesis is to interpret CDR for human activity recognition, in the light of background knowledge of the CDR data context. Indeed each entry of the CDR is as- sociated with a context, which describes the temporal and spatial location of the user when a particular network data has been generated by his/her mobile devices. Knowing, by leveraging available Web 2.0 data sources, (e.g., Openstreetmap) this research thesis proposes to develop a novel model from combination of logical and statistical reasoning standpoints for enabling human activity inference in qualitative terms. The results aimed at compiling human behavior predictions into sets of classification tasks in the CDRs. Our research results show that Point of Interest (POI)s are a good proxy for predicting the content of human activities in an area. So the model is proven to be effective for predicting the context of human activity, when its total level could be efficiently observed from cell phone data records.