Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

Distribution and state of fishery resources in the Northern and Central Adriatic Sea using trawl surveys data

The Ecosystem Approach to Fisheries represents the most recent research line in the international context, showing interest both towards the whole community and toward the identification and protection of all the “critical habitats” in which marine resources complete their life cycles. Using data coming from trawl surveys performed in the Northern and Central Adriatic from 1996 to 2010, this study provides the first attempt to appraise the status of the whole demersal community. It took into account not only fishery target species but also by-catch and discharge species by the use of a suite of biological indicators both at population and multi-specific level, allowing to have a global picture of the status of the demersal system. This study underlined the decline of extremely important species for the Adriatic fishery in recent years; adverse impact on catches is expected for these species in the coming years, since also minimum values of recruits recently were recorded. Both the excessive exploitation and environmental factors affected availability of resources. Moreover both distribution and nursery areas of the most important resources were pinpointed by means of geostatistical methods. The geospatial analysis also confirmed the presence of relevant recruitment areas in the North and Central Adriatic for several commercial species, as reported in the literature. The morphological and oceanographic features, the relevant rivers inflow together with the mosaic pattern of biocenoses with different food availability affected the location of the observed relevant nursery areas.

Shareholder protection and stock market development

The dissertation contains five parts: An introduction, three major chapters, and a short conclusion. The First Chapter starts from a survey and discussion of the studies on corporate law and financial development literature. The commonly used methods in these cross-sectional analyses are biased as legal origins are no longer valid instruments. Hence, the model uncertainty becomes a salient problem. The Bayesian Model Averaging algorithm is applied to test the robustness of empirical results in Djankov et al. (2008). The analysis finds that their constructed legal index is not robustly correlated with most of the various stock market outcome variables. The second Chapter looks into the effects of minority shareholders protection in corporate governance regime on entrepreneurs' ex ante incentives to undertake IPO. Most of the current literature focuses on the beneficial part of minority shareholder protection on valuation, while overlooks its private costs on entrepreneur's control. As a result, the entrepreneur trade-offs the costs of monitoring with the benefits of cheap sources of finance when minority shareholder protection improves. The theoretical predictions are empirically tested using panel data and GMM-sys estimator. The third Chapter investigates the corporate law and corporate governance reform in China. The corporate law in China regards shareholder control as the means to the ends of pursuing the interests of stakeholders, which is inefficient. The Chapter combines the recent development of theories of the firm, i.e., the team production theory and the property rights theory, to solve such problem. The enlightened shareholder value, which emphasizes on the long term valuation of the firm, should be adopted as objectives of listed firms. In addition, a move from the mandatory division of power between shareholder meeting and board meeting to the default regime, is proposed.

Modelling the dynamic response of rockfall protection barriers

Mountainous areas are prone to natural hazards like rockfalls. Among the many countermeasures, rockfall protection barriers represent an effective solution to mitigate the risk. They are metallic structures designed to intercept rocks falling from unstable slopes, thus dissipating the energy deriving from the impact. This study aims at providing a better understanding of the response of several rockfall barrier types, through the development of rather sophisticated three-dimensional numerical finite elements models which take into account for the highly dynamic and non-linear conditions of such events. The models are built considering the actual geometrical and mechanical properties of real systems. Particular attention is given to the connecting details between the structural components and to their interactions. The importance of the work lies in being able to support a wide experimental activity with appropriate numerical modelling. The data of several full-scale tests carried out on barrier prototypes, as well as on their structural components, are combined with results of numerical simulations. Though the models are designed with relatively simple solutions in order to obtain a low computational cost of the simulations, they are able to reproduce with great accuracy the test results, thus validating the reliability of the numerical strategy proposed for the design of these structures. The developed models have shown to be readily applied to predict the barrier performance under different possible scenarios, by varying the initial configuration of the structures and/or of the impact conditions. Furthermore, the numerical models enable to optimize the design of these structures and to evaluate the benefit of possible solutions. Finally it is shown they can be also used as a valuable supporting tool for the operators within a rockfall risk assessment procedure, to gain crucial understanding of the performance of existing barriers in working conditions.

Essays on Innovative Activity and the Protection of Innovations

This dissertation looks at three widely accepted assumptions about how the patent system works: patent documents disclose inventions; this disclosure happens quickly, and patent owners are able to enforce patents. The first chapter estimates the effect of stronger trade secret protection on the number of patented innovations. When firms find it easier to protect business information, there is less need for patent protection, and accordingly less need for the disclosure of technical information that is required by patent law. The novel finding is that when it is easier to keep innovations, there is not only a reduction in the number of patents but also a sizeable reduction in disclosed knowledge per patent. The chapter then shows how this endogeneity of the amount of knowledge per patent can affect the measurement of innovation using patent data. The second chapter develops a game-theoretic model to study how the introduction of fee-shifting in US patent litigation would influence firms’ patenting propensities. When the defeated party to a lawsuit has to bear not only their own cost but also the legal expenditure of the winning party, manufacturing firms in the model unambiguously reduce patenting, with small firms affected the most. For fee-shifting to have the same effect as in Europe, the US legal system would require shifting of a much smaller share of fees. Lessons from European patent litigation may, therefore, have only limited applicability in the US case. The third chapter contains a theoretical analysis of the influence of delayed disclosure of patent applications by the patent office. Such a delay is a feature of most patent systems around the world but has so far not attracted analytical scrutiny. This delay may give firms various kinds of strategic (non-)disclosure incentives when they are competing for more than a single innovation.

Data, Competition, and Consumer Privacy in Digital Markets

In digital markets personal information is pervasively collected by firms. In the first chapter I study data ownership and product customization when there is exclusive access to non rival but excludable data about consumer preferences. I show that an incumbent firm does not have an incentive to sell an exclusively held dataset with a rival firm, but instead it has an incentive to trade a customizing technology with the other firm. In the second chapter I investigate the effects of consumer information on the intensity of competition. In a two dimensional model of product differentiation, firms use information on preferences to practice price discrimination. I contrast a full privacy and a no privacy benchmark with a regime in which firms are able to target consumers only partially. When data is partially informative, firms are always better-off with price discrimination and an exclusive access to user data is not necessarily a competition policy concern. From a consumer protection perspective, the policy recommendation is that the regulator should promote either no privacy or full privacy. In the third chapter I introduce a data broker that observes either only one or both dimensions of consumer information and sells this data to competing firms for price discrimination purposes. When the seller exogenously holds a partially informative dataset, an exclusive allocation arises. Instead, when the dataset held is fully informative, the data broker trades information non exclusively but each competitor acquires consumer data on a different dimension. When data collection is made endogenous, non exclusivity is robust if collection costs are not too high. The competition policy suggestion is that exclusivity should not be banned per se, but it is data differentiation in equilibrium that rises market power in competitive markets. Upstream competition is sufficient to ensure that both firms get access to consumer information.

Vulnerable users' protection with advanced recycling paving materials. Design and characterisation of rubber-based impact-absorbing pavement materials for bike lanes and sidewalks

Our cities are constantly evolving, and the necessity to improve the condition and safety of the urban infrastructures is fundamental. However, on the roads, the specific needs of cyclists and pedestrians are often neglected. The Vulnerable Road Users (VRUs), among whom cyclists and pedestrians are, rarely benefit from the most innovative safety measures. Inspired by playgrounds and aiming to reduce VRUs injuries, the Impact-Absorbing Pavements (IAP) developed as novel sidewalks, and bike lanes surface layers may help decrease injuries, fatalities, and the related societal costs. To achieve this goal, the End-of-Life Tyres (ELTs) crumb rubber (CR) is used as a primary resource, bringing its elastic properties into the surface layer. The thesis is divided into five main chapters. The first concerns the formulation and the definition of a feasible mix. The second explores the mechanical and environmental properties in detail, and the ageing effect is also assessed. The third describes the modelling of the material to simulate accidents and measure the injury reduction, especially on the head. The fourth chapter is reserved for the field trial. The last gives some perspectives on the research and proposes a way to optimize and improve the data and results collected during the doctoral research. It was observed that the specimens made with cold protocol have noticeable performances and reduce the overall carbon footprint impact of this material. The material modelling and the accident simulation proved the performance of the IAP against head injuries, and the field trial confirmed the good results obtained in the laboratory for the cold-made material. Finally, the outcomes of this thesis opened many prospective to the IAP development, such as the use of a plant-based binder or recycled aggregates and gave a positive prospect of an innovative material to the urban road infrastructures.

Untangle sustainable development goal 8 through data visualization and HCI methods

Following the approval of the 2030 Agenda for Sustainable Development in 2015, sustainability became a hotly debated topic. In order to build a better and more sustainable future by 2030, this agenda addressed several global issues, including inequality, climate change, peace, and justice, in the form of 17 Sustainable Development Goals (SDGs), that should be understood and pursued by nations, corporations, institutions, and individuals. In this thesis, we researched how to exploit and integrate Human-Computer Interaction (HCI) and Data Visualization to promote knowledge and awareness about SDG 8, which wants to encourage lasting, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. In particular, we focused on three targets: green economy, sustainable tourism, employment, decent work for all, and social protection. The primary goal of this research is to determine whether HCI approaches may be used to create and validate interactive data visualization that can serve as helpful decision-making aids for specific groups and raise their knowledge of public-interest issues. To accomplish this goal, we analyzed four case studies. In the first two, we wanted to promote knowledge and awareness about green economy issues: we investigated the Human-Building Interaction inside a Smart Campus and the dematerialization process inside a University. In the third, we focused on smart tourism, investigating the relationship between locals and tourists to create meaningful connections and promote more sustainable tourism. In the fourth, we explored the industry context to highlight sustainability policies inside well-known companies. This research focuses on the hypothesis that interactive data visualization tools can make communities aware of sustainability aspects related to SDG8 and its targets. The research questions addressed are two: "how to promote awareness about SDG8 and its targets through interactive data visualizations?" and "to what extent are these interactive data visualizations effective?".