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BugBits: Making tangibles with children
Get PDFThe thesis presents and discusses the processes that lead to the development of a tangible toolkit for supporting design workshops aimed at building tangible interfaces with children. The toolkit, called BugBits, was used to explore and instantiate participatory design workshops with children enabling them to be creative and develop new prototypes. BugBits was tested in three case studies with children of different ages. The first study was conducted in a modern art museum, where children aged between 13 and 15 years old (N=185) built personalised artefacts with the toolkit. The artefacts were then used to perform an augmented visit to some of the exhibition rooms of the museum. The second study (N=31) was conducted in a kindergarten with children between 3 and 6 years old. The toolkit was adopted to perform two educational exercises about colours characteristics. The third study (N=24) explored how the toolkit can be used to instantiate creative processes during participatory design workshops with children between 7 and 11 years old. During the studies, qualitative and quantitative data were collected and analysed.
The outcomes of the analysis show that the toolkit can be used with success to keep the children engaged (study 1, 2, 3) and obtain an active and effective participation (study 3) and allow them to build new and evolving TUI prototypes (study 3). By retrospectively reflecting on the process, the thesis presents the KPW process to guide and instantiate the design of generative tools for TUI design with children. The KPW process poses particular attention to the children roles, and how the technological choices affect the design
Cellular mimics within lipid vesicles and in thermal out-of-equilibrium chambers
Get PDFThe absence of clear criteria to recognize life and evaluate attempts at building a cell from component parts has slowed progress towards the construction of cellular mimics that fully display the properties of natural living cells. In the first part of this PhD thesis, a method to objectively quantify progress is proposed. In the second part of the thesis, preliminary results are shown and discussed for the construction of out-of-equilibrium cellular mimics generated by thermal gradients that do not rely on compartments made from lipid membranes
Feeding Distinction: Constrictions and Constructions of Dietary Compliance
Get PDFIn this work, moving back and forth along complementary perspectives, I aim to provide an in-depth analysis of the social stratification of eating and feeding practices in an Italian context, with a special focus on the school canteen as a possible enhancer of children’s dietary compliance. Although the thesis cannot be read as a single monograph, the fil rouge that runs through the chapters presents new insights on the ways eating and feeding are organised, regulated, differentiated, and reproduced in Italy by adults and children.
In fact, each chapter reads as an autonomous contribution, accompanied by a specific literature review, that distinctively adds to a branch of the research on food sociology, from health to consumption passing through childhood. Nevertheless, this does not imply that the chapters are disconnected, and the reader will often find cross references throughout the manuscript. The thesis is constructed on two different blocks, divided by methodology, but held together by the first chapter, in which I discuss the socio-philosophical foundation of the research. Here I initially draw from Bourdieu’s practice theory to discuss the theoretical and methodological foundations of the thesis, and I subsequently examine the concepts of eating and feeding practices, eventually outlining the contribution of each empirical chapter. Therefore, the first block seeks to identify theoretically informed empirical regularities using Bourdieu’s (2011) theory of capitals, and its adaptation to health behaviours as proposed by Abel (2007; 2008). This part aims to ‘quantify’ how capital constrictions shape food consumption and beyond. Chapter 2, focusing on gender differences in health behaviours among adults (Courtenay, 2000), analyses the determinants of dietary compliance, drinking behaviour and smoking, and how gender differentials change depending on the respondent’s levels of cultural capital. Chapter 3, however, which paves the way for the subsequent ethnography, focuses on the determinants of dietary compliance among Italian schoolchildren, and specifically on the role of the school canteen as an equaliser that can mitigate health inequalities by improving the diet of most disadvantaged children. In the second block, I focus on eating and feeding practices as social constructions. This part of the work allows me to go behind and beyond the empirical regularities shown in the previous chapters. Behind, because qualitative data provide an opportunity to consider the epistemological foundations and the political implications of the construction of dietary compliance, in school and at home; beyond, because they allow us to excavate in vivo how eating and feeding are part of a contested field of knowledge that depends on family endowments. The three chapters are hence based on the ethnographic fieldwork and the in-depth interviews conducted in four Italian primary schools. Chapter 4, partially rooted in the Foucauldian tradition of governmentality studies, uses the concept of strategy and tactics (de Certeau, 1984) to analyse the construction and implementation of a healthy meal and the resistances that arise around and within the school canteen. On a different note, chapter 5 makes use of the in-depth interviews with parents and the fieldnotes gathered in Poversano and Goldazzo school canteens to study how cultural and economic family resources shape parental feeding practices, their perception of the school meal and children’s knowledge of healthy food and cuisine. Finally, chapter 6 illustrates what happens to food education programs when they are applied in extreme contexts, such as the school of a poverty-stricken neighbourhood of Palermo. In the conclusions, I summarise the most important findings of the manuscript, and I draw attention to the possible implications for school food programs as well as for future directions for research
Future Motorway. Design strategies for next generation infrastructure.
Get PDFThe research “Future motorway. Design strategies for next generation infrastructure”, in its path, deals with a double important and urgent issue: the need to consider mobility infrastructures as landscape devices and the definition of a new paradigm for the motorways of the future. The main objective of the thesis is the definition of a planning strategy for the infrastructures of the future, starting from the TechnoEcoSystem concept.
It is based around a double hypothesis: one theoretical, the other experimental. The first observes the definition of TechnoEcoSystem (Naveh, Lieberman,1990) from the ecology of the landscape and transfers it to the project/transformation process of the motorways.
The second one identifies one of the prototypes of the Motorway TechnoEcoSystem into the service areas. As a whole, the work combines theoretical and experimental aspects, within a path of design process that through qualitative and quantitative observations, defines
the 4.0 motorway through a holistic view of the system
Modelling and Recognizing Personal Data
Get PDFTo define what a person is represents a hard task, due to the fact that personal data, i.e., data that refer or describe a person, have a very heterogeneous nature. The issue is only worsening with the advent of technologies that, while allowing unprecedented collection and processing capabilities, cannot \textit{understand} the world as humans do. This problem is a well-known long-standing problem in computer science called the Semantic Gap Problem. It was originally defined in the research area of image processing as "... the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation...". In the context of this work, the semantic gap is the lack of coincidence is between sensor data collected by ubiquitous devices and the human knowledge about the world that relies on their intelligence, habits, and routines.
This thesis addresses the semantic gap problem from a representational point of view, proposing an interdisciplinary approach able to model and recognize personal data in real life scenarios. In fact, the semantic gap affects many communities, ranging from ubiquitous computing to user modelling, that must face the issue of managing the complexity of personal data in terms of modelling and recognition.
The contributions of this Ph. D. Thesis are:
1) The definition of a methodology based on an interdisciplinary approach that can account for how to represent and allow the recognition of personal data. The interdisciplinary approach relies on the entity-centric approach and on an interdisciplinary categorization to define and structure personal data.
2) The definition of an ontology of personal data to represent human in a general way while also accounting their different dimensions of their everyday life;
3) The instantiation of the personal data representation above in a reference architecture that allows implementing the ontology and that can exploit the methodology to account for how to recognize personal data.
4) The adoption of the methodology for defining personal data and its instantiation in three real-life use cases with different goals in mind, proving that our modelling works in different domains and can account for several dimensions of the user
Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision
Get PDFSemantic Image Interpretation (SII) is the process of generating a structured description of the content of an input image. This description is encoded as a labelled direct graph where nodes correspond to objects in the image and edges to semantic relations between objects. Such a detailed structure allows a more accurate searching and retrieval of images. In this thesis, we propose two well-founded methods for SII. Both methods exploit background knowledge, in the form of logical constraints of a knowledge base, about the domain of the images. The first method formalizes the SII as the extraction of a partial model of a knowledge base. Partial models are built with a clustering and reasoning algorithm that considers both low-level and semantic features of images. The second method uses the framework Logic Tensor Networks to build the labelled direct graph of an image. This framework is able to learn from data in presence of the logical constraints of the knowledge base. Therefore, the graph construction is performed by predicting the labels of the nodes and the relations according to the logical constraints and the features of the objects in the image. These methods improve the state-of-the-art by introducing two well-founded methodologies that integrate low-level and semantic features of images with logical knowledge. Indeed, other methods, do not deal with low-level features or use only statistical knowledge coming from training sets or corpora. Moreover, the second method overcomes the performance of the state-of-the-art on the standard task of visual relationship detection
Designing Wearables for Climbing: Integrating the Practice and the Experience Perspectives of Outdoor Adventure Sports
Get PDFThis thesis positions itself within the stream of research on HCI for sport and addresses the topic of designing wearable devices for sport. To date, the design of wearables for sport has focused on the measurable aspects of performance such as speed, heartbeat and calories burnt. Such design is driven by the possibilities offered by the miniaturisation of components and the trend to have a healthy lifestyle. The conjunction of these two trends, has created a breeding ground for technologies that offer self-tracking to improve personal fitness, health and wellbeing. Although these kinds of devices have great success on the market, several studies have shown poor long-term adoption, with people generally ceasing to use their devices around six months from the time of purchase. This thesis argues that the wearables produced until now do not address the full range of needs that sportspeople have and so aims to design wearables on the basis of a thorough understanding of the sport practice.
The leading research question in this work was: what are the elements to consider for the design of useful, acceptable and desirable wearable devices for sport? This broad research question was then operationalised in two sub-questions: what elements constitute the sport practice?; and how can wearable devices support such practice? By adopting a practice perspective and a subsequent research methodology based on situatedness, embodiment, and co-design, it was possible to identify aspects of sport other than performance. Emotions, trust and community values emerged as pivotal aspects of the climbing experience. These findings led to the design of wearables for augmenting the interpersonal communication of the actors involved. This introduces a new role for wearables supporting sportspeople, which as a facilitator of expertise rather than a tracker of activity.
The main contribution of this thesis is the articulation of a conceptual framework for the design of wearables for outdoor sports, with the goal of better acceptance and long-term adoption. The conceptual framework outlined here breaks down the complexity of the sport practice by identifying the elements that define it (i.e. type of performance, emotional involvement, social dynamics, physical context, values) and articulating their orchestration with product design aspects (such as ergonomics, comfort, and perceptibility) and the cultural value of wearing an artefact on the body
Privacy-Aware Risk-Based Access Control Systems
Get PDFModern organizations collect massive amounts of data, both internally (from their employees and processes) and externally (from customers, suppliers, partners). The increasing availability of these large datasets was made possible thanks to the increasing storage and processing capability. Therefore, from a technical perspective, organizations are now in a position to exploit these diverse datasets to create new data-driven businesses or optimizing existing processes (real-time customization, predictive analytics, etc.). However, this kind of data often contains very sensitive information that, if leaked or misused, can lead to privacy violations.
Privacy is becoming increasingly relevant for organization and businesses, due to strong regulatory frameworks (e.g., the EU General Data Protection Regulation GDPR, the Health Insurance Portability and Accountability Act HIPAA) and the increasing awareness of citizens about personal data issues. Privacy breaches and failure to meet privacy requirements can have a tremendous impact on companies (e.g., reputation loss, noncompliance fines, legal actions). Privacy violation threats are not exclusively caused by external actors gaining access due to security gaps. Privacy breaches can also be originated by internal actors, sometimes even by trusted and authorized ones. As a consequence, most organizations prefer to strongly limit (even internally) the sharing and dissemination of data, thereby making most of the information unavailable to decision-makers, and thus preventing the organization from fully exploit the power of these new data sources. In order to unlock this potential, while controlling the privacy risk, it is necessary to develop novel data sharing and access control mechanisms able to support risk-based decision making and weigh the advantages of information against privacy considerations. To achieve this, access control decisions must be based on an (dynamically assessed) estimation of expected cost and benefits compared to the risk, and not (as in traditional access control systems) on a predefined policy that statically defines what accesses are allowed and denied.
In Risk-based access control for each access request, the corresponding risk is estimated and if the risk is lower than a given threshold (possibly related to the trustworthiness of the requester), then access is granted or denied. The aim is to be more permissive than in traditional access control systems by allowing for a better exploitation of data. Although existing risk-based access control models provide an important step towards a better management and exploitation of data, they have a number of drawbacks which limit their effectiveness. In particular, most of the existing risk-based systems only support binary access decisions: the outcome is “allowed” or “denied”, whereas in real life we often have exceptions based on additional conditions (e.g., “I cannot provide this information, unless you sign the following non-disclosure agreement.” or “I cannot disclose this data, because they contain personal identifiable information, but I can disclose an anonymized version of the data.”). In other words, the system should be able to propose risk mitigation measures to reduce the risk (e.g., disclose partial or anonymized version of the requested data) instead of denying risky access requests. Alternatively, it should be able to propose appropriate trust enhancement measures (e.g., stronger authentication), and once they are accepted/fulfilled by the requester, more information can be shared.
The aim of this thesis is to propose and validate a novel privacy enhancing access control approach offering adaptive and fine-grained access control for sensitive data-sets. This approach enhances access to data, but it also mitigates privacy threats originated by authorized internal actors. More in detail:
1. We demonstrate the relevance and evaluate the impact of authorized actors’ threats. To this aim, we developed a privacy threats identification methodology EPIC (Evaluating Privacy violation rIsk in Cyber security systems) and apply EPIC in a cybersecurity use case where very sensitive information is used.
2. We present the privacy-aware risk-based access control framework that supports access control in dynamic contexts through trust enhancement mechanisms and privacy risk mitigation strategies. This allows us to strike a balance between the privacy risk and the trustworthiness of the data request. If the privacy risk is too large compared to the trust level, then the framework can identify adaptive strategies that can decrease the privacy risk (e.g., by removing/obfuscating part of the data through anonymization) and/or increase the trust level (e.g., by asking for additional obligations to the requester).
3. We show how the privacy-aware risk-based approach can be integrated to existing access control models such as RBAC and ABAC and that it can be realized using a declarative policy language with a number of advantages including usability, flexibility, and scalability.
4. We evaluate our approach using several industrial relevant use cases, elaborated to meet the requirements of the industrial partner (SAP) of this industrial doctorate
Cell-free expression systems for the construction of artificial cells
Get PDFCell-free expression systems are widely used to synthesize proteins for subsequent further characterization, to manufacture potentially useful commercial end products, and to construct cellular mimics in the laboratory. The first part of the thesis explores the feasibility of preparing two of the commercially available and widely used E. coli-based cell-free expression systems: the PURE System and the S30 Bacterial Extract. The second part focuses on the characterization of in vitro transcription and translation. The third part of the thesis features an example of an application of S30 Bacterial Extract cell-free expression systems i.e. the building of cell-like structures that can work together with engineered bacteria to achieve a predetermined task. Finally, the construction of a microfluidic dialysis device compatible with cell-free synthetic biology projects is presented
Deep neural network models for image classification and regression
Get PDFDeep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications. Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a decade ago for instance. Although it has demonstrated cutting-edge performance widely in computer vision, and particularly in object recognition and detection, deep learning is yet to find its way into other research areas. Furthermore, the performance of deep learning models has a strong dependency on the way in which these latter are designed/tailored to the problem at hand. This, thereby, raises not only precision concerns but also processing overheads. The success and applicability of a deep learning system relies jointly on both components. In this dissertation, we present innovative deep learning schemes, with application to interesting though less-addressed topics. In this respect, the first covered topic is rough scene description for visually impaired individuals, whose idea is to list the objects that likely exist in an image that is grabbed by a visually impaired person, To this end, we proceed by extracting several features from the respective query image in order to capture the textural as well as the chromatic cues therein. Further, in order to improve the representativeness of the extracted features, we reinforce them with a feature learning stage by means of an autoencoder model. This latter is topped with a logistic regression layer in order to detect the presence of objects if any. In a second topic, we suggest to exploit the same model, i.e., autoencoder in the context of cloud removal in remote sensing images. Briefly, the model is learned on a cloud-free image pertaining to a certain geographical area, and applied afterwards on another cloud-contaminated image, acquired at a different time instant, of the same area. Two reconstruction strategies are proposed, namely pixel-based and patch-based reconstructions.
From the earlier two topics, we quantitatively demonstrate that autoencoders can play a pivotal role in terms of both (i) feature learning and (ii) reconstruction and mapping of sequential data.
Convolutional Neural Network (CNN) is arguably the most utilized model by the computer vision community, which is reasonable thanks to its remarkable performance in object and scene recognition, with respect to traditional hand-crafted features. Nevertheless, it is evident that CNN naturally is availed in its two-dimensional version. This raises questions on its applicability to unidimensional data. Thus, a third contribution of this thesis is devoted to the design of a unidimensional architecture of the CNN, which is applied to spectroscopic data. In other terms, CNN is tailored for feature extraction from one-dimensional chemometric data, whilst the extracted features are fed into advanced regression methods to estimate underlying chemical component concentrations. Experimental findings suggest that, similarly to 2D CNNs, unidimensional CNNs are also prone to impose themselves with respect to traditional methods. The last contribution of this dissertation is to develop new method to estimate the connection weights of the CNNs. It is based on training an SVM for each kernel of the CNN. Such method has the advantage of being fast and adequate for applications that characterized by small datasets