Le disfluenze e la percezione degli ascoltatori. Valutazione di tre interpretazioni consecutive.

This study aims at exploring listeners’ perception of disfluencies, i.e. ungrammatical pauses, filled pauses, repairs, false starts and repetitions, which can irritate listeners and impede comprehension. As professional communicators, conference interpreters should be competent public speakers. This means that their speech should be easily understood by listeners and not contain elements that may be considered irritating. The aim of this study was to understand to what extent listeners notice disfluencies and consider them irritating, and to examine whether there are differences between interpreters and non-interpreters and between different age groups. A survey was therefore carried out among professional interpreters, students of interpreting and people who regularly attend conferences. The respondents were asked to answer a questionnaire after listening to three speeches: three consecutive interpretations delivered during the final exams held at the Advanced School of Languages, Literature, Translation and Interpretation (SSLLTI) in Forlì. Since conference interpreters’ public speaking skills should be at least as good as those of the speakers at a conference, the speeches were presented to the listeners as speeches delivered during a conference, with no mention of interpreting being made. The study is divided into five chapters. Chapter I outlines the characteristics of the interpreter as a professional communicator. The quality criterion “user-friendliness” is explored, with a focus on features that make a speech more user-friendly: fluency, intonation, coherence and cohesion. The Chapter also focuses on listeners’ quality expectations and evaluations. In Chapter II the methodology of the study is described. Chapter III contains a detailed analysis of the texts used for the study, focusing on those elements that may irritate listeners or impede comprehension, namely disfluencies, the wrong use of intonation and a lack of coherence or cohesion. Chapter IV outlines the results of the survey, while Chapter V presents our conclusions.

A novel marker for heart failure: Cross sectional area assessment in a large vein by conductance catheter.

The present study investigates the feasibility of a new application able to check the heart failure status in a patient through the estimation of the venous distension. In this way it would be possible to follow up patients, avoiding invasive or expensive exams such as cardiac catheterization and echocardiography. Moreover, the devices would also be able to diagnose the decline of the disease, in order to allow a new adaptation to therapy, and vice versa to check the improvement in the patient’s conditions after the CRT device implant. This thesis is essentially divided into three parts: an analytical model was used to obtain an estimation of the error committed for the calculation of the CSA and to understand how the accuracy and sensitivity depend on the different configurations of the electrodes and the catheter position inside the vein; secondly, an in-vitro experiment was carried out in order to verify the practical feasibility for these kinds of measurements, in a very simplified model; in the end, several animal experiments were done to test the in-vivo practicability of the proposed method. The obtained results showed the feasibility of this approach. In fact, the error committed in the estimation of CSA, during the animal experiments, can be considered acceptable (CSAerror_max ≈ -14%). Moreover, it has been demonstrated that the conductance catheter allows assessing, not only the vein CSA, but also the breathing of the animal.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.