Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

Understanding 3D structures from sketches

Sketches are a unique way to communicate: drawing a simple sketch does not require any training, sketches convey information that is hard to describe with words, they are powerful enough to represent almost any concept, and nowadays, it is possible to draw directly from mobile devices. Motivated from the unique characteristics of sketches and fascinated by the human ability to imagine 3D objects from drawings, this thesis focuses on automatically associating geometric information to sketches. The main research directions of the thesis can be summarized as obtaining geometric information from freehand scene sketches to improve 2D sketch-based tasks and investigating Vision-Language models to overcome 3D sketch-based tasks limitations. The first part of the thesis concerns geometric information prediction from scene sketches improving scene sketch to image generation and unlocking new creativity effects. The thesis proceeds showing a study conducted on the Vision-Language models embedding space considering sketches, line renderings and RGB renderings of 3D shape to overcome the use of supervised datasets for 3D sketch-based tasks, that are limited and hard to acquire. Following the obtained observations and results, Vision-Language models are applied to Sketch Based Shape Retrieval without the need of training on supervised datasets. We then analyze the use of Vision-Language models for sketch based 3D reconstruction in an unsupervised manner. In the final chapter we report the results obtained in an additional project carried during the PhD, which has lead to the development of a framework to learn an embedding space of neural networks that can be navigated to get ready-to-use models with desired characteristics.

New methodologies in CT perfusion and MRI analysis to develop cancer imaging biomarkers

Quantitative imaging in oncology aims at developing imaging biomarkers for diagnosis and prediction of cancer aggressiveness and therapy response before any morphological change become visible. This Thesis exploits Computed Tomography perfusion (CTp) and multiparametric Magnetic Resonance Imaging (mpMRI) for investigating diverse cancer features on different organs. I developed a voxel-based image analysis methodology in CTp and extended its use to mpMRI, for performing precise and accurate analyses at single-voxel level. This is expected to improve reproducibility of measurements and cancer mechanisms’ comprehension and clinical interpretability. CTp has not entered the clinical routine yet, although its usefulness in the monitoring of cancer angiogenesis, due to different perfusion computing methods yielding unreproducible results. Instead, machine learning applications in mpMRI, useful to detect imaging features representative of cancer heterogeneity, are mostly limited to clinical research, because of results’ variability and difficult interpretability, which make clinicians not confident in clinical applications. In hepatic CTp, I investigated whether, and under what conditions, two widely adopted perfusion methods, Maximum Slope (MS) and Deconvolution (DV), could yield reproducible parameters. To this end, I developed signal processing methods to model the first pass kinetics and remove any numerical cause hampering the reproducibility. In mpMRI, I proposed a new approach to extract local first-order features, aiming at preserving spatial reference and making their interpretation easier. In CTp, I found out the cause of MS and DV non-reproducibility: MS and DV represent two different states of the system. Transport delays invalidate MS assumptions and, by correcting MS formulation, I have obtained the voxel-based equivalence of the two methods. In mpMRI, the developed predictive models allowed (i) detecting rectal cancers responding to neoadjuvant chemoradiation showing, at pre-therapy, sparse coarse subregions with altered density, and (ii) predicting clinically significant prostate cancers stemming from the disproportion between high- and low- diffusivity gland components.

Insegnare l'italiano L2 per fini di studio: una proposta di letture graduate per gli studenti cinesi

Dati i problemi di comprensione linguistica riscontrati dagli studenti cinesi nel contesto accademico e la crescente necessità della didattica dell’italiano L2 per fini di studio, il presente lavoro ha come obiettivo finale la creazione di letture graduate, proposte come materiali didattici comprensibili e mirati agli studenti cinesi di italiano L2, in modo da agevolare il loro approccio ai testi impegnativi richiesti per gli studi artistico-professionali. In particolare, nei primi due capitoli si discute il ruolo significativo della distanza linguistica tra italiano e cinese nell’acquisizione della L2 da parte degli studenti cinesi e nello sviluppo della loro abilità di lettura in L2. In seguito, per capire come debba essere un input ideale per l’acquisizione linguistica a fini di studio, vengono esaminati vari approcci glottodidattici basati sull’input, e si osservano i tratti delle varietà di italiano presenti nel contesto accademico. Il lavoro procede poi con un’analisi delle specifiche caratteristiche linguistiche riscontrate in manuali universitari di storia dell’arte, utilizzando sia un approccio quantitativo che qualitativo, con l’obiettivo di avere un “panorama” delle complessità linguistiche che uno studente L2 deve affrontare nello studio. Successivamente, verrà presentata una sperimentazione di riscrittura con due gruppi, i quali sono stati sottoposti rispettivamente al testo originale e a quello riscritto secondo i criteri formulati dallo studio teorico sul confronto tipologico. I risultati ottenuti confermano sia le interferenze del cinese nella lettura in italiano, sia l’efficacia degli approcci linguistici individuati nel facilitare la comprensibilità del testo per gli studenti cinesi di livello A2-B1. Di conseguenze, viene proposto un percorso di letture graduate per gli studenti cinesi di belle arti; oltre a essere comprensibili, le letture mirano anche all’acquisizione delle varietà di italiano necessarie per lo studio accademico-professionale. L’ultima parte del lavoro è dedicata alle riflessioni teoriche e didattiche sviluppate nel corso della ricerca.

Path planning for image based visual servoing

This thesis deals with Visual Servoing and its strictly connected disciplines like projective geometry, image processing, robotics and non-linear control. More specifically the work addresses the problem to control a robotic manipulator through one of the largely used Visual Servoing techniques: the Image Based Visual Servoing (IBVS). In Image Based Visual Servoing the robot is driven by on-line performing a feedback control loop that is closed directly in the 2D space of the camera sensor. The work considers the case of a monocular system with the only camera mounted on the robot end effector (eye in hand configuration). Through IBVS the system can be positioned with respect to a 3D fixed target by minimizing the differences between its initial view and its goal view, corresponding respectively to the initial and the goal system configurations: the robot Cartesian Motion is thus generated only by means of visual informations. However, the execution of a positioning control task by IBVS is not straightforward because singularity problems may occur and local minima may be reached where the reached image is very close to the target one but the 3D positioning task is far from being fulfilled: this happens in particular for large camera displacements, when the the initial and the goal target views are noticeably different. To overcame singularity and local minima drawbacks, maintaining the good properties of IBVS robustness with respect to modeling and camera calibration errors, an opportune image path planning can be exploited. This work deals with the problem of generating opportune image plane trajectories for tracked points of the servoing control scheme (a trajectory is made of a path plus a time law). The generated image plane paths must be feasible i.e. they must be compliant with rigid body motion of the camera with respect to the object so as to avoid image jacobian singularities and local minima problems. In addition, the image planned trajectories must generate camera velocity screws which are smooth and within the allowed bounds of the robot. We will show that a scaled 3D motion planning algorithm can be devised in order to generate feasible image plane trajectories. Since the paths in the image are off-line generated it is also possible to tune the planning parameters so as to maintain the target inside the camera field of view even if, in some unfortunate cases, the feature target points would leave the camera images due to 3D robot motions. To test the validity of the proposed approach some both experiments and simulations results have been reported taking also into account the influence of noise in the path planning strategy. The experiments have been realized with a 6DOF anthropomorphic manipulator with a fire-wire camera installed on its end effector: the results demonstrate the good performances and the feasibility of the proposed approach.

A skin surface characterization system based on capacitive image analysis

During the last few years, several methods have been proposed in order to study and to evaluate characteristic properties of the human skin by using non-invasive approaches. Mostly, these methods cover aspects related to either dermatology, to analyze skin physiology and to evaluate the effectiveness of medical treatments in skin diseases, or dermocosmetics and cosmetic science to evaluate, for example, the effectiveness of anti-aging treatments. To these purposes a routine approach must be followed. Although very accurate and high resolution measurements can be achieved by using conventional methods, such as optical or mechanical profilometry for example, their use is quite limited primarily to the high cost of the instrumentation required, which in turn is usually cumbersome, highlighting some of the limitations for a routine based analysis. This thesis aims to investigate the feasibility of a noninvasive skin characterization system based on the analysis of capacitive images of the skin surface. The system relies on a CMOS portable capacitive device which gives 50 micron/pixel resolution capacitance map of the skin micro-relief. In order to extract characteristic features of the skin topography, image analysis techniques, such as watershed segmentation and wavelet analysis, have been used to detect the main structures of interest: wrinkles and plateau of the typical micro-relief pattern. In order to validate the method, the features extracted from a dataset of skin capacitive images acquired during dermatological examinations of a healthy group of volunteers have been compared with the age of the subjects involved, showing good correlation with the skin ageing effect. Detailed analysis of the output of the capacitive sensor compared with optical profilometry of silicone replica of the same skin area has revealed potentiality and some limitations of this technology. Also, applications to follow-up studies, as needed to objectively evaluate the effectiveness of treatments in a routine manner, are discussed.

Retrieval of atmospheric trace gases from DOAS/MAX-DOAS ground-based spectra

The study of the atmospheric chemical composition is crucial to understand the climate changes that we are experiencing in the last decades and to monitor the air quality over industrialized areas. The Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) ground-based instruments are particularly suitable to derive the concentration of some trace gases that absorb the Visible (VIS) and Ultra-Violet (UV) solar radiation. The zenith-sky spectra acquired by the Gas Analyzer Spectrometer Correlating Optical Differences / New Generation 4 (GASCOD/NG4) instrument are exploited to retrieve the NO2 and O3 total Vertical Column Densities (VCDs) over Lecce. The results show that the NO2 total VCDs are significantly affected by the tropospheric content, consequence of the anthropogenic activity. Indeed, they present systematically lower values during Sunday, when less traffic is generally present around the measurement site, and during windy days, especially when the wind direction measured at 2 m height is not from the city of Lecce. Another MAX-DOAS instrument (SkySpec-2D) is exploited to create the first Italian MAX-DOAS site compliant to the Fiducial Reference Measurements for DOAS (FRM4DOAS) standards, in San Pietro Capofiume (SPC), located in the middle of the Po Valley. After the assessment of the SkySpec-2D’s performances through two measurement campaigns taken place in Bologna and in Rome, SkySpec-2D is installed in SPC on the 1st October 2021. Its MAX-DOAS spectra are used to retrieve the NO2 and O3 total VCDs, and aerosol extinction and NO2 tropospheric vertical profiles over the Po Valley exploiting the Bremen Optimal estimation REtrieval for Aerosol and trace gaseS (BOREAS) algorithm. Promising results are found, with high correlations against both in-situ and satellite data. In the future, these data will play an important role for air quality studies over the Po Valley and for satellite validation purposes.