Development of a semantic graph-based scene description

Software representations of scenes, i.e. the modelling of objects in space, are used in many application domains. Current modelling and scene description standards focus on visualisation dimensions, and are intrinsically limited by their dependence upon their semantic interpretation and contextual application by humans. In this paper we propose the need for an open, extensible and semantically rich modelling language, which facilitates a machine-readable semantic structure. We critically review existing standards and techniques, and highlight a need for a semantically focussed scene description language. Based on this defined need we propose a preliminary solution, based on hypergraph theory, and reflect on application domains.

Software evolution: a graph based model

A model based on graph isomorphisms is used to formalize software evolution. Step by step we narrow the search space by an informed selection of the attributes based on the current state-of-the-art in software engineering and generate a seed solution. We then traverse the resulting space using graph isomorphisms and other set operations over the vertex sets. The new solutions will preserve the desired attributes. The goal of defining an isomorphism based search mechanism is to construct predictors of evolution that can facilitate the automation of ’software factory’ paradigm. The model allows for automation via software tools implementing the concepts.

Software evolution: a graph based model

A model based on graph isomorphisms is used to formalize software evolution. Step by step we narrow the search space by an informed selection of the attributes based on the current state-of-the-art in software engineering and generate a seed solution. We then traverse the resulting space using graph isomorphisms and other set operations over the vertex sets. The new solutions will preserve the desired attributes. The goal of defining an isomorphism based search mechanism is to construct predictors of evolution that can facilitate the automation of ’software factory’ paradigm. The model allows for automation via software tools implementing the concepts.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

The performance of conventional and fluorescence-based methods for occlusal caries detection An in vivo study with histologic validation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Diagnosis of Giardia infections by PCR-based methods in children of an endemic area

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Clinical applications of non-culture based methods for the diagnosis and management of opportunistic and endemic mycoses

Nonculture based methods for the detection of infections caused by fungal pathogens are becoming more important tools in the management of infected patients. Detection of fungal antigens and DNA appear to be the most promising in this respect for both opportunistic and endemic mycoses. In this article we present an overview of the most recent developments in nonculture based methods and examine their value in clinical practice.

The performance of conventional and fluorescence-based methods for occlusal caries detection: an in vivo study with histological validation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Kinematics and statics of cable-driven parallel robots by interval-analysis-based methods

In the past two decades the work of a growing portion of researchers in robotics focused on a particular group of machines, belonging to the family of parallel manipulators: the cable robots. Although these robots share several theoretical elements with the better known parallel robots, they still present completely (or partly) unsolved issues. In particular, the study of their kinematic, already a difficult subject for conventional parallel manipulators, is further complicated by the non-linear nature of cables, which can exert only efforts of pure traction. The work presented in this thesis therefore focuses on the study of the kinematics of these robots and on the development of numerical techniques able to address some of the problems related to it. Most of the work is focused on the development of an interval-analysis based procedure for the solution of the direct geometric problem of a generic cable manipulator. This technique, as well as allowing for a rapid solution of the problem, also guarantees the results obtained against rounding and elimination errors and can take into account any uncertainties in the model of the problem. The developed code has been tested with the help of a small manipulator whose realization is described in this dissertation together with the auxiliary work done during its design and simulation phases.

Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients

Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

An inter-comparison between a VIS/IR and a MW satellite-based methods for cloud detection and classification

This thesis is aimed to assess similarities and mismatches between the outputs from two independent methods for the cloud cover quantification and classification based on quite different physical basis. One of them is the SAFNWC software package designed to process radiance data acquired by the SEVIRI sensor in the VIS/IR. The other is the MWCC algorithm, which uses the brightness temperatures acquired by the AMSU-B and MHS sensors in their channels centered in the MW water vapour absorption band. At a first stage their cloud detection capability has been tested, by comparing the Cloud Masks they produced. These showed a good agreement between two methods, although some critical situations stand out. The MWCC, in effect, fails to reveal clouds which according to SAFNWC are fractional, cirrus, very low and high opaque clouds. In the second stage of the inter-comparison the pixels classified as cloudy according to both softwares have been. The overall observed tendency of the MWCC method, is an overestimation of the lower cloud classes. Viceversa, the more the cloud top height grows up, the more the MWCC not reveal a certain cloud portion, rather detected by means of the SAFNWC tool. This is what also emerges from a series of tests carried out by using the cloud top height information in order to evaluate the height ranges in which each MWCC category is defined. Therefore, although the involved methods intend to provide the same kind of information, in reality they return quite different details on the same atmospheric column. The SAFNWC retrieval being very sensitive to the top temperature of a cloud, brings the actual level reached by this. The MWCC, by exploiting the capability of the microwaves, is able to give an information about the levels that are located more deeply within the atmospheric column.