Approximation contexts in addressing graph data structures

While the application of machine learning algorithms to practical problems has been expanded from fixed sized input data to sequences, trees or graphs input data, the composition of learning system has developed from a single model to integrated ones. Recent advances in graph based learning algorithms include: the SOMSD (Self Organizing Map for Structured Data), PMGraphSOM (Probability Measure Graph Self Organizing Map,GNN (Graph Neural Network) and GLSVM (Graph Laplacian Support Vector Machine). A main motivation of this thesis is to investigate if such algorithms, whether by themselves individually or modified, or in various combinations, would provide better performance over the more traditional artificial neural networks or kernel machine methods on some practical challenging problems. More succinctly, this thesis seeks to answer the main research question: when or under what conditions/contexts could graph based models be adjusted and tailored to be most efficacious in terms of predictive or classification performance on some challenging practical problems? There emerges a range of sub-questions including: how do we craft an effective neural learning system which can be an integration of several graph and non-graph based models? Integration of various graph based and non graph based kernel machine algorithms; enhancing the capability of the integrated model in working with challenging problems; tackling the problem of long term dependency issues which aggravate the performance of layer-wise graph based neural systems. This thesis will answer these questions. Recent research on multiple staged learning models has demonstrated the efficacy of multiple layers of alternating unsupervised and supervised learning approaches. This underlies the very successful front-end feature extraction techniques in deep neural networks. However much exploration is still possible with the investigation of the number of layers required, and the types of unsupervised or supervised learning models which should be used. Such issues have not been considered so far, when the underlying input data structure is in the form of a graph. We will explore empirically the capabilities of models of increasing complexities, the combination of the unsupervised learning algorithms, SOM, or PMGraphSOM, with or without a cascade connection with a multilayer perceptron, and with or without being followed by multiple layers of GNN. Such studies explore the effects of including or ignoring context. A parallel study involving kernel machines with or without graph inputs has also been conducted empirically

Human-Mechanical system interaction in Virtual Reality

The present work aims to show the great potential of Virtual Reality (VR) technologies in the field of Human-Robot Interaction (HRI). Indeed, it is foreseeable that in not too distant future cooperating robots will be increasingly present in human environments. Many authors actually believe that after the current information revolution, we will witness the so-called "robotics revolution", with the spread of increasingly intelligent and autonomous robots capable of moving into our own environments. Since these machines must be able to interact with human beings in a safe way, new design tools for the study of Human-Robot Interaction (HRI) are needed. The author believes that VR is an ideal design tool for the study of the interaction between humans and automatic machines, since it allows the designers to interact in real-time with virtual robotic systems and to evaluate different control algorithms, without the need of physical prototypes. This also shields the user from any risk related to the physical experimentation. However, VR technologies have also a more immediate application in the field of HRI, such as the study of usability of interfaces for real-time controlled robots. In fact, these robots, such as robots for microsurgery or even "teleoperated" robots working in a hostile environments, are already quite common. VR allows the designers to evaluate the usability of such interfaces by relating their physical input with a virtual output. In particular, the author has developed a new software application aimed at simulating automatic robots and, more generally, mechanical systems in a virtual environment. The user can interact with one or more virtual manipulators and also control them in real-time by means of several input devices. Finally, an innovative approach to the modeling and control of a humanoid robot with high degree of redundancy is discussed. VR implementation of a virtual humanoid is useful for the study of both humanoid robots and human beings

MAPPA. Methodologies applied to archaeological potential Predictivity

The fruitful cooperation over the years between the university teaching staff of Univerità di Pisa (Pisa University), the officials of the Soprintendenza per i Beni Archeologici della Toscana (Superintendency for Archaeological Heritage of Tuscany), the officials of the Soprintendenza per i Beni Architettonici, Paesaggistici, Artistici ed Etnoantropologici per le Province di Pisa e Livorno (Superintendency for Architectural, Landscape and Ethno-anthropological Heritage for the Provinces of Pisa and Livorno), and the Comune di Pisa (Municipality of Pisa) has favoured a great deal of research on issues regarding archaeological heritage and the reconstruction of the environmental and landscape context in which Pisa has evolved throughout the centuries of its history. The desire to merge this remarkable know-how into an organic framework and, above all, to make it easily accessible, not only to the scientific community and professional categories involved, but to everyone, together with the wish to provide Pisa with a Map of archaeological potential (the research, protection and urban planning tool capable of converging the heritage protection needs of the remains of the past with the development requirements of the future) led to the development of the MAPPA project – Methodologies applied to archaeological potential predictivity - funded by Regione Toscana in 2010. The two-year project started on 1 July 2011 and will end on 30 June 2013. The first year of research was dedicated to achieving the first objective, that is, to retrieving the results of archaeological investigations from the archives of Superintendencies and University and from the pages of scientific publications, and to making them easily accessible; these results have often never been published or have often been published incompletely and very slowly. For this reason, a webGIS (“MappaGIS” that may freely accessed at http://mappaproject.arch.unipi.it/?page_id=452) was created and will be followed by a MOD (Mappa Open Data archaeological archive), the first Italian archive of open archaeological data, in line with European directives regarding access to Public Administration data and recently implemented by the Italian government also (the beta version of the archive can be viewed at http://mappaproject.arch.unipi.it/?page_id=454). Details are given in this first volume about the operational decisions that led to the creation of the webGIS: the software used, the system architecture, the organisation of information and its structuring into various information layers. But not only. The creation of the webGIS also gave us the opportunity to focus on a series of considerations alongside the work carried out by the MAPPA Laboratory researchers. We took the decision to publish these considerations with a view to promoting debate within the scientific community and, more in general, within the professional categories involved (e.g. public administrators, university researchers, archaeology professionals). This allowed us to overcome the critical aspects that emerged, such as the need to update the archaeological excavation documentation and data archiving systems in order to adjust them to the new standards provided by IT development; most of all, the need for greater and more rapid spreading of information, without which research cannot truly progress. Indeed, it is by comparing and connecting new data in every possible and, at times, unexpected way that research can truly thrive