Elementi postmoderni nell'horror americano contemporaneo. Forme testuali e culturali della mutazione 1968-1998

This research argues for an analysis of textual and cultural forms in the American horror film (1968- 1998), by defining the so-called postmodern characters. The “postmodern” term will not mean a period of the history of cinema, but a series of forms and strategies recognizable in many American films. From a bipolar re-mediation and cognitive point of view, the postmodern phenomenon is been considered as a formal and epistemological re-configuration of the cultural “modern” system. The first section of the work examines theoretical problems about the “postmodern phenomenon” by defining its cultural and formal constants in different areas (epistemology, economy, mass-media): the character of convergence, fragmentation, manipulation and immersion represent the first ones, while the “excess” is the morphology of the change, by realizing the “fluctuation” of the previous consolidated system. The second section classifies the textual and cultural forms of American postmodern film, generally non-horror. The “classic narrative” structure – coherent and consequent chain of causal cues toward a conclusion – is scattered by the postmodern constant of “fragmentation”. New textual models arise, fragmenting the narrative ones into the aggregations of data without causal-temporal logics. Considering the process of “transcoding”1 and “remediation”2 between media, and the principle of “convergence” in the phenomenon, the essay aims to define these structures in postmodern film as “database forms” and “navigable space forms.” The third section applies this classification to American horror film (1968-1998). The formal constant of “excess” in the horror genre works on the paradigm of “vision”: if postmodern film shows a crisis of the “truth” in the vision, in horror movies the excess of vision becomes “hyper-vision” – that is “multiplication” of the death/blood/torture visions – and “intra-vision”, that shows the impossibility of recognizing the “real” vision from the virtual/imaginary. In this perspective, the textual and cultural forms and strategies of postmodern horror film are predominantly: the “database-accumulation” forms, where the events result from a very simple “remote cause” serving as a pretext (like in Night of the Living Dead); the “database-catalogue” forms, where the events follow one another displaying a “central” character or theme. In the first case, the catalogue syntagms are connected by “consecutive” elements, building stories linked by the actions of a single character (usually the killer), or connected by non-consecutive episodes about a general theme: examples of the first kind are built on the model of The Wizard of Gore; the second ones, on the films such as Mario Bava’s I tre volti della paura. The “navigable space” forms are defined: hyperlink a, where one universe is fluctuating between reality and dream, as in Rosemary’s Baby; hyperlink b (where two non-hierarchical universes are convergent, the first one real and the other one fictional, as in the Nightmare series); hyperlink c (where more worlds are separated but contiguous in the last sequence, as in Targets); the last form, navigable-loop, includes a textual line which suddenly stops and starts again, reflecting the pattern of a “loop” (as in Lost Highway). This essay analyses in detail the organization of “visual space” into the postmodern horror film by tracing representative patterns. It concludes by examining the “convergence”3 of technologies and cognitive structures of cinema and new media.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

Classification algorithms for Intelligent Transport Systems

Intelligent Transport Systems (ITS) consists in the application of ICT to transport to offer new and improved services to the mobility of people and freights. While using ITS, travellers produce large quantities of data that can be collected and analysed to study their behaviour and to provide information to decision makers and planners. The thesis proposes innovative deployments of classification algorithms for Intelligent Transport System with the aim to support the decisions on traffic rerouting, bus transport demand and behaviour of two wheelers vehicles. The first part of this work provides an overview and a classification of a selection of clustering algorithms that can be implemented for the analysis of ITS data. The first contribution of this thesis is an innovative use of the agglomerative hierarchical clustering algorithm to classify similar travels in terms of their origin and destination, together with the proposal for a methodology to analyse drivers’ route choice behaviour using GPS coordinates and optimal alternatives. The clusters of repetitive travels made by a sample of drivers are then analysed to compare observed route choices to the modelled alternatives. The results of the analysis show that drivers select routes that are more reliable but that are more expensive in terms of travel time. Successively, different types of users of a service that provides information on the real time arrivals of bus at stop are classified using Support Vector Machines. The results shows that the results of the classification of different types of bus transport users can be used to update or complement the census on bus transport flows. Finally, the problem of the classification of accidents made by two wheelers vehicles is presented together with possible future application of clustering methodologies aimed at identifying and classifying the different types of accidents.