The red herring argument in everyday life, and in the film, "American Beauty"

The Red Herring Argument is a logical device found in many areas of life. This thesis will narrow the focus, however, and examine how it is employed in the legal technique called “Chewbacca defense,” and in the film, “American Beauty.”

Classificazione di oggetti in immagini attraverso il modello Bag of Visual Words

Generic object recognition is an important function of the human visual system and everybody finds it highly useful in their everyday life. For an artificial vision system it is a really hard, complex and challenging task because instances of the same object category can generate very different images, depending of different variables such as illumination conditions, the pose of an object, the viewpoint of the camera, partial occlusions, and unrelated background clutter. The purpose of this thesis is to develop a system that is able to classify objects in 2D images based on the context, and identify to which category the object belongs to. Given an image, the system can classify it and decide the correct categorie of the object. Furthermore the objective of this thesis is also to test the performance and the precision of different supervised Machine Learning algorithms in this specific task of object image categorization. Through different experiments the implemented application reveals good categorization performances despite the difficulty of the problem. However this project is open to future improvement; it is possible to implement new algorithms that has not been invented yet or using other techniques to extract features to make the system more reliable. This application can be installed inside an embedded system and after trained (performed outside the system), so it can become able to classify objects in a real-time. The information given from a 3D stereocamera, developed inside the department of Computer Engineering of the University of Bologna, can be used to improve the accuracy of the classification task. The idea is to segment a single object in a scene using the depth given from a stereocamera and in this way make the classification more accurate.

Proposta di sottotitolaggio del social movie italy in a day

The aim of this thesis is to analyse the main translating issues related to the subtitling of the Italian social movie Italy in a day into English: Italy in a day is a crowdsourced film, comprising a selection of video clips sent by ordinary people, showing occurrences of everyday life on a single day, October 26th, 2013. My dissertation consists of four chapters. The first provides a general overview of audiovisual translation, from the description of the characteristics of filmic products to a summary of the most important audiovisual translation modes; a theoretical framework of the discipline is also provided, through the analysis of the major contributions of Translations Studies and the multidisciplinary approach proposed by the scholar Frederic Chaume. The second chapter offers insight into the subtitling practice, examining its technical parameters, the spatial and temporal constraints, together with the advantages and pitfalls of this translation mode. The main criteria for quality assessment are also outlined, as well as the procedures carried out in the creation of subtitles within a professional environment, with a particular focus on the production of subtitles for the DVD industry. In the third chapter a definition of social movie is provided and the audiovisual material is accurately described, both in form and content. The creation of the subtitling project is here illustrated: after giving some information about the software employed, every step of the process is explained. In the final chapter the main translation challenges are highlighted. In the first part some text reduction techniques in the shift from oral to written are presented; then the culture-specific references and the linguistic variation in the film are analysed and the compensating strategies adopted to fill the linguistic and cultural gap are commented on and justified taking into account the needs and expectations of the target audience.

Lightweight Internet Traffic Classification - A Subject Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.