While the feature film has been the flagship of the Australian film revival and the short film has always played an important role in practitioner development; the role and effectiveness of the short feature is less clear. The short feature has emerged intermittently since the late 1980s and has been vulnerable to the conditions of the Australian film industry in general. Government renewed its support for the short feature in the 1990s viewing it as a development model for writers, directors and producers making the transition from short form drama to feature length production. While there is no doubt that the short feature model provides invaluable 'experience through practice' for writers, directors and producers, a lack of market demand, its continued dependence on government funding and the mixed development outcomes of those it supports suggests that the future of the short feature remains unclear. This thesis also includes School Friends, a 50-minute screenplay designed specifically to suit the parameters of short feature production. It is a redemption story about a man who attempts to get his life back on track after being released from prison.

Nowadays, when a keyword is provided, a search engine can return a large number of web pages, which makes it difficult for people to find the right information. Web page classification is a technology that can help us to make a relevant and quick selection of information that we are looking for. Moreover, web page classification is important for companies that provide marketing and analytics platforms, because it can help them to build a healthy mix of listings on search engines and large directories. This will provide more insight into the distribution of the types of web pages their local business listings are found on, and finally will help marketers to make better-informed decisions about marketing campaigns and strategies. In this thesis we perform a literature review that introduces web page classification, feature selection and feature extraction. The literature review also includes a comparison of three commonly used classification algorithms and a description of metrics for performance evaluation. The findings in the literature enable us to extend existing classification techniques, methods and algorithms to address a new web page classification problem faced by our industrial partner SweetIQ (a company that provides location-based marketing services and an analytics platform). We develop a classification method based on SweetIQ's data and business needs. Our method includes typical feature selection and feature extraction methods, but the features we use in this thesis are largely different from traditional ones used in the literature. We test selected features and find that the text extracted from the title and snippet of a web page can help a classifier to achieve good performance. Our classification method does not require the full content of a web page. Thus, it is fast and saves a lot of space.

Successful characterization of vortex structures in unsteady flow fields depend crucially upon an adequate choice of a reference frame. Vortex detection approaches in flow visualization aspire to be objective, i.e., invariant under time-dependent rotations and translations of the input reference frame. However, objectivity by itself does not guarantee good results as different specific approaches lead to different results. Moreover, recent more generic approaches to objectivity still require parameters to be specified beforehand which can significantly influence the resulting vortex detection, depending on the complexity and characteristics of the input flow field. With the assumption that human intervention is unavoidable to some extent, we tackle the problem of specifying parameters for vortex detection from a human-centered perspective. In this work, we present a novel system that enables users to interactively explore the parameter space of a flexible objective method, while jointly computing and visualizing the resulting vortex structures. We build on the computation of an objective field of reference frames and enable users to interactively change computation parameters as well as choose different observers, compute vortex structures on-the-fly during exploration, and visualize the flow field from the viewpoint of the chosen observers. Overall, we illustrate that such an interactive approach can be of significant value to the user for analyzing vortex structures visually and understanding why a computational method has detected a specific structure as a vortex.

Using topology for feature analysis in flow fields faces several problems. First of all, not all features can be detected using topology based methods. Second, while in flow feature analysis the user is interested in a quantification of feature parameters like position, size, shape, radial velocity and other parameters of feature models, many of these parameters can not be determined using topology based methods alone. Additionally, in some applications it is advantageous to regard the vector field as a superposition of several, possibly simple, features. As topology based methods are quite sensitive to superposition effects, their precision and usability is limited in these cases. In this paper, topology based analysis and visualization of flow fields is estimated and compared to other feature based approaches demonstrating these problems.

No description available.

Variability provides the ability to adapt and customize a software system's artifacts for a particular context or circumstance. Variability enables code reuse, but its mechanisms are often tangled within a software artifact or scattered over multiple artifacts. This makes the system harder to maintain for developers, and harder to understand for users that configure the software. Feature models provide a centralized source for describing the variability in a software system. A feature model consists of a hierarchy of features—the common and variable system characteristics—with constraints between features. Constructing a feature model, however, is a arduous and time-consuming manual process. We developed two techniques for feature model synthesis. The first, Feature-Graph-Extraction, is an automated algorithm for extracting a feature graph from a propositional formula in either conjunctive normal form (CNF), or disjunctive normal form (DNF). A feature graph describes all feature diagrams that are complete with respect to the input. We evaluated our algorithms against related synthesis algorithms and found that our CNF variant was significantly faster than the previous comparable technique, and the DNF algorithm performed similarly to a comparable, but newer technique, with the exception of several models where our algorithm was faster. The second, Feature-Tree-Synthesis, is a semi-automated technique for building a feature model given a feature graph. This technique uses both logical constraints and text to address the most challenging part of feature model synthesis—constructing the feature hierarchy—by ranking potential parents of a feature with a textual similarity heuristic. We found that the procedure effectively reduced a modeler's choices from thousands, to five or less when synthesizing the Linux and eCos variability models. Our third contribution is the analysis of Kconfig—a language similar to feature modeling used to specify the variability model of the Linux kernel. While large feature models are reportedly used in industry, these models have not been available to the research community for benchmarking feature model analysis and synthesis techniques. We compare Kconfig to feature modeling, reverse engineer formal semantics, and translate 12 open-source Kconfig models—including the Linux model with over 6000 features—to propositional logic.

This thesis compares the state of the art methods and proposes solutions for human activity classification from video data. Human activity classification is finding the meaning of human activities, which are captured by the video. Classification of human activity is needed in order to improve surveillance video analysis and summarization, video data mining and robot intelligence. This thesis focuses on the classification of low level human activities which are used as an important information source to determine high level activities. In this study, the feature relation histogram based activity description proposed by Ryoo et al. (2009) is implemented and extended. The feature histogram is widely used in feature based approaches / however, the feature relation histogram has the ability to represent the locational information of the features. Our extension defines a new set of relations between the features, which makes the method more effective for action description. Classifications are performed and results are compared using feature histogram, Ryoo&rsquo / s feature relation histogram and our feature relation histogram using the same datasets and the feature type. Our experiments show that feature relation histogram performs slightly better than the feature histogram, our feature relation histogram is even better than both of the two. Although the difference is not clearly observable in the datasets containing periodic actions, a 12% improvement is observed for the non-periodic action datasets. Our work shows that the spatio-temporal relation represented by our new set of relations is a better way to represent the activity for classification.

Due to the ever-increasing volume of textual documents, text mining is a rapidly growing application of knowledge discovery in databases. Past text mining techniques predominately concentrated on discovering intra-document patterns from textual documents, such as text categorization, document clustering, query expansion, and event tracking. Mining inter-document patterns from textual documents has been largely ignored in the literature. This research focuses on discovering inter-document patterns, called evolution patterns, from document-sequences and proposed the evolution pattern discovery (EPD) technique for mining evolution patterns from a set of ordered sequences of documents. The discovery of evolution patterns can be applied in such domains as environmental scanning and knowledge management, and can be used to facilitate existing document management and retrieval techniques (e.g., event tracking).

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the feature vector of a document set are grouped into clusters based on similarity test. Words that are similar to each other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We then have one extracted feature for each cluster. The extracted feature corresponding to a cluster is a weighted combination of the words contained in the cluster. By this algorithm, the derived membership functions match closely with and describe properly the real distribution of the training data. Besides, the user need not specify the number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. 20 Newsgroups data set and Cade 12 web directory are introduced to be our experimental data. We adopt the support vector machine to classify the documents. Experimental results show that our method can run faster and obtain better extracted features than other methods.

Požymis – tai savitas, charakteringas sistemos atributas. FODA (angl. Feature Orented Domain Analysis) požymius apibūdina kaip žinomas, savitas bei vartotojui matomas sistemos charakteristikas, tuo tarpu funkcijos, objektai ir aspektai yra naudojami apibūdinti vidines sistemos detales. Požymių modeliavimas susitelkia ties labai matomų išorinių produkto charakteristikų apibūdinimu, kalbant apie produkto bendrumą bei variantiškumą, o ne apie detalų sistemos apibūdinimą. Požymių modeliavimo rezultatas yra požymių diagramos. Tai yra grafinė kalba naudojama atvaizduoti bei modeliuoti sistemos arba komponento variantiškumus aukštesniame abstrakcijos lygyje, daţniausiai pradiniuose projektavimo lygiuose, tokiuose kaip reikalavimų specifikavime kuriant programinę įrangą. Šiame darbe atliekamas programų sistemų variantiškumo modelių aprašytų požymių diagramomis tyrimas specifikavimo, sintaksės validavimo, sudėtingumo įvertinimo ir konfigūravimo aspektais. Darbe aprašomas autoriaus (bendraautorius: P. Žaliaduonis) sukurtas požymių modeliavimo įrankis leidžia vartotojui specifikuoti, modeliuoti, validuoti, įvertinti ir dokumentuoti programų sistemos produktų linijos požymių variantiškumo modelius. / Feature Modeling is a domain modeling technique used in software product line development and generative software engineering that addresses the development of reusable software. A feature model defines common and variable elements of a family of software systems or products of a product line – the domain. It can be used to derive members of the system family built from a common set of reusable assets. The concept of product line, if applied systematically, allows for the dramatic increase of software design quality, productivity, provides a capability for mass customization and leads to the „industrial‟ software design. In this work, the author describes the way of product line variability specification using feature diagrams. The presented approach deals with specification of feature model elements, syntax validation, complexity evaluation and feature diagram configuration aspects. The developed software, described in this thesis, allows the user to specify features, design, validate, evaluate and document system product line variability models.