Discovering semantics from visualizations of film takes

In this paper, we study the application of a scene structure visualizing technique called Double-Ring Take-Transition-Diagram (DR-TTD). This technique presents takes and their transitions during a film scene via nodes and edges of a \u27graph\u27 consisting of two rings as its backbone. We describe how certain filmic elements such as montage, centre/cutaway, dialogue, temporal flow, zone change, dramatic progression, shot association, scene introduction, scene resolution, master shot and editing orchestration can be identified from a scene through the signature arrangements of nodes and edges in the DR-TTD

Computational Abstraction of Films for Quantitave Analysis of Cinematography

Currently, film viewers’ options for getting objective information about films before watching them, are limited. Comparisons are even harder to find and often require extensive film knowledge both by the author and the reader. Such comparisons are inherently subjective, therefore they limit the possibilities for scalable and effective statistical analyses. Apart from trailers, information about films cannot reach viewers audibly or visibly, which seems absurd considering the very nature of film. The thesis examines repeatable quantification methods for computationally abstracting films in order to extract informative data for visualizations and further statistical analy- ses. Theoretical background empowered by multidisciplinary approach and design processes are described. Visualizations of analyses are provided and evaluated for their accuracy and efficiency. Throughout the thesis foundations for the future automated quantification player/plugin, are described aiming to facilitate further developments. Theoretical structures of the website which may act as a gateway that collects and provides data for statistical cinematic research are also discussed

Multimodaalinen rekontekstualisaatio European University Associationin trendiraporteissa

This thesis studies multimodal recontextualization in European University Assocation Trend Reports. Recontextualization refers to the changes that are required for parts of discourse to move between contexts. Regular reports are published by the European University Association on the topic of the Bologna Process and its implementation across Europe. Two research questions guide the study: 1. What evidence of recontextualization can be found between textual elements and diagrams in higher education policy documents? 2. To what extent can multimodal analysis improve our understanding of recontextualization? The primary multimodal framework rests on Bateman’s (2011) abstract model for semiotic modes. These modes are realised through application of semiotic resources on a material substrate while being interpreted contextually based on an understanding of the relevant discourse semantics. Of particular note in this study are the semiotic modes of text-flow, page-flow, layout and the diagrammatic mode. The typology used for categorising and operationalizing diagrams is based on that of Engelhardt and Richards (2018). The most common types of diagrams in the dataset are various bar charts, table charts and choropleth maps. The total number of diagrams in the corpus is 212, of which these three categories form 88%. The way information is visually encoded is considered in terms of the principles of arranging, varying and linking performed by the diagrams. The recontextualization analysis proceeds by considering the transformations that take place when information within text-based contexts is transferred or translated over to a diagrammatic visualization. The methodology for this follows Van Leeuwen (2008), and primarily concerns the transformations of substitution, deletion and legitimation. The analysis shows that the diagrams in the dataset are used for three primary purposes: The summarization of information presented elsewhere, reinstatement of in-text claims for additional legitimation, and the presentation of temporal information in order to allow for comparison between trend reports or participating countries. The recontextualizing transformations of substitution and deletion are present in nearly every diagram in the dataset, while legitimation is used more conservatively to provide support for claims made in the text. These recontextualizations take advantage of tresources offered by the diagrammatic mode that allows them to present information by using otherwise unavailable dimensions such as temporal or the spatial. The results support the notion that analysis of recontextualization can be successfully combined with a multimodal approach, and this has been found to potentially support both approaches. This is in line with the interest multimodal researchers have shown in the demarcation of and transition of meaning between semiotic modes

Moscow-Petushki of Venedict Erofeev and Pulp Fiction of Quentin Tarantino in the Context of Postmodern Thinking

The aim of the article is the attempt of reading two texts of culture, i.e. Moscow-Petushki of Venedict Erofeev and Pulp Fiction of Quentin Tarantino using the comparative approach that goes beyond the range of factual relationships, historical influences or similarities functioning in the plot. The purpose of the publication is to see both works, representing totally different semiotic systems (cinema and literature), in their mutual intertextual relationships for which the style of postmodernism constitutes the constant reference point. Most literary studies associate each of the chosen texts (but never both of them together) with postmodern thinking, therefore in my research I concentrate on the categories which are considered crucial for understanding postmodern thematic quests and the change in thinking which took place at the end of the 20th century. I try to find out similarities existing in the structures of two texts, aesthetic ideas and tools used to express them. As a result, the interpretation focuses in particular on representations of the body shown in the American movie and the Russian “poem in prose”, as well as on the problems of beauty, space and addictions, which constitute the fundamental subjects in these texts

Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

Sentiment Analysis and Opinion Mining within Social Networks using Konstanz Information Miner

Evaluations, opinions, and sentiments have become very obvious due to rapid emerging interest in ecommerce which is also a significant source of expression of opinions and analysis of sentiment. In this study, a general introduction on sentiment analysis, steps of sentiment analysis, sentiments analysis applications, sentiment analysis research challenges, techniques used for sentiment analysis, etc., were discussed in detail. With these details given, it is hoped that researchers will engage in opinion mining and sentiment analysis research to attain more successes correlated to these issues. The research is based on data input from web services and social networks, including an application that performs such actions. The main aspects of this study are to statistically test and evaluate the major social network websites: In this case Twitter, because it is has rich data source and easy within social networks tools. In this study, firstly a good understanding of sentiment analysis and opinion mining research based on recent trends in the field is provided. Secondly, various aspects of sentiment analysis are explained. Thirdly, various steps of sentiment analysis are introduced. Fourthly, various sentiment analysis, research challenges are discussed. Finally, various techniques used for sentiment analysis are explained and Konstanz Information Miner (KNIME) that can be used as sentiment analysis tool is introduced. For future work, recent machine learning techniques including big data platforms may be proposed for efficient solutions for opinion mining and sentiment analysi