Automated annotation of multimedia audio data with affective labels for information management

The emergence of digital multimedia systems is creating many new opportunities for rapid access to huge content archives. In order to fully exploit these information sources, the content must be annotated with significant features. An important aspect of human interpretation of multimedia data, which is often overlooked, is the affective dimension. Such information is a potentially useful component for content-based classification and retrieval. Much of the affective information of multimedia content is contained within the audio data stream. Emotional features can be defined in terms of arousal and valence levels. In this study low-level audio features are extracted to calculate arousal and valence levels of multimedia audio streams. These are then mapped onto a set of keywords with predetermined emotional interpretations. Experimental results illustrate the use of this system to assign affective annotation to multimedia data

An affect-based video retrieval system with open vocabulary querying

Content-based video retrieval systems (CBVR) are creating new search and browse capabilities using metadata describing significant features of the data. An often overlooked aspect of human interpretation of multimedia data is the affective dimension. Incorporating affective information into multimedia metadata can potentially enable search using this alternative interpretation of multimedia content. Recent work has described methods to automatically assign affective labels to multimedia data using various approaches. However, the subjective and imprecise nature of affective labels makes it difficult to bridge the semantic gap between system-detected labels and user expression of information requirements in multimedia retrieval. We present a novel affect-based video retrieval system incorporating an open-vocabulary query stage based on WordNet enabling search using an unrestricted query vocabulary. The system performs automatic annotation of video data with labels of well defined affective terms. In retrieval annotated documents are ranked using the standard Okapi retrieval model based on open-vocabulary text queries. We present experimental results examining the behaviour of the system for retrieval of a collection of automatically annotated feature films of different genres. Our results indicate that affective annotation can potentially provide useful augmentation to more traditional objective content description in multimedia retrieval

Where Graph Topology Matters: The Robust Subgraph Problem

Robustness is a critical measure of the resilience of large networked systems, such as transportation and communication networks. Most prior works focus on the global robustness of a given graph at large, e.g., by measuring its overall vulnerability to external attacks or random failures. In this paper, we turn attention to local robustness and pose a novel problem in the lines of subgraph mining: given a large graph, how can we find its most robust local subgraph (RLS)? We define a robust subgraph as a subset of nodes with high communicability among them, and formulate the RLS-PROBLEM of finding a subgraph of given size with maximum robustness in the host graph. Our formulation is related to the recently proposed general framework for the densest subgraph problem, however differs from it substantially in that besides the number of edges in the subgraph, robustness also concerns with the placement of edges, i.e., the subgraph topology. We show that the RLS-PROBLEM is NP-hard and propose two heuristic algorithms based on top-down and bottom-up search strategies. Further, we present modifications of our algorithms to handle three practical variants of the RLS-PROBLEM. Experiments on synthetic and real-world graphs demonstrate that we find subgraphs with larger robustness than the densest subgraphs even at lower densities, suggesting that the existing approaches are not suitable for the new problem setting.Comment: 13 pages, 10 Figures, 3 Tables, to appear at SDM 2015 (9 pages only

Affect-based indexing and retrieval of multimedia data

Digital multimedia systems are creating many new opportunities for rapid access to content archives. In order to explore these collections using search, the content must be annotated with significant features. An important and often overlooked aspect o f human interpretation o f multimedia data is the affective dimension. The hypothesis o f this thesis is that affective labels o f content can be extracted automatically from within multimedia data streams, and that these can then be used for content-based retrieval and browsing. A novel system is presented for extracting affective features from video content and mapping it onto a set o f keywords with predetermined emotional interpretations. These labels are then used to demonstrate affect-based retrieval on a range o f feature films. Because o f the subjective nature o f the words people use to describe emotions, an approach towards an open vocabulary query system utilizing the electronic lexical database WordNet is also presented. This gives flexibility for search queries to be extended to include keywords without predetermined emotional interpretations using a word-similarity measure. The thesis presents the framework and design for the affectbased indexing and retrieval system along with experiments, analysis, and conclusions

Price formation at the Palmerston North fresh vegetable auction market : a thesis presented in partial fulfilment of the requirements for the degree of Master of Horticultural Science in Horticultural Economics and Marketing at Massey University

The aim was to analyse the short term price fluctuation of fresh vegetables at the Palmerston North auction market. A brief review of the theoretical and methodological aspects in relation to this topic is outlined. An econometric recursive model was developed in the "inductive phase" to represent the past behaviour of the industry. The simulation model was developed in the "deductive phase" for testing the sensitivity of the model and policy assessment. The results indicated that: 1) The wholesale demand for cabbages and cauliflowers is relatively inelastic (-0.5034 and -0.8142 respectively) while that of lettuces (-1.434) was elastic. Carrots showed nonsignificant positive relationship between quantity purchased and price (+1.935). 2) The simulation model was relatively insensitive to changes in its parameters. It was proved that the supply of fresh vegetables was mainly governed by the seasonal factor. 3) The policy of supply rationalisation could reduce price variance and supply variance by 18% and 45% respectively, while the gross income and unweighted mean price could be increased by 8.7% and 0.3% respectively

Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social Media (ICWSM17