Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

09081 Abstracts Collection -- Similarity-based learning on structures

From 15.02. to 20.02.2009, the Dagstuhl Seminar 09081 ``Similarity-based learning on structures \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

SOMvisua: Data Clustering and Visualization Based on SOM and GHSOM

Text in web pages is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. Search in text usually finds text similarities between paragraphs in documents. This paper proposes a framework for data clustering and visualization called SOMvisua. SOMvisua is based on a graph representation of data input for Self-Organizing Map (SOM) and Growing Hierarchically Self-Organizing Map (GHSOM) algorithms. In SOMvisua, sentences from an input article are represented as graph model instead of vector space model. SOM and GHSOM clustering algorithms construct knowledge from this article

Friend Suggestion and Friend Browsing in Web 2.0 Applications

Web 2.0 and social network applications have become increasingly popular. It is important for these applications to help users in maintaining their social networks by providing functions on friend suggestion and friend browsing. However, little study in this area has been reported in the literature. This paper proposes the design of two modules for friend suggestion and friend browsing. The first module is based on Hopfield Net spreading activation, while the second module is based on hyperbolic tree and self-organizing map. The proposed evaluation plan is also presented in the paper

The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning)

The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data

Network Analysis on Incomplete Structures.

Over the past decade, networks have become an increasingly popular abstraction for problems in the physical, life, social and information sciences. Network analysis can be used to extract insights into an underlying system from the structure of its network representation. One of the challenges of applying network analysis is the fact that networks do not always have an observed and complete structure. This dissertation focuses on the problem of imputation and/or inference in the presence of incomplete network structures. I propose four novel systems, each of which, contain a module that involves the inference or imputation of an incomplete network that is necessary to complete the end task. I first propose EdgeBoost, a meta-algorithm and framework that repeatedly applies a non-deterministic link predictor to improve the efficacy of community detection algorithms on networks with missing edges. On average EdgeBoost improves performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook. The second system, Butterworth, identifies a social network user's topic(s) of interests and automatically generates a set of social feed ``rankers'' that enable the user to see topic specific sub-feeds. Butterworth uses link prediction to infer the missing semantics between members of a user's social network in order to detect topical clusters embedded in the network structure. For automatically generated topic lists, Butterworth achieves an average top-10 precision of 78%, as compared to a time-ordered baseline of 45%. Next, I propose Dobby, a system for constructing a knowledge graph of user-defined keyword tags. Leveraging a sparse set of labeled edges, Dobby trains a supervised learning algorithm to infer the hypernym relationships between keyword tags. Dobby was evaluated by constructing a knowledge graph of LinkedIn's skills dataset, achieving an average precision of 85% on a set of human labeled hypernym edges between skills. Lastly, I propose Lobbyback, a system that automatically identifies clusters of documents that exhibit text reuse and generates ``prototypes'' that represent a canonical version of text shared between the documents. Lobbyback infers a network structure in a corpus of documents and uses community detection in order to extract the document clusters.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133443/1/mattburg_1.pd