We describe the use of meeting metadata, acquired using a computerized meeting organization and note-taking system, to improve automatic transcription of meetings. By applying a two-step language model adaptation process based on notes and agenda items, we were able to reduce perplexity by 9% and word error rate by 4% relative on a set of ten meetings recorded in-house. This approach can be used to leverage other types of metadata.

AbstractÐMany recent database applications must deal with similarity queries. For such applications, it is important to measure the similarity between two objects using the distance between them. Focusing on this problem, this paper proposes the Slim-tree, a new dynamic tree for organizing metric data sets in pages of fixed size. The Slim-tree uses the triangle inequality to prune distance calculations needed to answer similarity queries over objects in metric spaces. The proposed insertion algorithm uses new policies to select the nodes where incoming objects are stored. When a node overflows, the Slim-tree uses a Minimal Spanning Tree to help with the split. The new insertion algorithm leads to a tree with high storage utilization and improved query performance. The Slim-tree is the first metric access method to tackle the problem of overlap between nodes in metric spaces and to propose a technique to minimize it. The proposed ªfat-factorº is a way to quantify whether a given tree can be improved and also to compare two trees. We show how to use the fat-factor to achieve accurate estimates of the search performance and also how to improve the performance of a metric tree through the proposed ªSlim-downº algorithm. This paper also presents a new tool in the arsenal of resources of Slim-tree aimed at visualizing it. Visualization is a powerful tool for interactive data mining and for the visual tracking of the behavior of a tree under updates. Finally, we present a formula to estimate the number of disk accesses in range queries. Results from experiments with real and synthetic data sets show that the new algorithms of the Slim-tree lead to performance improvements. These results show that the Slim-tree outperforms the M-tree up to 200 percent for range queries. For insertion and split, the Minimal-Spanning-Tree-based algorithm achieves up to 40 times faster insertions. We observed improvements up to 40 percent in range queries after applying the Slim-down algorithm.

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Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.

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