1,720,955 research outputs found
Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes
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Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities. However, they fail to make use of abnormal event patterns, which are elements to comprise abnormal events. Moreover, normal patterns in testing videos may be divergent from training ones, due to the existence of abnormalities. To address these problems, in this paper, an abnormality detector is proposed to detect abnormal events based on a statistical hypothesis test. The proposed detector treats each sample as a combination of a set of event patterns. Due to the unavailability of labeled abnormalities for training, abnormal patterns are adaptively extracted from incoming unlabeled testing samples. Contributions of this paper are listed as follows: 1) we introduce the idea of a statistical hypothesis test into the framework of abnormality detection, and abnormal events are identified as ones containing abnormal event patterns while possessing high abnormality detector scores; 2) due to the complexity of video events, noise seldom follows a simple distribution. For this reason, we approximate the complex noise distribution by employing a mixture of Gaussian. This benefits the modeling of video events and improves abnormality detection accuracies; and 3) because of the existence of abnormalities, there are always some unusually occurring normal events in the testing videos, which differ from the training ones. To represent normal events precisely, an online updating strategy is proposed to cover these cases in the normal event patterns. As a result, false detections are eliminated mostly. Extensive experiments and comparisons with state-of-the-art methods verify the effectiveness of the proposed algorithm.</p
Deep representation for abnormal event detection in crowded scenes
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Abnormal event detection is extremely important, especially for video surveillance. Nowadays, many detectors have been proposed based on hand-crafted features. However, it remains challenging to effectively distinguish abnormal events from normal ones. This paper proposes a deep representation based algorithm which extracts features in an unsupervised fashion. Specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. Subsequently, long-term temporal clues are modeled with a long short-term memory (LSTM) recurrent network, in order to discover meaningful regularities of video events. The abnormal events are identified as samples which disobey these regularities. Moreover, this paper proposes a spatial anomaly detection strategy via manifold ranking, aiming at excluding false alarms. Experiments and comparisons on real world datasets show that the proposed algorithm outper-forms state of the arts for the abnormal event detection problem in crowded scenes. © 2016 ACM.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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