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Search Results: 1 - 10 of 130549 matches for " Anatole V. Gershman "
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Corporate Training in Virtual Worlds
Charles Nebolsky,Nicholas K. Yee,Valery A. Petrushin,Anatole V. Gershman
Journal of Systemics, Cybernetics and Informatics , 2004,
Abstract: This paper presents virtual training worlds that are relatively low-cost distributed collaborative learning environments suitable for corporate training. A virtual training world allows a facilitator, experts and trainees communicating and acting in the virtual environment for practicing skills during collaborative problem solving. Using these environments is beneficial to both trainees and corporations. Two system prototypes – the sales training and the leadership training virtual worlds – are described. The leadership training course design is discussed in details.
Kilin-Klyosov TMRCA Calculator for Time Spans up to Millions of Years  [PDF]
Anatole A. Klyosov, Vladimir V. Kilin
Advances in Anthropology (AA) , 2016, DOI: 10.4236/aa.2016.63007
Abstract: A TMRCA (Time to the Most Recent Common Ancestor) calculator has been developed, with a capacity to handle up to 10,000 haplotypes simultaneously, for haplotypes being in any format within the 111 markers in the FTDNA (Family Tree DNA, a leading company in systematics of the haplotypes) nomenclature, for haplotypes being in any combination with respect to number of their markers, and for the TMRCA values from a few hundred years to millions of years. The calculator shows the TMRCA data calculated separately and simultaneously in the 6-, 12-, 25-, 37, 67, and 111-marker formats by the linear method, and for haplotypes of any format, such as 7-, 8-, 9-, 10-, 17-, 19-, 23- and any other format by the quadratic method. The calculator also shows a number of mutations (in the whole given dataset of haplotypes), so the TMRCA values can be verified manually, if desired so. The calculator automatically makes corrections for back mutations (in the linear method; there is no need for corrections in the quadratic method), and considers multi-marker mutations and zero alleles, counting them correctly as one mutation. The calculator can be navigated to exclude markers which show an excessive dispersion, which likely is an indication of “admixtures”, which do not belong to the given set of haplotypes. The paper provides a number of examples of TMRCA calculations for datasets of different haplogroups, and shows that the mutation rate constants are the same in different haplogroups. The papers provides a comparison of mutation rate tables by Chandler (2006), Ballantyne et al. (2010), Heinila (2012) and an anonymous investigator (2014) with the mutation rate constants determined and examined in this study. It is shown that the above authors noticeably and significantly overestimated their mutation rates, which often lead to unrealistic TMRCAs.
Co-Multistage of Multiple Classifiers for Imbalanced Multiclass Learning
Luis Marujo,Anatole Gershman,Jaime Carbonell,David Martins de Matos,Jo?o P. Neto
Computer Science , 2013,
Abstract: In this work, we propose two stochastic architectural models (CMC and CMC-M) with two layers of classifiers applicable to datasets with one and multiple skewed classes. This distinction becomes important when the datasets have a large number of classes. Therefore, we present a novel solution to imbalanced multiclass learning with several skewed majority classes, which improves minority classes identification. This fact is particularly important for text classification tasks, such as event detection. Our models combined with pre-processing sampling techniques improved the classification results on six well-known datasets. Finally, we have also introduced a new metric SG-Mean to overcome the multiplication by zero limitation of G-Mean.
Ensemble Detection of Single & Multiple Events at Sentence-Level
Luís Marujo,Anatole Gershman,Jaime Carbonell,Jo?o P. Neto,David Martins de Matos
Computer Science , 2014,
Abstract: Event classification at sentence level is an important Information Extraction task with applications in several NLP, IR, and personalization systems. Multi-label binary relevance (BR) are the state-of-art methods. In this work, we explored new multi-label methods known for capturing relations between event types. These new methods, such as the ensemble Chain of Classifiers, improve the F1 on average across the 6 labels by 2.8% over the Binary Relevance. The low occurrence of multi-label sentences motivated the reduction of the hard imbalanced multi-label classification problem with low number of occurrences of multiple labels per instance to an more tractable imbalanced multiclass problem with better results (+ 4.6%). We report the results of adding new features, such as sentiment strength, rhetorical signals, domain-id (source-id and date), and key-phrases in both single-label and multi-label event classification scenarios.
Key Phrase Extraction of Lightly Filtered Broadcast News
Luis Marujo,Ricardo Ribeiro,David Martins de Matos,Jo?o P. Neto,Anatole Gershman,Jaime Carbonell
Computer Science , 2013,
Abstract: This paper explores the impact of light filtering on automatic key phrase extraction (AKE) applied to Broadcast News (BN). Key phrases are words and expressions that best characterize the content of a document. Key phrases are often used to index the document or as features in further processing. This makes improvements in AKE accuracy particularly important. We hypothesized that filtering out marginally relevant sentences from a document would improve AKE accuracy. Our experiments confirmed this hypothesis. Elimination of as little as 10% of the document sentences lead to a 2% improvement in AKE precision and recall. AKE is built over MAUI toolkit that follows a supervised learning approach. We trained and tested our AKE method on a gold standard made of 8 BN programs containing 110 manually annotated news stories. The experiments were conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio news/programs, running daily, and monitoring 12 TV and 4 radio channels.
Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
Luis Marujo,Anatole Gershman,Jaime Carbonell,Robert Frederking,Jo?o P. Neto
Computer Science , 2013,
Abstract: Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a "Gold Standard" - a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores.
Recognition of Named-Event Passages in News Articles
Luis Marujo,Wang Ling,Anatole Gershman,Jaime Carbonell,Jo?o P. Neto,David Matos
Computer Science , 2013,
Abstract: We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes. We propose a method for finding specific passages in news articles that contain information about such events and report our preliminary evaluation results. Collecting "Gold Standard" data presents many problems, both practical and conceptual. We present a method for obtaining such data using the Amazon Mechanical Turk service.
Hourly Traffic Prediction of News Stories
Luis Marujo,Miguel Bugalho,Jo?o Paulo da Silva Neto,Anatole Gershman,Jaime Carbonell
Computer Science , 2013,
Abstract: The process of predicting news stories popularity from several news sources has become a challenge of great importance for both news producers and readers. In this paper, we investigate methods for automatically predicting the number of clicks on a news story during one hour. Our approach is a combination of additive regression and bagging applied over a M5P regression tree using a logarithmic scale (log10). The features included are social-based (social network metadata from Facebook), content-based (automatically extracted keyphrases, and stylometric statistics from news titles), and time-based. In 1st Sapo Data Challenge we obtained 11.99% as mean relative error value which put us in the 4th place out of 26 participants.
Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach
Luís Marujo,Ricardo Ribeiro,David Martins de Matos,Jo?o P. Neto,Anatole Gershman,Jaime Carbonell
Computer Science , 2015,
Abstract: The increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-document summarization methods to multi-document summarization. The proposed methods are based on the hierarchical combination of single-document summaries, and achieves state of the art results.
Algorithmic Simulation and Mathematical Modeling in Studying the Kinetics of Iron (II)-Ascorbate-Dependent Lipid Peroxidation
Anatole D. Ruslanov,Anton V. Bashylau
Lecture Notes in Engineering and Computer Science , 2009,
Abstract:
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