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Author

Raúl Monroy

Other affiliations: University of Edinburgh
Bio: Raúl Monroy is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Cryptographic protocol & Intrusion detection system. The author has an hindex of 17, co-authored 102 publications receiving 941 citations. Previous affiliations of Raúl Monroy include University of Edinburgh.


Papers
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Journal ArticleDOI
TL;DR: A utility function that measures the quality of proposed sensing locations, gives a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map is developed.

103 citations

Journal ArticleDOI
TL;DR: This paper proposes to use one-class classification to enhance Twitter bot detection, as this allows detecting novel bot accounts, and requires only from examples of legitimate accounts, without requiring previous information about them.

63 citations

Journal ArticleDOI
TL;DR: From the experimental results, it can be concluded that the proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems.
Abstract: Contrast pattern-based classifiers are an important family of both understandable and accurate classifiers. Nevertheless, these classifiers do not achieve good performance on class imbalance problems. In this paper, we introduce a new contrast pattern-based classifier for class imbalance problems. Our proposal for solving the class imbalance problem combines the support of the patterns with the class imbalance level at the classification stage of the classifier. From our experimental results, using highly imbalanced databases, we can conclude that our proposed classifier significantly outperforms the current contrast pattern-based classifiers designed for class imbalance problems. Additionally, we show that our classifier significantly outperforms other state-of-the-art classifiers not directly based on contrast patterns, which are also designed to deal with class imbalance problems.

60 citations

Journal ArticleDOI
TL;DR: A pattern-based classification mechanism is used to social bot detection, specifically for Twitter, and a new feature model is introduced, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis.
Abstract: Detecting non-human activity in social networks has become an area of great interest for both industry and academia. In this context, obtaining a high detection accuracy is not the only desired quality; experts in the application domain would also like having an understandable model, with which one may explain a decision. An explanatory decision model may help experts to consider, for example, taking legal action against an account that has displayed offensive behavior, or forewarning an account holder about suspicious activity. In this paper, we shall use a pattern-based classification mechanism to social bot detection, specifically for Twitter. Furthermore, we shall introduce a new feature model for social bot detection, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis. From our experimental results, we shall see that our mechanism outperforms other, state-of-the-art classifiers, not based on patterns; and that our feature model yields better classification results than others reported on in the literature.

53 citations

Journal ArticleDOI
TL;DR: This paper introduces and applies a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve, and finds that all the evaluated minutian descriptors obtained identification rates lower than 10% for Rank−1 and 24% forRank−100 comparing the minutiae in the database NIST SD27.
Abstract: Latent fingerprint identification is attracting increasing interest because of its important role in law enforcement. Although the use of various fingerprint features might be required for successful latent fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform other methods. However, as many fingerprint feature representations exist, we sought to determine if the selection of feature representation has an impact on the performance of automated fingerprint identification systems. In this paper, we review the most prominent fingerprint feature representations reported in the literature, identify trends in fingerprint feature representation, and observe that representations designed for verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of the most popular fingerprint feature representations over a common latent fingerprint database. Therefore, we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than 10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating the need of new minutia descriptors for latent fingerprint identification.

51 citations


Cited by
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01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

Journal ArticleDOI
23 Sep 1974-JAMA
TL;DR: A great strength of the subject of pathology is that it bonds strongly with many other medical sciences and specialties and thus occupies the top spot in the field.
Abstract: Pathologic Basis of Diseaseby Stanley L. Robbins is really the fourth edition of hisPathology. Appropriate updating and addition enhance the otherwise identical format, sequence, writing, and illustrations. So many medical students have benefited from this source that it may be the best known general book in the field. I recommend it even more now. Like his former texts, this will be enjoyed for its readability. He clearly lays out a great deal of information. When he includes minutiae, the reasons are clear and one feels that all the material is pertinent. Robbins keeps the whole field in perspective—that is, he does not dwell so long or so heavily on pathologic anatomy or pathogenesis as to tempt the reader to overlook clinical presentation or prognosis. A great strength of the subject of pathology is that it bonds strongly with many other medical sciences and specialties and thus occupies the

1,230 citations

Journal ArticleDOI
TL;DR: A survey of the state-of-the-art in Intrusion Detection Systems (IDSs) that are proposed for WSNs is presented, followed by the analysis and comparison of each scheme along with their advantages and disadvantages.
Abstract: Wireless Sensor Networking is one of the most promising technologies that have applications ranging from health care to tactical military. Although Wireless Sensor Networks (WSNs) have appealing features (e.g., low installation cost, unattended network operation), due to the lack of a physical line of defense (i.e., there are no gateways or switches to monitor the information flow), the security of such networks is a big concern, especially for the applications where confidentiality has prime importance. Therefore, in order to operate WSNs in a secure way, any kind of intrusions should be detected before attackers can harm the network (i.e., sensor nodes) and/or information destination (i.e., data sink or base station). In this article, a survey of the state-of-the-art in Intrusion Detection Systems (IDSs) that are proposed for WSNs is presented. Firstly, detailed information about IDSs is provided. Secondly, a brief survey of IDSs proposed for Mobile Ad-Hoc Networks (MANETs) is presented and applicability of those systems to WSNs are discussed. Thirdly, IDSs proposed for WSNs are presented. This is followed by the analysis and comparison of each scheme along with their advantages and disadvantages. Finally, guidelines on IDSs that are potentially applicable to WSNs are provided. Our survey is concluded by highlighting open research issues in the field.

743 citations