1,720,957 research outputs found
Harnessing Artificial Intelligence - Rule-Based AI (Lecture #4) [video]
No full textHarnessing Artificial Intelligence - Rule-Based AI (Lecture #4), Oct. 9. 2019); By Dr. Vinnie Monaco, Assistant Professor, NPS Department of Computer Scienc
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
ERINYES: A CONTINUOUS AUTHENTICATION PROTOCOL
Full text linkThe need for user authentication in the digital domain is paramount as the number of digital interactions that involve sensitive data continues to increase. Advances in the fields of machine learning (ML) and biometric encryption have enabled the development of technologies that can provide fully remote continuous user authentication services. This thesis introduces the Erinyes protocol. The protocol leverages state of the art ML models, biometric encryption of asymmetric cryptographic keys, and a trusted third-party client-server architecture to continuously authenticate users through their behavioral biometrics. The goals in developing the protocol were to identify if biometric encryption using keystroke timing and mouse cursor movement sequences were feasible and to measure the performance of a continuous authentication system that utilizes biometric encryption. Our research found that with a combined keystroke and mouse cursor movement dataset, the biometric encryption system can perform with a 0.93% False Acceptance Rate (FAR), 0.00% False Reject Rate (FRR), and 99.07% accuracy. Using a similar dataset, the overall integrated system averaged 0% FAR, 2% FRR and 98% accuracy across multiple users. These metrics demonstrate that the Erinyes protocol can achieve continuous user authentication with minimal user intrusion.Approved for public release. Distribution is unlimited.Lieutenant, United States NavyLieutenant, United States Nav
ACTIVE BAYESIAN DEEP LEARNING WITH AN ACOUSTIC VECTOR SENSOR
Full text linkTraditional passive monitoring of the ocean’s acoustic signals is conducted with an omnidirectional hydrophone, which only measures acoustic pressure. Vector sensors, unlike hydrophones, respond to both the acoustic pressure and the vector motion of water, providing additional information. This thesis focuses on utilizing vector sensor data as input to a neural network and studies the advantage of utilizing all four channels over single-channel data from the acoustic pressure sensor. A Bayesian deep learning approach is used to build multi-class classification models that provide estimates of uncertainty. The best model had an F1 score of .798 using single-channel data and .81 when using four-channel data from the vector sensor. However, the addition of information from the four-channel signal significantly reduces predictive uncertainty, demonstrating the advantage of utilizing all four channels for passive sonar classification. Next, active learning is examined, an algorithm that typically depends on uncertainty estimates to select the best training data. This is likely the first study on active learning with Bayesian deep learning models in passive sonar classification. With active learning using 23% of the training data, we trained within two percent of the F1 score compared to the entire training data. Additionally, the active learning experiments demonstrated that uncertainty-based acquisition functions increased performance using four channels over single-channel data.Distribution Statement A. Approved for public release: Distribution is unlimited.Outstanding ThesisLieutenant, United States Nav
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
ENHANCED MULTI-LABEL CLASSIFICATION OF HETEROGENEOUS UNDERWATER SOUNDSCAPES BY CONVOLUTIONAL NEURAL NETWORKS USING BAYESIAN DEEP LEARNING
Full text linkThe classification of underwater soundscapes is a challenging task for humans as well as machine learning
systems. This is largely due to the heterogenous nature of these soundscapes, especially in coastal zones close to
human settlements, where multiple ships and other man-made and natural sound sources are often present
simultaneously. This thesis proposes a Bayesian deep learning approach that can accurately classify multiple ships
simultaneously present in the vicinity of a sensor (multi-label classification) while also providing an uncertainty
measurement for the classification. This is achieved by assuming a Bayesian formulation of standard
convolutional neural network architectures to not only assign multi-labels per inference but also to provide per
inference uncertainty. The best performing Bayesian architecture on the multi-label task achieves a weighted F1
score of 0.84, where each prediction is accompanied by a measurement of uncertainty that is used to further
enhance the understanding of model predictions. Ships, submarines, and unmanned underwater vehicles can use
this classification system to aid in the identification, tracking, and/or targeting of contacts to help maintain safety
of navigation, to aid in the real-time interdiction of illicit activities (such as drug or human smuggling and covert
vessel transits), and to provide port security monitoring while uncertainty filters can help sonar operators prioritize
contacts for further analysis.Approved for public release; distribution is unlimited.Lieutenant Commander, United States Nav
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