Jeffrey Ellen
Space and Naval Warfare Systems Center Pacific
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Publication
Featured researches published by Jeffrey Ellen.
acm symposium on applied computing | 2009
Marion G. Ceruti; Vincent Vinh Dinh; Nghia Tran; Hoa Van Phan; LorRaine Duffy; Tu-Anh Ton; Guy Leonard; Emily W. Medina; Omar Amezcua; Sunny Fugate; Gary J. Rogers; Robert Luna; Jeffrey Ellen
Military personnel need better ways to communicate in hostile, noisy, silence-mandated, and/or extreme environments. Typing on a keyboard is difficult and impractical while wearing comprehensive protective clothing. Wireless data gloves were researched and developed to transmit and receive ASCII code and other signals as hand gestures. Two categories of glove prototypes were constructed: gloves with and without a haptic-IO capability. All data gloves detect motion, such as gestures, using magnetic sensors. Non-haptic gloves only transmit static and dynamic gestures. Haptic gloves have vibro-mechanical devices on the fingertips for feedback about transmitted signals and for covert-signal reception. Many potential communications applications include hazardous and covert military operations, space operations, fire fighting, mining, training, underwater use, and aids for the visually and hearing impaired.
international conference on social computing | 2012
Jeffrey Ellen; Joan L. Kaina; Shibin Parameswaran
Our thesis is that members of the same group have shared tendencies and nuances in communication style and substance, particularly online. In this paper, we dicuss some potential applications of accuarate authorship affiliation technology. We also discuss related work in similar author identification efforts and the research issues that currently exist when trying to perform automated authorship affiliation. We provide quantitative results from our recent Machine Learning experimenation using Support Vector Machines as some initial validation of our theory. In this paper, we applied our work towards the task of classifying website forum posts by the affiliation of their author. We discuss in detail the stylometric features we used to perform the automated classification and split the original features into individual groups to isolate their respective contributions and/or discriminating capability. Our results show promise towards automating group representation, an important first step in studying group formation.
international conference on machine learning and applications | 2011
Jeffrey Ellen; Shibin Parameswaran
Although there have been previous studies performing authorship attribution to a specific individual, we find a shortage of efforts to group authors based on their affiliations. This paper presents our work on classification of website forum posts by the authors group affiliation. Specifically, we seek to classify translated website forum posts by the (inferred) political affiliation of the author. The two datasets that we attempt to classify consist of real-world data discussing current issues -- Israeli/Palestinian dialogue (Bitter Lemons corpus) and translated Extremist/Moderate forum entries (from internet websites). To achieve our goal of reliable authorship affiliation, we extract term frequency-based features (that are conventional in document classification) along with less commonly used linguistic style-based features. The resulting set of stylometric features are then utilized in two widely used supervised classification algorithms, namely k-Nearest Neighbor algorithm and Support Vector Machines. Specifically, we used k-NN with cosine distance and Support Vector Machines with two different kernel functions. In addition to the popular RBF kernels, we also evaluate the applicability and performance of the recently introduced arc-cosine kernels for group affiliation. The results of our experiments show strong performance across a range of pertinent metrics.
international conference on machine learning and applications | 2016
Casey A. Graff; Jeffrey Ellen
This paper describes three metrics used to asses the filter diversity learned by convolutional neural networks during supervised classification. As our testbed we use four different data sets, including two subsets of ImageNet and two planktonic data sets collected by scientific instruments. We investigate the correlation between our devised metrics and accuracy, using normalization and regularization to alter filter diversity. We propose that these metrics could be used to improve training CNNs. Three potential applications are determining the best preprocessing method for non-standard data sets, diagnosing training efficacy, and predicting performance in cases where validation data is expensive or impossible to collect.
granular computing | 2010
Sunny Fugate; Jeffrey Ellen; Stuart Harvey Rubin
A microformat is a set of design principles for including semantic information within standard X/HTML markup. Individual microformat entities are distributed yet share a common semantics. Each microformat is a granule of structured information containing a set of attributes. These information granules can be produced, distributed, aggregated, and consumed without reliance on centralized services. We describe the impact on the gathering, distribution, and analysis of concepts within our visual communication research. In this paper we present solutions for managing lexical consistency when microformat-structured information granules are distributed and maintained independently and asynchronously. Lexical groups and hierarchies leverage the resulting inconsistencies, utilizing term aggregation across visual and linguistic features to dynamically compose lexicons and to perform lexical analysis. We think of this as creating semantic and orthographic ’projections’ of the lexicon into different feature spaces. We show how to use this approach to construct situated lexicons which derive from shared context and social communities.
international conference on machine learning and applications | 2009
Kevin Dela Rosa; Jeffrey Ellen
international conference on human computer interaction | 2009
Nghia Tran; Hoa Van Phan; Vince V. Dinh; Jeffrey Ellen; Bryan Berg; Jason Lum; Eldridge Alcantara; Mike Bruch; Marion G. Ceruti; Charles Kao; Daniel Garcia; Sunny Fugate; LorRaine Duffy
Archive | 2009
Nghia Tran; Sunny Fugate; Jeffrey Ellen; LorRaine Duffy; Hoa Phan
Archive | 2010
Jeffrey Ellen; Marion G. Ceruti; Emily W. Medina; LorRaine Duffy
international conference on machine learning and applications | 2015
Aidan Macdonald; Jeffrey Ellen