28 Slices
Medium 9781601324078

Machine Translation, Natural Language Processing and Related Methods

Editied By Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica,Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti (Editors) CSREA Press PDF


WorldComp is an
international conference that serves researchers, scholars, professionals,
students, and academicians who are looking to both foster working
relationships and gain access to the latest research results. It is being
held jointly (same location and dates) with a number of other research
conferences; namely, The 2015 World Congress in Computer Science, Computer
Engineering, and Applied Computing (WORLDCOMP'15). The Congress is among the
top five largest annual gathering of researchers in computer science,
computer engineering and applied computing. We anticipate to have attendees
from about 85 countries/territories.

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Medium 9781601324078

Knowledge Discovery and Machine Learning

Editied By Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica,Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti (Editors) CSREA Press PDF

Int'l Conf. Artificial Intelligence | ICAI'15 |

183

Cognitive RF Systems and EM Fratricide – Part III

Gerard T. Capraro

Capraro Technologies, Inc., 401 Herkimer Road, Utica, NY 13502 USA

Abstract

The United States Department of Defense and researchers throughout the world have been addressing the overcrowding of the radio frequency (RF) spectrum. When the frequency spectrum is measured over time, technologists have shown that the spectrum is underutilized. This has led to numerous studies concerning cognitive radios, networks, and radar systems to intelligently choose frequencies, waveform parameters, antenna beam patterns, etc. to operate with conventional receivers without causing electromagnetic (EM) fratricide. In many of these studies there is an inherent assumption that the cognitive system knows when and where the fratricide occurs. In two previous papers [11, 17] we presented two approaches for determining if a cognitive solution is causing EMI in nearby receivers. In this paper a solution requiring a new cognitive user to perform an analysis and negotiate a solution before transmitting is presented, thereby establishing that the EMI is due to fratricide and not some other phenomena such as spoofing. Five major issues are addressed related to this problem area and four are discussed in detail.

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Medium 9781601324078

Knowlege Discovery and Machine Learning

Editied By Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica,Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti (Editors) CSREA Press PDF

Int'l Conf. Artificial Intelligence | ICAI'15 |

183

Cognitive RF Systems and EM Fratricide – Part III

Gerard T. Capraro

Capraro Technologies, Inc., 401 Herkimer Road, Utica, NY 13502 USA

Abstract

The United States Department of Defense and researchers throughout the world have been addressing the overcrowding of the radio frequency (RF) spectrum. When the frequency spectrum is measured over time, technologists have shown that the spectrum is underutilized. This has led to numerous studies concerning cognitive radios, networks, and radar systems to intelligently choose frequencies, waveform parameters, antenna beam patterns, etc. to operate with conventional receivers without causing electromagnetic (EM) fratricide. In many of these studies there is an inherent assumption that the cognitive system knows when and where the fratricide occurs. In two previous papers [11, 17] we presented two approaches for determining if a cognitive solution is causing EMI in nearby receivers. In this paper a solution requiring a new cognitive user to perform an analysis and negotiate a solution before transmitting is presented, thereby establishing that the EMI is due to fratricide and not some other phenomena such as spoofing. Five major issues are addressed related to this problem area and four are discussed in detail.

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Medium 9781601324078

Pattern Recognition and Supporting Algorithms + Machine Learning and Applications + Learning Methods (Supervised and Unsupervised) and Data Mining

Editied By Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica,Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti (Editors) CSREA Press PDF

Int'l Conf. Artificial Intelligence | ICAI'15 |

571

Using Google Glass and Machine Learning to

Assist People with Memory Deficiencies

Thomas Way, Adam Bemiller, Raghavender Mysari and Corinne Reimers

Applied Computing Technology Laboratory

Department of Computing Sciences

Villanova University, Villanova PA 19085 thomas.way@villanova.edu

Abstract – Memory deficiencies may occur naturally with age and for a variety of reasons including

Alzheimer’s disease, depression, side-effects of drug use, stroke, and traumatic brain injury. Because memory loss can significantly interferes with daily life, many external memory aids have been developed, though most use a passive approach. In this paper, we report on the design and prototype development of a dynamic, wearable system, called ELEPHANT, to assist those with memory deficiencies. Our design uses the Google

Glass platform and a machine learning approach to intelligently retrieve stored photographic “memories” annotated with location, date, time, and activity information to enhance the memory of the user.

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Medium 9781601324078

Artificial Intelligence: Theory, Algorithms and Applications + Applications + Cognitive Science + Modeling

Editied By Hamid R. Arabnia, David de la Fuente, Roger Dziegiel, Elena B. Kozerenko, Peter M. LaMonica,Raymond A. Liuzzi, Jose A. Olivas, Todd Waskiewicz, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti (Editors) CSREA Press PDF

Int'l Conf. Artificial Intelligence | ICAI'15 |

407

Visual Intelligence: Toward Machine

Understanding of Video Content

Michael C. Burl, Russell L. Knight, Anthony C. Barrett

Jet Propulsion Laboratory, California Institute of Technology

4800 Oak Grove Drive, Pasadena, CA 91109

Abstract - This paper describes progress toward developing visual intelligence algorithms (VI) that can produce humanlike text descriptions (captions) from video inputs. Video frames are assumed to be generated according to an underlying “script’ that specifies a camera model and the content and action in a scene. VI is formulated as the problem of recovering the script (or relevant portions of the script) given a sequence of video frames. Three types of scripts at different levels of abstraction are recovered: C-scripts contain object detections, poses, and descriptive information on a frame-by-frame basis; B-scripts assign persistent IDs to objects across frames and “smooth” frame-by-frame information; A-scripts provide a symbolic representation of video content using a sparse timeline in which Planning

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