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

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.

See All Chapters
Medium 9781601324078

Artificial Intellegence: Theory, Algorithms and 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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Medium 9781601324078

Robitics and Applications

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 |

3

Place Recognition and Topological Map Learning in a Virtual

Cognitive Robot

Paul R. Smart1 and Katia Sycara2

1 Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ UK ps02v@ecs.soton.ac.uk

2 Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA katia@cs.cmu.edu

Abstract— An ACT-R cognitive model is used to control the spatial behavior of a virtual robot that is embedded in a three-dimensional virtual environment, implemented using the Unity game engine. The environment features a simple maze that the robot is required to navigate. Communication between ACT-R and Unity is established using a networkbased inter-operability framework. The ability of the robot to learn about the spatial structure of its environment and navigate to designated goal locations serves as a test of the ability of the framework to support the integrative use of cognitive architectures and virtual environments in a range of research and development contexts.

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Medium 9781601324078

Late Poster Papers

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 |

825

Predicting Correctness of “Google Translate”

Yulia Rossikova, J. Jenny Li, and Patricia Morreale

Computer Science, Kean University

1000 Morris Ave, Union NJ 08340 USA juli@kean.edu

Abstract— This paper presents a new modeless approach for Machine Learning predictions, called Radius of

Neighbors (RN). We applied RN to predict the correctness of Google translator and found it to be an improvement over K-Nearest Neighbors (KNN) in terms of prediction accuracy. Both methods are applicable to situations when a mathematical prediction model does not exist or is unknown. With RN, we will be able to create new applications that rely on the users’ awareness of translation accuracy, e.g. an online instant messager, which allows users to chat in various natural languages.

Keywords – K-Nearest Neighbors (KNN), Machine

Learning, Prediction.

EXTENDED ABSTRACT

Machine learning in recent years has given us many new breakthrough applications such as self-driving cars, phone contact center voice recognision, effective web search and automatic natural language translators. Prediction is a key feature of machine learning and K-Nearest-Neighbor (KNN) is a well known prediction method that doesn't need any known model in advance, and thus is suitable for the situations when the model doesn't exist or is unknown. However the accuracy of KNN hinders his wider usage in prediction.

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