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Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater 2019 Selected Works

Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater

Joe Pater

The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This paper traces the development of these two approaches to cognitive science, from their largely autonomous early development in their first thirty years, through their collision in the 1980s around the past tense debate (Rumelhart and McClelland 1986, Pinker and Prince 1988), and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines ...


Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler 2018 Department of Compter Science

Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a prominent declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Sometimes these encodings may display significantly different performance. Uncovering precise formal links between these programs is often important and yet far from trivial. This paper claims the correctness   of a number of interesting program rewritings. Notably, they  assume  programs with variables and  such important language features as choice, disjunction, and aggregates. We showcase the utility of some considered rewritings  by using ...


Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler 2018 University of Nebraska at Omaha

Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler

Yuliya Lierler

Answer set programming is a popular constraint programming paradigm that has seen wide use across various industry applications. However, logic programs under answer set semantics often require careful design and nontrivial expertise from a programmer to obtain satisfactory solving times. In order to reduce this burden on a software engineer we propose an automated rewriting technique for non-ground logic programs that we implement in a system Projector. We conduct rigorous experimental analysis, which shows that applying system Projector to a logic program can improve its performance, even after significant human-performed optimizations.


End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai 2018 University of New Mexico

End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai

Shared Knowledge Conference

Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an ...


Becoming Human: The Darwinian Evolution Of Ai, Alexander Aviles 2018 University of North Georgia

Becoming Human: The Darwinian Evolution Of Ai, Alexander Aviles

Georgia Undergraduate Research Conference (GURC)

Public fear about the rise of artificial intelligence (AI) has created growing interest in understanding the differences from what created humans and machines. This project contrasts the two major models for creating intelligent life, Darwin’s Theory of Evolution and the neural mapping use to construct AI. Beginning by defining the particulars of Darwinian evolution, the paper explains how evolution relies upon interaction between populations and environmental factors. Turning to research in computer science by the likes of Alan Turing and John McCarthy, the paper then explains how artificial neural networks are programmed to work locally to accomplish a set ...


Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal 2018 Stanford University

Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap, we tested whether social media is of utility when physical surveillance cameras went off-line during Hurricane Irma in 2017. Specifically, we collected and compared geo-tagged Instagram and Twitter posts in the state of Florida during times and in areas where public surveillance cameras went off-line. We report social media content ...


Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker 2018 Maurice A. Deane School of Law at Hofstra University

Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker

Legal Tech Boot Camp

No abstract provided.


March Of The Silent Bots, Paul Robert GRIFFIN 2018 Singapore Management University

March Of The Silent Bots, Paul Robert Griffin

MITB Thought Leadership Series

Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.


Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler 2018 University of Nebraska at Omaha

Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler

Yuliya Lierler

Constraint answer set programming integrates answer set programming with constraint processing. System Ezsmt+ is a constraint answer set programming tool that utilizes satisfiability modulo theory solvers for search. The truly unique feature of ezsmt+ is its capability to process linear as well as nonlinear constraints simultaneously containing integer and real variables.


Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore 2018 Dartmouth College

Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore

Open Dartmouth: Faculty Open Access Articles

In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is ...


Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo 2018 The Graduate Center, City University of New York

Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo

All Dissertations, Theses, and Capstone Projects

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions ...


Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, Yuliya Lierler 2018 Department of Compter Science

Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, Yuliya Lierler

Yuliya Lierler

Answer set programming  is a  declarative programming paradigm used in formulating combinatorial search problems and implementing distinct knowledge representation and reasoning formalisms. It is common that several related and yet substantially different answer set programs exist for a given problem. Uncovering precise formal links between these programs is often of value. This paper develops a methodology for establishing such links. This methodology relies on the notions of strong equivalence and conservative extensions and a body of earlier theoretical work related to these concepts. We use distinct answer set programming formalizations  of an action language C and a syntactically restricted action ...


Natural Language Understanding: Deep Learning For Abstract Meaning Representation, William Roger Foland Jr. 2018 University of Colorado at Boulder

Natural Language Understanding: Deep Learning For Abstract Meaning Representation, William Roger Foland Jr.

Computer Science Graduate Theses & Dissertations

In the last few years there have been major improvements in the performance of hard nat- ural language processing tasks due to the application of artificial neural network models. These models replace complex hand-engineered systems for extracting and representing the meaning of human language with systems which learn features based on processing examples of language. In this dissertation, I present deep neural networks for semantic role labeling, and then for Abstract Meaning Representation parsing, and a novel Distributed Abstract Meaning Representation, or DAMR. I then describe a model used to create fixed vector representations of sentence meaning from DAMR. Finally ...


Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia 2018 Southern Methodist University

Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia

SMU Data Science Review

Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research [1] predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural ...


Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick 2018 Purdue University

Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick

The Summer Undergraduate Research Fellowship (SURF) Symposium

Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as ...


Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin 2018 Penn State University

Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced ...


Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal 2018 Purdue University

Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal

The Summer Undergraduate Research Fellowship (SURF) Symposium

In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples ...


Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa 2018 Boise State University

Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa

Boise State University Theses and Dissertations

Describing scenes such as rooms, city streets, or routes, is a very common human task that requires the ability to identify and describe the scene sufficiently for a hearer to develop a mental model of the scene. When people talk about such scenes, they mention some objects of the scene at the exclusion of others. We call the mentioned objects salient objects as people consider them noticeable or important in comparison to other non-mentioned objects. In this thesis, we look at saliency of visual scenes and how visual saliency informs what can and should be said about a scene when ...


Thermal Fluid Model Development Of Steam Methane Reformer Using Artificial Neural Network, M. A. Rafe Biswas 2018 University of Texas at Tyler

Thermal Fluid Model Development Of Steam Methane Reformer Using Artificial Neural Network, M. A. Rafe Biswas

M. A. Rafe Biswas

NASA’s Johnson Space Center has recently begun efforts to eventually integrate air-independent Solid Oxide Fuel Cell systems, with landers that can be propelled by LOX-CH4, for long duration missions. Using landers that utilize such propellants, provides the opportunity to use such systems as a power option, especially since they are able to process methane into a reactant through fuel reformation. To ensure fuel reformation in the systems, Steam Methane Reformation (SMR) are being employed. Various lead-up activities, such as hardware testing and computational modelling, have been initiated to assist with this developmental effort. One modeling approach, currently being explored ...


A Novel Multirobot System For Distributed Phenotyping, Tianshuang Gao, Homagni Saha, Hamid Emadi, Jiaoping Zhang, Alec Lofquist, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh Singh, Sourabh Bhattacharya 2018 Iowa State University

A Novel Multirobot System For Distributed Phenotyping, Tianshuang Gao, Homagni Saha, Hamid Emadi, Jiaoping Zhang, Alec Lofquist, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh Singh, Sourabh Bhattacharya

Mechanical Engineering Publications

Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.


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