Nondestructive Testing And Structural Health Monitoring Based On Adams And Svm Techniques, 2016 Southwest University of Science and Technology
Nondestructive Testing And Structural Health Monitoring Based On Adams And Svm Techniques, Gang Jiang, Yi Ming Deng, Ji Tai Niu
The 8th International Conference on Physical and Numerical Simulation of Materials Processing
No abstract provided.
Constraint Cnf: A Sat And Csp Language Under One Roof, 2016 KU Leuven
Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler
A new language, called constraint CNF, is proposed. It integrates propositional logic with constraints stemming from constraint programming (CP). A family of algorithms is designed to solve problems expressed in constraint CNF. These algorithms build on techniques from both propositional satisfiability (SAT) and CP. The result is a uniform language and an algorithmic framework, which allow us to gain a deeper understanding of the relation between the solving techniques used in SAT and in CP and apply them together.
Smt-Based Constraint Answer Set Solver Ezsmt (System Description), 2016 University of Nebraska at Omaha
Smt-Based Constraint Answer Set Solver Ezsmt (System Description), Benjamin Susman, Yuliya Lierler
Computer Science Faculty Proceedings & Presentations
Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. Recently, the formal link between this research area and satisfiability modulo theories (or SMT) was established. This link allows the cross-fertilization between traditionally different solving technologies. The paper presents the system EZSMT, one of the first SMT-based solvers for constraint answer set programming. It also presents the comparative analysis of the performance of EZSMT in relation to its peers including solvers EZCSP and CLINGCON that rely on the hybrid solving approach based on the combination of answer set solvers and constraint solvers. Experimental ...
A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, 2016 The Graduate Center, City University of New York
A Study Of The Impact Of Interaction Mechanisms And Population Diversity In Evolutionary Multiagent Systems, Sadat U. Chowdhury
All Graduate Works by Year: Dissertations, Theses, and Capstone Projects
In the Evolutionary Computation (EC) research community, a major concern is maintaining optimal levels of population diversity. In the Multiagent Systems (MAS) research community, a major concern is implementing effective agent coordination through various interaction mechanisms. These two concerns coincide when one is faced with Evolutionary Multiagent Systems (EMAS).
This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one ...
Communication, Machines & Human Augmentics, 2016 The University of Illinois at Chicago
Communication, Machines & Human Augmentics, John Novak, Jason Archer, Victor Mateevitsi, Steve Jones
This essay reformulates the question of human augmentation as a problem of advanced human-machine communication, theorizing that such communication implies robust artificial intelligence and necessitates understanding the relational role new technologies play in human-machine communication. We focus on the questions, “When do electronic tools cease to be ‘simply’ tools, and become meaningfully part of ourselves,” and, “When might we think of these tools as augmenting our selves, rather than simply amplifying our capabilities?” These questions, already important to the medical and rehabilitative fields, loom larger with increasing commodification of pervasive augmentation technologies, and indicate the verge on which human-machine communication ...
Pagi World: A Physically Realistic, General-Purpose Simulation Environment For Developmental Ai Systems, 2016 Indiana University - Purdue University Fort Wayne
Pagi World: A Physically Realistic, General-Purpose Simulation Environment For Developmental Ai Systems, John Licato, Selmer Bringsjord
Computer Science Faculty Presentations
There has long been a need for a simulation environment rich enough to support the development of an AI system sufficiently knowledgeable about physical causality to pass certain tests of Psychometric Artificial Intelligence (PAI) and Psychometric Artificial General Intelligence (PAGI). In this article, we present a simulation environment, PAGI World, which is: cross-platform (as it can be run on all major operating systems); open-source (and thus completely free of charge to use); able to work with AI systems written in almost any programming language; as agnostic as possible regarding which AI approach is used; and easy to set up and ...
Agora: A Knowledge Marketplace For Machine Learning, 2016 The University of Western Ontario
Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro
Electronic Thesis and Dissertation Repository
More and more data are becoming part of people's lives. With the popularization of technologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models ...
Toward Autonomous Multi-Rotor Indoor Aerial Vehicles, 2016 Western Kentucky University
Toward Autonomous Multi-Rotor Indoor Aerial Vehicles, Connor Brooks
Honors College Capstone Experience/Thesis Projects
In this project, we worked to create an indoor autonomous micro aerial vehicle (MAV) using a multi-layer architecture with modular hardware and software components. We required that all computing was done onboard the vehicle during time of flight so that no remote connection of any kind was necessary for successful control of the vehicle, even when flying autonomously. We utilized environmental sensors including ultrasonic sensors, light detection and ranging modules, and inertial measurement units to acquire necessary environment information for autonomous flight. We used a three-layered system that combined a modular control architecture with distributed on-board computing to allow for ...
V3nlp Framework: Tools To Build Applications For Extracting Concepts From Clinical Text, 2016 VA Salt Lake City Health Care System and University of Utah School of Medicine
V3nlp Framework: Tools To Build Applications For Extracting Concepts From Clinical Text, Guy Divita, Marjorie Carter Ms, Le-Thuy Tran Phd, Doug Redd Ms, Qing T. Zeng Phd, Scott Duvall Phd, Matthew H. Samore Md, Phd, Adi V. Gundlapalli Md, Phd, Ms
eGEMs (Generating Evidence & Methods to improve patient outcomes)
Introduction: Substantial amounts of clinically significant information are contained only within the narrative of the clinical notes in electronic medical records. v3NLP Framework is a set of best of breed functionalities developed to transform this information into structured data for use in quality improvement, research, population health surveillance, and decision support.
Background: MetaMap, cTAKES and similar well known NLP tools do not have sufficient scalability out of the box. v3NLP Framework evolved out of the necessity to scale these tools up and provide a framework to customize and tune techniques to fit a variety of tasks, including document classification, tuned ...
Paper-Android-Based Health Care Management System, 2016 Gomal University, Dera Ismail Khan
Paper-Android-Based Health Care Management System, Fazal Masud Kundi, Anam Habib, Ammara Habib, Dr. Muhammad Zubair Asghar
Dr. Muhammad Zubair Asghar
Classifying Pattern Formation In Materials Via Machine Learning, 2016 Purdue University
Classifying Pattern Formation In Materials Via Machine Learning, Lukasz Burzawa, Shuo Liu, Erica W. Carlson
The Summer Undergraduate Research Fellowship (SURF) Symposium
Scanning probe experiments such as scanning tunneling microscopy (STM) and atomic force microscopy (AFM) on strongly correlated materials often reveal complex pattern formation that occurs on multiple length scales. We have shown in two disparate correlated materials that the pattern formation is driven by proximity to a disorder-driven critical point. We developed new analysis concepts and techniques that relate the observed pattern formation to critical exponents by analyzing the geometry and statistics of clusters observed in these experiments and converting that information into critical exponents. Machine learning algorithms can be helpful correlating data from scanning probe experiments to theoretical models ...
Conditional Computation In Deep And Recurrent Neural Networks, 2016 University of Tennessee, Knoxville
Conditional Computation In Deep And Recurrent Neural Networks, Andrew Scott Davis
Recently, deep learning models such as convolutional and recurrent neural networks have displaced state-of-the-art techniques in a variety of application domains. While the computationally heavy process of training is usually conducted on powerful graphics processing units (GPUs) distributed in large computing clusters, the resulting models can still be somewhat heavy, making deployment in resource- constrained environments potentially problematic. In this work, we build upon the idea of conditional computation, where the model is given the capability to learn how to avoid computing parts of the graph. This allows for models where the number of parameters (and in a sense, the ...
A Genetic Algorithmic Approach To Automated Auction Mechanism Design, 2016 CUNY Guttman Community College
A Genetic Algorithmic Approach To Automated Auction Mechanism Design, Jinzhong Niu, Simon Parsons
Publications and Research
In this paper, we present a genetic algorithmic approach to automated auction mechanism design in the context of \cat games. This is a follow-up to one piece of our prior work in the domain, the reinforcement learning-based grey-box approach. Our experiments show that given the same search space the grey-box approach is able to produce better auction mechanisms than the genetic algorithmic approach. The comparison can also shed light on the design and evaluation of similar search solutions to other domain problems.
Formalization Of The Ad Hominem Argumentation Scheme, 2016 University of Windsor
Formalization Of The Ad Hominem Argumentation Scheme, Douglas Walton
In this paper, several examples from the literature, and one central new one, are used as case studies of texts of discourse containing an argumentation scheme that has now been widely investigated in literature on argumentation. Argumentation schemes represent common patterns of reasoning used in everyday conversational discourse. The most typical ones represent defeasible arguments based on nonmonotonic reasoning. Each scheme has a matching set of critical questions used to evaluate a particular argument fitting that scheme. The project is to study how to build a formal computational model of this scheme for the circumstantial ad hominem argument using argumentation ...
Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, 2016 University of Windsor
Examination Dialogue: An Argumentation Framework For Critically Questioning An Expert Opinion, Douglas Walton
Recent work in argumentation theory (Walton and Krabbe, 1995; Walton, 2005) and artificial intelligence (Bench-Capon, 1992, 2003; Cawsey, 1992; McBurney and Parsons, 2002; Bench-Capon and Prakken, 2005) uses types of dialogue as contexts of argument use. This paper provides an analysis of a special type called examination dialogue, in which one party questions another party, sometimes critically or even antagonistically, to try to find out what that party knows about something. This type of dialogue is most prominent in law and in both legal and non-legal arguments based on expert opinion. It is also central to dialogue systems for questioning ...
Critical Questions In Computational Models Of Legal Argument, 2016 University of Windsor
Critical Questions In Computational Models Of Legal Argument, Douglas Walton, Thomas F. Gordon
Two recent computational models of legal argumentation, by Verheij and Gordon respectively, have interpreted critical questions as premises of arguments that can be defeated using Pollock’s concepts of undercutters and rebuttals. Using the scheme for arguments from expert opinion as an example, this paper evaluates and compares these two models of critical questions from the perspective of argumentation theory and competing legal theories about proof standardsfor defeating presumptions. The applicable proof standard is found to be a legal issue subject to argument. Verheij’smodel is shown to have problems because the proof stan-dards it applies to different kinds of ...
Climbing Up Cloud Nine: Performance Enhancement Techniques For Cloud Computing Environments, 2016 The University of Western Ontario
Climbing Up Cloud Nine: Performance Enhancement Techniques For Cloud Computing Environments, Mohamed Abusharkh
Electronic Thesis and Dissertation Repository
With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on ...
Constraint Answer Set Programming Versus Satisfiability Modulo Theories, 2016 University of Nebraska at Omaha
Constraint Answer Set Programming Versus Satisfiability Modulo Theories, Yuliya Lierler, Benjamin Susman
Computer Science Faculty Proceedings & Presentations
Constraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of Satisfiability Modulo Theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, we make the link between these two areas precise.
A Hybrid Tabu/Scatter Search Algorithm For Simulation-Based Optimization Of Multi-Objective Runway Operations Scheduling, 2016 Old Dominion University
A Hybrid Tabu/Scatter Search Algorithm For Simulation-Based Optimization Of Multi-Objective Runway Operations Scheduling, Bulent Soykan
Engineering Management & Systems Engineering Theses & Dissertations
As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class ...
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Electrical and Computer Engineering Publications
In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is ...