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Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy GUNAWAN, Hoong Chuin LAU, Pieter VANSTEENWEGEN 2016 Singapore Management University

Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen

Research Collection School Of Information Systems

The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the Generalized OP, the Arc OP ...


Nonlocal Automated Comparative Static Analysis, Leigh Tesfatsion 2016 Iowa State University

Nonlocal Automated Comparative Static Analysis, Leigh Tesfatsion

Leigh Tesfatsion

This paper reviews work on the^ development of a program Nasa for the automated comparative static analysis of parametrized nonlinear systems over parameter intervals. Nasa incorporates a fast and efficient algorithm Feed for the automatic evaluation of higher-order partial derivatives, as well as an adaptive homotopy continuation algorithm for obtaining all required iiutial conditions. Applications are envisioned for fields such as economics where models tend to be complex and closed-form solutions are difficult to obtain...


Computer Vision Based Object Detection And Tracking In Micro Aerial Vehicles, Richard F. Chapman, H. David Mathias 2016 Florida Southern College

Computer Vision Based Object Detection And Tracking In Micro Aerial Vehicles, Richard F. Chapman, H. David Mathias

Papers & Publications: Interdisciplinary Journal of Undergraduate Research

­­­­The ultimate goal of Computer Vision is to instruct a computer to understand and interpret visual signals and images in real time and to instruct a computer to react to the environment around them. In this work, we describe a system that allows a micro aerial vehicle (MAV), equipped with an onboard camera, to detect and track a moving target object. In an alternative implementation, the MAV instead searches the environment for the target object and flies to it. Due to the limited capability of the drone’s integrated processor, image processing is performed by a ground-based computer that also ...


Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. MacArthur, Manuel Trujillo-Silva, Thomas MacGillivray, Chris Ripa, Peter A. Hancock 2016 University of Central Florida

Perceptions Of Planned Versus Unplanned Malfunctions: A Human-Robot Interaction Scenario, Theresa T. Kessler, Keith R. Macarthur, Manuel Trujillo-Silva, Thomas Macgillivray, Chris Ripa, Peter A. Hancock

Keith Reid MacArthur

The present study investigated the effect of malfunctions on trust in a human-robot interaction scenario. Participants were exposed to either a planned or unplanned robot malfunction and then completed two different self-report trust measures. Resulting trust between planned and unplanned exposures was analyzed, showing that trust levels impacted by planned malfunctions did not significantly differ from those impacted by unplanned malfunctions. Therefore, it can be surmised that the methods used for the manipulation of the planned malfunctions were effective and are recommended for further study use.


Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration And Sensor Fusion - A Feasibility Study, Utsav Pardasani 2016 The University of Western Ontario

Neurosurgical Ultrasound Pose Estimation Using Image-Based Registration And Sensor Fusion - A Feasibility Study, Utsav Pardasani

Electronic Thesis and Dissertation Repository

Modern neurosurgical procedures often rely on computer-assisted real-time guidance using multiple medical imaging modalities. State-of-the-art commercial products enable the fusion of pre-operative with intra-operative images (e.g., magnetic resonance [MR] with ultrasound [US] images), as well as the on-screen visualization of procedures in progress. In so doing, US images can be employed as a template to which pre-operative images can be registered, to correct for anatomical changes, to provide live-image feedback, and consequently to improve confidence when making resection margin decisions near eloquent regions during tumour surgery.

In spite of the potential for tracked ultrasound to improve many neurosurgical procedures ...


Nondestructive Testing And Structural Health Monitoring Based On Adams And Svm Techniques, Gang Jiang, Yi Ming Deng, Ji Tai Niu 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, Broes De Cat, Yuliya Lierler 2016 KU Leuven

Constraint Cnf: A Sat And Csp Language Under One Roof, Broes De Cat, Yuliya Lierler

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), Benjamin Susman, Yuliya Lierler 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, Sadat U. Chowdhury 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 ...


Stochastic Cosamp: Randomizing Greedy Pursuit For Sparse Signal Recovery, Dipan K. Pal, Ole J. Mengshoel 2016 Carnegie Mellon University

Stochastic Cosamp: Randomizing Greedy Pursuit For Sparse Signal Recovery, Dipan K. Pal, Ole J. Mengshoel

Ole J Mengshoel

In this paper, we formulate the K-sparse compressed signal recovery problem with the L0 norm within a Stochastic Local Search (SLS) framework. Within this randomized framework, we generalize the popular recovery algorithm CoSaMP, creating Stochastic CoSaMP (StoCoSaMP). Interestingly, our deterministic worst case analysis shows that under RIP, even a purely random version of StoCoSaMP is guaranteed to recover a notion of strong components of a sparse signal, thereby leading to support convergence. Empirically, we find that randomness helps StoCoSaMP to outperform CoSaMP both in terms of signal recoverability and computational cost, even in high dimensions (upto 1 million dimensions), on ...


Communication, Machines & Human Augmentics, John Novak, Jason Archer, Victor Mateevitsi, Steve Jones 2016 The University of Illinois at Chicago

Communication, Machines & Human Augmentics, John Novak, Jason Archer, Victor Mateevitsi, Steve Jones

communication +1

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 ...


A Genetic Algorithm For Learning Parameters In Bayesian Networks Using Expectation Maximization, Priya K. Sundararajan, Ole J. Mengshoel 2016 Carnegie Mellon University

A Genetic Algorithm For Learning Parameters In Bayesian Networks Using Expectation Maximization, Priya K. Sundararajan, Ole J. Mengshoel

Ole J Mengshoel

Expectation maximization (EM) is a popular algorithm for parameter estimation in situations with incomplete data. The EM algorithm has, despite its popularity, the disadvantage of often converging to local but non-global optima. Several techniques have been proposed to address this problem, for example initializing EM from multiple random starting points and then selecting the run with the highest likelihood. Unfortunately, this method is computationally expensive. In this paper, our goal is to reduce computational cost while at the same time maximizing likelihood. We propose a Genetic Algorithm for Expectation Maximization (GAEM) for learning parameters in Bayesian networks. GAEM combines the ...


Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross 2016 Dublin Institute of Technology

Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross

Conference papers

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we ...


Pagi World: A Physically Realistic, General-Purpose Simulation Environment For Developmental Ai Systems, John Licato, Selmer Bringsjord 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 ...


Important Considerations For Human Activity Recognition Using Sensor Data, Matt Buckner 2016 Rose-Hulman Institute of Technology

Important Considerations For Human Activity Recognition Using Sensor Data, Matt Buckner

Rose-Hulman Undergraduate Research Publications

Automated human activity recognition has received much attention in recent years due to increasing focus on interconnected devices in The Internet of Things (IoT) and the miniaturization and proliferation of sensor systems with the adoption of smartphones. In this work, we focus on the current status of human activity recognition across multiple studies, including methodology, accuracy of results, and current challenges to implementation. We include some preliminary work we have completed on a sensor system for classifying treadmill usage.


Real Time Activity Recognition Of Treadmill Usage Via Machine Learning, Nathan Blank, Matt Buckner, Christian Owen, Anna Scott 2016 Rose-Hulman Institute of Technology

Real Time Activity Recognition Of Treadmill Usage Via Machine Learning, Nathan Blank, Matt Buckner, Christian Owen, Anna Scott

Rose-Hulman Undergraduate Research Publications

Our objective is to provide real-time classification of treadmill usage patterns based on accelerometer and magnetometer measurements. We collected data from treadmills in the Rose-Hulman Student Recreation Center (SRC) using Shimmer3 sensor units. We identified useful data features and classifiers for predicting treadmill usage patterns. We also prototyped a proof of concept wireless, real-time classification system.


Agora: A Knowledge Marketplace For Machine Learning, Mauro Ribeiro 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, Connor Brooks 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 ...


Improving Demand Prediction In Bike Sharing System By Learning Global Features, Ming Zeng, Tong Yu, Xiao Wang, Vincent Su, Le T. Nguyen, Ole J. Mengshoel 2016 Carnegie Mellon University

Improving Demand Prediction In Bike Sharing System By Learning Global Features, Ming Zeng, Tong Yu, Xiao Wang, Vincent Su, Le T. Nguyen, Ole J. Mengshoel

Ole J Mengshoel

A bike sharing system deploys bicycles at many open docking stations and makes them available to the public for shared use. These bikes can be checked-in and checked-out at any of the docking stations. Predicting daily visits is important for service providers to optimize bike allocation and station maintenance. In this paper, we formulate this prediction problem as a regression task. Through data analysis, we develop several features that are very helpful in predictions. Moreover, we demonstrate that there are significant differences among the patterns of visits at different stations. To improve prediction accuracy, we propose station-centric augmented with global ...


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 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 ...


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