Second-Order Know-How Strategies, 2018 Lafayette College
Second-Order Know-How Strategies, Pavel Naumov, Jia Tao
Faculty Research and Reports
The fact that a coalition has a strategy does not mean that the coalition knows what the strategy is. If the coalition knows the strategy, then such a strategy is called a know-how strategy of the coalition. The paper proposes the notion of a second-order know-how strategy for the case when one coalition knows what the strategy of another coalition is. The main technical result is a sound and complete logical system describing the interplay between the distributed knowledge modality and the second-order coalition know-how modality.
Smt-Based Answer Set Solver Cmodels-Diff (System Description), 2018 University of Nebraska at Omaha
Smt-Based Answer Set Solver Cmodels-Diff (System Description), Da Shen, Yuliya Lierler
Augustana Invitational Robotics Challenge 2018, 2018 Augustana College, Rock Island Illinois
Augustana Invitational Robotics Challenge 2018, Forrest Stonedahl
Celebration of Learning
We will be hosting the 3rd Annual Augustana Invitational Robotics Challenge. This event will involve student teams from Augustana and potentially several other schools in the region bringing forth the robots that they have designed, built, and programmed, to compete against one another. This year's challenge task involves the careful relocation of soda pop cans.
Cuoricino Thermal Pulse Classification By Machine Learning Algorithms, 2018 California Polytechnic State University, San Luis Obispo
Cuoricino Thermal Pulse Classification By Machine Learning Algorithms, Joshua Mann
Many of the various properties of neutrinos are still a mystery. One unknown is whether neutrinos are Majorana fermions or Dirac fermions. Cuoricino and CUORE are experiments that aim to solve this mystery. Noise reduction in these experiments hinges on the ability to discern among alpha, beta and gamma particle detections using the thermal pulses they create. In this paper, we look at Cuoricino data and attempt to classify pulses, not as alpha, beta or gamma particles, but rather as signal, noise or calibration data. We will use this preliminary testing ground to examine various machine learning algorithms' abilities in ...
Self-Coaching With Ai: Developing Thinking Skills, Thinking Dispositions, And Well-Being, 2018 Florida International University
Self-Coaching With Ai: Developing Thinking Skills, Thinking Dispositions, And Well-Being, Olivier Malafronte, Isla Reddin, Roy Van Den Brink-Budgen
ICOT 18 - International Conference on Thinking - Cultivating Mindsets for Global Citizens
Being motivated by the need to address the challenges of our Volatile Uncertain Complex Ambiguous world, we strive to create tools to improve people’s lives and help them become more resilient, resourceful, self-confidant, and successful.
In a digital world, we must understand how to efficiently connect to digital systems. Connecting “with AI” doesn’t mean spending more time on digital devices, but spending time in a deliberate way with purpose and intentional learning outcomes.
As a society, we want to see graduates with emotional intelligence and reflective skills in order to address global economic and social issues. As ...
Applications Of Artificial Intelligence In Power Systems, 2018 srastgou
Applications Of Artificial Intelligence In Power Systems, Samin Rastgoufard
University of New Orleans Theses and Dissertations
Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems.
The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation ...
Detecting Metagame Shifts In League Of Legends Using Unsupervised Learning, 2018 University of New Orleans
Detecting Metagame Shifts In League Of Legends Using Unsupervised Learning, Dustin P. Peabody
University of New Orleans Theses and Dissertations
Over the many years since their inception, the complexity of video games has risen considerably. With this increase in complexity comes an increase in the number of possible choices for players and increased difficultly for developers who try to balance the effectiveness of these choices. In this thesis we demonstrate that unsupervised learning can give game developers extra insight into their own games, providing them with a tool that can potentially alert them to problems faster than they would otherwise be able to find. Specifically, we use DBSCAN to look at League of Legends and the metagame players have formed ...
Real-Time Object Recognition Using A Multi-Framed Temporal Approach, 2018 Bowdoin College
Real-Time Object Recognition Using A Multi-Framed Temporal Approach, Corinne Alini
Computer Vision involves the extraction of data from images that are analyzed in order to provide information crucial to many modern technologies. Object recognition has proven to be a difficult task and programming reliable object recognition remains elusive. Image processing is computationally intensive and this issue is amplified on mobile platforms with processor restrictions. The real-time constraints demanded by robotic soccer in RoboCup competition serve as an ideal format to test programming that seeks to overcome these challenges. This paper presents a method for ball recognition by analyzing the movement of the ball. Major findings include enhanced ball discrimination by ...
Effectively Enforcing Minimality During Backtrack Search, 2018 University of Nebraska-Lincoln
Effectively Enforcing Minimality During Backtrack Search, Daniel J. Geschwender
Computer Science and Engineering: Theses, Dissertations, and Student Research
Constraint Processing is an expressive and powerful framework for modeling and solving combinatorial decision problems. Enforcing consistency during backtrack search is an effective technique for reducing thrashing in a large search tree. The higher the level of the consistency enforced, the stronger the pruning of inconsistent subtrees. Recently, high-level consistencies (HLC) were shown to be instrumental for solving difficult instances. In particular, minimality, which is guaranteed to prune all inconsistent branches, is advantageous even when enforced locally. In this thesis, we study two algorithms for computing minimality and propose three new mechanisms that significantly improve performance. Then, we integrate the ...
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, 2018 University of Arkansas, Fayetteville
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday
Theses and Dissertations
We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)
algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart
reinforcement learning performance with the introduction of the A3C algorithm. The goal of
this research is to show that A3C can be improved by the use of a new novel exploration strategy we
call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path
at the beginning of a training episode and then later in the episode the agent is forced to “forage”
and explores randomly ...
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, 2018 University of Arkansas, Fayetteville
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Theses and Dissertations
The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are omitted, even deep neural networks reduce to simple linear regression due to the fact that a linear combination of linear combinations is still a linear combination. In much of the existing literature on neural networks, just one or two activation functions are selected for the entire network, even though the use of heterogenous activation functions has been shown to produce superior results in some cases. Even less often employed are activation functions that can adapt their ...
Ai-Human Collaboration Via Eeg, 2018 College of Saint Benedict/Saint John's University
Ai-Human Collaboration Via Eeg, Adam Noack
All College Thesis Program, 2016-present
As AI becomes ever more competent and integrated into our lives, the issue of AI-human goal misalignment looms larger. This is partially because there is often a rift between what humans explicitly command and what they actually mean. Most contemporary AI systems cannot bridge this gap. In this study we attempted to reconcile the goals of human and machine by using EEG signals from a human to help a simulated agent complete a task.
File Fragment Classification Using Neural Networks With Lossless Representations, 2018 East Tennessee State University
File Fragment Classification Using Neural Networks With Lossless Representations, Luke Hiester
Undergraduate Honors Theses
This study explores the use of neural networks as universal models for classifying file fragments. This approach differs from previous work in its lossless feature representation, with fragments’ bits as direct input, and its use of feedforward, recurrent, and convolutional networks as classifiers, whereas previous work has only tested feedforward networks. Due to the study’s exploratory nature, the models were not directly evaluated in a practical setting; rather, easily reproducible experiments were performed to attempt to answer the initial question of whether this approach is worthwhile to pursue further, especially due to its high computational cost. The experiments tested ...
Multimodal Depression Detection: An Investigation Of Features And Fusion Techniques For Automated Systems, 2018 The Graduate Center, City University of New York
Multimodal Depression Detection: An Investigation Of Features And Fusion Techniques For Automated Systems, Michelle Renee Morales
All Dissertations, Theses, and Capstone Projects
Depression is a serious illness that affects a large portion of the world’s population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems ...
Computer Vision Evidence Supporting Craniometric Alignment Of Rat Brain Atlases To Streamline Expert-Guided, First-Order Migration Of Hypothalamic Spatial Datasets Related To Behavioral Control, 2018 University of Texas at El Paso
Computer Vision Evidence Supporting Craniometric Alignment Of Rat Brain Atlases To Streamline Expert-Guided, First-Order Migration Of Hypothalamic Spatial Datasets Related To Behavioral Control, Arshad M. Khan, Jose G. Perez, Claire E. Wells, Olac Fuentes
Arshad M. Khan, Ph.D.
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning, 2018 University of Massachusetts Medical School
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning, Tsendsuren Munkhdalai, Feifan Liu, Hong Yu
Open Access Articles
BACKGROUND: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data.
OBJECTIVE: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and ...
Comparative Study Of Deep Learning Models For Network Intrusion Detection, 2018 Southern Methodist University
Comparative Study Of Deep Learning Models For Network Intrusion Detection, Brian Lee, Sandhya Amaresh, Clifford Green, Daniel Engels
SMU Data Science Review
In this paper, we present a comparative evaluation of deep learning approaches to network intrusion detection. A Network Intrusion Detection System (NIDS) is a critical component of every Internet connected system due to likely attacks from both external and internal sources. A NIDS is used to detect network born attacks such as Denial of Service (DoS) attacks, malware replication, and intruders that are operating within the system. Multiple deep learning approaches have been proposed for intrusion detection systems. We evaluate three models, a vanilla deep neural net (DNN), self-taught learning (STL) approach, and Recurrent Neural Network (RNN) based Long Short ...
Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, 2018 Southern Methodist University
Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, Andrew Abbott, Alex Deshowitz, Dennis Murray, Eric C. Larson
SMU Data Science Review
This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a ...
Cognitive Virtual Admissions Counselor, 2018 Southern Methodist University
Cognitive Virtual Admissions Counselor, Kumar Raja Guvindan Raju, Cory Adams, Raghuram Srinivas
SMU Data Science Review
Abstract. In this paper, we present a cognitive virtual admissions counselor for the Master of Science in Data Science program at Southern Methodist University. The virtual admissions counselor is a system capable of providing potential students accurate information at the time that they want to know it. After the evaluation of multiple technologies, Amazon’s LEX was selected to serve as the core technology for the virtual counselor chatbot. Student surveys were leveraged to collect and generate training data to deploy the natural language capability. The cognitive virtual admissions counselor platform is currently capable of providing an end-to-end conversational dialog ...
Pelee: A Real-Time Object Detection System On Mobile Devices, 2018 The University of Western Ontario
Pelee: A Real-Time Object Detection System On Mobile Devices, Jun Wang
Electronic Thesis and Dissertation Repository
There has been a rising interest in running high-quality Convolutional Neural Network (CNN) models under strict constraints on memory and computational budget. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and NASNet-A. However, all these architectures are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. Meanwhile, there are few studies that combine efficient models with fast object detection algorithms. This research tries to explore the design of an efficient CNN architecture for both image classification tasks and object detection tasks. We propose an efficient architecture named ...