Computer Sciences Commons^™

A Collaboration Between Neural Networks And Reinforcement Learning: Applying Concepts To A Brick Breaking Game, Bryce Kadrlik

Augsburg Honors Review

The intent of this work is to explore the interactions of artificial neural networks and digital games. It details the development of an artificial neural network trained upon a brick breaking game like the Atari game Breakout. This network was designed with the goals of not dropping the ball and maximizing the game score. Full game and network integration was not completed. However, two versions of the network were developed to move the paddle to the right or left based on the ball's point of impact on the paddle. In preliminary testing using manual inputs, these networks eventually learned to …

Combing Texts: A Quest To Increase The Timeliness And Accuracy Of Geotagging Multilingual Toponyms And Tagging Persons In Large Corpora Using Tensors For Disambiguation, Anthony D. Davis

Theses and Dissertations

This research demonstrates an effective algorithm and provides a tool for efficient and accurate disambiguation of domain specific texts. The goals outlined in the research are: 1) to demonstrate that a simple knowledge base can be used to tag texts, 2) to demonstrate that a tensor can be used to efficiently and accurately tag entities in raw text documents, and, 3) create and provide a machine learning digital humanities tool for scholars interested in early Christian documents in English and Greek from late-antiquity. The research shows that a simple knowledgebase using a comma delimited file along with a rank-3 tensor …

An Alternative To The One-Size-Fits-All Approach To Isa Training: A Design Science Approach To Isa Regarding The Adaption To Student Vulnerability Based On Knowledge And Behavior, Thomas Jernejcic

SDSU Data Science Symposium

Any connection to the university’s network is a conduit that has the potential of being exploited by an attacker, resulting in the possibility of substantial harm to the infrastructure, to the university, and to the student body of whom the university serves. While organizations rightfully “baton down the hatches” by building firewalls, creating proxies, and applying important updates, the most significant vulnerability, that of the student, continues to be an issue due to lack of knowledge, insufficient motivation, and inadequate or misguided training. Utilizing the Design Science Research (DSR) methodology, this research effort seeks to address the latter concern of …

Evaluation Of Text Mining Techniques Using Twitter Data For Hurricane Disaster Resilience, Joshua Eason, Sathish Kumar

SDSU Data Science Symposium

Data obtained from social media microblogging websites such as Twitter provide the unique ability to collect and analyze conversations of the public in order to gain perspective on the thoughts and feelings of the general public. Sentiment and volume analysis techniques were applied to the dataset in order to gain an understanding of the amount and level of sentiment associated with certain disaster-related tweets, including a topical analysis of specific terms. This study showed that disaster-type events such as a hurricane can cause some strong negative sentiment in the period of time directly preceding the event, but ultimately returns quickly …

Psidb: A Framework For Batched Query Processing And Optimization, Mehrad Eslami

USF Tampa Graduate Theses and Dissertations

Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of logical foundation for query processing and optimization. In this paper, we present PsiDB, a system model for processing a large number of database queries in a batch. The key idea is to generate a single query expression that returns a global relation containing all the data needed for individual queries. For that, we propose the use of a type of relational operators …

Wireless Underground Communications In Sewer And Stormwater Overflow Monitoring: Radio Waves Through Soil And Asphalt Medium, Usman Raza, Abdul Salam

Faculty Publications

Storm drains and sanitary sewers are prone to backups and overflows due to extra amount wastewater entering the pipes. To prevent that, it is imperative to efficiently monitor the urban underground infrastructure. The combination of sensors system and wireless underground communication system can be used to realize urban underground IoT applications, e.g., storm water and wastewater overflow monitoring systems. The aim of this article is to establish a feasibility of the use of wireless underground communications techniques, and wave propagation through the subsurface soil and asphalt layers, in an underground pavement system for storm water and sewer overflow monitoring application. …

Virtual Water Flows Embodied In International And Interprovincial Trade Of Yellow River Basin: A Multiregional Input-Output Analysis, Guilian Tian, Xiaosheng Han, Chen Zhang, Jiaojiao Li, Jinjing Liu

Information Systems and Analytics Department Faculty Journal Articles

With the imminent need of regional environmental protection and sustainable economic development, the concept of virtual water is widely used to solve the problem of regional water shortage. In this paper, nine provinces, namely Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong in the Yellow River Basin (YRB), are taken as the research objects. Through the analysis of input-output tables of 30 provinces in China in 2012, the characteristics of virtual water trade in this region are estimated by using a multi-regional input-output (MRIO) model. The results show that: (1) The YRB had a net inflow of …

Critical Temperature Prediction Of Superconductors Based On Atomic Vectors And Deep Learning, Shaobo Li, Yabo Dan, Xiang Li, Tiantian Hu, Rongzhi Dong, Zhuo Cao, Jianjun Hu

Faculty Publications

In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical …

Who Owns Bitcoin? Private Law Facing The Blockchain, Matthias Lehmann

Minnesota Journal of Law, Science & Technology

No abstract provided.

Developing Big Data Projects In Open University Engineering Courses: Lessons Learned, Juan A. Lara, Aurea Anguera De Sojo, Shadi Aljawarneh, Robert P. Schumaker, Bassam Al-Shargabi

Computer Science Faculty Publications and Presentations

Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students' academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop …

Hierarchical Clustering Analyses Of Plasma Proteins In Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based On Differential Levels Of Angiogenic And Inflammatory Biomarkers, Zachary Winder, Tiffany L. Sudduth, David W. Fardo, Qiang Cheng, Larry B. Goldstein, Peter T. Nelson, Frederick A. Schmitt, Gregory A. Jicha, Donna M. Wilcock

Sanders-Brown Center on Aging Faculty Publications

Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to …

2020 February 04 - Computation And Research In Data Science (Cards) Minutes, Computation And Research In Data Science, East Tennessee State University

Computation and Research in Data Science (CaRDS) Board Meeting Minutes

No abstract provided.

Integrated Quantum Information Processing Controlled Phase Gate, Richard S. Kim, Attila A. Szep, Michael L. Fanto, Paul M. Alsing, Gordon E. Lott, Christopher C. Tison

AFIT Patents

An electro-optical directional coupler is provided having a substrate and a first and second optical waveguide formed on the substrate, where the second waveguide extends adjacent to and parallel with the first waveguide for at least one interaction length. The interaction length has a first end and a second end such that an optical signal applied only to one of the first and second waveguides couples to the other of the first and second waveguides between the ends. A first electrode is proximate the first and second waveguides and between the ends of the interaction length. A first voltage applied …

Classifying Emotions With Eeg And Peripheral Physiological Data Using 1d Convolutional Long Short-Term Memory Neural Network, Rupal Agarwal

USF Tampa Graduate Theses and Dissertations

Recognizing emotions is very important while building robust and interactive Affective Brain-Computer Interfaces as it allows the machines to have some degree of emotional intelligence with the help of which they can understand the changing emotional state of users. In the past, emotions have been recognized via unimodal data such as electroencephalography (EEG) signals, speech, facial expressions or peripheral physiological signals. However, emotions are complex as they are a combination of human behavior, thinking and feeling. Therefore, as compared to unimodal methods, multi-modal techniques, recognize emotions with more reliability. This thesis aims to recognize and classify human emotions into high/low …

A Cost Analysis Of Internet Of Things Sensor Data Storage On Blockchain Via Smart Contracts, Yesem Kurt Peker, Xavier Rodriguez, James Ericsson, Suk Jin Lee, Alfredo J. Perez

Computer Science Faculty Publications

Blockchain is a developing technology that can be utilized for secure data storage and sharing. In this work, we examine the cost of Blockchain-based data storage for constrained Internet of Things (IoT) devices. We had two phases in the study. In the first phase, we stored data retrieved from a temperature/humidity sensor connected to an Ethereum testnet blockchain using smart contracts in two different ways: first, appending the new data to the existing data, storing all sensor data; and second, overwriting the new data onto the existing data, storing only a recent portion of the data. In the second phase, …

A Monte Carlo Approach To Closing The Reality Gap, Damian Lyons, James Finocchiaro, Michael Novitzky, Christopher Korpela

Faculty Publications

We propose a novel approach to the ’reality gap’ problem, i.e., modifying a robot simulation so that its performance becomes more similar to observed real world phenomena. This problem arises whether the simulation is being used by human designers or in an automated policy development mechanism. We expect that the program/policy is developed using simulation, and subsequently deployed on a real system. We further assume that the program includes a monitor procedure with scalar output to determine when it is achieving its performance objectives. The proposed approach collects simulation and real world observations and builds conditional probability functions. These are …

A Cloud Based Approach For Secure Storage And Retrieval Of Standard Electronic Health Records, Raghavendra Ganiga

Manipal Institute of Technology, Manipal Theses and Dissertations

No abstract provided.

Singapore’S National Ai Strategy, Singapore Management University

Perspectives@SMU

The island state is banking on industry-wide projects and building an AI ecosystem to transform its economy

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an …

How Quantum Cryptography And Quantum Computing Can Make Cyber-Physical Systems More Secure, Deepak Tosh, Oscar Galindo, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

For cyber-physical systems, cyber-security is vitally important. There are many cyber-security tools that make communications secure -- e.g., communications between sensors and the computers processing the sensor's data. Most of these tools, however, are based on RSA encryption, and it is known that with quantum computing, this encryption can be broken. It is therefore desirable to use an unbreakable alternative -- quantum cryptography -- for such communications. In this paper, we discuss possible consequences of this option. We also explain how quantum computers can help even more: namely, they can be used to optimize the system's design -- in particular, …

Why Squashing Functions In Multi-Layer Neural Networks, Julio Urenda, Orsoly Csiszár, Gábor Csiszár, József Dombi, Olga Kosheleva, Vladik Kreinovich, György Eigner

Departmental Technical Reports (CS)

Most multi-layer neural networks used in deep learning utilize rectified linear neurons. In our previous papers, we showed that if we want to use the exact same activation function for all the neurons, then the rectified linear function is indeed a reasonable choice. However, preliminary analysis shows that for some applications, it is more advantageous to use different activation functions for different neurons -- i.e., select a family of activation functions instead, and select the parameters of activation functions of different neurons during training. Specifically, this was shown for a special family of squashing functions that contain rectified linear neurons …

A Mystery Of Human Biological Development -- Can It Be Used To Speed Up Computations?, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

For many practical problems, the only known algorithms for solving them require non-feasible exponential time. To make computations feasible, we need an exponential speedup. A reasonable way to look for such possible speedup is to search for real-life phenomena where such a speedup can be observed. A natural place to look for such a speedup is to analyze the biological activities of human beings -- since we, after all, solve many complex problems that even modern super-fast computers have trouble solving. Up to now, this search was not successful -- e.g., there are people who compute much faster than others, …

Automating Deep-Sea Video Annotation Using Machine Learning, Hanson Egbert, Lubomir Stanchev, Benjamin Ruttenberg

Computer Science and Software Engineering

As the world explores opportunities to develop offshore renewable energy capacity, there will be a growing need for pre-construction biological surveys and post-construction monitoring in the challenging marine environment. Underwater video is a powerful tool to facilitate such surveys, but the interpretation of the imagery is costly and time-consuming. Emerging technologies have improved automated analysis of underwater video, but these technologies are not yet accurate or accessible enough for widespread adoption in the scientific community or industries that might benefit from these tools. To address these challenges, we developed a website that allows us to: (1) Quickly play and annotate …

Establishing An Information System For Documenting Valuable Buildings By Using Gis In Egypt, Mona Mahrous Abdel Wahed

Emirates Journal for Engineering Research

Valuable heritage buildings are the history of nations, and history forms the identities of these nations. Many of these buildings are exposed to deterioration, destruction and distortion. Therefore, it is essential to protect and maintain these buildings to protect history. Effective documentation of valuable buildings is necessary to guide and assist stakeholders in making decisions regarding valuable buildings. Documentation requires robust and scientific methods. Therefore, it is important to utilize new technology in general and geographic information system GIS in particular in documenting valuable buildings. GIS has the potential to contribute and deal with valuable buildings at various stages and …

How To Gauge The Quality Of A Testing Method When Ground Truth Is Known With Uncertainty, Nicholas Gray, Scott Ferson, Vladik Kreinovich

Departmental Technical Reports (CS)

The quality of a testing method is usually measured by using sensitivity, specificity, and/or precision. To compute each of these three characteristics, we need to know the ground truth, i.e., we need to know which objects actually have the tested property. In many applications (e.g., in medical diagnostics), the information about the objects comes from experts, and this information comes with uncertainty. In this paper, we show how to take this uncertainty into account when gauging the quality of testing methods.

Stealthy And Efficient Adversarial Attacks Against Deep Reinforcement Learning, Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu

Research Collection School Of Computing and Information Systems

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to …

Experiential Learning Framework For Smaller Computer Science Programs, Zachary A. Kissel, Christopher S. Stuetzle

Computer and Data Science Faculty Publications

Experiential learning (EL) permeates the Computer Science discipline. This work seeks to codify EL practices for computer science pedagogy into ve key pillars. These pillars have been successfully applied at a small to mid-sized college within the heavily competitive Boston area. This paper further describes how a computer science department may eectively implement the pillars in their own curriculum.

Experiential Learning In The Technology Disciplines February 2020, Patricia Sendall, Christopher S. Stuetzle, Zachary A. Kissel, Tahir Hameed

Computer and Data Science Faculty Publications

Learning-by-doing has long been a tradition in the technology disciplines. It is the "hands-on" work, combined with student reflection, feedback and assessment, that reinforces theory into practice. Over the past 40 years, experiential learning (EL) in higher education has grown beyond in-class assignments to include internships, cooperative education, team-based learning, project-based learning, community engagement, service learning, international and study-away experiences, capstone projects and research opportunities. This paper provides an overview of experiential education theory and practice in the undergraduate technology disciplines, and presents examples of how experiential learning practices have evolved over time at a medium-sized institution in the Northeast …

Information Security Recommendation System, Vildan Bina

Theses and Dissertations

Konsiderimi gjithëpërfshirës i rreziqeve të TI-së rezulton nga një përdorim i shtuar i TI-së dhe ankesave dhe skandaleve që rezultojnë në fushën e teknologjisë së informacionit. Në veçanti, humbjet financiare që vijnë nga dëmtimi në reputacion dhe pretendimet për dëme kontribuojnë në një vetëdije të rritur për rreziqet themelore të TI.

Ky studim vë në pah disa prej shkaqeve të cilat bëjnë kanosjen e rrezikut ne sferen e sigurisë së informacionit,duke analizuar situatën nga ana e bizneseve në gjërat të cilat nuk u kushtojnë rëndësinë e merituar deri tek një zhvillues me qëllim të keq i cili tenton të bëj …

Cache-Enabled In Cooperative Cognitive Radio Networks For Transmission Performance, Jiachen Yang, Houbing Song, Chaofan Ma, Jiabao Man, Huifang Xu, Gan Zheng

Publications

The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay …