Computer Sciences Commons^™

Using Natural Language Processing To Categorize Fictional Literature In An Unsupervised Manner, Dalton J. Crutchfield

Electronic Theses and Dissertations

When following a plot in a story, categorization is something that humans do without even thinking; whether this is simple classification like “This is science fiction” or more complex trope recognition like recognizing a Chekhov's gun or a rags to riches storyline, humans group stories with other similar stories. Research has been done to categorize basic plots and acknowledge common story tropes on the literary side, however, there is not a formula or set way to determine these plots in a story line automatically. This paper explores multiple natural language processing techniques in an attempt to automatically compare and cluster …

Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao

Electronic Theses and Dissertations

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC).

We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in …

Automated Change Detection In Privacy Policies, Andrick Adhikari

Electronic Theses and Dissertations

Privacy policies notify Internet users about the privacy practices of websites, mobile apps, and other products and services. However, users rarely read them and struggle to understand their contents. Also, the entities that provide these policies are sometimes unmotivated to make them comprehensible. Due to the complicated nature of these documents, it gets even harder for users to understand and take note of any changes of interest or concern when these policies are changed or revised.

With recent development of machine learning and natural language processing, tools that can automatically annotate sentences of policies have been developed. These annotations can …

Real-Time Detection Of Demand Manipulation Attacks On A Power Grid, Srinidhi Madabhushi

Electronic Theses and Dissertations

An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Based on the time and situation of the attack, an adversary needs access to a fixed number of IoT devices to synchronously switch on/off all of them, resulting in an imbalance between the supply and demand. When the frequency of the power generators drops below a threshold value, it can lead to the generators …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Certification-Driven Testing Of Safety-Critical Systems, Aiman S. Gannous

Electronic Theses and Dissertations

Safety-critical systems are those systems that when they fail they could cause loss of life or significant physical damages. Since software now is an essential component of these types of systems, failures caused by software faults could come from flaws in the software development life-cycle. As a result, challenges unfold in two directions. First, in verifying that the software will not put the system in an unsafe state, and identifying external failures and mitigate them properly. Second, in providing sufficient evidence for an efficient safety certification process. In this study, we propose an approach for testing safety-critical systems called Model-Combinatorial …

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

The Oceans Above Us: An Augmented Reality Experience, Chris Nalani Dimeo

Mahurin Honors College Capstone Experience/Thesis Projects

Augmented reality holds the potential to be the new fabric of our everyday lives.

Also known as AR, augmented reality is any technology that superimposes graphical information over a real-world environment, whether it be through a smartphone screen or visually projected onto the environment. Though it has existed in various forms for decades, augmented reality development is still widely considered the work of experts in technology-related fields.

In November 2019, however, Adobe unveiled a new augmented reality development platform, Project Aero, along with boasts that the app’s intuitive design and integration with other Adobe programs would place AR creation into …

Can Trait Emotional Intelligence Variables Of Well-Being, Self-Control, Emotionality, And Sociability Individually Or Collectively Predict A Software Development Engineer's Creativity?, Mwoyondishe Jonathan Mvududu

Doctor of Business Administration (DBA)

This quantitative research study was carried out as a partial requirement for earning a Doctor of Business Administration degree from George Fox University. The main goal of the research study was to investigate whether trait emotional intelligence variables of well-being, self-control, emotionality, and sociability can individually or collectively predict a software development engineer’s creativity or creativeness potential. Employee innovativeness is the primary focus of many organizations in today’s turbulent business environment whereby employees are increasingly gaining autonomy in self-managed teams. The study discusses the theoretical frameworks of creativity and trait emotional intelligence (Trait EI), an extension of the emotional intelligence …

Advancement Of Predictive Modeling Of Zeta Potentials (Ζ) In Metal Oxide Nanoparticles With Correlation Intensity Index (Cii), Andrey A. Toropov, Natalia Sizochenko, Alla P. Toropova, Danuta Leszczynska, Jerzy Leszczynski

Articles

It was expected that index of the ideality of correlation (IIC) and correlation intensity index (CII) could be used as possible tools to improve the predictive power of the quantitative model for zeta potential of nanoparticles. In this paper, we test how the statistical quality of quantitative structure-activity models for zeta potentials (ζ, a common measurement that reflects surface charge and stability of nanomaterial) could be improved with the use of these two indexes. Our hypothesis was tested using the benchmark data set that consists of 87 measurements of zeta potentials in water. We used quasi-SMILES molecular representation to take …

An Enhanced Ride Sharing Model Based On Human Characteristics And Machine Learning Recommender System, Govind Pramod Yatnalkar, Husnu S. Narman, Haroon Malik

Computer Sciences and Electrical Engineering Faculty Research

Ride Sharing provides benefits like reducing traffic and pollution, but currently, the usage is significantly low due to social barriers, long rider waiting time, and unfair pricing models. Considering the aforementioned issues, we present an Enhanced Ride Sharing Model (ERSM) in which riders are matched based on a specific set of human characteristics using Machine Learning. After trip completion, we record the user feedback and compute two main characteristics that are most important to riders. The registered and the computed characteristics are fed to a classification module, which later predicts the two main characteristics for new riders. We have carried …

Smart Green Communication Protocols Based On Several-Fold Messages Extracted From Common Sequential Patterns In Uavs, Iván García-Magariño, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Articles

Green communications can be crucial for saving energy in UAVs and enhancing their autonomy. The current work proposes to extract common sequential patterns of communications to gather each common pattern into a single several- fold message with a high-level compression. Since the messages of a pattern are elapsed from each other in time, the current approach performs a machine learning approach for estimating the elapsed times using off-line training. The learned predictive model is applied by each UAV during flight when receiving a several-fold compressed message. We have explored neural networks, linear regression and correlation analyses among others. The current …

Fusion-Net: Integration Of Dimension Reduction And Deep Learning Neural Network For Image Classification, Mohammad Masum, Philippe Laval

Published and Grey Literature from PhD Candidates

Building a deep network using original digital images requires learning many parameters which may reduce the accuracy rates. The images can be compressed by using dimension reduction methods and extracted reduced features can be feeding into a deep network for classification. Hence, in the training phase of the network, the number of parameters will be decreased. Principal Component Analysis is a well-known dimension reduction technique that leverage orthogonal linear transformation of the original data. In this paper, we propose a neural network-based framework, named Fusion-Net, which implements PCA on an image dataset (CIFAR-10) and then a neural network applies on …

Renewable Energy Integration In Distribution System With Artificial Intelligence, Yi Gu

Electronic Theses and Dissertations

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data …

Gmdh-Based Models For Mid-Term Forecast Of Cryptocurrencies (On Example Of Waves), Pavel Mogilev, Anna Boldyreva, Mikhail Alexandrov, John Cardiff

Conference Papers

Cryptocurrencies became one of the main trends in modern economy. However by the moment the forecast of cryptocurrencies values is an open problem, which is almost non-reflected in publications related to finance market. Reasons consist in its novelty, large volatility and its strong dependence on subjective factors. In this experimental research we show possibilities of GMDH-technology to give weekly and monthly forecast for values of cryptocurrency 'Waves' (waves/euro rate). The source information is week data covering the period 2017-2019. We tests 4 algorithms from the GMDH Shell platform on the whole period and on the crisis period 4-th quarter 2017 …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

We’Ve Only Just Begun: Children Searching In The Classroom, Monica Landoni, Theo Huibers, Emiliana Murgia, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

In this extended abstract, we present an overview of our ongoing project. Specifically, we briefly discuss the motivation for our research agenda, research goals in the short and long term, and the body of work we have published thus far that serves as the foundation upon which we build the next steps related to Information Retrieval and Children in the Classroom Setting.

Kidspell: A Child-Oriented, Rule-Based, Phonetic Spellchecker, Brody Downs, Oghenemaro Anuyah, Aprajita Shukla, Jerry Alan Fails, Maria Soledad Pera, Katherine Wright, Casey Kennington

Computer Science Faculty Publications and Presentations

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess …

Detection Of Weak Ties, Jerry Scripps

Technical Reports

In a small world social network, strong and weak ties exist that define tightly clustered areas and isolated links between them. Granovetter provided an heuristic that strong links have a higher common neighbor count than weak ones. The problem of identifying weak links is the central focus of this paper. The proposed metric vett will be shown that within certain constraints of a (modified) small world network the accuracy of the proposed method is 100%.

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

The Unpopularity Of The Software Tester Role Among Software Practitioners: A Case Study, Yadira Lizama, Daniel Varona, Pradeep Waychal, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

As software systems are becoming more pervasive, they are also becoming more susceptible to failures, resulting in potentially lethal combinations. Software testing is critical to preventing software failures but is, arguably, the least understood part of the software life cycle and the toughest to perform correctly. Adequate research has been carried out in both the process and technology dimensions of testing, but not in the human dimensions. This work attempts to fill in the gap by exploring the human dimension, i.e., trying to understand the motivation/de-motivation of software practitioners to take up and sustain testing careers. One hundred and forty …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Effect Of User Involvement In Supply Chain Cloud Innovation: A Game Theoretical Model And Analysis, Yun Chen, Lian Duan, Weiyong Zhang

Information Technology & Decision Sciences Faculty Publications

Cloud innovation has become increasingly important to supply chain innovation and performance. User involvement is a crucial part of cloud innovation. However, the effect of user involvement in supply chain cloud innovation has not been thoroughly studied, particularly its effect on product cost and optimal price. In this paper, the authors attempted to bridge this major gap in the literature. The authors reviewed the relevant literature to define cloud innovation and user involvement in supply chain cloud innovation. Then the authors developed a game model based on the Bertrand model. Analysis of the model showed that user involvement affects product …

Mac Protocols For Terahertz Communication: A Comprehensive Survey, Saim Ghafoor, Noureddine Boujnah, Mubashir Husain Rehmani, Alan Davy

Publications

Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency. However, the unique features of the Terahertz band such as high path loss, scattering, and reflection pose new challenges and results in short communication distance. The antenna directionality, in turn, is required to enhance the communication distance and to overcome the high path loss. However, these features in combine negate the use of traditional medium access protocols (MAC). Therefore, novel MAC protocol designs are required to fully exploit their potential benefits including efficient channel access, control message …

Helping Language Education Teachers Using Ai, Hyechan Jun, Kenneth C. Arnold

Summer Research

Not every language student is equal. That is the reason for differentiated instruction, where the difficulty of the material and lessons is adjusted to suit the needs of each individual student. Unfortunately, differentiated instruction requires spending a significant amount of time and effort to produce individualized content, which— though not impossible—is certainly not easy. That is why in our research we ask: “How can we use artificial intelligence to aid language education teachers in differentiated instruction?”

Medical Education And Assisted Surgery By Ar, Sadan Suneesh Menon, Thomas Wischgoll, Sharon Farra, Cindra Holland

Computer Science and Engineering Faculty Publications

No abstract provided.

An Analysis Of The Success Of Farmers Markets In Kentucky Using Logistic Regression And Support Vector Machines, Jeron Russell

Mahurin Honors College Capstone Experience/Thesis Projects

The purpose of this research is to look at the relationship that market-specific, economic, and demographic variables have with the success of farmers markets in Kentucky. It additionally seeks to build a tool for predicting farmers market success that could be used by policy makers to aid in decision-making processes concerning farmers markets. Logistic regression and Support Vector Machines (SVMs) are used on data acquired from the Kentucky Department of Agriculture and the American Community Survey in order to analyze the data in a traditional statistical approach as well as a machine learning approach. The results included an SVM model …

A Description Of A Humans Knowledge Using Artificial Intelligence, Dj Price

Mahurin Honors College Capstone Experience/Thesis Projects

There currently does not exist a way to easily view the relationships between a collection of written items (e.g. sports articles, diary entries, research papers). In recent years, novel machine learning methods have been developed which are very good at extracting semantic relationships from large numbers of documents. One of them is the (unsupervised) machine learning model Doc2Vec which constructs vectors for documents. The research project detailed in this paper uses this and other already existing algorithms to analyze the relationship between pieces of text. We set forth a broader ambition for this project before discussing the use and need …

The Mathematics, Computer Science, And Data Science Student Research Showcase, Seton Hall University

Petersheim Academic Exposition

No abstract provided.

Recommender System Projects, T. Marlowe, G. Chang

Petersheim Academic Exposition

No abstract provided.