Digital Commons Network^™

Automated Lyrical Narrative Writing, Divya Singh

Master's Projects

Computational Creativity studies the potential of computers to act as autonomous creators and co-creators in addition to tools helping people. Creativity is evident in music, visual art, problem solving and languages. Significant work has been conducted in the area of linguistic creation mainly in the generation of stories, puns, rhymes, jokes, similes, and poetry. One of the major challenges of computational creativity is to generate lyrics that exhibit human-level creativity. On one hand, the lyrics generated should be meaningful and coherent, while on the other hand, they should satisfy poetry constraints such as rhyme scheme, rhyme type, and the number …

Subtopics In Yelp Reviews, Riya Suchdev

Master's Projects

Yelp is a review platform that connects people to local businesses. It is a very popular platform that helps customers decide which business to choose. It relies on crowd sourced plain text reviews. From the business’s description some facts can be determined, such as category and location. However, more detailed description can be extracted from the reviews. Discovering latent topics and subtopics in Yelp reviews, can help summarize the reviews to gain knowledge. For example, we can deduce that reviews related to the Restaurant category tend to emphasize on service, food, order etc. Additionally, one can deduce positive or negative …

Sentiment Analysis Using An Ensemble Of Feature Selection Algorithms, Manankumar Bhagat

Master's Projects

To determine the opinion of any person experiencing any services or buying any product, the usage of Sentiment Analysis, a continuous research in the field of text mining, is a common practice. It is a process of using computation to identify and categorize opinions expressed in a piece of text. Individuals post their opinion via reviews, tweets, comments or discussions which is our unstructured information. Sentiment analysis gives a general conclusion of audits which benefit clients, individuals or organizations for decision making. The primary point of this paper is to perform an ensemble approach on feature reduction methods identified with …

Wright State University's Symposium Of Student Research, Scholarship & Creative Activities From Friday April 13, 2018, Wright State University

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

The student abstract booklet is a compilation of abstracts from students' oral and poster presentations at Wright State University's Symposium of Student Research, Scholarship & Creative Activities on April 13, 2018.

Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors Of Food Journalers, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek

Research Collection School Of Computing and Information Systems

Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and …

Using Sentiment Analysis And Pattern Matching To Signal User Review Abnormalities, Stefan S. Gloutnikov

Master's Projects

User opinions on websites like Amazon, Yelp, and TripAdvisor are a key input for consumers when figuring out what to purchase, or where and what to eat. This means that in order for such websites to provide a better service to their customers, they must guard against fake and targeted reviews. Detecting such users and reviews automatically is a very complex multi-step process, and there is no direct mechanism for solving the problem reliably. Multiple AI and Machine Learning algorithms are coupled together when examining user reviews in determining if a review is fake or not. In this project we …

Stock Price Prediction Using Deep Learning, Abhinav Tipirisetty

Master's Projects

Stock price prediction is one among the complex machine learning problems. It depends on a large number of factors which contribute to changes in the supply and demand. This paper presents the technical analysis of the various strategies proposed in the past, for predicting the price of a stock, and evaluation of a novel approach for the same. Stock prices are represented as time series data and neural networks are trained to learn the patterns from trends. Along with the numerical analysis of the stock trend, this research also considers the textual analysis of it by analyzing the public sentiment …

Detect Rumor And Stance Jointly By Neural Multi-Task Learning, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

In recent years, an unhealthy phenomenon characterized as the massive spread of fake news or unverified information (i.e., rumors) has become increasingly a daunting issue in human society. The rumors commonly originate from social media outlets, primarily microblogging platforms, being viral afterwards by the wild, willful propagation via a large number of participants. It is observed that rumorous posts often trigger versatile, mostly controversial stances among participating users. Thus, determining the stances on the posts in question can be pertinent to the successful detection of rumors, and vice versa. Existing studies, however, mainly regard rumor detection and stance classification as …

Issue 16

The Synapse: Intercollegiate science magazine

No abstract provided.

Design And Implementation Of An Artificial Neural Network Controller For Quadrotor Flight In Confined Environment, Ahmed Mekky

Mechanical & Aerospace Engineering Theses & Dissertations

Quadrotors offer practical solutions for many applications, such as emergency rescue, surveillance, military operations, videography and many more. For this reason, they have recently attracted the attention of research and industry. Even though they have been intensively studied, quadrotors still suffer from some challenges that limit their use, such as trajectory measurement, attitude estimation, obstacle avoidance, safety precautions, and land cybersecurity. One major problem is flying in a confined environment, such as closed buildings and tunnels, where the aerodynamics around the quadrotor are affected by close proximity objects, which result in tracking performance deterioration, and sometimes instability. To address this …

Deep Learning For Chatbots, Vyas Ajay Bhagwat

Master's Projects

Natural Language Processing (NLP) requires modelling complex relationships between the semantics of the language. While traditional machine learning techniques are used for NLP, the models built for conversations, called chatbots, are unable to be truly generic. While chatbots have been made with traditional machine learning techniques, deep learning has allowed the complexities within NLP to be easier to model and can be leveraged to build a chatbot which has a real conversation with a human. In this project, we explore the problems and techniques used to build chatbots and where improvements can be made. We analyze different architectures to build …

Deep Learning Algorithm Recommender, Rajat Kabra

Master's Projects

Deep learning contains a set of algorithms that are based on the functioning of human brain i.e. neural networks. These algorithms require a lot of computation power and time along with complex setup to get good results. The project contains several artificial neural network implementation for a variety of tasks like data classification, image classification, natural language processing and more. The project contains an exploratory analysis of hyperparameters of deep learning algorithms in domain of deep learning applications to prove that it is possible to achieve a good accuracy with less resources.

Artificial Intelligence: An Analysis Of Alan Turing’S Role In The Conception And Development Of Intelligent Machinery, Erika L. Furtado

Selected Honors Theses

The purpose of this thesis is to follow the thread of Alan Turing’s ideas throughout his decades of research and analyze how his predictions have come to fruition over the years. Turing’s Computing Machinery and Intelligence is the paper in which the Turing Test is described as an alternative way to answer the question “can machines think?” (Turing 433). Since the development of Turing’s original paper, there has been a tremendous amount of advancement in the field of artificial intelligence. The field has made its way into art classification as well as the medical industry. The main concept researched in …

Perspective On And Re-Orientation Of Physical Proxies In Object-Focused Remote Collaboration, Martin Feick, Terrance Mok, Anthony Tang, Lora Oehlberg, Ehud Sharlin

Research Collection School Of Computing and Information Systems

Remote collaborators working together on physical objects have difficulty building a shared understanding of what each person is talking about. Conventional video chat systems are insufficient for many situations because they present a single view of the object in a flattened image. To understand how this limited perspective affects collaboration, we designed the Remote Manipulator (ReMa), which can reproduce orientation manipulations on a proxy object at a remote site. We conducted two studies with ReMa, with two main findings. First, a shared perspective is more effective and preferred compared to the opposing perspective offered by conventional video chat systems. Second, …

Eat & Tell: A Randomized Trial Of Random-Loss Incentive To Increase Dietary Self-Tracking Compliance, Palakorn Achananuparp, Ee Peng Lim, Vibhanshu Abhishek, Tianjiao Yun

Research Collection School Of Computing and Information Systems

A growing body of evidence has shown that incorporating behavioral economics principles into the design of financial incentive programs helps improve their cost-effectiveness, promote individuals' short-term engagement, and increase compliance in health behavior interventions. Yet, their effects on long-term engagement have not been fully examined. In study designs where repeated administration of incentives is required to ensure the regularity of behaviors, the effectiveness of subsequent incentives may decrease as a result of the law of diminishing marginal utility. In this paper, we introduce random-loss incentive-a new financial incentive based on loss aversion and unpredictability principles-to address the problem of individuals' …

Domain-Specific Cross-Language Relevant Question Retrieval, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Chinese developers often cannot effectively search questions in English, because they may have difficulties in translating technical words from Chinese to English and formulating proper English queries. For the purpose of helping Chinese developers take advantage of the rich knowledge base of Stack Overflow and simplify the question retrieval process, we propose an automated cross-language relevant question retrieval (CLRQR) system to retrieve relevant English questions for a given Chinese question. CLRQR first extracts essential information (both Chinese and English) from the title and description of the input Chinese question, then performs domain-specific translation of the essential Chinese information into English, …

The Application Of Text Mining And Data Visualization Techniques To Textual Corpus Exploration, Jeffrey R. Smith Jr.

Theses and Dissertations

Unstructured data in the digital universe is growing rapidly and shows no evidence of slowing anytime soon. With the acceleration of growth in digital data being generated and stored on the World Wide Web, the prospect of information overload is much more prevalent now than it has been in the past. As a preemptive analytic measure, organizations across many industries have begun implementing text mining techniques to analyze such large sources of unstructured data. Utilizing various text mining techniques such as n -gram analysis, document and term frequency analysis, correlation analysis, and topic modeling methodologies, this research seeks to develop …

Perceived Access And Barriers To Care Among Illicit Drug Users And Hazardous Drinkers: Findings From The Seek, Test, Treat, And Retain Data Harmonization Initiative (Sttr)., Mika Matsuzaki, Quan M Vu, Marya Gwadz, Joseph A C Delaney, Irene Kuo, Maria Esther Perez Trejo, William E Cunningham, Chinazo O Cunningham, Katerina Christopoulos

Epidemiology Faculty Publications

BACKGROUND: Illicit drug use (DU) and hazardous drinking (HD) among marginalized populations may be associated with greater barriers to care.

METHODS: We used baseline data on the participants of the Seek, Test, Treat, and Retain data harmonization initiative. DU includes use of any illicit drugs within the past 6 months. HD was defined as scores ≥8 for men and ≥ 7 for women on Alcohol Use Disorders Identification Test within the past 12 months. Social support scores were assigned by summing scores from individual questions related to social support. Two outcomes for multivariable regression models and mediation analysis were perceived …

Trolling Pathologies, Hal Berghel

Computer Science Faculty Research

The netizen watch phrase is: don't feed the trolls. A better guide would be: learn to recognize the trolls.

Speech Emotion Recognition Using Convolutional Neural Networks, Somayeh Shahsavarani

School of Computing: Dissertations, Theses, and Student Research

Automatic speech recognition is an active field of study in artificial intelligence and machine learning whose aim is to generate machines that communicate with people via speech. Speech is an information-rich signal that contains paralinguistic information as well as linguistic information. Emotion is one key instance of paralinguistic information that is, in part, conveyed by speech. Developing machines that understand paralinguistic information, such as emotion, facilitates the human-machine communication as it makes the communication more clear and natural. In the current study, the efficacy of convolutional neural networks in recognition of speech emotions has been investigated. Wide-band spectrograms of the …

The Journal Of Undergraduate Research: 2017

The Journal of Undergraduate Research

This is the complete issue of the South Dakota State University Journal of Undergraduate Research, Volume 15.

Why Zipf's Law: A Symmetry-Based Explanation, Daniel Cervantes, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we have probability distributions for which, for large values of the corresponding quantity x, the probability density has the form ρ(x) ~ x^−αfor some α > 0. While, in principle, we have laws corresponding to different α, most frequently, we encounter situations -- first described by Zipf for linguistics -- when α is close to 1. The fact that Zipf's has appeared frequently in many different situations seems to indicate that there must be some fundamental reason behind this law. In this paper, we provide a possible explanation.

Kratylos: A Tool For Sharing Interlinearized And Lexical Data In Diverse Formats, Daniel Kaufman, Raphael Finkel

Computer Science Faculty Publications

In this paper we present Kratylos, at www.kratylos.org/, a web application that creates searchable multimedia corpora from data collections in diverse formats, including collections of interlinearized glossed text (IGT) and dictionaries. There exists a crucial lacuna in the electronic ecology that supports language documentation and linguistic research. Vast amounts of IGT are produced in stand-alone programs without an easy way to share them publicly as dynamic databases. Solving this problem will not only unlock an enormous amount of linguistic information that can be shared easily across the web, it will also improve accountability by allowing us to verify analyses …

Https://Onlinelibrary.Wiley.Com/Doi/10.1002/Spy2.15#:~:Text=A%20review%20and%20an%20empirical%20analysis%20of%20privacy%20policy%20and%20notices%20for%20consumer%20internet%20of%20things, Alfredo J. Perez, Sherali Zeadally, Jonathan Cochran

Computer Science Faculty Publications

The privacy policies and practices of six consumer Internet of things (IoT) devices were reviewed and compared. In addition, an empirical verification of the compliance of privacy policies for data collection practices on two voice-activated intelligent assistant devices, namely the Amazon Echo Dot and Google Home devices was performed. The review shows that IoT privacy policies may not be usable from the human-computer interaction perspective because IoT policies are included as part of the manufacturers' general privacy policy (which may include policies unrelated to the device), or the IoT policy requires to read (in addition to the IoT policies) the …

Forecasting Country Conflict Within Modified Combatant Command Regions Using Statistical Learning Methods, Sarah Neumann

Theses and Dissertations

Conflict forecasts are crucial to Combatant Commanders’ understanding of the dynamic environment encompassing countries within their area of responsibility. The current structure of the Combatant Commands (COCOMs) is rooted in geography by grouping nations in geographic proximity to the same regional command. However, leaders today question the effectiveness of the current structure. A novel modified k-means clustering algorithm is developed and implemented that groups countries based on data similarities and geographic proximity resulting in new COCOM groupings that improve conflict forecasts. The data spans various political, military, economic, and social characteristics of countries, and is used to develop conditional logistic …

An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our first model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we …

Front Matter, Adfsl

Annual ADFSL Conference on Digital Forensics, Security and Law

No abstract provided.

The Prospect Of Human Spiritual Unity Through The Cosmic Story, Philip Novak

Collected Faculty Scholarship

Thomas Berry and Brian Swimme have noted that, “we seem to be moving beyond any religious expression so far known to the human into a meta-religious age that seems to be a new comprehensive context for all religions.” That “new comprehensive context” is of course now known as Big History -- a.k.a. the Evolutionary Epic, Universe Story, or New Cosmic Story—the astonishing contemporary synthesis of modern sciences that tells a coherent story of the evolution of the universe from the Big Bang 13.7 billion years ago to the present.

Yet with the notable exception of the writings of Berry and …

Patent Keyword Extraction Algorithm Based On Distributed Representation For Patent Classification, Jie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu

Faculty Publications

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard …

Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis

Faculty Publications

In response to the need for examples of test validation from which everyday language programs can benefit, this paper reports on a study that used Bachman’s (2005) assessment use argument (AUA) framework to examine evidence to support claims made about the intended interpretations and uses of scores based on a new web-based Spanish language placement test. The test, which consisted of 100 items distributed across five item types (sound discrimination, grammar, listening comprehension, reading comprehension, and vocabulary), was tested with 2,201 incoming first-year and transfer students at a large, Midwestern public university. Analyses of internal consistency and validity revealed the …