Computer Sciences Commons^™

An Authoring Tool To Provide Group And Crowd Animation Using Natural Language Scripts, Guido Mainardi, Aline Normoyle, Vinícius Cassol, Norman Badler, Soraia Raupp Musse

Computer Science Faculty Research and Scholarship

Virtual environments have become ubiquitous, expanding beyond games into the domains of architecture, engineering, psychology, education, and archaeology. Furthermore, virtual humans can further enhance these environments when they provide compelling and coherent behaviors. In this paper, we present a scripting language based on simple, plain English commands. Our system assists people without game and animation expertise to populate large environments and complex scenarios. To validate our approach, we develop a prototype using Unreal Engine 4 and author a variety of indoor and outdoor agent simulations. Furthermore, we test our prototype with both experienced and inexperienced users, creating scenarios for a …

The U-Net-Based Active Learning Framework For Enhancing Cancer Immunotherapy, Vishwanshi Joshi

Theses, Dissertations and Capstones

Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. …

Human-Ai Teaming For Dynamic Interpersonal Skill Training, Xavian Alexander Ogletree

Browse all Theses and Dissertations

In almost every field, there is a need for strong interpersonal skills. This is especially true in fields such as medicine, psychology, and education. For instance, healthcare providers need to show understanding and compassion for LGBTQ+ and BIPOC (Black, Indigenous, and People of Color), or individuals with unique developmental or mental health needs. Improving interpersonal skills often requires first-person experience with expert evaluation and guidance to achieve proficiency. However, due to limited availability of assessment capabilities, professional standardized patients and instructional experts, students and professionals currently have inadequate opportunities for expert-guided training sessions. Therefore, this research aims to demonstrate leveraging …

Stellar Classification Of Folded Spectra Using The Mk Classification Scheme And Convolutional Neural Networks, John Magee

Dissertations

The year 1943 saw the introduction of the Morgan-Keenan (MK) classification scheme and this replaced the existing Harvard Classification scheme. Both stellar classification scheme are fundamentally grounded in the field of spectroscopy. The Harvard Classification scheme classified stars based on stellar surface temperature. The MK Classification scheme introduced the concept of a luminosity class that is intrinsically linked to the surface gravity of a star. Temperature and luminosity class values are estimated directly from the stellar spectrum.

Machine learning is a well-established technique in astronomy. Traditionally, a spectrum is treated as a one-dimensional sequence of data. Techniques such as artificial …

A Deep Understanding Of Structural And Functional Behavior Of Tabular And Graphical Modules In Technical Documents, Michail Alexiou

Browse all Theses and Dissertations

The rapid increase of published research papers in recent years has escalated the need for automated ways to process and understand them. The successful recognition of the information that is contained in technical documents, depends on the understanding of the document’s individual modalities. These modalities include tables, graphics, diagrams and etc. as defined in Bourbakis’ pioneering work. However, the depth of understanding is correlated to the efficiency of detection and recognition. In this work, a novel methodology is proposed for automatic processing of and understanding of tables and graphics images in technical document. Previous attempts on tables and graphics understanding …

Hybrid Models As Transdisciplinary Research Enablers, Andreas Tolk, Alison Harper, Navonil Mustafee

Computational Modeling & Simulation Engineering Faculty Publications

Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in …

Sustainable International Engagement Using A Partner Co-Hosted Teaching Model, Brian Gillespie, Paul Doyle, Zy Jiang, Darryl Humble

Conference Papers

Internationalisation is a significant activity of Higher Education Institutions (HEIs) worldwide and is typically embedded within the aims, ambitions, vision, and strategy of the institution. It incorporates the policies and procedures required to facilitate participation within a global academic environment, and is often considered to be a transformative process that impacts practices in teaching and learning, research, and administration. With formal protocols to establish partnerships, such as memoranda of understanding and articulation agreements, the business of formally creating international partnerships is well defined. However, the motivations, corresponding metrics and key performance indicators (KPIs) of successful partnerships are not as well …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Blockchain technology facilitates the flow of information and the speed of information through a faster and more decentralized network. It has its advantages as compared to more centralized networks and legacy networks. With the evolution of mainstream technology, blockchains is predicted to be more effective and sufficient to consumers and commercial companies. In this paper, blockchains will be scaled to cryptocurrency mining, where cryptocurrencies utilize blockchain technology to record transactions and orders. Mining will also be examined through energy consumption, the algorithms behind some cryptocurrencies, their sustainability issue, and resolutions to combat high energy consumption. While the pace of energy …

Combination Of Facebook Prophet And Attention-Based Lstm With Multi- Source Data For Indian Stock Market Prediction, Pavan Nagesh

Dissertations

The stock market prediction has been the subject of interest to various researchers and analysts due to its highly unpredictable nature and serves as a perfect example for time series forecasting. Over the years deep learning models such as Long-Term Short-Term Memory and statistical models such as Autoregressive Integrated Moving Average have shown promising results in predicting future stock prices. But the results from these models cannot be generalized as they fail to incorporate the dynamics of the market and influence of several external factors such as political, social, investor's emotion, etc on stock markets. Recently Facebook’s creation of the …

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam

Dissertations

Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

How To Estimate Time Needed For Software Migration, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to migrate the existing software package to a new programming language and/or a new operating system. In such a migration, it is important to be able to accurately estimate time needed for this migration: if we underestimate this time, we will lose money and may go bankrupt; if we overestimate this time, other companies who estimate more accuracy will outbid us, and we will lose the contract. The formulas currently used for estimating migration time often lead to underestimation. In this paper, we start with the main ideas behind the existing formulas, and show …

Distributions On An Interval As A Scale-Invariant Combination Of Scale-Invariant Functions: Theoretical Explanation Of Empirical Marchenko-Pastur-Type Distributions, Vladik Kreinovich, Kevin Alvarez, Chon Van Le

Departmental Technical Reports (CS)

In many practical situations, we know the lower and upper bounds L and U on possible values of a quantity x. In such situations, the probability distribution of this quantity is also located on the corresponding interval [L, U]. In many such cases, the empirical probability distribution has the form d(x) = const * (x − L)α− * (U − x)α+ * xα. In the particular case α− = α+ = 0.5 and α = −1, we get the Marchenko-Pastur distribution that describes the distribution of the eigenvalues of a random matrix. However, in some cases, the empirical distribution corresponds …

When To Stop Computing And Start Investing, Sean R. Aguilar, Olga Kosheleva

Departmental Technical Reports (CS)

Purpose: The purpose of the study is to analyze when -- while predicting the future price of a financial instrument -- we should stop computations and start using this information for the actual investment.

Design/methodology/approach: We derive the explicit formulas explaining how the resulting gain depends on the duration of computations.

Findings: We provide an algorithm that enables us to decide the computation time that leads to the largest possible gain.

Originality/value: To the best of our knowledge, this is the first solution to the problem. Following our recommendations will allow investors to select the computation time for which the …

Tents Of Israel Revisited: Audio Privacy, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In one of the Biblical stories, prophet Balaam blesses the tents of Israel for being good. But what can be so good about the tents? The traditional Rabbinical interpretation is that the placement of the tents provided full privacy. In our previous paper, we considered the consequences of visual privacy: from each entrance, one cannot see what is happening at any other entrance. In this paper, we analyze the possible consequences of audio privacy: from each tent, you cannot hear what is going on in other tents.

How To Gauge Reliability Of A Binary Classification Result: A Simple Case, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to make a binary decision based on the available data: whether an incoming email is a spam or not, whether to give a bank loan to a company, etc. In many such situations, we can (and do) use machine learning to come up with such a decision. The problem is that while the results of a machine learning model are not 100% reliable, the existing machine learning algorithms do not allow us to decide how reliable is each result. In this paper, for simple examples, we provide a technique for gauging this reliability.

Lanchester's Equations And Cyberwarfare, George Markowsky, Linda Markowsky

Computer Science Faculty Research & Creative Works

In his classic book Aircraft in Warfare, F. W. Lanchester discussed different types of warfare and presented equations, called the Lanchester equations, that can be used to model the results of battles between two forces of different sizes or capabilities. This paper introduces the Lanchester equations and provides a theoretical discussion leading to an analysis of the relative value of increasing the effectiveness of military assets vs. increasing the quantity of those assets. In particular, we show that increasing the effectiveness contributes only linearly to the power of a combatant, but increasing the quantity contributes quadratically. This paper also presents …

Abn: Agent-Aware Boundary Networks For Temporal Action Proposal Generation, Khoa Vo, Kashu Yamazaki, Sang Truong, Minh-Triet Tran, Akihiro Sugimoto, Ngan Le

Computer Science and Computer Engineering Faculty Publications and Presentations

Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding. Despite the great achievement in TAPG, most existing works ignore the human perception of interaction between agents and the surrounding environment by applying a deep learning model as a black-box to the untrimmed videos to extract video visual representation. Therefore, it is beneficial and potentially improves the performance of TAPG if we can capture these interactions between agents and the environment. In this paper, we propose a novel …

Change Request Prediction And Effort Estimation In An Evolving Software System, Lamees Abdullah Alhazzaa

Electronic Theses and Dissertations

Prediction of software defects has been the focus of many researchers in empirical software engineering and software maintenance because of its significance in providing quality estimates from the project management perspective for an evolving legacy system. Software Reliability Growth Models (SRGM) have been used to predict future defects in a software release. Modern software engineering databases contain Change Requests (CR), which include both defects and other maintenance requests. Our goal is to use defect prediction methods to help predict CRs in an evolving legacy system.

Limited research has been done in defect prediction using curve-fitting methods evolving software systems, with …

The Design, Development, And Determination Of A Virtual Reality Classroom, Victoria Alexxis Reddington

Electronic Theses and Dissertations

The COVID-19 pandemic has radically changed the way students learn and engage with their peers and instructors. Likewise, instructors have had to quickly transform their course materials to suit the online classroom format. Results from a survey of students and instructors at the University of Denver revealed that perceived levels of learning and collaboration were lessened with the transition to online learning. Moreover, the sense of presence in an educational atmosphere with other individuals was reported to be significantly stronger in a real physical classroom, as compared to an online classroom. This thesis therefore seeks to provide a new, alternative …

Leveraging Sequential Nature Of Conversations For Intent Classification, Shree Gotteti

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Conversations are more than just a sequence of text, it is where two or more participants interact in order to achieve their goals. Conversation Understanding (CU) requires all participants to understand each others intent. In the past decade, CU has been extended from automated human-human text processing to build automated conversational agents for human-machine interactions. Despite their popularity, these automated conversational agents (like Siri, Alexa, etc) can't handle more than one or two utterances, and they don't recognize conversations as intents. The development of approaches that extract intents behind an utterance is essential for the advancements of Question Answering (QA) …

Content Adaption And Design In Mobile Learning Of Wind Instruments, Neha Priyadarshani

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People in today's world seek things that are simple to use. Learning is one of the most crucial aspects of the ongoing digital transformation. Everything is now accessible with a single click on mobile devices, making access to instructional materials faster, easier, and more comfortable. It takes time and effort to build abilities and become an expert in the fields of learning, training, and teaching; and music learning demands a great deal of both practice and mentoring. Initially, music teachers and band directors must maintain a steady attention and devote a significant amount of time to manually teaching materials. This …

The 12th Annual Graduate Research Symposium 2021 Poster Tu Dublin: How To Recruit And Retain Women In Computer Science, Alina Berry, Susan Mckeever, Brenda Murphy, Sarah Jane Delany

Other resources

While in recent decades a number of efforts have been coordinated to address the issue of gender imbalance in STEM (science, technology, engineering and mathematics) disciplines, the problem still persists. Many authors speak of the ‘leaky’ pipeline metaphor that describes the loss of women in STEM areas before reaching senior roles. Research shows that women who leave are unlikely to return. The issue is particularly severe in the area of computer science, where women represent less than 20% of the labour force across the EU.

This poster introduces a summary of findings from the literature on how to effectively recruit …

Multimodal Learning For Hateful Memes Detection, Yi Zhou, Zhenhao Chen, Huiyuan Yang

Computer Science Faculty Research & Creative Works

Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes …

Complex Interactions Between Multiple Goal Operations In Agent Goal Management, Sravya Kondrakunta

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A significant issue in cognitive systems research is to make an agent formulate and manage its own goals. Some cognitive scientists have implemented several goal operations to support this issue, but no one has implemented more than a couple of goal operations within a single agent. One of the reasons for this limitation is the lack of knowledge about how various goals operations interact with one another. This thesis addresses this knowledge gap by implementing multiple-goal operations, including goal formulation, goal change, goal selection, and designing an algorithm to manage any positive or negative interaction between them. These are integrated …

A Deep Understanding Of Structural And Functional Behavior Of Tabular And Graphical Modules In Technical Documents, Michail Alexiou

Browse all Theses and Dissertations

The rapid increase of published research papers in recent years has escalated the need for automated ways to process and understand them. The successful recognition of the information that is contained in technical documents, depends on the understanding of the document’s individual modalities. These modalities include tables, graphics, diagrams and etc. as defined in Bourbakis’ pioneering work. However, the depth of understanding is correlated to the efficiency of detection and recognition. In this work, a novel methodology is proposed for automatic processing of and understanding of tables and graphics images in technical document. Previous attempts on tables and graphics understanding …

Text Classification Using Novel Term Weighting Scheme-Based Improved Tf-Idf For Internet Media Reports, Zhiying Jiang Phd, Bo Gao, Yanlin He, Yongming Han, Paul Doyle, Qunxiong Zhu

Other

With the rapid development of the internet technology, a large amount of internet text data can be obtained. The text classification (TC) technology plays a very important role in processing massive text data, but the accuracy of classification is directly affected by the performance of term weighting in TC. Due to the original design of information retrieval (IR), term frequency-inverse document frequency (TF-IDF) is not effective enough for TC, especially for processing text data with unbalanced distributions in internet media reports. Therefore, the variance between the DF value of a particular term and the average of all DFs , namely, …