Computer Sciences Commons^™

Exclusion Cycles: Reinforcing Disparities In Medicine, Ana Bracic, Shawneequa L. Callier, Nicholson Price

Articles

Minoritized populations face exclusion across contexts from politics to welfare to medicine. In medicine, exclusion manifests in substantial disparities in practice and in outcome. While these disparities arise from many sources, the interaction between institutions, dominant-group behaviors, and minoritized responses shape the overall pattern and are key to improving it. We apply the theory of exclusion cycles to medical practice, the collection of medical big data, and the development of artificial intelligence in medicine. These cycles are both self-reinforcing and other-reinforcing, leading to dismayingly persistent exclusion. The interactions between such cycles offer lessons and prescriptions for effective policy.

A Novel Handover Method Using Destination Prediction In 5g-V2x Networks, Pooja Shyamsundar

Master's Projects

This paper proposes a novel approach to handover optimization in fifth generation vehicular networks. A key principle in designing fifth generation vehicular network technology is continuous connectivity. This makes it important to ensure that there are no gaps in communication for mobile user equipment. Handovers can cause disruption in connectivity as the process involves switching from one base station to another. Issues in the handover process include poor load management for moving traffic resulting in low bandwidth or connectivity gaps, too many hops resulting in multiple unneccessary handovers, short dwell times and ineffective base station selection resulting in delays and …

Analysis Of Public Sentiment Of Covid-19 Pandemic, Vaccines, And Lockdowns, Devinesh Singh

Master's Projects

CoV-2 pandemic prompted lockdown measures to be implemented worldwide; these directives were implemented nationwide to stunt the spread of the infection. Throughout the lockdowns, millions of individuals resorted to social media for entertainment, communicate with friends and family, and express their opinions about the pandemic. Simultaneously, social media aided in the dissemination of misinformation, which has proven to be a threat to global health. Sentiment analysis, a technique used to analyze textual data, can be used to gain an overview of public opinion behind CoV-2 from Twitter and TikTok. The primary focus of the project is to build a deep …

Caption And Image Based Next-Word Auto-Completion, Meet Patel

Master's Projects

With the increasing number of options or choices in terms of entities like products, movies, songs, etc. which are now available to users, they try to save time by looking for an application or system that provides automatic recommendations. Recommender systems are automated computing processes that leverage concepts of Machine Learning, Data Mining and Artificial Intelligence towards generating product recommendations based on a user’s preferences. These systems have given a significant boost to businesses across multiple segments as a result of reduced human intervention. One similar aspect of this is content writing. It would save users a lot of time …

Empirical Evaluation Of The Shift And Scale Parameters In Batch Normalization, Yashna Peerthum

Master's Projects

Batch Normalization (BatchNorm) is a technique that enables the training of deep neural networks, especially Convolutional Neural Networks (CNN) for computer vision tasks. It has been empirically demonstrated that BatchNorm increases per- formance, stability, and accuracy, although the reasons for these improvements are unclear. BatchNorm consists of a normalization step with trainable shift and scale parameters. In this paper, we examine the role of normalization and the shift and scale parameters in BatchNorm. We implement two new optimizers in PyTorch: a version of BatchNorm that we refer to as AffineLayer, which includes the shift and scale transform without normalization, and …

Multi-Step Prediction Using Tree Generation For Reinforcement Learning, Kevin Prakash

Master's Projects

The goal of reinforcement learning is to learn a policy that maximizes a reward function. In some environments with complete information, search algorithms are highly useful in simulating action sequences in a game tree. However, in many practical environments, such effective search strategies are not applicable since their state transition information may not be available. This paper proposes a novel method to approximate a game tree that enables reinforcement learning to use search strategies even in incomplete information environments. With an approximated game tree, the agent predicts all possible states multiple steps into the future and evaluates the states to …

Graph Neural Networks For Malware Classification, Vrinda Malhotra

Master's Projects

Malware is a growing threat to the digital world. The first step to managing this threat is malware detection and classification. While traditional techniques rely on static or dynamic analysis of malware, the generation of these features requires expert knowledge. Function call graphs (FCGs) consist of program functions as their nodes and their interprocedural calls as their edges, providing a wealth of knowledge that can be utilized to classify malware without feature extraction that requires experts. This project treats malware classification as a graph classification problem, setting node features using the Local Degree Profile (LDP) model and using different graph …

Using Machine Learning To Maximize First-Generation Student Success A Contribution To The Mission Of Aiding The Underserved, Mustafa Emre Yesilyurt

Master's Projects

The Leadership and Career Accelerator (UNVS 101) is a course offered at San José State University (SJSU) designed to hone industry skills in and provide support to students of underserved backgrounds. The main goal of this study is to determine which features are most significant to identifying the students at risk of failing the course. This will allow faculty to better focus data collection efforts and facilitate an increase in classifier accuracy. The data came as three distinct sets (sources). One contained features describing student demographics and academic history, another described the students’ experience in the course, and a third …

Abstractive Text Summarization For Tweets, Siyu Chen

Master's Projects

In the high-tech age, we can access a vast number of articles, information, news, and opinion online. The wealth of information allows us to learn about the topics we are interested in more easily and cheaply, but it also requires us to spend an enormous amount of time reading online. Text summarization can help us save a lot of reading time so that we can know more information in a shorter period. The primary goal of text summarization is to shorten the text while including as much vital information as possible in the original text so fewer people use this …

A Study On Human Face Expressions Using Convolutional Neural Networks And Generative Adversarial Networks, Sriramm Muthyala Sudhakar

Master's Projects

Human beings express themselves via words, signs, gestures, and facial emotions. Previous research using pre-trained convolutional models had been done by freezing the entire network and running the models without the use of any image processing techniques. In this research, we attempt to enhance the accuracy of many deep CNN architectures like ResNet and Senet, using a variety of different image processing techniques like Image Data Generator, Histogram Equalization, and UnSharpMask. We used FER 2013, which is a dataset containing multiple classes of images. While working on these models, we decided to take things to the next level, and we …

Gesture Recognition Using Neural Networks, Ashwini Kurady

Master's Projects

The advances in technology have brought in a lot of changes in the way humans go about their lives. This has enhanced the significance of Artificial Neural Networks and Computer Vision- based interactions with the world. Gesture Recognition is one of the major focus areas in Computer Vision. This involves Human Computer Interfaces (HCI) that would capture and understand human actions. In this project, we will explore how Neural Network concepts can be applied in this challenging field of Computer Vision. By leveraging the latest research for Gesture Recognition, we researched on how to capture the movement across different frames …

Predicting Externally Visible Traits From A Dna Sample For Law Enforcement Applications, Niraj Pandkar

Master's Projects

A large majority of crimes such as homicides, sexual assaults and missing person cases are not solved within a reasonable timeframe and become cold cases. The ability to predict visual appearance and ancestry from a DNA sample will provide an unprecedented advancement in such criminal investigations. DNA based prediction of craniofacial features, phenotypes and ancestry can be used to reduce the pool of candidates onto which to perform further investigations. To achieve the above goal, it is first essential to substantiate, model and measure the intrinsic relationship between the genomic markers and phenotypic features. The first step is to standardize …

Generative Adversarial Networks For Image-Based Malware Classification, Huy Nguyen

Master's Projects

Malware detection and analysis are important topics in cybersecurity. For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. With the rise in computing power and the advent of cloud computing, deep learning models for malware analysis has gained in popularity. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine

(SVM), XGBoost, and Restricted Boltzmann Machines …

Contextualized Vector Embeddings For Malware Detection, Vinay Pandya

Master's Projects

Malware classification is a technique to classify different types of malware which form an integral part of system security. The aim of this project is to use context dependant word embeddings to classify malware. Tansformers is a novel architecture which utilizes self attention to handle long range dependencies. They are particularly effective in many complex natural language processing tasks such as Masked Lan- guage Modelling(MLM) and Next Sentence Prediction(NSP). Different transfomer architectures such as BERT, DistilBert, Albert, and Roberta are used to generate context dependant word embeddings. These embeddings would help in classifying different malware samples based on their similarity …

Adversarial Attacks On Speech Separation Systems, Kendrick Trinh

Master's Projects

Speech separation is a special form of blind source separation in which the objective is to decouple two or more sources such that they are distinct. The need for such an ability grows as speech activated device usage increases in our every day life. These systems, however, are susceptible to malicious actors. In this work, we repurpose proven adversarial attacks and leverage them against a combination speech separation and speech recognition system. The attack adds adversarial noise to a mixture of two voices such that the two outputs of the speech separation system are similarly transcribed by the speech recognition …

Faking Sensor Noise Information, Justin Chang

Master's Projects

Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …

Exploring Algorithmic Literacy For College Students: An Educator’S Roadmap, Susan Gardner Archambault

LMU Theses and Dissertations

Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general …

Security Concerns On Machine Learning Solutions For 6g Networks In Mmwave Beam Prediction, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Devrim Unal

Engineering Technology Faculty Publications

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial …

Bfv-Based Homomorphic Encryption For Privacy-Preserving Cnn Models, Febrianti Wibawa, Ferhat Ozgur Catak, Salih Sarp, Murat Kuzlu

Engineering Technology Faculty Publications

Medical data is frequently quite sensitive in terms of data privacy and security. Federated learning has been used to increase the privacy and security of medical data, which is a sort of machine learning technique. The training data is disseminated across numerous machines in federated learning, and the learning process is collaborative. There are numerous privacy attacks on deep learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in medical data applications. Homomorphic encryption-based model security from the adversarial collaborator is one of the answers …

A Citizen-Science Approach For Urban Flood Risk Analysis Using Data Science And Machine Learning, Candace Agonafir

Dissertations and Theses

Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. The main goal of this dissertation is to develop novel approaches toward the comprehension of urban flood risk using data science techniques on crowd-sourced data. This is accomplished by developing a series of data-driven models to identify flood factors of significance and localized areas of flood vulnerability in New York City (NYC). First, the infrastructural (catch basin clogs, …

Secure Multi-Robot Adaptive Information Sampling With Continuous, Periodic And Opportunistic Connectivity, Tamim Khatib

UNF Graduate Theses and Dissertations

Multi-robot teams are an increasingly popular approach for information gathering in large geographic areas, with applications in precision agriculture, natural disaster aftermath surveying, and pollution tracking. In a coordinated multi-robot information sampling scenario, robots share their collected information amongst one another to form better predictions. These robot teams are often assembled from untrusted devices, making the verification of the integrity of the collected samples an important challenge. Furthermore, such robots often operate under conditions of continuous, periodic, or opportunistic connectivity and are limited in their energy budget and computational power. In this thesis, we study how to secure the information …

A Machine Learning Approach To Intended Motion Prediction For Upper Extremity Exoskeletons, Justin Berdell

Graduate Research Theses & Dissertations

A fully solid-state, software-defined, one-handed, handle-type control device built around a machine-learning (ML) model that provides intuitive and simultaneous control in position and orientation each in a full three degrees-of-freedom (DOF) is proposed in this paper. The device, referred to as the “Smart Handle”, and it is compact, lightweight, and only reliant on low-cost and readily available sensors and materials for construction. Mobility chairs for persons with motor difficulties could make use of a control device that can learn to recognize arbitrary inputs as control commands. Upper-extremity exoskeletons used in occupational settings and rehabilitation require a natural control device like …

Application Of Artificial Intelligence And Machine Learning For Hiv Prevention Interventions, Yang Xiang, Jingcheng Du, Kayo Fujimoto, Fang Li, John Schneider, Cui Tao

Faculty, Staff and Student Publications

In 2019, the US Government announced its goal to end the HIV epidemic within 10 years, mirroring the initiatives set forth by UNAIDS. Public health prevention interventions are a crucial part of this ambitious goal. However, numerous challenges to this goal exist, including improving HIV awareness, increasing early HIV infection detection, ensuring rapid treatment, optimising resource distribution, and providing efficient prevention services for vulnerable populations. Artificial intelligence has had a pivotal role in revolutionising health care and has shown great potential in developing effective HIV prevention intervention strategies. Although artificial intelligence has been used in a few HIV prevention intervention …

Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

Electrical & Computer Engineering Faculty Publications

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …

Monitoring Plants Growth In Indoor Vertical Farms Using Computer Vision And Ai Techniques, Bhama Krishna Pillutla

Graduate Research Theses & Dissertations

Climatic conditions like temperature, drought, and heavy metals disturb plant cell structures and, ultimately, plant growth that significantly affects crop production. Due to increasing climate change, maize crop yields are projected to decline by 24% by the end of century. With the increase in food demands and decrease in agricultural land and water resources, the space for effective farming is left much desired. Though limited to a few crops at this moment, Indoor Vertical Farming is one technique that requires much less land space, water, soil, and sunlight when compared to traditional farming. Vertical farming allows artificial control of temperature, …

Modeling And Visualization Of Long-Term Public Opinion On Covid-19 Vaccine, Ashiqur Rahman

Graduate Research Theses & Dissertations

The coronavirus pandemic created significant dependence on social media. While the social web was crucial in spreading timely information and informing the public, misinformation has also spread with little to no oversight. Several works have focused on identifying misinformation and topic analysis in COVID-19 (SARS-COV-2) tweets. While most of the previous studies focus on a shorter time frame, we analyzed a larger dataset starting from the beginning of the pandemic until the end of December 2021. Our work focuses on a novel area that identifies the motivating and demotivating topics of COVID-19 vaccination and analyzes these topics based on time, …

Visual Descriptor Extraction From Patent Figure Captions: A Case Study Of Data Efficiency Between Bilstm And Transformer, Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen

Computer Science Faculty Publications

Technical drawings used for illustrating designs are ubiquitous in patent documents, especially design patents. Different from natural images, these drawings are usually made using black strokes with little color information, making it challenging for models trained on natural images to recognize objects. To facilitate indexing and searching, we propose an effective and efficient visual descriptor model that extracts object names and aspects from patent captions to annotate benchmark patent figure datasets. We compared two state-of-the-art named entity recognition (NER) models and found that with a limited number of annotated samples, the BiLSTM-CRF model outperforms the Transformer model by a significant …

Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He

Computer Science Faculty Publications

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow …

Theory Entity Extraction For Social And Behavioral Sciences Papers Using Distant Supervision, Xin Wei, Lamia Salsabil, Jian Wu

Computer Science Faculty Publications

Theories and models, which are common in scientific papers in almost all domains, usually provide the foundations of theoretical analysis and experiments. Understanding the use of theories and models can shed light on the credibility and reproducibility of research works. Compared with metadata, such as title, author, keywords, etc., theory extraction in scientific literature is rarely explored, especially for social and behavioral science (SBS) domains. One challenge of applying supervised learning methods is the lack of a large number of labeled samples for training. In this paper, we propose an automated framework based on distant supervision that leverages entity mentions …

A Synthetic Prediction Market For Estimating Confidence In Published Work, Sarah Rajtmajer, Christopher Griffin, Jian Wu, Robert Fraleigh, Laxmann Balaji, Anna Squicciarini, Anthony Kwasnica, David Pennock, Michael Mclaughlin, Timothy Fritton, Nishanth Nakshatri, Arjun Menon, Sai Ajay Modukuri, Rajal Nivargi, Xin Wei, Lee Giles

Computer Science Faculty Publications

[First paragraph] Concerns about the replicability, robustness and reproducibility of findings in scientific literature have gained widespread attention over the last decade in the social sciences and beyond. This attention has been catalyzed by and has likewise motivated a number of large-scale replication projects which have reported successful replication rates between 36% and 78%. Given the challenges and resources required to run high-powered replication studies, researchers have sought other approaches to assess confidence in published claims. Initial evidence has supported the promise of prediction markets in this context. However, they require the coordinated, sustained effort of collections of human experts …