Physical Sciences and Mathematics Commons^™

Cat Tracks – Tracking Wildlife Through Crowdsourcing Using Firebase, Tracy Ho

Master's Projects

Many mountain lions are killed in the state of California every year from roadkill. To reduce these numbers, it is important that a system be built to track where these mountain lions have been around. One such system could be built using the platform-as-a-service, Firebase. Firebase is a platform service that collects and manages data that comes in through a mobile application. For the development of cross-platform mobile applications, Flutter is used as a toolkit for developers for both iOS and Android. This entire system, Cat Tracks is proposed as a crowdsource platform to track wildlife, with the current focus …

A Neat Approach To Malware Classification, Jason Do

Master's Projects

Current malware detection software often relies on machine learning, which is seen as an improvement over signature-based techniques. Problems with a machine learning based approach can arise when malware writers modify their code with the intent to evade detection. This leads to a cat and mouse situation where new models must constantly be trained to detect new malware variants. In this research, we experiment with genetic algorithms as a means of evolving machine learning models to detect malware. Genetic algorithms, which simulate natural selection, provide a way for models to adapt to continuous changes in a malware families, and thereby …

Multi-Agent Deep Reinforcement Learning For Walkers, Inhee Park

Master's Projects

This project was motivated by seeking an AI method towards Artificial General Intelligence (AGI), that is, more similar to learning behavior of human-beings. As of today, Deep Reinforcement Learning (DRL) is the most closer to the AGI compared to other machine learning methods. To better understand the DRL, we compares and contrasts to other related methods: Deep Learning, Dynamic Programming and Game Theory.

We apply one of state-of-art DRL algorithms, called Proximal Policy Op- timization (PPO) to the robot walkers locomotion, as a simple yet challenging environment, inherently continuous and high-dimensional state/action space.

The end goal of this project is …

End-To-End Learning Utilizing Temporal Information For Vision- Based Autonomous Driving, Dapeng Guo

Master's Projects

End-to-End learning models trained with conditional imitation learning (CIL) have demonstrated their capabilities in driving autonomously in dynamic environments. The performance of such models however is limited as most of them fail to utilize the temporal information, which resides in a sequence of observations. In this work, we explore the use of temporal information with a recurrent network to improve driving performance. We propose a model that combines a pre-trained, deeper convolutional neural network to better capture image features with a long short-term memory network to better explore temporal information. Experimental results indicate that the proposed model achieves performance gain …

Lidar Object Detection Utilizing Existing Cnns For Smart Cities, Vinay Ponnaganti

Master's Projects

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with …

Detecting Deepfakes With Deep Learning, Eric C. Tjon

Master's Projects

Advances in generative models and manipulation techniques have given rise to digitally altered videos known as deepfakes. These videos are difficult to identify for both humans and machines. Typical detection methods exploit various imperfections in deepfake videos, such as inconsistent posing and visual artifacts. In this paper, we propose a pipeline with two distinct pathways for examining individual frames and video clips. The image pathway contains a novel architecture called Eff-YNet capable of both segmenting and detecting frames from deepfake videos. It consists of a U-Net with a classification branch and an EfficientNet B4 encoder. The video pathway implements a …

Image Spam Classification With Deep Neural Networks, Ajay Pal Singh, Katerina Potika

Faculty Publications, Computer Science

Image classification is a fundamental problem of computer vision and pattern recognition. We focus on images that contain spam. Spam is unwanted bulk content, and image spam is unwanted content embedded inside the images. Image spam potentially creates a threat to the credibility of any email-based communication system. While a lot of machine learning techniques are successful in detecting textual based spam, this is not the case for image spams, which can easily evade these textual-spam detection systems. In our work, we explore and evaluate four deep learning techniques that detect image spams. First, we train deep neural networks using …

Findfur: A Tool For Predicting Furin Cleavage Sites Of Viral Envelope Substrates, Christine Gu

Master's Projects

Most biologically active proteins of eukaryotic cells are initially synthesized in the secretory pathway as inactive precursors and require proteolytic processing to become functionally active. This process is performed by a specialized family of endogenous enzymes known as proproteases convertases (PCs). Within this family of proteases, the most notorious and well-research is furin. Found ubiquitously throughout the human body, typical furin substrates are cleaved at sites composed of paired basic amino acids, specifically at the consensus sequence, R-X-[K/R]-R↓. Furin is often exploited by many pathogens, such as enveloped viruses, for proteolytic processing and maturation of their proteins. Glycoproteins of enveloped …

Malware Classification With Gaussian Mixture Model-Hidden Markov Models, Jing Zhao

Master's Projects

Discrete hidden Markov models (HMM) are often applied to the malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this study, we apply GMM-HMMs to the malware classification problem and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve on the classification results that we can attain with …

The Use Of Evidential Reasoning Model With Biomarkers In Pancreatic Cancer Prediction, Qianhui Fan

Master's Projects

In this project, an evidential reasoning model is built to amalgamate factors that could be used in early detection of pancreatic cancer. Our machine learning model outputs a probability of a given patient having prostate cancer based on various input variables. These variables include health history factors, such as smoking and medical history, technical artifacts, such as biopsy sequencing technology, and genomic biomarkers such as mutational, transcriptional and methylomic profiles, cfDNA, and copy number variation. The dataset used in this project is a part of The Cancer Genome Atlas (TCGA) project and was collected from the National Cancer Institute (NIH) …

Visualization Of Large Networks Using Recursive Community Detection, Xinyuan Fan

Master's Projects

Networks show relationships between people or things. For instance, a person has a social network of friends, and websites are connected through a network of hyperlinks. Networks are most commonly represented as graphs, so graph drawing becomes significant for network visualization. An effective graph drawing can quickly reveal connections and patterns within a network that would be difficult to discern without visual aid. But graph drawing becomes a challenge for large networks. Am- biguous edge crossings are inevitable in large networks with numerous nodes and edges, and large graphs often become a complicated tangle of lines. These issues greatly reduce …

Bioinformatics Metadata Extraction For Machine Learning Analysis, Zachary Tom

Master's Projects

Next generation sequencing (NGS) has revolutionized the biological sciences. Today, entire genomes can be rapidly sequenced, enabling advancements in personalized medicine, genetic diseases, and more. The National Center for Biotechnology Information (NCBI) hosts the Sequence Read Archive (SRA) containing vast amounts of valuable NGS data. Recently, research has shown that sequencing errors in conventional NGS workflows are key confounding factors for detecting mutations. Various steps such as sample handling and library preparation can introduce artifacts that affect the accuracy of calling rare mutations. Thus, there is a need for more insight into the exact relationship between various steps of the …

Malware Classification Using Lstms, Dennis Dang

Master's Projects

Signature and anomaly based detection have long been quintessential techniques used in malware detection. However, these techniques have become increasingly ineffective as malware becomes more complex. Researchers have therefore turned to deep learning to construct better performing models. In this project, we create four different long-short term memory (LSTM) models and train each model to classify malware by family type. Our data consists of opcodes extracted from malware executables. We employ techniques used in natural language processing (NLP) such as word embedding and bidirection LSTMs (biLSTM). We also use convolutional neural networks (CNN). We found that our model consisting of …

Quantifying Deepfake Detection Accuracy For A Variety Of Natural Settings, Pratikkumar Prajapati

Master's Projects

Deep fakes are videos generated from a starting video of a person where that person's face has been swapped for someone else's. In this report, we describe our work to develop general, deep learning-based models to classify Deep Fake content. Our first experiments involved simple Convolution Neural Network (CNN)-based models where we varied how individual frames from the source video were passed to the CNN. These simple models tended to give low accuracy scores for discriminating fake versus non-fake videos of less than 60%. We then developed three more sophisticated models: one based on choosing test frames, one based on …

How Live Streaming And Twitch Have Changed The Gaming Industry, Krystal Ruiz

ART 108: Introduction to Games Studies

Live streaming in itself has become a booming industry in which its content consists of “streamers” who live broadcast numerous events and real-time interactions while simultaneously chatting with viewers drawing huge and increasing numbers (Adamovich). Twitch has especially excelled at garnering attention as one of the most popular live streaming platforms that focuses on broadcasting and viewing video game content (Adamovich). Twitch has grown rapidly within the last few years asserting its dominance as one of the major forces in the games industry and becoming a multi-billion-dollar industry (Adamovich). For example, according to Descrier, in 2016 there were approximately 292 …

The Impact Of Shigeru Miyamoto On The Game Design Industry, Luan Tran

ART 108: Introduction to Games Studies

Nintendo started as a small company in the 1970s that sold playing cards. Having seen the exemplary gift in his son, Miyamoto's father arranged for an interview with the president of Nintendo Hiroshi Yamauchi. Consequently, Miyamoto got a position in 1977 as an apprentice in the planning department after showing his toy creations to the president. He became the first Nintendo artist as he helped create the art for the first original coin-operated arcade game. The approach demonstrated his innate abilities that would help him become the ultimate guru in the industry. Through individual discovery, Miyamoto has managed to produce …

Feedback Induced Magnetic Phases In Binary Bose-Einstein Condensates, Hilary M. Hurst, Shangjie Guo, I. B. Spielman

Faculty Research, Scholarly, and Creative Activity

Weak measurement in tandem with real-time feedback control is a new route toward engineering novel non-equilibrium quantum matter. Here we develop a theoretical toolbox for quantum feedback control of multicomponent Bose-Einstein condensates (BECs) using backaction-limited weak measurements in conjunction with spatially resolved feedback. Feedback in the form of a single-particle potential can introduce effective interactions that enter into the stochastic equation governing system dynamics. The effective interactions are tunable and can be made analogous to Feshbach resonances -- spin-independent and spin-dependent -- but without changing atomic scattering parameters. Feedback cooling prevents runaway heating due to measurement backaction and we present …

Development Of A Statistical Model To Predict Materials’ Unit Prices For Future Maintenance And Rehabilitation In Highway Life Cycle Cost Analysis, Changmo Kim, Ghazan Khan, Brent Nguyen, Emily L. Hoang

Mineta Transportation Institute Publications

The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting pavement materials’ unit price. Additionally, the unit price records of the popular pavement materials were …

The History Of Nintendo: The Company, Consoles And Games, Laurie Takeda

ART 108: Introduction to Games Studies

From the start, Nintendo has evolved overall as a company; from a playing card manufacturer to developing a wide variety of video game consoles and video games that are played worldwide, they continue to research and expand upon what the company can offer.

Non-Hermitian Topology Of One-Dimensional Spin-Torque Oscillator Arrays, Benedetta Flebus, Rembert A. Duine, Hilary M. Hurst

Faculty Research, Scholarly, and Creative Activity

Magnetic systems have been extensively studied both from a fundamental physics perspective and as building blocks for a variety of applications. Their topological properties, in particular those of excitations, remain relatively unexplored due to their inherently dissipative nature. The recent introduction of non-Hermitian topological classifications opens up new opportunities for engineering topological phases in dissipative systems. Here, we propose a magnonic realization of a non-Hermitian topological system. A crucial ingredient of our proposal is the injection of spin current into the magnetic system, which alters and can even change the sign of terms describing dissipation. We show that the magnetic …

Ru(Ii)-Diimine Complexes And Cytochrome P450 Working Hand-In-Hand, Celine Eidenschenk, Lionel Cheruzel

Faculty Publications, Chemistry

With a growing interest in utilizing visible light to drive biocatalytic processes, several light-harvesting units and approaches have been employed to harness the synthetic potential of heme monooxygenases and carry out selective oxyfunctionalization of a wide range of substrates. While the fields of cytochrome P450 and Ru(II) photochemistry have separately been prolific, it is not until the turn of the 21st century that they converged. Non-covalent and subsequently covalently attached Ru(II) complexes were used to promote rapid intramolecular electron transfer in bacterial P450 enzymes. Photocatalytic activity with Ru(II)-modified P450 enzymes was achieved under reductive conditions with a judicious choice of …

Is The Transit Industry Prepared For The Cyber Revolution? Policy Recommendations To Enhance Surface Transit Cyber Preparedness, Scott Belcher, Terri Belcher, Eric Greenwald, Brandon Thomas

Mineta Transportation Institute Publications

The intent of this study is to assess the readiness, resourcing, and structure of public transit agencies to identify, protect from, detect, respond to, and recover from cybersecurity vulnerabilities and threats. Given the multitude of connected devices already in use by the transit industry and the vast amount of data generated (with more coming online soon), the transit industry is vulnerable to malicious cyber-attack and other cybersecurity-related threats. This study reviews the state of best cybersecurity practices in public surface transit; outlines U.S. public surface transit operators’ cybersecurity operations; assesses U.S. policy on cybersecurity in public surface transportation; and provides …

Analysis Of The Benefits Of Green Streets, Christopher E. Ferrell, John M. Eells, Richard W. Lee, Reyhane Hosseinzade

Mineta Transportation Institute Publications

Green streets offer many potential benefits that include improving water quality, absorbing carbon (sequestration), and reducing urban heat island effects. This report summarizes: (1) the research team’s analysis of 14 tools calculating green streets benefits; and (2) the results of applying the most promising calculators to a select group of green streets case studies. The researchers are affiliated with the Mineta Transportation Institute, which serves the California Department of Transportation (“Caltrans”). The report presents the results of the case study analyses, with an emphasis on carbon sequestration benefits and improvements to pedestrian levels of service (PLOS).

Trees absorb carbon dioxide …

Find Me If You Can: Aligning Users In Different Social Networks, Priyanka Kasbekar, Katerina Potika, Chris Pollett

Faculty Publications, Computer Science

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user's behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is …

Relative Impacts Of Different Grade Scales On Student Success In Introductory Physics, David J. Webb, Cassandra A. Paul, Mary A. Chessey

Faculty Publications

In deciding on a student’s grade in a class, an instructor generally needs to combine many individual grading judgments into one overall judgment. Two relatively common numerical scales used to specify individual grades are the 4-point scale (where each whole number 0–4 corresponds to a letter grade) and the percent scale (where letter grades A through D are uniformly distributed in the top 40% of the scale). This paper uses grading data from a single series of courses offered over a period of 10 years to show that the grade distributions emerging from these two grade scales differed in many …

Small Gaps Between Almost Primes, The Parity Problem, And Some Conjectures Of Erdős On Consecutive Integers Ii, Daniel A. Goldston, Sidney W. Graham, Apoorva Panidapu, Janos Pintz, Jordan Schettler, Cem Y. Yıldırım

Faculty Publications

We show that for any positive integer n, there is some fixed A such that d(x) = d(x +n) = A infinitely often where d(x) denotes the number of divisors of x. In fact, we establish the stronger result that both x and x +n have the same fixed exponent pattern for infinitely many x. Here the exponent pattern of an integer x > 1is the multiset of nonzero exponents which appear in the prime factorization of x.

Evidence-Based Detection Of Pancreatic Canc, Rajeshwari Deepak Chandratre

Master's Projects

This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85% …

Predicting Students’ Performance By Learning Analytics, Sandeep Subhash Madnaik

Master's Projects

The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to monitor and self-assess …

Using Machine Learning To Optimize Predictive Models Used For Big Data Analytics In Various Sports Events, Akhil Kumar Gour

Master's Projects

In today’s world, data is growing in huge volume and type day by day. Historical data can hence be leveraged to predict the likelihood of the events which are to occur in the future. This process of using statistical or any other form of data to predict future outcomes is commonly termed as predictive modelling. Predictive modelling is becoming more and more important and is trending because of several reasons. But mainly, it enables businesses or individual users to gain accurate insights and allows to decide suitable actions for a profitable outcome.

Machine learning techniques are generally used in order …

Higher-Order Link Prediction Using Graph Embeddings, Neeraj Chavan

Master's Projects

Link prediction is an emerging field that predicts if two nodes in a network are likely to be connected or not in the near future. Networks model real-world systems using pairwise interactions of nodes. However, many of these interactions may involve more than two nodes or entities simultaneously. For example, social interactions often occur in groups of people, research collaborations are among more than two authors, and biological networks describe interactions of a group of proteins. An interaction that consists of more than two entities is called a higher-order structure. Predicting the occurrence of such higher-order structures helps us solve …