Physical Sciences and Mathematics Commons^™

Deep Learning Approaches For Chaotic Dynamics And High-Resolution Weather Simulations In The Us Midwest, Vlada Volyanskaya, Kabir Batra, Shubham Shrivastava

Discovery Undergraduate Interdisciplinary Research Internship

Weather prediction is indispensable across various sectors, from agriculture to disaster forecasting, deeply influencing daily life and work. Recent advancement of AI foundation models for weather and climate predictions makes it possible to perform a large number of predictions in reasonable time to support timesensitive policy- and decision-making. However, the uncertainty quantification, validation, and attribution of these models have not been well explored, and the lack of knowledge can eventually hinder the improvement of their prediction accuracy and precision. Our project is embarking on a two-fold approach leveraging deep learning techniques (LSTM and Transformer) architectures. Firstly, we model the Lorenz …

Closing The Gap: Leveraging Aes-Ni To Balance Adversarial Advantage And Honest User Performance In Argon2i, Nicholas Harrell, Nathaniel Krakauer

CERIAS Technical Reports

The challenge of providing data privacy and integrity while maintaining efficient performance for honest users is a persistent concern in cryptography. Attackers exploit advances in parallel hardware and custom circuit hardware to gain an advantage over regular users. One such method is the use of Application-Specific Integrated Circuits (ASICs) to optimize key derivation function (KDF) algorithms, giving adversaries a significant advantage in password guessing and recovery attacks. Other examples include using graphical processing units (GPUs) and field programmable gate arrays (FPGAs). We propose a focused approach to close the gap between adversarial advantage and honest user performance by leveraging the …

Research Instrumentation Center (Ric), Ryan Hilger, Purdue University Office Of Research

University Research Core Facility Boilerplate Descriptions

No abstract provided.

The Future Of Indiana’S Water Resources: A Report From The Indiana Climate Change Impacts Assessment, Keith Cherkauer, Robert Barr, Laura C. Bowling, Kyuhyun Byun, Indrajeet Chaubey, Natalie Chin, Chun-Mei Chiu, Darren Ficklin, Alan Hamlet, Stephen Kines, Charlotte Lee, Ram Neupane, Garett Pignotti, Sanoar Rahman, Sarmistha Singh, Pandara Valappil Femeena, Tanja Williamson, Melissa Widhalm, Jeffrey Dukes

Water Report

This report from the Indiana Climate Change Impacts Assessment (IN CCIA) applies climate change projections for the state to explore how continued changes in Indiana’s climate are going to affect all aspects of water resources, including soil water, evaporation, runoff, snow cover, streamflow, drought, and flooding. As local temperatures continue to rise and rainfall patterns shift, managing the multiple water needs of communities, natural systems, recreation, industry, and agriculture will become increasingly difficult. Ensuring that enough water is available in the right places and at the right times will require awareness of Indiana’s changing water resources and planning at regional …

Improving Developers' Understanding Of Regex Denial Of Service Tools Through Anti-Patterns And Fix Strategies, Sk Adnan Hassan, Zainab Aamir, Dongyoon Lee, James C. Davis, Francisco Servant

Department of Electrical and Computer Engineering Faculty Publications

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

On The Use Of Machine Learning For Causal Inference In Extreme Weather Events, Yuzhe Wang

Discovery Undergraduate Interdisciplinary Research Internship

Machine learning has become a helpful tool for analyzing data, and causal Inference is a powerful method in machine learning that can be used to determine the causal relationship in data. In atmospheric and climate science, this technology can also be applied to predicting extreme weather events. One of the causal inference models is Granger causality, which is used in this project. Granger causality is a statistical test for identifying whether one time series is helpful in forecasting the other time series. In granger causality, if a variable X granger-causes Y: it means that by using all information without …

Optimizing Cybersecurity Budgets With Attacksimulation, Alexander Master, George Hamilton, J. Eric Dietz

Faculty Publications

Modern organizations need effective ways to assess cybersecurity risk. Successful cyber attacks can result in data breaches, which may inflict significant loss of money, time, and public trust. Small businesses and non-profit organizations have limited resources to invest in cybersecurity controls and often do not have the in-house expertise to assess their risk. Cyber threat actors also vary in sophistication, motivation, and effectiveness. This paper builds on the previous work of Lerums et al., who presented an AnyLogic model for simulating aspects of a cyber attack and the efficacy of controls in a generic enterprise network. This paper argues that …

The Impact Of Service Dogs On Objective And Perceived Sleep Quality For Veterans With Ptsd, Madhuri Vempati, Elise A. Miller, Sarah C. Leighton, Leanne O. Nieforth, Marguerite O’Haire

Discovery Undergraduate Interdisciplinary Research Internship

One in four post-9/11 veterans (Fulton et al., 2015) have been diagnosed with posttraumatic stress disorder (PTSD), facing sleep disruptions as one of their most common symptoms. Service dogs have become an increasingly popular complementary intervention and anecdotes suggest they may impact sleep for veterans with PTSD. There is a need for empirical investigation into these claims through measurement and analysis of sleep quality.

The purpose of this study was to longitudinally investigate the impact of service dogs on sleep quality through both objective and subjective measures.

Participants in the treatment group (n=92) received a service dog after baseline, while …

Hhl Algorithm On The Honeywell H1 Quantum Computer, Adrik B. Herbert, Eric A. F. Reinhardt

Discovery Undergraduate Interdisciplinary Research Internship

The quantum algorithm for linear systems of equations (HHL algorithm) provides an efficient tool for finding solutions to systems of functions with a large number of variables and low sensitivity to changes in inputs (i.e. low error rates). For complex problems, such as matrix inversion, HHL requires exponentially less computational time as compared with classical computation methods. HHL can be adapted to current quantum computing systems with limited numbers of qubits (quantum computation bits) but a high reusability rate such as the Honeywell H1 quantum computer. Some methods for improving HHL have been proposed through the combination of quantum and …

Crowd-Machine Partnership On Road Infrastructure Quality Recognition And Resilience, Eric J. Thompson

Discovery Undergraduate Interdisciplinary Research Internship

Public roads are a vital component of modern-day society, as they are necessary for the transportation of people and capital; consequently, it is important that they are regularly and effectively maintained. Unfortunately, this maintenance is difficult to manage due to the sheer area that roads span. It is an arduous task to locate every instance of road damage, as well as to determine the urgency that each bit of damage necessitates. Repairing road damage has high costs in labor, time, and money. To provide a more efficient way to monitor road conditions, we are designing a mobile application that collects …

Climate Justice In Engineering Education, Tyler J. Morgan, Donna Riley, Caroline M. Camfield

Discovery Undergraduate Interdisciplinary Research Internship

The goal of this research is to design a learning module for Purdue first-year engineering (FYE) students to learn climate fundamentals, and the role of engineers in responding to climate justice challenges. There is a lack of climate material within these classes currently, leading to a lack of climate conscious engineers in the future. The project entailed reviewing and synthesizing a wide variety of previous research on climate change education in engineering, including key learning objectives and their assessment. Because one of the key foci of the first-year engineering sequence relates to data analysis and management, we focused our work …

Comparison Of The Performance Of The Observation-Based Hybrid Edmf And Edmf-Tke Pbl Schemes In 2020 Tropical Cyclone Forecasts From The Globalnested Hurricane Analysis And Forecast System, Andrew Hazelton, Jun A. Zhang, Sundararaman Gopalakrishnan

Department of Earth, Atmospheric, and Planetary Sciences Faculty Publications

Better representation of the planetary boundary layer (PBL) in numerical models is one of the keys to improving forecasts of TC structure and intensity, including rapid intensification. To meet this goal, our recent work has used observations to improve the eddy-diffusivity mass flux with prognostic turbulent kinetic energy (EDMF-TKE) PBL scheme in the Hurricane Analysis and Forecast System (HAFS). This study builds on that work by comparing a modified version of EDMF-TKE (MEDMF-TKE) with the hybrid EDMF scheme based on a K-profile method (HEDMF-KP) in the 2020 HAFS-globalnest model. Verification statistics based on 101 cases in the 2020 season demonstrate …

Password Managers: Secure Passwords The Easy Way, Alexander Master

CERIAS Technical Reports

Poor passwords are often the central problem identified when data breaches, ransomware attacks, and identity fraud cases occur. This Purdue Extension publication provides everyday users of Internet websites and computer systems with tools and strategies to protect their online accounts. Securing information access with password managers can be convenient and often free of cost, on a variety of devices and platforms. “Do’s and Don’ts” of password practices are highlighted, as well as the benefits of multi-factor authentication. The content is especially applicable for small businesses or non-profits, where employees often share access to systems or accounts.

Sok: Analysis Of Software Supply Chain Security By Establishing Secure Design Properties, Chinenye Okafor, Taylor R. Schorlemmer, Santiao Torres-Arias, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered …

Reflecting On Recurring Failures In Iot Development, Dharun Anandayuvaraj, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate …

Exploiting Input Sanitization For Regex Denial Of Service, Efe Barlas, Xin Du, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Web services use server-side input sanitization to guard against harmful input. Some web services publish their sanitization logic to make their client interface more usable, e.g., allowing clients to debug invalid requests locally. However, this usability practice poses a security risk. Specifically, services may share the regexes they use to sanitize input strings — and regex-based denial of service (ReDoS) is an emerging threat. Although prominent service outages caused by ReDoS have spurred interest in this topic, we know little about the degree to which live web services are vulnerable to ReDoS.

In this paper, we conduct the first black-box …

Discrepancies Among Pre-Trained Deep Neural Networks: A New Threat To Model Zoo Reliability, Diego Montes, Pongpatapee Peerapatanapokin, Jeff Schultz, Chengjun Guo, Wenxin Jiang, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Training deep neural networks (DNNs) takes significant time and resources. A practice for expedited deployment is to use pre-trained deep neural networks (PTNNs), often from model zoos.collections of PTNNs; yet, the reliability of model zoos remains unexamined. In the absence of an industry standard for the implementation and performance of PTNNs, engineers cannot confidently incorporate them into production systems. As a first step, discovering potential discrepancies between PTNNs across model zoos would reveal a threat to model zoo reliability. Prior works indicated existing variances in deep learning systems in terms of accuracy. However, broader measures of reliability for PTNNs from …

An Empirical Study On The Impact Of Deep Parameters On Mobile App Energy Usage, Qiang Xu, James C. Davis, Y Charlie Hu, Abhilash Jindal

Department of Electrical and Computer Engineering Faculty Publications

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these "deep" parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. …

Reflections On Software Failure Analysis, Paschal C. Amusuo, Aishwarya Sharma, Siddharth R. Rao, Abbey Vincent, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Failure studies are important in revealing the root causes, behaviors, and life cycle of defects in software systems. These studies either focus on understanding the characteristics of defects in specific classes of systems or the characteristics of a specific type of defect in the systems it manifests in. Failure studies have influenced various software engineering research directions, especially in the area of software evolution, defect detection, and program repair.

In this paper, we reflect on the conduct of failure studies in software engineering. We reviewed a sample of 52 failure study papers. We identified several recurring problems in these studies, …

Towards A Burden-Free Implicit Authentication For Wearable Device Users, Bryan Lee, Sudip Vhaduri

Discovery Undergraduate Interdisciplinary Research Internship

The state of current knowledge-based wearable authentication systems requires users to physically interact with a device to initiate and validate their presence, thereby imposing a burden on the user. However, with the recent advancements of sensor technologies in consumer smart wearables (e.g., Fitbit and Apple watches), we were able to utilize vectors of statistical features extracted from the continuous stream of data from these IoT devices to implicitly validate a user's activities and its spatiotemporal context via the use of machine learning techniques. To improve the performance of our models, additional soft biometric data (i.e., respiratory sounds) was collected, and …

Physics-Informed Machine Learning To Predict Extreme Weather Events, Rthvik Raviprakash, Jonathan Buchanan, Mahdi Bu Ali

Discovery Undergraduate Interdisciplinary Research Internship

Extreme weather events refer to unexpected, severe, or unseasonal weather events, which are dynamically related to specific large-scale atmospheric patterns. These extreme weather events have a significant impact on human society and also natural ecosystems. For example, natural disasters due to extreme weather events caused more than $90 billion global direct losses in 2015. These extreme weather events are challenging to predict due to the chaotic nature of the atmosphere and are highly correlated with the occurrence of atmospheric blocking. A key aspect for preparedness and response to extreme climate events is accurate medium-range forecasting of atmospheric blocking events.

Unlike …

Characterization Of Landfill Leachate For Enhanced Metal Recovery, Hanna Fulford, Amisha Shah, Inez Hua, Nadezhda Zyaykina, Lori Hoagland, Alejandro Rodriguez Sanchez, Umut Bicim

Discovery Undergraduate Interdisciplinary Research Internship

Landfills contain a trove of valuable materials, such as critical, precious, and rare earth metals, that are integral to the United State’s economy and national security. The leachate that filters through landfills picks up these materials, which allows for the possibility of recovery. For this research, samples will be analyzed from landfills throughout the Midwestern United States to provide a baseline on water quality constituents, elements present, and microbial activity. Preliminary data for this study was acquired by analyzing samples of landfill leachate from a landfill in northern Indiana. pH readings indicate that the leachate is slightly basic. It also …

Frames For Justice Consciousness, Colin M. Gray, Rua M. Williams, Paul Parsons, Austin L. Toombs, Abbee Westbrook

Computer Graphics Technology Open Educational Resources

We describe how UX design students become aware of citizen-engaged design work, and indicate the extent to which a progression toward social justice-focused design work might be possible in a single project cycle. Our study site is a sophomore-level UX design studio at a large Midwestern US university—part of a five-semester sequence in which students engage in a range of projects that address competence in user research, prototyping, and evaluation. The project cycle we focus on directly challenges the apolitical framing in most foundational UX methods literature, explicitly asking students to engage with issues of power disparities. We analyzed …

Formation Of Microcages From A Collagen Mimetic Peptide Via Metal-Ligand Interactions, Jeremy Gleaton, Ryan W. Curtis, Jean Chmielewski

Department of Chemistry Faculty Publications

Here, the hierarchical assembly of a collagen mimetic peptide (CMP) displaying four bipyridine moieties is described. The CMP was capable of forming triple helices followed by self-assembly into disks and domes. Treatment of these disks and domes with metal ions such as Fe(II), Cu(II), Zn(II), Co(II), and Ru(III) triggered the formation of microcages, and micron-sized cup-like structures. Mechanistic studies suggest that the formation of the microcages proceeds from the disks and domes in a metal-dependent fashion. Fluorescently-labeled dextrans were encapsulated within the cages and displayed a time-dependent release using thermal conditions.

Catalytic Pyrolysis Of Lignin Model Compounds (Pyrocatechol, Guaiacol, Vanillic And Ferulic Acids) Over Nanoceria Catalyst For Biomass Conversion, Nataliia Nastasiienko, Tetiana Kulik, Borys Palianytsia, Julia Laskin, Tetiana Cherniavska, Mykola Kartel, Mats Larsson

Department of Chemistry Faculty Publications

Understanding the mechanisms of thermal transformations of model lignin compounds (MLC) over nanoscale catalysts is important for improving the technologic processes occurring in the pyrolytic conversion of lignocellulose biomass into biofuels and value-added chemicals. Herein, we investigate catalytic pyrolysis of MLC (pyrocatechol (P), guaiacol (G), ferulic (FA), and vanillic acids (VA)) over nanoceria using FT-IR spectroscopy, temperature-programmed desorption mass spectrometry (TPD MS), and thermogravimetric analysis (DTG/DTA/TG). FT-IR spectroscopic studies indicate that the active groups of aromatic rings of P, G, VA, and FA as well as carboxylate groups of VA and FA are involved in the interaction with nanoceria surface. …

Radiation Effects On Space Solar Cells At Various Earth And Jupiter Orbital Altitudes, Naazneen Rana

Discovery Undergraduate Interdisciplinary Research Internship

Solar cells are used as the primary power source for earth-orbiting satellites and as a primary/secondary power source for various missions within the solar system. However, high energy particles from the sun, planetary magnetospheres, and the galaxy can affect the performance and life expectancy of the space solar cell and associated power systems. As the interests for interplanetary travel and the exploration of planets within our solar system increase, the need to understand a device’s performance within a particular planet’s environment is necessary. Therefore, this study will analyze the performance of space solar cells, particularly the SolAero IMM-α, at various …

A Wireguard Exploration, Alexander Master, Christina Garman

CERIAS Technical Reports

Internet users require secure means of communication. Virtual Private Networks (VPNs) often serve this purpose, for consumers and businesses. The research aims of this paper were an analysis and implementation of the new VPN protocol WireGuard. The authors explain the cryptographic primitives used, build server and client code implementations of WireGuard peers, and present the benefits and drawbacks of this new technology. The outcome was a functional WireGuard client and server implementation, capable of tunneling all Internet traffic through a cloud-based virtual private server (VPS), with minimal manual configuration necessary from the end user. The code is publicly available.

Characterizing Students’ Engineering Design Strategies Using Energy3d, Jasmine Singh, Viranga Perera, Alejandra Magana, Brittany Newell

Discovery Undergraduate Interdisciplinary Research Internship

The goals of this study are to characterize design actions that students performed when solving a design challenge, and to create a machine learning model to help future students make better engineering design choices. We analyze data from an introductory engineering course where students used Energy3D, an open source computer-aided design software, to design a zero-energy home (i.e. a home that consumes no net energy over a period of a year). Student design actions within the software were recorded into text files. Using a sample of over 300 students, we first identify patterns in the data to assess how students …

Characterization Of Microbial Populations In Landfill Leachate, Umut M. Bicim, Hanna Fulford, Lori A. Hoagland, Alejandro R. Sanchez, Amisha D. Shah, Inez Hua

Discovery Undergraduate Interdisciplinary Research Internship

In the United States, municipal solid waste (MSW) landfills remain a potential mining source of recoverable materials, including but not limited to critical, precious, and rare earth metals found in electronic waste. This is possible due to collectible leachate that filters through MSW landfills, carrying metals, nutrients of value, and microbes—some of which may hold key metal bioleaching properties—within. The purpose of this study is to begin analyzing leachate from MSW landfills in the American Midwest to understand the composition of microbial communities within these landfills. Landfill leachate samples sourced in northern Indiana, representing the landfill process during unique times …