Digital Commons Network^™

Building Lean Standalone Web Servers, And Routing Engine, Ajita Shrivastava

Master's Projects

As a result of advancement in technology the web and email servers have greatly expanded in size.
This has created a need for miniaturization, and people are trying to minimize technology whilst
making it fast and efficient. This report discusses the development of a set of servers aligned with
the miniaturization trend: Atto servers. These are simple to use single file PHP servers created for
moderate usages including web traffic and email tasks. The purpose of this project is to develop
small server solutions which could act as working counterparts of products like Apache or Nginx.
It makes the server …

Predicting Remaining Useful Life Of Turbofan Engines On Cmapss And N-Cmapss Using Deep Recurrent Neural Networks, Samaikya Tippareddy

Master's Projects

Aircraft engines are susceptible to failure at multiple points over their lifespan and need replacement or repairs. The ability to proactively determine how long an engine will function helps avoid fatalities and build a reliable prognostic system. To accomplish this, predictive models are being developed using various approaches like physics-based and data-driven techniques. Physics-based models need huge computing power for simulations and domain knowledge for understanding and implementing the models. Alternatively, if we have substantial data for prediction, data-driven models can be used. In this research, we use data-driven approach for engine Remaining-Useful-Life (RUL) prediction on the NASA Commercial Modular …

Formula 1 Commentary Generator Using Generative Artificial Intelligence, Hiral Moliya

Master's Projects

This project aims to create a high-quality commentary generation system utilizing cutting-edge Generative AI technologies, with a particular focus on the T5 transformer-based text-to-text transfer transformer (T5). The primary goal is to create a fully autonomous and contextually aware commentary system that will be able to provide consistent and insightful commentary on dynamic events that reflect the level of detail normally associated with human commentary. Upon giving the input as a text input of the race events the model using large language models that are trained on a large range of datasets creates text-based commentary. The system in order to …

An Empirical Analysis Of Adversarial Attacks In Federated Learning, Rohit Mapakshi

Master's Projects

In this paper, we experimentally analyze the susceptibility of selected Federated Learning (FL) systems to the presence of adversarial clients. We find that temporal attacks significantly affect model performance in FL, especially when the adversaries are active throughout and during the ending rounds of the FL process. Machine Learning models like Multinominal Logistic Regression, Support Vector Classifier (SVC), Neural Network models like Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and tree-based machine learning models like Random Forest and XGBoost were considered. These results highlight the effectiveness of temporal attacks and the need …

Multimodal Techniques For Malware Classification, Jonathan Jiang

Master's Projects

The threat of malware has remained a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. This research adopted the structured nature of PE files incorporated with a multi-modal machinelearning approach, to classify malware types. Features extracted from the PE headers were used to train an LSTM model. Features extracted from the PE sections were used to train a CNN model. Probabilities produced from these two models were then concatenated and fed into an SVM classifier. This multi-modal approach demonstrated high accuracy by experimenting with and verifying the approach on a large and labeled dataset. …

Explainable Reinforcement Learning For Network Routing Optimization, Yu Xiu

Master's Projects

Software-defined Networking (SDN) provides a solution for configuring multiple network devices by offering a centralized controller architecture. Network routing is one of the most crucial problems in network configuration. In particular, the surging network traffic demands require efficient routing techniques to load balance communication links. In order to optimize the communication path’s utilization and reduce request blocking, this project utilizes Reinforcement Learning (RL) to decide the routes for given network requests. Furthermore, we adopt Explainable Reinforcement Learning (XRL) to explain the RL learning agent’s decision-making process to enhance the trustworthiness of our approach. In particular, we focus on Feature Importance …

The Global Partners Program A New International Volunteer Opportunity For Ams Members, Walter Dabberdt, Darrel Baumgardner, Robert Bornstein, Gregory Carmichael, Richard Clark, Jeffrey Collett, Harindra Fernando, Efi Foufoula-Georgiou, Dev Niyogi, Mohan Ramamurthy, Alan Robock, Julie Winkler

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

Erratum To: Reconfigurations In Brain Networks Upon Awakening From Slow Wave Sleep: Interventions And Implications In Neural Communication (Network Neuroscience), Cassie J. Hilditch, Kanika Bansal, Ravi Chachad, Lily R. Wong, Nicholas G. Bathurst, Nathan H. Feick, Amanda Santamaria, Nita L. Shattuck, Javier O. Garcia, Erin E. Flynn-Evans

Faculty Research, Scholarly, and Creative Activity

After publication of our paper Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication, we noticed an error in our condition labels that required some revision of figures and text and some new interpretations throughout the manuscript. The error was a consequence of the double-blind nature of the original experimentation which contained two interventions: (1) a blue-enriched light intervention, as originally mentioned; and (2) an olfactory odorant condition. In review of previous analyses and presentations of the data, there was a time when non-descriptive labels (e.g., “A”, “B”) were accidentally swapped, indicating the …

Lithium And Water: Hydrosocial Impacts Across The Life Cycle Of Energy Storage, James J.A. Blair, Noel Vineyard, Dustin Mulvaney, Alida Cantor, Ali Sharbat, Kate Berry, Elizabeth Bartholomew, Ariana Firebaugh Ornelas

Faculty Research, Scholarly, and Creative Activity

As a key ingredient of batteries for electric vehicles (EVs), lithium plays a significant role in climate change mitigation, but lithium has considerable impacts on water and society across its life cycle. Upstream extraction methods—including open-pit mining, brine evaporation, and novel direct lithium extraction (DLE)—and downstream processes present different impacts on both the quantity and quality of water resources, leading to water depletion and contamination. Regarding upstream extraction, it is critical for a comprehensive assessment of lithium's life cycle to include cumulative impacts related not only to freshwater, but also mineralized or saline groundwater, also known as brine. Legal frameworks …

A Comparative Multivariate Analysis Of Var And Deep Learning-Based Models For Forecasting Volatile Time Series Data, Saroj Gopali, Sima Siami-Namini, Faranak Abri, Akbar Siami Namin

Faculty Research, Scholarly, and Creative Activity

The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), Simple Exponential Smoothing (SES), deep learning models, and, most notably, univariate Long Short-Term Memory (LSTM) built based on univariate variable where the next lag of time series is leveraged for forecasting the next cycle of data. This paper takes this line of research to the next level by focusing on forecasting time series data based on “multivariate” modeling and analysis. To have a better insight of the performance of various deep learning-based models when multivariate …

Assessment Of Digital Collaboration Skills, Esperanza Huerta, Ana Lidia Franzoni Velázquez, Scott Jensen

Faculty Research, Scholarly, and Creative Activity

This exploratory study proposes a methodology for assessing digital collaboration skills based on the students' actual behavior. Students' comments using a digital collaboration tool during a one-month long project were manually coded. This methodology is not context specific and can be used across different domains. This assessment contrasts with self-reported measures in which students rate themselves as already possessing collaboration skills. Finally, the study explores the use of generative AI to automatically code student's comments to alleviate the labor-intensive process that coding requires and to enable scalability in coding data.

Comparison Of Different Drought Monitoring Indices In Different Climatic Conditions In Iran, Samira Rahnama, Ali Shahidi, Mostafa Yaghoobzadeh, Ali Akbar Mehran

Faculty Research, Scholarly, and Creative Activity

This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014–2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that …

Is Knowledge Management (Finally) Extractive? – Fuller’S Argument Revisited In The Age Of Ai, Norman Mooradian

Faculty Research, Scholarly, and Creative Activity

Aim/Purpose The rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background This paper re-examines Fuller’s argument …

Modeling And Vibration Suppression Of Rotating Machines Using The Sparse Identification Of Nonlinear Dynamics And Terminal Sliding Mode Control, Sina Piramoon, Mohammad Ayoubi, Saeid Bashash

Faculty Research, Scholarly, and Creative Activity

This paper presents a novel physics-based data-driven approach for reconstructing the nonlinear governing equations and suppressing vibrations in vertical-shaft rotary machines during transient motion. We first identify the key nonlinear terms using a physics-based methodology. Subsequently, a data-driven approach, known as the Sparse Identification of Nonlinear Dynamical Systems (SINDy), is employed to reconstruct the nonlinear governing equations of a typical rotary machine. After validating the model, a robust nonlinear controller is designed using the terminal sliding mode control (TSMC) technique to reduce lateral vibrations in the machine’s shaft. Extensive experimental tests on a laboratory-scale rotary system confirm the stability and …

Oculometric Biomarkers Of Visuomotor Deficits In Clinically Asymptomatic Patients With Systemic Lupus Erythematosus Undergoing Long-Term Hydroxychloroquine Treatment, Andrew R. Berneshawi, Kimia Seyedmadani, Rahul Goel, Mark R. Anderson, Terence L. Tyson, Yasmin M. Akay, Metin Akay, Loh Shan B. Leung, Leland S. Stone

Faculty Research, Scholarly, and Creative Activity

Introduction: This study examines a set of oculomotor measurements, or “oculometric” biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. Methods: We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical …

Computer Vision Intelligence Test Modeling And Generation: A Case Study On Smart Ocr, Jing Shu, Bing Jiun Miu, Eugene Chang, Jerry Gao, Jun Liu

Faculty Research, Scholarly, and Creative Activity

AI-based systems possess distinctive characteristics and introduce challenges in quality evaluation at the same time. Consequently, ensuring and validating AI software quality is of critical importance. In this paper, we present an effective AI software functional testing model to address this challenge. Specifically, we first present a comprehensive literature review of previous work, covering key facets of AI software testing processes. We then introduce a 3D classification model to systematically evaluate the image-based text extraction AI function, as well as test coverage criteria and complexity. To evaluate the performance of our proposed AI software quality test, we propose four evaluation …

Effect Of Fermentation, Malting And Ultrasonication On Sorghum, Mopane Worm And Moringa Oleifera: Improvement In Their Nutritional, Techno-Functional And Health Promoting Properties, Mpho Sebabiki Maleke, Oluwafemi Ayodeji Adebo, Jonathan Wilkin, Moira Ledbetter, Xi Feng, John Gieng, Tumisi Beiri Jeremiah Molelekoa

Faculty Research, Scholarly, and Creative Activity

Background: Food processing offers various benefits that contribute to food nutrition, food security and convenience. This study investigated the effect of three different processes (fermentation, malting and ultrasonication) on the nutritional, techno-functional and health-promoting properties of sorghum, mopane worm and Moringa oleifera. Methods: The fermented and malted flours were prepared at 35°C for 48 h, and for ultrasonication, samples were subjected to 10 min at 4°C with amplitudes of 40–70 Hz. The biochemical, nutritional quality and techno-functional properties of the obtained flours were analysed using standard procedures. Results: Fermentation resulted in significantly lower pH and higher titratable acidity in sorghum …

Webometrics Analysis Of Government Medical Colleges Of Bihar: A Study, Debashish Pandit Paul, Mohd. Aslam Khan, Anubhaw Kumar Suman

Library Philosophy and Practice (e-journal)

The study presents the webometrics analysis of 9 websites of Government Medical Colleges under the Health Department, Government of Bihar. The Purpose of in study to identify the web presence, domain analysis and other webometrics indicators of the selected website. The study uses SEO MOZ for data collection. It was found that the website of Sri Krishna Medical College and Hospital, Muzaffarpur (SKMCH) has the highest domain authority of 27 and the highest page authority of Nalanda Medical College and Hospital, Patna (NMCH) of 35. The study concluded that all websites should be inter-connected, well-maintained and …

Social Media Bot Detection Using Dropout-Gan, Anant Shukla

Master's Projects

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. We demonstrate that our approach outperforms---in terms of accuracy---the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification …

Gradual Typing For Information Flow Control In Typescript Using Es Lint, Ashish Agarwal

Master's Projects

Current state-of-the-art systems tackle data security threats by incorporating information flow control (IFC) to ensure that a piece of information reaches only its intended recipient. However, most IFC implementations introduce a custom language built on top of a well-known language. Adaptations of such languages are limited due to limited support and updates, along with difficulty in learning new syntaxes. Implementations without a custom language offer incomplete IFC support. We present a comprehensive framework by leveraging Typescript, in conjunction with ESLint and NodeJS, aiming to resolve some of the limitations of IFC and intending to facilitate acceptance by a wide range …

Zookeeper Through The Ages: A Comparative Performance Analysis Of Evolutionary Versions, Ashish Khanchandani

Master's Projects

The rise in the need for scalable, fault-tolerant, and high-performance systems is the primary factor driving the developments in distributed computing. However, the proliferation of distributed computing creates significant difficulties. In an internet- scale setting where errors and network delays are frequent, coordinating distributed applications presents difficulties that ZooKeeper attempts to solve. It ensures several crucial characteristics that guarantee reliability and consistency. However, performance and latency play a huge part when it comes to dealing with systems built for handling very high loads. ZooKeeper has very low latency for read-heavy workloads, making it suitable for real-time applications. These factors contribute …

A Two-Stage Machine Learning Approach For Fake News Detection And News Article Categorization, Snegdha Adusumilli

Master's Projects

This project employs machine learning techniques to develop a sequential model for detecting and categorizing fake news, aiming to mitigate its proliferation in today's digital landscape. The model operates in two phases: in the first phase, the classification algorithms like Naïve Bayes, XGBoost and Random Forest are used to distinguish between true and false news stories and in the second phase the capabilities of Naïve Bayes, XGBoost, Random Forest, and the Transformer-based BERT (Bidirectional Encoder Representations from Transformers) model are leveraged to further categorize the news into specific topics.

The methodology encompasses several key steps: data acquisition, preprocessing, feature extraction, …

A Censorship Resistant News Website Using The Ethereum Blockchain, Hoang Lai

Master's Projects

Misleading information, false claims, and fabricated news articles not only misguide readers but also undermine the trustworthiness of the news platforms themselves. The blockchain provides decentralized, immutable data storage and offers a promising solution to prevent censorship on news websites. Compared to traditional news websites, a decentralized application (dApp) offers benefits such as greater stability and resistance to information manipulation. A decentralized web app is harder to attack than centralized servers since the database is stored across a blockchain network. Moreover, blockchain prevents censorship by letting readers check data across all blocks in the Blockchain, which is good for a …

Echo: A Browser Extension That Runs Experimental Javascript, Prayuj Pillai

Master's Projects

Narcissus is a JavaScript interpreter written in JavaScript. While it is a good engine for experimenting with JavaScript’s design, it does not integrate easily into the browser. This project introduces ‘‘Echo’’, a browser add-on designed to execute Narcissus JavaScript files and scripts within web browsers. The project explores the performance of the Narcissus interpreter against native browser JavaScript engines and benchmarks the results, showcasing the trade-offs in running an experimental engine—the Narcissus interpreter—on the browser versus native JavaScript. Additionally, as a proof of concept, we implement taint tracking, a capability meant to boost security by preventing sensitive data from being …

Fine-Tuning Large Language Models For Folder Structure Generation, Likhith Nemani

Master's Projects

Starting a new project is a significant challenge in the software development world. Building a new project skeleton and configurations will require vast amounts of time and effort. This project aims to overcome the difficulty presented by this challenge using advanced large language models, specifically fine-tuning LLMs. Our initial focus with the implementation is to use the powerful capabilities of advanced modern models to simplify and accelerate the complicated process of getting new projects started. The solution process begins with a user posting a README file to a predetermined repository. This README file then is used as a source for …

Credit Score-Based Lending System On The Ethereum Platform, Mayuri Shimpi

Master's Projects

Traditional banking systems act as intermediaries, assessing risks and profiting from interest rate differentials. Credit scores, provided by trusted bureaus, are commonly used to evaluate the creditworthiness of borrowers. Cryptocurrencies have emerged as a significant and innovative medium due to their decentralized nature, operating without reliance on a central authority, such as a government.

This report describes a project to implement the Autonomous Lending system on the Ethereum Platform (ALOE), as proposed in [1], aiming to seamlessly integrate traditional credit scoring methodologies for evaluating a borrower's risk of default. The objective of this project report is to establish a robust …

Domain Switch On Sentiment Analysis Using Gradient Reversal Layer, Hemish Veeraboina

Master's Projects

Switching domains in sentiment analysis presents the challenge of transferring learned knowledge from one context to another without the need to label data. Traditional methods often struggle when dealing with differences in data distribution a problem known as the domain shift issue. To tackle this using Gradient Reversal Layers (GRL) has emerged as a solution for adapting to different domains in an unsupervised learning setting. This study introduces an enhancement to the standard GRL approach by incorporating a sigmoid function that gradually adjusts how intensely domain adaptation occurs during training. This upgraded GRL technique ensures controlled learning outcomes making it …

Scalable Container Caching Optimization With Action Masking For Serverless Edge Computing, Manikanta Sanjay Veera

Master's Projects

Serverless edge computing is an emerging technology that realizes the low latency and resource-efficient function calls for responsive computing. In cloud-based serverless computing, it is a common practice to cache sufficiently many function containers for future reuse to reduce the overhead of container initiation. In contrast, the capacity limitation of edge nodes poses a complex problem to the caching strategy in serverless edge computing of selecting an appropriate set of container caches based on the request distribution. Deep Reinforcement Learning (DRL) can play a crucial role in optimizing the caching decisions under dynamic request arrivals. In this paper, we propose …

Emotion Detection Using Ensemble Learning, Priya Harika Yerapothu

Master's Projects

Emotion detection is gaining exponential necessity in today’s technological age. This research seeks to delve into ways conversational AI could be enhanced by integrating emotional intelligence using an ensemble learning approach. Traditional machine learning along with advanced neural network architectures are implemented to improve the understanding and intricacies of emotion detection from textual data. The dataset we use is GoEmotions dataset, annotated with 27 emotional labels, to conduct a detailed analysis of emotion recognition. Various machine learning models, such as HistGradientBoosting, LightGBM, CatBoost, and MLP, will be evaluated side by side with advanced models of Bidirectional Long Short-Term Memory (BiLSTM) …

Regional Sea Level Rise Prediction In Monterey Bay With Lstms And Vertical Land Motion, Branden Lopez

Master's Projects

Earth system data is vast in volume and variety, and is used to forecast weather,

hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of

clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of …