Computer Sciences Commons^™

Several Thoughts On Information Technology Driving Future Industries, Yungang Bao, Miao Liu, Pinyan Lu, Xipeng Qiu, Jiang Xu

Bulletin of Chinese Academy of Sciences (Chinese Version)

No abstract provided.

Boosting Adversarial Training Using Robust Selective Data Augmentation, Bader Rasheed, Asad Masood Khattak, Adil Khan, Stanislav Protasov, Muhammad Ahmad

All Works

Artificial neural networks are currently applied in a wide variety of fields, and they are near to achieving performance similar to humans in many tasks. Nevertheless, they are vulnerable to adversarial attacks in the form of a small intentionally designed perturbation, which could lead to misclassifications, making these models unusable, especially in applications where security is critical. The best defense against these attacks, so far, is adversarial training (AT), which improves the model’s robustness by augmenting the training data with adversarial examples. In this work, we show that the performance of AT can be further improved by employing the neighborhood …

Explainable Software Defect Prediction From Cross Company Project Metrics Using Machine Learning, Susmita Haldar, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Predicting the number of defects in a project is critical for project test managers to allocate budget, resources, and schedule for testing, support and maintenance efforts. Software Defect Prediction models predict the number of defects in given projects after training the model with historical defect related information. The majority of defect prediction studies focused on predicting defect-prone modules from methods, and class-level static information, whereas this study predicts defects from project-level information based on a cross-company project dataset. This study utilizes software sizing metrics, effort metrics, and defect density information, and focuses on developing defect prediction models that apply various …

Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson

Computer Science Faculty Publications

[First paragraph] This page details concrete recipes that platforms that host research outputs (e.g. data repositories, institutional repositories, publisher platforms, etc.) can follow to implement Signposting, a lightweight yet powerful approach to increase the FAIRness of scholarly objects.

Vitreoscilla Globin Promoter Cloning And Testing In Escherichia Coli, Lauren J. Coffey

Rose-Hulman Undergraduate Research Publications

No abstract provided.

A Neutrosophic Framework For Assessment Of Distributed Circular Water To Give Neighborhoods Analysis To Prepare For Unexpected Stressor Events, Mohamed Abouhawwash, Mohammed Jameel, Sameh S. Askar

Neutrosophic Systems with Applications

The global water issue is caused by a number of factors, including extreme weather, population increase, and industrial activity. One of the reasons we're running out of water as a planet is because of the outdated take-make-use-throw-away linear paradigm of handling water. It has been suggested that the circular economy may help alleviate water shortages by inspiring a fundamental change in municipal water infrastructure. Reduced consumption, recovered natural resources, and minimal waste are the three pillars of a circular water supply. A dispersed water supply is more adaptable and robust than a centralized one because it gives communities more time …

A Neutrosophic Framework For Assessment Of Distributed Circular Water To Give Neighborhoods Analysis To Prepare For Unexpected Stressor Events, Mohamed Abouhawwash, Mohammed Jameel, Sameh S. Askar

Neutrosophic Systems with Applications

The global water issue is caused by a number of factors, including extreme weather, population increase, and industrial activity. One of the reasons we're running out of water as a planet is because of the outdated take-make-use-throw-away linear paradigm of handling water. It has been suggested that the circular economy may help alleviate water shortages by inspiring a fundamental change in municipal water infrastructure. Reduced consumption, recovered natural resources, and minimal waste are the three pillars of a circular water supply. A dispersed water supply is more adaptable and robust than a centralized one because it gives communities more time …

The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer

MODVIS Workshop

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!

We look forward to the group’s suggestions for these projects.

Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer

MODVIS Workshop

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …

How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; …

A Dynamical Model Of Binding In Visual Cortex During Incremental Grouping And Search, Daniel Schmid, Daniel A. Braun, Heiko Neumann

MODVIS Workshop

Binding of visual information is crucial for several perceptual tasks. To incrementally group an object, elements in a space-feature neighborhood need to be bound together starting from an attended location (Roelfsema, TICS, 2005). To perform visual search, candidate locations and cued features must be evaluated conjunctively to retrieve a target (Treisman&Gormican, Psychol Rev, 1988). Despite different requirements on binding, both tasks are solved by the same neural substrate. In a model of perceptual decision-making, we give a mechanistic explanation for how this can be achieved. The architecture consists of a visual cortex module and a higher-order thalamic module. While the …

Implementing A Functional Logic Programming Language Via The Fair Scheme, Andrew Michael Jost

Dissertations and Theses

This document presents a new compiler for the Functional Logic programming language Curry based on a novel pull-tabbing evaluation strategy called the Fair Scheme. A simple version of the Fair Scheme is proven sound, complete, and optimal. An elaborated version is also developed, which supports narrowing computations and other features of Curry, such as constraint programming, equational constraints, and set functions.

The Fair Scheme is used to develop a new Curry system called Sprite, a high-quality, performant implementation whose aims are to promote practical uses of Curry and to serve as a laboratory for further research. An important aspect of …

Artificial Intelligence In Neuroradiology: A Scoping Review Of Some Ethical Challenges, Pegah Khosravi, Mark Schweitzer

Publications and Research

Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. …

An Assistive Interface For Displaying Novice's Code History, Ruiwei Xiao

McKelvey School of Engineering Graduate Student Theses & Dissertations

As Teaching Assistant (TA) programs grow in number and size in introductory CS courses, TAs play a significant role in novice programmers' experience and contribute to their success. However, many TAs are also relative beginners themselves and thus have limited experience in programming and teaching. Thus the effectiveness and consistency of their guidance can vary significantly. To improve interaction quality and assist TAs in providing better support, we examine the difficulties encountered by inexperienced TAs in previous literature and then identify the potential for the high cognitive load as an unaddressed difficulty that may prevent new TAs from initiating effective …

Expressive Marks: Art In The Age Of Augmented Reality, Carson G. Levine

Dartmouth College Master’s Theses

Augmented reality (AR) and non-fungible tokens (NFTs) introduce new considerations for the long-standing debate of what it means for digital art to be “real.” However, the ability to create AR experiences is limited to those who are technically skilled or who can afford to consult someone else. This paper addresses the need for an accessible tool that enables artists of all technical backgrounds to expressively create marks in AR. The solution includes a mobile application called CrayonAR. The system was designed to be modular, minimal, and physically engaging, and was developed in Unity using ARFoundation and Firebase Storage and Realtime …

Ptmtorrent: A Dataset For Mining Open-Source Pre-Trained Model Packages, Wenxin Jiang, Nicholas Synovic, Purvish Jajal, Taylor R. Schorlemmer, Arav Tewari, Bhavesh Pareek, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing …

Chargex: Exploring State Switching Attack On Electric Vehicle Charging Systems, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of …

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

Computer Science: Faculty Publications and Other Works

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. …

Feature Selection From Clinical Surveys Using Semantic Textual Similarity, Benjamin Warner

McKelvey School of Engineering Graduate Student Theses & Dissertations

Survey data collected from human subjects can contain a high number of features while having a comparatively low quantity of examples. Machine learning models that attempt to predict outcomes from survey data under these conditions can overfit and result in poor generalizability. One remedy to this issue is feature selection, which attempts to select an optimal subset of features to learn upon. A relatively unexplored source of information in the feature selection process is the usage of textual names of features, which may be semantically indicative of which features are relevant to a target outcome. The relationships between feature names …

Costs And Benefits Of Authentication Advice, Hazel Murray, David Malone

Department of Computer Science Publications

Authentication security advice is given with the goal of guiding users and organisations towards secure actions and practices. In this article, a taxonomy of 270 pieces of authentication advice is created, and a survey is conducted to gather information on the costs associated with following or enforcing the advice. Our findings indicate that security advice can be ambiguous and contradictory, with 41% of the advice collected being contradicted by another source. Additionally, users reported high levels of frustration with the advice and identified high usability costs. The study also found that end-users disagreed with each other 71% of the time …

Optimizing Tumor Xenograft Experiments Using Bayesian Linear And Nonlinear Mixed Modelling And Reinforcement Learning, Mary Lena Bleile

Statistical Science Theses and Dissertations

Tumor xenograft experiments are a popular tool of cancer biology research. In a typical such experiment, one implants a set of animals with an aliquot of the human tumor of interest, applies various treatments of interest, and observes the subsequent response. Efficient analysis of the data from these experiments is therefore of utmost importance. This dissertation proposes three methods for optimizing cancer treatment and data analysis in the tumor xenograft context. The first of these is applicable to tumor xenograft experiments in general, and the second two seek to optimize the combination of radiotherapy with immunotherapy in the tumor xenograft …

Visualized Algorithm Engineering On Two Graph Partitioning Problems, Zizhen Chen

Computer Science and Engineering Theses and Dissertations

Concepts of graph theory are frequently used by computer scientists as abstractions when modeling a problem. Partitioning a graph (or a network) into smaller parts is one of the fundamental algorithmic operations that plays a key role in classifying and clustering. Since the early 1970s, graph partitioning rapidly expanded for applications in wide areas. It applies in both engineering applications, as well as research. Current technology generates massive data (“Big Data”) from business interactions and social exchanges, so high-performance algorithms of partitioning graphs are a critical need.

This dissertation presents engineering models for two graph partitioning problems arising from completely …

Understanding The Impacts Of Topobathymetric Data On Storm Surge Model Predictions, Sydni Crain

Master's Theses

The topobathymetric characteristics of a region are regularly altered by natural and anthropogenic causes. This directly impacts the resulting storm surge during a hurricane. The primary goal of this research was to gain a better understanding of the impact that topography and bathymetry have on storm surge models, particularly the Advanced Circulation (ADCIRC) Model. Hurricane Zeta (2020) and Hurricane Ida (2021) were chosen as case studies; therefore, the Gulf of Mexico (GOM) was chosen as the study site. This research was completed by comparing ADCIRC storm surge results which were based on older, lower-resolution data with results derived from more …

Identifying Key Activity Indicators In Rats' Neuronal Data Using Lasso Regularized Logistic Regression, Avery Woods

Honors Theses

This thesis aims to identify timestamps of rats’ neuronal activity that best determine behavior using a machine learning model. Neuronal data is a complex and high-dimensional dataset, and identifying the most informative features is crucial for understanding the underlying neuronal processes. The Lasso regularization technique is employed to select the most relevant features of the data to the model’s prediction. The results of this study provide insights into the key activity indicators that are associated with specific behaviors or cognitive processes in rats, as well as the effect that stress can have on neuronal activity and behavior. Ultimately, it was …

An Analysis And Examination Of Consensus Attacks In Blockchain Networks, Thomas R. Clark

Senior Honors Projects, 2020-current

This paper examines consensus attacks as they relate to blockchain networks. Consensus attacks are a significant threat to the security and integrity of blockchain networks, and understanding these attacks is crucial for developers and stakeholders. The primary contribution of the paper is to present blockchain and consensus attacks in a clear and accessible manner, with the aim of making these complex concepts easily understandable for a general audience. Using literature review, the paper identifies various methods to prevent consensus attacks, including multi-chain networks, proof-of-work consensus algorithms, and network auditing and monitoring. An analysis revealed that these methods for preventing consensus …

Integrated Neutrosophic Best-Worst Method For Comprehensive Analysis And Ranking Of Flood Risks: A Case Study Approach From Aswan, Egypt, Safa Al-Saeed, Nabil M. Abdelaziz

Neutrosophic Systems with Applications

This paper presents a comprehensive framework for the analysis and ranking of flood risks with respect to regional variations and data uncertainties under a Neutrosophic environment. The research introduces a novel approach to flood risk mitigation and management, bringing together the scientifically robust Best-Worst Method (BWM) and single-valued Neutrosophic set for the first time. The unique application of a single-valued Neutrosophic set allows us to better illustrate and manage uncertainty, imprecision, and vagueness in data. Additionally, we employ BWM, a multi-factor decision-making method, for discerning and ranking the most influential flood risk factors. Together, the integrated methodologies provide a balanced, …

Analyzing Motivation And Sense Of Belonging Belonging In Cs1 Review Sessions, Cory Longenecker

Senior Honors Projects, 2020-current

The Computer Science Department at James Madison University has a Teaching Assistant program which aims to help students succeed in early-level Computer Science courses. Part of this program is a review session, the Fourth Hour, which provides students extra help on the concepts taught each week in class. Historically, attendance at this review session has been low. Because of this, the study aimed to increase attendance by motivating students through interventions, primarily offering quiz retakes to students who attended. Additionally, this study looked at the reported sense of belonging for participants who attended.

We made three conclusions from survey data …

Automatic Identification Of Jetting Behavior In 3d Printing With Binary Classification And Anomaly Detection, Alexander Chandy

Honors Scholar Theses

Consistently jetting different materials from the print head of a 3D printer is a key, yet challenging task in manufacturing processes. By using active machine learning, we can efficiently predict complex diagrams that illustrate the region of printing conditions under which “desirable jetting”, “jetting”, and “no jetting” of ink occurs for different substances. However, labeling the images of printed ink droplets that are fed to the active learning model can be time intensive. Therefore, it is ideal to use computer vision to automate the classification of this image data. This classification can be broken down into two steps. In the …

Generative And Pseudo-Relevant Feedback For Sparse, Dense And Learned Sparse Retrieval, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Computer Science Faculty Research & Creative Works

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectiveness. This work extends GRF to dense and learned sparse retrieval paradigms with experiments over six standard document ranking benchmarks. We find that GRF improves over comparable PRF techniques by around 10% on both precision and recall-oriented measures. Nonetheless, query analysis shows that GRF and PRF have contrasting benefits, with GRF providing external context …

U-No: U-Shaped Neural Operators, Md Ashiqur Rahman, Zachary E Ross, Kamyar Azizzadenesheli

Department of Computer Science Faculty Publications

Neural operators generalize classical neural networks to maps between infinite-dimensional spaces, e.g., function spaces. Prior works on neural operators proposed a series of novel methods to learn such maps and demonstrated unprecedented success in learning solution operators of partial differential equations. Due to their close proximity to fully connected architectures, these models mainly suffer from high memory usage and are generally limited to shallow deep learning models. In this paper, we propose U-shaped Neural Operator (U-NO), a U-shaped memory enhanced architecture that allows for deeper neural operators. U-NOs exploit the problem structures in function predictions and demonstrate fast training, data …