Digital Commons Network^™

Singleadv: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems, Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed

Computer Science: Faculty Publications and Other Works

In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretations. The universal perturbation is stochastically and iteratively optimized by minimizing the adversarial loss that is designed to consider both the classifier and interpreter costs in targeted and non-targeted categories. In this optimization framework, ruled by the first- and second-moment estimations, the desired loss surface promotes high confidence and interpretation score of adversarial samples. By …

Ghost Connect-Net: A Connectivity-Based Companion Network To Enhance Pruning Methods, Mary Isabelle Wisell

Honors College

Deep neural network (DNN) approaches excel in various real-world applications like robotics and computer vision, yet their computational demands and memory requirements hinder usability on advanced devices. Also, larger models heighten overparameterization risks, making networks more vulnerable to input disturbances. Recent studies aim to boost DNN efficiency by trimming redundant neurons or filters based on task relevance. Instead of introducing a new pruning method, this project aims to enhance existing techniques by introducing a companion network, Ghost Connect-Net (GC-Net), to monitor the connections in the original network. The initial weights of GC- Net are equal to the connectivity measurements of …

Deep Learning-Based Predictive Classification Of Functional Subpopulations Of Hematopoietic Stem Cells And Multipotent Progenitors., Shen Wang, Jianzhong Han, Jingru Huang, Khayrul Islam, Yuheng Shi, Yuyuan Zhou, Dongwook Kim, Jane Zhou, Zhaorui Lian, Yaling Liu, Jian Huang

Cooper Medical School of Rowan University Faculty Scholarship

BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.

METHODS: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images.

RESULTS: After rigorous training and validation using extensive image …

Functional Data Learning Using Convolutional Neural Networks, Jose Galarza, Tamer Oraby

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of …

Gliopredictor: A Deep Learning Model For Identification Of High-Risk Adult Idh-Mutant Glioma Towards Adjuvant Treatment Planning, Shuhua Zheng, Nikhil Rammohan, Timothy Sita, P Troy Teo, Yilin Wu, Maciej Lesniak, Sean Sachdev, Tarita O Thomas

2020-Current year OA Pubs

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and …

Ds 677-852: Deep Learning, Ioannis Koutis

Data Science Syllabi

No abstract provided.

Deep Learning-Based Phenotyping Reclassifies Combined Hepatocellular-Cholangiocarcinoma, Julien Calderaro, Pooja Navale, Et Al.

2020-Current year OA Pubs

Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical …

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 …

Single-Cell Analysis Of Chromatin Accessibility In The Adult Mouse Brain, Songpeng Zu, Yang Eric Li, Et Al.

2020-Current year OA Pubs

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete

Optimizing Uncertainty Quantification Of Vision Transformers In Deep Learning On Novel Ai Architectures, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Deep Learning (DL) methods have shown substantial efficacy in computer vision (CV) and natural language processing (NLP). Despite their proficiency, the inconsistency in input data distributions can compromise prediction reliability. This study mitigates this issue by introducing uncertainty evaluations in DL models, thereby enhancing dependability through a distribution of predictions. Our focus lies on the Vision Transformer (ViT), a DL model that harmonizes both local and global behavior. We conduct extensive experiments on the ImageNet-1K dataset, a vast resource with over a million images across 1,000 categories. ViTs, while competitive, are vulnerable to adversarial attacks, making uncertainty estimation crucial for …

Flacgec: A Chinese Grammatical Error Correction Dataset With Fine-Grained Linguistic Annotation, Hanyue Du, Yike Zhao, Qingyuan Tian, Jiani Wang, Lei Wang, Yunshi Lan, Xuesong Lu

Research Collection School Of Computing and Information Systems

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences …

Deep Learning Integrates Histopathology And Proteogenomics At A Pan-Cancer Level, Joshua M Wang, Yize Li, Li Ding, Et Al.

2020-Current year OA Pubs

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level …

Emotion-Aware Music Recommendation, Hieu Tran, Tuan Le, Anh Do, Tram Vu, Steven Bogaerts, Brian T. Howard

Computer Science Faculty publications

It is common to listen to songs that match one's mood. Thus, an AI music recommendation system that is aware of the user's emotions is likely to provide a superior user experience to one that is unaware. In this paper, we present an emotion-aware music recommendation system. Multiple models are discussed and evaluated for affect identification from a live image of the user. We propose two models: DRViT, which applies dynamic routing to vision transformers, and InvNet50, which uses involution. All considered models are trained and evaluated on the AffectNet dataset. Each model outputs the user's estimated valence and arousal …

Gpachov At Checkthat! 2023: A Diverse Multi-Approach Ensemble For Subjectivity Detection In News Articles, Georgi Pachov, Dimitar Dimitrov, Ivan Koychev, Preslav Nakov

Natural Language Processing Faculty Publications

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task 2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered …

Modeling Islet Enhancers Using Deep Learning Identifies Candidate Causal Variants At Loci Associated With T2d And Glycemic Traits., Sanjarbek Hudaiberdiev, D Leland Taylor, Wei Song, Narisu Narisu, Redwan M Bhuiyan, Henry J Taylor, Xuming Tang, Tingfen Yan, Amy J Swift, Lori L Bonnycastle, Diamante Consortium, Shuibing Chen, Michael L. Stitzel, Michael R Erdos, Ivan Ovcharenko, Francis S Collins

Faculty Research 2023

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, …

Sctiger: A Deep-Learning Method For Inferring Gene Regulatory Networks From Case Versus Control Scrna-Seq Datasets., Madison Dautle, Shaoqiang Zhang, Yong Chen

Faculty Scholarship for the College of Science & Mathematics

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among …

Training Certified Detectives To Track Down The Intrinsic Shortcuts In Covid-19 Chest X-Ray Data Sets, Ran Zhang, Dalton Griner, John W. Garrett, Zhihua Qi, Guang-Hong Chen

Diagnostic Radiology Articles

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can …

Non-Invasive Arterial Blood Pressure Measurement And Spo2 Estimation Using Ppg Signal: A Deep Learning Framework, Yan Chu, Kaichen Tang, Yu-Chun Hsu, Tongtong Huang, Dulin Wang, Wentao Li, Sean I Savitz, Xiaoqian Jiang, Shayan Shams

Journal Articles

BACKGROUND: Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection.

METHOD: Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers' interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct …

Opportunistic Detection Of Type 2 Diabetes Using Deep Learning From Frontal Chest Radiographs, Ayis Pyrros, Stephen M. Borstelmann, Ramana Mantravadi, Zachary Zaiman, Kaesha Thomas, Brandon Price, Eugene Greenstein, Nasir Siddiqui, Melinda Willis, Ihar Shulhan, John Hines-Shah, Jeanne M. Horowitz, Paul Nikolaidis, Matthew P. Lungren, Jorge Mario Rodríguez-Fernández, Judy Wawira Gichoya, Sanmi Koyejo, Adam E. Flanders, Nishith Khandwala, Amit Gupta, John W. Garrett, Joseph Paul Cohen, Brian T. Layden, Perry J. Pickhardt, William Galanter

Department of Radiology Faculty Papers

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of …

Spatial & Temporal Agnostic Deep-Learning Based Radio Fingerprinting, Fahmida Afrin

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Radio fingerprinting is a technique that validates wireless devices based on their unique radio frequency (RF) signals. This method is highly feasible because RF signals carry distinct hardware variations introduced during manufacturing. The security and trustworthiness of current and future wireless networks heavily rely on radio fingerprinting. In addition to identifying individual devices, it can also differentiate mission-critical targets. Despite significant efforts in the literature, existing radio fingerprinting methods require improved robustness, scalability, and resilience. This study focuses on the challenges of spatial-temporal variations in the wireless environment. Many prior approaches overlook the complex numerical structure of the in-phase and …

Duplicate Bug Report Detection: How Far Are We?, Ting Zhang, Donggyun Han, Venkatesh Vinayakarao, Ivana Clairine Irsan, Bowen Xu, Thung Ferdian, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Many Duplicate Bug Report Detection (DBRD) techniques have been proposed in the research literature. The industry uses some other techniques. Unfortunately, there is insufficient comparison among them, and it is unclear how far we have been. This work fills this gap by comparing the aforementioned techniques. To compare them, we first need a benchmark that can estimate how a tool would perform if applied in a realistic setting today. Thus, we first investigated potential biases that affect the fair comparison of the accuracy of DBRD techniques. Our experiments suggest that data age and issue tracking system choice cause a significant …

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 …

A Novel Graph Neural Network-Based Framework For Automatic Modulation Classification In Mobile Environments, Pejman Ghasemzadeh

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Automatic modulation classification (AMC) refers to a signal processing procedure through which the modulation type and order of an observed signal are identified without any prior information about the communications setup. AMC has been recognized as one of the essential measures in various communications research fields such as intelligent modem design, spectrum sensing and management, and threat detection. The research literature in AMC is limited to accounting only for the noise that affects the received signal, which makes their models applicable for stationary environments. However, a more practical and real-world application of AMC can be found in mobile environments where …

Automated Classification Of Pectinodon Bakkeri Teeth Images Using Machine Learning, Jacob A. Bahn

MS in Computer Science Project Reports

Microfossil dinosaur teeth are studied by paleontologists in order to better under- stand dinosaurs. Currently, tooth classification is a long, manual, error-ridden process. Deep learning offers a solution that allows for an automated way of classifying images of these microfossil teeth. In this thesis, we aimed to use deep learning in order to develop an automated approach for classifying images of Pectinodon bakkeri teeth. The proposed model was trained using a custom topology and it classified the images based on clusters created via K-Means. The model had an accuracy of 71%, a precision of 71%, a recall of 70.5%, and …

Automatic Detection Of Circulating Tumor Cells And Cancer Associated Fibroblasts Using Deep Learning, Cheng Shen, Siddarth Rawal, Rebecca Brown, Haowen Zhou, Ashutosh Agarwal, Mark A Watson, Richard J Cote, Changhuei Yang

2020-Current year OA Pubs

Circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) from whole blood are emerging as important biomarkers that potentially aid in cancer diagnosis and prognosis. The microfilter technology provides an efficient capture platform for them but is confounded by two challenges. First, uneven microfilter surfaces makes it hard for commercial scanners to obtain images with all cells in-focus. Second, current analysis is labor-intensive with long turnaround time and user-to-user variability. Here we addressed the first challenge through developing a customized imaging system and data pre-processing algorithms. Utilizing cultured cancer and CAF cells captured by microfilters, we showed that images from our …

Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping, Junxiao Zhang

Department of Biological Systems Engineering: Dissertations and Theses

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which …

Ad-Syn-Net: Systematic Identification Of Alzheimer's Disease-Associated Mutation And Co-Mutation Vulnerabilities Via Deep Learning, Xingxin Pan, Zeynep H Coban Akdemir, Ruixuan Gao, Xiaoqian Jiang, Gloria M Sheynkman, Erxi Wu, Jason H Huang, Nidhi Sahni, S Stephen Yi

Journal Articles

Alzheimer's disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework ('AD-Syn-Net'), and propose deep learning models named Deep-SMCI and Deep-CMCI configured …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

A Deep Learning Approach To Identify Missing Is-A Relations In Snomed Ct, Rashmie Abeysinghe, Fengbo Zheng, Elmer V Bernstam, Jay Shi, Olivier Bodenreider, Licong Cui

Journal Articles

Objective

SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT.

Materials and Methods

Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances …

A Deep Learning Approach To Identify Missing Is-A Relations In Snomed Ct, Rashmie Abeysinghe, Fengbo Zheng, Elmer V Bernstam, Jay Shi, Olivier Bodenreider, Licong Cui

Journal Articles

OBJECTIVE: SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT.

MATERIALS AND METHODS: Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances …