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- Computer Science--Machine Learning (3)
- Computer Science--Information Retrieval (2)
- Computer Science--Social and Information Networks (2)
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- Pain intensity quantification, Pain pattern identification, Physiological signals, Sickle cell anemia--Pain (1)
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- Pain perception (1)
- Pain threshold (1)
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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Ufuzzer: Lightweight Detection Of Php-Based Unrestricted File Upload Vulnerabilities Via Static-Fuzzing Co-Analysis, Jin Huang, Junjie Zhang, Jialun Liu, Chuang Li
Ufuzzer: Lightweight Detection Of Php-Based Unrestricted File Upload Vulnerabilities Via Static-Fuzzing Co-Analysis, Jin Huang, Junjie Zhang, Jialun Liu, Chuang Li
Computer Science and Engineering Faculty Publications
Unrestricted file upload vulnerabilities enable attackers to upload malicious scripts to a web server for later execution. We have built a system, namely UFuzzer, to effectively and automatically detect such vulnerabilities in PHP-based server-side web programs. Different from existing detection methods that use either static program analysis or fuzzing, UFuzzer integrates both (i.e., static-fuzzing co-analysis). Specifically, it leverages static program analysis to generate executable code templates that compactly and effectively summarize the vulnerability-relevant semantics of a server-side web application. UFuzzer then “fuzzes” these templates in a local, native PHP runtime environment for vulnerability detection. Compared to static-analysis-based methods, UFuzzer preserves …
Clustering Of Pain Dynamics In Sickle Cell Disease From Sparse, Uneven Samples, Gary K. Nave Jr, Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Nirmish Shah, Daniel M. Abrams
Clustering Of Pain Dynamics In Sickle Cell Disease From Sparse, Uneven Samples, Gary K. Nave Jr, Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Nirmish Shah, Daniel M. Abrams
Computer Science and Engineering Faculty Publications
Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different …
Uncertainty-Aware Visualization In Medical Imaging - A Survey, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Gerik Scheuermann
Uncertainty-Aware Visualization In Medical Imaging - A Survey, Christina Gillmann, Dorothee Saur, Thomas Wischgoll, Gerik Scheuermann
Computer Science and Engineering Faculty Publications
Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be …
Nomophobia Before And After The Covid-19 Pandemic-Can Social Media Be Used To Understand Mobile Phone Dependency, Vaishnavi Visweswaraiah, Tanvi Banerjee, William Romine, Sarah Fryman
Nomophobia Before And After The Covid-19 Pandemic-Can Social Media Be Used To Understand Mobile Phone Dependency, Vaishnavi Visweswaraiah, Tanvi Banerjee, William Romine, Sarah Fryman
Computer Science and Engineering Faculty Publications
No abstract provided.
Neuro-Symbolic Deductive Reasoning For Cross-Knowledge Graph Entailment, Monireh Ebrahimi, Md Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Aaron Eberhart, Derek Doran, Hyeongsik Kim, Pascal Hitzler
Neuro-Symbolic Deductive Reasoning For Cross-Knowledge Graph Entailment, Monireh Ebrahimi, Md Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Aaron Eberhart, Derek Doran, Hyeongsik Kim, Pascal Hitzler
Computer Science and Engineering Faculty Publications
A significant and recent development in neural-symbolic learning are deep neural networks that can reason over symbolic knowledge graphs (KGs). A particular task of interest is KG entailment, which is to infer the set of all facts that are a logical consequence of current and potential facts of a KG. Initial neural-symbolic systems that can deduce the entailment of a KG have been presented, but they are limited: current systems learn fact relations and entailment patterns specific to a particular KG and hence do not truly generalize, and must be retrained for each KG they are tasked with entailing. We …
Leveraging Natural Language Processing To Mine Issues On Twitter During The Covid-19 Pandemic, Ankita Agarwal, Preetham Salehundam, Swati Padhee, William Romine, Tanvi Wright State University - Main Campus
Leveraging Natural Language Processing To Mine Issues On Twitter During The Covid-19 Pandemic, Ankita Agarwal, Preetham Salehundam, Swati Padhee, William Romine, Tanvi Wright State University - Main Campus
Computer Science and Engineering Faculty Publications
The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe. The international travel ban, panic buying, and the need for self-quarantine are among the many other social challenges brought about in this new era. Twitter platforms have been used in various public health studies to identify public opinion about an event at the local and global scale. To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets and identify the important topics of discussion on social media platforms like Twitter …
Topic-Centric Unsupervised Multi-Document Summarization Of Scientific And News Articles, Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer
Topic-Centric Unsupervised Multi-Document Summarization Of Scientific And News Articles, Amanuel Alambo, Cori Lohstroh, Erik Madaus, Swati Padhee, Brandy Foster, Tanvi Banerjee, Krishnaprasad Thirunarayan, Michael Raymer
Computer Science and Engineering Faculty Publications
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to generate extractive and abstractive summaries for groups of scientific articles across 20 Fields of Study (FoS) in …
Can Subjective Pain Be Inferred From Objective Physiological Data? Evidence From Patients With Sickle Cell Disease, Mark J. Panaggio, Daniel M. Abrams, Fan Yang, Tanvi Banerjee, Nirmish R. Shah
Can Subjective Pain Be Inferred From Objective Physiological Data? Evidence From Patients With Sickle Cell Disease, Mark J. Panaggio, Daniel M. Abrams, Fan Yang, Tanvi Banerjee, Nirmish R. Shah
Computer Science and Engineering Faculty Publications
Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient's subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients' pain levels indirectly using vital signs that are routinely collected and documented in …
An Analysis Of C/C++ Datasets For Machine Learning-Assisted Software Vulnerability Detection, Daniel Grahn, Junjie Zhang
An Analysis Of C/C++ Datasets For Machine Learning-Assisted Software Vulnerability Detection, Daniel Grahn, Junjie Zhang
Computer Science and Engineering Faculty Publications
As machine learning-assisted vulnerability detection research matures, it is critical to understand the datasets being used by existing papers. In this paper, we explore 7 C/C++ datasets and evaluate their suitability for machine learning-assisted vulnerability detection. We also present a new dataset, named Wild C, containing over 10.3 million individual opensource C/C++ files – a sufficiently large sample to be reasonably considered representative of typical C/C++ code. To facilitate comparison, we tokenize all of the datasets and perform the analysis at this level. We make three primary contributions. First, while all the datasets differ from our Wild C dataset, some …
Augmented Reality Headset Facilitates Exposure For Surgical Stabilization Of Rib Fractures, T. Sensing, Pratik Parikh, Claire Hardman, Thomas Wischgoll, Sadan Suneesh Menon
Augmented Reality Headset Facilitates Exposure For Surgical Stabilization Of Rib Fractures, T. Sensing, Pratik Parikh, Claire Hardman, Thomas Wischgoll, Sadan Suneesh Menon
Computer Science and Engineering Faculty Publications
Recent advances in augmented reality (AR) technology have made it more accessible, portable, and powerful. AR headsets differentiate themselves from virtual reality in that they allow the wearer an unobstructed view of the “real world” but with an image superimposed upon it. The technology has many potential applications in medicine, including surgical planning, simulation, and medical education. The aim of this project was to provide proof of concept that using an AR headset during surgical stabilization of rib fractures (SSRF) is feasible. We theorized that the use of AR could allow for more precise localization of fractures, allowing for smaller …
Pain Intensity Assessment In Sickle Cell Disease Patients Using Vital Signs During Hospital Visits, Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M. Abrams, Gary K. Nave, Nirmish Shah
Pain Intensity Assessment In Sickle Cell Disease Patients Using Vital Signs During Hospital Visits, Swati Padhee, Amanuel Alambo, Tanvi Banerjee, Arvind Subramaniam, Daniel M. Abrams, Gary K. Nave, Nirmish Shah
Computer Science and Engineering Faculty Publications
Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from …