Computer Sciences Commons^™

Missing Teeth And Restoration Detection Using Dental Panoramic Radiography Based On Transfer Learning With Cnns, Shih-Lun Chen, Tsung-Yi Chen, Yen-Cheng Huang, Chiung-An Chen, He-Sheng Chou, Ya-Yun Huang, Wei-Chi Lin, Tzu-Chien Li, Jia-Jun Yuan, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

Quantum Key-Length Extension, Joseph Jaeger, Fang Song, Stefano Tessaro

Computer Science Faculty Publications and Presentations

Should quantum computers become available, they will reduce the effective key length of basic secret-key primitives, such as blockciphers. To address this we will either need to use blockciphers with inherently longer keys or develop key-length extension techniques to amplify the security of a blockcipher to use longer keys.

We consider the latter approach and revisit the FX and double encryption constructions. Classically, FX was proven to be a secure key-length extension technique, while double encryption fails to be more secure than single encryption due to a meet-in-the-middle attack. In this work we provide positive results, with concrete and tight …

Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs

Articles

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …

Robust Time Series Chain Discovery With Incremental Nearest Neighbors, Li Zhang, Yan Zhu, Yifeng Gao, Jessica Lin

Computer Science Faculty Publications

Time series motif discovery has been a fundamental task to identify meaningful repeated patterns in time series. Recently, time series chains were introduced as an expansion of time series motifs to identify the continuous evolving patterns in time series data. Informally, a time series chain (TSC) is a temporally ordered set of time series subsequences, in which every subsequence is similar to the one that precedes it, but the last and the first can be arbitrarily dissimilar. TSCs are shown to be able to reveal latent continuous evolving trends in the time series, and identify precursors of unusual events in …

An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong Wen Deng, Chaoyang Zhang

Michigan Tech Publications, Part 1

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …

Compilation Optimizations To Enhance Resilience Of Big Data Programs And Quantum Processors, Travis D. Lecompte

LSU Doctoral Dissertations

Modern computers can experience a variety of transient errors due to the surrounding environment, known as soft faults. Although the frequency of these faults is low enough to not be noticeable on personal computers, they become a considerable concern during large-scale distributed computations or systems in more vulnerable environments like satellites. These faults occur as a bit flip of some value in a register, operation, or memory during execution. They surface as either program crashes, hangs, or silent data corruption (SDC), each of which can waste time, money, and resources. Hardware methods, such as shielding or error correcting memory (ECM), …

Enabling The Human Perception Of A Working Camera In Web Conferences Via Its Movement, Anish Shrestha

LSU Master's Theses

In recent years, video conferencing has seen a significant increase in its usage due to the COVID-19 pandemic. When casting user’s video to other participants, the videoconference applications (e.g. Zoom, FaceTime, Skype, etc.) mainly leverage 1) webcam’s LED-light indicator, 2) user’s video feedback in the software and 3) the software’s video on/off icons to remind the user whether the camera is being used. However, these methods all impose the responsibility on the user itself to check the camera status, and there have been numerous cases reported when users expose their privacy inadvertently due to not realizing that their camera is …

An Autoencoder-Based Deep Learning Method For Genotype Imputation, Meng Song, Jonathan Greenbaum, Joseph Luttrell Iv, Weihua Zhou, Chong Wu, Zhe Luo, Chuan Qiu, Lan Juan Zhao, Kuan-Jui Su, Qing Tian, Hui Shen, Huixiao Hong, Ping Gong, Xinghua Shi, Hong-Wen Deng, Chaoyang Zhang

Faculty Publications

Genotype imputation has a wide range of applications in genome-wide association study (GWAS), including increasing the statistical power of association tests, discovering trait-associated loci in meta-analyses, and prioritizing causal variants with fine-mapping. In recent years, deep learning (DL) based methods, such as sparse convolutional denoising autoencoder (SCDA), have been developed for genotype imputation. However, it remains a challenging task to optimize the learning process in DL-based methods to achieve high imputation accuracy. To address this challenge, we have developed a convolutional autoencoder (AE) model for genotype imputation and implemented a customized training loop by modifying the training process with a …

Playing Unbound: Towards A Radically Intersectional Hci, Johanna Brewer

Computer Science: Faculty Publications

This short essay is a call to action for digital games researchers to positively transform the future of HCI by meaningfully adopting an explicitly intersectional approach to our work as a scholarly community.

Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro

The Journal of Purdue Undergraduate Research

No abstract provided.

Research Software Science: Expanding The Impact Of Research Software Engineering, Michael A. Heroux

Computer Science Faculty Publications

Software plays a central role in scientific discovery. Improving how we develop and use software for research can have both broad and deep impacts on a spectrum of challenges and opportunities society faces today. The emergence of the research software engineer (RSE) role correlates with the growing complexity of scientific challenges and the diversity of software team skills. In this article, research software science (RSS), an idea related to RSE and particularly suited to research software teams, is described. RSS promotes the use of scientific methodologies to explore and establish broadly applicable knowledge. Using RSS, we can pursue sustainable, repeatable, …

Design Of Secure Communication Schemes To Provide Authentication And Integrity Among The Iot Devices, Vidya Rao Dr.

Technical Collection

The fast growth in Internet-of-Things (IoT) based applications, has increased the number of end-devices communicating over the Internet. The end devices are made with fewer resources and are low battery-powered. These resource-constrained devices are exposed to various security and privacy concerns over publicly available Internet communication. Thus, it becomes essential to provide lightweight security solutions to safeguard data and user privacy. Elliptic Curve Cryptography (ECC) can be used to generate the digital signature and also encrypt the data. The method can be evaluated on a real-time testbed deployed using Raspberry Pi3 devices and every message transmitted is subjected to ECC. …

Application Of Machine Learning And Cyber Security In Smart Grid, Soham Dutta Dr.

Technical Collection

Unplanned islanding of microgrids is a major hindrance in providing continuous power supply to the critical loads. The detection of these islanding instants needs to be very fast so that the distributed generators (DG) are able to take control actions in minimum time. Due to high quality data at a rapid rate, micro phasor measurement unit (μ-PMU) are becoming widely popular in distribution system and micro grids. These μ-PMUs can be leveraged for island detection. However, the working of μ-PMU is hugely dependent on communication network for data transmission which is prone to cyber-attacks. In view of the above facts, …

Quality Evaluation Of Agricultural And Food Products By Using Image Processing And Soft Computing Paradigm, Narendra Vg

Technical Collection

My research interests revolve around the problem of quality evaluation of Agricultural and Food Products by using Image Processing and Soft Computing Paradigm. Much of my recent work focuses on develop a framework for quality evaluation of Edible Nuts using Computer Vision and Soft Computing Techniques. Also, my interest in developing a framework for defects recognition and classification of Fruits and Vegetables using deep learning methods. My research has also explored many problems related to Blockchain Technology while considering the supply chain management of Agricultural and Food products in between with formers, retailers, and consumers.

1. http://doi.org/10.1109/DELCON54057.2022.9752836
2. http://doi.org/10.1007/978-3-031-07012-9_56
3. http://doi.org/10.1007/978-981-15-8603-3_30
4. http://doi.org/10.1007/978-981-15-8603-3_29
5. http://doi.org/10.1007/978-981-15-8603-3_29

Computer Vision Based Analysis Of Crowd Behavior For Efficient Video Surveillance In Public Places, Abhilash K. Pai

Technical Collection

Automated crowd behavior analysis and monitoring is a challenging task due to the unpredictable nature of the crowd, making it an open problem. Within this domain, we are focusing on two research problems which deal with low to mid-level analysis of crowd videos. The first problem focuses on how to effectively utilize the readily available compressed motion vector information in an input crowd video to model the motion of the crowd and perform crowd behavior analysis in varying densities of crowds. For this purpose, we consider a mid-level crowd scene analysis task called as motion pattern segmentation. The second research …

Classification Of Mudra And Posture Images Of Bharatanatyam, Venkatesh Bhandage Dr.

Technical Collection

Bharatanatyam is an Indian classical dance form which has to be studies under the supervision of experts/gurus. In this modern era there is scarcity of Bharatanatyam teachers and also the youth is less fascinated towards the classical dance forms. To promote and propagate this Indian classical dance form it is required to take leverage of technology. Hence, the automatic classification of Mudras and Postures was attempted as part of the research work. The research work can be used in training the novice learners, to provide online commentary during the concerts and e-learning of Bharatanatyam dance. The research work can help …

Zeroth-Order Hard-Thresholding: Gradient Error Vs. Expansivity, William De Vazelhes, Hualin Zhang, Huimin Wu, Xiao Tong Yuan, Bin Gu

Machine Learning Faculty Publications

ℓ0 constrained optimization is prevalent in machine learning, particularly for high-dimensional problems, because it is a fundamental approach to achieve sparse learning. Hard-thresholding gradient descent is a dominant technique to solve this problem. However, first-order gradients of the objective function may be either unavailable or expensive to calculate in a lot of real-world problems, where zeroth-order (ZO) gradients could be a good surrogate. Unfortunately, whether ZO gradients can work with the hard-thresholding operator is still an unsolved problem. To solve this puzzle, in this paper, we focus on the ℓ0 constrained black-box stochastic optimization problems, and propose a new stochastic …

Zeroth-Order Negative Curvature Finding: Escaping Saddle Points Without Gradients, Hualin Zhang, Huan Xiong, Bin Gu

Machine Learning Faculty Publications

We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them require either second or first-order information, and only a few of them have exploited zeroth-order methods, particularly the technique of negative curvature finding with zeroth-order methods which has been proven to be the most efficient method for escaping saddle points. To fill this gap, in this paper, we propose two zeroth-order negative curvature finding frameworks that can replace Hessian-vector product computations without increasing the iteration complexity. We apply the proposed frameworks to …

How To Get The Most Accurate Measurement-Based Estimates, Salvador Robles, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we want to estimate a quantity y that is difficult -- or even impossible -- to measure directly. In such cases, often, there are easier-to-measure quantities x₁, ..., x_n that are related to y by a known dependence y = f(x₁,...,x_n). So, to estimate y, we can measure these quantities x_i and use the measurement results to estimate y. The two natural questions are: (1) within limited resources, what is the best accuracy with which we can estimate y, and (2) to reach a given accuracy, what amount …

Dialogs Re-Enacted Across Languages, Nigel Ward, Jonathan E. Avila, Emilia Rivas

Departmental Technical Reports (CS)

To support machine learning of cross-language prosodic mappings and other ways to improve speech-to-speech translation, we present a protocol for collecting closely matched pairs of utterances across languages, a description of the resulting data collection, and some observations and musings. This report is intended for 1) people using this corpus, 2) people extending this corpus, and 3) people designing similar collections of bilingual dialog data.

Anomaly Detection In Crowdsourcing: Why Midpoints In Interval-Valued Approach, Alejandra De La Pena, Damian L. Gallegos Espinoza, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations -- e.g., when preparing examples for a machine learning algorithm -- we need to label a large number of images or speech recordings. One way to do it is to pay people around the world to perform this labeling; this is known as crowdsourcing. In many cases, crowd-workers generate not only answers, but also their degrees of confidence that the answer is correct. Some crowd-workers cheat: they produce almost random answers without bothering to spend time analyzing the corresponding image. Algorithms have been developed to detect such cheaters. The problem is that many crowd-workers cannot describe …

Dielectric Barrier Discharge (Dbd) Thrusters -- Aerospace Engines Of The Future: Invariance-Based Analysis, Alexis Lupo, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the most prospective aerospace engines is a Dielectric Barrier Discharge (DBD) thruster -- an effective electric engine without moving parts. Originally designed by NASA for flights over other planets, it has been shown to be very promising for Earth-based flights as well. The efficiency of this engine depends on the proper selection of the corresponding electric field. To make this selection, we need to know, in particular, how its thrust depends on the atmospheric pressure. At present, for this dependence, we only know an approximate semi-empirical formula. In this paper, we use natural invariance requirements to come up …

Hunting Habits Of Predatory Birds: Theoretical Explanation Of An Empirical Formula, Adilene Alaniz, Jiovani Hernandez, Andres D. Munoz, Vladik Kreinovich

Departmental Technical Reports (CS)

Predatory birds play an important role in an ecosystem. It is therefore important to study their hunting behavior, in particular, the distribution of their waiting time. A recent empirical study showed that the waiting time is distributed according to the power law. In this paper, we use natural invariance ideas to come up with a theoretical explanation for this empirical dependence.

Aquatic Ecotoxicology: Theoretical Explanation Of Empirical Formulas, Demetrius R. Hernandez, George M. Molina Holguin, Francisco Parra, Vivian Sanchez, Vladik Kreinovich

Departmental Technical Reports (CS)

To analyze the effect of pollution on marine life, it is important to know how exactly the concentration of toxic substances decreases with time. There are several semi-empirical formulas that describe this decrease. In this paper, we provide a theoretical explanation for these empirical formulas.

Transcriptional Regulatory Network Topology With Applications To Bio-Inspired Networking: A Survey, Satyaki Roy, Preetam Ghosh, Nirnay Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment - the very optimization challenges that are dealt with by biological networks as a consequence of millions of years …

Stable Matching Based Resource Allocation For Service Provider's Revenue Maximization In 5g Networks, Ajay Pratap, Sajal K. Das

Computer Science Faculty Research & Creative Works

5G technology is foreseen to have a heterogeneous architecture with the various computational capability, and radio-enabled service providers (SPs) and service requesters (SRs), working altogether in a cellular model. However, the coexistence of heterogeneous network model spawns several research challenges such as diverse SRs with uneven service deadlines, interference management, and revenue maximization of non-uniform computational capacities enabled SPs. Thus, we propose a coexistence of heterogeneous SPs and SRs enabled cellular 5G network and formulate the SPs' revenue maximization via resource allocation, considering different kinds of interference, data rate, and latency altogether as an optimization problem and further propose a …

Recall Distortion In Neural Network Pruning And The Undecayed Pruning Algorithm, Aidan Good, Jiaqi Lin, Hannah Sieg, Mikey Ferguson, Xin Yu, Shandian Zhe, Jerzy Wieczorek, Thiago Serra

Faculty Conference Papers and Presentations

Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a dataset may be more negatively affected. In this work, we study such relative distortions in recall by hypothesizing an intensification effect that is inherent to the model. Namely, that pruning makes recall relatively worse for a class with recall below accuracy and, conversely, that it makes recall relatively better for a class with recall above accuracy. In addition, we propose a new pruning algorithm aimed …

Testing Research Software: A Survey, Nasir U. Eisty, Jeffrey C. Carver

Computer Science Faculty Publications and Presentations

Background Research software plays an important role in solving real-life problems, empowering scientific innovations, and handling emergency situations. Therefore, the correctness and trustworthiness of research software are of absolute importance. Software testing is an important activity for identifying problematic code and helping to produce high-quality software. However, testing of research software is difficult due to the complexity of the underlying science, relatively unknown results from scientific algorithms, and the culture of the research software community.

Aims The goal of this paper is to better understand current testing practices, identify challenges, and provide recommendations on how to improve the testing process …

Resource Allocation For Multi-Tasking Optimization: Explanation Of An Empirical Formula, Alan Gamez, Antonio Aguirre, Christian Cordova, Alberto Miranda, Vladik Kreinovich

Departmental Technical Reports (CS)

For multi-tasking optimization problems, it has been empirically shown that the most effective resource allocation is attained when we assume that the gain of each task logarithmically depends on the computation time allocated to this task. In this paper, we provide a theoretical explanation for this empirical fact.