Physical Sciences and Mathematics Commons^™

Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang

Coal Geology & Exploration

A highly efficient means is provided by remote sensing and deep learning to keep tracking of land use in open-pit coal mining area. Based on the high–resolution images from the domestic GF-2 satellite, a DeepLabv3+ model was utilized to achieve recognition of land use on open-pit coal mining area. In addition, a comparison was made among Deeplabv3+, U-Net, FCN, Random Forest, Support Vector Machine, and Maximum Likelihood Method. Firstly, samples data from high-resolution images were produced and sensitivity tests were conducted to determine the optimal cutting size and mode of the sample. Then, the deep neural network model (DeepLabv3+) was …

Assessing The Reidentification Risks Posed By Deep Learning Algorithms Applied To Ecg Data, Arin Ghazarian, Jianwei Zheng, Daniele Struppa, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. …

Towards Color-Based Two-Hand 3d Global Pose Estimation, Fanqing Lin

Theses and Dissertations

Pose estimation and tracking is essential for applications involving human controls. Specifically, as the primary operating tool for human activities, hand pose estimation plays a significant role in applications such as hand tracking, gesture recognition, human-computer interaction and VR/AR. As the field develops, there has been a trend to utilize deep learning to estimate the 2D/3D hand poses using color-based information without depth data. Within the depth-based as well as color-based approaches, the research community has primarily focused on single-hand scenarios in a localized/normalized coordinate system. Due to the fact that both hands are utilized in most applications, we propose …

Learning To Generalize Dispatching Rules On The Job Shop Scheduling, Zangir Iklassov, Dmitrii Medvedev, Ruben Solozabal, Martin Takac

Machine Learning Faculty Publications

This paper introduces a Reinforcement Learning approach to better generalize heuristic dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do not focus on generalization, although, as we show in this work, this is key to learning better heuristics on the problem. A well-known technique to improve generalization is to learn on increasingly complex instances using Curriculum Learning (CL). However, as many works in the literature indicate, this technique might suffer from catastrophic forgetting when transferring the learned skills between different problem sizes. To address this issue, we introduce a novel Adversarial Curriculum Learning (ACL) strategy, …

The Smart In Smart Cities: A Framework For Image Classification Using Deep Learning, Rabiah Al-Qudah, Yaser Khamayseh, Monther Aldwairi, Sarfraz Khan

All Works

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. …

An Overview Of Technologies Deployed In Gcc Countries To Combat Covid-19, Samia Loucif, Murad Al-Rajab, Reem Salem, Nadine Akkila

All Works

Since December 2019, COVID-19 and all of its variants continue to ravage the planet with consequent negative impact that has completely changed our lives within a short period of time after the outbreak of the Virus. On March 11, 2020, COVID-19 was declared a global pandemic by the World Health Organization. Since then, a group of new COVID-19 variants has emerged posing a greater danger to humanity. By the start of August 2021, the reported COVID-19 related death toll across the globe has rocketed to 4,233,139. To deal with the COVID-19 pandemic, countries across the world have rushed to develop …

Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe

Research Collection School Of Computing and Information Systems

A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the …

Adversarially Robust And Accurate Machine Learning For Image Classification, Yanan Yang

Dissertations

Machine learning techniques in medical imaging systems are accurate, but minor perturbations in the data known as adversarial attacks can fool them. These attacks make the systems vulnerable to fraud and deception, and thus a significant challenge has been posed in practice. This dissertation presents the gradient-free trained sign activation networks to detect and deter adversarial attacks on medical imaging AI (Artificial Intelligence) systems. Experimental results show a higher distortion value is required to attack the proposed model than other state-of-the-art models on brain MRI (Magnetic resonance imaging), Chest X-ray, and histopathology image datasets. Moreover, the proposed models outperform the …

Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang

Michigan Tech Publications

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …

Language Learning Using Models Of Intentionality In Repeated Games With Cheap Talk, Jonathan Berry Skaggs

Theses and Dissertations

Language is critical to establishing long-term cooperative relationships among intelligent agents (including people), particularly when the agents' preferences are in conflict. In such scenarios, an agent uses speech to coordinate and negotiate behavior with its partner(s). While recent work has shown that neural language modeling can produce effective speech agents, such algorithms typically only accept previous text as input. However, in relationships among intelligent agents, not all relevant context is expressed in conversation. Thus, in this paper, we propose and analyze an algorithm, called Llumi, that incorporates other forms of context to learn to speak in long-term relationships modeled as …

Challenges In Migrating Imperative Deep Learning Programs To Graph Execution: An Empirical Study, Tatiana Castro Vélez, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the "best of both worlds," the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved …

Missing Value Estimation Using Clustering And Deep Learning Within Multiple Imputation Framework, Manar D. Samad, Sakib Abrar, Norou Diawara

Computer Science Faculty Research

Missing values in tabular data restrict the use and performance of machine learning, requiring the imputation of missing values. Arguably the most popular imputation algorithm is multiple imputation by chained equations (MICE), which estimates missing values from linear conditioning on observed values. This paper proposes methods to improve both the imputation accuracy of MICE and the classification accuracy of imputed data by replacing MICE’s linear regressors with ensemble learning and deep neural networks (DNN). The imputation accuracy is further improved by characterizing individual samples with cluster labels (CISCL) obtained from the training data. Our extensive analyses of six tabular data …

Cell Tracking At Low Frame Rate Using Deep Learning And Bayesian Integration, Xiang Zhang

All Dissertations

Tracking cells over time is a fundamental task in live-cell imaging, and often requires costly manual analysis if images are not acquired with high enough frame rate. Acquiring high frame rate images, however, can limit the number of conditions explored and cells analyzed, and contribute to photobleaching, which makes fluorophores dimmer and phototoxicity, which affects cell health and renders the resulting data unusable.

Assuming a relatively high frame rate in image acquisition, state-of-the-art cell tracking approaches rely on either spatial proximity or morphological similarity to link cells in consecutive frames. The problem is that, at low frame rate, both approaches …

Identifying Noisy Labels In The Ground Truth Of Eating Episodes Self-Reported By Button Press On A Wrist-Worn Device, Tianyi Zhang

All Theses

This thesis considers the problem of identifying noisy labels in the ground truth of eating episodes (meals, snacks) as self-reported by participants collecting data in the wild. Participants wore a smartwatch-like device that tracked their wrist motion all day. They were instructed to press a button on the device at the start and end of each eating episode. The device and instructions were designed to be as simple to use as possible, but post-review of the ground truth provided by participants revealed a strong likelihood that a significant portion of the button presses may contain errors. For example, an error …

Deep Learning Object-Based Detection Of Manufacturing Defects In X-Ray Inspection Imaging, Juan C. Parducci

Mechanical & Aerospace Engineering Theses & Dissertations

Current analysis of manufacturing defects in the production of rims and tires via x-ray inspection at an industry partner’s manufacturing plant requires that a quality control specialist visually inspect radiographic images for defects of varying sizes. For each sample, twelve radiographs are taken within 35 seconds. Some defects are very small in size and difficult to see (e.g., pinholes) whereas others are large and easily identifiable. Implementing this quality control practice across all products in its human-effort driven state is not feasible given the time constraint present for analysis.

This study aims to identify and develop an object detector capable …

Anomaly Detection In Rotating Machinery Using Autoencoders Based On Bidirectional Lstm And Gru Neural Networks, Krishna Patra, Rabi Narayan Sethi, Dhiren Kkumar Behera

Turkish Journal of Electrical Engineering and Computer Sciences

A time series anomaly is a form of anomalous subsequence that indicates future faults will occur. The development of novel techniques for detecting this type of anomaly is significant for real-time system monitoring. Several algorithms have been used to classify anomalies successfully. However, the time series anomaly detection algorithm was not studied well. We use a new bidirectional LSTM and GRU neural networks-based hybrid autoencoder to detect if a machine is operating normally in this research. An autoencoder is trained on a set of 12 features taken from healthy operating data gathered promptly after a planned maintenance period using vibration …

Simple Or Complex? Together For A More Accurate Just-In-Time Defect Predictor, Xin Zhou, Donggyun Han, David Lo

Research Collection School Of Computing and Information Systems

Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful …

Robust Noise-Based Attacks Against Audio Event Detection Systems, Rodrigo Augusto Silva Dos Santos

Computer Science and Engineering Dissertations

The massive advances on the field of deep neural networks in the 2000 and 2010 decades led to an overwhelming adoption of these algorithms on all sorts of domains and applications. Under this widespread adoption scenario, it is natural that these neural networks have also been employed on safety-related use cases, bringing substantial improvements to the performance of existing as well as novel systems. Examples of these safety-inclined applications include scene recognition, object detection and tracking, speech recognition, audio event detection and classification, just to cite a few ones. Unfortunately, these neural network algorithms have been shown to be vulnerable …

Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly …

Indoor Localization Using Solar Cells, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef

Research Collection School Of Computing and Information Systems

The development of the Internet of Things (IoT) opens the doors for innovative solutions in indoor positioning systems. Recently, light-based positioning has attracted much attention due to the dense and pervasive nature of light sources (e.g., Light-emitting Diode lighting) in indoor environments. Nevertheless, most existing solutions necessitate carrying a high-end phone at hand in a specific orientation to detect the light intensity with the phone's light sensing capability (i.e., light sensor or camera). This limits the ease of deployment of these solutions and leads to drainage of the phone battery. We propose PVDeepLoc, a device-free light-based indoor localization system that …

Transparscit: A Transformer-Based Citation Parser Trained On Large-Scale Synthesized Data, Md Sami Uddin

Computer Science Theses & Dissertations

Accurately parsing citation strings is key to automatically building large-scale citation graphs, so a robust citation parser is an essential module in academic search engines. One limitation of the state-of-the-art models (such as ParsCit and Neural-ParsCit) is the lack of a large-scale training corpus. Manually annotating hundreds of thousands of citation strings is laborious and time-consuming. This thesis presents a novel transformer-based citation parser by leveraging the GIANT dataset, consisting of 1 billion synthesized citation strings covering over 1500 citation styles. As opposed to handcrafted features, our model benefits from word embeddings and character-based embeddings by combining the bidirectional long …

Radar Remote Sensing Data Augmentation Method Based On Generative Adversarial Network, Xu Kang, Xiaofeng Zhang

Journal of System Simulation

Abstract: In the research field of radar remote sensing, both the completeness and diversity of radar data samples cannot meet the requirement of effective training of deep learning models, and the models are prone to over-fitting, which significantly limits the wide application of deep learning techniques in this field. Targeting on the needs of intelligent application in radar remote sensing, a microwave imaging radar suited data augmentation method is proposed to solve the issue of insufficient radar data samples by leveraging the general framework of generative adversarial network. Aiming at the features of radar samples being not obvious, the label …

Visual Attention Methods In Deep Learning: An In-Depth Survey, Mohammed Hassanin, Anwar Saeed, Ibrahim Radwan, Fahad Shahbaz Khan, Ajmal Mian

Computer Vision Faculty Publications

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated in one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey specific to attention techniques to guide researchers in employing attention in their deep models. …

Physics-Guided Machine Learning In Ocean Acoustics Using Fisher Information, Michael Craig Mortenson

Theses and Dissertations

Waterborne acoustic signals carry information about the ocean environment. Ocean geoacoustic inversion is the task of estimating environmental parameters from received acoustic signals by matching the measured sound with the predictions of a physics-based model. A lower bound on the uncertainty associated with environmental parameter estimates, the Cramér-Rao bound, can be calculated from the Fisher information, which is dependent on derivatives of a physics-based model. Physics-based preconditioners circumvent the need for variable step sizes when computing numerical derivatives. This work explores the feasibility of using a neural network to perform geoacoustic inversion for environmental parameters and their associated uncertainties from …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Deep Convolutional Neural Network-Based System For Fish Classification, Ahmad Al Smadi, Atif Mehmood, Ahed Abugabah, Eiad Almekhlafi, Ahmad Mohammad Al-Smadi

All Works

In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 …

How Can Generative Adversarial Networks Impact Computer Generated Art? Insights From Poetry To Melody Conversion, Sakib Shahriar, Noora Al Roken

All Works

Recent advances in deep learning and generative adversarial networks (GANs), in particular, has enabled interesting applications including photorealistic image generation, image translation, and automatic caption generation. This has opened up possibilities for many cross-domain applications in computer generated arts and literature. Although there are existing software-based approaches for generating musical accompaniment of a given poetry, there are no existing implementation using GANs. This work proposes a novel poetry to melody generation conditioned on poem emotion using GANs. A dataset containing pairs of poetry and melody based on three emotion categories is introduced. Furthermore, various GAN architectures including SpecGAN and WaveGAN …

General Purpose Computing On Graphics Processing Units For Accelerated Deep Learning In Neural Networks, Conor Helmick

Senior Honors Theses

Graphics processing units (GPUs) contain a significant number of cores relative to central processing units (CPUs), allowing them to handle high levels of parallelization in multithreading. A general-purpose GPU (GPGPU) is a GPU that has its threads and memory repurposed on a software level to leverage the multithreading made possible by the GPU’s hardware, and thus is an extremely strong platform for intense computing – there is no hardware difference between GPUs and GPGPUs. Deep learning is one such example of intense computing that is best implemented on a GPGPU, as its hardware structure of a grid of blocks, each …

Quantitative Multidimensional Stress Assessment From Facial Videos, Lin He

Dissertations (1934 -)

Stress has a significant impact on the physical and mental health of an individual and is a growing concern for society, especially during the COVID-19 pandemic. Facial video-based stress evaluation from non-invasive cameras has proven to be a significantly more efficient method to evaluate stress in comparison to approaches that use questionnaires or wearable sensors. Plenty of classification models have been built for stress detection. However, most do not consider individual differences. Also, the results for such models are limited by a uni-dimensional definition of stress levels lacking a comprehensive quantitative definition of stress. The dissertation focuses on building a …