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Articles 91 - 120 of 890
Full-Text Articles in Physical Sciences and Mathematics
A Systematic Collection Of Medical Image Datasets For Deep Learning, Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, Basheer Bennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed A. A. Shah, Mohammed Bennamoun
A Systematic Collection Of Medical Image Datasets For Deep Learning, Johann Li, Guangming Zhu, Cong Hua, Mingtao Feng, Basheer Bennamoun, Ping Li, Xiaoyuan Lu, Juan Song, Peiyi Shen, Xu Xu, Lin Mei, Liang Zhang, Syed A. A. Shah, Mohammed Bennamoun
Research outputs 2022 to 2026
The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to medical data. Thus, as comprehensively as possible, this article …
Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Multi-View Information Fusion Using Multi-View Variational Autoencoder To Predict Proximal Femoral Fracture Load, Chen Zhao, Joyce H. Keyak, Xuewei Cao, Qiuying Sha, Li Wu, Zhe Luo, Lan Juan Zhao, Qing Tian, Michael Serou, Chuan Qiu, Kuan Jui Su, Hui Shen, Hong Wen Deng, Weihua Zhou
Michigan Tech Publications, Part 2
Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or “strength”) and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to …
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
Uavs And Deep Neural Networks: An Alternative Approach To Monitoring Waterfowl At The Site Level, Zachary J. Loken
LSU Master's Theses
Understanding how waterfowl respond to habitat restoration and management activities is crucial for evaluating and refining conservation delivery programs. However, site-specific waterfowl monitoring is challenging, especially in heavily forested systems such as the Mississippi Alluvial Valley (MAV)—a primary wintering region for ducks in North America. I hypothesized that using uncrewed aerial vehicles (UAVs) coupled with deep learning-based methods for object detection would provide an efficient and effective means for surveying non-breeding waterfowl on difficult-to-access restored wetland sites. Accordingly, during the winters of 2021 and 2022, I surveyed wetland restoration easements in the MAV using a UAV equipped with a dual …
Smart Street Light Control: A Review On Methods, Innovations, And Extended Applications, Fouad Agramelal, Mohamed Sadik, Youssef Moubarak, Saad Abouzahir
Smart Street Light Control: A Review On Methods, Innovations, And Extended Applications, Fouad Agramelal, Mohamed Sadik, Youssef Moubarak, Saad Abouzahir
Computer Vision Faculty Publications
As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each …
A Comprehensive Survey Of Complex Brain Network Representation, Haoteng Tang, Guixiang Ma, Yanfu Zhang, Kai Ye, Lei Guo, Guodong Liu, Qi Huang, Yalin Wang, Olusola Ajilore, Alex D. Leow
A Comprehensive Survey Of Complex Brain Network Representation, Haoteng Tang, Guixiang Ma, Yanfu Zhang, Kai Ye, Lei Guo, Guodong Liu, Qi Huang, Yalin Wang, Olusola Ajilore, Alex D. Leow
Computer Science Faculty Publications and Presentations
Highlights
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Major traditional and deep learning methods on brain network representation are overviewed.
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Brain network datasets and algorithm implementation tools are summarized.
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Promising research directions in brain network analysis are discussed.
Abstract
Recent years have shown great merits in utilizing neuroimaging data to understand brain structural and functional changes, as well as its relationship to different neurodegenerative diseases and other clinical phenotypes. Brain networks, derived from different neuroimaging modalities, have attracted increasing attention due to their potential to gain system-level insights to characterize brain dynamics and abnormalities in neurological conditions. Traditional methods aim to pre-define multiple topological features of brain …
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel. Chen, Nan Hu, Peng. Liang, Morgan. Swink
Research Collection School Of Computing and Information Systems
Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …
On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng
On The Sustainability Of Deep Learning Projects: Maintainers' Perspective, Junxiao Han, Jiakun Liu, David Lo, Chen Zhi, Yishan Chen, Shuiguang Deng
Research Collection School Of Computing and Information Systems
Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open-source software (OSS) projects. In this regard, we first investigate …
Optimized Uncertainty Estimation For Vision Transformers: Enhancing Adversarial Robustness And Performance Using Selective Classification, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Optimized Uncertainty Estimation For Vision Transformers: Enhancing Adversarial Robustness And Performance Using Selective Classification, Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja
Computer Science: Faculty Publications and Other Works
Deep Learning models often exhibit undue confidence when encountering out-of-distribution (OOD) inputs, misclassifying with high confidence. The ideal outcome, in these cases, would be an "I do not know" verdict. We enhance the trustworthiness of our models through selective classification, allowing the model to abstain from making predictions when facing uncertainty. Rather than a singular prediction, the model offers a prediction distribution, enabling users to gauge the model’s trustworthiness and determine the need for human intervention. We assess uncertainty in two baseline models: a Convolutional Neural Network (CNN) and a Vision Transformer (ViT). By leveraging these uncertainty values, we minimize …
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Prediction Of Rockburst Intensity Grade Based On Convolutional Neural Network, Li Kangnan, Wu Yaqin, Du Feng, Zhang Xiang, Wang Yiqiao
Coal Geology & Exploration
Rockburst is one of the urgent problems to be addressed in the process of deep resource extraction. In order to predict the rockburst disasters safely and efficiently, a rockburst intensity grade prediction model (MICE-CNN) based on the Multiple Imputation by Chained Equations (MICE) and Convolutional Neural Network (CNN) was proposed. Specifically, a predictive indicator system was established based on the main influencing factors and the acquisition conditions of rockburst. A total of 120 sets of raw data from rockburst cases were collected, with the outliers processed by pauta criterion. Then, the missing data were interpolated with the four interpolation models …
Deep Learning For Plant Bioinformatics: An Explainable Gradient-Based Approach For Disease Detection, Muhammad Shoaib, Babar Shah, Nasir Sayed, Farman Ali, Rafi Ullah, Irfan Hussain
Deep Learning For Plant Bioinformatics: An Explainable Gradient-Based Approach For Disease Detection, Muhammad Shoaib, Babar Shah, Nasir Sayed, Farman Ali, Rafi Ullah, Irfan Hussain
All Works
Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a …
Dtitd: An Intelligent Insider Threat Detection Framework Based On Digital Twin And Self-Attention Based Deep Learning Models, Zhi Qiang Wang, Abdulmotaleb El Saddik
Dtitd: An Intelligent Insider Threat Detection Framework Based On Digital Twin And Self-Attention Based Deep Learning Models, Zhi Qiang Wang, Abdulmotaleb El Saddik
Computer Vision Faculty Publications
Recent statistics and studies show that the loss generated by insider threats is much higher than that generated by external attacks. More and more organizations are investing in or purchasing insider threat detection systems to prevent insider risks. However, the accurate and timely detection of insider threats faces significant challenges. In this study, we proposed an intelligent insider threat detection framework based on Digital Twins and self-attentions based deep learning models. First, this paper introduces insider threats and the challenges in detecting them. Then this paper presents recent related works on solving insider threat detection problems and their limitations. Next, …
Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras
Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras
Computer Science Faculty and Staff Publications
Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …
Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi
Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi
Turkish Journal of Electrical Engineering and Computer Sciences
Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni
Turkish Journal of Electrical Engineering and Computer Sciences
Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …
An Ai-Based Framework For Translating American Sign Language To English And Vice Versa, Vijayendra D. Avina, Md Amiruzzaman, Stefanie Amiruzzaman, Linh B. Ngo, M. Ali Akber Dewan
An Ai-Based Framework For Translating American Sign Language To English And Vice Versa, Vijayendra D. Avina, Md Amiruzzaman, Stefanie Amiruzzaman, Linh B. Ngo, M. Ali Akber Dewan
Computer Science Faculty Publications
Abstract: In this paper, we propose a framework to convert American Sign Language (ASL) to English and English to ASL. Within this framework, we use a deep learning model along with the rolling average prediction that captures image frames from videos and classifies the signs from the image frames. The classified frames are then used to construct ASL words and sentences to support people with hearing impairments. We also use the same deep learning model to capture signs from the people with deaf symptoms and convert them into ASL words and English sentences. Based on this framework, we developed a …
Object Recognition With Deep Neural Networks In Low-End Systems, Lillian Davis
Object Recognition With Deep Neural Networks In Low-End Systems, Lillian Davis
Mahurin Honors College Capstone Experience/Thesis Projects
Object recognition is an important area in computer vision. Object recognition has been advanced significantly by deep learning that unifies feature extraction and classification. In general, deep neural networks, such as Convolution Neural Networks (CNNs), are trained in high-performance systems. Aiming to extend the reach of deep learning to personal computing, I propose a study of deep learning-based object recognition in low-end systems, such as laptops. This research includes how differing layer configurations and hyperparameter values used in CNNs can either create or resolve the issue of overfitting and affect final accuracy levels of object recognition systems. The main contribution …
Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai
Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai
Computer Science Faculty Research & Creative Works
Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The …
Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii
Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii
Mechanical & Aerospace Engineering Theses & Dissertations
In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.
This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Doctoral Dissertations and Master's Theses
With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …
Attention-Based Human Age Estimation From Face Images To Enhance Public Security, Md. Ashiqur Rahman, Shuhena S. Aonty, Kaushik Deb, Iqbal H. Sarker
Attention-Based Human Age Estimation From Face Images To Enhance Public Security, Md. Ashiqur Rahman, Shuhena S. Aonty, Kaushik Deb, Iqbal H. Sarker
Research outputs 2022 to 2026
Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, …
Perceptual Cue-Guided Adaptive Image Downscaling For Enhanced Semantic Segmentation On Large Document Images, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang
Perceptual Cue-Guided Adaptive Image Downscaling For Enhanced Semantic Segmentation On Large Document Images, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang
School of Computing: Faculty Publications
Image downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both …
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
An Intelligent Prediction Method And Interpretability For Drag And Torque Of Drill String, Liu Muchen, Song Xianzhi, Li Dayu, Zhu Shuo, Fu Li, Zhu Zhaopeng, Zhang Chengkai, Pan Tao
Coal Geology & Exploration
The accurate characterization and dynamic analysis of drilling string mechanics are essential to ensure the safe and efficient drilling. In the classical soft/rigid string model for drag & torque of drilling string, the friction coefficient of the drilling string is determined by empirical estimation or post-drilling inversion, of which the accuracy and timeliness needs to be improved. Based on the effectiveness of artificial intelligence technology applied in complex nonlinear mapping, a drag and torque prediction method of drill string with mechanism-data fusion was proposed by predicting the friction coefficient. Firstly, the friction coefficient was inversed using the drilled and logged …
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Drilling Core Identification Based On Natural Image, Gao Hui, Wu Zhenkun, Ke Yu, Tan Songcheng, He Siqi, Duan Longchen
Coal Geology & Exploration
The traditional on-site core identification and recording mainly rely on the experience of technicians, and there are many uncertain factors. Limited by the site conditions, using mobile phones or cameras to capture the natural images is the most convenient way to collect the core information. Therefore, it is necessary to study the feature information extraction technology of core image and apply it to the identification and prediction of core type and other information. Specifically, a large number of core samples were collected, the thin-section identification method was employed to determine the core types and names, and then the core images …
Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang
Style Transfer Network For Generating Opera Makeup Details, Fengquan Zhang, Duo Cao, Xiaohan Ma, Baijun Chen, Jiangxiao Zhang
Journal of System Simulation
Abstract: To address the problem of the loss of local style details in cross-domain image simulation, a ChinOperaGAN network framework suitable for opera makeup is designed from the perspective of protecting the excellent traditional culture. In order to solve the style translation of differences in two image domains, multiple overlapping local adversarial discriminators are proposed in the generative adversarial network. Since paired opera makeup data are difficult to obtain, a synthetic image is generated by combining the source image makeup mapping to effectively guide the transfer of local makeup details between images. In view of the characteristics of opera makeup …
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Intelligent Lithology Prediction Method Based On Vibration Signal While Drilling And Deep Learning, Wang Sheng, Lai Kun, Zhang Zheng, Bai Jun, Luo Zhongbin, Li Bingle, Zhang Jie
Coal Geology & Exploration
Intelligent lithology prediction is of great importance in geological drilling, capable of improving exploration and mining efficiency, as well as the quality of results. In this study, a method of intelligent lithology prediction while drilling was proposed based on the vibration signals produced by the drill bit breaking rocks during drilling. Specifically, seven types of rocks with the same size and different lithologies were selected, and a micro-drilling experiment was designed to apply different drilling rates and rotary speeds to the rocks, in order to collect the triaxial vibration signals while drilling under multiple drilling conditions. The signals were preprocessed …
A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan
A Deep Learning-Based Object Detection Framework For Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images, Ibrahim Hassan Syed, Susan Mckeever Dr., Kieran Feighan, David Power, Dympna O'Sullivan
Conference papers
Road maintenance and the early detection of road defects rely on routine pavement inspections. While advanced 3D laser profiling systems have the capability to automatically identify certain types of distress such as cracks and ruts, more complex pavement damage, including patches, often require manual identification. To address this limitation, this study proposes an automated patch detection system that employs object detection techniques. The results demonstrate the ability of object detection models to accurately identify patches in laser profiling images, indicating that the proposed approach has the capability to significantly enhance automation in visual inspection processes. This has the potential for …
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Takvor Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi Takvor Khatchadourian Ph.D., Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, 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—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph,D,, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja
Towards Safe Automated Refactoring Of Imperative Deep Learning Programs To Graph Execution, Raffi T. Khatchadourian Ph,D,, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, 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—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution. We present our ongoing work on automated refactoring that assists developers in specifying whether …
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
Sentence Embedding Approach Using Lstm Auto-Encoder For Discussion Threads Summarization, Abdul Wali Khan, Feras Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
All Works
Online discussion forums are repositories of valuable information where users interact and articulate their ideas and opinions, and share experiences about numerous topics. These online discussion forums are internet-based online communities where users can ask for help and find the solution to a problem. A new user of online discussion forums becomes exhausted from reading the significant number of irrelevant replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) …
Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert
Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert
Research Collection School Of Computing and Information Systems
Android malware detection has been an active area of research. In the past decade, several machine learning-based approaches based on different types of features that may characterize Android malware behaviors have been proposed. The usually-analyzed features include API usages and sequences at various abstraction levels (e.g., class and package), extracted using static or dynamic analysis. Additionally, features that characterize permission uses, native API calls and reflection have also been analyzed. Initial works used conventional classifiers such as Random Forest to learn on those features. In recent years, deep learning-based classifiers such as Recurrent Neural Network have been explored. Considering various …