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Articles 61 - 67 of 67
Full-Text Articles in Physical Sciences and Mathematics
Towards Misleading Connection Mining, Md Main Uddin Rony
Towards Misleading Connection Mining, Md Main Uddin Rony
Electronic Theses and Dissertations
This study introduces a new Natural Language Generation (NLG) task – Unit Claim Identification. The task aims to extract every piece of verifiable information from a headline. The Unit Claim identification has applications in other domains; such as fact-checking where the identification of each verifiable information from a check-worthy statement can lead to an effective fact-check. Moreover, the extracting of the unit claims from headlines can identify a misleading news article, by mapping evidence from contents. For addressing the unit claim identification problem, we outlined a set of guidelines for data annotation, arranged in-house training for the annotators and obtained …
Optimizing The Performance Of Complex Engineering Systems Aided By Artificial Neural Networks, Khalil Qatu
Optimizing The Performance Of Complex Engineering Systems Aided By Artificial Neural Networks, Khalil Qatu
Electronic Theses and Dissertations
In the first problem Polyetherimide graphene nanoplatelets papers (PEIGNP) were tested with different graphene loadings varying from 0-97 weight percent (WT%). The resulting stress-strain curves were utilized to develop two ANN models. Stress-controlled and strain-controlled models. Both models shoan excellent correlation to the experimental. Several Mechanical properties were calculated from the predicted stress-strain curves namely; toughness maximum strength maximum strain and maximum tangent modulus. Both models captured the same overall behavior of the PEIGNP composite. However the strain-controlled model was found to predict lower stress than the stress-controlled model. Finally a Graphical User Interface (GUI) was developed to aid in …
Performance Evaluation Of Blocking And Non-Blocking Concurrent Queues On Gpus, Hossein Pourmeidani
Performance Evaluation Of Blocking And Non-Blocking Concurrent Queues On Gpus, Hossein Pourmeidani
Electronic Theses and Dissertations
The efficiency of concurrent data structures is crucial to the performance of multi-threaded programs in shared-memory systems. The arbitrary execution of concurrent threads, however, can result in an incorrect behavior of these data structures. Graphics Processing Units (GPUs) have appeared as a powerful platform for high-performance computing. As regular data-parallel computations are straightforward to implement on traditional CPU architectures, it is challenging to implement them in a SIMD environment in the presence of thousands of active threads on GPU architectures. In this thesis, we implement a concurrent queue data structure and evaluate its performance on GPUs to understand how it …
An Approach To Semi-Autonomous Indoor Drone System: Software Architecture And Integration Testing, Shobhan Singh
An Approach To Semi-Autonomous Indoor Drone System: Software Architecture And Integration Testing, Shobhan Singh
Electronic Theses and Dissertations
To address these problems, we establish a semi-autonomous functionality by removing the RC transmitter, and remotely connecting the Drone System to track status and executing user-based input commands. In order to resolve the limitation in hardware connections on the Flight Controller, we integrated the sonar sensor into a companion computer, from where the data is continuously fed to an embedded system through MAVLink (Micro Aerial Vehicle Link) network communication protocol. In this study, we also implemented a modular architecture which enables scalable integration of sensor modules into the Drone System to streamline the process of development, deployment, testing and debugging.
Utilizing Various Neural Network Architectures To Play A Game Developed For Human Players, Michael Blake Arender
Utilizing Various Neural Network Architectures To Play A Game Developed For Human Players, Michael Blake Arender
Electronic Theses and Dissertations
Neural Networks have received an explosive amount of attention and interest in recent years. Despite the fact that Neural Network algorithms having existed for many decades, it was not until recent advances in computer hardware that they saw widespread use. This is in no small part due to the success these algorithms have had in tasks ranging from image classification, voice recognition, game theory, and many other applications. Thanks to recent strides in hardware development, most importantly in the advancements in Graphics Processor Units including the capabilities of modern GPU Computing, Neural Networks are now capable of solving tasks at …
Scheduling Irregular Workloads On Gpus, David Arthur Troendle
Scheduling Irregular Workloads On Gpus, David Arthur Troendle
Electronic Theses and Dissertations
This doctoral research aims at understanding the nature of the overhead for data irregular GPU workloads, proposing a solution, and examining the consequences of the result. We propose a novel, retry-free GPU workload scheduler for irregular workloads. When used in a Breadth First Search (BFS) algorithm, the proposed simple, monolithic concurrent queue scales to within 10% of ideal scalability on AMD’s Fiji GPU with 14,336 active threads. The dissertation presents an important finding that the retry overhead associated with Compare and Swap (CAS) operations is the principle reason why concurrent queues do not scale well as the number of clients …
Improving Random Forests By Feature Dependence Analysis, Silu Zhang
Improving Random Forests By Feature Dependence Analysis, Silu Zhang
Electronic Theses and Dissertations
Random forests (RFs) have been widely used for supervised learning tasks because of their high prediction accuracy good model interpretability and fast training process. However they are not able to learn from local structures as convolutional neural networks (CNNs) do when there exists high dependency among features. They also cannot utilize features that are jointly dependent on the label but marginally independent of it. In this dissertation we present two approaches to address these two problems respectively by dependence analysis. First a local feature sampling (LFS) approach is proposed to learn and use the locality information of features to group …