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Full-Text Articles in Engineering
Semantic Segmentation Based Deep Learning Approaches For Weed Detection, Puranjit Singh
Semantic Segmentation Based Deep Learning Approaches For Weed Detection, Puranjit Singh
Department of Biological Systems Engineering: Dissertations and Theses
Global increase in herbicide use to control weeds has led to issues such as evolution of herbicide-resistant weeds, off-target herbicide movement, etc. Precision agriculture advocates Site Specific Weed Management (SSWM) application to achieve precise and right amount of herbicide spray and reduce off-target herbicide movement. Recent advancements in Deep Learning (DL) have opened possibilities for adaptive and accurate weed recognitions for field based SSWM applications with traditional and emerging spraying equipment; however, challenges exist in identifying the DL model structure and train the model appropriately for accurate and rapid model applications over varying crop/weed growth stages and environment. In our …
Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan
Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …
Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …
Foreign Object Debris Detection For Airport Pavement Images Based On Self-Supervised Localization And Vision Transformer, Travis Munyer, Daniel Brinkman, Xin Zhong, Chenyu Huang, Iason Konstantzos
Foreign Object Debris Detection For Airport Pavement Images Based On Self-Supervised Localization And Vision Transformer, Travis Munyer, Daniel Brinkman, Xin Zhong, Chenyu Huang, Iason Konstantzos
Durham School of Architectural Engineering and Construction: Faculty Publications
Supervised object detection methods provide subpar performance when applied to Foreign Object Debris (FOD) detection because FOD could be arbitrary objects according to the Federal Aviation Administration (FAA) specification. Current supervised object detection algorithms require datasets that contain annotated examples of every to-be-detected object. While a large and expensive dataset could be developed to include common FOD examples, it is infeasible to collect all possible FOD examples in the dataset representation because of the openended nature of FOD. Limitations of the dataset could cause FOD detection systems driven by those supervised algorithms to miss certain FOD, which can become dangerous …