Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Bert (1)
- Computer Vision (1)
- Convolutional LSTM (1)
- Deep learning (1)
- Delphi study (1)
-
- Engineering education (1)
- Extractive summarization (1)
- Head pose estimation (1)
- LSTM (1)
- LiDAR (1)
- Neural networks (1)
- Online exam Proctoring (1)
- Point Semantic Segmentation (1)
- Professional skills (1)
- Semantic KITTI (1)
- Semantic Segmentation (1)
- Sensor Fusion (1)
- Temporal (1)
- Text summarization (1)
- Transformer architecture (1)
- User authentication (1)
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Comparing Importance Of Knowledge And Professional Skill Areas For Engineering Programming Utilizing A Two Group Delphi Survey, John F. Hutton
Comparing Importance Of Knowledge And Professional Skill Areas For Engineering Programming Utilizing A Two Group Delphi Survey, John F. Hutton
Theses and Dissertations
All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and …
A Component-Based Analysis For Online Proctoring, Salma Roshdy Ali
A Component-Based Analysis For Online Proctoring, Salma Roshdy Ali
Theses and Dissertations
The switch to online learning due to the COVID-19 revealed flaws in the existing learning methods, especially with online proctored assessments. Hence, online proctoring using computers was needed for a fair evaluation. Many studies develop cheating detection systems using several approaches. However, to the best of our knowledge, none of the existing studies investigated the impact of their system components in detecting cheating behaviors. Combining system components, even if they do not significantly improve the system performance in cheating detection, can cause an overload on the system. Therefore, our goal is to investigate the system components’ impact, individually and combined, …
Camera And Lidar Fusion For Point Cloud Semantic Segmentation, Ali Abdelkader
Camera And Lidar Fusion For Point Cloud Semantic Segmentation, Ali Abdelkader
Theses and Dissertations
Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from …
Adding Temporal Information To Lidar Semantic Segmentation For Autonomous Vehicles, Mohammed Anany
Adding Temporal Information To Lidar Semantic Segmentation For Autonomous Vehicles, Mohammed Anany
Theses and Dissertations
Semantic segmentation is an essential technique to achieve scene understanding for various domains and applications. Particularly, it is of crucial importance in autonomous driving applications. Autonomous vehicles usually rely on cameras and light detection and ranging (LiDAR) sensors to gain contextual information from the environment. Semantic segmentation has been employed to process images and point clouds that were captured from cameras and LiDAR sensors respectively. One important research direction to consider is investigating the impact of utilizing temporal information in the domain of semantic segmentation. Many contributions exist in the field with regards to utilizing temporal information for semantic segmentation …
Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed
Extractive Text Summarization On Single Documents Using Deep Learning, Shehab Mostafa Abdel-Salam Mohamed
Theses and Dissertations
The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for …