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Articles 1 - 6 of 6
Full-Text Articles in Engineering
User Profiling Through Zero-Permission Sensors And Machine Learning, Ahmed Elhussiny
User Profiling Through Zero-Permission Sensors And Machine Learning, Ahmed Elhussiny
Theses and Dissertations
With the rise of mobile and pervasive computing, users are often ingesting content on the go. Services are constantly competing for attention in a very crowded field. It is only logical that users would allot their attention to the services that are most likely to adapt to their needs and interests. This matter becomes trivial when users create accounts and explicitly inform the services of their demographics and interests. Unfortunately, due to privacy and security concerns, and due to the fast nature of computing today, users see the registration process as an unnecessary hurdle to bypass, effectively refusing to provide …
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 …
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 …
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 …
Efficient End-To-End Autonomous Driving, Hesham Eraqi
Efficient End-To-End Autonomous Driving, Hesham Eraqi
Theses and Dissertations
Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to train artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolution Long Short-Term Memory Recurrent Neural Network (C-LSTM), which is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the …