White supremacist hate speech is one of the most recently observed harmful content on social media. The critical influence of these radical groups is no longer limited to social media and can negatively affect society by promoting racial hatred and violence. Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information and the implicit nature of hate speech. Therefore, it is necessary to detect such speech automatically and in a timely manner. This research investigates the feasibility of automatically detecting white supremacist hate speech on Twitter using deep learning and natural language processing techniques. Two deep learning models are investigated in this research. The first approach utilizes a bidirectional Long Short-Term Memory (BiLSTM) model along with domain-specific word embeddings extracted from white supremacist corpus to capture the semantic of white supremacist slangs and coded words. The second approach utilizes one of the most recent language models, which is Bidirectional Encoder Representations from Transformers (BERT). The BiLSTM model achieved 0.75 F1-score and BERT reached a 0.80 F1-score. Both models are tested on a balanced dataset combined from Twitter and a Stormfront dataset compiled from white supremacist forum.
Person re-identification systems (person Re-ID) have recently gained more attention between computer vision researchers. They are playing a key role in intelligent visual surveillance systems and have widespread applications like applications for public security. The person Re-ID systems can identify if a person has been seen by a non-overlapping camera over large camera network in an unconstrained environment. It is a challenging issue since a person appears differently under different camera views and faces many challenges such as pose variation, occlusion and illumination changes. Many methods had been introduced for generating handcrafted features aimed to handle the person Re-ID problem. In recent years, many studies have started to apply deep learning methods to enhance the person Re-ID performance due the deep learning yielded significant results in computer vision issues. Therefore, this paper is a survey of the recent studies that proposed to improve the person Re-ID systems using deep learning. The public datasets that are used for evaluating these systems are discussed. Finally, the paper addresses future directions and current issues that must be considered toward improving the person Re-ID systems.INDEX TERMS Deep learning, person re-identification, video surveillance.
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.
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