Reviewerly/exHarmony huggingface dataset:
Ultrasonic Smart Stick Vision-Based EyeTech Digital System for Movement Tracking

There is no doubt that contemporary technology is the main engine of society's acceleration of growth and progress worldwide. It has become tactile for everything that people do daily. The smart stick approach is highly needed in the special needs group to help the blind and visually impaired on movement and mobility easily surround their environment. This paper uses a group of sensors and Arduino-Mega microcontroller to receive, process and analysis RF signals and issue an alert in a voice message that distinguishes the type of obstacles for the blind. This approach uses ultrasonic sensors to detect obstacles and constraints in the way of the blind and differentiate whether they are small in size and can be bypassed or a significant obstacle which highly required to change the direction directly. The proposed stick sensors also recognize the water puddles on the blind way. Moreover, the system contains a remote control and IR receiver to help the blind in case it falls or is lost in the surround of him, the stick is equipped with an RTC module to help the blind know the time anywhere easily by clicking on the remote control. In this project, we seek to provide an affordable lightweight, and easy-to-use this smart stick that serves the blind and helps them to move from one place to another.
Enhance the Aspect Category Detection in Arabic Language using AraBERT and Text Augmentation

Research into Arabic Aspect Category Detection (ACD), a subtask of Aspect-Based Sentiment Analysis (ABSA) is primarily focused on experimenting with Machine Learning (ML) and Deep Learning (DL) models, but rarely consider methods for robust dataset creation or augmentation. Therefore, this study investigates and compares two applications of AraBERT vr2 on the Arabic SemEval2016 hotel dataset, where the objective is to predict aspect categories in the reviews. The baseline pipeline uses the AraBERT classification layer to predict 34 aspect categories, and the proposed pipeline performs a data augmentation process prior to classification, where the HARD dataset is used with a Word2Vec model to extend the original training of the SemEval2016 dataset. Both implementations of AraBERT were evaluated on Gold Standard dataset derived from SemEval2016. Results indicate that both applications achieved a closely similar f1-score of 0.663 for the baseline and 0.661 for the augmented model. The by-class results are further discussed.
A Restaurant Recommendation Engine Using Feature-based Explainable Matrix Factorization

Black box algorithms have demonstrated their superiority in handling the sparse data that the modern Internet is saturated with. Transparency, which is essential for building trust in recommender systems, is missing, nevertheless. Collaborative Filtering techniques require lots of items ratings for the model to function properly, moreover, item-side information greatly aids in creating interpretations because filtering models frequently rely on ratings. In our study, we suggest a new approach for producing justifications while maintaining high accuracy. The findings of the research demonstrate that our suggested solution performs better in terms of accuracy and transparency.
Security and Privacy Challenges in Big Data

In recent years, there has been a growing interest in the field of Big Data thanks to its benefits, such as cost optimization, improved efficiency, and potential risks identification. Big data has become a widely exploited research area in academia and industry as it benefits both organizations and consumers. However, like any new technology, there are still several challenges relevant to this field and most notably data security. The security challenges of individual’s sensitive information associated with Big Data have been widely studied in the literature. In addition, current studies of Big Data mainly focus on how to minimize the privacy risk brought by Big Data operations; however, unwanted disclosure of the user’s sensitive information may also happen in the process of data collection and analysis. In this paper, we provide a comprehensive study of the security and privacy issues related to Big Data from a wider perspective and investigate various mechanisms that can be used to protect sensitive information and thus, achieve better data security.
Protecting Digital Currency Transactions on Bitcoin Networks Against Delay-Based Attacks*

Bitcoin (₿) synchronizes a public ledger over a large decentralized blockchain network as an underlying infrastructure. Propagation delay among long chains of network nodes is a major threat to message dissemination and integrity of digital currency transactions. In this paper, a comprehensive study presents state-of-the-art protection methods of digital currency transactions against security threats caused by the propagation delay on the Bitcoin network. We discuss the network propagation algorithms and illustrate the main factors affecting the propagation delay and the major security attacks threatening digital currency transactions on these networks. Based on these factors, we describe and compare the possible countermeasures for securing the Bitcoin network against the major threats.
Opinion Mining using Sentiment Analysis and Topic Modelling for an Entertainment Event

The entertainment sector is one of the critical sectors fostering a vibrant creative economy. Saudi Arabia has recently constructed several entertainment venues in Riyadh to attract visitors and tourists. One such place is Winter Wonderland. Although this event is held yearly, there is not enough information about visitor opinions and satisfaction. Therefore, this paper aims to collect a dataset and analyze the visitors’ sentiments and their discussed topics on Twitter. We employ two methods. The first is classification using three variations of Support Vector Machine for sentiment analysis, and the second is topic modelling using BERTopic. We further examine the discussed topics in light of time and place. The outcome demonstrates that both Linear SVM and RBF outperform Poly SVM. In addition, most of the tweets discuss topics related to tickets, with Saudi cities being the largest source of tweets selling tickets.
Improving Web Application Development Course

Providing students with the expertise of scientific and practical skills in Web development requires good preparation and practical competencies, the formation of positive attitudes toward the given course and determining suitable learning outcomes is one of the most important trends and issues in instructional design and technology. One of the most important challenges in this paper is to develop the relatively old course plan to cope with the rapid developments in the world of web application development, this requires Curriculum mapping to identify and address academic gaps, and redundancies. For purposes of improving the overall coherence of the course, its relationship with the other prerequisite courses should be explained more, some learning outcomes may be achieved through more than one course, therefore this study traced other related courses to the problem statement, such as Object-Oriented-Programming (OOP), Software Modelling and Design and other courses. This interconnected set of courses was motivated by the idea of Crosscutting concepts (CCCs) in Web Application Development, such as design patterns, Model-View-Controller (MVC), Entity Framework, Object Relational Mapping, Application Programming Interface (API), and others. This paper proposed some best practices and guidelines to take students gradually from a simple to more complicated tasks in learning Web development.
Feature Selection Based Universal Image Steganalysis: A Survey

Steganography is the technique of hiding communication into multimedia objects such as image, video, audio, and text. On the other hand, steganalysis is the art and science of detecting the existence of an embedded message. Steganalysis can be classified into targeted\specific steganalysis and universal\blind steganalysis. Targeted steganalysis is designed for specific steganography techniques. Whereas, in universal steganography techniques are not familiar to the user. Moreover, the convectional universal steganalysis consists of two main phases: the extraction of features as well as classifying images into stego or clean. Images containing hidden messages are called Stego images. Feature selection is the technique used to decrease feature dimensionality. This literature review provides an overview of universal image steganalysis based on feature selection.
Digital Forensics and Machine Learning to Fraudulent Email Prediction

E-mail is widespread in the modern commercial environment, providing an appropriate and efficient method for communication. However, today’s e-mail security threats are multiplying at an unprecedented rate. Sending phishing, spoofing, spam, and scam e-mails attempting to gain access to victims’ personal or financial information is a common way e-mail is used to commit a crime. The criminal activity needs to be combated through digital forensics. Unfortunately, cyber events are becoming significantly challenging, and human capabilities are limited. Using the SeFACED dataset, this research proposes a content base, E-mail multi-classification, into four different classes: Normal, Fraudulent, Threatening, and Suspicious, using four primary Machine Learning algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). In addition, the Term FrequencyInverse Document Frequency (TF-IDF) and Word2vec are also used as feature extraction techniques to compare their results. The findings show that the best accuracy is achieved with the RF, LR, SVM model by the TF-IDF feature extraction with an accuracy of 95%. In addition, the result for classification with TF-IDF outperformed Word2vec classification.
Detecting Hate speech in Arabic Literahire Tweets

Middle Eastern people are one of the largest populations on Twitter. In recent years, more Arabic writers, especially young ones, have used Twitter to publish their literary works to a wider audience. This research focused on two literary genres: prose and poetry, to detect hate speech in Arabic literary texts that were published in Twitter platform. Arabic tweets wm be scraped, pre-processed, and classified into hate or non-hate tweets using five different machine learning algorithms; namely: Support Vector Machines, Naive Bayes, Random Forest, Gradient Boosted Decision Trees, and Extra Tree Classmer in three different scenarios: unbalance, undersampling data, and over-sampling data. We compare the performance of these algorithms based on four evaluation metrics; namely: accuracy, precision, recall, and Fl-score. Our results show that the RF algorithm produces 95.45% accuracy, 98.63% precision, 92.17% recall, and 95.29% F1-score; hence, we decided to display RF classified tweets on a website called (ميراث الأدباء), that means Wnters’ Legacy in the English language, to preserve and enable readers to easily access Arabic literature tweets.
EBSAR: Detecting of Objects that Hinder Visually Impaired in a Controlled Area Using Deep Learning

With more than 285 million visually impaired people globally, there is a critical need for an assistive system that helps blind and visually impaired people to navigate easily. The EBSAR system is proposed to detect the objects using mobile video cameras and make a sound alert with the distance and right direction in Arabic language to the user. In the EBSAR system, the updated Convolutional Neural Networks (CNN) and You Only Look Once version four (YOLOv4) algorithms are used for object detection. Besides, the key matching as these algorithms give more accuracy. The camera of the phone is enough for detecting the objects and no special hardware is required. Thus, the system requires minimal effort from the user to use the system during everyday life.
Utilizing Sentiment Analysis to Enhance the Quality of Online Learning

The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: “Positive,” “Negative,” and “Neutral.” Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique.
Leveraging Ground Test Data for Aero-Engine Thrust Estimation

Accurate aero-engine thrust estimation plays an important role in direct thrust control. This work proposes to leverage the ground test data to estimate the thrust with an artificial neural network. The Savitzky Golay method is used to denoise the data collected by sensors. The random forest and Pearson correlation coefficient methods are combined to reduce the dimension of the data. Considering the limitation of sensors on flight comparing to the ground test, we propose to use the on flight sensor data to estimate the missing data first, and then integrate them as inputs to estimate the thrust. The results of case study validate the effectiveness of our proposed model.
A Semantic Enhanced Chinese Text Recognition Method Based on Stroke Decomposing

Chinese text recognition has been one of the hot topics. Although there are many text recognition methods available, some problems are still not effectively solved, such as the appearance of Chinese characters that do not appear in the training process. Previous Chinese text recognition methods have not thoroughly solved this problem, and many of them are based on English text recognition methods. English words can be split into different character, and Chinese character can be similarly split Chinese characters into multiple stroke sequences, then recognize the stroke sequences, and finally connect the stroke sequences into sentences. Howerver, the same sequence of strokes can form different Chinese characters, so a sequence of strokes can also form different sentences. To solve this confusion problem, a text classification model is trained to rank all possible sentences and then find the one that best matches the normal semantics.
Efficient Audio Encryption Using a Discrete Cosine Transform and Baker Map

With the expansion and improvement of technology and communication in recent years, the necessity to secure and defend these communications from various threats has increased. In this research, we provide a novel method for encrypting audio signals utilizing Baker and discrete cosine transform (DCT) chaotic maps. Three typical audio samples of funky, muted trumpet, and flute are converted into 2D to enable the use of chaotic maps. Then, a DCT is used to extract the coefficients, and the Baker chaotic map is used to replace them. Finally, an inverse DCT is used to perform the encryption. The DCT is applied to the sample during the decryption stage, and the coefficients are altered using the Baker map. An inverse DCT is then applied, and the output is transformed from 2D to 1D to obtain the original audio signal. The effectiveness of the proposed coding model based on Baker maps and DCT is evaluated using a variety of measures, including entropy, histogram, correlation coefficient, spectrogram, noise effect, and differential tests, and the results demonstrate the effectiveness of the proposed encryption model.
Enhancing Feature Fusion Using Attention for Small Object Detection

At present, object detection performance can meet some routine tasks' requirements. However, the detection performance for small-sized objects is far from satisfactory. Therefore, we propose the feature layer attention module and nonlinear positioning loss penalty based on size to improve small object detection performance. Our work proposes the feature layer attention module, which introduces an attention mechanism in the feature layer to enhance the model's attention to small objects. Through the feature fusion scheme proposed in this paper, we solve the problem of insufficient features of small objects to a certain extent and reduce the difficulty of model training. Besides, we introduce a size-based nonlinear penalty in the loss function, which can enhance the penalty for small object positioning errors. The effectiveness of our method has been demonstrated on small object data sets. On VisDrone2019 dataset, the proposed method improves the detection's AP by 2.2%. On TT100k dataset, the proposed method improves the detection's AP by 1.0%.
Contrastive learning of visual representations-based DDTF for geophysical data denoising and interpolation

A new artificial intelligence denoising and interpolation algorithm is proposed for geophysical data processing. The contrastive learning of visual representations (CLR) and the data-driven tight frame (DDTF) are incorporated in this new algorithm to achieve a accurate patch selection and direction learning for complex datasets with low amplitudes and high noise levels. Three cases are shown to test the effectiveness of this new method.
Incremental Update based Closed-Loop Malicious URL Detection System

In recent years, deep learning has been widely applied to the detection of malicious URLs and has made a great success. However, the latent patterns of malicious URLs change constantly, which requires frequent model update for the detection system. Existing URL detection system update basically relies on model retaining, which is time-consuming and resource-consuming. In this paper, we propose an incremental update based closed-loop URL detection system, where the URL detection model continually updates to fuse the knowledge of the samples with patterns different from the previously known samples in the training set. We develop a convolutional neural network (CNN) based automatic semantic feature extraction and a fully connected network based URL detection method. In order to detect the samples with new patterns, we conceive a least confidence algorithm based suspicious URL detection. A knowledge distillation based lightweight incremental update method is proposed to rapidly aggregate the knowledge of newly added URLs with only a small part of history data. In this way, a closed-loop URL detection system is formed by automatic feature extraction, initial model training, suspicious URL detection, and detection model update, so that the detection model can keep pace with the evolution of URLs. Finally, the experiments implemented on the public URL dataset ISCX-URL2016 demonstrate that our proposed closed-loop URL detection system and method can obtain a high detection accuracy with a low update time.
A Self-Attention Based SRGAN for Super-Resolution of Astronomical Image

High-resolution (HR) astronomical image play a vital role in the development of scientific research, cosmic exploration, astronomy, and physics. In this paper, we propose a self- attention based generative adversarial network of astronomical image super-resolution (SR) aiming at the problem of low- resolution (LR) of astronomical imaging systems. We adopt SRGAN as the benchmark model and add self-attention, which captures more global dependencies and deepens the network for enhanced high-frequency feature representation. To achieve fast and stable training, the BN layer is deleted from the proposed networks. The Charbonnier loss is introduced as the loss function to handle outliers and improve SR performance. Experimental results demonstrate that the proposed method is able to reduce artifacts and obtains better performance in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) on the astronomical image testset.
Bayesian Generation Model for Snow Removal

Snowflakes in the image usually reduce the visibility of the background image and affect the image quality. Nowadays, most of the single image snow removal tasks have made some progress through the design of more complex deep learning models, but most of them use specific datasets, and the generalization ability is not enough. We deal with the snow removal problem from the point of view of the datasets and propose a more effective method to synthesize snow images. The full Bayesian generation model of snow image is established, in which the snow layer is parameterized as a generator and input as some latent variables. To resolve this model, the variational reasoning framework is used to close the expected statistical distribution of snow in a data-driven way. Using the model generator, different and non-repetitive training pairs can be generated automatically, thus enriching and expanding the existing benchmark datasets. In addition, we design a more simple and feasible deep learning snow removal network based on this model. The experimental results show that the generated model can extract the complex snowflake distribution and can effectively improve the current deep learning snow removal performance. the snow removal effect of the snow removal network based on extended datasets is better than that of the existing snow removal models, and has better processing efficiency.
Pose Face Recognition Based on Supervised Subspace Learning

The pose changes involved in face images have always been a difficulty in face recognition. In order to solve this difficulty, we propose a pose face recognition method based on supervised subspace learning (SSL). In SSL, we first propose a supervised subspace learning algorithm (SSLA). On the one hand, SSLA reduces the intra-class differences by constructing the difference term. On the other hand, the samples can be represented by the samples of the same category by constructing the block-diagonal regularization term. Besides, SSLA is not disturbed by noise by constructing a noise robust term. Then, the samples in the original space are mapped to the learned subspace by using SSLA. Finally, those mapped samples are classified by ESRC. The experimental results on Georgia Tech dataset and FEI dataset show that SSL has achieved good recognition results.