Reviewerly/exHarmony huggingface dataset:
Machine Learning Approach for Negation Handling in Sentiment Analysis

With the growing popularity of online shopping, product reviews have emerged as significant source of product information. Sentiment analysis was developed as a result of the growing customer interest in reading specific product reviews before making a purchase, which uses natural language processing to examine client opinions. The influence of negation on word scope and order, as well as the inability to handle linguistic features, is an essential task in sentiment analysis. In this study, we first tackle the challenge of assigning the polarity of a phrase while identifying the scope of negation. Second, we suggest an approach to dealing with negations that relies on linguistic features to identify the impact of various genres of negation in lexicon- dependent on sentiment analysis. The data imbalance problem was overcome using SMOTE. The results indicate that the hybrid approach, which includes a lexicon-based dictionary for training the machine learning classifier, performs better at detecting negative sentences. When comparing classifiers, Random Forest outperformed Support Vector Machine and Naive Bayes. However, Naive Bayes (NB) outperforms in terms of computing time.
DeepFake Detection System using Deep Learning

Due to the growing usage of DeepFake technology to produce fake photos and videos, the problem of DeepFake identification has become a serious concern in recent years. With the use of this technology, fake material that is difficult to tell apart from real content may be produced. Therefore, the development of accurate DeepFake detection algorithms is essential to identify and prevent the spread of manipulated content. However, current publicly available DeepFake detection datasets suffer from a lack of diversity, with only a few actors appearing in multiple videos. This results in an oversampled training dataset, leading to model overfitting and inadequate performance when tested on new data. The overfitting problem can cause deep neural networks to focus more on facial features than on specific traits of DeepFake content, thereby reducing the model's ability to generalize. To address this issue and broaden the training dataset's variety, we suggest applying data augmentation methods like Face Cutout and Random Erase. The Face Cutout technique randomly removes a rectangular area of an image containing the face, while the Random Erase technique randomly removes a rectangular area of the image. These techniques introduce variations in the images, making it more challenging for the model to overfit and focus on specific traits of DeepFake content. We evaluated our approach using several models and achieved significant accuracy improvements using the EfficientnetV2B0 model and Random Erase augmentation.
Effectiveness of Feature Selection in Text Summarization

Text summarization and classification are crucial techniques for managing the massive and steadily growing volume of text documents published every day. Feature Selection (FS) is a significant problem in text summarization and classification. This paper provides a summary of the most common feature selection methods and recent techniques depending on feature selections utilized in text summarization and classification. It includes a discussion of 13 scientific publications. The feature selection methods discussed in the paper are unsupervised such as correlation and supervised such as embedded, filter, and wrapper. The feature selection techniques that are presented in this paper are correlation, Term Frequency-Inverse Document Frequency(TF-IDF), textrank, x^2-statistic (CHI-square), mutual information, itemset mining, Information Gain (IG), Improved Fruit Fly Optimization Algorithm (IFFOA), Firefly Algorithm (FA), Harris Hawks Optimization (HHO), and Support Vector Machine-Recursive Feature Elimination(SVM-RFE).
Deepfake Detection Using EfficientNet and XceptionNet

The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves preprocessing the Celeb- DF dataset by extracting frames and isolating faces, training the models, and evaluating their performance using log loss and Area Under the Curve (AUC) metrics. The study shows that both models are effective in accurately classifying real and fake videos and highlights the importance of continuously updating deepfake detection algorithms in response to evolving deepfake generation techniques.
BERT-Based Model for Reading Comprehension Question Answering

Question Answering (QA) has been an open research topic in the past years due to its significance in different domains. Moreover, it has different challenges to be solved to reach a stage where models can mimic human reasoning and answer various questions and tasks efficiently. This paper proposes an implementation of a pre-trained BERT model that aims to answer reading comprehension questions. The proposed model can be applied to different downstream tasks that are either single sentence, such as sentiment analysis tasks, or pair of sentences, such as question-answering tasks with a given context. The existence of different input embedding representations and output representations supported the model to integrate together to overcome such any downstream task. The proposed model is a BERTBASE with a total of 110M pre-trained parameters. The model has been fine-tuned and evaluated using the SQuAD 2.0 dataset. The model results reached an Exact Match score of 61.12 and 72.5 F1 score.
Quantum Gate Count Analysis

In recent years, interest in quantum computers has grown significantly. While commercial quantum computers are not available yet, both private and public sector investment into developing quantum computers has developed considerably. One of the key challenges in quantum computing is optimizing the performance of the quantum hardware devices. Different factors can be put into consideration regarding this point, including the number of gates used by the processors to execute different quantum computations and operations. This paper focuses on developing quantum hardware metrics that can be used to measure the performance of a quantum processor in terms of number of quantum gates needed to execute different quantum algorithms. The proposed metrics have been implemented and experimental evaluations have been conducted on various IBM back-end Quantum Computers and using 4 different algorithms: a 5-Qubit entangled circuit algorithm, a 5-Qubit implementation of Grover’s quantum search algorithm, a 5-Qubit implementation of Simon’s quantum algorithm and the 7-Qubit implementation of Steane’s error correction quantum algorithm. In addition, quantitative data on the number of quantum gates used by quantum processors to execute the different quantum algorithms were gathered, which enabled the analysis of how the availability and compatibility of different gate sets affect the overall number of quantum gates needed in the execution of the different quantum algorithms.
Text-to-Image Synthesis using Generative Adversarial Networks

Text-to-image synthesis is considered a unique problem in image synthesis, which is the task of generating image from input text. Previously, the purpose of text-to-image synthesiswas to match words and images using retrieval methods basedon a phrase or collection of keywords. With the evolution ofdeep learning, particularly the usage of generative models, image synthesis has made potentially significant advancements. One of the most significant generative models are Generative Adversarial Networks (GANs), and they have been widely successfully utilized in various domains. In this study proposes our model, examines and summarizes recent work on GAN-based text-to-image synthesis, as well as providing an overview of the evolution of both classic and cutting-edge models. The usual structure of each category is described in depth. The survey presents a thorough review of the development of GAN-based text to image synthesis. Finally, Dynamic Memory GAN with Contrastive Learning (DMGAN+CL) with prompt engineering, which optimizes and acquires consistent textual representations for captions using the contrastive learning technique. We tested our technique on the CUB-200 dataset, and the findings demonstrate that our framework achieved comparable results with other typical GANs architectures in text-to-image synthesis.
Using NLP for Functional Size Measurement of IoT Devices

The Internet of Things (IoT) was born when embedded systems represented in edge devices were able to send and receive data back and forth via the Internet and process these data in real-time near the same physical environments they are deployed. Hence, enabling the existence of numerous smart applications and devices used in daily life. The ubiquity of IoT applications required means to measure software-provided functionality. Functional size measurement is a tool used for this purpose in addition to obtaining technical indicators early in the design phase. The Common Software Measurement International Consortium (COSMIC) method is an international standard for functional size measurement of a software. A study [?] has represented a tool to automatically retrieve source codes of IoT edge devices and measure their COSMIC functional size through analysing the code. Nevertheless, the tool was limited to certain Arduino libraries. In this paper, Natural language processing (NLP), a sub-field of Artificial intelligence that enables computers to process human language is integrated with the tool presented to analyse the source code. Hence, enabling the tool to measure the COSMIC functional size of any source code retrieved automatically. This paper presents an approach to modify the tool presented in [1] by using NLP to automatically analyse and measure the COSMIC functional size of source codes without being limited to a certain number of libraries.
Improved Ingredients-based Recipe Recommendation Software using Machine Learning

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.
Identifying Fake Products using Blockchain Technology in Supply Chain System

With the rise of globalization and the rapid advancements in technology, the volume of production has surged, making it easier to acquire counterfeit goods to an unprecedented degree. All types of industrial manufacturers and distributors are currently working to increase supply chain operations' openness to prevent counterfeiting, whether it be for food, medicine, or luxury goods. In order to identify counterfeit products within the supply chain system, this paper proposes a decentralized Blockchain-based application system (DApp) to authenticate products and detect counterfeit products. With the quick development of the Blockchain technology, It is widely acknowledged that the data stored there is secure and unchangeable. As a result, this idea is used in the proposed system to handle the transfer of ownership of goods. A Quick Response (QR) code issued by the DApp for each product connected to the Blockchain can be scanned by a customer to confirm the distribution and ownership information of the goods.
A Comparitive Study on Point-of-Interest Recommendation Techniques in Location-Based Social Network

Location-Based Social Networks (LBSNs) are a special form of social Web system where users can create social connections with other system users or register each time they visit a particular Point-of-Interest (POI) through so-called check-ins. Location information enables a closer knowledge of consumers' interests and behavior by bridging the gap between the digital and physical worlds. Large-scale geospatial dataset has sparked research into innovative recommender systems that aim to ease users' travel and social interactions. In this study, we give a thorough review of the work done on this subject in recent years. A novel structure is presented that is divided into four phases, subsequently we analyse each phase and the related work that corresponds to it. We propose a taxonomy to categorise the recommender systems based on these four factors. The potential and challenges that still exist in the field are also been highlighted.
Hybrid Deep Learning-Based Approach for Enhanced Multi-Label Emotion Classification

Nowadays, social networking is controlling our lives. Every passing second, millions of tweets are shared by individuals through online social media platforms like Twitter as a means to express their feelings and thoughts. Exploring and examining the emotions conveyed within social media content, along with extracting valuable insights from extensive data, categorizing it into various groups, and foreseeing end-user behavior or emotions, holds potential advantages across multiple domains such as public health, social welfare, commerce, etc. This paper offered a deep learning-based solution for addressing multiple emotion classification challenges on Twitter. The novel proposed presents an advanced deep learning system with a hybrid approach that harnesses the strengths of combining Bidirectional Long-Short Term Memory and Recurrent Neural Network with an additional attention mechanism. This model has been trained using the "SemEval 2018 Task1 (Twitter Data)" to evaluate the performance of the suggested model. The experimental outcomes demonstrated that, compared to individual SVM, RNN, or LSTM models, as well as hybrid models, it produces classification results that are more accurate and have greater F1 scores and recall. Our model surpasses the existing cutting-edge systems, achieving a superior accuracy score of 0.90. The proposed model provides an alternate resolution to the issue of long-term dependency within current models while also addressing the challenge of data loss with growing training data sizes. The presented model achieved improved accuracy, which was amplified further with larger training data sizes as well as an increased number of training epochs.
Investigating the Relation between Code Comments and Issue Resolution Time in Java Repositories

Source code comments play a critical role in software development by facilitating issue resolution and reducing maintenance time and cost. However, the impact of code comments, particularly those aiding program comprehension, on expediting issue resolution remains insufficiently explored. This study investigates the impact of code comments on issue resolution time for issues related to pulling requests involving code maintenance. To this end, ten of the most popular Java repositories on GitHub were selected, and closed pull request-related issues were identified using a specific criterion. Comments were then extracted from classes modified by the pull requests, and a trained BERT model was utilized to classify them according to their usefulness. The percentage of comments relative to code size, the percentage of useful comments relative to all comments, and the average issue resolution time were computed and examined for correlations. The results indicate a negative correlation (-0.68) between the percentage of code comments in modified classes and the average issue resolution time. A stronger negative correlation (-0.73) was found between the percentage of useful code comments to all comments in modified classes and the average issue resolution time per class. These findings demonstrate that code with more comments, particularly useful ones, leads to faster issue resolution. This serves as a basis for further research into the impact of various fine-grained comment types on issue resolution time in different programming languages. Also, it can assist developers in writing more effective comments that accelerate bug-fixing and reduce software maintenance efforts.
Chartlytics: Chart Image Classification and Data Extraction

Chart images play a vital role in visualizing data, but automatically extracting the values from these charts poses a significant challenge. The diverse range of chart styles makes it difficult to employ pure rule-based data extraction methods effectively. Developing a system that generates textual descriptions of images would improve the accessibility of digital content for individuals with visual impairments. To address this issue, Chartlytics was developed, a user-friendly, accessible application that can create textual descriptions for a given chart image and display them for the user in the form of plain understandable text or a sound that people with complete loss of vision can easily hear. Our proposed framework has three main parts that work together in a sequence. First, an image is passed through a classification model to decide its chart type. Then, based on the chart type, the image is processed by a specific component detection model to identify the bounding boxes of chart components. Next, the image is fed into the data extraction module, where rule-based techniques are used to extract the relevant information from the chart. Finally, the output data is processed by a chart description generator that is based on a comprehensive predefined template for each chart type. Our solution achieves an accuracy of 99.42% in classifying the chart image using the MobileNet model, a 0.99 mAP50 score in detecting chart components using a YOLO model, and an accuracy that ranges between 89.2% and 98.5% in extracting data from charts according to each chart type. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1 2</sup>
Predicting Unsettled Debts in Imbalanced Data using Resampling Methods

Unsettled debt is a central global problem for banks and businesses. Being able to accurately predict which loans will default would be incredibly valuable for understanding the state of the economy. Machine learning models can be used to predict debt defaults, which can help these organizations make better decisions and reduce their risk. This study contributed a unique dataset collected from Egyptian Social Insurance organization (NOSI) with a proposed approach for defaulted credit prediction using classification learning and resampling methods for solving imbalanced data, which is a common challenge in debt default and credit data analysis studies. The approach was evaluated using three tree-based classification algorithms: Decision Tree (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). The Similarity Oversampling and Under-sampling Pre-processing (SOUP) resampling technique outperformed all other resampling techniques when applied with RF, achieving high micro-average precision and recall of 66.67% and 67.38%, respectively, compared to the original dataset's 50.42% and 50.39%.
Auto-encoders for Detection and Counting of Live/Dead Cells

Counting cells and detecting their viability is a task of utmost importance in the medical field, but this task has only 2 ways, namely manual and automatic cell counting. Manual cell counting is reliant on the human counting the cells and classifying them as viable or non-viable and therefore is prone to human error is monotonous and time-consuming and relies on the experience of the counter. Automatic cell counting is accurate, but not all labs are equipped with an automatic counter, and that is because of its high price. One could leverage the power of machine learning to create a machine that could count cells and classify them effectively and without the high cost of automatic cell counters. The methodology contains the methods of acquiring a dataset, how to create binary images for this dataset, and experimentation with 3 different machine learning models, namely VGG-16 Unet, ResUnet, and Attention ResUnet. The models predict maps of the following accuracy values: VGG-16 U-net scored 82%, ResUnet scored 86%, and Attention ResUnet 82%, with training images equal to 402 images, which is considered very good compared to other models that usually use data sets with double or triple the images used in this paper.
Detection and Discrimination of Arabic Phonemes Using Long Short-Term Memory (LSTM) Model

Phonetics is a crucial branch of linguistics that studies human speech sounds and is essential for language learning, speech therapy, and speech technology development. However, current Arabic speech systems cannot instantly analyze and detect mispronunciation of Arabic phonemes, only offering a recording feature for self-evaluation. So, this paper presents a state-of-the-art approach using a Long Short-Term Memory (LSTM) model for detecting and discriminating between Arabic phonemes. The model was tested on a collected dataset for the Arabic phonemes /s/ 'sīn' and /sˤ/ 'ṣād and demonstrated excellent performance in discriminating between them despite their close articulation. The model achieved an accuracy of 97.33% and outperformed other techniques proposed for small datasets. The model can be utilized either independently or integrated into a computer-assisted language learning (CALL) system. Furthermore, our findings suggest that there is significant room for improvement in this area, particularly in collecting large audio datasets for all the Arabic phonemes, refining the algorithms, and optimizing the data processing pipeline.
Improving Deep Learning-Based Eye Movements Classification Using Bayesian Optimization

Eye tracking technology has emerged as a touchless solution for gaze-based object selection, holding immense promise in the field of assistive technology. Despite its potential, accurately classifying various eye movements remains a formidable challenge, critical for dependable object selection. This paper introduces an innovative approach to enhance the performance of deep learning-based eye movements classification. We leveraged Bayesian Optimization (BO) to optimize the Temporal Convolutional Networks (TCNs), addressing a critical gap in prior research by optimizing hyperparameters. BO, a model-based optimization technique, efficiently explores the hyperparameter search space that leads to significant improvements in classification performance. To rigorously assess our approach, we conducted experiments on the GazeCom dataset, a rich resource annotated for diverse eye movements with a specific emphasis on smooth pursuit that is vital for calibration-free eye tracking applications. Using a lighter model, our approach significantly improved the classification of different types of eye movements—including fixation, saccade, and smooth pursuit. This result outperformed the baseline TCNs model by a margin of 1% to 7.21%. A notable improvement was observed in the classification result of smooth pursuit eye movement (F1 score: 0.8346). This achievement marks a decisive step toward refining the performance of assistive technology based on gaze interaction. Furthermore, our study can be used as a guide for future implementation of hyperparameters optimization in deep learning-based eye movements classification.