Reviewerly/exHarmony huggingface dataset:
A survey on hate speech detection and sentiment analysis using machine learning and deep learning models

In today's digital era, the rise of hate speech has emerged as a critical concern, driven by the rapid information-sharing capabilities of social media platforms and online communities. As the internet expands, the proliferation of harmful content, including hate speech, presents considerable obstacles in ensuring a secure and inclusive online environment. In response to this challenge, researchers have embraced machine learning and deep learning methods to create automated systems that can effectively detect hate speech and conduct sentiment analysis, offering potential solutions to address this pressing issue. This survey article provides a comprehensive overview of recent advancements in hate speech detection and sentiment analysis using machine learning and deep learning models. We present an in-depth analysis of various methodologies and datasets employed in this domain. Additionally, we explore the unique challenges faced by these models in accurately identifying and classifying hate speech and sentiment in online text. Finally, we outline areas where more study is needed and suggest potential new avenues for exploration in the field of hate speech identification and sentiment analysis. Using the results of this survey, we hope to encourage the development of more effective machine learning and deep learning-based solutions to curb hate speech and promote a more inclusive online environment.
Measuring similarity based on user activeness in recommender systems to improve algorithm scalability

Recommender systems analyze user preferences and predict their preferred products based on available data, saving users time in the search for preferred products. However, accurate recommendations for both the system and the user may require complicated computations or the need to store more neighbors, leading to a less scalable algorithm. To address this issue, we define user activity coefficients and then analyze the relationship between different activity coefficients and similarity to design a novel approach for measuring user activity-based similarity in recommender systems. By examining the similarity of inactive user groups, our approach considers the accurate calculation of their similarity as one of the keys to influencing the number of required neighbors for optimal performance. As compared to several advanced collaborative filtering algorithms on two datasets, the algorithm developed in this paper can reduce the number of neighbors by 35.58%–75.38% while maintaining high prediction accuracy. The results indicate that user activity in recommender systems has a significant impact on similarity evaluation, which should be considered in relevant models.
Deep learning in Transportation: Optimized driven deep residual networks for Arabic traffic sign recognition

Car manufacturers around the globe are in a race to design and build driverless cars. The concept of driverless is also being applied to any moving vehicle such as wheelchairs, golf cars, tourism carts in recreational parks, etc. To achieve this ambition, vehicles must be able to drive safely on streets stay within required lanes, sense moving objects, sense obstacles, and be able to read traffic signs that are permanent and even temporary signs. It will be a completely integrated system of the Internet of Things (IoT), Global Positioning System (GPS), Machine Learning (ML)/Deep Learning (DL), and Smart Technologies. A lot of work has been done on traffic sign recognition in the English language, but little has been done for Arabic traffic sign recognition. The concepts used for traffic sign recognition can also be applied to indoor signage, smart cities, supermarket labels, and others. In this paper, we propose two optimized Residual Network (ResNet) models (ResNet V1 and ResNet V2) for automatic traffic sign recognition using the Arabic Traffic Signs (ArTS) dataset. Additionally, the authors developed a new dataset specifically for Arabic Traffic Sign recognition consisting of 2,718 images taken from random places in the Eastern province of Saudi Arabia. The optimized proposed ResNet V1 model achieved the highest training and validation accuracies of 99.18% and 96.14%, respectively. It should be noted here that the authors accounted for both overfitting and underfitting in the proposed models. It is also important to note that the results achieved using the proposed models outperform similar methods proposed in the extant literature for the same dataset or similar-size dataset.
Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice

Saudi Arabia’s tourism sector has recently started to play a significant role as an economic driver. The restaurant industry in Riyadh has experienced rapid growth in recent years, making it increasingly challenging for customers to choose from the large number of restaurants available. This paper proposes a matrix factorization collaborative-based recommender system for Riyadh city restaurants. The system leverages user reviews and ratings to predict users’ preferences and recommend restaurants likely to be of interest to them. The system incorporates three different approaches, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++). To the best of our knowledge, this is the first recommender system specifically designed for Riyadh restaurants. A comprehensive dataset of restaurants in Riyadh was collected, scraped from Foursquare.com, which includes a wide range of restaurant features and attributes. The dataset is publicly available, enabling other researchers to replicate the experiments and build upon the work. The performance of the system was evaluated using a real-world dataset, and its effectiveness was demonstrated by comparing it to a state-of-the-art recommender system. The evaluation results showed that SVD and NMF are effective methods for generating recommendations, with SVD performing slightly better in terms of RMSE and NMF performing slightly better in terms of MAE. Overall, the findings suggest that the collaborative-based approach using matrix factorization algorithms is an effective way to capture the complex relationships between users and restaurants.
Detecting signs of model change with continuous model selection based on descriptive dimensionality

Abstract We address the issue of detecting changes of models that lie behind a data stream. The model refers to an integer-valued structural information such as the number of free parameters in a parametric model. Specifically we are concerned with the problem of how we can detect signs of model changes earlier than they are actualized. To this end, we employ continuous model selection on the basis of the notion of descriptive dimensionality (Ddim). It is a real-valued model dimensionality, which is designed for quantifying the model dimensionality in the model transition period. Continuous model selection is to determine the real-valued model dimensionality in terms of Ddim from a given data. We propose a novel methodology for detecting signs of model changes by tracking the rise-up/descent of Ddim in a data stream. We apply this methodology to detecting signs of changes of the number of clusters in a Gaussian mixture model and those of the order in an auto regression model. With synthetic and real data sets, we empirically demonstrate its effectiveness by showing that it is able to visualize well how rapidly model dimensionality moves in the transition period and to raise early warning signals of model changes earlier than they are detected with existing methods.
Sub-Pixel Convolutional Neural Network for Image Super-Resolution Reconstruction

Image super-resolution (SR) reconstruction technology can improve the quality of low-resolution (LR) images. There are many available deep learning networks different from traditional machine learning algorithms. However, these networks are usually prone to poor performance on complex computation, vanishing gradients, and loss of useful information. In this work, we propose a sub-pixel convolutional neural network (SPCNN) for image SR reconstruction. First, to reduce the strong correlation, the RGB mode was translated into YCbCr mode, and the Y channel data was chosen as the input LR image. Meanwhile, the LR image was chosen as the network input to reduce computation instead of the interpolation reconstructed image as used in the super-resolution convolutional neural network (SRCNN). Then, two convolution layers were built to obtain more features, and four non-linear mapping layers were used to achieve different level features. Furthermore, the residual network was introduced to transfer the feature information from the lower layer to the higher layer to avoid the gradient explosion or vanishing gradient phenomenon. Finally, the sub-pixel convolution layer based on up-sampling was designed to reduce the reconstruction time. Experiments on three different data sets proved that the proposed SPCNN performs superiorly to the Bicubic, sparsity constraint super-resolution (SCSR), anchored neighborhood regression (ANR), and SRCNN methods on reconstruction precision and time consumption.
Short-Term Solar Insolation Forecasting in Isolated Hybrid Power Systems Using Neural Networks

Solar energy is an unlimited and sustainable energy source that holds great importance during the global shift towards environmentally friendly energy production. However, integrating solar power into electrical grids is challenging due to significant fluctuations in its generation. This research aims to develop a model for predicting solar radiation levels using a hybrid power system in the Gorno-Badakhshan Autonomous Oblast of Tajikistan. This study determined the optimal hyperparameters of a multilayer perceptron neural network to enhance the accuracy of solar radiation forecasting. These hyperparameters included the number of neurons, learning algorithm, learning rate, and activation functions. Since there are numerous combinations of hyperparameters, the neural network training process needed to be repeated multiple times. Therefore, a control algorithm of the learning process was proposed to identify stagnation or the emergence of erroneous correlations during model training. The results reveal that different seasons require different hyperparameter values, emphasizing the need for the meticulous tuning of machine learning models and the creation of multiple models for varying conditions. The absolute percentage error of the achieved mean for one-hour-ahead forecasting ranges from 0.6% to 1.7%, indicating a high accuracy compared to the current state-of-the-art practices in this field. The error for one-day-ahead forecasting is between 2.6% and 7.2%.
Consumer Personalized Gesture Recognition in UAV-Based Industry 5.0 Applications

In the era of Industry 5.0, technology promises the rapid and smart growth of industries to improve the system performance substantially and be sustainable and more secure. Industry 5.0 enabled the conventional unmanned aerial vehicle (UAV) industries to represent a dynamic result that can offer various services irrespective of time, place, and in broader physical range. Hence, satellite systems of high performance having advanced pre-processing transmission capabilities are gaining considerable attention to increase overall system throughput. In enhancing the efficiency of interaction between humans and machines, gesture recognition in UAV-based industry 5.0 can perform a vital role as the transmission medium in end-user based applications with low latency periods. However, few smart consumer personalized gesture recognition (CPGR) systems are insufficient in the privacy preservation in UAV-based industry 5.0 applications. Also, privacy-preserving is time-consuming, especially for model training and real-time CPGR. Although wireless technologies with big-data schemes provide such applications but lack emotional attachments. This article proposes a gesture-aware system with pre-processing connected systems using a powerful emotion detection system by incorporating a conventional analysis-modification-synthesis framework used for industry 5.0 applications. This study demonstrates the effectiveness of different noise estimation techniques on the proposed Modulation domain Recognition (MDSR) approach for UAV-based Industry 5.0.
ELM parameter estimation in view of maximum likelihood

Extreme Learning Machine (ELM) can be considered a probabilistic model, wherein the output is a random variable characterized by its mean and variance, both crucial for assessing and enhancing ELM's generalization ability. Despite its importance, limited research has been conducted on quantitative variance estimation and the underlying factors that influence connecting weight regularization. In this paper, we establish a connection between the variance and weights of ELM for regression problems, and propose maximum likelihood-based estimations for model parameters. We introduce a novel activation function and algorithms to generate hidden layer outputs and predicted outputs that adhere or approximate a normal distribution, as validated through experiments. Additionally, by leveraging the established relationship, we derive estimators for weights, variance, and confidence intervals of predicted values using Maximum Likelihood Estimation (MLE). The variance estimator enables a quantitative evaluation of generalization, and the link between variance and weights offers a theoretical basis for ELM regularization techniques.
Style transformation super-resolution GAN for extremely small infrared target image

With the development of generative adversarial networks, the super-resolution technique of reconstructing a high-resolution image from a low-resolution has achieved excellent resolution results. However, small, low-resolution images are widespread, such as images taken by a thermal camera or with a lens far from the target. Extremely small target image super-resolution is a challenging problem. The main reason is that the small infrared target has fewer pixels and weaker features. The current optimization methods for the tiny target are mainly based on multi-scale feature fusion or super-resolution enhancement. The low-resolution images characterizing small targets are usually obtained by downsampling with high-resolution images during training, which is different from the style of the tiny target in actual detection applications, resulting in poor resolution. In order to solve the problem, we propose a new resolution network: Style Transformation Super-Resolution Generative Adversarial Network (STSRGAN). It contains two sub-networks: one is style transformation GAN to convert the style of the image, and the other is super-resolution GAN. STSRGAN transforms a blurry infrared small target into a clear target with a distribution similar to the training set. Then the resolution can be increased to get a better enhancement effect. The discriminator distinguishes whether the input comes from the generator or the actual image to assist in generating a better super-resolution image. Meanwhile, we produced an infrared Unmanned Aerial Vehicle (UAV) small target dataset with target pixels below 16 × 16. Our method proves better resolution enhancement of small IR targets and shows superior performance over other methods through experiments.
Early Diagnosis: End-to-End CNN–LSTM Models for Mass Spectrometry Data Classification

Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.
Personalization Disentanglement for Federated Learning

Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific personalization, which leads to more reliable and effective PFL. The disentanglement is achieved by a novel Federated Dual Variational Autoencoder (FedDVA), which employs two encoders to infer the two types of representations. FedDVA can produce a better understanding of the trade-off between global knowledge sharing and local personalization in PFL. Moreover, it can be integrated with existing FL methods and turn them into personalized models for heterogeneous downstream tasks. Extensive experiments validate the advantages caused by disentanglement and show that models trained with disentangled representations substantially outperform those vanilla methods.
Prototype calibration for long tailed recognition

In the real world, data distribution usually presents imbalanced characteristics, such as long-tailed distribution, which is generally divided into the head and tail classes. For tail classes, image features cannot be represented well due to insufficient training samples. It is a vital task to learn discriminative image representation on imbalanced data distribution. In our work, through exploring prototype information, we propose a prototype-based contrastive learning(PCL) loss and prototype-based feature augmentation(PFA) module to improve the accuracy of the classifier on the imbalanced dataset. Specifically, we utilize the classifier parameters to generate learnable embeddings, which can be regarded as the class centers after using metric learning. The PFA module generates the image features of each tail class with the help of head class information. We validate our approach on common long-tailed benchmarks. Our results indicate that the PCL and PFA make the classification model achieve significant performance boosts on these benchmarks.
Exploring Opinion-Unaware Video Quality Assessment with Semantic Affinity Criterion

Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions. This motivates our exploration on opinion-unaware (a.k.a zero-shot) VQA approaches. Existing approaches only considers low-level naturalness in spatial or temporal domain, without considering impacts from high-level semantics. In this work, we introduce an explicit semantic affinity index for opinion-unaware VQA using text-prompts in the contrastive language-image pre-training (CLIP) model. We also aggregate it with different traditional low-level naturalness indexes through gaussian normalization and sigmoid rescaling strategies. Composed of aggregated semantic and technical metrics, the proposed Blind Unified Opinion-Unaware Video Quality Index via Semantic and Technical Metric Aggregation (BUONA-VISTA) outperforms existing opinion-unaware VQA methods by at least 20% improvements, and is more robust than opinion-aware approaches.
Multi-level Part-aware Feature Disentangling for Text-based Person Search

Text-based person search is an important sub-task in cross-modality image retrieval, aiming to capture interested person images by giving textual descriptions. The huge information differences between image and text modalities make this task challenging. Recent methods take local-aligned feature learning strategy into consideration, but lack sufficient mining of more local information. Accordingly, we explore a Multi-level Part-aware Feature Disentangling (MPFD) framework to more fully extract visual and textual representations from multiple angles. Specifically, we introduce a Textual Part-aware Matching (TPM) module into the existing baseline, to disentangle local features for detailed information from both visual and textual part-aware aspects. Besides, in order to fuse multiple local features and improve discrimination of global features, we propose a Multi-level Feature Integration (MFI) module which is capable to perceive the relations between features. We carry out adequate experiments on CUHK-PEDES and ICFG-PEDES datasets to verify our proposed framework, and the results demonstrate that MPFD framework performs favorably against the state-of-the-art methods.
Security Analytics Framework Validation Based on Threat Intelligence

Logical analysis of the ontology of digital security in banking helps us to identify the possible entry points for illegal access. The threats described in the ontology are detected by Machine Learning engines. The theoretical analysis is validated by verifying the framework and Machine Learning algorithms. Intelligence Graphs (original term) which are adding the actions to knowledge graphs to form workflows, are a base for validation of the framework through simulated execution of the scenarios specified in them. The output is a method for analysing live network traffic data (machine learning algorithm) combined with semantic model to give a hybrid framework for threat intelligence in digital banking, leading to a complete threat detection platform. The model is validated using operation workflows, namely 12 scenarios of banking “journeys” under the duress of various threats. In this work we are presenting the validation of the framework by simulation of the banking operations and transactions stemming from the Ontology of Digital Banking used as a model of the banking infrastructure (assets, vulnerabilities and threats included). This validation is based on the use of the Intelligence Graphs to demonstrate the capability of the framework to deal with typical scenarios by detecting the threats through ML, identifying them by checking the situations in which they appear in the ontology of threats, selecting appropriate counteraction using the security rules and planning their execution as containerized services. For better understanding of the subject matter, the authors would like to refer the reader to a previous article on general framework we developed.