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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Investigating the combinations of operating parameters of PEMFC computational results using the Taguchi Method

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International Journal of Thermofluids 2025年 27卷

作者： Thiyagarajan, Prem Kumar Gokuldas, Nithesh Kumble G, Srinivasa Rajendran, Avinash Kumar Saravanakumar, Kavin Selvan Vasudevan, Mohanaharish Kannan, Mohith Prasad, C. Durga Aden, Adem Abdirkadir Department of Mechanical Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Mechanical Engineering Siddaganga Institute of Technology Karnataka Tumakuru572103 India Department of Mathematics New Horizon College of Engineering Karnataka Bengaluru India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka Bengaluru560076 India Department of Mechanical Engineering Bulehora University Ethiopia

Proton Exchange Membrane Fuel Cells (PEMFC) is considered a promising energy source due to higher energy efficiency, low pollution, fast startup time, and low operating temperature. Under simplified conditions of constant temperature and one-dimensional flow, through the channel, and zero-flux boundaries the model was solved. The model was validated using COMSOL Multiphysics software and experimental results with a 4.22 % and 5.5 % deviation. The Taguchi Method was used to study the operating parameters of PEMFCs and to identify optimal combinations for best output along with developing equations to predict maximum power density. The delta i.e. the difference between the mean of high- and low-level Signal to Noise (S/N) ratios was calculated and found that the relative humidity was significant with value 11.07. The optimum combination is found with the help of the S/N ratio graph based on the larger the better for the performance application. The maximum power density value was predicted using the equation and found to be deviated by 16.5 % with the help of an L8 orthogonal array. Modified Taguchi approach with L4 orthogonal array with a fixed level of higher derivation parameter, reduced the error deviation further to 6.5 % with respect to the simulation results. The approach will be handy for predicting the performance with fewer trials. © 2025 The Author(s)

关键词： Energy efficiency

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Learning algorithms for mean field optimal control

arXiv

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arXiv 2025年

作者： Soner, H. Mete Teichmann, Josef Yan, Qinxin Department of Operations Research and Financial Engineering Princeton University PrincetonNJ08540 United States Department of Mathematics ETH Zürich Switzerland Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States

We analyze an algorithm to numerically solve the mean-field optimal control problems by approximating the optimal feedback controls using neural networks with problem specific architectures. We approximate the model by an N-particle system and leverage the exchangeability of the particles to obtain substantial computational efficiency. In addition to several numerical examples, a convergence analysis is provided. We also developed a universal approximation theorem on Wasserstein *** Codes 35Q89, 35D40, 49L25, 60G99 © 2025, CC BY-NC-ND.

关键词： Optimal control systems

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Improved Compact Genetic Algorithms with Efficient Caching

arXiv

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arXiv 2025年

作者： Dutta, Prasanta Mukhopadhyay, Anirban Physics and Applied Mathematics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering University of Kalyani West Bengal Kalyani741235 India

Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to those of classical GAs. However, cGAs have a tendency to repeatedly generate the same chromosomes as they approach convergence, resulting in unnecessary evaluations of identical chromosomes. This article introduces the concept of caching in cGAs as a means of avoiding redundant evaluations of the same chromosomes. Our proposed approach operates equivalently to cGAs, but enhances the algorithm’s time efficiency by reducing the number of function evaluations. We also present a data structure for efficient cache maintenance to ensure low overhead. The proposed caching approach has an asymptotically constant time complexity on average. The proposed method further generalizes the caching mechanism with higher selection pressure for elitism-based cGAs. We conduct a rigorous analysis based on experiments on benchmark optimization problems using two well-known cache replacement strategies. The results demonstrate that caching significantly reduces the number of function evaluations required while maintaining the same level of performance accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Genetic algorithms

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A fully-decoupled second-order-in-time and unconditionally energy stable scheme for the Cahn-Hilliard-Navier-Stokes equations with variable density

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Journal of Computational Physics 2025年 532卷

作者： Zhang, Jinpeng Luo, Li Wang, Xiaoping Department of Mathematics University of Macau Macao SAR China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China Department of Mathematics the Hong Kong University of Science and Technology Hong Kong

In this paper, we develop a second-order, fully decoupled, and energy-stable numerical scheme for the Cahn-Hilliard-Navier-Stokes model for two phase flow with variable density and viscosity. We propose a new decoupling Constant Scalar Auxiliary Variable (D-CSAV) method which is easy to generalize to schemes with high order accuracy in time. The method is designed using the "zero-energy-contribution" property while maintaining conservative time discretization for the "non-zero-energy-contribution" terms. A new set of scalar auxiliary variables is introduced to develop second-order-in-time, unconditionally energy stable, and decoupling-type numerical schemes. We also introduce a stabilization parameter α to improve the stability of the scheme by slowing down the dynamics of the scalar auxiliary variables. Our algorithm simplifies to solving three independent linear elliptic systems per time step, two of them with constant coefficients. The update of all scalar auxiliary variables is explicit and decoupled from solving the phase field variable and velocity field. We rigorously prove unconditional energy stability of the scheme and perform extensive benchmark simulations to demonstrate accuracy and efficiency of the method. © 2025 Elsevier Inc.

关键词： Navier Stokes equations

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No Imputation Without Representation 35th

No Imputation Without Representation

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35th Benelux Conference on Artificial Intelligence and Machine Learning, BNAIC/Benelearn 2023

作者： Lenz, Oliver Urs Peralta, Daniel Cornelis, Chris Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Research Group for Computational Web Intelligence Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Information Technology Ghent University Ghent Belgium

ISBN: (纸本)9783031746499

By filling in missing values in datasets, imputation allows these datasets to be used with algorithms that cannot handle missing values by themselves. However, missing values may in principle contribute useful information that is lost through imputation. The missing-indicator approach can be used in combination with imputation to instead represent this information as a part of the dataset. There are several theoretical considerations why missing-indicators may or may not be beneficial, but there has not been any large-scale practical experiment on real-life datasets to test this question for machine learning predictions. We perform this experiment for three imputation strategies and a range of different classification algorithms, on the basis of twenty real-life datasets. In a follow-up experiment, we determine attribute-specific missingness thresholds for each classifier above which missing-indicators are more likely than not to increase classification performance. And in a second follow-up experiment, we evaluate numerical imputation of one-hot encoded categorical attributes. We reach the following conclusions. Firstly, missing-indicators generally increase classification performance. Secondly, with missing-indicators, nearest neighbour and iterative imputation do not lead to better performance than simple mean/mode imputation. Thirdly, for decision trees, pruning is necessary to prevent overfitting. Fourthly, the thresholds above which missing-indicators are more likely than not to improve performance are lower for categorical attributes than for numerical attributes. Lastly, mean imputation of numerical attributes preserves some of the information from missing values. Consequently, when not using missing-indicators it can be advantageous to apply mean imputation to one-hot encoded categorical attributes instead of mode imputation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Transformer-based Multivariate Time Series Anomaly Localization

arXiv

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arXiv 2025年

作者： Shimillas, Charalampos Malialis, Kleanthis Fokianos, Konstantinos Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decision-making. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the Space-Time Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and space-time statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks. © 2025, CC BY-SA.

关键词： Time series

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Hybrid Profile based Multi-document Text Summarisation

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Procedia computer Science 2025年 252卷 862-872页

作者： Jean Louis K.E. Fendji Dounia Donatien Marcellin Atemkeng Department of Computer Engineering University Institute of Technology University of Ngaoundere 455 Ngaoundere Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre at Stellenbosch University Stellenbosch South Africa Department Mathematics and Computer Science Faculty of Science University of Ngaoundere 454 Ngaoundere Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

The internet has become a crucial component of the daily routines, offering numerous resources and documents for a variety of tasks and information retrieval. However, the large volume of available information often leads to ”information saturation,” posing a challenge to efficient processing and extraction of relevant information. To mitigate this issue, extensive research has been conducted exploring a range of methods, including machine learning and deep learning techniques. A significant advancement in this field is automatic text summarisation, which employs Natural Language Processing (NLP). Despite their efficacy, traditional summarisation methods typically fall short as they fail to consider the unique needs and preferences of individual users. This study introduces a novel, hybrid and profile-based multi-document summarisation method that selects relevant documents according to user queries and preferences, as defined in a user profile. By leveraging NLP algorithms, the proposed system creates personalised summaries by initially extracting sentences from documents that closely match the user’s profile, followed by the generation of a concise abstract summary. The model, specifically developed for French, results in a success rate of 87.5%, and delivering semantically coherent summaries for up to three documents concurrently. This method enhances the user experience by providing succinct and customised information.

关键词： Text Summarisation User Profile Multi-document preferences

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Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

arXiv

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arXiv 2025年

作者： Nytko, Nicolas Reisner, Andrew Moulton, J. David Olson, Luke N. West, Matthew Department of Computer Science University of Illinois at Urbana-Champaign United States Applied Computer Science Los Alamos National Laboratory New Zealand Applied Mathematics and Plasma Physics Los Alamos National Laboratory New Zealand Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States

Legacy codes are in ubiquitous use in scientific simulations;they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of new, sometimes incompatible computing paradigms, such as GPU hardware. In this paper, we explore using automated code translation to enable execution of legacy multigrid solver code on GPUs without significant time investment and while avoiding intrusive changes to the codebase. We developed a thin, reusable translation layer that parses Fortran 2003 at compile time, interfacing with the existing library Loopy (Klöckner 2014) to transpile to C++/GPU code, which is then managed by a custom MPI runtime system that we created. With this low-effort approach, we are able to achieve a payoff of an approximately 2–3× speedup over a full CPU socket, and 6× in multi-node settings. © 2025, CC BY-SA.

关键词： Investments

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Transformer-Based Multivariate Time Series Anomaly Localization

Transformer-Based Multivariate Time Series Anomaly Localizat...

引用

Computational Intelligence on engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Charalampos Shimillas Kleanthis Malialis Konstantinos Fokianos Marios M. Polycarpou KIOS Research and Innovation Center of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Mathematics and Statistics University of Cyprus Nicosia Cyprus

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

With the growing complexity of Cyber-Physical Systems (CPS) and the integration of Internet of Things (IoT), the use of sensors for online monitoring generates large volume of multivariate time series (MTS) data. Consequently, the need for robust anomaly diagnosis in MTS is paramount to maintaining system reliability and safety. While significant advancements have been made in anomaly detection, localization remains a largely underexplored area, though crucial for intelligent decisionmaking. This paper introduces a novel transformer-based model for unsupervised anomaly diagnosis in MTS, with a focus on improving localization performance, through an in-depth analysis of the self-attention mechanism's learning behavior under both normal and anomalous conditions. We formulate the anomaly localization problem as a three-stage process: time-step, window, and segment-based. This leads to the development of the SpaceTime Anomaly Score (STAS), a new metric inspired by the connection between transformer latent representations and spacetime statistical models. STAS is designed to capture individual anomaly behaviors and inter-series dependencies, delivering enhanced localization performance. Additionally, the Statistical Feature Anomaly Score (SFAS) complements STAS by analyzing statistical features around anomalies, with their combination helping to reduce false alarms. Experiments on real world and synthetic datasets illustrate the model's superiority over state-of-the-art methods in both detection and localization tasks.

关键词： Location awareness Measurement Time series analysis Transformers Sensor systems Safety Internet of Things Reliability Monitoring Synthetic data

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