The growing prevalence of text reuse and plagiarism in various fields has led to an urgent need for reliable computational methods for detection. However, current commercial plagiarism detection systems are ineffective in identifying paraphrased cases of text reuse, highlighting the need for improvement. Previous research on paraphrased text reuse and plagiarism detection has mainly focused on English, European, Persian, and Arabic languages, and very few studies have been reported on the under-resourced Urdu language. This study aims to overcome this research gap by using a Deep Neural Network (DNN) based architecture and pre-trained Large Language Models (LLMs) for the task of Urdu paraphrased text reuse and plagiarism detection. The architecture called Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD), relies on LLMs for input and utilizes CNN and LSTM to extract essential textual features. Moreover, we have proposed and evaluated two D-TRaPPD variants, Word Embeddings-D-TRaPPD (WE-D-TRaPPD) and Sentence Embeddings-D-TRaPPD (SE-D-TRaPPD), using two gold standard document-level corpora containing both real and simulated cases of Urdu paraphrased text reuse and plagiarism. The results demonstrate the effectiveness of the D-TRaPPD architecture, with SE-D-TRaPPD achieving the highest $$F_1$$ scores of 91.77 for real cases and 95.15 for simulated cases. Furthermore, the results highlight the superiority of our approaches over the state-of-the-art methods for Urdu paraphrased text reuse and plagiarism detection.

Plagiarism, the unauthorized reuse of text, fueled by the ease of access to online content, is a pressing concern for academia, publishers, and authors. Paraphrasing, a common tactic in textual plagiarism, compounds the problem further. The automatic detection of paraphrased plagiarism in text documents is a fundamental task in Natural Language Processing (NLP), crucial for maintaining academic integrity and authenticity. This article presents an extensive investigation into Urdu sentential paraphrased plagiarism detection leveraging advanced Deep Neural Networks (DNNs) and Large Language Models (LLMs). The study builds upon the foundational work and proposes modifications to the Deep Text Reuse and Paraphrased Plagiarism Detection (D-TRaPPD) architecture to incorporate state-of-the-art pre-trained LLMs. The proposed approach, SELLM-D-TRaPPD, integrates various language models, including contextualized sentence embedding-based LLMs, language-agnostic and multilingual transformer-based LLMs, and multilingual knowledge-distilled transformer-based LLMs. We evaluated these models against three benchmark Urdu sentential paraphrase corpora—Urdu Sentential Paraphrase Corpus, Urdu Short Text Reuse Corpus, and Semi-automatic Urdu Sentential Paraphrase Corpus. The results demonstrate the effectiveness of SELLM-D-TRaPPD with LLMs, achieving F1 scores of 92.09%, 96.70%, and 98.23%, respectively. A comparative analysis with existing state-of-the-art methods shows significant performance improvements, establishing SELLM-D-TRaPPD as the new leading approach for Urdu sentential paraphrased plagiarism detection. These findings highlight the value of leveraging advanced neural network architectures and pre-trained LLMs in improving the accuracy and effectiveness of paraphrased plagiarism detection in Urdu, addressing a crucial gap in Urdu NLP research.

Abstract We show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.