Muhammad Asif Khan¹, Dildar Hussain², Bhuyan Kaibalya Prasad³, Irfan Ullah⁴, Inayat Khan⁵, Jawad Khan^6,*, Yeong Hyeon Gu^2,*, Pavlos Kefalas⁷

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5451-5468, 2025, DOI:10.32604/cmc.2025.069134 - 23 October 2025

Abstract Dialogue State Tracking (DST) is a critical component of task-oriented spoken dialogue systems (SDS), tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values. Recent advances leverage Large Language Models (LLMs) with prompt-based tuning to improve tracking accuracy and efficiency. However, these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts, without explicitly modeling the complex dependencies between slots and values. In this work, we propose PUGG, a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer… More >

David He^1,*, Miao He², Jay Yoon³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1955-1969, 2025, DOI:10.32604/cmes.2025.063975 - 30 May 2025

Abstract Full ceramic bearings are mission-critical components in oil-free environments, such as food processing, semiconductor manufacturing, and medical applications. Developing effective fault diagnosis methods for these bearings is essential to ensuring operational reliability and preventing costly failures. Traditional supervised deep learning approaches have demonstrated promise in fault detection, but their dependence on large labeled datasets poses significant challenges in industrial settings where fault-labeled data is scarce. This paper introduces a few-shot learning approach for full ceramic bearing fault diagnosis by leveraging the pre-trained GPT-2 model. Large language models (LLMs) like GPT-2, pre-trained on diverse textual data,… More >

Stefan Ćirković¹, Vladimir Mladenović¹, Siniša Tomić², Dalibor Drljača², Olga Ristić^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4409-4430, 2025, DOI:10.32604/cmc.2025.059696 - 06 March 2025

Abstract With the increasing use of web applications, challenges in the field of cybersecurity are becoming more complex. This paper explores the application of fine-tuned large language models (LLMs) for the automatic generation of synthetic attacks, including XSS (Cross-Site Scripting), SQL Injections, and Command Injections. A web application has been developed that allows penetration testers to quickly generate high-quality payloads without the need for in-depth knowledge of artificial intelligence. The fine-tuned language model demonstrates the capability to produce synthetic payloads that closely resemble real-world attacks. This approach not only improves the model’s precision and dependability but… More >

Ou Ye^1,2, Mimi Wang¹, Zhenhua Yu^1,*, Yan Fu¹, Shun Yi¹, Jun Deng²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5675-5696, 2023, DOI:10.32604/cmc.2023.037503 - 29 April 2023

Abstract Currently, the video captioning models based on an encoder-decoder mainly rely on a single video input source. The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning, which is not conducive to the accurate description and understanding of video content. To address this issue, a novel video captioning method guided by a sentence retrieval generation network (ED-SRG) is proposed in this paper. First, a ResNeXt network model, an efficient convolutional network for online video understanding (ECO) model, and a long short-term memory (LSTM) network… More >

Bao Rong Chang^1,*, Hsiu-Fen Tsai², Po-Wen Su¹

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 253-277, 2021, DOI:10.32604/cmes.2021.015673 - 24 August 2021

Abstract This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer (GPT-2) reasonably, improving the program execution performance significantly. Besides, a theoretical estimation in statistics has given the minimum number of generated programs as required, which guarantees to find the best one within them. The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code. In addition to GPT-2, this study develops the variational Simhash algorithm to check the code similarity between sample program More >