Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Emotion recognition in text is a growing area in Natural Language Processing ( NLP ), essential for improving human-computer interactions by allowing systems to interpret emotional expressions. While much progress has been made in English, Persian emotion recognition has seen limited devel- opment due to resource constraints and linguistic challenges. In this study, we address these gaps by leveraging two key Persian datasets, ArmanEmo and ShortEmo, to train an efficient emotion recognition model. Using FaBERT, a BERT-based model optimized for Persian, we employ interme- diate fine-tuning on a large collection of informal and formal Persian texts to enhance the model’s adaptability to colloquial language. This step significantly improves comprehension of Persian text
variations, as reflected in reduced perplexity scores. Our final evaluations, incorporating accuracy, precision, recall, and F1 score metrics, demonstrate that this fine-tuned FaBERT model achieves strong performance in emotion recognition, providing a promising approach for NLP in low-resource languages...

Abstract Tokenization is a critical stage in text preprocessing and presents numerous challenges in languages like Persian, where there is no deterministic word boundary. These challenges include the identification of multi-function morphemes, separation of punctuation marks, omission of spaces between tokens, and handling extra spaces inside words.



Typically, the evaluation of tokenizers focuses on overall performance, and test data does not necessarily cover all challenging linguistic phenomena. As a result, strengths and weaknesses of tokenizers in addressing specific challenges are not independently assessed. This paper examines the challenges posed by the Persian script in detecting word boundaries and evaluates the performance of seven tokenizers in handling these issues. A test set of 4091 tokens across 483 sentences was prepared, with 1010 considered as challenging tokens. The tokenizers were evaluated using this dataset.



The results indicate varying performance among tokenizers when dealing with Persian orthography. Some tokenizers performed better in separating compound words, while others excelled in identifying and preserving zero-length joiners (half-space). A detailed comparison reveals that no tokenizer fully addresses all challenges, highlighting the need for improved algorithms and more sophisticated solutions for Persian word boundary detection.



By introducing a comprehensive benchmark and identifying the strengths and weaknesses of available tokenizers, this study paves the way for the development of better Persian language processing tools.

Abstract I. Abstract— This paper examines the legal frameworks governing the criminal liability of artificial intelligence (AI) in Iran and Saudi Arabia, focusing on how both countries address the evolving role of AI in criminal acts. With the rapid advancement of AI technologies—from narrow (weak) AI, which performs specific tasks, to general (strong) AI, capable of autonomous decision-making—complex legal and ethical questions have emerged. Specifically, this paper examines the applicability of three theoretical models of AI criminal liability: Perpetration-By-Another Liability, Natural-Probable-Consequence Liability, and Direct Liability.

II. The comparative analysis highlights that despite differences in legal traditions and societal contexts, both Iran and Saudi Arabia recognize that AI itself cannot bear criminal responsibility, and instead, liability is attributed to human actors, such as developers, users, and operators. The findings suggest that integrating technological progress with ethical and legal safeguards, grounded in Islamic jurisprudence, is essential for addressing the challenges posed by AI-related crimes in both jurisdictions.

Abstract The stock market plays an imperative role in the entire financial market. The intricate and multifaceted nature of the stock market poses a challenge for investors seeking to establish a reliable and profitable trading approach. This paper aims to address this issue by leveraging two methodologies based on Deep Reinforcement Learning (DRL), namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), incorporating Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU) architectures, along with an attention mechanism to boost the decision making based on time-series stock data. This adaptation enables the model to focus on essential features and time periods within the stock data, leading to more successful and higher-quality trading choices. Following extensive experimentation and analysis, our proposed RLbased trading demonstrates improved accuracy and profitability compared to similar approaches. The proposed methodology strives to offer investors a dependable and lucrative trading strategy, ultimately leading to a more prosperous and efficient stock trading experience.

Abstract 3D object detection for self-driving cars has emerged as a critical challenge, largely due to the constraints of computing resources and the requirement for real-time processing. For evaluation, we utilize the KITTI benchmark dataset, which is widely used for self-driving cars and 3D object detection research. In this paper, an adaptive Weighted knowledge distillation approach is proposed to enhance detection accuracy while improving computational efficiency. Based on the performance of the teacher model, point clouds are divided into TPS (Teacher Performs Strongly) and TPW (Teacher Performs Weakly). The student model adapts its learning strategy dynamically: for TPS point clouds, it closely imitates the teacher by increasing the distillation weight, whereas for TPW point clouds, it prioritizes learning from raw data, reducing reliance on the teacher’s guidance. Additionally, a data pruning mechanism creates a smaller dataset from the KITTI benchmark based on the teacher model’s performance, while maintaining the TPS-TPW ratio. Experimental results indicate that the student model achieves comparable, and in some cases superior, performance to the teacher model. Specifically, it enhances recall by up to 1.5% and precision by up to 2.2% in complex scenarios. The student model is a lightweight network that learns from the teacher through knowledge distillation [28] and is solely used during execution. This design reduces execution time by nearly 50% while maintaining high detection accuracy. These findings emphasize the effectiveness of the proposed framework in real-time 3D object detection, making it well-suited for deployment in resource-constrained environments such as self-driving cars.

Abstract Abstract— Intent detection and slot filling are crucial for understanding human language and are essential for creating intelligent virtual assistants, chatbots, and other interactive systems that interpret user queries accurately. Recent advancements, especially in transformer-based architectures and large language models (LLMs), have significantly improved the effectiveness of intent detection and slot filling. This paper, proposes a method for effectively utilizing low volume fine-tuning data samples to enhance the natural language comprehension of lightweight language models, yielding a nimble and efficient approach. Our approach involves augmenting new data while increasing model layers to enhance understanding of desired intents and slots. We explored various synonym replacement methods and prompt-generated data samples created by large language models. To prevent semantic meaning disturbance, we established a lexical retention list containing non-O slots to preserve the sentence's core meaning. This strategy enhances the model's slot precision, recall, F1-score, and exact match metrics by 1.41%, 1.8%, 1.61%, and 3.81%, respectively, compared to not using it. The impact of increasing model layers was studied under different layer arrangement scenarios. Our results show that our proposed solution outperforms the baseline by 10.95% and 4.89% in exact match and slot F1-score evaluation metrics.

Abstract The de Bruijn graph, initially proposed as a topological architecture for interconnection networks, offers unique attributes. These graphs are regular, Eulerian, and Hamiltonian, boasting a small diameter close to optimal connectivity. Their low average distance, small diameter, and high connectivity contribute to remarkable fault tolerance against both node and edge failures. Comprehensively, these graphs serve as pivotal components in word-representing networks, finding applications in various scientific and engineering domains, particularly in genome assembly. They play a significant role in bioinformatics, information theory, coding, communication networks, and multiprocessors. Additionally, de Bruijn graphs are utilized in peer-to-peer (P2P) networks and distributed hash tables (DHT), demonstrating their versatility. Moreover, de Bruijn graphs can serve as a robust infrastructure for modeling online/offline user behavior. In this article, we delve into the different types of de Bruijn graphs and their unique properties from a graph theory perspective. Our focus is on evaluating the reliability of these graphs concerning resilience, fragility, and vulnerability to random failures and targeted attacks on both nodes and edges.