Original/Review Paper
H.3.15.3. Evolutionary computing and genetic algorithms
Mohsen Kiani; Mohammad Reza Khayyambashi
Abstract
The present study investigates the effectiveness of several new meta-heuristic (MH) methods in solving virtual machine (VM) to physical machine (PM) placement (VMP) in cloud data centers. More specifically, Coati optimization algorithm (COA) is properly adapted for solving VMP by introducing several ...
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The present study investigates the effectiveness of several new meta-heuristic (MH) methods in solving virtual machine (VM) to physical machine (PM) placement (VMP) in cloud data centers. More specifically, Coati optimization algorithm (COA) is properly adapted for solving VMP by introducing several operators for the phases of the algorithm. Several emerging and classic meta-heuristics are also included in the evaluations, including genetic algorithm, chemical reaction optimization, Harris hawk optimization (HHO), and electron valley optimizer (EVO). Two main parameters are included in our evaluations, including power consumption and resource wastage. The algorithms are evaluated in terms of their ability to reduce power consumption and resource wastage in VMP, and also in terms of their execution times. A set of evaluations with synthetic VMs are performed. The results indicate that all MHs perform almost similarly, while emerging methods (COA, HHO, EVO) have a marginal benefit.
Technical Paper
H.6.5.14. Text processing
Amir Ali Kharazmi; Hamid Hassanpour
Abstract
Advancements in artificial intelligence have produced powerful language models that enhance scientific writing through automated evaluation and proofreading. Effective use of these models relies on prompt engineering—the precise formulation of requests—which directly influences output quality. ...
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Advancements in artificial intelligence have produced powerful language models that enhance scientific writing through automated evaluation and proofreading. Effective use of these models relies on prompt engineering—the precise formulation of requests—which directly influences output quality. As the saying goes, "Asking correctly is half of knowledge," emphasizing the importance of well-crafted prompts. In this study, we introduce a novel approach utilizing the simple language model Gemma-7b-it to improve scientific writing. By detailing the specific characteristics and structures of each section of a scientific paper, we prompt the model to evaluate and proofread text for clarity, coherence, and adherence to academic standards. Our method comprises three stages: initial evaluation, feedback-based proofreading, and iterative refinement using textual gradient optimization. Tested on a dataset of 25 scientific articles, expert evaluations confirm that this method achieves significant enhancements in abstract quality. These findings demonstrate that meticulous prompt engineering can enable simpler language models to produce results comparable to advanced models like GPT-4, underscoring the critical role of prompt optimization in achieving high-quality scientific writing.
Original/Review Paper
H.3.2.2. Computer vision
Elahe Yadolahi; Sheis Abolmaali
Abstract
Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency ...
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Semantic segmentation is a critical task in computer vision, focused on extracting and analyzing detailed visual information. Traditional artificial neural networks (ANNs) have made significant strides in this area, but spiking neural networks (SNNs) are gaining attention for their energy efficiency and biologically inspired time-based processing. However, existing SNN-based methods for semantic segmentation face challenges in achieving high accuracy due to limitations such as quantization errors and suboptimal membrane potential distribution. This research introduces a novel spiking approach based on Spiking-DeepLab, incorporating a Regularized Membrane Potential Loss (RMP-Loss) to address these challenges. Built upon the DeepLabv3 architecture, the proposed model leverages RMP-Loss to enhance segmentation accuracy by optimizing the membrane potential distribution in SNNs. By optimizing the storage of membrane potentials, where values are stored only at the final time step, the model significantly reduces memory usage and processing time. This enhancement not only improves the computational efficiency but also boosts the accuracy of semantic segmentation, enabling more accurate temporal analysis of network behavior. The proposed model also demonstrates better robustness against noise, maintaining its accuracy under varying levels of Gaussian noise, which is common in real-world scenarios. The proposed approach demonstrates competitive performance on standard datasets, showcasing its potential for energy-efficient image processing applications.
Original/Review Paper
H.7. Simulation, Modeling, and Visualization
Ju Xiaolin; Vaskar Chakma; Misbahul Amin; Arkhid Chakma Joy
Abstract
This research examines the key factors influencing house prices, focusing on how size, condition, and structural features contribute to property valuation. Using a dataset from Washington State, USA, covering the year 2014 with over 4,600 entries, a multivariate analysis was conducted with a Linear Regression ...
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This research examines the key factors influencing house prices, focusing on how size, condition, and structural features contribute to property valuation. Using a dataset from Washington State, USA, covering the year 2014 with over 4,600 entries, a multivariate analysis was conducted with a Linear Regression model to assess the relationships between crucial features such as square footage, number of bedrooms, bathrooms, floors, and additional structural elements like garage presence and yard size. The analysis revealed that square footage and bathrooms exhibit the strongest positive correlations with house prices (both with correlation values of 0.76, statistically significant at p < 0.05), indicating their substantial impact on property valuation. In contrast, factors like condition and view demonstrated weaker correlations, suggesting a more limited influence. The Linear Regression model explained 75% of the variation in house prices (R2 = 0.75), with validation conducted using a holdout test set to ensure generalizability. While the model effectively highlights key price determinants, its limitations in handling non-linear relationships and sensitivity to outliers were addressed through data transformation and outlier removal. Compared to prior studies, this research reinforces established findings on square footage and bathrooms while providing new insights into the comparatively lower impact of property condition. Future work could explore advanced predictive models, such as non-linear regression and machine learning techniques, to better capture complex relationships and improve forecasting accuracy. These findings offer valuable insights for buyers, sellers, and industry professionals, emphasizing the importance of a data-driven approach to understanding house price dynamics.
Original/Review Paper
F.2.7. Optimization
Soheil Rezashoar; Amir Abbas Rassafi
Abstract
This study performs a thorough comparative analysis of the Red Deer Optimization Algorithm (RDOA) in comparison to five well-established metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Whale Optimization ...
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This study performs a thorough comparative analysis of the Red Deer Optimization Algorithm (RDOA) in comparison to five well-established metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), and Whale Optimization Algorithm (WOA). The main objective is to evaluate the performance of RDOA on a range of benchmark problems, including essential unimodal and sophisticated multimodal functions. The methodology incorporates hyperparameters optimization for each algorithm to optimize performance and assesses them on six standard benchmark problems (Sphere, Rosenbrock, Bohachevsky, Griewank, Rastrigin, and Eggholder). Convergence plots are examined to demonstrate the rate at which convergence occurs and the level of stability achieved. The results demonstrate that RDOA performs well compared to other algorithms in all benchmarks and excels in dealing with multimodal functions. However, the selection of an algorithm should be based on the specific characteristics of the problem, taking into account their distinct advantages.
Original/Review Paper
H.3. Artificial Intelligence
Monireh Azimi Hemat; Ezat Valipour; Laya Ali Ahmadipoor
Abstract
Visual features extracted from images in content-based image retrieval systems are inherently ambiguous. Consequently, applying fuzzy sets for image indexing in image retrieval systems has improved efficiency. In this article, the intuitionistic fuzzy sets are used to enhance the performance of the Fuzzy ...
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Visual features extracted from images in content-based image retrieval systems are inherently ambiguous. Consequently, applying fuzzy sets for image indexing in image retrieval systems has improved efficiency. In this article, the intuitionistic fuzzy sets are used to enhance the performance of the Fuzzy Content-Based Image Retrieval (F-CBIR) system. To this aim, an Intuitionistic Fuzzy Content-Based Image Retrieval (IF-CBIR) is proposed by applying intuitionistic fuzzy generators on fuzzy sets. Due to the diversity of the intuitionistic fuzzy distance measure, several are assessed in IF-CBIR; in these assessments, the measure with higher performance is identified. Finally, the proposed IF-CBIR and the existing crisp CBIR and F-CBIR simulate on Corel 5K and Corel 10K databases. The results show that our proposed method has higher (10-15%) precision compared to the mentioned methods.
Original/Review Paper
H.5. Image Processing and Computer Vision
Kimia Peyvandi
Abstract
Image inpainting is one of the important topics in the field of image processing, and various methods have been proposed in this area. However, this problem still faces multiple challenges, as an inpainting algorithm may perform well for a specific class of images but may have poor performance for other ...
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Image inpainting is one of the important topics in the field of image processing, and various methods have been proposed in this area. However, this problem still faces multiple challenges, as an inpainting algorithm may perform well for a specific class of images but may have poor performance for other images. In this paper, we attempt to decompose the image into a low-rank component and a sparse component using (Principal Component Analysis) PCA, and then independently restore each component. For inpainting the low-rank component, we use an algorithm based on low-rank minimization, and for restoring the sparse component, we use the concept of splines. Using splines, we can effectively restore edges and lines, whereas the restoration of these regions is challenging in most algorithms. Also, in restoring the low-rank component, we construct a tensor at each step and approximate the missing pixels in the tensor, thereby significantly improving the efficiency of the low-rank minimization idea in image inpainting. Finally, we have applied our proposed method to restore various types of images, which demonstrates the effectiveness of our proposed method compared to other inpainting methods based on PSNR and SSIM.
Original/Review Paper
H.3.8. Natural Language Processing
Milad Allhgholi; Hossein Rahmani; Amirhossein Derakhshan; Saman Mohammadi Raouf
Abstract
Document similarity matching is essential for efficient text retrieval, plagiarism detection, and content analysis. Existing studies in this field can be categorized into three approaches: statistical analysis, deep learning, and hybrid approaches. However, to the best of our knowledge, none have incorporated ...
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Document similarity matching is essential for efficient text retrieval, plagiarism detection, and content analysis. Existing studies in this field can be categorized into three approaches: statistical analysis, deep learning, and hybrid approaches. However, to the best of our knowledge, none have incorporated the importance of named entities into their methodologies. In this paper, we propose DOSTE, a method that first extracts name entities and then utilizes them to enhance document similarity matching through statistical and graph-based analysis. Empirical results indicate that DOSTE achieves better results by emphasizing named entities, resulting in an average improvement of 9% in the average recall metric compared to baseline methods. Also, DOSTE unlike LLM-based approaches, does not require extensive GPU resources. Additionally, non-empirical interpretations of the results indicate that DOSTE is particularly effective in identifying similarity in short documents and complex document comparisons.
Original/Review Paper
H.3.2.2. Computer vision
Razieh Rastgoo
Abstract
Sign language (SL) is the primary mode of communication within the Deaf community. Recent advances in deep learning have led to the development of various applications and technologies aimed at facilitating bidirectional communication between the Deaf and hearing communities. However, challenges remain ...
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Sign language (SL) is the primary mode of communication within the Deaf community. Recent advances in deep learning have led to the development of various applications and technologies aimed at facilitating bidirectional communication between the Deaf and hearing communities. However, challenges remain in the availability of suitable datasets for deep learning-based models. Only a few public large-scale annotated datasets are available for sign sentences, and none exist for Persian Sign Language sentences. To address this gap, we have collected a large-scale dataset comprising 10,000 sign sentence videos corresponding to 100 Persian sign sentences. This dataset includes comprehensive annotations such as the bounding box of the detected hand, class labels, hand pose parameters, and heatmaps. A notable feature of the proposed dataset is that it contains isolated signs corresponding to the sign sentences within the dataset. To analyze the complexity of the proposed dataset, we present extensive experiments and discuss the results. More concretely, the results of the models in key sub-domains relevant to Sign Language Recognition (SLR), including hand detection, pose estimation, real-time tracking, and gesture recognition, have been included and analyzed. Moreover, the results of seven deep learning-based models on the proposed datasets have been discussed. Finally, the results of Sign Language Production (SLP) using deep generative models have been presented. We report the experimental results of these models from these sub-areas, showcasing their performance on the proposed dataset.
Original/Review Paper
H.3.8. Natural Language Processing
Ali Reza Ghasemi; Javad Salimi Sartakhti
Abstract
This paper evaluates the performance of various fine-tuning methods in Persian natural language processing (NLP) tasks. In low-resource languages like Persian, which suffer from a lack of rich and sufficient data for training large models, it is crucial to select appropriate fine-tuning ...
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This paper evaluates the performance of various fine-tuning methods in Persian natural language processing (NLP) tasks. In low-resource languages like Persian, which suffer from a lack of rich and sufficient data for training large models, it is crucial to select appropriate fine-tuning techniques that mitigate overfitting and prevent the model from learning weak or surface-level patterns. The main goal of this research is to compare the effectiveness of fine-tuning approaches such as Full-Finetune, LoRA, AdaLoRA, and DoRA on model learning and task performance. We apply these techniques to three different Persian NLP tasks: sentiment analysis, named entity recognition (NER), and span question answering (QA). For this purpose, we conduct experiments on three Transformer-based multilingual models with different architectures and parameter scales: BERT-base multilingual (~168M parameters) with Encoder only structure, mT5-small (~300M parameters) with Encoder-Decoder structure, and mGPT (~1.4B parameters) with Decoder only structure. Each of these models supports the Persian language but varies in structure and computational requirements, influencing the effectiveness of different fine-tuning approaches. Results indicate that fully fine-tuned BERT-base multilingual consistently outperforms other models across all tasks in basic metrics, particularly given the unique challenges of these embedding-based tasks. Additionally, lightweight fine-tuning methods like LoRA and DoRA offer very competitive performance while significantly reducing computational overhead and outperform other models in Performance-Efficiency Score introduced in the paper. This study contributes to a better understanding of fine-tuning methods, especially for Persian NLP, and offers practical guidance for applying Large Language Models (LLMs) to downstream tasks in low-resource languages.
