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Image Generation from Arabic Text: Comparative Study of Proposed Architectures 8th

Image Generation from Arabic Text: Comparative Study of Prop...

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8th international conference on Arabic Language Processing

作者： Bahani, Mourad El Ouaazizi, Aziza Maalmi, Khalil Essahlaoui, Abdelouahed Sidi Mohamed Ben Abdellah Univ Natl Sch Appl Sci Artificial Intelligence Data Sci & Emerging Syst Fes Morocco Sidi Mohamed Ben Abdellah Univ Lab Engn Sci FPT Taza Morocco

ISBN: (纸本)9783031804373;9783031804380

Text-to-image generation is a cutting-edge technology that enables computers to generate images from textual descriptions. While this technology has been extensively researched and applied to English language text, applying it to Arabic language text is still in its early stages. Additionally, the Arabic language is challenging due to its right-to-left writing system and extensive vocabulary of 1.3 million words. In this paper, we explore text-to-image generation for generating images from Arabic language text descriptions. Firstly, we fine-tune a transformer-based model pre-trained on the Arabic text to transform the text information into affine transformation within the DF-GAN generator. Secondly, we present a text transformer that combines LSTM layers to address the limitation of unrecognized words. Thirdly, a mask predictor is trained into the generator using a weakly supervised method and incorporated into the affine transformation for a more effective integration of image and text features. In addition, we add the DAMSM loss function as a regularization to the loss function to achieve convergences and stability in the training phase. The experiment on two challenging datasets CUB and Oxford-flower shows that our architectures can accurately generate high-quality images faithfully representing the Arabic textual descriptions. We believe the scaling of this task could have critical applications in fields such as Arabic visual learning, e-commerce, advertising, and entertainment.

关键词： Machine Learning Deep Learning computer Vision Generative Adversarial Networks Text-to-Image Generation Arabic Text Processing

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Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Radio resource management and path planning in intelligent t...

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international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Norouzi, S. Azarasa, N. Abedi, M.R. Mokari, N. Seyedabrishami, S.E. Saeedi, H. Jorswieck, E.A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran College of Engineering Doha University for Science and Technology Doha Qatar Institute of Communications Technology TU Braunschweig Lower Saxony Germany

ISBN: (纸本)9781032803722

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. © 2025 The Author(s).

关键词： Resource allocation

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Specular Region Detection and Covariant Feature Extraction 27th

Specular Region Detection and Covariant Feature Extraction

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Bappy, D.M. Kang, Donghwa Lee, Jinkyu Lee, Youngmoon Koo, Minsuk Baek, Hyeongboo Department of Computer Science and Engineering Incheon National University Incheon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Robotics Hanyang University Ansan Korea Republic of Department of Artificial Intelligence University of Seoul Seoul Korea Republic of

ISBN: (纸本)9783031781971

Endoscopy images pose a distinct set of challenges, such as specularity, uniformity, and deformation, which can obstruct surgeons’ observations and decision-making processes. These hurdles complicate feature extraction and may ultimately lead to the failure of a surgical navigation system. To tackle these obstacles, we introduce a Modified Maximal Stable Extremal Region (MMSER) detector that specifically targets fine specular regions. Subsequently, we ingeniously fuse the capabilities of MMSER and saturation region properties to precisely identify specular regions within endoscopy images. Furthermore, our approach harnesses the shared properties of covariant features and endoscopic imaging to detect features in intricate regions, such as low-textured and deformed areas. Surpassing contemporary methods, our proposed technique demonstrates remarkable performance when evaluated on the available CVC-ClinicSpec datasets. Our method has shown improvements in accuracy, recall, f1-score, and Jaccard index by 0.21%,25.42%,7.77% snd 11.77%, respectively, when compared to recent techniques. Owing to its exceptional ability to accurately pinpoint specular regions and extract features from complex areas, our approach holds the potential to significantly advance surgical navigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Endoscopy

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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Light Weighted YOLO Vehicle Tracker for Toll Gate Using Centroid Algorithm 3rd

Light Weighted YOLO Vehicle Tracker for Toll Gate Using Cen...

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3rd international conference on Emerging Electronics and automation, E2A 2023

作者： Singh, Pankaj Pratap Prasad, Shitala Kalita, Gyanjyoti Das, Jahnu Mahanta, Pallab Jyoti Department of Computer Science and Engineering CIT Kokrajhar Assam India School of Mathematics and Computer Science IIT Goa Goa India

ISBN: (纸本)9789819730896

This paper focuses on vehicle detection at highway toll gates. The study involves the detection of vehicles within a custom dataset, which comprises four vehicle classes: car, truck, bus, and motorcycle. The YOLOv4 model is utilized for training and is complemented by the implementation of the Centroid tracking algorithm. In the proposed vehicle track dataset, each class comprises 1500 annotated images for training and 300 images in the testset. These images are sourced from the Google Open Images V6 dataset, and the OIDv4 toolkit is employed to scrape dataset images. The primary objective of this study is to train a vehicle detection model and evaluate the effectiveness of various tracking algorithms, with a specific focus on the centroid tracker for real-time application at the toll gates. Importantly, this model is designed to operate efficiently on conventional and cost-effective GPUs and demonstrates a commendable level of precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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Silicon Solar Cell-Integrated Flexible Retinal Prosthesis for Artificial Vision 38

Silicon Solar Cell-Integrated Flexible Retinal Prosthesis fo...

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38th IEEE international conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Kim, Chaesung Chung, Seung-Han Kim, Yong-Jin Roh, Hyeonhee Lee, Seung-Ki Kim, Yong-Kweon Lee, Hyung-Min Park, Jae-Hyoung Kang, Min Gu Im, Maesoon Korea University School of Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Photovoltaics Research Department Daejeon Korea Republic of Dankook University Department of Electronics and Electrical Engineering Yongin Korea Republic of Brain Science Institute Seoul Korea Republic of Division of Bio-Medical Science and Technology Seoul Korea Republic of Kyung Hee University KHU-KIST Department of Converging Science and Technology Seoul Korea Republic of

ISBN: (纸本)9798331508890

To date, no flexible silicon solar cell capable of directly converting visible light into electrical current has been developed for use in retinal prosthetic devices. In this study, we successfully fabricated silicon solar cells into small, pixelated structures and demonstrated their potential for retinal prostheses. To minimize surgical incisions during implantation and enhance device stability on the retina, we incorporated a flexible polymer substrate. Our results underscore the promise of silicon solar cells in biomedical engineering, especially for retinal prosthesis applications © 2025 IEEE.

关键词： Cell engineering

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LEAF: Learning Edge Attraction Filed for Precise Roof Wireframe Reconstruction 24

LEAF: Learning Edge Attraction Filed for Precise Roof Wirefr...

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8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Hao, Qiaoqiao Liu, Zhaoliang Zhu, Duxin Su, Jinhe Gong, Zheng Wu, Yundong Cai, Guorong School of Computer Engineering Jimei University Fujian Xiamen China

ISBN: (纸本)9798400710094

Significant progress has been made in roof wireframe reconstruction. However, previous work faced a major issue of positive-negative sample imbalance in edge detection using fully connected graphs of corner points. This led to incomplete roof structures and redundant edges. In this paper, we introduce a new learning point for edge attraction fields, called LEAF, for roof wireframe reconstruction. We observed that points distributed along the edges determine whether there is a connection between diagonal points. To address this, we designed an edge point detector that screens edge points based on their spatial relationship to the edge. Additionally, we developed an edge attraction field extractor to compute the direction vector of the nearest edge for these points. This allows us to use the main directional tendencies of points around corner lines to verify true connections between corner points. By selecting diagonal connections, our method reduces false connections in the fully connected graph and addresses the sample imbalance, resulting in a complete roof wireframe. In experiments on synthetic datasets Point2Roof and the Real-world Building3D dataset, our method achieved superior performance. Notably, we improved Edge recall by 20 percentage points on Building3D. Our approach enhances all edge parameters on both datasets, outperforming existing wireframe reconstruction methods. © 2024 Copyright held by the owner/author(s).

关键词： Edge detection

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An In-Label Prioritizing Variable Branching Strategy of SAT Solvers for a Preferred Extension of Argumentation Frameworks 21st

An In-Label Prioritizing Variable Branching Strategy of SAT...

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21st Pacific Rim international conference on Artificial Intelligence, PRICAI 2024

作者： Luo, Mao Xiong, Jiao He, Ningning Xiong, Caiquan Wu, Xinyun Wu, Jun Hubei University of Technology Wuhan430068 China Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819601271

The SAT-based reduction methods have shown significant effectiveness in solving preferred extensions problem in argumentation frameworks. As a general-purpose solver, SAT solvers does not fully utilize the inherent properties of argumentation framework problems for customized design, resulting in suboptimal performance. Therefore, this paper proposes an in-label prioritizing variable branching strategy to improve the SAT solver from two aspects: the initialization of the polarity values of decision variables and the evaluation criteria for variable’s activity, enabling efficient solving of a preferred extension of argumentation frameworks. We implement this method in the state-of-the-art argumentation algorithm ArgSemSAT and conducted experimental analysis using the internationally recognized benchmarks ICCMA 2023 and ICCMA 2021. The experimental results show that the proposed method significantly enhances the performance of the original algorithm in solving a preferred extension, increasing the proportion of instances solved by the original algorithm by 6.96% for ICCMA 2023 and 37.5% for ICCMA 2021. Furthermore, the optimized algorithm outperforms μ-toksia (cmsat) which ranks the third-place in the competition ICCMA 2023. Notably, the proposed algorithm demonstrates very efficient performance on the AFGen instances, reducing the average solving time of the original algorithm from 350.88 s to 5.75 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Consensus algorithm

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Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

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2nd international conference on Next-Generation Computing and Information Systems, ICNGCIS 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. Shabaz, Mohammad Kollu, Archana Sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based Support Vector Machine (KSVM) optimized by Whales algorithm. The combination kernel SVM shows better accuracy and less computational time compared to single kernel of SVM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： Support vector machines

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Automatic Generation of Training Samples Based on Local Samples Phenological Information for Crop Type Classification 24

Automatic Generation of Training Samples Based on Local Samp...

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2nd international conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Liu, Yuhao Xiao, Xingyuan College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798400712883

Reliable crop type classification is essential for ensuring food security. With advancements in computer technology, machine learning has become an indispensable tool in this domain. However, their successful implementation is often constrained by the scarcity of training samples. To address this challenge, this study proposes an effective method for automatically generating training samples using a limited number of localized ground-truth labels. The proposed method involves two steps: (1) automatically generating candidate samples by leveraging the phenological information from a small number of local samples within the study area using Time-Weighted Dynamic Time Warping (TWDTW) technology;(2) Cleaning the candidate samples using temporal NDVI features. We conducted multiple experiments in the study area located at the border of North Dakota and Minnesota. By combining samples collected from CDL data with generated samples, we demonstrated the effectiveness of the proposed method in enhancing classifier performance. The results indicated that, compared to using only the original samples, the average overall accuracy of RF and SVM classifications improved by 7.66% and 4.24%, respectively, when employing the generated training samples. This indicates that the generated training samples exhibit high reliability. This study provides a feasible approach for crop classification in regions with scarce ground data. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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