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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13281-13295页

作者： Wang, Yu Chen, Hao Zhang, Ye Li, Guozheng Harbin Institute of Technology Dept. of Information Engineering Harbin China

Existing thin cloud removal methods primarily rely on generative paradigms or discriminative paradigms. Generative paradigms often suffer from training instability, while discriminative paradigms exhibit insufficient feature representation, and their loss strategies lack physical consistency, resulting in suboptimal performance. To address these issues, we propose a dynamic bidirectional feature enhancement network for thin cloud removal in optical remote sensing images. Firstly, we design a multidimensional attention module comprising skip-recursive dilated convolution-based spatial attention module and frequency-domain-driven channel attention module. These modules efficiently capture long-range contextual dependencies and suppress cloud noise interference. Next, an adaptive local feature enhancement block is constructed using cross-fusion and adjacent feature propagation between dynamic convolutions, aimed at enhancing the ability of model to recover details. Then, we present a dynamic enhancement-based bidirectional information flow module to model the dynamic interaction between multi-task features, guiding detail recovery and feedback for optimized cloud removal features. Finally, we design a physics-aware joint loss function incorporating atmospheric light consistency constraints to ensure the physical authenticity of cloud-free images. Evaluation on the RICE and WHUS2-CR datasets demonstrates that the proposed method is superior to compared methods in thin cloud removal and can improve the performance of baseline. © 2008-2012 IEEE.

关键词： Adaptive optics

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Feature Fusion Multi-Modal Sentiment Analysis Model Based on Cross-Attention Mechanism 9

Multi-Feature Fusion Multi-Modal Sentiment Analysis Model Ba...

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9th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2024

作者： Pan, Zhengjun Zhao, Lianfen Tan, Yanxian Software Engineering Institute of Guangzhou Dept. of Software Engineering Guangzhou China Software Engineering Institute of Guangzhou Dept. of Network Technology Guangzhou China

ISBN: (纸本)9798350373554

To address the problem of insufficient intra-modal feature extraction and inter-modal interaction information fusion in current multimodal sentiment analysis, a multi-feature fusion multimodal sentiment analysis model based on cross-attention mechanism is proposed. The model first uses subnetworks and self-attention mechanism to obtain important features of multiple modalities such as text, audio, and video. Then, it calculates the correlation between modalities through cross-modal cross-attention mechanism to achieve the interaction and fusion of multimodal information. After that, soft attention mechanism is used to assign attention weights to each modality feature. Finally, the modalities features are spliced to output the final sentiment classification result. The experimental results show that compared with the benchmark model on the public datasets CH-SIMS and CMU-MOSEI, this model has certain improvements in two-class accuracy, three-class accuracy, and F1 value. © 2024 IEEE.

关键词： Sentiment analysis

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Hybrid CNN-ViT architecture to exploit spatio-temporal feature for fire recognition trained through transfer learning

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Multimedia Tools and Applications 2025年第8期84卷 4703-4732页

作者： Shahid, Mohammad Wang, Hong-Cyuan Chen, Yung-Yao Hua, Kai-Lung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Fires are becoming one of the major natural hazards that threaten the ecology, economy, human life and even more worldwide. Therefore, early fire detection systems are crucial to prevent fires from spreading out of control and causing destruction. Based on vision sensors, many fire detection techniques have evolved with the recent surge of curiosity in deep learning, which exploits the spatial features of individual images. However, fire can take different forms, scales, and combustion materials can produce different colors, making accurate fire detection from an image challenging. Small fires captured from long-distance cameras lack salient features, further complicating detection. This paper proposes a hybrid structure that uses attention-enhanced convolutional neural networks and vision transformers (CNN-ViT) to detect fire. The proposed CNN-ViT first pays spatial attention to every frame and then aggregates temporal contextual information from neighboring frames to improve detection performance. Due to the limited availability of training fire datasets, the study employs deep transfer learning for feature extraction using pre-trained CNN. We used various metrics to examine the efficacy of the proposed approach. The results showed that the CNN-ViT method outperformed previous models based on spatial-temporal characteristics by achieving a relative improvement in accuracy and F1 score. The satisfactory results on images contaminated with different intensities of noise confirm the robustness of the approach. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fires

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Low-Data Welding Defects Detection 7

Low-Data Welding Defects Detection

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7th International Conference on information Technologies in engineering Education, Inforino 2024

作者： Misnik, Anton Kapelko, Eduard Belarussian-Russian university Dept. Information technology software Mogilev Belarus

ISBN: (纸本)9798350371369

The study delves into the analysis of binary image classification techniques tailored for detecting defects within welded seams amidst constraints posed by limited data availability. Extensive scrutiny was conducted on methodologies, including augmentation, hard-mining, Transfer Learning, and Few-shot Learning, dissecting their efficacy within scenarios of restricted training data. Findings from this rigorous analysis served as the cornerstone for the formulation of a model training method dubbed 'task switch.' This innovative approach showcases notable advancements in object classification accuracy, particularly evident when dealing with modest data volumes. Such enhancements stem from the unique characteristics exhibited by the selected models' receptive fields subsequent to the transition to detection. The 'task switch' methodology demonstrates a remarkable ability to harness these attributes, leading to a substantial surge in the precision of defect identification within binary image classifications. The amalgamation of task switch and refined detection mechanisms establishes a paradigm shift, significantly amplifying the accuracy of defect detection in welded seams despite constrained data availability. The study also underscores the significance of adaptability in model architecture, showcasing the task switch's capacity to dynamically adjust to varying data constraints, solidifying its position as a pioneering framework in addressing the challenges of limited data in defect detection within welded seams. © 2024 IEEE.

关键词： Image classification

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Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket

Developing and Training a Mathematical Model for Optimizing ...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Alahakon, Shalitha Siriwardana, Tharindu Udupihilla, Deshan Wickramasinghe, Tharukshi Rajapaksha, Samantha Sri Lanka Institute of Information Technology Dept. Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers. © 2023 IEEE.

关键词： Python

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J-NECORA: A Framework for Optimal Resource Allocation in Cloud-Edge-Things Continuum for Industrial Applications With Mobile Nodes

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IEEE Internet of Things Journal 2025年第11期12卷 16525-16542页

作者： Pettorali, Marco Righetti, Francesca Vallati, Carlo Das, Sajal K. Anastasi, Giuseppe Univ. of Pisa Dept. of Information Engineering Italy Missouri Univ. of Science and Technology Dept. of Computer Science RollaMO United States

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of MNs. If the optimal allocation does not exist, J-NECORA provides a best-effort allocation that minimizes the delay. The proposed framework is highly modular and flexible, and can be easily customized to different scenarios. We validated its effectiveness through simulation experiments, focusing on a specific use case. For the considered scenarios, the performance predicted by our framework matches quite closely the simulation results. © 2014 IEEE.

关键词： Cloud platforms

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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Single Image Super-Resolution Reconstruction Based on Multi-Scale Attention Mechanism 16

Single Image Super-Resolution Reconstruction Based on Multi-...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Su, Jin-Sheng Zhang, Ming-Jun Yu, Wen-Jing Dept. of Network Technology Software Engineering Institute of Guangzhou 510990 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This study is dedicated to solving the problem of single-image super-resolution reconstruction, particularly by introducing a multi-scale attention mechanism to enhance the reconstruction effectiveness. Advances in super-resolution technology have provided unprecedented possibilities for reconstructing images with low clarity, yet challenges regarding reconstruction quality and efficiency remain. Based on a review of the literature concerning attention mechanisms and their applications in super-resolution, this paper proposes a new network architecture that incorporates a carefully designed multi-scale attention module. The research demonstrates through experiments that the new algorithm can effectively capture the feature information of images and achieve improvements in detail recovery. Both quantitative and qualitative experimental results indicate that the proposed method outperforms existing approaches in the task of super-resolving images, showcasing significant advantages and practical application value. The paper also provides a detailed analysis of key modules within the algorithm and discusses the results obtained. Overall, this research successfully opens up new avenues for enhancing image reconstruction performance and offers valuable insights for future work. © 2025 SPIE.

关键词： Image reconstruction

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