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SCCA-Net: A Novel Network for Image Manipulation Localization Using Split-Channel Contextual Attention 17th

SCCA-Net: A Novel Network for Image Manipulation Localizati...

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17th Asian conference on computer Vision, ACCV 2024

作者： Xiang, Yan Zhao, Kaiqi Yin, Haichang Guangdong Polytechnic of Science and Technology Guangdong Zhuhai519090 China School of Cyberspace Security Shandong University of Political Science and Law Shandong Jinan250014 China

ISBN: (纸本)9789819609628

this paper introduces SCCA-Net, an advanced end-to-end network designed specifically for Image Manipulation Localization (IML). SCCA-Net comprises four critical modules: Split-Channel Contextual Attention (SCCA), Extractor, Encoder, and Decoder. the SCCA module fuses dynamic frequency contextual features and similarity features extracted from RGB feature pyramids of images, overcoming the common shortcomings of existing attention-based IML technologies that typically overlook the frequency adaptation of contextual information. the SCCA module’s pivotal component, the Parallel Dynamic Frequency Aggregator (PDFA), integrates Parallel Low-pass (PL) and Similarity Attention (SA) blocks to merge contextual and similarity vectors. the Extractor produces an RGB feature pyramid, channeling varied frequency features into the SCCA. the Encoder, utilizing Transformer, establishes a robust global feature representation. To reconstruct the predicted mask, the Decoder employs uniquely designed cascaded upsampling-convolution (Up-Conv) blocks. Rigorous testing demonstrates that SCCA-Net surpasses conventional models, achieving F1 score improvements of +14.3% on Coverage and +11.8% on CASIA, matching top performances on NIST2016. SCCA-Net pushes the field’s boundaries and redefines the benchmarks for assessing IML. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Portability of Multiphysics Applications on Heterogeneous Modular Supercomputers 15th

Portability of Multiphysics Applications on Heterogeneous ...

引用

15th International conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Caviedes-Voullième, Daniel Koh, Seong-Ryong Poll, Stefan Suarez, Estela Arakawa, Takashi Nakajima, Kengo Sumimoto, Shinji Jülich Supercomputing Centre Forschungszentrum Jülich Jülich Germany CliMTech Inc. Chiba Japan The University of Tokyo Kashiwa Chiba Japan Kobe Japan Department of Computer Science University of Bonn Bonn Germany

ISBN: (纸本)9783031857027

the Modular Supercomputer Architecture (MSA) integrates various processing units, with compute modules tailored for specific algorithms, forming a unified heterogeneous system. Modules operate as parallel clustered systems, interconnected via a common or federated network. Optimized resource management allows flexible node selection, aiming for better performance. Hardware heterogeneity poses challenges for developers, who must port and optimize codes for various configurations. this study evaluated the performance of two applications-TSMP (regional Earth-system simulation) and mAIA (fluid dynamics)-across two MSA platforms (DEEP, Wisteria/BDEC-01), considering both hardware and software configurations. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Supercomputers

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Manna Methodology: A Novel Approach to education 5.0 through Learning and Socio-Emotional Assessment in IoD Bootcamps

Manna Methodology: A Novel Approach to Education 5.0 Through...

引用

17th International conference on computer Supported education, CSEDU 2025

作者： Madrigar, T.T. Calvo, R. Ruiz Aylon, L.B. Manna Ecosystem State University of Maringá Brazil

ISBN: (纸本)9789897587467

this study explores the practical application of education 5.0 through a methodology that integrates exponential technologies, emphasizing the Internet of Drones (IoD) to enhance students' technical and socio-emotional competencies. Conducted within the Manna Ecosystem-Brazil's largest platform for teaching, research, and innovation in Exponential Technologies-the study evaluates the impact of personalized pedagogical approaches in public schools. the proposed methodology utilizes David Kolb's learning style model (Accommodator, Converger, Assimilator, and Diverger) and specific socio-emotional competencies, including emotional awareness, emotional regulation, and relationship skills. Based on these learning profiles and participants' socio-emotional competencies, teams were organized with complementary profiles, using Artificial Intelligence to create personalized groups that promoted greater engagement and synergy, enhancing performance in innovation activities. Four case studies were conducted, comparing experimental groups formed with the personalized methodology and control groups organized randomly, through diagnostic, formative, and summative assessments. Statistical analyses revealed that the experimental group showed significantly superior performance compared to the control group, highlighting the potential of the methodology in creating an interactive and collaborative learning environment. these findings reinforce the relevance of integrating exponential technologies, such as IoD, with active and adaptive teaching methodologies, contributing to the advancement of education 5.0 and preparing students for the challenges of the 21st century. Copyright © 2025 by SCITEPRESS - science and technology Publications, Lda.

关键词： Students

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Unknown Category Malicious Traffic Detection Based on Contrastive Learning 6th

Unknown Category Malicious Traffic Detection Based on Contra...

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6th International conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Yan, Leiming Zhou, Tao Chen, Xianyi Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Cyber Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

In response to the continuous emergence of novel unknown malicious traffic and the limitations of traditional detection methods, this paper presents an unknown-category malicious traffic detection approach based on contrastive learning. the proposed method utilizes contrastive learning to train an encoder for distinguishing normal traffic and multiple types of known malicious traffic, thereby enhancing inter-class separability. Subsequently, the model is fine-tuned to optimize the representations of malicious traffic categories with limited samples, amplifying the separation between different classes in the feature space. this ensures that the distance between unknown malicious traffic and other categories exceeds a predefined threshold, thereby achieving effective detection. Experimental results on the CICIoT2023 and UNSW-NB15 datasets demonstrate the superior performance of the proposed method, achieving multi-class F1-scores of 93.23 and 89.93, respectively, outperforming traditional approaches. Additionally, the method achieves an accuracy of over 83% in simulated scenarios involving unknown-category malicious traffic detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Optimizing Lane Configuration and ETC Adoption to Minimize Congestion and Costs at Toll Plazas 17th

Optimizing Lane Configuration and ETC Adoption to Minimize C...

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17th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Klaykul, Pattarapon Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

At toll plazas, congestion frequently occurs during peak hours due to inadequate lane management and misalignment of toll payment lanes with traffic volume and payment patterns. this study proposes a model to determine the optimal number of Manual Toll Collection (MTC) lanes to open or close and to optimize the proportion of Electronic Toll Collection (ETC) lanes. the objective is to minimize average queue lengths while striving to meet secondary-level service requirements. the current system often exceeds these standards, and this study aims to simulate the effects of increasing ETC usage and ETC lanes to improve queue management. the analysis shows that ETC lanes are more efficient and can handle a higher traffic volume than MTC lanes, leading to shorter average queue lengths based on queueing theory. It was observed that lane adjustments are only feasible during off-peak periods, optimizing queue lengths to meet standards. During peak hours, simulations show significant reductions in average queue lengths for various ETC usage scenarios, with reductions of up to 91.96% depending on lane configuration. Additionally, converting one MTC lane to ETC reduces hourly operating costs per lane from 73.17 Baht to 5.42 Baht, a cost reduction of approximately 92.59%. these results emphasize the importance of strategic planning and scheduling for dynamic lane adjustments and advocate for increased ETC adoption through targeted policies and incentives to enhance overall toll plaza efficiency. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Queueing theory

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Masking Cascaded Self-attentions for Few-Shot Font-Generation Transformer 17th

Masking Cascaded Self-attentions for Few-Shot Font-Generati...

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17th Asian conference on computer Vision, ACCV 2024

作者： Ma, Jing Xiang, Xiang He, Yan National Key Lab of Multi-Spectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609161

Few-shot Font Generation (FFG) is a practical technology widely used in designing artistic characters, handwriting imitation, and identification, etc., which aims to generate realistic font images with a few reference samples. Typically, convolutional neural networks (CNNs) are employed to learn the representations of style and content from the font images. However, owing to the locality of convolutional operations, CNNs are not good at capturing the global structure of fonts, which leads to the generated images with blurry components and distorted space layouts. To address this problem, we consider cascading self-attention modules to exploit long-range dependencies for font generation and propose a transformer-based approach called FGTr. Following the style-content disentanglement paradigm, FGTr contains two different transformer encoders to extract the style and content sequences. A multi-layer transformer decoder is adopted to merge the two sequences and generate target images. In order to smoothen the transition of patch edges, we utilize a Local Self-Attention Mask (LSAM) to restrict the attention scope of each patch to a fixed-size sliding window, which plugs into the Transformer with no extra parameters. We also propose an Auxiliary Generation Module (AGM) in favor of generating glyphs closer to the real. Extensive experiments demonstrate the effectiveness and superiority of our method compared with state-of-the-art CNN-based models. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval Using Fuzzy Feature Fusion 16

Fuzzy-AttnNet: A Deep Learning Framework for Image Retrieval...

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

作者： Liu, Shengyu Pan, Jingshan Li, Na Hu, Tao Tian, Tangbin Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Image retrieval is the process of searching for similar images based on their visual content, which has significant applications in the field of medical imaging. Quickly retrieving similar images from a medical image database can assist doctors in rapid browsing and diagnosis. Although feature fusion-based descriptors have achieved great success in the field of image retrieval, there are relatively few cases of using fusion feature descriptors for image retrieval in the medical imaging field. To address this gap, we propose a novel feature extraction model, Fuzzy-AttnNet, aimed at enhancing the performance of medical image retrieval using fusion descriptors. the model simulates dilated convolutions and self-attention mechanisms, comprehensively modeling both local and global features of medical images and performing feature fusion. the fused descriptors are then applied to image retrieval. Additionally, Fuzzy-AttnNet employs a parallel optimization strategy using Arcface loss and Softmax loss, which increases intra-class spacing and significantly improves the model's recognition performance across different categories. In subclass classification tasks, Fuzzy-AttnNet demonstrates significant advantages, with an average precision improvement of approximately 10% and a recall increase of about 30%. the experimental results indicate that our proposed feature extraction model significantly enhances the accuracy of medical image retrieval. © 2025 SPIE.

关键词： Medical imaging

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E2CANet: An Efficient and Effective Convolutional Attention Network for Semantic Segmentation 17th

E2CANet: An Efficient and Effective Convolutional Attention...

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17th Asian conference on computer Vision, ACCV 2024

作者： Mu, Yuerong Guo, Qiang School of Computer Science and Technology Shandong University of Finance and Economics Jinan China

ISBN: (纸本)9789819626434

Many semantic segmentation methods employ various attention mechanisms to improve segmentation accuracy. However, as the accuracy of the model increases, the computational cost is relatively expensive, which is not favorable for some practical applications. To solve this problem, this paper presents an efficient and effective convolutional attention network (E2CANet), which is designed to achieve a good trade-off between segmentation accuracy and computational efficiency. E2CANet adopts an encoder-decoder architecture with skip connections to preserve details and semantic information. For the encoder, we use cheap convolutional operations to introduce two different attentions, i.e. global attention and multi-scale attention, which can significantly reduce the computational cost while highlighting important features and suppressing unnecessary ones. A lightweight All-MLP decoder, which only consists of six linear layers, is used to aggregate features from the encoder. the simple design of this decoder is also the key to reduce computational complexity. Extensive experiments are performed on ADE20K, Cityscapes, and COCO-stuff datasets. the proposed E2CANet delivers very competitive results on all datasets. Especially, E2CANet-Tiny (a lightweight version of E2CANet) achieves 41.92% mIoU on ADE20K dataset with less than 4.4M parameters, which demonstrates the efficiency and effectiveness of our method. Code is available at https://***/muyuerong/E2CANet. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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TSR-Jack: An In-Browser Crypto-Jacking Detection Method Based on Time Series Representation Learning 26th

TSR-Jack: An In-Browser Crypto-Jacking Detection Method Base...

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26th International conference on Information and Communications Security, ICICS 2024

作者： Cui, Bo Liu, Shuai Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819788002

As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized cryptocurrency mining. this form of browser-based mining, termed crypto-jacking, imparts substantial impact and harm to society, enterprises, and users. However, existing research methods lead to poor transferability because they require model training using labeling data from detection devices. To solve this problem, we introduce a crypto-jacking detection method based on time series representation learning, called TSR-jack. Specifically, we first collect hardware runtime system data for a period of time and generate datasets. After slicing the time series datasets, they are used to train a time series representation model, which introduces the loss of temporal information to extract temporal features. the temporal features can capture similarities between different devices and therefore address model transfer difficulties. Finally, the time vectors obtained from representation learning are fed into SVM for classification. Experimental results show that our TSR-jack method outperforms similar dynamic feature detection methods, especially when considering transferability, with a precision improvement of more than 5%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Direction-Guided Sparse Representation Method for Finger Vein Recognition 18th

Direction-Guided Sparse Representation Method for Finger Vei...

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18th Chinese conference on Biometric Recognition, CCBR 2024

作者： Zhou, Lizhen Yang, Lu Meng, Qinggang Yang, Gongping Department of Information ZiBo Normal College Zibo255130 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China School of Software Shandong University Jinan250101 China

ISBN: (纸本)9789819610679

the extended sparse representation method is robust to image deformation and has shown promising performance in finger vein recognition. However, its large number of dictionary atoms results in high memory usage and long computation time. To address these issues, we propose a direction-guided sparse representation (DGSR) method for finger vein recognition. Our method computes the guidance direction based on rough matching results between the testing and training images, and then selects the corresponding dictionary atoms, reducing the dictionary size and computation time. Additionally, this method uses vein backbones, which are robust to noise, as image features in sparse representation. Experimental results on the open finger vein database from Hong Kong Polytechnic University demonstrate the effectiveness of the proposed method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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