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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68642 条记录，以下是361-370 订阅

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Soft Computing Paradigms for Load Balancing in Cloud Computing 3rd

Soft Computing Paradigms for Load Balancing in Cloud Computi...

引用

3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ghafir, Shabina Alam, M. Afshar Alankar, Bhavya Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819719457

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Numerical Analysis of Optimal Control of Assets and Liabilities by a Bank 4th

Numerical Analysis of Optimal Control of Assets and Liabi...

引用

4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Alexey Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Vavilov str 44 bld. 2 Moscow Russia Moscow Institute of Physics and Technology Institutki per. 9 Dolgoprudny141701 Russia

ISBN: (纸本)9783031812408

The formal description of the assets and liabilities management (ALM) by a bank within regulatory requirements leads to an optimal control problem with phase constraints. The phase constraints arise from the restrictions on banking activities, which are set by law to maintain the banking process. Solving such model explicitly is rarely possible, and numerical calculations enable to study its properties. The indirect method of the solution involves formulating the system of necessary optimality conditions and then computation of the ordinary differential equations system combined with specific equations for dual variables. This paper provides the details of application of the numerical methods and its specifics in the proposed economic model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Banking

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Waste Classification Using Deep Neural Networks 15th

Waste Classification Using Deep Neural Networks

引用

15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Kumar, Naween Dash, Yajnaseni Abraham, Ajith Nayak, Akanksha Mehta, Ansh Tandon, Kushagra School of Computer Science Engineering and Technology Bennett University UP Greater Noida India School of Artificial Intelligence Bennett University UP Greater Noida India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810824

Waste management has been rapidly increasing worldwide lately due to the skyrocketing waste production and environmental and health-related issues. However, solving this issue using conventional approaches is not feasible. Various efforts are being made to make it more accessible through better technology, including automation. In this research, the Deep Neural Networks (DNN) techniques are utilized to classify waste with the help of image data. The proposed method is tested using multiple experiments for different waste classification tasks, including separating recyclables and organics from the waste. Different models, such as CNN, InceptionV3, ResNet50, and VGG16 are trained for the classification task. The presented work contributes to understanding the advancement of technology use in one of our generation’s most critical challenges – waste management. The obtained findings indicate an effective utilization of DNNs to acquire the necessary knowledge to enhance our future with more sustainable technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Waste treatment

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

引用

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

作者： Wang, Zhigang Fan, Shaojing Liu, Zhenguang Wu, Zheqi Wu, Sifan Jiao, Yingying College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatio-temporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset. © 2025 IEEE.

关键词： Large datasets

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DocHFormer: Document Image Dewarping via Harmonized Modeling of Hierarchical Priors 27th

DocHFormer: Document Image Dewarping via Harmonized Modelin...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Zhou, Xinyue Li, Guanting Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Zhu, ShunZhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Document Image Dewarping (DID) task aims to address the issue of geometry distortion and improve image quality. In this paper, we propose a simple but effective method, named DocHFormer, that can take hierarchical priors features of images, including document image mask and coordinate positions, as additional information to realize accurate representation. To better exploit these fused information for dewarping, we take them into a harmonized space random shuffle operation, which can stochastically rearrange the pixels across spatial space and further use inverse operation to recover the original order. This way can adapt to allocate each feature pixel with equal probability and thus make full use of multi-type features. Furthermore, we introduce this mechanism into local self-attention to use linear complexity to input resolution and also design a new feed-forward network with structural modeling to boost representation. With the help of the above components, our proposed DocHFormer can achieve competitive performance with lower complexity and also outperform the existing state-of-the-art on several popular datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Graph Topology Identification Based on Covariance Matching

Graph Topology Identification Based on Covariance Matching

引用

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

作者： Han, Yongsheng Natali, Alberto Leus, Geert Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350368741

This paper addresses graph topology identification for applications where the underlying structure of systems like brain and social networks is not directly observable. Traditional approaches based on signal matching and spectral templates have limitations, particularly in handling scale issues and sparsity assumptions. We introduce a novel covariance matching methodology that efficiently reconstructs the graph topology using observable data. For the structural equation model (SEM) using an undirected graph, we demonstrate that our method can converge to the correct result under relatively soft conditions. Furthermore, we extend our methodology to polynomial models and any known distribution of latent variables, broadening its applicability and utility in diverse graph-based systems. © 2025 IEEE.

关键词： covariance matching graph topology identification polynomial model structural equation model

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Anti-concept Drift-enabled Recognition Method of Encrypted Traffic Behavior 24

Anti-concept Drift-enabled Recognition Method of Encrypted T...

引用

2nd international conference on Communication Networks and Machine Learning, CNML 2024

作者： Zheng, Jicheng Huang, Shuangshuang Ji, Qingbing School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Key Laboratory of Security Communication Sichuan Chengdu China

ISBN: (纸本)9798400711688

Although encrypted traffic ensures information security, it can also be exploited by malicious actors, leading to security risks. This paper addresses the limitations of existing behavior recognition technologies, particularly the concept drift issue in the"training-testing" phase. We propose an Anti-Concept Drift Framework (ACD-WFP), which includes a Confusion-Aware Adversarial Concept Drift Detection Algorithm (CACD) and an Incremental Learning-Based Multi-Modal Behavior Recognition Algorithm (IM-WFP). In experiments involving 75GB of encrypted traffic across 12 concept drift scenarios, CACD efficiently detects and tracks drift with low storage requirements, while IM-WFP improves recognition accuracy by 2% to 22% through continuous learning of new concepts. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Innovations and Transformations in Teaching Systems from the Perspective of Human-computer Interaction: A Case Study on College Students' Mental Health Education Courses 24

Innovations and Transformations in Teaching Systems from the...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Min, Min Cheng, Junlin School of Foreign languages Jiangsu University of Science and Technology Jiangsu Zhenjiang China School of Energy and Power Jiangsu University of Science and Technology Jiangsu Zhenjiang China

ISBN: (纸本)9798400712692

The arrival of human-computer interaction (HCI) technology has mainly transformed all areas of education. This study discusses the use of HCI technology in college mental health education and its impact on teaching method. This study explores the current challenges of traditional mental health education, such as limited interaction, lack of individualization, and low participation of students. By combining HCI technologies such as interactive platforms, virtual reality (VR), and personalized feedback systems, this research proposes and implements a novel teaching model. In this study, the effectiveness of HCI technology-based teaching model was evaluated through a survey involving 200 students and the results showed that the HCI technology-based program effectively increased the participation and satisfaction of mental health education among college students and also help improve their mental heath level. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Teaching

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Document Image Shadow Removal via Frequency Information-Oriented Network 27th

Document Image Shadow Removal via Frequency Information-Ori...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Yang, Fan Zhou, Xinyue Jiang, Nanfeng Wang, Da-Han Zhang, Xu-Yao Li, Guantin Man, Wang Wu, Yun School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031781186

Removing shadows from document images can significantly improve the Quality of Experience (QoE) and boost the performance of the downstream document analysis and recognition tasks. However, existing methods still have limited generalization ability on complex document images and are prone to disrupt the image details. To address this issue, we consider the different shadow types that impact the image content on different frequency sub-bands. This motivates us to exploit frequency-domain information and further design a Frequency Information-oriented Deshadow Network (FID-Net). The proposed FID-Net mainly uses two elaborated modules, named Frequency Feature Extractor (FFE) and a Frequency Feature Refinement (FFR). FFE can generate low/high-frequency features through adaptively decomposing spectra of the shadow image. After that, FFR further refines both frequency features with mutual information operations. With the proposed key designs, extensive experimental results on the commonly used benchmarks demonstrate that the proposed method can learn discriminative shadows and achieve favorable performance against state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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