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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

This paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. The features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. The system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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Functional Tensor Decompositions for Physics-Informed Neural Networks 27th

Functional Tensor Decompositions for Physics-Informed Neural...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Vemuri, Sai Karthikeya Büchner, Tim Niebling, Julia Denzler, Joachim Computer Vision Group Friedrich Schiller University Jena Jena07743 Germany Institute of Data Science German Aerospace Center Jena07745 Germany

ISBN: (纸本)9783031783883

Physics-Informed Neural Networks (PINNs) have shown continuous and increasing promise in approximating partial differential equations (PDEs), although they remain constrained by the curse of dimensionality. In this paper, we propose a generalized PINN version of the classical variable separable method. To do this, we first show that, using the universal approximation theorem, a multivariate function can be approximated by the outer product of neural networks, whose inputs are separated variables. We leverage tensor decomposition forms to separate the variables in a PINN setting. By employing Canonic Polyadic (CP), Tensor-Train (TT), and Tucker decomposition forms within the PINN framework, we create robust architectures for learning multivariate functions from separate neural networks connected by outer products. Our methodology significantly enhances the performance of PINNs, as evidenced by improved results on complex high-dimensional PDEs, including the 3d Helmholtz and 5d Poisson equations, among others. This research underscores the potential of tensor decomposition-based variably separated PINNs to surpass the state-of-the-art, offering a compelling solution to the dimensionality challenge in PDE approximation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Poisson equation

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A Simple Finetuning Strategy Based on Bias-Variance Ratios of Layer-Wise Gradients 17th

A Simple Finetuning Strategy Based on Bias-Variance Ratios ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Tomita, Mao Sato, Ikuro Kawakami, Rei Inoue, Nakamasa Ikehata, Satoshi Tanaka, Masayuki Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9789819609659

Finetuning is an effective method for adapting pretrained networks to downstream tasks. However, the success of finetuning depends heavily on the selection of layers to be tuned, as full finetuning can lead to overfitting, while tuning only the last layer may not capture the necessary task-specific features. This requires a balanced approach of automatic layer selection to achieve higher performance. In this context, we propose the Bias-Variance Guided Layer Selection (BVG-LS), a simple yet effective strategy that adaptively selects a layer to be tuned at each training iteration. More specifically, BVG-LS computes the bias-variance ratios of mini-batch gradients for each layer and updates the parameters of the layer with the largest ratio. This strategy reduces the risk of overfitting while maintaining the model’s capacity to learn task-specific features. In our experiments, we demonstrate the effectiveness of the BVG-LS strategy on seven image classification tasks. We show that BVG-LS outperforms full finetuning on all tasks with the WideEMAs of mini-batch gradients and theResNet-50-2 model and on six out of seven tasks with the ViT-S model (The reproduction code is available online. http://***/proj/BVGLS/). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bias-variance tradeoff Finetuning strategy Image classification

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Geospatial Webservices and Reproducibility of Research: Challenges and Needs 21st

Geospatial Webservices and Reproducibility of Research: Chal...

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21st International Symposium on Web and Wireless Geographical Information Systems (W2GIS)

作者： Collombin, Maxime Cannata, Massimiliano Ertz, Olivier Giuliani, Gregory Ingensand, Jens Primerano, Claudio Strigaro, Daniele Univ Appl Sci Western Switzerland HEIG VD INSIT Inst Canton Of Vaud Switzerland Univ Appl Sci Western Switzerland HEIG VD MEI Inst Canton Of Vaud Switzerland Univ Appl Sci Southern Switzerland SUPSI IST Inst Canton Ticino Switzerland Univ Geneva UNIGE Inst Environm Sci Geneva Switzerland

ISBN: (纸本)9783031607950;9783031607967

This article investigates challenges and requirements related to the reproducibility of geospatial research using geospatial web-services. Several researchers have identified hinders related to technology on the one hand, as well as challenges regarding existing well-known standards that respect FAIR principles (findable, accessible, interoperable, reusable). Therefore four hypotheses are established regarding reproducibility using geospatial webservices. These four hypotheses are addressed in an online survey. The results shows correlations between academic affiliations, open standards, and reproducibility in geospatial research.

关键词： open science geospatial web services reproducibility FAIR interoperability reproducible research

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Compustory: A Virtual Museum Game for Modeling the History of computer Evolution 8th

Compustory: A Virtual Museum Game for Modeling the History o...

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8th International Visual Informatics Conference, IVIC 2023

作者： Azzahra, Warda Munir Wahyudin Program Studi Pendidikan Ilmu Komputer Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (纸本)9789819973385

Museums have an educational function that can be used as a means of education, especially in learning history at school. As we know, the COVID-19 pandemic has changed the face-to-face learning system to virtual. So, teachers cannot invite students to study history in museums. Virtual museum games can be an alternative solution to this problem by presenting remote facilities that can make it easier for students to explore knowledge about history without any restrictions. This study aims to develop a virtual museum game to model the history of computers evolution. This study uses the DDD-E model in developing this game. The reason for using this model is that every development and addition of new features to the game will be evaluated. Development evaluation is carried out by conducting expert validation using the LORI instrument. The virtual museum game "Compustory" was developed using a first-person perspective with 3D models to visualize the evolutionary history of computers. The results of the validation show that the average percentage score obtained is 99% with the eligibility qualification of "Very Eligible" used as an educational medium to teach the history of computer development in schools and is considered capable of making learning more enjoyable and recreation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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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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CESegNet:Context-Enhancement Semantic Segmentation Network Based on Transformer 30th

CESegNet:Context-Enhancement Semantic Segmentation Network B...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Chen, Xu Zhang, Zhibin School of Computer Science Inner Mongolia University Hohhot010021 China Key Laboratory of Wireless Networks and Mobile Computing School of Computer Science Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9783031533075

CNN-based methods have achieved success in semantic segmentation. However, research on improving network robustness in this domain has been limited. Similarly, transformer and its variants have recently shown state-of-the-art results in many vision tasks, from image classification to dense prediction, because transformer has a global receptive field, but transformer-based methods have much higher computational complexity compared to CNN-based methods. To address these problems, we introduce a context-enhancement network based on the transformer. Firstly, we enhance global contextual information through a hierarchical simplified transformer encoder. Then, we design two different Context-Enhancement Modules (CEM) to enrich contextual features further. Finally, we propose a Contextual Fusion Decoder (CFD) to fuse multi-scale contextual information. Extensive experiments demonstrate that our method achieves significant performance and robustness compared to previous counterparts. Our best model, CESegNet-Large, achieves 82.21% mIoU and 48.77% mIoU on the Cityscapes and the ADE20K validation sets, and it also demonstrates excellent zero-shot robustness on the Cityscapes-N dataset. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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PCR: A Large-Scale Benchmark for Pig Counting in Real World 6th

PCR: A Large-Scale Benchmark for Pig Counting in Real World

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6th Chinese Conference on Pattern Recognition and computer Vision (PRCV)

作者： Jia, Jieru Zhang, Shuorui Ruan, Qiuqi Shanxi Univ Inst Big Data Sci & Ind Taiyuan Peoples R China Engn Res Ctr Machine Vis & Data Min Shanxi Prov Taiyuan 030006 Peoples R China Shanxi Univ Sch Informat & Technol Taiyuan 030006 Peoples R China Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Automatic pig counting with pattern recognition and computer vision techniques, despite its significance in intelligent agriculture, remains to be a relatively unexplored area and calls for further study. In this paper, we propose a large-scale image-based Pig Counting in Real world (PCR) dataset, covering a variety of real-world scenarios and environmental factors. The dataset consists of two subsets, i.e., PartA captured on real-world pig pens and PartB collected from the Internet, with center point annotations of pig torsos in 4844 images. Moreover, we develop an automatic pig counting algorithm based on weakly-supervised instance segmentation, which can output a single segmentation blob per instance via the proposed Segmentation-Split-Regression (SSR) loss, utilizing point-level annotations only. Experiments show that the proposed algorithm achieves state-of-the-art counting accuracy and exhibits superior robustness against challenging environmental factors. The dataset and source codes are available at https://***/jierujia0506/PCR.

关键词： pig counting computer vision instance segmentation deep neural network

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Vehicle Appearance Dataset 7th

Vehicle Appearance Dataset

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7th Chinese Conference on Pattern Recognition and computer Vision

作者： Li, Baojun Liu, Zeyang Xu, Ziang Li, Jianuo Wang, Bo Song, Mingliang Dalian Univ Technol Dalian Peoples R China Wuhan Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9789819786916;9789819786923

Aesthetic evaluation of vehicle appearance design is an important part of the vehicle design process. How to conduct a high-confidence, rapid, objective and systematic evaluation of a given vehicle appearance is of great significance to vehicle designers, consumers, decision makers and other groups. Big data-driven deep learning approach to automated, reliable, multi-dimensional and systematic evaluation of vehicle appearance relies on high-quality datasets. There is a lack of datasets for exterior styling of automobiles. And the previous methods have the subjective problems of results, limitation of evaluation dimensions, and lower consumer involvement. Aiming at the above problems, this paper combines intelligent mechanisms such as quantitative scoring and perceptual imagery evaluation of vehicle appearance from the perspectives of vehicle users, styling experts, etc., and the main research contents are as follows: Create a large-scale and multi-dimensional vehicle appearance datasets (VAD), with near 140000 images, which is merged by three datasets: MVVA, VAS and VAPIE. Multi-view vehicle appearance image dataset (MVVA) contains 2093 automobile models and 78590 images, with small samples in the aesthetic evaluation of vehicle appearance design;Vehicle appearance scoring data set (VAS) with 19610 images is collected, cleaned and preprocessed online then labeled by the quantitative scoring and perceptual imagery evaluation of the two dimensions of users and experts;And vehicle appearance perceptual imagery evaluation dataset (VAPIE) with 40017 images based on the perspectives of the users and the experts is labeled from the image data. Our dataset is now available on Github: https://***/KDafu/Vehicle-Appearance-Dataset.

关键词： Vehicle Appearance Vehicle Dataset computer Vision Appearance Evaluation

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An Emotion-Aware Human-computer Negotiation Model Powered by Pretrained Language Model 17th

An Emotion-Aware Human-Computer Negotiation Model Powered by...

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17th International Conference on Knowledge science, Engineering and Management (KSEM)

作者： Luo, Xudong Deng, Zhiqi Sun, Kaili Lin, Pingping Guangxi Normal Univ Sch Comp Sci & Engn Guilin 541004 Guangxi Peoples R China Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin 541004 Guangxi Peoples R China Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9789819755004;9789819755011

The growth of e-commerce has led to increasing online transactions, inevitably requiring online negotiations. However, manual negotiation cannot meet the growing need. As a result, automated negotiation attracts lots of researchers. However, most work of this kind is on computer-computer negotiation and a little on human-computer one. Moreover, even the research on human-computer negotiation tends to ignore the emotional factors of human negotiators. So, it cannot deal with human emotions during a negotiation, which may significantly influence its outcome. To this end, this paper proposes a novel human-computer negotiation model. First, we fine-tune the ERNIE pretrained language model on a dataset we create. Then, the negotiating agent uses it to understand the intents and emotions of human dialogue in negotiation. Finally, the agent responds to humans according to the sentiment-related negotiation strategy we designed in this paper. Our extensive experiments show the effectiveness of our model.

关键词： Automated Negotiation Sentiment Analysis Dialogue System Pre-trained language model Natural language processing

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