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1st International Conference on Extended Reality, ICXR 2024

作者： Cai, Dongning Ren, Le Chang, Yaqi Li, Xiaodan Lin, Yuxin Yao, Junfeng Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen361005 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism New Taipei City Taiwan School of Informatics Xiamen University Xiamen361005 China

ISBN: (纸本)9789819636785

In reality, galleries usually display paintings in a protective manner, partly to delay the damage to the original artwork and ensure the long-term preservation of the artwork. On the other hand, it also hinders visitors’ deep appreciation and learning of the artwork. In the face of this challenge, our research aims to provide users with immersive viewing and entertainment, as well as interactive gaming experiences with artwork, through the use of virtual reality technology. So the paper proposes PaintingScapeVR, which is based on a virtual gallery and reproduces the three-dimensional scenes corresponding to different paintings. Visitors can freely explore in this virtual environment and gain a deeper understanding of the meaning and artistic value of artworks through interaction with them. Our results indicate that the reproduction of 3D scenes and gamified interactive design can not only significantly enhance visitors’ appreciation of painting art, but also leave a deeper learning impression. This immersive experience stimulates visitors’ desire to appreciate and explore, prompting them to actively delve into the background and techniques of art works, thereby enhancing their artistic appreciation abilities. At the same time, this work also demonstrates its potential in enhancing visitors’ understanding of painting works, allowing more people to explore the joy of painting and art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interactive gamification Three-dimensional displays Virtual reality

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Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

Not All Diffusion Model Activations Have Been Evaluated as D...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutual Information 48

Improving Bert Fine-Tuning via Stabilizing Cross-Layer Mutua...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Li, Jicun Li, Xingjian Wang, Tianyang Wang, Shi Cao, Yanan Xu, Chengzhong Dou, Dejing Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Big Data Lab Baidu Research China University of Alabama Birmingham United States Institute of Information Engineering Chinese Academy of Sciences China State Key Lab of Iotsc University of Macau China

ISBN: (纸本)9781728163277

Fine-tuning pre-trained language models, such as BERT, has shown enormous success among various NLP tasks. Though simple and effective, the process of fine-tuning has been found unstable, which often leads to unexpected poor performance. To increase stability and generalizability, most existing works resort to maintaining the parameters or representations of pre-trained models during fine-tuning. Nevertheless, very little work explores mining the reliable part of pre-learned information that can help to stabilize fine-tuning. To address this challenge, we introduce a novel solution in which we fine-tune BERT with stabilized cross-layer mutual information. Our method aims to preserve the reliable behaviors of cross-layer information propagation, instead of preserving the information itself, of the pre-trained model. Therefore, our method circumvents the domain conflicts between pre-trained and target tasks. We conduct extensive experiments with popular pre-trained BERT variants on NLP datasets, demonstrating the universal effectiveness and robustness of our method. © 2023 IEEE.

关键词： Fine-tuning Stability Mutual Information Pre-trained Language Model

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Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning

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Computers in Biology and Medicine 2024年 174卷 108428-108428页

作者： Xu, Xuebin Liu, Dehua Huang, Guohua Wang, Muyu Lei, Meng Jia, Yang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China Weinan Central Hospital Shaanxi Xi'an714099 China

Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors’ output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR. © 2024 The Authors

关键词： Computer aided diagnosis

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Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

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Frontiers of Computer Science 2021年第6期15卷 165-173页

作者： Xia-an BI Yiming XIE Hao WU Luyun XU Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410081China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Business School of Hunan Normal University Changsha 410081China

Mild cognitive impairment(MCI)as the potential sign of serious cognitive decline could be divided into two stages,i.e.,late MCI(LMCI)and early MCI(EMCI).Although the different cognitive states in the MCI progression have been clinically defined,effective and accurate identification of differences in neuroimaging data between these stages still needs to be further *** this paper,a new method of clustering-evolutionary weighted support vector machine ensemble(CEWSVME)is presented to investigate the alterations from cognitively normal(CN)to EMCI to *** CEWSVME mainly includes two *** first step is to build multiple SVM classifiers by randomly selecting samples and *** second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs,thereby improving the final classification ***,we extracted the optimal features to detect the differential brain regions in MCI progression,and confirmed that these differential brain regions changed dynamically with the development of *** exactly,this study found that some brain regions only have durative effects on MCI progression,such as parahippocampal gyrus,posterior cingulate gyrus and amygdala,while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the *** work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.

关键词： machine learning MCI progression optimal feature extraction differential brain regions functional magnetic resonance imaging

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A Low-profile Quadrifilar Helix Antenna for Beidou Navigation System

A Low-profile Quadrifilar Helix Antenna for Beidou Navigatio...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiahong Wang Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Ahstract-A new low-profile quadrifilar helical antenna (QHA) is proposed and investigated. Four spiral arms of the QHA are divided and connected to for a sleeve structure to reduce the height of the antenna. In this case, four spiral arms on the inner cylindrical sleeve are connected with the ground, while the upper ends of the four spiral arms on the outer cylindrical sleeve are short-circuited, and inner and outer spiral arms are connected in the middle. The QHA has a beam width reaches to above $120 ^{\circ}$ and a cardioid pattern at 1.575 GHz. As a result, the QHA has a very low profile of only 22 mm. The simulation and optimization from the CST show that the center frequency of the QHA is 1.575 GHz, which is used for the satellite navigation system, with axis ratio in the operating band less than 3 dB.

关键词： Satellite antennas Helical antennas Spirals Satellite navigation systems Optimization Antenna radiation patterns

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Reliable Multi-Modal Object Re-Identification via Modality-Aware Graph Reasoning

arXiv

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arXiv 2025年

作者： Wan, Xixi Zheng, Aihua Wang, Zi Jiang, Bo Tang, Jin Ma, Jixin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China School of Biomedical Engineering Anhui Medical University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) tasks. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in the case of low-quality features. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multimodal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Modal analysis

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2023 7th International Conference on Communication and Information Systems An Improved IOTA Protocol Based on Optimal Midway Walk

2023 7th International Conference on Communication and Infor...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Hongcheng Wu Hui Zhi Yangyang Liu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Hefei Zhilian Technology Industry Investment Co. Ltd Hefei China

IOTA (Internet of Things Applications) protocol aims to provide a secure and trustworthy protocol for realizing data-value interactions between IoT devices, and it has become the next possible direction for the development of Blockchain applications. IOTA protocol has the problem that the time-consuming cumulative weight update grows too fast with the increase of the number of transactions, this paper proposed an improved IOTA protocol based on optimal midway walk, the improved protocol uses cutting-edge transaction selection algorithm of weighted Monte Carlo Markov random walk to reduce cumulative weight update delay. Firstly, this improved IOTA protocol mainly ensures the efficiency of transaction confirmation without losing the security of consensus by introducing the optimal midway walk method, i.e., the starting point of each walk is chosen to depart at an optimal location. Secondly, this paper designs the indirect verification identity record algorithm and the layer degree of confirmation principle are designed to realize the improved IOTA protocol's resilience and resistance to Parasite chain attack. The experimental results show that the improved IOTA using random function to select the midway walk starting point from i-50λ, to i-75λsubstantially solves the system computational burden problem and greatly reduces the cumulative weight update delay.

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Sparse Linear Array Designed via ULA Fitting

Sparse Linear Array Designed via ULA Fitting

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wanlu Shi Yingsong Li Zhixiang Huang College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A sparse array (SA), termed as ATLF3-2BL, is proposed based on the uniform linear array (ULA) fitting (UF) principle. UF is a general SA devising scheme, based on which designers can devise SAs using a series of ULAs. SAs devised upon UF enjoy closed-form expressions for both array configuration and uniform degrees of freedom. Comprised of 7 ULAs, the proposed ATLF3-2BL can provide a long virtual difference coarray than most existing SAs. Besides, the mutual coupling in ATLF3-2BL remains a low level. Therefore, the ATLF3-2BL gives a considerable compromise between mutual coupling and uniform degrees of freedom. Simulations attest the good performance of the proposed ATLF3-2BL.

关键词： Couplings Mutual coupling Closed-form solutions Fitting Sensors Antenna arrays

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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