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IEEE International Conference on Systems, Man and Cybernetics

作者： Shasha Hu Han Su Ruixuan Gao College of Computer Science Sichuan Normal University Chengdu China Visual Computing and visual reality Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Few-shot image classification stands as a pivotal task within the realm of computer vision. However, obtaining accurate class prototypes from limited annotated samples is a challenging problem. In recent years, many methods based on prototype networks have shown excellent performance. Nevertheless, existing methods overlook the discriminative semantic information lost due to sample scarcity and the hidden category information in the query set, failing to address the issue of unreliable prototypes generated from limited annotated samples. In this paper, we propose a feature enhancement network for few-shot classification. To improve the accuracy and robustness of few-shot classification models, we first enhance the support set through learning a weight matrix and then align the enhanced support set prototypes with textual semantics. To avoid being influenced by introduced prior noise, we fuse between semantically aligned prototypes and mean prototypes and ultimately utilize query prototypes for dynamic updating to obtain more accurate class prototypes. Extensive experiments demonstrate that our method achieves competitive performance on miniImageNet and tieredImageNet datasets. Furthermore, it exhibits excellent results in cross-domain few-shot classification.

关键词： Computer vision Accuracy Fuses Semantics Noise Prototypes Robustness Few shot learning Cybernetics Image classification

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Web usage mining with variable precision rough set approach

Web usage mining with variable precision rough set approach

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2011 4th International Symposium on Knowledge Acquisition and Modeling, KAM 2011

作者： Feng, Lin Guan, Baohua Sichuan Key Laboratory of Visualization Computing and Virtual Reality Sichuan Normal University Chengdu Sichuan Province China College of Computer Science Sichuan Normal University Chengdu Sichuan Province China Department of Planning and Construction Sichuan University Chengdu Sichuan Province China

ISBN: (纸本)9780769545479

The rich Web information makes the Web users to be drowned in the huge Web data. This paper proposes a new navigation approach termed WUMVPRSM (Web Usage Mining based on Variable Precision Rough Set Model) for Web users browsing a website. First, Log training data sets are reduced using attribute reduction module by rough set. And then, a reduced Log data set is trained to create a rough classifier. The final classification result for identifying Web user is obtained according to rough decision rules. Simulation results illustrate the efficiency of the proposed approaches. © 2011 IEEE.

关键词： Websites

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G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks

G-MAP: General Memory-Augmented Pre-trained Language Model f...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Huawei Cloud Computing Zhejiang Lab China

Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However, this Domain-Adaptive Pre-Training (DAPT;Gururangan et al. (2020)) tends to forget the previous general knowledge acquired by general PLMs, which leads to a catastrophic forgetting phenomenon and sub-optimal performance. To alleviate this problem, we propose a new framework of General Memory-Augmented Pre-trained Language Model (G-MAP), which augments the domain-specific PLM by a memory representation built from the frozen general PLM without losing any general knowledge. Specifically, we propose a new memory-augmented layer, and based on it, different augmented strategies are explored to build the memory representation and then adaptively fuse it into the domain-specific PLM. We demonstrate the effectiveness of G-MAP on various domains (biomedical and computer science publications, news, and reviews) and different kinds (text classification, QA, NER) of tasks, and the extensive results show that the proposed G-MAP can achieve SOTA results on all tasks. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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An Effective Deployment Scheme for Elimination of Phase Cancellation in Backscatter-based WPCN

An Effective Deployment Scheme for Elimination of Phase Canc...

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IEEE Conference on Wireless Communications and Networking

作者： Qiang Wang Yuzhuo Ma Chenglong Zhang Tang Liu Jilin Yang Dié Wu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China

Without the need for batteries, backscatter-based Wireless Powered Communication Network (WPCN) has been envisioned as a promising alternative to conventional wireless networks. Unfortunately, the unique phase cancellation problem in backscatter-based WPCN is essentially a phenomenon that severely affects connectivity and reliability of the network. Many arts have tried to tackle this issue either by using multiple antennas to employ the signal diversity, which increases the size and is not cost-efficient, or by making a repetition of the same information with different load impedances, which significantly decreases the throughput of network. In our paper, we propose an effective deployment scheme, aiming to fundamentally eliminate the phase cancellation problem. Specifically, we first build a practical communication model seeking the blind areas caused by phase cancellation. Then, a greedy algorithm and a minimum-weight graph based algorithm are proposed to elaborate topology of the network to ensure the connectivity. Finally, extensive experiments are carried out to evaluate the performance.

关键词： Greedy algorithms Network topology Wireless networks Throughput Topology Reliability Communication networks

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CBRM: Computational Reputation Model for C2C e-Commerce Platform

CBRM: Computational Reputation Model for C2C e-Commerce Plat...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Xueqin Li Cong Li Li Ma College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu China Library Sichuan Normal University Chengdu China

ISBN: (纸本)9781479970063

Online shopping has become an important and popular way of life. With the rapid C2C platform development, the credit problem in online shopping was being discussed. Serious loss of credit will leads to bad online business environment. However, till now lots of researchers just focus on seller reputation model to solve credit problem, thus buyer reputation model is seldom being considered. To solve the above problem, we proposed a Computational Buyer Reputation Model for C2C e-Commerce platform, i.e. CBRM. Five buyer reputation indicators, including transactions amount, seller ratings, buyer operations, chargeback ratio and real name authentication, were integrated into CBRM. By using AHP to distribute various indicator weights, we can obtain numeric value of buyer reputation. Therefore, these values can be used for sellers to choose appropriate buyers to deal with. Experiment results showed the efficiency and effectiveness of CBRM.

关键词： Mathematical model Computational modeling Solid modeling Authentication Numerical models visualization virtual reality

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S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

arXiv

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arXiv 2022年

作者： Zhang, Chiyu Xu, Xiaogang Wang, Lei Dai, Zaiyan Yang, Jun Sichuan Normal University China Nanjing University of Aeronautics and Astronautics China Zhejiang Lab China Zhejiang University China Visual Computing and Virtual Reality Key Laboratory of Sichuan Provience China

Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling. This paper introduces Strips Window Attention Transformer (S2WAT), a novel hierarchical vision transformer designed for style transfer. S2WAT employs attention computation in diverse window shapes to capture both short- and long-range dependencies. The merged dependencies utilize the "Attn Merge" strategy, which adaptively determines spatial weights based on their relevance to the target. Extensive experiments on representative datasets show the proposed method's effectiveness compared to state-of-the-art (SOTA) transformer-based and other approaches. The code and pre-trained models are available at https://***/AlienZhang1996/S2WAT. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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To determine neighborhoods in ultrasound image denoising

To determine neighborhoods in ultrasound image denoising

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International Conference on Machine Learning and Cybernetics

作者： Liao, Zhiwu Hu, Shaoxiang Wang, Jianjun Yu, Zhiqiang Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China School of Computer Science Sichuan Normal University Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Chinese Flight Test Establishment Xi'an China Chengdu Aircraft Industrial Co. Ltd. Chengdu China

ISBN: (纸本)9781424465262

Ultrasound images are effected by serious noises. In this paper, we propose an adaptive method by determining a variable neighborhood of each pixel in ultrasound image denoising. The real neighbors of a pixel can be chosen according to the local energy of the pixel, the distance between the pixel and its close pixels, the requirement of reliable estimate. All of the real neighbors of a pixel form a neighborhood, named the real neighborhood (RN) of the pixel. The RN s can preserve edges and obtain reliable estimates simultaneously, which can not be met at the same time and becomes a tradeoff problem in existing methods. The experiments also show better performance both in denoising and edges preserving than the state-of-art techniques. © 2010 IEEE.

关键词： Pixels

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Correction to: Personality trait and group emotion contagion based crowd simulation for emergency evacuation

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Multimedia Tools and Applications 2018年第5期79卷 3105-3106页

作者： Yan Mao Shanwen Yang Zuning Li Yongjian Li School of Economics and Management Southwest Jiaotong University Chengdu China College of Movie and Media Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China

In the original publication, Figs. 1 and 4 contain an error. Figure 1 on page 7, in the lower right corner of the image below “Leader 2” should be corrected to “Group 2” instead of “Group 1”. Figure 4 on pa...

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Dynamic Semi-structured Data Clustering Based on Frequently Changing Structure

Dynamic Semi-structured Data Clustering Based on Frequently ...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Junren Yang Wei Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In order to cluster dynamic semi-structured data documents, a dynamic semi-structured data clustering algorithm based on frequently changing structure is proposed. The algorithm uses dynamic model to store the historical change information of dynamic semi-structured data, builds frequent structure path tree based on the spatial frequent changing structure, uses frequent structure path tree to calculate document similarity, and finally uses DBSCAN algorithm to cluster. Experimental results show that the Jaccard coefficient and Rand index of the proposed method are above 0.99, and the performance consumption and clustering effect are better than the traditional static clustering algorithm.

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