检索结果-内蒙古大学图书馆

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing Data centers Costs Blockchains Behavioral sciences

来源：评论

学校读者我要写书评

暂无评论

Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint 5

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

引用

5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Ji, Lu Liu, Rui Zhou, Dongsheng Zhang, Qiang Wei, Xiaopeng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781728168968

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art. © 2020 IEEE.

关键词： Least squares approximations

来源：评论

学校读者我要写书评

暂无评论

DEVICE: Depth and Visual Concepts Aware Transformer for OCR-based Image Captioning

arXiv

引用

arXiv 2023年

作者： Xu, Dongsheng Huang, Qingbao Zhang, Xingmao Cheng, Haonan Shuang, Feng Cai, Yi School of Artificial Intelligence Guangxi University Nanning530004 China College of General Education Guangxi Arts University Nanning530022 China The State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Nanning530004 China School of Software Engineering South China University of Technology Guangzhou510006 China

OCR-based image captioning is an important but under-explored task, aiming to generate descriptions containing visual objects and scene text. Recent studies have made encouraging progress, but they are still suffering from a lack of overall understanding of scenes and generating inaccurate captions. One possible reason is that current studies mainly focus on constructing the plane-level geometric relationship of scene text without depth information. This leads to insufficient scene text relational reasoning so that models may describe scene text inaccurately. The other possible reason is that existing methods fail to generate fine-grained descriptions of some visual objects. In addition, they may ignore essential visual objects, leading to the scene text belonging to these ignored objects not being utilized. To address the above issues, we propose a Depth and Visual Concepts Aware Transformer (DEVICE) for OCR-based image captinong. Concretely, to construct three-dimensional geometric relations, we introduce depth information and propose a depth-enhanced feature updating module to ameliorate OCR token features. To generate more precise and comprehensive captions, we introduce semantic features of detected visual concepts as auxiliary information, and propose a semantic-guided alignment module to improve the model’s ability to utilize visual concepts. Our DEVICE is capable of comprehending scenes more comprehensively and boosting the accuracy of described visual entities. Sufficient experiments demonstrate the effectiveness of our proposed DEVICE, which outperforms state-of-the-art models on the TextCaps test set. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

IRS-Aided Secure Communications over an Untrusted AF Relay System

IRS-Aided Secure Communications over an Untrusted AF Relay S...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Ying Gao Deli Qiao Haifeng Qian School of Communication and Electronic Engineering East China Normal University Shanghai China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Software Engineering East China Normal University Shanghai China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

ISBN: (纸本)9781665450867

In this paper, an intelligent reflecting surface (IRS) assisted wireless secure communication system in the presence of an untrusted relay is studied. A multi-antenna access point (AP) intends to send confidential information to a single-antenna user in the presence of an amplify-and-forward (AF) untrusted relay, which may eavesdrop the message when helping relay the signal. It is assumed that the direct link between the AP and the user is blocked by the obstacles. The achievable secrecy rate maximization problem is then formulated. To overcome the non-convexity of the formulated problem, an alternating iteration algorithm is proposed to jointly optimize the active and passive beamforming. Specifically, the transmit beamforming vector at the AP, the phase shift matrix at the IRS, and the relay beamforming matrix are jointly optimized to maximize the secrecy rate. Moreover, the asymptotic expressions for the maximum secrecy rates of the proposed scheme in the high and low signal-to-noise ratio (SNR) regimes of the AP-relay and relay-user links are derived as well. Finally, numerical evaluations demonstrate the superiority of the proposed scheme compared with other benchmark schemes, and validate the theoretical analysis. Some useful insights are also obtained, including the number of antennas at the AP, the scale selection and the phase shift settings at the IRS, and the potential cooperation gain of the untrusted relay.

关键词： Wireless communication Array signal processing Simulation Signal processing algorithms Benchmark testing Communication system security Relays

来源：评论

学校读者我要写书评

暂无评论

Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition 1

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Su, Yukun Lin, Guosheng Zhu, Jinhui Wu, Qingyao School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Beijing China Nanyang Technological University Singapore Singapore

ISBN: (数字)9783030585488

ISBN: (纸本)9783030585471

This paper introduces a new method for recognizing violent behavior by learning contextual relationships between related people from human skeleton points. Unlike previous work, we first formulate 3D skeleton point clouds from human skeleton sequences extracted from videos and then perform interaction learning on these 3D skeleton point clouds. A novel Skeleton Points Interaction Learning (SPIL) module, is proposed to model the interactions between skeleton points. Specifically, by constructing a specific weight distribution strategy between local regional points, SPIL aims to selectively focus on the most relevant parts of them based on their features and spatial-temporal position information. In order to capture diverse types of relation information, a multi-head mechanism is designed to aggregate different features from independent heads to jointly handle different types of relationships between points. Experimental results show that our model outperforms the existing networks and achieves new state-of-the-art performance on video violence datasets. © 2020, Springer Nature Switzerland AG.

关键词： Musculoskeletal system

来源：评论

学校读者我要写书评

暂无评论

Fer-Chc: Facial Expression Recognition with Cross-Hierarchy Contrast

SSRN

引用

SSRN 2022年

作者： Wu, Xuemei He, Jie Huang, Changqin Huang, Qionghao Zhu, Jia Huang, Xiaodi Fujita, Hamido Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China School of Computing Mathematics and Engineering Charles Sturt University NSW Australia Software and Information Science Iwate Prefectural University Takizawa020-0693 Japan

Remarkable progress has been made in facial expression recognition (FER) tasks with convolutional neural networks (CNNs). However, these CNN-based approaches do not well capture the detailed and crucial features that can distinguish different facial expressions from a global perspective among multiple facial expressions. The performance of existing CNN-based models for FER is still much room for improvement. As such, we propose a novel cross-hierarchy contrast (CHC) framework named FER-CHC for facial expression recognition tasks. The basic idea of FER-CHC is to employ a CHC to regularize the feature learning of the backbone network and enhance global representations of facial expressions. Specifically, the CHC captures common and differential features from different facial expressions with a cross-hierarchy contrast mechanism. The fusion network globally integrates the features learned from a backbone network and CHC to learn a more robust feature representation. Comprehensive experiments were conducted on six popular datasets: CK+, FER2013, FER+, RAF-DB, AffectNet, and JAFFE. The results show that our proposed FER-CHC achieves state-of-the-art performances on these datasets. In addition, the ablation study also demonstrates the effectiveness of the proposed components in FER-CHC. © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

Robust Generalization Against Photon-Limited Corruptions via...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University JD Explore Academy Department of Automation University of Science and Technology of China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Department of Computer Science Hong Kong Baptist University

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Label Embedding Enhanced Multi-label Sequence Generation Model 9th

Label Embedding Enhanced Multi-label Sequence Generation Mod...

引用

9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Wang, Yaqiang Yan, Feifei Wang, Xiaofeng Tang, Wang Shu, Hongping College of Software Engineering Chengdu University of Information Technology ChengduSichuan610225 China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service ChengduSichuan610225 China School of Electronic Engineering Chengdu University of Information Technology ChengduSichuan610225 China Sunsheen Inc. ChengduSichuan610225 China

ISBN: (纸本)9783030604561

Existing sequence generation models ignore the exposure bias problem when they apply to the multi-label classification task. To solve this issue, in this paper, we proposed a novel model, which disguises the label prediction probability distribution as label embedding and incorporate each label embedding from previous step into the current steps LSTM decoding process. It allows the current step can make a better prediction based on the overall output of the previous prediction, rather than simply based on a local optimum output. In addition, we proposed a scheduled sampling-based learning algorithm for this model. The learning algorithm effectively and appropriately incorporates the label embedding into the process of label generation procedure. Through comparing with three classical methods and four SOTA methods for the multi-label classification task, the results demonstrated that our proposed method obtained the highest F1-Score (reaching 0.794 on a chemical exposure assessment task and reaching 0.615 on a clinical syndrome differentiation task of traditional Chinese medicine). © 2020, Springer Nature Switzerland AG.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Deciphering circRNA-drug sensitivity associations via global-local heterogeneous matrix factorization and hypergraph contrastive learning

引用

Expert Systems with Applications 2025年 292卷

作者： Tiyao Liu Shudong Wang Yuanyuan Zhang Shanchen Pang Wenjing Yin Wenhao Wu Yingye Liu College of Computer Science and Technology Institute of Software China University of Petroleum Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China The School of Information and Control Engineering Qingdao University of Technology Qingdao 266525 China

Growing evidence highlights the critical role of circular RNAs (circRNAs) as regulators of cellular drug sensitivity, significantly influencing drug efficacy. While matrix factorization has proven feasible in uncovering circRNA-drug sensitivity associations, existing methods rely solely on decomposing the association matrix and fail to efficiently incorporate richer biological information. Moreover, current predictive models are limited in representing multi-perspective relationships and higher-order relationships in circRNA-drug sensitivity associations. To address these limitations, we propose a novel model based on global-local heterogeneous matrix factorization and hypergraph contrastive learning (HMFHCL). HMFHCL first calculates the global and local similarities of circRNAs and drugs, then constructs global and local circRNA-drug heterogeneous networks and performs matrix factorization of the adjacency matrices of these networks to extract information-rich feature representations. By utilizing multi-source information, HMFHCL effectively captures richer structural features and reveals potential connections in heterogeneous networks. Next, we constructed multiple circRNA and drug hypergraphs using global and local association matrices to capture higher-order interactions between nodes via hypergraph convolution. To enhance feature learning, comparative learning is applied to both global and local views of circRNA/drugs, effectively mining the similarities and differences between global structures and local details, improving the model’s ability to perceive underlying patterns and the consistency of representation learning. Finally, HMFHCL integrates circRNA and drug features from different perspectives to predict circRNA-drug associations effectively. Comprehensive experiments on three benchmark datasets demonstrate that HMFHCL outperforms state-of-the-art models, highlighting its superior ability in uncovering complex circRNA-drug sensitivity associations.

关键词： CircRNA-drug sensitivity associations Global heterogeneous network Heterogeneous matrix factorization Hypergraph contrastive learning Local heterogeneous network

来源：评论

学校读者我要写书评

暂无评论

Formation control of multiple nonholonomic vehicles with local measurements in 3D space

Formation control of multiple nonholonomic vehicles with loc...

引用

IEEE Conference on Decision and Control

作者： Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

ISBN: (纸本)9781665436601

We study the formation control problem for multiple nonholonomic vehicles in 3D space, where vehicles are required to maintain a prescribed pattern and rotate around a target in the target plane, with the direction of rotation along with the normal vector of the target plane. Unlike the most existing works that study formation problems of agents modeled by mass-point in a 2D plane, control of multiple nonholonomic vehicles in 3D space means a grand challenge since both the space and dynamics are complex. We give explicit formulations of the problem and vehicles’ dynamics. Then, control laws to solve the problem are proposed and further transformed into the body-fixed frames of vehicles to show that they can be easily implemented using only local measurements and the direction of gravity. Theoretical analysis is presented and simulations show the effectiveness of the proposed laws.

关键词： Space vehicles Solid modeling Analytical models Three-dimensional displays Conferences Aerospace electronics Extraterrestrial measurements

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：