检索结果-内蒙古大学图书馆

IEEE International Conference on Image Processing

作者： Minglong Xue Zhengyang He Yanyi He Peiqi Xie Xin Feng School of Computer Science and Engineering Chongqing University of Technology Chongqing P.R. China State Key Laboratory for Novel Software Technology Nanjing University Nanjing P.R. China

How to balance lighting and texture details to achieve the desired visual effect remains the bottleneck of existing low-light image enhancement methods. In this paper, we propose a novel Unpaired Textual-attention Generative Adversarial N network (UT-GAN) for low-light text image enhancement task. UT-GAN first uses the Zero-DCE net for initial illumination recovery and our TAM module is proposed to translate text information into a textual attention mechanism for the overall network, emphasizing attention to the details of text regions. Moreover, the method constructs an AGM-Net module to mitigate noise effects and fine-tune the illumination. Experiments show that UT-GAN outperforms existing methods in qualitative and quantitative evaluation on the widely used the low-light datasets LOL and SID.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs

SemLog: A Semantics-based Approach for Anomaly Detection in ...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaoman Tan Ningning Han Siyang Lu Wu Chen Dongdong Wang College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Civil Environmental and Construction Engineering University of Central Florida Orlando FL US

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system’s run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization

arXiv

引用

arXiv 2022年

作者： Dong, Wei Wu, Junsheng Luo, Yi Ge, Zongyuan Wang, Peng School of Computer Science and Engineering Northwestern Polytechnical University China School of Software Northwestern Polytechnical University China Monash University Australia University of Wollongong Australia

The key towards learning informative node representations in graphs lies in how to gain contextual information from the neighbourhood. In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing. Following InfoNCE, our framework is optimized via a surrogate contrastive loss, where the positive selection underpins the quality and efficiency of representation learning. To this end, we propose a topology-aware positive sampling strategy, which samples positives from the neighbourhood by considering the structural dependencies between nodes and thus enables positive selection upfront. In the extreme case when only one positive is sampled, we fully avoid expensive neighbourhood aggregation. Our methods achieve promising performance on various node classification datasets. It is also worth mentioning by applying our loss function to MLP based node encoders, our methods can be orders of faster than existing solutions. Our codes and supplementary materials are available at https://***/dongwei156/n2n. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Embedding Representation of Academic Heterogeneous Informati...

引用

IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Junfu Wang Yawen Li Meiyu Liang Ang Li Beijing Key Laboratory of Intelligent Communication Software and Multimedia School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing School of Economics and Management Beijing University of Posts and Telecommunications Beijing

ISBN: (纸本)9781665456579

Existing studies for representation learning of homogeneous information networks cannot be applicable to academic heterogeneous information networks (HINs) with multi-type nodes and multi-relationships because of the lack of ability to issue heterogeneity. Meanwhile, due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of academic HINs and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated.

关键词： Representation learning Training Cloud computing Federated learning Semantics Intelligent systems Business

来源：评论

学校读者我要写书评

暂无评论

DDA: A Dynamic Difficulty-aware Data Augmenter for Image Super-resolution

DDA: A Dynamic Difficulty-aware Data Augmenter for Image Sup...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Xinyi Zhang Tao Dai Bin Chen Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University College of Computer Science and Software Engineering Shenzhen University Harbin Institute of Technology Shenzhen Peng Cheng Laboratory Research Center of Artificial Intelligence

Deep neural networks (DNNs) have been recently widely used in image super-resolution (SR) and have achieved remarkable performance. However, most existing methods focus on elaborate network design, while rarely considering the training strategy, which affects the model performance and training efficiency. In practice, most SR methods still train the networks with the commonly-used data augmentation (e.g., random crop and sampling), which is shown to converge slowly for deep SR networks. To address this issue, in this paper, we propose a dynamic difficulty-aware data augmenter, named DDA, by considering the restoration difficulty and distribution of input patches. Our DDA mainly consists of difficulty-aware divider, dynamic sampler, and adaptive re-weighter. Specifically, our DDA first uses the difficulty-aware divider to divide the input image into small over-lapping patches, followed by classification into $N$ different classes based on the restoration difficulty. Next, dynamic sampler samples the training patches from each class with probability based on training loss. Furthermore, to remedy the imbalance of training patches between different classes, adaptive re-weighter updates the weight of each training patch according to the accumulated training loss. Extensive experiments demonstrate the effectiveness of our DDA on different SR methods by improving training efficiency and model performance across a wide range of scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Loss-tolerant authentication with digital signatures

引用

Security and Communication Networks 2020年第11期7卷 2054-2062页

作者： Lv, Xixiang Mu, Yi Li, Hui National Key Laboratory of ISN Xidian University Xi'an710071 China School of Computer Science and Software Engineering University of Wollonggong NSW2522 Australia

Consider a signed file that cannot be verified because of some fraction loss or noise, which might not affect the business of users. It would be desirable for the verifier to locate the damaged fractions so that the authenticity of other components of the file can be ensured. Thereafter, the verifier can decide whether or not to accept it. To address this issue, in this paper, we present a loss-tolerant authentication scheme, which allows the verifier to locate the damaged elements in the signed file stream. Our idea is to allow the signed file to be fragmented, and each fragment is appended a short authentication tag, which enables the verifier to find the modified fragments in the signed file and verify the signature even if some fragments are lost. The proposed scheme adopts a combination of Reed-Solomon error correction and erasure codes, aiming to allow verifiers to locate the modified fractions and thereafter reconstruct the signature when sufficient original fractions of the file are obtained. Our scheme offers many potential applications such as loss-tolerant transmission and storage. © 2013 John Wiley & Sons, Ltd.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

来源：评论

学校读者我要写书评

暂无评论

A Virtual Farm Tour for Public Education about Dairy Industry

A Virtual Farm Tour for Public Education about Dairy Industr...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Anh Nguyen Emma Windfeld Michael Francis Guillaume Lhermie Kangsoo Kim Department of Computer Science University of Calgary The Simpson Centre The School of Public Policy University of Calgary Department of Electrical and Software Engineering University of Calgary The Simpson Centre Faculty of Veterinary Medicine University of Calgary

In this paper, we present an immersive virtual farm simulation developed to provide realistic on-farm experiences to the public. Users could visit the virtual farm, walk through various sites where dairy cows are raised, and learn how the dairy products are produced through the virtual experience. Public users' responses about the virtual experience were collected in various measures, e.g., user experience and learning efficacy, via showcases at local libraries. We present preliminary results regarding the potential of the simulation as an effective agricultural education, and discuss our future plans.

关键词： Industries Solid modeling Three-dimensional displays Dairy products Conferences Virtual reality User interfaces

来源：评论

学校读者我要写书评

暂无评论

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

Adaptive Sparse Convolutional Networks with Global Context E...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Bowei Du Yecheng Huang Jiaxin Chen Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional network (CEASC). It first develops a context-enhanced group normalization (CE-GN) layer, by replacing the statistics based on sparsely sampled features with the global contextual ones, and then designs an adaptive multi-layer masking strategy to generate optimal mask ratios at distinct scales for compact foreground coverage, promoting both the accuracy and efficiency. Extensive experimental results on two major benchmarks, i.e. VisDrone and UAVDT, demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the inference procedure when plugging into the typical state-of-the-art detection frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is available at https://***/Cuogeihong/CEASC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SCALABLE EDGE BLOCKING ALGORITHMS FOR DEFENDING ACTIVE DIRECTORY STYLE ATTACK GRAPHS :A PREPRINT

arXiv

引用

arXiv 2022年

作者： Guo, Mingyu Ward, Max Neumann, Aneta Neumann, Frank Nguyen, Hung School of Computer Science University of Adelaide Australia School of Physics Maths and Computing Computer Science and Software Engineering University of Western Australia Australia Department of Molecular and Cellular Biology Harvard University CambridgeMA United States

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender’s aim is to minimize the attacker’s success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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：