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Robust Zero Watermarking Algorithm for Medical Images Using Local Binary pattern and Discrete Cosine Transform 8th

Robust Zero Watermarking Algorithm for Medical Images Using ...

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Liu, Wenyi Li, Jingbing Shao, Chunyan Ma, Jixin Huang, Mengxing Bhatti, Uzair Aslam School of Information and Communication Engineering Hainan University Haikou570228 China School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China

ISBN: (纸本)9783031067631

thanks to the rapid development of the telemedicine, medical treatment has turned in great convenience. However, as the internet network can be addressed by any user, the medical images cannot avoid the defects of suffering from information leakage and require information encryption techniques. To protect the privacy of patients in medical images, a robust watermarking method, which combines medical images with watermark information, is proposed in this paper. First, the low-frequency feature vector of the medical image is extracted by combining local binary pattern (LBP) and transformation with discrete cosine transform (DCT). Second, hash sequence is implemented on to convert feature vector into binary sequences. At last, the medical image is embedded with hash sequence and watermark preprocessed by chaotic encryption technology using the zero-watermark technology. the experimental results show that the method can encrypt the medical images’ watermark effectively and has good robustness against geometric attacks. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Local binary pattern

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Layer-2 Routing Analytic Model by Linear Programming

Layer-2 Routing Analytic Model by Linear Programming

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international conference on Advanced Communication Technology (ICACT)

作者： Changjin Suh Shin-woong Jung School of Computing Soongsil University Seoul South Korea

Carrier Ethernet integrates distributed layer-2 bridge networks to a backbone network. Since carrier Ethernets use simple layer-2 routing, its performance is lower than that of layer-3 routing methods. We introduce an analytic model that evaluates the routing methods in backbone networks. the model solves the multi- input and multi-output maximum flow problem in backbone networks using linear programming. Our method can be used as follows. Firstly, the model suggests appropriate link capacity if network topology and traffic pattern is given. Secondly, the model shows and compares the performance of layer-2 routing methods including perfect routing and our proposed routing called ENDIST(Edge Node Divided Spanning Tree). In this study, the performance is the maximum throughput that a given routing method can reach. We additionally found many good results about ENDIST. Our analytic model proved that ENDIST outperforms the existing layer-2 standard protocols such as STP, MSTP for any offered load and performs perfectly under medium and light traffic load in the given network, thus enough to say that ENDIST is as good as layer 3 routing from the point of throughput.

关键词： Routing Linear programming Spine Ethernet networks Telecommunication traffic throughput Bridges Network topology Traffic control Performance analysis

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the Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy

The Effect of Spectrogram Reconstruction on Automatic Music ...

引用

international conference on pattern recognition

作者： Kin Wai Cheuk Yin-Jvun Luo Emmanouil Benetos Dorien Herremans Institute of High Performance Computing Agency for Science Technology and Research School of Electronic Engineering and Computer Science Queen Mary University of London

ISBN: (纸本)9781728188089;9781728188096

Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. these predictions are then concatenated together and used as the input to train another model with the pitch labels to obtain the final transcription. We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks. In this paper, we do not aim at achieving state-of-the-art transcription accuracy, instead, we explore the effect that spectrogram reconstruction has on our AMT model. Our proposed model consists of two U-nets: the first U-net transcribes the spectrogram into a posteriorgram, and a second U-net transforms the posteriorgram back into a spectrogram. A reconstruction loss is applied between the original spectrogram and the reconstructed spectrogram to constrain the second U-net to focus only on reconstruction. We train our model on three different datasets: MAPS, MAESTRO, and MusicNet. Our experiments show that adding the reconstruction loss can generally improve the note-level transcription accuracy when compared to the same model without the reconstruction part. Moreover, it can also boost the frame-level precision to be higher than the state-of-the-art models. the feature maps learned by our U-net contain gridlike structures (not present in the baseline model) which implies that with the presence of the reconstruction loss, the model is probably trying to count along both the time and frequency axis, resulting in a higher note-level transcription accuracy.

关键词： Training Measurement Time-frequency analysis Transforms Predictive models Market research pattern recognition

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Data Privacy Protection of Industrial Blockchain 10th

Data Privacy Protection of Industrial Blockchain

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10th EAI international conference on Cloud computing, CloudComp 2020

作者： Long, Huaqiu Hou, Jun Li, Qianmu Ma, Na Jiang, Jian Qi, Lianyong Xu, Xiaolong Zhang, Xuyun Intelligent Manufacturing Department Wuyi University Jiangmen529020 China Nanjing Institute of Industry Technology Nanjing210023 China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Jiangsu Zhongtian Internet Technology Co. Ltd. Nantong226009 China School of Information Science and Engineering Qufu Normal University Jining272000 China Nanjing University of Information Science and Technology Nanjing210023 China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9783030699918

this paper studies the data privacy protection of industrial block chain. Aiming at the privacy leakage problem of industrial blockchain, combining symmetric encryption and homomorphic encryption, the data privacy protection method is proposed to ensure the confidentiality and privacy of industrial block chain and improve the privacy security of industrial enterprises. this paper designs common basic chain code, security service chain code and privacy protection chain code in the chain code layer, USES AES algorithm to encrypt sensitive information of industrial enterprises, and USES Paillier algorithm to encrypt maintenance cost. the chain code of privacy protection can be used for security access and fault event audit to guarantee the privacy security of industrial enterprises. In addition, in order to achieve the unity of call chain code, the call chain code is encapsulated to provide support for fast and highly concurrent upload data and access data. Finally, the existing sensitive and private data storage methods of industrial block chain are analyzed, and the experimental comparison with the method in this paper proves the effectiveness of this method. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Blockchain

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Feature Fusion for Online Mutual Knowledge Distillation

Feature Fusion for Online Mutual Knowledge Distillation

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international conference on pattern recognition

作者： Jangho Kim Minsung Hyun Inseop Chung Nojun Kwak Seoul National University Seoul Korea

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks and generates meaningful feature maps. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. the fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each subnetwork, mutually teaching one another in an online-knowledge distillation manner. this mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial than other alternative methods because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performances of both sub-networks and the fused classifier, and the aspect of generating meaningful feature maps. the code is available at this link^1.

关键词： Neural networks Education Performance gain pattern recognition

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Learning Dynamics for Video Facial Expression recognition 21

Learning Dynamics for Video Facial Expression Recognition

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Proceedings of the 2021 4th international conference on Algorithms, computing and Artificial Intelligence

作者： Yu Wang XinMin Xu Yao Zhuang Zhejiang University China Alibaba Group China

ISBN: (纸本)9781450385053

Video-based facial expression recognition has always been a focus of attention in the computer vision community for decades. It aims to automatically identify one of several emotions represented by the video according to the input audio or visual information. Capturing the dynamics, namely motion pattern plays an important role in video-based facial expression recognition. In this paper, we explore an effective and efficient motion pattern to model temporal relationships called Diff-based Canny Operator (DCO) to guide the inter-frame aggregation and generate a novel feature modality. Our proposed DCO add rare computational consumption and can be easily inserted into any frameworks, so we incorporate it with exist networks to form a unified structure for video-based facial expression recognition task, which enable the network to Ideally extract temporal information. With extensive experiments on CK+ and AFEW dataset, our proposed method shows its superiority with better or comparable performance compared to the state-of-the-art approaches at low FLOPs.

关键词： Diff-based Canny Operator dynamics video-based facial expression recognition motion pattern

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Towards Robust Learning with Different Label Noise Distributions

Towards Robust Learning with Different Label Noise Distribut...

引用

international conference on pattern recognition

作者： Diego Ortego Eric Arazo Paul Albert Noel E. O'Connor Kevin McGuinness Insight Centre for Data Analytics Dublin City University (DCU)

Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important step towards preventing performance degradations in Convolutional Neural Networks. Discarding noisy labels avoids a harmful memorization, while the associated image content can still be exploited in a semi-supervised learning (SSL) setup. Clean samples are usually identified using the small loss trick, i.e. they exhibit a low loss. However, we show that different noise distributions make the application of this trick less straightforward and propose to continuously relabel all images to reveal a discriminative loss against multiple distributions. SSL is then applied twice, once to improve the clean-noisy detection and again for training the final model. We design an experimental setup based on ImageNet32/64 for better understanding the consequences of representation learning with differing label noise distributions and find that non-uniform out-of-distribution noise better resembles real-world noise and that in most cases intermediate features are not affected by label noise corruption. Experiments in CIFAR-10/100, ImageNet32/64 and WebVision (real-world noise) demonstrate that the proposed label noise Distribution Robust Pseudo-Labeling (DRPL) approach gives substantial improvements over recent state-of-the-art. Code is available at https://***/JJ0PV.

关键词： Training Degradation Semisupervised learning Robustness pattern recognition Noise measurement Labeling

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Early Prediction of Complex Business Processes Using Association Rule Based Mining 4th

Early Prediction of Complex Business Processes Using Associ...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Khan, Naveed Tariq, Zeeshan Ali, Aftab McClean, Sally Taylor, Paul Nauck, Detlef School of Computing Ulster University JordanstownBT37 0QB United Kingdom Applied Research BT Ipswich United Kingdom

ISBN: (纸本)9783031070044

Complex business processes are challenging and hard to analyse. the objective here is to enhance delivery of processes in terms of improving quality of service and customer satisfaction. therefore, an automated process prediction system is desirable to monitor and evaluate complex business processes and forecast process outcome during execution time. the analysis of such processes would help domain experts to make in-time decisions to improve the process. the in-time response greatly effects the quality of service and customer satisfaction. therefore, in this paper, the early process prediction framework using Classification Based on Association rules (CBA) has been proposed to predict outcomes for such incomplete processes. the essential part of the proposed system is to extract association rules from the process data up to a certain point in time (i.e. the cut-off time) at which the prediction needs to be made;in an live process this would usually be the current time. the CBA algorithm generates rules with user specified support and confidence which are then utilised for early process prediction. the experimental results based on real business process data are presented for on-time and delayed processes. the proposed early process prediction system is evaluated using different metrics such as accuracy, precision, recall and the F-measure. Moreover, the proposed system is also compared with our prior published work in terms of accuracy, recall and F-measure. the analysis shows that the performance of proposed system outperforms schemes in the literature. © 2022, Springer Nature Switzerland AG.

关键词： Association rules

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Activation Density Driven Efficient Pruning in Training

Activation Density Driven Efficient Pruning in Training

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international conference on pattern recognition

作者： Timothy Foldy-Porto Yeshwanth Venkatesha Priyadarshini Panda Yale University

Neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy. Typical pruning methods require large, fully trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining procedure to regain the original accuracy. We propose a novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network. We introduce an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network. Our method is architecture agnostic, allowing it to be employed on a wide variety of systems. For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200 x reduction in parameters and over 60 x reduction in inference compute operations in the best case) with accuracy comparable to the baseline network. By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods. Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity (1.6 x to 3.2 x lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.

关键词： Training Neural networks Computer architecture Real-time systems pattern recognition Complexity theory Iterative methods

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Stacked Dark COVID-Net: A Multi-class Multi-label Classification Approach for Diagnosing COVID-19 Using Chest X-Ray Images 4th

Stacked Dark COVID-Net: A Multi-class Multi-label Classifica...

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4th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2021

作者： Anila Glory, H. Meghana, S. Kesav Kumar, J.S. Shankar Sriram, V.S. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur613401 India

ISBN: (纸本)9783031070044

Most challenging yet, the need of the hour is accurate diagnosis of COVID-19, as the Coronavirus cases are increasing drastically day-by-day. Ceaseless efforts by the researchers and innovators have led to the development of several diagnostic models based on Deep Learning for effective diagnosis of COVID-19. However, the Deep Learning techniques that have been developed so far, fail to address major challenges such as overfitting, stability, computation overhead due to the usage of the massive volume of parameters and problems associated with the multi-class classification. Also in the medical perspective, researchers often suffer to identify the infinitesimal difference that exists in the radiographic images among the several lung diseases which makes the decision-making process difficult. thus, to curb the crisis and to provide promising solutions & expertise for accurate diagnosis, this paper presents a novel lightweight multi-class multi-label COVID-19 detection model to assist physicians with greater ease to fight against this pandemic situation. Radiographic images are pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and classified using novel Stacked Dark COVID-Net. the proposed model is validated using chest X-ray images and the results confirm the efficacy of the proposed model in terms of classification accuracy, sensitivity, specificity and stability. © 2022, Springer Nature Switzerland AG.

关键词： Deep learning

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