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Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot network

Self-supervised Image Denoising with Downsampled Invariance ...

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International conference on Computer Vision (ICCV)

作者： Yeong Il Jang Keuntek Lee Gu Yong Park Seyun Kim Nam Ik Cho Department of ECE INMC Seoul National University Seoul Korea Gauss Labs Inc. IPAI Seoul National University Seoul Korea

There have been many image denoisers using deep neural networks, which outperform conventional model-based methods by large margins. Recently, self-supervised methods have attracted attention because constructing a large real noise dataset for supervised training is an enormous burden. The most representative self-supervised denoisers are based on blind-spot networks, which exclude the receptive field’s center pixel. However, excluding any input pixel is abandoning some information, especially when the input pixel at the corresponding output position is excluded. In addition, a standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise, though it successfully removes independently distributed synthetic noise. Hence, to realize a more practical denoiser, we propose a novel self-supervised training framework that can remove real noise. For this, we derive the theoretic upper bound of a supervised loss where the network is guided by the downsampled blinded output. Also, we design a conditional blind-spot network (C-BSN), which selectively controls the blindness of the network to use the center pixel information. Furthermore, we exploit a random subsampler to decorrelate noise spatially, making the C-BSN free of visual artifacts that were often seen in downsample-based methods. Extensive experiments show that the proposed C-BSN achieves state-of-the-art performance on real-world datasets as a self-supervised denoiser and shows qualitatively pleasing results without any post-processing or refinement.

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Image reconstruction attacks on distributed machine learning models 2

Image reconstruction attacks on distributed machine learning...

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2nd ACM International Workshop on distributed Machine Learning, distributedML 2021, co-located with the 17th International conference on emerging networking EXperiments and Technologies, CoNEXT 2021

作者： Benkraouda, Hadjer Nahrstedt, Klara University of Illinois at Urbana-Chamapaign ChamapaignIL United States

ISBN: (纸本)9781450391344

Recent developments in Deep neural networks have resulted in their wide deployment for services around many aspects of human life, including security critical domains that handle sensitive data. Congruently, we have seen a proliferation of IoT devices with limited resources. Together, these two trends have led to the distribution of data analysis, processing, and decision making between edge devices and third parties such as cloud services. In this work we assess the security of the previously proposed distributed machine learning (ML) schemes by analyzing the information leaked from the output of the edge devices, i.e. the intermediate representation (IR). We particularly look at a Deep neural network that is used for video/image classification and tackle the problem of image/frame reconstruction from the output of the edge device. Our work focuses on assessing whether the proposed scheme of partitioned enclave execution is secure against chosen-image attacks (CIA). Given the attacker has the capability of querying the model under attack (victim model) to create image-IR pairs, can the attacker reconstruct the private input images? In this work we show that it is possible to carry out a black-box reconstruction attack by training a CNN based encoder-decoder architecture (reconstruction model) using image-IR pairs. Our tests show that the proposed reconstruction model achieves a 70% similarity between the original image and the reconstructed image. © 2021 ACM.

关键词： Image reconstruction

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Analysis of Effective E-Commerce Coordination Big Data processing Strategies under Infinite Deep neural network Topology

Analysis of Effective E-Commerce Coordination Big Data Proce...

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Communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Ailing Zhang Fuxiao Yao Shandong Vocational and Technical University of International Studies Rizhao Shandong China

ISBN: (纸本)9781665415385;9781665447034

This paper researches and analyses the effective e-commerce coordination big data processing strategy through the infinite-depth neural network topology. This paper firstly proposes a neural network training model neural network-Storm (NN-S) based on Storm streaming distributed architecture, which decomposes the neural network training task into multiple computing units by data-parallel method, the parameters are updated synchronously after the training of a single batch of data is completed. In the Storm architecture, a Zookeeper network is used for multi-server distributed deployment. The training results show that the NN-S model can significantly improve the training speed of neural networks. At the same time, the NN-S architecture can quickly recover from node failures and network resource scheduling abnormalities with strong robustness. In this paper, we investigate the streaming-based distributed neural network training and design a Storm-based distributed neural network training model and optimized training algorithms, which are of reference significance for distributed neural network training.

关键词： Training Industries Job shop scheduling Processor scheduling Computational modeling Transportation Computer architecture

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Enhancing Cloud Storage Security with Intrusion Detection System using CNN and Grey Wolf Optimization Algorithm

Enhancing Cloud Storage Security with Intrusion Detection Sy...

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Sustainable Computing and Data Communication Systems (ICSCDS), International conference on

作者： G. Eswari G.K. Monica V. Deepak K.M. Sunil B. Prem Kumar Department of Information Technology Velammal Institute of Technology Panchetti Chennai India

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. This research article proposes a novel approach for enhancing the security of cloud storage systems by developing an Intrusion Detection System (IDS) that utilizes feature extraction, pre-processing, and advanced analysis tools. Specifically, the proposed IDS leverage a Convolutional neural network (CNN) model and a grey wolf optimization algorithm to detect potential cyber-attacks on cloud storage. The research work focuses on extracting relevant features from the data and performing preprocessing to remove any irrelevant or noisy data points. The CNN model is then trained using the pre-processed data, while the grey wolf optimization algorithm is engaged to optimize the runtime performance of the IDS. The outcome of the proposed approach demonstrates its ability to detect various types of cyber-attacks with high accuracy and efficiency, which can help to improve the security of cloud storage systems. By measuring the effectiveness and efficiency of the algorithm, it can be determined whether GWO is a suitable optimization algorithm for intrusion detection in cloud security.

关键词： Cloud computing Privacy Runtime Intrusion detection Feature extraction Security Convolutional neural networks

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CWAI-CNER: Chinese entity recognition based on adaptive incorporation of characters and words

CWAI-CNER: Chinese entity recognition based on adaptive inco...

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IEEE International conference on Consumer Electronics and Computer Engineering (ICCECE)

作者： Peng, Pai Wu, Xu Xie, Xiaqing Wu, Jingchen BUPT Key Lab Trustworthy Distributed Comp & Serv Sch Cyberspace Secur Minist Educ Beijing Peoples R China BUPT Sch Cyberspace Secur Key Lab Trustworthy Distributed Comp & Serv Beijing Univ Posts & Telecommun LibMinist Educ Beijing Peoples R China

ISBN: (纸本)9781728183190

Chinese Named hint Recognition (CNER) is an portantant sub topic in the field of Chinese Natural Language processing. which plays an important role in multi tasks. however. it's difficult to determine the boundaries of entities in Chinese texts because the Chinese words are not naturally separated, which further causes the task of CNER much more difficult. In addition, the mainstream Named Entity Recogni-tion (NER) is based on sequence tagging, which causes the cost of training set labeling very high so many NER tasks are limited to training sets deficiency. In this work, we propose a new CNER method based on adaptive incorporation of characters and words-CWAI to solve the problem of words information loss caused by lacking of words boundaries, which uses convolution neural network (CNN) to capture the local semantics for energy character, and then adaptively calculates the weights of potential north;that match a lexicon for each character based on attention mechanism between characters and words. And for the problem of limited model effects due to insufficient training set, we combined our model with pretrained models to mike that.

关键词： Named Entity Recognition attention mechanism pre-trained language model

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A Graph Convolution neural network Based Method for Insider Threat Detection

A Graph Convolution Neural Network Based Method for Insider ...

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

作者： Kexiong Fei Jiang Zhou Lin Su Weiping Wang Yong Chen Fan Zhang School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Computer Science Texas Tech University Lubbock USA Information Technology Center China Mobile Beijing China

In this research, we propose Log2Graph, a new insider threat detection method based on graph convolution neural network (GCN). This method first retrieves the corresponding logs and features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe relationship between entities, such as users and hosts, instead of establish complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationship. At last, the graph convolution neural network constructed is used to detect insider threats. Our validation and evaluation results confirm that Log2Graph can greatly improve the performance of detecting insider threats compared against baseline and existing methods.

关键词： Knowledge engineering Convolution neural networks Supervised learning Feature extraction Time measurement Data models

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Towards Crowdsourced Training of Large neural networks using Decentralized Mixture-of-Experts 34

Towards Crowdsourced Training of Large Neural Networks using...

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34th conference on neural Information processing Systems (NeurIPS)

作者： Ryabinin, Max Gusev, Anton Natl Res Univ Higher Sch Econ Yandex St Petersburg Russia

ISBN: (纸本)9781713829546

Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, the cluster used to train GPT-3 costs over $250 million(2). As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of a hundred thousand $2500 desktops dwarfs that of a $250M server pod, but one cannot utilize that power efficiently with conventional distributed training methods. In this work, we propose Learning@home: a novel neural network training paradigm designed to handle large amounts of poorly connected participants. We analyze the performance, reliability, and architectural constraints of this paradigm and compare it against existing distributed training techniques.

关键词： Deep learning

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Higher Reconstruction Performance with Dual Deep neural networks for Wireless Sensor networks Compressed Temporal Signals

Higher Reconstruction Performance with Dual Deep Neural Netw...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Sébastien Rousseau Romain Négrier Fabien Courreges Frank Itoua Engoti Raymond Quéré XLIM Institute University of Limoges Brive-la-Gaillarde France NAIoBEE company Brive-la-Gaillarde France

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals.

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Performance Modeling for distributed Training of Convolutional neural networks

Performance Modeling for Distributed Training of Convolution...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Adrián Castelló Mar Catalán Manuel F. Dolz Jose I. Mestre Enrique S. Quintana-Ortí José Duato Universitat Jaume I Castellón Spain Universitat Politècnica de València Valencia Spain

We perform a theoretical analysis comparing the scalability of data versus model parallelism, applied to the distributed training of deep convolutional neural networks (CNNs), along five axes: batch size, node (floating-point) arithmetic performance, node memory bandwidth, network link bandwidth, and cluster dimension. Our study relies on analytical performance models that can be configured to reproduce the components and organization of the CNN model as well as the hardware configuration of the target distributed platform. In addition, we provide evidence of the accuracy of the analytical models by performing a validation against a Python library for distributed deep learning training.

关键词： Training Analytical models Scalability neural networks Bandwidth Organizations Parallel processing

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HyperSack: distributed Hyperparameter Optimization for Deep Learning using Resource-Aware Scheduling on Heterogeneous GPU Systems

HyperSack: Distributed Hyperparameter Optimization for Deep ...

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International conference on High Performance Computing

作者： Nawras Alnaasan Bharath Ramesh Jinghan Yao Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Hyperparameter Optimization (HPO) can unlock the full potential of Deep Learning (DL) models; however, it is considered one of the most compute-intensive tasks in the DL do-main due to multi-dimensional search spaces and complex neural network architectures. A common method for accelerating HPO workloads is parallelizing training jobs on multiple computing devices, such as modern GPUs in High-Performance Computing (HPC) environments. Nonetheless, existing HPO parallelization strategies underutilize powerful GPU devices, like the NVIDIA AI00 and HI00, especially for training lightweight Deep Neu-ral networks. Resource-sharing mechanisms can improve G PU utilization; nonetheless, naive adaptations for HPO workloads lead to poor performance. Therefore, we propose HyperSack-a distributed HPO framework for dynamic and resource-aware scheduling on heterogeneous GPU-based HPC systems with resource elasticity and fault tolerance. HyperSack reduces the execution time of HPO workloads by orchestrating the placement of DL training jobs on GPU devices with different computational capabilities. It supports different hardware architectures, HPO workloads, and scheduling policies. Our evaluations on vision and language models HPO workloads show up to 2.8x performance improvement in execution time on AI00 GPUs, 4.0x on HI00 GPUs, and 3.9x on a combination of 12 AI00 and 4 HI00 GPUs using HyperSack over standard HPO parallelization methods.

关键词： Training Performance evaluation Deep learning Processor scheduling High performance computing Computational modeling Graphics processing units Computer architecture Hyperparameter optimization Dynamic scheduling

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