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Data compression methods based on neural networks 21

Data compression methods based on Neural Networks

引用

Proceedings of the 5th International conference on Future networks and distributed Systems

作者： Aleksandr Berezkin Alexey Slepnev Ruslan Kirichek Dmitry Kukunin Daniil Matveev Communication Networks and Data Transmission Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia Software Engineering and Computer Science Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia

ISBN: (纸本)9781450387347

The modern stage of the development of telecommunication systems is characterized by the widespread development of wired and wireless data transmission networks. The growth in the number of users of such networks, the emergence of new multimedia services impose high demands on speed, reliability, and delay time in information processing. One of the factors in increasing the data transfer rate is the on-the-fly compression of information at the link level. The paper describes a method for compressing data in a communication channel using error-correcting BCH codes and a feed-forward neural network autoencoder. This method converts a binary vector of user data into BCH codewords, which are used to train the autoencoder. This ultimately makes it possible to reduce the number of transmitted bits in the communication channel.

关键词： autoencoder

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Online Runtime Environment Prediction for Complex Colocation Interference in distributed Streaming processing 23rd

Online Runtime Environment Prediction for Complex Colocatio...

引用

23rd International conference on Computational Science, ICCS 2023

作者： Liu, Fan Zhu, Weilin Mu, Weimin Zhang, Yun Li, Mingyang Ma, Can Wang, Weiping Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031360206

To improve system resource utilization, multiple operators are co-located in the distributed stream processing systems. In the colocation scenarios, the node runtime environment and co-located operators affect each other. The existing methods mainly study the impact of the runtime environment on operator performance. However, there is still a lack of in-depth research on the interference of operator colocation to the runtime environment. It will lead to inaccurate prediction of the performance of the co-located operators, and further affect the effect of operator placement. To solve these problems, we propose an online runtime environment prediction method based on the operator portraits for complex colocation interference. The experimental results show that compared with the existing works, our method can not only accurately predict the runtime environment online, but also has strong scalability and continuous learning ability. It is worth noting that our method exhibits excellent online prediction performance for runtime environments in large-scale colocation scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices

PipeEdge: Pipeline Parallelism for Large-Scale Model Inferen...

引用

Euromicro Symposium on Digital System Design

作者： Yang Hu Connor Imes Xuanang Zhao Souvik Kundu Peter A. Beerel Stephen P. Crago John Paul Walters Ming Hsieh Department of Electrical Computer Engineering University of Southern California USA Information Sciences Institute University of Southern California USA Unaffiliated China

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision and natural language processing. However, such models are too compute- or memory-intensive for resource-constrained edge devices. Prior works on parallel and distributed execution primarily focus on training-rather than inference-using homogeneous accelerators in data centers. We propose PipeEdge, a distributed framework for edge systems that uses pipeline parallelism to both speed up inference and enable running larger, more accurate models that otherwise cannot fit on single edge devices. PipeEdge uses an optimal partition strategy that considers heterogeneity in compute, memory, and network bandwidth. Our empirical evaluation demonstrates that PipeEdge achieves 11.88× and 12.78× speedup using 16 edge devices for the ViT-Huge and BERT-Large models, respectively, with no accuracy loss. Similarly, PipeEdge improves throughput for ViT-Huge (which cannot fit in a single device) by 3.93× over a 4-device baseline using 16 edge devices. Finally, we show up to 4.16× throughput improvement over the state-of-the-art PipeDream when using a heterogeneous set of devices.

关键词： Deep learning Digital systems Computational modeling Pipelines neural networks Parallel processing Throughput

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MapReduce-based BP neural network classification of aquaculture water quality

MapReduce-based BP neural network classification of aquacult...

引用

2020 International conference on Computer Information and Big Data Applications, CIBDA 2020

作者： Wen, Yan Li, Min Ye, Yu Chengdu Agricultural College Academy of Mechatromcs and Information Chengdu China

ISBN: (纸本)9781728198378

As information technology develops and prevails across the globe, the informatization and data processing efficiency in the aquaculture field are exerting increasing impact on the intelligent conversion of this field. The aquaculture water quality indicators were analyzed by a feedforward error back propagation algorithm (BP neural network) with strong nonlinear mapping capacity, and the complicated nonlinear relations among the parameters of the aquaculture environment were solved. An aquaculture element analysis model was proposed, the Johnson attribute reduction algorithm based on the discernibility matrix was used to optimize the traditional algorithm, and the network convergence speed was increased under a given accuracy. The MapReduece distributed programming model was then used to perform parallel design of the BP neural network algorithm to meet the needs of massive data processing in aquaculture platforms. Also, case studies were performed to analyze the aquaculture element model and the parallel learning algorithm, and the big data framework design and data analysis method are integrated to develop an efficient, fault-tolerant aquaculture data management, mining, visualization big data system. © 2020 IEEE.

关键词： Aquaculture

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Imaging from Temporal Data via Spiking Convolutional neural networks 3

Imaging from Temporal Data via Spiking Convolutional Neural ...

引用

conference on Emerging Imaging and Sensing Technologies for Security and Defence V / conference on Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III

作者： Kirkland, Paul Kapitany, Valentin Lyons, Ashley Soraghan, John Turpin, Alex Faccio, Daniele Di Caterina, Gaetano Univ Strathclyde Glasgow Lanark Scotland Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9781510638945

A new approach for imaging that is solely based on the time of flight of photons coming from the entire imaged scene, combined with a novel machine learning algorithm for image reconstruction: a spiking convolutional neural network (SCNN) named Spike-SPI (Spiking - Single Pixel Imager). The approach uses a single point detector and the corresponding time-counting electronics, which provide the arrival time of photons in the form of spikes distributed over time. This data is transformed into a temporal histogram containing the number of photons per arrival time. A SCNN that converts the 1D temporal histograms into a 3D image (2D image with depth map) by exploiting the feature extraction capabilities of convolutional neural networks (CNNs), the high dimensional compressed latent space representations of a variational encoder-decoder network structure, and the asynchronous processing capabilities of a spiking neural network (SNN). The performance of the proposed SCNN is analysed to demonstrate the state-of-the-art feature extraction capabilities of CNNs and the low latency asynchronous processing of SNNs that offer both higher throughput and higher accuracy in image reconstruction from the ToF data, when compared to standard ANNs. The results of Spike-SPI show an increase in spatial accuracy of 15% over then ANN, using the Intersection of Union (IoU) for the objects in the scene. While also delivering a 100% increase over then ANN in object reconstruction signal to noise ratio (RSNR) from similar to 3dB to similar to 6dB. These results are also consistent across a range of IRF (Instrument Response Functions) values and photo counts, highlighting the robust nature of the new network structure. Moreover, the asynchronous processing nature of the spiking neurons allow for a faster throughput and less computational overhead, benefiting from the operational sparsity in the single point sensor.

关键词： LiDAR SPAD SCNN CNN neural network Imaging Depth

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MMA Regularization: DecorrelatingWeights of neural networks by Maximizing the Minimal Angles 34

MMA Regularization: DecorrelatingWeights of Neural Networks ...

引用

34th conference on neural Information processing Systems (NeurIPS)

作者： Wang, Zhennan Xiang, Canqun Zou, Wenbin Xu, Chen Shenzhen Univ Shenzhen Key Lab Adv Machine Learning & Applicat Guangdong Key Lab Intelligent Informat Proc Inst Artificial Intelligence & Adv CommunColl El Shenzhen Peoples R China Shenzhen Univ Inst Artificial Intelligence & Adv Commun Coll Math & Stat Shenzhen Peoples R China

ISBN: (纸本)9781713829546

The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a novel diversity regularization method to address this issue, which makes the normalized weight vectors of neurons or filters distributed on a hypersphere as uniformly as possible, through maximizing the minimal pairwise angles (MMA). This method can easily exert its effect by plugging the MMA regularization term into the loss function with negligible computational overhead. TheMMAregularization is simple, efficient, and effective. Therefore, it can be used as a basic regularization method in neural network training. Extensive experiments demonstrate that MMA regularization is able to enhance the generalization ability of various modern models and achieves considerable performance improvements on CIFAR100 and TinyImageNet datasets. In addition, experiments on face verification show that MMA regularization is also effective for feature learning. Code is available at: https://***/wznpub/MMA_Regularization.

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neural network-based distributed denial of service (DDoS) attack detection in smart home networks

Neural network-based distributed denial of service (DDoS) at...

引用

6th Smart Cities Symposium (SCS 2022)

作者： I. Ahamed F. Ahamad V. Palade A. Ahamed Centre for Future Transport and Cities Coventry University Coventry UK Centre for Computational Science and Mathematical Modelling Coventry University Coventry UK School of Engineering Canterbury Christ Church University Canterbury UK

Due to various limitations, such as limited power supply, the lack of storage capability and processing power, Internet of Things-based smart home networks have become vulnerable to various cyber-security attacks including distributed Denial of Service (DDoS) attacks. These attacks are a malicious attempt to exhaust and overwhelm the target system resources, which has significant impact on the operation of smart home net- works. This paper proposes a novel, efficient and lightweight DDoS attack detection scheme in smart home networks, which employs artificial neural networks (ANN) to classify smart home networks traffic into DDoS attacks or normal traffic. The proposed solution is evaluated on four datasets, namely, IoT-23, DS2OS, NUSW-NB15GT and CICDDOS2019. Experiments were conducted on two types of ANN models, i.e., Multilayered Perceptron (MLP) and Long- Short-Term Memory (LSTM), which achieved 99.78% and 99.98% accuracy, respectively.

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ROCK-CNN: a distributed RockPro64-based Convolutional neural network Cluster for IoT. Verification and Performance Analysis 26

ROCK-CNN: a Distributed RockPro64-based Convolutional Neural...

引用

26th conference of Open Innovations Association FRUCT

作者： Khaydarova, Rezeda Fishchenko, Vladislav Mouromtsev, Dmitriy Shmatkov, Vladislav Lapaev, Maksim ITMO Univ St Petersburg Russia TPP Lab LTD St Petersburg Russia

ISBN: (纸本)9789526924427

The paper is dedicated to optimization of machine learning and neural networks applications by replacing common servers with Single Board Computer (SBC) clusters to minimize mounting and service expenses, simplify node mounting process and organize parallel computing in IoT applications. Authors focus on former experience of using distributed computing, mainly, light-weight and cost-optimized SBCs to classify use cases, then, choose an appropriate hardware platform enabling sufficient data processing and easy hot-replacement of nodes. This task requires organizing an efficient software architecture to make use of advantages of SBCs. A comparison for various SBSs is presented. Authors suggest their formerly-designed architecture with changes allowing using it for neural network applications. Authors pay attention to thorough parameter examination based on numerous tests. Parameter timelines are presented. The paper describes a number of test-cases to validate the efficiency of suggested architecture based on common use-cases. Performance analysis and cluster scalability potential estimation are conducted as well to estimate an efficient number of nodes required for future tasks.

关键词： Mountings

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Multi-features-Based Automatic Clinical Coding for Chinese ICD-9-CM-3 30th

Multi-features-Based Automatic Clinical Coding for Chinese I...

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30th International conference on Artificial neural networks (ICANN)

作者： Gao, Yue Fu, Xiangling Liu, Xien Wu, Ji Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ Beijing Peoples R China

ISBN: (纸本)9783030863838;9783030863821

ICD-9-CM Volume 3 (ICD-9-CM-3), as a subset of the ICD-9-CM, is a standard system used to classify operations and medical procedures for billing purposes. With the gradual maturity of the DRG system, the precise coding of ICD-9-CM-3 is increasingly important for both hospital revenue and patients' health. In this paper, a method based on BERT and NER capturing Multi-Features for automatic Chinese ICD-9-CM-3 Coding (BNMF) is proposed to support doctors to make decisions so that traditional manual coding in hospitals can be replaced. The method is designed focusing on short text information from electronic medical records (EMR) and make decisions by combining the semantic features of the clinical text, the structured features of Chinese ICD-9-CM-3, and the axis knowledge of Chinese ICD-9-CM-3 text. Meanwhile, fusion of spatial and temporal features is made to better capture semantic features. The experiments demonstrate that the efficiency of our framework is higher than those of state-of-the-art methods in the field of classification of the short medical corpus.

关键词： ICD coding Decision support system Medical informatics Natural language processing

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DIPool: Degree-Induced Pooling for Hierarchical Graph Representation Learning

DIPool: Degree-Induced Pooling for Hierarchical Graph Repres...

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

作者： Hualei Yu Yirong Yao Jinliang Yuan Chongjun Wang Department of Computer Science and Technology Nanjing University Nanjing China

Graph neural networks have been popularly investigated and achieved promising performance in node-wise and graph-level tasks, like node classification and graph classification. For graph classification, correctly defining pooling operations to coarsen an input graph for distilling hierarchical knowledge from node representations is demanded. Some works have devised pooling strategies to summarize a graph by evaluating and gathering the ranked top-k representative nodes. Yet there is few literature to explicitly consider the node's degree-specific structure information in pooling procedure. One intuition is the larger the node's degree is, the more important it probably is in the entire graph. In this work, we propose a novel Degree-Induced Pooling (DIPool), which can be integrated into various graph neural network architectures. Specifically, the DIPool layer devises two approaches for explicitly incorporating the characteristics and degree-specific structures of nodes into a unified criterion to evaluate the importance of the nodes within an input graph. This setting permits to retain more representative nodes to generate the newly pooled graph. Experimental results on a collection of public benchmarks have demonstrated the effectiveness of the proposed DIPool operator. Especially, DIPool achieves relative improvements of 3.69% on NCI1 and 3.24% on NCI109 compared with the best baseline ASAP.

关键词： Representation learning Convolution Biological system modeling Aggregates Predictive models Benchmark testing Graph neural networks

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