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22nd IEEE International Symposium on Parallel and distributed processing with Applications, ISPA 2024

作者： Li, Mingyue Zheng, Liudong Liu, Jiahui Du, Ruizhong Jia, Chunfu Hebei University Baoding China Nankai University Tianjin China

ISBN: (纸本)9798331509712

The retrieval of encrypted images in cloud computing is a research hotspot at present. However, the existing schemes have the problem of low image retrieval accuracy since it is difficult to obtain accurate feature information through convolutional neural network from encrypted image. In this paper, a secure image retrieval method using dual-stream convolution structure for feature fusion is proposed. Specifically, the stream cryptographic image containing contour features and the Fourier transform image containing frequency domain features are used as the two inputs of the convolutional network stream, where the weighted average gate function helps to integrate the feature information in these streams to achieve a more comprehensive feature representation and improve the retrieval accuracy. Furthermore, to keep the contour features of the encrypted image, the fuzzy image and Arnold mapping algorithm are selected randomly to encrypt the encrypted image twice. Experimental evaluation on three datasets, MNIST, Fashion-MNIST and CIFAR-100, shows that the proposed encryption method has higher security than AES and stream cipher encryption. In addition, the retrieval average accuracy (mAP) of this scheme is superior to the existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Reinforcement Learning for Real-Time Multi-Access Edge Computing 23

Reinforcement Learning for Real-Time Multi-Access Edge Compu...

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24th International Conference on distributed Computing and networking (ICDCN)

作者： Rajashekar, Kolichala Indian Inst Technol Bhilai Raipur Madhya Pradesh India

ISBN: (纸本)9781450397964

We examine data-intensive real-time applications, such as forest fire detection, medical emergency services, oil pipeline monitoring, etc., that require relatively low response time in processing data from the Internet of Things (IoT) devices. Typically, in such circumstances, the edge computing paradigm is utilised to drastically reduce the processing delay of such applications. However, with the growing IoT devices, the edge device cluster needs to be configured properly such that the real-time requirements are met. Therefore, the cluster configuration must be dynamically adapted to the changing network topology of the edge cluster in order to minimise the observed overall communication delay incurred by edge devices when processing data from IoT devices. To this end, we propose an intelligent assignment of IoT devices to edge devices based on Reinforcement Learning such that communication delay is minimised and none of the edge devices is overloaded. We demonstrate, with some preliminary results, that our algorithm outperforms the state-of-the-art.

关键词： datasets neural networks gaze detection text tagging

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A neural network to Estimate Isolated Performance from Multi-Program Execution 30

A Neural Network to Estimate Isolated Performance from Multi...

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30th Euromicro International Conference on Parallel, distributed and network-Based processing (PDP)

作者： Lurbe, Manel Feliu, Josue Petit, Salvador Gomez, Maria E. Sahuquillo, Julio Univ Politecn Valencia Dept Informat Sistemas & Computadores Valencia Spain Univ Murcia Dept Ingn & Tecnol Computadores Murcia Spain

ISBN: (纸本)9781665469586

When multiple applications are running on a platform with shared resources like multicore CPUs, the behaviour of the running application can be altered by the co-runners. In this case, the system resources need to be managed (e.g. by repartitioning the cache space, re-schedule applications in distinct cores, modifying the prefetcher configuration, etc.) to reduce the inter-application interference in order to minimize the performance losses over isolated execution. In this context, a main challenge in different computing scenarios like the public cloud or soft real-time systems is knowing the performance impact of a given management action on each application with respect to its isolated execution. With this aim, in this work we present a neural network-based approach that estimates the performance an application would have had in isolation from multi-program executions. Experimental results show that the proposal dynamically adapts to changes in application behavior. On average, the predicted performance presents an error deviation by 11.7% and 2.3% for MAPE and MSE respectively.

关键词： IBM POWER8 System Performance Inter-Application Interference Machine Learning Deep Learning

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Frequency Hopping Modulation Format Recognition Based on Higher-Order Cyclic Cumulants and neural network Classifier 5

Frequency Hopping Modulation Format Recognition Based on Hig...

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5th International Conference on Information Science, Parallel and distributed Systems, ISPDS 2024

作者： Xie, Wenjun Li, Longqing Zhong, Lei Xiao, Qingxin Zhao, Yongjie Hu, Deming National University of Defense Technology College of Intelligence Science and Technology Changsha China Guangzhou China

ISBN: (纸本)9798350352719

In the field of wireless communications, the recognition of frequency hopping (FH) signal modulation formats is of significant importance for the detection and analysis of anti-drone communication signals. However, existing methods for FH signal modulation have certain limitations in handling complex signal environments, especially in terms of frequency offset resistance, timing error, anti-interference capability, and generalization ability. To address these issues, this study proposes a novel method for recognizing the modulation formats of FH signals. The Tektronix real-time spectrum analyzer is used to capture the FH signals generated by the signal source in real-time, and the signals are then processed to remove the FH. Based on this, higher-order cyclic cumulants are further calculated to deeply analyze the statistical characteristics of the signals. Finally, with the aid of a well-trained neural network classifier, precise recognition of modulation formats such as BPSK, QPSK, 8PSK, and 16QAM is achieved. This study not only improves the accuracy of FH signal recognition but also demonstrates excellent performance in resisting frequency offset and timing errors through the collection and processing of real signals. It provides a novel and efficient method for recognizing the modulation formats of FH signals in communication systems. © 2024 IEEE.

关键词： Spectrum analyzers

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SPRINT: A High-Performance, Energy-Efficient, and Scalable Chiplet-Based Accelerator With Photonic Interconnects for CNN Inference

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2022年第10期33卷 2332-2345页

作者： Li, Yuan Louri, Ahmed Karanth, Avinash George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

Chiplet-based convolution neural network (CNN) accelerators have emerged as a promising solution to provide substantial processing power and on-chip memory capacity for CNN inference. The performance of these accelerators is often limited by inter-chiplet metallic interconnects. Emerging technologies such as photonic interconnects can overcome the limitations of metallic interconnects due to several superior properties including high bandwidth density and distance-independent latency. However, implementing photonic interconnects in chiplet-based CNN accelerators is challenging and requires combined effort of network architectural optimization and CNN dataflow customization. In this article, we propose SPRINT, a chiplet-based CNN accelerator that consists of a global buffer and several accelerator chiplets. SPRINT introduces two novel designs: (1) a photonic inter-chiplet network that can adapt to specific communication patterns in CNN inference through wavelength allocation and waveguide reconfiguration, and (2) a CNN dataflow that can leverage the broadcasting capability of photonic interconnects while minimizing the costly electrical-to-optical and optical-to-electrical signal conversions. Simulations using multiple CNN models show that SPRINT achieves up to 76% and 68% reduction in execution time and energy consumption, respectively, as compared to other state-of-the-art chiplet-based architectures with either metallic or photonic interconnects.

关键词： Photonics Convolutional neural networks Optical waveguides Optical switches Optical filters Convolution Optical network units Convolution neural network chiplet accelerator photonic interconnects

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A Privacy Enforcing Framework for Data Streams on the Edge

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第3期12卷 852-863页

作者： Sedlak, Boris Murturi, Ilir Donta, Praveen Kumar Dustdar, Schahram Tech Univ Wien Distributed Syst Grp A-1040 Vienna Austria

Recent developments in machine learning (ML) allow for efficient data stream processing and also help in meeting various privacy requirements. Traditionally, predefined privacy policies are enforced in resource-rich and homogeneous environments such as in the cloud to protect sensitive information from being exposed. However, large amounts of data streams generated from heterogeneous IoT devices often result in high computational costs, cause network latency, and increase the chance of data interruption as data travels away from the source. Therefore, this article proposes a novel privacy-enforcing framework for transforming data streams by executing various privacy policies close to the data source. To achieve our proposed framework, we enable domain experts to specify high-level privacy policies in a human-readable form. Then, the edge-based runtime system analyzes data streams (i.e., generated from nearby IoT devices), interprets privacy policies (i.e., deployed on edge devices), and transforms data streams if privacy violations occur. Our proposed runtime mechanism uses a Deep neural networks (DNN) technique to detect privacy violations within the streamed data. Furthermore, we discuss the framework, processes of the approach, and the experiments carried out on a real-world testbed to validate its feasibility and applicability.

关键词： Data anonymization data stream transformations edge computing privacy models Data anonymization data stream transformations edge computing privacy models

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The network Science of distributed Representational Systems

The Network Science of Distributed Representational Systems

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作者： Ju, Harang University of Pennsylvania

学位级别：Ph.D., Doctor of Philosophy

From brains to science itself, distributed representational systems store and process information about the world. In brains, complex cognitive functions emerge from the collective activity of billions of neurons, and in science, new knowledge is discovered by building on previous discoveries. In both systems, many small individual units—neurons and scientific concepts—interact to inform complex behaviors in the systems they comprise. The patterns in the interactions between units are telling; pairwise interactions not only trivially affect pairs of units, but they also form structural and dynamic patterns with more than just pairs, on a larger scale of the network. Recently, network science adapted methods from graph theory, statistical mechanics, information theory, algebraic topology, and dynamical systems theory to study such complex systems. In this dissertation, we use such cutting-edge methods in network science to study complex distributed representational systems in two domains: cascading neural networks in the domain of neuroscience and concept networks in the domain of science of science. In the domain of neuroscience, the brain is a system that supports complex behavior by storing and processing information from the environment on long time scales. Underlying such behavior is a network of millions of interacting neurons. Many recent studies measure neural activity on the scale of the whole brain with brain regions as units or on the scale of brain regions with individual neurons as units. While many studies have explored the neural correlates of behaviors on these scales, it is less explored how neural activity can be decomposed into low-level patterns. network science has shown potential to advance our understanding of large-scale brain networks, and here, we apply network science to further our understanding of low-level patterns in small-scale neural networks. Specifically, we explore how the structure and dynamics of biological neural networks suppor

关键词： distributed representational systems network science Neurons

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A neural network-Based distributed Compressed Sensing Algorithm to Derive Continuous Hyperspectral Images in Wireless Sensor networks

Research Square

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Research Square 2022年

作者： Sarkar Hasanuzzaman, Md. Department of Computer Science and Engineering Faculty of Engineering and Technology University of Dhaka Bangladesh

Hyperspectral imaging is a versatile and powerful technology for gathering geo-data. Planes and satellites equipped with hyperspectral cameras are currently the leading contenders for large-scale imaging projects. Aiming at the shortcomings of traditional methods for detecting sparse representation of multi-spectral images, this paper proposes wireless sensor networks (WSNs) based single-hyperspectral image super-resolution method based on deep residual convolutional neural networks. We propose a different strategy that involves merging cheaper multispectral sensors to achieve hyperspectral-like spectral resolution while maintaining the WSN's spatial resolution. This method studies and mines the nonlinear relationship between low-resolution remote sensing images and high-resolution remote sensing images, constructs a deep residual convolutional neural network, connects multiple residual blocks in series, and removes some unnecessary modules. For this purpose, a decision support system is used that provides the outcome to the next layer. Finally, this paper, fully explores the similarities between natural images and hyperspectral images, use natural image samples to train convolutional neural networks, and further use migration learning to introduce the trained network model to the super-resolution problem of high-resolution remote sensing images, and solve the lack of training samples problem. A comparison between different algorithms for processing data on datasets collected in situ and via remote sensing is used to evaluate the proposed approach. The experimental results show that the method has good performance and can obtain better super-resolution effects. © 2022, CC BY.

关键词： Hyperspectral imaging

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Few-Shot Classification with Meta-Learning for Urban Infrastructure Monitoring Using distributed Acoustic Sensing

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SENSORS 2024年第1期24卷 49页

作者： Van Luong, Huynh Deligiannis, Nikos Wilhelm, Roman Drapp, Bernd AP Sensing GmbH Herrenberger Str 130 D-71034 Boblingen Germany Vrije Univ Brussel Dept Elect & Informat Pleinlaan 2 B-1050 Brussels Belgium Interuniv Microelect Ctr IMEC Kapeldreef 75 B-3001 Leuven Belgium

This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processing methods for DAS data and (ii) the implementation of a neural network model based on meta-learning to learn a representation of the processed data. In the context of urban infrastructure monitoring, we develop a few-shot classification framework that classifies query samples with only a limited number of support samples. The model consists of an embedding network trained on a meta dataset for feature extraction and is followed by a classifier for performing few-shot classification. This research thoroughly explores three types of data pre-processing, that is, decomposed phase, power spectral density, and frequency energy band, as inputs to the neural network. Experimental results show the efficient learning capabilities of the embedding model when working with various pre-processed data, offering a range of pre-processing options. Furthermore, the results demonstrate outstanding few-shot classification performance across a large number of event classes, highlighting the framework's potential for urban infrastructure monitoring applications.

关键词： meta-learning few-shot classification distributed acoustic sensing artificial intelligence neural networks

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Two accurate hybrid islanding detection schemes for distribution network

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第2期42卷 755-766页

作者： Ray, Papia Salkuti, Surender Reddy Biswal, Monalisa Veer Surendra Sai Univ Technol VSSUT Dept Elect Engn Burla Odisha India Woosong Univ Dept Railrd & Elect Engn Daejeon South Korea Natl Inst Technol NIT Dept Elect Engn Raipur Chattisgarh India

In this paper, two accurate hybrid islanding detection schemes are proposed based on Wavelet Transform and Stockwell transform (S-transform). The proposed methods use the potential of sequence voltage (negative) retrieved at the target distributed Generation (DG) location of the distribution network under study. In one of the schemes, DiscreteWavelet transform (DWT) is applied to process the negative sequence voltage signal and for its decomposition, which is further used to extract six statistical features like energy, entropy, mean, kurtosis, standard deviation, and skewness from the reconstructed DWT coefficients. Test and train data sets are generated with the wide variation of loading conditions, and optimal features are chosen from the full feature set by forward feature selection method (FFS) during the training process by an artificial neural network (ANN). After that, the trained system is tested to get the detection result. Another scheme presented in this paper for islanding detection is based on S-transform, which is used to decompose the negative sequence voltage signal. Amplitude, frequency, and phase are the three coefficients acquired from the pre-processing of the raw signal by S-transform. Then the cumulative sums of the energy content of the S-transform coefficients are determined and are compared with a threshold value to get the detection result. The proposed schemes are tested in a distribution network consisting of two 9MWwind farm driven by six 1.5MW wind turbine connected to 120 kV main grid through a 25 kV, 30 km feeder. Several cases have been investigated like normal condition, islanding, DG line trip, disconnection of point of common coupling, and sudden change in load to test the performance of the proposed schemes. It can be observed from the results that both the approaches gave high accuracy in the detection of islanding conditions and demarcates properly from the non-islanding state. However, results show that the S-transform based

关键词： Artificial neural network islanding detection wavelet transforms distributed generation S-transform

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