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Intrusion Detection Using neural networks: A Grid Computing Based Data Mining Approach

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16th International conference on neural Information processing (ICONIP 2009)

作者： Castellano, Marcello Mastronardi, Giuseppe Tarricone, Gianfranco Politecn Bari Dipartimento Elettrotecn & Elettron I-70125 Bari Italy

ISBN: (纸本)9783642106828

Scientific disciplines such as life sciences as well as security and business fields depend on Knowledge Discovery because of the increasing amount of data being collected and for the complex analyses that need to be performed on them. New techniques, such as parallel, distributed, and grid-based data mining, are often able to overcome some of the characteristics of current data sources such as their large scale, high dimensionality, heterogeneity, and distributed nature. In several of these data mining applications, neural networks can be successfully applied. Moreover, an approach using neural networks seems to be one of the most promising methods for intrusion detection in a computer system or network security today. In this paper we describe a grid computing data mining approach for an intrusion detection application based on neural networks. Detection is carried out through the analyses of internet traffic generated by users in a network computer system.

关键词： Knowledge Discovery Data Mining neural networks Intrusion Detection Grid Computing

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Application of Self-tuning of PID Control Based on BP neural networks in the Mobile Robot Target Tracking

Application of Self-tuning of PID Control Based on BP Neural...

引用

3rd International conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC)

作者： Cui, Shi-Gang Pan, Hui-Liang Li, Ji-Gong Tianjin Univ Technol & Educ Tianjin Key Lab Informat Sensing & Intelligent Co Tianjin Peoples R China

ISBN: (纸本)9780769551227

Artificial neural network (ANN), also known as parallel distributed processing model or connection mechanism model, is an information processing system or a computer system based on the structure and the ability to mimic the human brain [1]. BP neural network self-tuning PID controller combines BP neural network and the traditional PID control advantages which tuning PID three coefficients based on neural network in real time online learning [2]. This will give full play to their respective advantages, so as to broaden the applications of the PID control. Mobile robot as a controlled object modeling with BP neural network self-tuning PID control is conducted a simulation study of robot tracking moving objects. The simulation results show that: Tracking Performance of the BP neural network self-tuning PID controller is quite good. The experiments show that: this controller has better robustness and adaptability than traditional PID controller, which can meet the requirements of the mobile robot on the low-speed two-dimensional moving object tracking applications.

关键词： BP neural network PID Control Mobile Robot Target Tracking

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distributed SPEECH SEPARATION IN SPATIALLY UNCONSTRAINED MICROPHONE ARRAYS

DISTRIBUTED SPEECH SEPARATION IN SPATIALLY UNCONSTRAINED MIC...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Furnon, Nicolas Serizel, Romain Illina, Irina Essid, Slim Univ Lorraine CNRS INRIA Loria F-54000 Nancy France Inst Polytech Paris LTCI Telecom Paris Palaiseau France

ISBN: (纸本)9781728176055

Speech separation with several speakers is a challenging task because of the non-stationarity of the speech and the strong signal similarity between interferent sources. Current state-of-the-art solutions can separate well the different sources using sophisticated deep neural networks which are very tedious to train. When several microphones are available, spatial information can be exploited to design much simpler algorithms to discriminate speakers. We propose a distributed algorithm that can process spatial information in a spatially unconstrained microphone array. The algorithm relies on a convolutional recurrent neural network that can exploit the signal diversity from the distributed nodes. In a typical case of a meeting room, this algorithm can capture an estimate of each source in a first step and propagate it over the microphone array in order to increase the separation performance in a second step. We show that this approach performs even better when the number of sources and nodes increases. We also study the influence of a mismatch in the number of sources between the training and testing conditions.

关键词： Speech separation microphone arrays distributed processing

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distributed Deep Convolutional neural network For Smart Camera Image Recognition 2017

Distributed Deep Convolutional Neural Network For Smart Came...

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11th International conference on distributed Smart Cameras (ICDSC)

作者： Castillo, Emmanuel Ayuyao Ahmadinia, Ali Calif State Univ San Marcos Dept Comp Sci 333 S Twin Oaks Valley Rd San Marcos TX 92069 USA

ISBN: (纸本)9781450354875

Smart Cameras brought sight to the idea of cameras as sensors for visual data rather than strictly for taking pictures. One set of applications that leverages this idea are image recognition applications. Unfortunately, a main drawback on these applications is the hardware limitations that they compromise with due to many of its applications residing in the embedded domain. As a matter of fact, a Deep Convolutional neural network (DCNN) is the main general algorithm used in handling the image recognition tasks that can be costly in response time, power consumption and data transfer bandwidth. However, the general architecture of a DCNN is consisted of layers that do specific processing tasks. These distribution of layers can be efficiently organized on a system consisted of Smart Cameras that can improve response time, limit power usage and reduce data transfer bandwidth. A concept is proposed to distribute the layers of a DCNN within a distributed set of Smart Cameras on embedded devices, an edge device and the Cloud that may improve response time, power consumption and data transfer bandwidth.

关键词： distributed neural network Edge Computing

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EmbRace: Accelerating Sparse Communication for distributed Training of Deep neural networks 51

EmbRace: Accelerating Sparse Communication for Distributed T...

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51st International conference on Parallel processing (ICPP)

作者： Li, Shengwei Lai, Zhiquan Li, Dongsheng Zhang, Yiming Ye, Xiangyu Duan, Yabo Univ Def Technol Natl Key Lab Parallel & Distributed Proc Comp Coll Changsha Peoples R China Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9781450397339

distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like image classification models, we find that these DL frameworks have relatively low scalability for sparse models like natural language processing (NLP) models that have highly sparse embedding tables. Most existing works overlook the sparsity of model parameters thus suffering from significant but unnecessary communication overhead. In this paper, we propose EmbRace, an efficient communication framework to accelerate communications of distributed training for sparse models. EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters. To effectively overlap sparse communication with both backward and forward computation, EmbRace further designs a 2D Communication Scheduling approach which optimizes the model computation procedure, relaxes the dependency of embeddings, and schedules the sparse communications of each embedding row with a priority queue. We have implemented a prototype of EmbRace based on PyTorch and Horovod, and conducted comprehensive evaluations with four representative NLP models. Experimental results show that EmbRace achieves up to 2.41x speedup compared to the state-of-the-art distributed training baselines.

关键词： distributed training deep learning sparsity of NLP models communication scheduling

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Room Transfer Function Reconstruction Using Complex-valued neural networks and Irregularly distributed Microphones 32

Room Transfer Function Reconstruction Using Complex-valued N...

引用

32nd European Signal processing conference (EUSIPCO)

作者： Ronchini, Francesca Comanducci, Luca Pezzoli, Mirco Antonacci, Fabio Sarti, Augusto Politecn Milan Dipartimento Elettron Informaz & Bioingn Piazza Leonardo Da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9789464593617;9798331519773

Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal processing methods, deep learning techniques have been applied to reconstruct the room transfer function starting from a very limited set of measurements at scattered points in the room. In this paper, we employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances, using a few irregularly distributed microphones. To the best of our knowledge, this is the first time that complex-valued neural networks are used to estimate room transfer functions. To analyze the benefits of applying complex-valued optimization to the considered task, we compare the proposed technique with a state-of-the-art kernel-based signal processing approach for sound field reconstruction, showing that the proposed technique exhibits relevant advantages in terms of phase accuracy and overall quality of the reconstructed sound field. For informative purposes, we also compare the model with a similarly-structured data-driven approach that, however, applies a real-valued neural network to reconstruct only the magnitude of the sound field.

关键词： sound field reconstruction RIR interpolation complex-valued neural network room acoustics

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Parallel distributed implementation of the shortest path algorithm

Parallel distributed implementation of the shortest path alg...

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Proceedings of 1993 International Joint conference on neural networks. Part 2 (of 3)

作者： Zhao, Songhe Dillon, T.S. La Trobe Univ Bundoora

ISBN: (纸本)0780314212

neural network (or Parallel distributed processing) models have been shown to have some potential for solving optimization problems. Most formulations result in NP-complete problems and solutions rely on energy based models, so there is no guarantee that the network converges to a global optimal solution. In this paper, we propose a non-energy based neural shortest path network based on the principle of dynamic programming and least take all network. No problem of local minima exists and it guarantees to reach the optimal solution. The network can work purely in an asynchronous mode which greatly increases the computation speed.

关键词： neural networks

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network representation of Biology-Inspired Analog Signal-processing: Correlated neural Cells Array in Diffuse Noisy Stochastic EM Fields 19

Network representation of Biology-Inspired Analog Signal-Pro...

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19th International conference on Electromagnetics in Advanced Applications (ICEAA)

作者： Wane, Sidina Bajon, Damienne NXP Semicond Caen France SUPAERO ISAE Toulouse France

ISBN: (纸本)9781509044511

We exploit the concept of Stochastic-Resonance (SR) for investigating new architecture solutions which take benefit of noise in mimicking the operating principle of biological neural systems. network-based implementation of synaptic neuromorphic integrated circuits using distributed X-Topology lattice equivalent circuits with local grounding are proposed for biology-inspired Signal-processing. Perspectives for real-time processing solutions of wavelet-based Fast Fourier-Transforms are drawn.

关键词： Stochastic Resonance X-Topology Lattice network Biology-inspired Signal processing

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ShadowTutor: distributed Partial Distillation for Mobile Video DNN Inference 20

ShadowTutor: Distributed Partial Distillation for Mobile Vid...

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49th International conference on Parallel processing (ICPP)

作者： Chung, Jae-Won Kim, Jae-Yun Moon, Soo-Mook Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450388160

Following the recent success of deep neural networks (DNN) on video computer vision tasks, performing DNN inferences on videos that originate from mobile devices has gained practical significance. As such, previous approaches developed methods to offload DNN inference computations for images to cloud servers to manage the resource constraints of mobile devices. However, when it comes to video data, communicating information of every frame consumes excessive network bandwidth and renders the entire system susceptible to adverse network conditions such as congestion. Thus, in this work, we seek to exploit the temporal coherence between nearby frames of a video stream to mitigate network pressure. That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model. Moreover, we update only a subset of the student's parameters, which we call partial distillation, to reduce the data size of each network transmission. Specifically, the server runs a large and general teacher model, and the mobile device only runs an extremely small but specialized student model. On sparsely selected key frames, the server partially trains the student model by targeting the teacher's response and sends the updated part to the mobile device. We investigate the effectiveness of ShadowTutor with HD video semantic segmentation. Evaluations show that network data transfer is reduced by 95% on average. Moreover, the throughput of the system is improved by over three times and shows robustness to changes in network bandwidth.

关键词： Deep neural networks distributed inference knowledge distillation video semantic segmentation

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Minimizing makespan on identical parallel machines using neural networks

Minimizing makespan on identical parallel machines using neu...

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13th International conference on neural Informational processing

作者： Akyol, Derya Eren Bayhan, G. Mirac Dokuz Eylul Univ Dept Ind Engn TR-35100 Izmir Turkey

ISBN: (纸本)3540464840

This paper deals with the problem of minimizing the maximum completion time (makespan) of jobs on identical parallel machines. A Hopfield type dynamical neural network is proposed for solving the problem which is known to be NP-hard even for the case of two machines. A penalty function approach is employed to construct the energy function of the network and time evolving penalty coefficients are proposed to be used during simulation experiments to overcome the tradeoff problem. The results of proposed approach tested on a scheduling problem across 3 different datasets for 5 different initial conditions show that the proposed network converges to feasible solutions for all initialization schemes and outperforms the LPT (longest processing time) rule.

关键词： distributed computer systems

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