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检索条件"任意字段=Conference on Machine Intelligence and Bio-inspired Computation - Theory and Applications VIII"

共 45 条记录，以下是11-20 订阅

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Energy-efficient STDP-based Learning Circuits with Memristor Synapses

Energy-efficient STDP-based Learning Circuits with Memristor...

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conference on machine intelligence and bio-inspired computation - theory and applications viii

作者： Wu, Xinyu Saxena, Vishal Campbell, Kristy A. Boise State Univ Dept Elect & Comp Engn Boise ID 83725 USA

ISBN: (纸本)9781628410563

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of a dense network of neurons and the synapses formed between the axons and dendrites. Further, unsupervised or supervised competitive learning schemes are being investigated for global training of the network. In contrast to a software implementation, hardware realization of these networks requires massive circuit overhead for addressing and individually updating network weights. Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STDP) to efficiently update the network weights locally. To realize SNNs on a chip, we propose to use densely integrating mixed-signal integrate-and-fire neurons (IFNs) and cross-point arrays of memristors in back-end-of-the-line (BEOL) of CMOS chips. Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<1 pJ/spike/synapse), even at spike rate greater than 10 MHz. We present circuit design details and simulation results of the IFN with memristor synapses, its response to incoming spike trains and STDP learning characterization.

关键词： machine Learning Memristors Spiking Neural Networks Spike-Timing-Dependent Plasticity (STDP)

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computational intelligence and Neuromorphic Computing Potential for Cybersecurity applications

Computational Intelligence and Neuromorphic Computing Potent...

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conference on machine intelligence and bio-inspired computation - theory and applications VII

作者： Pino, Robinson E. Shevenell, Michael J. Cam, Hasan Mouallem, Pierre Shumaker, Justin L. Edwards, Arthur H. ICF Int Baltimore MD 21202 USA

ISBN: (纸本)9780819495426

In today's highly mobile, networked, and interconnected internet world, the flow and volume of information is overwhelming and continuously increasing. Therefore, it is believed that the next frontier in technological evolution and development will rely in our ability to develop intelligent systems that can help us process, analyze, and make-sense of information autonomously just as a well-trained and educated human expert. In computational intelligence, neuromorphic computing promises to allow for the development of computing systems able to imitate natural neurobiological processes and form the foundation for intelligent system architectures.

关键词： Neuromorphic intelligent cognitive autonomous neural network security cyberspace

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Subset selection of training data for machine learning: a situational awareness system case study 2

Subset selection of training data for machine learning: a si...

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conference on Next-Generation Robotics II and machine intelligence and bio-inspired computation - theory and applications IX

作者： McKenzie, M. Wong, S. C. Def Sci & Technol Org Canberra ACT Australia

ISBN: (纸本)9781628416107

Recent advances in machine learning with big data sets has allowed for significant advances in the optimisation of classification and recognition systems. However, for applications such as situational awareness systems, the entirety of the available data dwarfs the amount permissible for a training set with tractable machine learning optimization times. Furthermore, the performance of any optimized system is highly dependent of the training set correctly and completely representing the entire data space of scenarios. In this paper we present a technique to characterize the entire data space to ascertain the key factors for representation and subsequently select a subset that statistically represents the correct mix of scenarios. We demonstrate the effectiveness of these characterization and subset selection techniques by using a genetic algorithm to optimize the performance of a gunfire recognition system.

关键词： machine learning big data optimisation

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Optimized Hardware Framework of MLP with Random Hidden Layers for Classification applications 10

Optimized Hardware Framework of MLP with Random Hidden Layer...

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conference on machine intelligence and bio-inspired computation - theory and applications X

作者： Zyarah, Abdullah M. Ramesh, Abhishek Merkel, Cory Kudithipudi, Dhireesha Rochester Inst Technol NanoComp Res Lab Rochester NY 14623 USA Univ Baghdad Dept Elect Engn Baghdad 10071 Iraq

ISBN: (纸本)9781510600911

Multilayer Perceptron Networks with random hidden layers are very efficient at automatic feature extraction and offer significant performance improvements in the training process. They essentially employ large collection of fixed, random features, and are expedient for form-factor constrained embedded platforms. In this work, a reconfigurable and scalable architecture is proposed for the MLPs with random hidden layers with a customized building block based on CORDIC algorithm. The proposed architecture also exploits fixed point operations for area efficiency. The design is validated for classification on two different datasets. An accuracy of approximate to 90% for MNIST dataset and 75% for gender classification on LFW dataset was observed. The hardware has 299 speed-up over the corresponding software realization.

关键词： Multilayer Perceptrons ELM Classification CORDIC Algorithm Random Hidden Layers

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Comparison of Artificial intelligence Classifiers for SIP Attack Data 10

Comparison of Artificial Intelligence Classifiers for SIP At...

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conference on machine intelligence and bio-inspired computation - theory and applications X

作者： Safarik, Jakub Slachta, Jiri Cesnet Zspo Zikova 4 Prague 6 Czech Republic

ISBN: (纸本)9781510600911

Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work;the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

关键词： artificial neural network attack classifier multilayer perceptron neural network classifier SIP attacks

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machine intelligence and bio-inspired computation: theory and applications VII

Machine Intelligence and Bio-Inspired Computation: Theory an...

引用

machine intelligence and bio-inspired computation: theory and applications VII conference

ISBN: (纸本)9780819495426

The proceedings contain 11 papers. The topics discussed include: optimization of background subtraction for image enhancement;statistical recognition of 3D objects using integral imaging;spatial context for moving vehicle detection in wide area motion imagery with multiple kernel learning;fusing video and text data by integrating appearance and behavior similarity;trusted computation through biologically inspired processes;a developmental approach to learning causal models for cyber security;computational intelligence and neuromorphic computing potential for cybersecurity applications;a pipelined FPGA implementation of an encryption algorithm based on genetic algorithm;vehicle tracking and analysis within a city;applying manifold learning to vehicle classification using vibrometry signatures;and electro-optical seasonal weather and gender data collection.

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Pattern Activity Clustering and Evaluation (PACE)

Pattern Activity Clustering and Evaluation (PACE)

引用

conference on Evolutionary and bio-inspired computation - theory and applications VI

作者： Blasch, Erik Banas, Christopher Paul, Michael Bussjager, Becky Seetharaman, Guna USAF Res Lab Informat Directorate Rome NY 13441 USA

ISBN: (纸本)9780819490803

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches;as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

关键词： Group Tracking Behavior Analysis Activity-based intelligence Information Fusion Classification recognition

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Performance Evaluation and Clinical applications of 3D Plenoptic Cameras 2

Performance Evaluation and Clinical Applications of 3D Pleno...

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conference on Next-Generation Robotics II and machine intelligence and bio-inspired computation - theory and applications IX

作者： Decker, Ryan Shademan, Azad Opfermann, Justin Leonard, Simon Kim, Peter C. W. Krieger, Axel Childrens Natl Med Ctr Sheikh Zayed Inst Pediat Surg Innovat Washington DC 20010 USA

ISBN: (纸本)9781628416107

The observation and 3D quantification of arbitrary scenes using optical imaging systems is challenging, but increasingly necessary in many fields. This paper provides a technical basis for the application of plenoptic cameras in medical and medical robotics applications, and rigorously evaluates camera integration and performance in the clinical setting. It discusses plenoptic camera calibration and setup, assesses plenoptic imaging in a clinically relevant context, and in the context of other quantitative imaging technologies. We report the methods used for camera calibration, precision and accuracy results in an ideal and simulated surgical setting. Afterwards, we report performance during a surgical task. Test results showed the average precision of the plenoptic camera to be 0.90mm, increasing to 1.37mm for tissue across the calibrated FOV. The ideal accuracy was 1.14mm. The camera showed submillimeter error during a simulated surgical task.

关键词： Imaging systems Three-dimensional (3D) image acquisition Medical and biological imaging plenoptic camera calibration quantitative measurement

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Realistic Computer Network Simulation for Network Intrusion Detection Dataset Generation 2

Realistic Computer Network Simulation for Network Intrusion ...

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conference on Next-Generation Robotics II and machine intelligence and bio-inspired computation - theory and applications IX

作者： Payer, Garrett ICF Int Columbia MD 21046 USA

ISBN: (纸本)9781628416107

The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup;its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

关键词： machine Learning Cyber Security Intrusion Detection

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bio-inspired approach for intelligent Unattended Ground Sensors 2

Bio-inspired approach for intelligent Unattended Ground Sens...

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conference on Next-Generation Robotics II and machine intelligence and bio-inspired computation - theory and applications IX

作者： Hueber, Nicolas Raymond, Pierre Hennequin, Christophe Pichler, Alexander Perrot, Maxime Voisin, Philippe Moeglin, Jean-Pierre ELSI French German Res Inst St Louis F-68300 St Louis France

ISBN: (纸本)9781628416107

Improving the surveillance capacity over wide zones requires a set of smart battery-powered Unattended Ground Sensors capable of issuing an alarm to a decision-making center. Only high-level information has to be sent when a relevant suspicious situation occurs. In this paper we propose an innovative bio-inspired approach that mimics the human bi-modal vision mechanism and the parallel processing ability of the human brain. The designed prototype exploits two levels of analysis: a low-level panoramic motion analysis, the peripheral vision, and a high-level event-focused analysis, the foveal vision. By tracking moving objects and fusing multiple criteria (size, speed, trajectory, etc.), the peripheral vision module acts as a fast relevant event detector. The foveal vision module focuses on the detected events to extract more detailed features (texture, color, shape, etc.) in order to improve the recognition efficiency. The implemented recognition core is able to acquire human knowledge and to classify in real-time a huge amount of heterogeneous data thanks to its natively parallel hardware structure. This UGS prototype validates our system approach under laboratory tests. The peripheral analysis module demonstrates a low false alarm rate whereas the foveal vision correctly focuses on the detected events. A parallel FPGA implementation of the recognition core succeeds in fulfilling the embedded application requirements. These results are paving the way of future reconfigurable virtual field agents. By locally processing the data and sending only high-level information, their energy requirements and electromagnetic signature are optimized. Moreover, the embedded Artificial intelligence core enables these bio-inspired systems to recognize and learn new significant events. By duplicating human expertise in potentially hazardous places, our miniature visual event detector will allow early warning and contribute to better human decision making.

关键词： Artificial neural network Change detection bio-inspired sensing Decision support Unattended Ground Sensor On-Chip Artificial intelligence Event recognition Real-time analysis Situational awareness Embedded electronics Movement analysis

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