TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

While accounting researchers have explored international publishing patterns in the accounting literature generally, little is known about recent contributions to the specialist international accounting history journals. Specifically, this study surveys publishing patterns in the three specialist, internationally refereed, accounting history journals in the English language during the period 1996 to 1999. The survey covers 149 contributions in total and provides empirical evidence on the location of their authors, the subject country or region in each investigation, and the time span of each study. It also classifies the literature examined based on the literature classification framework provided by Carnegie and Napier [1996].

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Requirements Prioritization Based on Benefit and Cost Prediction: A Method Classification Framework

2008 34th Euromicro Conference Software Engineering and Advanced Applications ◽

10.1109/seaa.2008.46 ◽

2008 ◽

Cited By ~ 18

Author(s):

Maya Daneva ◽

Andrea Herrmann

Keyword(s):

Cost Prediction ◽

Requirements Prioritization ◽

Classification Framework

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A Classification Framework for IoT Scenarios

Business Process Management Workshops - Lecture Notes in Business Information Processing ◽

10.1007/978-3-030-11641-5_36 ◽

2019 ◽

pp. 458-469 ◽

Cited By ~ 3

Author(s):

Sankalita Mandal ◽

Marcin Hewelt ◽

Maarten Oestreich ◽

Mathias Weske

Keyword(s):

Classification Framework

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A league-winner algorithm for defect classification in an industrial web inspection system

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114753 ◽

2021 ◽

Vol 175 ◽

pp. 114753

Author(s):

Angel Gaspar Gonzalez-Rodriguez ◽

Antonio Gonzalez-Rodriguez ◽

Fernando Jose Castillo-Garcia

Keyword(s):

Inspection System ◽

Defect Classification ◽

Web Inspection

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Workmanship defect classification in medium voltage cable terminations with convolutional neural network

Electric Power Systems Research ◽

10.1016/j.epsr.2021.107105 ◽

2021 ◽

Vol 194 ◽

pp. 107105

Author(s):

Halil Ibrahim Uckol ◽

Suat Ilhan ◽

Aydogan Ozdemir

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Defect Classification ◽

Medium Voltage

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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing

Sensors ◽

10.3390/s21041065 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1065

Author(s):

Moshe Bensimon ◽

Shlomo Greenberg ◽

Moshe Haiut

Keyword(s):

Neural Network ◽

Network Structure ◽

Analog Circuits ◽

Continuous Time ◽

Spiking Neurons ◽

Spiking Neural Network ◽

Sound Classification ◽

Classification Framework ◽

Neural Network Structure ◽

Simple Analog

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

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