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Element-Wise Layer Normalization for Continuous Signal Representation 23

Element-Wise Layer Normalization for Continuous Signal Repre...

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15th international conference on Digital Image Processing, ICDIP 2023

作者： Chen, Weifeng Ding, Hui He, Zhifen Li, Bo Liu, Bin Wang, Kang School of Mathematics and Information Science Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Jiangxi Science and Technology Infrastructure Center Jiangxi Computing Center Jiangxi Computing Center Nanchang China

ISBN: (纸本)9798400708237

Implicit neural representation (INR), sometimes also referred to coordinate-based representation or fitting, has gained the state-of-the-art performance in numerous research fields including computer vision and computer graphics due to the powerful continuous representation ability. Recent researches indicate that Fourier embedding is critical for INR to fit realistic images with high-frequency details. However, is Fourier embedding all we need for high-frequency coordinate (image) fitting? In this paper, we revisit the problem of coordinate fitting from a novel perspective of distribution mapping. Fourier embedding, as a preprocessing step of coordinate fitting, essentially performs the operation of mapping the uniform coordinate distribution to a normal distribution, and makes the learning of mapping function between two similar smooth distribution become easier. However, the number of discrete Fourier basis function affects the fitting performance dramatically and cannot be determined automatically. Based on the above analysis, a simple yet efficient INR coordinate fitting method is proposed in this paper, which demonstrates that the Fourier embedding is not the only way to improve INR. The proposed method only adds an element-wise layer normalization (ELN) module to the vanilla multi-layer perception (MLP) with ReLU activation. Experimental results on public database demonstrate that the proposed method outperforms the state-of-the-art methods using Fourier embedding. © 2023 ACM.

关键词： Mapping

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A Novel ML classifier Using Modern Hopfield Neural Networks

A Novel ML classifier Using Modern Hopfield Neural Networks

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computing and Machine Intelligence (ICMI), international conference on

作者： Mohamed Abdelkarim Hanan Ahmed Kamal Doaa Shawky Siemens Digital Industries Software Faculty of Engineering Cairo University Zewail City of Science and Technology

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

State-of-the-art classification neural networks, used for images and various data types, are complex and require significant energy and computational resources relying on super-vised gradient back-propagation. In contrast, the Hopfield Neural Network (HNN) is simpler, being a single-layer, fully connected network that mimics the human brain's associative memory network, making it easy to implement and computationally efficient. Its compatibility with oscillatory neural networks (ONNs) makes it ideal for lightweight machine learning applications in the Internet of Things (IoT) era. Normally, HNN has been primarily used for associative memory aiding in image processing, pattern recognition, and more, but this paper introduces it as a classifier. The proposed HNN classifier is adaptable to various datasets, including images and tabular data, and requires zero training time, making it suitable for resource-limited environments. It represents a significant leap in classification, with the highest accuracy reported for HNN classifiers to the best of our knowl-edge, achieving 96% accuracy on the MNIST dataset, a 36% percentage improvement over previous models.

关键词： Training Associative memory Accuracy MIMICs Hopfield neural networks Machine learning pattern recognition

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SAI-ChileanDiet: A multi-label food dataset with self-acquired images of the Chilean diet

SAI-ChileanDiet: A multi-label food dataset with self-acquir...

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pattern recognition Systems (ICPRS), international conference on

作者： Roberto Morales Bastián Muñoz Angela Martínez-Arroyo Eduardo Aguilar Dept. of Computing and Systems Engineering Universidad Católica del Norte Antofagasta Chile Escuela Nutrición y Dietética Universidad de Valparaíso Valparaíso Chile

ISBN: (数字)9798350375657

ISBN: (纸本)9798350375664

The lack of data representative of the Chilean diet precludes providing accurate applications based on real foods consumed by Chilean society. Moreover, for the multi-label food recognition problem, there are few datasets available, which is mandatory for food analysis considering the great variability of dishes that can be made by combining different foods, which do not have a particular name and therefore cannot be treated as a single-label recognition problem. In this paper, we introduce a new challenging multi-label food dataset, SAI-ChileanDiet, focused on the Chilean diet. The dataset includes foods of popular dishes from Chilean cuisine and others included in the Chilean diets. SAI-ChileanDiet considers different sources of data for training and test sets. The training set comprises images from the web and the test set comprises self-acquired images. This feature of the dataset allows us to assess the representativeness of public images in real-world Chilean foods and encourages the generation of advanced methods capable of dealing with the distribution shift of these sets. We evaluate on the proposed SAI-ChileanDiet dataset the performance of several state-of-the-art deep networks and AI platforms for multi-label food recognition. The experiments demonstrate how challenging the dataset is and the importance of validating self-acquired data to be aware of the performance it could offer in real-life conditions.

关键词： Training Image recognition Accuracy pattern recognition Artificial intelligence

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User Specific Real-Time Noise Cancellation using Deep Learning

User Specific Real-Time Noise Cancellation using Deep Learni...

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international conference on computing and Networking Technology (ICCNT)

作者： Anil Kadu Advay Argade Aditya Deore Asit Anand Atharva Nehete Arya Alurkar Dept. of Instrumentation Engineering Vishwakarma Institute of Technology Pune Maharashtra Dept. of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Noise cancellation is a critical concern in various domains, from personal audio experiences to professional settings. This paper presents a novel approach utilizing machine learning, specifically Convolutional Neural Networks (CNNs) trained on urban sound datasets and Yet Another Multilayered Network (YAMNet), to identify and cancel real-time noise. Note that the system is designed as an anti-mechanism for rather repetitive sounds and not for those which vary unpredictably. The methodology involves converting sound inputs into 2D arrays and extracting Mel-Frequency Cepstral Coefficients (MFCC) features, conducting pattern recognition, and employing inversion techniques for noise cancellation. The results exhibit promising noise reduction, showcased through visual representations of input and output sound waves. The potential implications and prospects of this approach are also discussed.

关键词： Deep learning Visualization Noise reduction Neural networks Noise cancellation Feature extraction Real-time systems pattern recognition Convolutional neural networks Mel frequency cepstral coefficient

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4th international conference on Innovative computing, IC 2021

4th International Conference on Innovative Computing, IC 202...

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4th international conference on Innovative computing, IC 2021

ISBN: (纸本)9789811642579

The proceedings contain 241 papers. The special focus in this conference is on Innovative computing. The topics include: Inertial Navigation Method for Spacecraft Based on General Relativity;influencing Mechanism of Live Broadcasting Marketing pattern on Consumers’ Purchase Decision Internet Background;Reinventing the Wheel of Marketing: Assessing the Impact of Artificial Intelligence (AI) on Digital Marketing and Consumer Buying Behavior;Fraud Identification Model of Profit and Loss Adjustment Financial Report Based on Lib SVM Algorithm;stock Price Prediction Based on Deep Reinforcement Learning;space Design of Medical and Maintenance Building Under the Background of Computer;application of Computer Technology in Concrete;design and Implementation of Educational Information System Based on Cloud computing;Evaluation Model of English–Chinese Cross-Language Initiation Oral Teaching Based on SOFMNN;exploration of Optimized Teaching Mode in Applied Technology Colleges Under the Background of Big Data;efficient-net Speaker recognition Master—The New Speaker recognition System Built Base on Efficient-net;intelligent Invoice Identification Technology Based on Zxing Technology;author Identification of Handwritten Numbers Based on Transfer Learning and Multi-task Learning;influence of Oil Price on Corn Price Based on Multiple Linear Regression Model;An Empirical Analysis of the VAR Model of Consumption Structure, Industrial Structure and Economic Growth in the Internet Age: Taking Henan Province as an Example;a Research on the Influence of Raising the Threshold of Individual Income Tax on Urban Residents’ Consumption Based on Eviews software—A Case Study of Shanghai;effects of Minimum Wage on Enterprise Productivity—Empirical Analysis Based on Database of Industrial Enterprises.

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Algebraic Bayesian Networks: The Generation of the Knowledge pattern Canonical Representation 24

Algebraic Bayesian Networks: The Generation of the Knowledge...

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24th international conference on soft computing and Measurements, SCM 2021

作者： Kharitonov, Nikita A. Tulupyev, Alexander L. Saint-Petersburg State University Saint Petersburg Russia St.Petersburg State University Saint-Petersburg Federal Research Center Russian Academy of Sciences Saint Petersburg Russia

ISBN: (纸本)9781665439749

Algebraic Bayesian networks relate to the class of probabilistic graphical models. The data in the network is divided into smaller parts - knowledge patterns. Each knowledge pattern can be presented as a quanta set. Each element of the knowledge pattern has a scalar or interval estimates of probability. The handling of networks with scalar estimates has large complexity. This is why one of the tasks during working with Algebraic Bayesian networks with interval estimates is to find the canonical representation of these networks. The canonical representation is the network with scalar estimates, which presents the reduced information from the first one. The task of this study is to generate the canonical representation for a separately taken knowledge pattern. This task is solved with the usage of Monte-Carlo method, approach and algorithm are described, an example of algorithm's usage is shown and a naive approach for generation of the whole network canonical representation is proposed. © 2021 IEEE.

关键词： Bayesian networks

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Semi-supervised Wafer Map pattern recognition using Domain-Specific Data Augmentation and Contrastive Learning

Semi-supervised Wafer Map Pattern Recognition using Domain-S...

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IEEE international Test conference (ITC)

作者： Hu, Hanbin He, Chen Li, Peng Univ Calif Santa Barbara Elect & Comp Engn Santa Barbara CA 93105 USA NXP Semicond Austin TX 78735 USA

ISBN: (纸本)9781665416955

Wafer map pattern recognition is instrumental for detecting systemic manufacturing process issues. However, high cost in labeling wafer patterns renders it impossible to leverage large amounts of valuable unlabeled data in conventional machine learning based wafer map pattern prediction. We proposed a contrastive learning framework for semi-supervised learning and prediction of wafer map patterns. Our framework incorporates an encoder to learn good representation for wafer maps in an unsupervised manner, and a supervised head to recognize wafer map patterns. In particular, contrastive learning is applied for the unsupervised encoder representation learning supported by augmented data generated by different transformations (views) of wafer maps. We identified a set of transformations to effectively generate similar variants of each original pattern. We further proposed a novel rotation-twist transformation to augment wafer map data by rotating each given wafer map for which the angle of rotation is a smooth function of the radius. Experimental results demonstrate that the proposed semi-supervised learning framework greatly improves recognition accuracy compared to traditional supervised methods, and the rotation-twist transformation further enhances the recognition accuracy in both semi-supervised and supervised tasks.

关键词： wafer map pattern recognition semi-supervised learning contrastive learning

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Efficient FPGA Implementation of Feedback Perceptron for Hardware Acceleration

Efficient FPGA Implementation of Feedback Perceptron for Har...

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computing and Machine Intelligence (ICMI), international conference on

作者： Tamador Mohaidat Azeemuddin Syed Mohammed Alqodah Kasem Khalil Department of Electrical and Computer Engineering University of Mississippi Mississippi USA CVEST International Institute of Information Technology Hyderabad India

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Artificial Neural Networks (ANNs) have revolutionized machine learning, mimicking human cognitive abilities to recognize patterns and make decisions. The perceptron, a fundamental unit in ANNs, forms the basis for complex network structures. This paper introduces a novel approach to perceptrons by incorporating a feedback mechanism using a gain factor, replacing conventional learning rates. The proposed method aims to optimize network performance while adapting to hardware constraints. Implementation of MNIST and Heart Attack datasets showcase the superiority of the proposed approach over tra-ditional methods, revealing substantial accuracy improvements across various activation functions (Sign, Step, and Sigmoid) in both single perceptron and multilayer perceptron (MLP) architectures. The proposed method achieves significant accuracy of 96.80% with the MNIST dataset, and 88.52% with the Heart Attack dataset compared to other methods. It has a small overhead in power consumption. The proposed approach is implemented in Verilog HDL on Xilinx Virtex-7 FPGA XC7A35T-IFGG484C. The proposed method demonstrates remarkable accuracy enhancements in pattern recognition tasks, promising advancements in real-world applications.

关键词： Accuracy Power demand Cardiac arrest Machine learning Computer architecture Artificial neural networks pattern recognition

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Modified Expressions to Evaluate the Correlation Coefficient between Two Dual Hesitant Fuzzy soft Sets and Their Application in Decision-Making

Modified Expressions to Evaluate the Correlation Coefficient...

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2022 international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Singh, Akanksha Department of Mathematics-UIS Chandigarh University Mohali Punjab Gharuan India

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

This paper is the study of the existing correlation coefficients (CCoefs) to evaluate the meaningful relation/dependency between two variables of the extended fuzzy sets for solving decision-making (DeM) problems. To understand the weighted CCoefs between two variables the considered domain is the dual hesitant fuzzy soft set (DHFSS) which is an extension of a fuzzy set incorporating properties of both the dual hesitant fuzzy set (DHFS) (which considers the hesitancy degree of both the membership and non-membership elements of a set) and soft set (SS) (which gives the importance to each parameter in an alternative). This strive suggests improved weighted CCoefs for solving DHFSS problems from the existing weighted CCoefs for solving DHFSS multi-criteria decision-making (MCDM) problems (DeM problems in which the rating value of each alternative over each criterion is depicted by a DHFSS) in a real-world. Also, mathematical verification is presented which clearly states the validation of the proposed expressions of weighted CCoefs for solving DHFSS MCDM problems. To show the practical application of the proposed weighted CCoefs for solving DHFSS MCDM problems, solutions to some real-world problems like- recruitment, medical diagnosis, and pattern recognition are determined, and a methodical comparison of the results are shown between the considered real-life MCDM problems by the existing approach and by the proposed approach interpretation. © 2022 IEEE.

关键词： Decision making

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Technical Aspects of Using IoE (Internet of Energy) Metadata in Smart Cities

Technical Aspects of Using IoE (Internet of Energy) Metadata...

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international conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Islambek Rustambekov Balkibayeva Zhanagul Shahida Karakhodjaeva Aziz Ashurov Mukhammad Ali Turdialiev Akmal Akromov Jahongir Yuldashev Tashkent State University of Law Tashkent Uzbekistan Judge of the Constitutional Court of the Republic of Uzbekistan Tashkent Uzbekistan Department of International Private Law Tashkent State University of Law Tashkent Uzbekistan

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper examines the technical aspects of utilizing Internet of Energy (IoE) metadata in smart city environments. Through comparative and inductive analysis of existing literature and technical reports, we explore how advanced metadata management techniques can address inefficiencies in processing and analyzing large volumes of energy data in urban systems. Key findings include the potential for standardized metadata ontologies, edge computing for data preprocessing, and machine learning algorithms for pattern recognition in energy consumption. We propose strategies for developing unified IoE metadata management systems that integrate with broader smart city infrastructure. While metadata-driven approaches show promise for enhancing energy efficiency and sustainability in urban environments, challenges in data privacy and security remain. This research highlights IoE metadata as a critical tool for optimizing energy systems in smart cities through advanced data analytics and management techniques.

关键词： Machine learning algorithms Data analysis Smart cities Energy Internet Metadata Ontologies Energy efficiency pattern recognition Sustainable development Edge computing

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