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Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss 48

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeineldeen, Mohammad Audhkhasi, Kartik Baskar, Murali Karthick Ramabhadran, Bhuvana Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany Google Llc New York United States

ISBN: (纸本)9781728163277

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data. © 2023 IEEE.

关键词： Recurrent neural networks

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5W1H Extraction With Large Language Models

5W1H Extraction With Large Language Models

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international Joint conference on Neural Networks (IJCNN)

作者： Cao, Yang Lan, Yangsong Zhai, Feiyan Li, Piji Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol MIIT Key Lab Pattern Anal & Machine Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

The extraction of essential news elements through the 5W1H framework (What, When, Where, Why, Who, and How) is critical for event extraction and text summarization. The advent of Large language models (LLMs) such as ChatGPT presents an opportunity to address language-related tasks through simple prompts without fine-tuning models with much time. While ChatGPT has encountered challenges in processing longer news texts and analyzing specific attributes in context, especially answering questions about What, Why, and How. The effectiveness of extraction tasks is notably dependent on highquality human-annotated datasets. However, the absence of such datasets for the 5W1H extraction increases the difficulty of finetuning strategies based on open-source LLMs. To address these limitations, first, we annotate a high-quality 5W1H dataset based on four typical news corpora (CNN/DailyMail, XSum, NYT, RAMDS);second, we design several strategies from zero-shot/fewshot prompting to efficient fine-tuning to conduct 5W1H aspects extraction from the original news documents. The experimental results demonstrate that the performance of the fine-tuned models on our labelled dataset is superior to the performance of ChatGPT. Furthermore, we also explore the domain adaptation capability by testing the source-domain (e.g. NYT) models on the target domain corpus (e.g. CNN/DailyMail) for the task of 5W1H extraction.

关键词： 5W1H Extraction LLMs Prompting Fine-tuning

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Partial Discharge pattern recognition of Power Transformer by Distributed computing Framework

Partial Discharge Pattern Recognition of Power Transformer b...

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2022 IEEE international conference on High Voltage Engineering and Applications, ICHVE 2022

作者： Jiang, Tianyan Yuan, Haoxiang Liu, Haicheng Zhu, Hanlin Chen, Xi Yang, Zhangang School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing400054 China State Grid Chongqing Shibei Electric Power Supply Branch Chongqing400023 China

ISBN: (数字)9781665407502

ISBN: (纸本)9781665407502

This paper presents a back propagation neural network (BPNN) pattern recognition algorithm based on distributed framework. In this paper, four typical partial discharge(PD) models of artificial oil-paper insulation defects are designed. PD signal characteristic parameters are extracted to form PD spectrum statistical feature library under pulsating DC voltage. This algorithm is applied to pattern recognition of four typical PD models. This algorithm is dissimilar from the traditional BPNN algorithm. The distributed BPNN algorithm can greatly reduce the training speed of the model, so as to achieve the purpose of rapid identification of fault modes. Compared with the traditional BPNN algorithm, the calculation speed of the algorithm is faster than that of the traditional algorithm. © 2022 IEEE.

关键词： Partial discharges

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pattern Inversion as a pattern recognition Method for Machine Learning 2

Pattern Inversion as a Pattern Recognition Method for Machin...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Mikhailov, Alexei Karavay, Mikhail Institute for Control Problems Russian Academy of Sciences Moscow Russia

Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provides automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesizes that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes. © Published under licence by IOP Publishing Ltd.

关键词： Search engines

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Investigation of Machine Learning Algorithms for pattern recognition in Image Processing 5

Investigation of Machine Learning Algorithms for Pattern Rec...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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Automated Computer Aided Detection of Diabetic Retinopathy using Machine Learning Hybrid Model 13

Automated Computer Aided Detection of Diabetic Retinopathy u...

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13th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Kubde, Aishwarya Mohod, Sharad W. Dept. of Electronic Engineering Prof. Ram Meghe Institute of Technolgy & Research Badnera Amravati India

ISBN: (纸本)9781713857938

Diabetic retinopathy is spreading dangerously worldwide among people with diabetes, leading to reduced vision and completely blindness. In this paper, a technique is proposed to identify diabetic retinopathy using the fundus image obtained from the patient's retina. The method involves processing the digital image on the fundus image, which help the ophthalmologist in examine DR. Micro-aneurysm, which is considered as first stage of diabetic retinopathy has been diagnosed using Neural Network. The proposed Support Vector Machine has been compared to existing methods - Naive Bayes classifier. Experimental validation is done with MATLAB/SIMULINK software. The preprocess image has been applied as input data for neural network pattern recognition. A significant improvement has been observed in terms of Sensitivity, Specificity and Accuracy compared to the aforementioned existing methods. © Grenze Scientific Society, 2022.

关键词： pattern recognition

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Approaches to developing an audio system as part of a smart house 8

Approaches to developing an audio system as part of a smart ...

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8th IEEE international conference on Energy Smart Systems, ESS 2022

作者： Bevz, Svitlana Voitko, Viktoriia Burbelo, Sergii Stavytskyi, Pavlo Vinnytsia National Technical University Vinnytsia Ukraine

ISBN: (数字)9798350334418

ISBN: (纸本)9798350399202

Approaches to the development of the system of music synthesis and recognition are considered. In addition, such audio software as part of the smart house system can bring additional benefits and increased experience for its users. It is important to consider an audio recognition state that considers fingerprint creation and optimization. Moreover, an approach of parallel GPU computing is considered as a solution to the fingerprint matching process optimization. © 2022 IEEE.

关键词： pattern matching

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CamCarv - Expose the Source Camera at the Rear of Seam Insertion 21st

CamCarv - Expose the Source Camera at the Rear of Seam Inser...

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21st international conference on Artificial Intelligence and soft computing (ICAISC)

作者： Irshad, Muhammad Law, Ngai Fong Loo, Ka Hong Hong Kong Polytech Univ Dept Elect & Informat Engn Hung Hom Hong Kong Peoples R China

ISBN: (纸本)9783031234798;9783031234804

It is well known that photo response non-uniformity (PRNU) noise based source attribution helps to verify the camera used to take an image. Recent advances in content-aware image resizing method such as seam carving allow an image to be resized while the critical content is retained. In this paper, we propose identifying the source camera from seam inserted images using blocks as small as 20 x 20. In particular, the correlation is computed between the noise residue of the seam inserted image and the camera PRNU constructed using different numbers of im-ages. We found that different correlation patterns with the camera PRNUs are ob-served, depending on whether the image is taken by that camera or not. Addition-ally, based on this observation, features are extracted from the correlation patterns which are then weighted and combined to form a decision metric for source cam-era identification. We demonstrate by our experimental results that our approach is effective in identifying the source camera in seam insertion images.

关键词： Source camera identification Seam insertion Correlation pattern

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Improving the Efficiency of Image Captured via PGC and Stereo Vision Using Novel VJ Algorithm in Comparison with Local Binary pattern Histogram Algorithm

Improving the Efficiency of Image Captured via PGC and Stere...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), international conference

作者： Harshitha Ramisetty N P G Bhavani Department of Electronics and Communication Engineering Saveetha School of Engineering (SSE) Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

The study compares the local binary pattern histogram method to a PGC and stereo vision picture acquired utilising innovative violas jones algorithm. An automated face identification and recognition system was tested utilising the innovative violas jones algorithm and Local Binary pattern Histogram (LBPH) Algorithm using 20 samples at various periods. The G power with 0.8 pre-testing power, 0.05 alpha, and 0.95 confidence interval is suggested for SPSS accuracy prediction. The model is tested and trained on 40 kaggle (***) samples. Automatic face identification and recognition system performance was assessed using the innovative violas jones method and Local Binary pattern Histogram method with 20 samples at various periods. After statistical analysis, the violas jones algorithm achieved 93% accuracy, while the local binary pattern histogram algorithm achieved 84% accuracy using the default setting. The independent sample t-test significance was =0.001 (<0.05).

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recognition of Partial Discharge in Medium Voltage Switchgear via Gray-Scale Diagram and Support Vector Machine 12

Recognition of Partial Discharge in Medium Voltage Switchgea...

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12th international conference on Power and Energy Systems (ICPES)

作者： Wu, Xutao Jin, Lei Liu, Weifeng Niu, Bo Li, Zecheng Ma, Yunlong State Grid Ningxia Elect Power Co Ltd Elect Power Res Inst Yinchuan Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9781665451451

Partial discharge (PD) has been proved to be a sensitive indicator to reveal the insulation condition of medium voltage (MV) switchgear. Since the influence of a PD on MV switchgear strongly depends on its type and location, accurate recognition of the PD is crucial to improve the efficiency of on-site measurement and optimize the maintenance strategy. Accordingly, this paper develops a PD recognition method based on gray-scale diagram and support vector machine. Labeled signals including many PD pulses are transformed into a gray-scale diagram via a two-dimensional maximum margin criterion, significantly reducing the dimensionality of the PD data thus improving computing efficiency. Models of different types of PD defects are then trained by applying the support vector machine to the labeled gray-scale diagrams. Eventually, the type of a PD is identified via the trained models. The effectiveness and feasibility of the proposed method are verified on four artificial PD models and three simulated PD defects on a real 10-kV switchgear.

关键词： switchgear partial discharge SVM pattern recognition simulated real defects

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