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

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

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mohammad Zeineldeen Kartik Audhkhasi Murali Karthick Baskar Bhuvana Ramabhadran Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Google LLC New York

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.

关键词： Knowledge engineering Training Recurrent neural networks Transducers Production Signal processing Acoustics

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Twofold Siamese Network with Attentional Feature Fusion for Object Tracking 22

Twofold Siamese Network with Attentional Feature Fusion for ...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Huang, Huang Chen, Si Wang, Da-Han Xu, Huarong School of Computer and Information Engineering Xiamen University of Technology China Fujian Key Laboratory of Pattern Recognition and Image Understanding China

ISBN: (纸本)9781450396110

Object tracking is still a critical and challenging problem in computer vision. More and more researchers pay attention to applying deep learning to obtain the powerful feature for robust tracking. Nowadays, feature fusion is an essential part of Siamese tracking architectures. However, the existing feature fusion methods usually provide a fixed linear aggregation of feature maps, and this combination may not be appropriate for a specific object. In this paper, a twofold Siamese network, named SD-Siam, is proposed to extract the features of the object effectively. The template branch and the search branch are both composed of a deep layer sub-network and a shallow layer sub-network, which is used for feature fusion of the different network layers. Moreover, an attentional feature fusion scheme is employed to better fuse scale-inconsistent features, where a multi-scale channel attention module is used to fuse different scales of features. In addition, we respectively evaluate similarity measures for the features of deep layer sub-networks and the fused features of the template branch and the search branch, and then these two similarity response maps are added to obtain the tracking result. Experiments show the proposed SD-Siam outperforms representative trackers on several challenging benchmarks. © 2022 ACM.

关键词： Network layers

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A Study on pattern Analysis of Extracted Features from Bearing Fault Vibration Signals 3

A Study on Pattern Analysis of Extracted Features from Beari...

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3rd IEEE international Virtual conference on Innovations in Power and Advanced computing Technologies, i-PACT 2021

作者： Nayana, B.R. Geethanjali, P. Sir M Visvesvaraya Institute of Technology Dept. of Electrical and Electronics Engineering Bangalore India Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9781665426916

In any pattern recognition challenge, extracted features play a vital role. It is important to extract useful features and then proceed with classification. Especially in applications like mechanical fault diagnosis of machinery, where the characteristics features of the fault are to be captured. This can avoid the need for feature reduction/ selection techniques that would be needed once the number of features is greater and does not help advance the act of the classifier. This work presents an attempt to investigate the efficacy of eighteen features for classification. Among 18 features, 12 are statistical time domain features in which 4 are 1st 4 moments of the signal, 5 are simple statistical features and 3 are counters. Furthermore, 6 frequency domain features are computed directly in time domain, which are log transformed higher order moments of frequency domain. Subsequently, the features are studied for their pattern and their correlation with each other by visualization. Visualization observations are validated using classification performance parameters. The features are extracted from a benchmark dataset and a 13-class classification is implemented using well-established classifiers. Finally, the paper concludes that 4 effective features will suffice to achieve 99.7% accuracy of classification. © 2021 IEEE.

关键词： Visualization

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819603121

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819601875

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34th international conference on Architecture of computing Systems, ARCS 2021

34th International Conference on Architecture of Computing S...

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34th international conference on Architecture of computing Systems, ARCS 2021

ISBN: (纸本)9783030816810

The proceedings contain 14 papers. The special focus in this conference is on Architecture of computing Systems. The topics include: Energy Efficient Power-Management for Out-of-Order Processors Using Cyclic Power-Gating;BCH 2-Bit and 3-Bit Error Correction with Fast Multi-Bit Error Detection;evaluating soft Error Mitigation Trade-offs During Early Design Stages;static Extraction of Memory Access Profiles for Multi-core Interference Analysis of Real-Time Tasks;Transparent Resilience for Approximate DRAM;automatic Mapping of Parallel pattern-Based Algorithms on Heterogeneous Architectures;Assessing and Improving the Suitability of Model-Based Design for GPU-Accelerated Railway Control Systems;DRT: A Lightweight Runtime for Developing Benchmarks for a Dataflow Execution Model;Performance Gain of a Data Flow Oriented ISA as Replacement for Java Bytecode;Towards Transparent Dynamic Binary Translation from RISC-V to a CGRA;an Organic computing System for Automated Testing;preface.

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Self-Optimizing 6G RAN: A Unified Approach with Image and Video Analytics

Self-Optimizing 6G RAN: A Unified Approach with Image and Vi...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Rakshesh P Bhatt Divya G Nair Sivaramakrishnan Swaminathan Andrianov Anatoly Nokia Bangalore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Nokia Schaumburg USA

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The advent of 6G wireless technology heralds a new era of network optimization driven by AI/ML algorithms across its entirety. As outlined in 3GPP TS 28.104 (Release 19), Management Data Analytics (MDA) presents diverse use cases and requirements. However, the variety of data formats and AI/ML application methods introduces complexity and limits generic implementation. Compounded by the exponential deployment of cells and a range of new devices like XR devices, UAVs, etcs., the resulting data deluge poses a formidable big-data challenge for 5G advanced, 6G, and beyond. Anticipating the unique demands of 6G, characterized by metaverse-enabled networking and AR/VR applications, there arises a need for AI/ML-driven visual data processing services. This paper presents a novel approach: representing wireless network data in the form of images and videos. Such a representation not only optimizes storage and processing requirements but also facilitates the reuse of AI/ML models across various use cases and data formats. Experimental validation is provided through two key avenues: Firstly, text-based logs are transformed into images, enabling the identification of recurrent issues through visual pattern recognition. Secondly, Comma Seperated Variables (CSV) data is converted into a series of images, effectively simulating video frames, thus extending the realm of video analytics to encompass all data types pertinent to self-optimizing networks in 6G RAN. The advantages of this image and video-based data representation paradigm are meticulously elucidated, offering a compelling proposition for the future landscape of wireless network optimization in 6G and beyond.

关键词： 6G mobile communication Data analysis Visual analytics Wireless networks Key performance indicator Data visualization Data models pattern recognition Optimization Videos

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2021 international conference on computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

2021 International Conference on Computing, Communication, E...

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2021 international conference on computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

The proceedings contain 235 papers. The topics discussed include: intelligent defaulter prediction using data science process; power generation using sound by piezo electric material; pineal eye for blind using ultrasonic sensors; a novel approach for pattern string matching in intrusion detection system; identification of plant leaf diseases using adaptive neuro fuzzy classification; number plate detection and recognition using a novel computer vision approach for Indian motor vehicles; Patang Abhidhani - convolution neural network based butterfly research survey; performance of CH-DVR during phase angle jump and fault riding; experimental validation with FPGA controller to a switched-capacitor interleaved bidirectional DC-DC converter for a renewable energy storage systems; texture based clustering technique for fetal ultrasound image segmentation; analysis and classification of active sludge in wastewater; improved haar cascade feature extraction and access control framework for rich internet applications; and face recognition and database management system for event participant authentication.

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Research on Classification of Watermelon Ripeness Based on Neural Network pattern recognition 2021

Research on Classification of Watermelon Ripeness Based on N...

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2nd international conference on computing and Data Science, CONF-CDS 2021

作者： Chen, Yuchan Hangzhou High School Hangzhou China

ISBN: (纸本)9781450389570

Analysis and identification of watermelon ripeness is a very important link in agricultural production and melon and fruit sales, which can greatly improve the market circulation efficiency of watermelon. In this paper, watermelon ripeness discrimination was studied based on back propagation neural network (BPNN), watermelon acoustic modeling and svm classification model. In this project, mechanical acquisition equipment also was made. The reasonable circuit structure was designed to drive the automatic arm whipping machine based on Arduino control board, so as to beat watermelon and generate sound signals. 4 microphone sensors were used to capture audio information and send it to Matlab port through USB serial port for saving and preprocessing sound data. Finally, the comprehensive recognition rate reached 90.325%, which can accurately identify watermelon individuals corresponding to each signal, so as to provide scientific reference for the production and circulation of fruits and vegetables. © 2021 ACM.

关键词： Fruits

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An Approach to Recognize Human Activities based on ConvLSTM and LRCN

An Approach to Recognize Human Activities based on ConvLSTM ...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Shradha Bhatia Tushar Chauhan Sumita Gupta Sapna Gambhir Jitesh H. Panchal Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida Department of Computer Engineering J. C Bose University of Engineering & Technology Faridabad Department of Mechanical Engineering Purdue University

In recent times, approaches based on deep learning (DL) have been effectively used to predict a variety of human actions using time series data from smartphones and wearable sensors. Time series data handling remains a barrier for DL-based techniques, even though they did quite well in activity detection. Traditional pattern recognition techniques have achieved significant advancements in recent years. However, the performance of the generalization model may be hampered by the approaches’ heavy reliance on human feature extraction. Deep learning methods are becoming more and more successful, and employing these approaches to understand human behaviours in mobile and wearable computing situations or using vision-based technologies has garnered a lot of interest. ConvLSTM and LRCN which is a combination of Convolutional Neural Network (CNN) and Long shirt-term memory (LSTM) are the machine learning methods we employed in this research. With the help of CNNLSTM, it is possible to anticipate human actions more accurately while also simplifying the model and doing away with the necessity for sophisticated feature engineering. Both in terms of space and time, the CNN-LSTM network is deep. In this paper, the LRCN model gets 92% accuracy when we compare the performance of all the models that were utilized against on each other.

关键词： Deep learning Computational modeling Wearable computers Time series analysis Feature extraction pattern recognition Convolutional neural networks

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