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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pavel Golik Boulos Harb Ananya Misra Michael Riley Alex Rudnick Eugene Weinstein Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Google Inc. New York NY USA School of Informatics and Computing Indiana University Bloomington IN USA

We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the system's robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.

关键词： Training Accuracy Hidden Markov models Music Speech recognition USA Councils

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On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting recognition

On the Benefits of Convolutional Neural Network Combinations...

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International Workshop on Frontiers in Handwriting recognition

作者： Dewi Suryani Patrick Doetsch Hermann Ney King Mongkut's University of Technology North Bangkok The Sirindhorn International Thai-German Graduate School of Engineering Bangkok Thailand Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781509009824

In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.

关键词： Hidden Markov models Neurons Training Handwriting recognition Biological neural networks computer architecture Logic gates

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

An evolutionary lion optimization algorithm-based image comp...

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作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

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AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning

arXiv

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arXiv 2022年

作者： Wen, Jiaxin Zhu, Yeshuang Zhang, Jinchao Zhou, Jie Huang, Minlie The CoAI group Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Pattern Recognition Center WeChat AI Tencent Inc China

Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models’ reliance on spurious features and improving their generalizability. However, current methods still heavily rely on human efforts or task-specific designs to generate counterfactuals, thereby impeding CAD’s applicability to a broad range of NLU tasks. In this paper, we present AutoCAD, a fully automatic and task-agnostic CAD generation framework. AutoCAD first leverages a classifier to unsupervisedly identify rationales as spans to be intervened, which disentangles spurious and causal features. Then, AutoCAD performs controllable generation enhanced by unlikelihood training to produce diverse counterfactuals. Extensive evaluations on multiple out-of-domain and challenge benchmarks demonstrate that AutoCAD consistently and significantly boosts the out-of-distribution performance of powerful pre-trained models across different NLU tasks, which is comparable or even better than previous state-of-the-art human-in-the-loop or task-specific CAD methods. The code is publicly available at https://***/thu-coai/AutoCAD. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Flexible synapse detection in fluorescence micrographs by modeling human expert grading

Flexible synapse detection in fluorescence micrographs by mo...

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IEEE International Symposium on Biomedical Imaging

作者： Julia Herold Manuela Friedenberger Marcus Bode Nasir Rajpoot Walter Schubert Tim W. Nattkemper Applied Neuroinformatics Group Faculty of Technology University of Bielefeld Germany Molecular Pattern Recognition Research Group Institute of Medical Neurobiology University of Magdeburg Germany Department of Computer Science University of Warwick UK

A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffuse signals. To automate synapse detection in such images, one has to simulate aspects of human pattern recognition skills to account for low signal-to-noise-ratios. We propose a machine learning based method that allows a direct integration of the experts' visual expertise who tag a low number of referential synapses according to their degree of synapse likeness. The sensitivity and positive predictive values show that by using graded likeness information in our learning algorithm we can provide an intuitively tunable tool for neural tissue slide evaluation.

关键词： Fluorescence humans Microscopy Object detection Image analysis Image resolution Image edge detection Image texture analysis High-resolution imaging Medical signal detection

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Trade-Offs Between Fairness and Privacy in language Modeling

arXiv

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arXiv 2023年

作者： Matzken, Cleo Eger, Steffen Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany Natural Language Learning Group Faculty of Technology Universität Bielefeld Germany

Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. In this paper, we explore the extent to which this tradeoff really holds when we incorporate both privacy preservation and debiasing techniques into training text generation models. How does improving the model along one dimension affect the other dimension as well as the utility of the model? We conduct an extensive set of experiments that include bias detection, privacy attacks, language modeling, and performance on downstream tasks. © 2023, CC BY.

关键词： Economic and social effects

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Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error

Refined Statistical Bounds for Classification Error Mismatch...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Zijian Yang Vahe Eminyan Ralf Schlüter Hermann Ney Computer Science Department Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 RWTH Aachen University Germany AppTek GmbH Germany

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

In statistical classification/multiple hypothesis testing and machine learning, a model distribution estimated from the training data is usually applied to replace the unknown true distribution in the Bayes decision rule, which introduces a mismatch between the Bayes error and the model-based classification error. In this work, we derive the classification error bound to study the relationship between the Kullback-Leibler divergence and the classification error mismatch. We first reconsider the statistical bounds based on classification error mismatch derived in previous works, employing a different method of derivation. Then, motivated by the observation that the Bayes error is typically low in machine learning tasks like speech recognition and pattern recognition, we derive a refined Kullback-Leibler-divergence-based bound on the error mismatch with the constraint that the Bayes error is lower than a threshold.

关键词： Analytical models Conferences Training data Machine learning Speech recognition Data models Bayes methods Testing Information theory

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Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error

arXiv

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arXiv 2024年

作者： Yang, Zijian Eminyan, Vahe Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 Computer Science Department RWTH Aachen University Germany AppTek GmbH Germany

关键词： Adversarial machine learning

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Bridging the Native language and language Variety Identification Tasks

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Procedia computer science 2017年 112卷 1554-1561页

作者： Marc Franco-Salvador Greg Kondrak Paolo Rosso Symanto Research 90425 Nuremberg Germany Pattern Recognition and Human Language Technology (PRHLT) Research Center Universitat Politècnica de València 46022 Valencia Spain Department of Computing Science University of Alberta Edmonton AB T6G 2E8 Canada

The objective of Native language Identification is to determine the native language of the author of a text that he or she wrote in another language. By contrast, language Variety Identification aims at classifying texts representing different varieties of a single language. We postulate that both tasks may be reduced to a single objective, which is to identify the language variety of the text. We design a general approach that combines string kernels and word embeddings, which capture different characteristics of texts. The results of our experiments show that the approach achieves excellent results on both tasks, without any task-specific adaptations.

关键词： Native language Identification language Variety Identification String Kernels Word Embeddings Classifier Combination

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A Theoretical Review: Risk Mitigation Through Trusted human Framework for Insider Threats

A Theoretical Review: Risk Mitigation Through Trusted Human ...

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International Conference on Cybersecurity (ICoCSec)

作者： Mohd Nazer Apau Muliati Sedek Rabiah Ahmad Faculty of Information and Communication Technology Information Security and Network Research Group Center for Advanced Computing Technology Department of Computer Science Department of Language and Human Development Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka MALAYSIA

This paper discusses the possible effort to mitigate insider threats risk and aim to inspire organizations to consider identifying insider threats as one of the risks in the company's enterprise risk management activities. The paper suggests Trusted human Framework (THF) as the on-going and cyclic process to detect and deter potential employees who bound to become the fraudster or perpetrator violating the access and trust given. The mitigation's control statements were derived from the recommended practices in the “Common Sense Guide to Mitigating Insider Threats” produced by the Software Engineering Institute, Carnegie Mellon University (SEI-CMU). The statements validated via a survey which was responded by fifty respondents who work in Malaysia.

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