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arXiv 2024年

作者： Zhang, Yanzhe Bi, Zhonghao Xiao, Feiyang Yang, Xuefeng Zhu, Qiaoxi Guan, Jian Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University China Faculty of Computing Harbin Institute of Technology China Acoustics Lab University of Technology Sydney Australia

This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 signal processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker. However, differences between feature distributions of original and anonymized speech complicate this task. To address this challenge, we propose an attacker system that combines Data Augmentation enhanced feature representation and Speaker Identity Difference enhanced classifier to improve verification performance, termed DA-SID. Specifically, data augmentation strategies (i.e., data fusion and SpecAugment) are utilized to mitigate feature distribution gaps, while probabilistic linear discriminant analysis (PLDA) is employed to further enhance speaker identity difference. Our system significantly outperforms the baseline, demonstrating exceptional effectiveness and robustness against various voice anonymization systems, ultimately securing a top-5 ranking in the challenge. Copyright © 2024, The Authors. All rights reserved.

关键词： Anonymity

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Effect of Pectoral Muscles on CNN based Mammographic Cancer Detection

Effect of Pectoral Muscles on CNN based Mammographic Cancer ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Santoresh Kumari Dhimann Tinny Sawhney Rakesh Kumar Yadav Department of Computer Science & Engineering Maharishi University of Information Technology Lucknow India Department of Electronics Digital Signal Processing Lab University of Jammu Jammu India

Cancer is attributed to abnormal growth of tissue cells in any part of the living body. The number of cancer patients is increasing worldwide. The reason of the development of abnormal cells within the body is still unknown to the researchers. The most common is breast cancer and it is affective affecting one out of every ten women. The reason of breast lesions formation may be calcification or mass deposit and digital mammography is generally used for verifying it. It is a cheap, non-toxic, and user-friendly test, but needs expert opinion for ascertaining the presence of cancer. The accuracy of mammographic detection varies between 80% and 90%. Recently, Convolutional neural networks (CNNs) have shown their application in automatic detection of the cancer, but inclusion of pectoral muscle in the mammogram reduces the efficiency of the CNN system as the texture of the muscle resembles with cancerous tissues. Further, CNN based detection requires high resolution images which in turn requires state of art technology-based machines, generally, not availab.e in rural area-based clinics. The focus of the proposed research is to investigate effect of low-quality blurred input mammograms on the detection of pectoral muscle boundary, so that the pectoral muscle removed mammograms can be applied to CNN system for automatic prediction of cancerous images. The investigations carried out have shown that low quality blurred images can also be employed for detecting cancerous tissues provided the pectoral muscle is removed from the mammograms before using CNN based classifier.

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A demonstration of vector symbolic architecture as an effective integrated technology for AI at the network edge 2

A demonstration of vector symbolic architecture as an effect...

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Artificial Intelligence for Security and Defence Applications II 2024

作者： Bent, Graham Davies, Cai Vilamala, Marc Roig Li, Yuhua Preece, Alun Di Caterina, Gaetano Sola, Alex Vicente Kirkland, Paul Pearson, Gavin Tutcher, Benomy Neurosynapse Ltd. United Kingdom Security Crime and Intelligence Institute Cardiff University United Kingdom Neuromorphic Sensor Signal Processing Lab. EEE Dept. University of Strathclyde United Kingdom Defence Science and Technology Laboratory Porton Down United Kingdom Frazer-Nash Consultancy Ltd. Bristol United Kingdom

ISBN: (纸本)9781510681200

Vector Symbolic Architecture (VSA), a.k.a. Hyperdimensional Computing has transformative potential for advancing cognitive processing capabilities at the network edge. This paper presents a technology integration experiment, demonstrating how the VSA paradigm offers robust solutions for generation-after-next AI deployment at the network edge. Specifically, we show how VSA effectively models and integrates the cognitive processes required to perform intelligence, surveillance, and reconnaissance (ISR). The experiment integrates functions across the observe, orientate, decide and act (OODA) loop, including the processing of sensed data via both a neuromorphic event-based camera and a standard CMOS frame-rate camera;declarative knowledge-based reasoning in a semantic vector space;action planning using VSA cognitive maps;access to procedural knowledge via large language models (LLMs);and efficient communication between agents via highly-compact binary vector representations. In contrast to previous ‘point solutions’ showing the effectiveness of VSA for individual OODA tasks, this work takes a ‘whole system’ approach, demonstrating the power of VSA as a uniform integration technology. © 2024 SPIE.

关键词： Photons

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Adversarial Example Generation Method Based on Probability Histogram Equalization

Adversarial Example Generation Method Based on Probability H...

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Chinese Control Conference (CCC)

作者： Youqing Fang Jingwen Jia Yuhai Yang Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively.

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Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity

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SN computer Science 2022年第1期3卷 15页

作者： Mian Qaisar, Saeed AbdelGawad, Amal Essam ElDin Srinivasan, Kathiravan College of Engineering Effat University Jeddah 22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah 22332 Saudi Arabia School of Computer Science and Engineering Vellore Institute of Technology (VIT) Vellore India

In almost all contemporary power systems, the battery is an elementary component, and it is routinely used in a variety of critical applications such as drones, avionics, and cell phones. Due to their superior characteristics compared to the concurrent technologies, Li-ion batteries are widely utilized. Since batteries are costly, their usage is closely monitored by battery management systems (BMSs). It ensures that batteries survive and serve longer. Modern BMSs’ are complex and sophisticated and can deal with hundreds of cells in a battery pack. It results in an increased processing resources requirement and can cause an overhead power consumption. The aim of this work is to improve current BMSs by redesigning their associative processing chain. It focuses on improving data collection, processing and prediction processes for Li-ion battery cell capacities. To prevent the processing of a large amount of unnecessary data, the classical sensing approach that is fix-rate is avoided and replaced by event-driven sensing (EDS) mechanism to digitize battery cell parameters such as voltages, currents, and temperatures in a way that allows for real-time data compressing. A new approach is proposed for event-driven feature extraction. The robust machine-learning algorithms are employed for processing the extracted features and to predict the capacity of considered battery cell. Results show a considerable compression gain with a correlation coefficient of 0.999 and the relative absolute error (RAE) and root relative squared error (RRSE) of 1.88% and 2.08%, respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Battery capacity Computational complexity Event-driven processing Features extraction Li-ion battery Remaining useful life

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is availab.e at https://***/boyuh/AUCSeg.

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Korean-Chinese Bilingual Sentence Alignment Method Based on Character Length 2nd

Korean-Chinese Bilingual Sentence Alignment Method Based on ...

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2nd Big Data Analytics for Cyber-Physical System in Smart City, BDCPS 2020

作者： Wang, Qi Zhao, Yahui Cui, Rongyi Intelligent Information Processing Lab. Department of Computer Science and Technology Yanbian University Yanji133002 China

ISBN: (纸本)9789813345737

Sentence-level aligned bilingual parallel corpus is an indispensable and valuable resource for machine translation, translation knowledge acquisition, and bilingual dictionary compilation. Based on the Korean-Chinese parallel corpus, this paper uses a Korean-Chinese sentence alignment algorithm based on character length to realize automatic Korean-Chinese sentence alignment, and proposes a sentence alignment evaluation method. Firstly, preprocess and segment the Korean-Chinese corpus;Secondly, calculate the mean and variance of the corpus distribution based on the Korean Chinese corpus, and use the probability score to find the maximum likelihood probability of the sentence under the framework of dynamic programming;Finally, proposed a sentence alignment judgment method, with the help of Hanjaja tool to convert Sino-Korean words in Korean sentences into Chinese words to form Korean sentences containing Chinese words (abbreviated as C-K sentences), then calculate the Jaccard coefficient between the C-K sentence and the Chinese sentence, by determining the appropriate threshold to automatically determine the alignment or not. Experiments show that the length-based sentence alignment method has a good effect on automatic alignment of Korean-Chinese sentences, and the accuracy of sentence alignment reaches 88.61%. The proposed sentence alignment judgment method is simple and effective. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Dynamic programming

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Machine Learning Approaches for Automated Detection and Classification of Dysarthria Severity

Machine Learning Approaches for Automated Detection and Clas...

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Electronics, Energy and Measurement (IC2EM), International Conference on

作者： Amina Hamza Djamel Addou Hamza Kheddar Speech and Signal Processing Lab University of Sciences and Technology Houari Boumediene Algiers Algeria Dept. Electrical Engineering LSEA Lab. Faculty of Technology University of MEDEA Medea Algeria

Dysarthria, a speech disorder caused by neuro-motor problems resulting in impaired articulation, requires an assessment of its severity for diagnostic and monitoring purposes. Additionally, accurate severity classification facilitates the development of automated dysarthric speech detection and classification systems. This paper presents a comprehensive investigation into detecting dysarthric voices within a collection of normal voice samples, followed by the dysarthria severity classification utilizing neural network frameworks, specifically long short-term memory network (LSTM) and recurrent neural network (RNN). The study employs various features including Mel frequency cepstral coefficients (MFCC), formants, prosodic parameters, and voice quality. The performance of these models is evaluated against a baseline support vector machine (SVM) classifier using the Nemours corpus database. Remarkably, the highest classification accuracy achieved for this corpus is 99.69%. Detailed analysis demonstrates that selecting an appropriate neural network architecture yields superior performance compared to the conventional SVM classifier.

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Automatic Speech Recognition with BERT and CTC Transformers: A review

arXiv

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arXiv 2024年

作者： Djeffal, Noussaiba Kheddar, Hamza Addou, Djamel Mazari, Ahmed Cherif Himeur, Yassine Speech and Signal Processing Lab. USTHB University Algiers Algeria LSEA Lab. Faculty of Technology University of MEDEA Medea26000 Algeria LSEA Lab Faculty of Science University of MEDEA Medea26000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates

This review paper provides a comprehensive analysis of recent advances in automatic speech recognition (ASR) with bidirectional encoder representations from transformers BERT and connectionist temporal classification (CTC) transformers. The paper first introduces the fundamental concepts of ASR and discusses the challenges associated with it. It then explains the architecture of BERT and CTC transformers and their potential applications in ASR. The paper reviews several studies that have used these models for speech recognition tasks and discusses the results obtained. Additionally, the paper highlights the limitations of these models and outlines potential areas for further research. All in all, this review provides valuable insights for researchers and practitioners who are interested in ASR with BERT and CTC transformers. © 2024, CC BY.

关键词： Distribution transformers

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Heterogeneous Multi-Attribute Group Decision-Making Integrating Multi-Granulation Weighting Model and Improved VIKOR in Uncertain Linguistic Environment 9

Heterogeneous Multi-Attribute Group Decision-Making Integrat...

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9th International Conference on Fuzzy Systems and Data Mining, FSDM 2023

作者： Pang, Jifang Song, Peng Wang, Baoli Zhang, Chao Key Lab. of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China School of Economics and Management Shanxi University Shanxi Taiyuan030006 China School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng044000 China

ISBN: (纸本)9781643684703

Heterogeneous multi-attribute group decision-making (HMAGDM) is a complex decision-making problem that widely exists in the real world. However, there is relatively little research on the HMAGDM problems when the attribute set and alternative set are both heterogeneous, and the existing studies still have some limitations, such as the weight calculation is too simple, lacking objectivity and comprehensiveness;the ranking methods does not consider the utility of both group and individuals simultaneously, lacking flxibility and practicality. In order to obtain more effective decision results, a HMAGDM method integrating multi-granulation weighting model and improved VIKOR in uncertain linguistic environment is proposed in this paper. Our contributions can be identified as follows: (1) On the basis of the uncertainty and closeness of uncertain linguistic terms (ULTs), a measure indicator for the effectiveness of experts' opinions is proposed, and a finest-granulation weight optimization model for experts is established by maximizing the effectiveness;(2) Based on comprehensive consideration of effectiveness and deviation, a bi-objective optimization model is proposed to obtain the multi-granulation weights of attributes;(3) An improved VIKOR method combining the boundedness of ULTs and the multi-granulation weights of attributes is proposed to obtain more stable and effective ranking results. Finally, the case study and comparative analysis illustrate the feasibility and characteristics of the proposed method. © 2023 The authors and IOS Press.

关键词： Granulation

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