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International Symposium on Chinese Spoken Language processing

作者： Hengzhi Zhou Mingyue Shi Qinglin Meng Acoustics Laboratory School of Physics and Optoelectronics South China University of Technology Guangzhou Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Evaluating speech intelligibility is a crucial stage in the development of sound processing algorithms for cochlear implants (CI). While the cost of recruiting actual CI patients and carrying out subjective experiments is high, objective measurements provide a cost-effective alternative with the added benefit of enabling large-scale testing. Most objective measurements were designed to evaluate different font-end noise reduction algorithms, but another critical aspect in CI development is coding strategy. In this study, the automatic speech recognition (ASR) network OpenAI Whisper was used to simulate a human perception model listening to CI-simulated speech. The speech was generated by a vocoder simulating the most used strategy, the Advanced Combination Encoder (ACE) strategy. An objective ASR experiment was conducted to compare with a previous subjective study that explored the relationship between two key parameters, i.e., the number of maxima (nmax) and electrical dynamic range (EDR), in the ACE strategy with both normal hearing and CI listeners. Although ASR performed poorly in recognizing words under severely distorted conditions, it exhibited a similar result trend to that of normal-hearing and CI users. This pilot study demonstrates the potential of using well-trained ASR and vocoder systems for objective CI speech intelligibility evaluation. Future works on fine-tuning ASR and comparison to traditional objective metrics are warranted.

关键词： Cochlear implants Vocoders Noise reduction Dynamic range Market research Encoding Noise measurement Speech processing Automatic speech recognition Testing

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors Data models Medical diagnosis Medical diagnostic imaging

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Multi-view Graph Learning with Fuzzy Linear Discriminant Analysis 22

Multi-view Graph Learning with Fuzzy Linear Discriminant Ana...

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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Hong Jia Jian Huang Guangdong Key Laboratory of Intelligent Information Processing China and College of Electronics and Information Engineering Shenzhen University China

ISBN: (纸本)9781450398336

The multi-view clustering method based on graph learning has been extensively studied because of its good clustering effect. However, most of the graph learning methods are based on the original data features, which often contain noise and outliers, and using them directly may lead to learning suboptimal graph. To address the above problems, we propose a multi-view graph learning method based on linear discriminant analysis. On the one hand, the manifold structure obtained using the global graph guides the learning of the discriminative projections for each view, and on the other hand, the common graph is learned using the projection data of each view. Then, an efficient solution is proposed by dividing the problem into two sub-problems. Finally, experiments are conducted on some public multi-view datasets to verify the effectiveness of the method.

关键词： graph learning

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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SCDMSC: Deep Multi-View Subspace Clustering for Single-Cell Multi-Omics Data

SCDMSC: Deep Multi-View Subspace Clustering for Single-Cell ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ke-Pei Wen Ying Li Le Ou-Yang College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Key Laboratory of Media Security and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

With the rapid development of single-cell sequencing technologies, various clustering methods have emerged, significantly enhancing our understanding of cellular heterogeneity and biological processes. However, many existing clustering methods are primarily designed for single-cell RNA sequencing data, mono-omic data typically only capture information from a specific aspect, failing to comprehensively reveal the diversity of cells. The emergence of single-cell multi-omics sequencing technologies facilitates better exploration of cell diversity, but it has also posed challenges. How to effectively utilize the available information across multiple omics data and explore the global structure of cells has become a new challenge. To address this issue, we propose a deep multi-view subspace clustering (scDMSC) model to identify cell types from single-cell multi-omics data, which can learn both cell representations and subspaces simultaneously. To explore the global structure of cells, scDMSC introduces a novel subspace affinity. Furthermore, a collaborative training strategy is used to learn the consensus and complementary information of single-cell multi-omics data. The superiority of scDMSC is demonstrated through experimental results on three diverse single-cell multi-omics datasets.

关键词： Training Sequential analysis Clustering methods RNA Collaboration Biological processes Machine learning Data models Cybernetics

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Improving VGG-Style Convnet for JPEG Steganalysis

Improving VGG-Style Convnet for JPEG Steganalysis

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

作者： Zhuofan Yang Qiushi Li Shenghai Luo Shunquan Tan Bin Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Shenzhen Key Laboratory of Media Security Shenzhen University China

The steganalysis of JPEG images is a crucial area of research. Deep-learning based steganalysis methods have achieved superior detection performance. All methods for JPEG steganalysis rely on residual networks. Although the incorporation of residual connections has enhanced detection performance, it has also led to a notable increase in computational complexity. Furthermore, most of these methods are not complete end-to-end models. In their approaches, traditional hand-crafted filters are employed for image preprocessing. To avoid relying on residual connections and prior knowledge, we propose an end-to-end VGG-style ConvNet. During training, the model utilizes a multi-branch architecture, while it is transformed into a VGG-style ConvNet through structural reparameterization during inference. We conduct extensive experiments on ALASKA KAGGLE dataset and ALASKA II dataset, demonstrating that the proposed method achieves state-of-the-art results in the JPEG domain comparable to other CNN-based steganalyzers such as UCNet and EfficientNet, with clearly better convergence capacity and lower model complexity.

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A Keyless Extraction Framework Targeting at Deep Learning Based Image-Within-Image Models

A Keyless Extraction Framework Targeting at Deep Learning Ba...

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

作者： Rongxuan Peng Xianbo Mo Shunquan Tan Bin Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Shenzhen Key Laboratory of Media Security Shenzhen University China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Image-within-image technique aims to establish covert communication by concealing a secret image within a cover image. Compared with traditional steganography algorithms, the security of image-within-image technique has not been rigorously evaluated by steganalysis. Existing attack methods just brutally destroy the container image, resulting in the secret image cannot be revealed by the original decryption model (key). This paper introduces a novel keyless extraction framework, carrying out steganalysis on the container image without destroying it. Our approach utilizes collected pairs of container and revealed images to construct a master key, enabling us to extract secret image from container image without relying on the original key. Remarkably, the master key remains effective for multiple image-within-image techniques simultaneously, even when their encryption and decryption models are re-trained. In addition, we propose a patch-based data augmentation technique to adapt to scenarios with limited training samples, and we design a weighted loss function with three components to further enhance the visual quality of the extracted secret image. All the experiments are conducted on datasets derived from ImageNet, COCO and DIV2k. The results demonstrate that our approach can extract secret images with comparable visual quality to the original ones.

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Robust Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems

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IEEE Transactions on Vehicular Technology 2025年

作者： Guo, Fang Zhou, Zhenhua Liao, Bin Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen518060 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Hangzhou Dianzi University School of Communication Engineering Hangzhou310018 China

In this paper, the downlink channel estimation of the intelligent reflecting surface (IRS) assisted MIMO systems is considered in the millimeter-wave (mmWave) band. It is challenging to acquire the channel state information (CSI) of the IRS-assisted wireless communication systems, due to the large amount of passive reflecting elements at the IRS. On account of the low-rank property of the mmWave channels, a bivariate optimization problem, which balances the minimization of the fitting errors and the kernel norms, is formulated to address the estimation of the cascaded channel. By leveraging the alternating optimization method, the problem is decoupled into two subproblems, each of which is solved with the alternating direction method of multipliers (ADMM) together with the singular value thresholding (SVT) technique. In comparison with its counterparts, the proposed method provides higher estimation accuracy and stronger resistance to the uncertainty of the ranks of the channel state information matrices. © 2025 IEEE.

关键词： Channel estimation

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A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

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Journal of Systems engineering and Electronics 2020年第1期31卷 142-155页

作者： WANG Na SU Yuchao CHEN Xiaohong LI Xia LIU Dui College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto *** tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising *** paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto *** with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.

关键词： evolutionary algorithm many-objective optimization shuffled frog leaping algorithm(SFLA) ε-indicator

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Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization

arXiv

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arXiv 2023年

作者： Tan, Yuxuan Ye, Jiaxiong Li, Yuanman Wang, Wei Zeng, Limin Li, Xia Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Department of Engineering Shenzhen MSU-BIT University Shenzhen China

Constrained image splicing detection and localization (CISDL) is a fundamental task of multimedia forensics, which detects splicing operation between two suspected images and localizes the spliced region on both images. Recent works regard it as a deep matching problem and have made significant progress. However, existing frameworks typically perform feature extraction and correlation matching as separate processes, which may hinder the model’s ability to learn discriminative features for matching and can be susceptible to interference from ambiguous background pixels. In this work, we propose a multi-scale target-aware framework to couple feature extraction and correlation matching in a unified pipeline. In contrast to previous methods, we design a target-aware attention mechanism that jointly learns features and performs correlation matching between the probe and donor images. Our approach can effectively promote the collaborative learning of related patches, and perform mutual promotion of feature learning and correlation matching. Additionally, in order to handle scale transformations, we introduce a multi-scale projection method, which can be readily integrated into our target-aware framework that enables the attention process to be conducted between tokens containing information of varying scales. Our experiments demonstrate that our model, which uses a unified pipeline, outperforms state-of-the-art methods on several benchmark datasets and is robust against scale transformations. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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