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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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HSBNN: A High-Scalable Bayesian Neural Networks Accelerator Based on Field Programmable Gate Arrays (FPGA)

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Cognitive Computation 2025年第3期17卷 1-14页

作者： Liu, Yinghao Zhang, Hao Sun, Zhe Duan, Feng Ma, Yuan Lu, Wenyi Caiafa, Cesar F. Solé-Casals, Jordi Graduate School of Arts and Sciences The University of Tokyo Meguro City153-8902 Japan Gusu Laboratory of Materials Suzhou215000 China Faculty of Health Data Science Juntendo University Chiba279-0013 Japan College of Artificial Intelligence Nankai University Tianjin300350 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Instituto Argentino de Radioastronomía CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa Argentina Data and Signal Processing Research Group University of Vic - Central University of Catalonia Catalonia Vic Spain

Traditional artificial neural networks have inherent overfitting problems and tend to produce overly confident predictions due to their reliance on point estimation methods. In contrast, Bayesian theory offers a probabilistic framework that replaces point estimation with probability distributions, effectively addressing issues of overconfidence. The brain is also believed working under the Bayesian rules, the neural networks of which evaluate the precision of prior knowledge and incoming evidence, achieving the balance of weight updating to the most reliable information sources [1]. By integrating Bayesian principles with artificial neural networks, the bio-inspired Bayesian Neural Networks (BNNs) can generate predictions accompanied by confidence evaluations, enhancing their practical applicability. To further improve the computational efficiency of BNNs and enable scalable deployment on edge devices, we propose a High-Scalable Bayesian Neural Network (HSBNN) accelerator based on field-programmable gate arrays (FPGAs) with multiple optimizations. A resource-saving Gaussian random number generator (RS-GRNG) optimized for FPGAs shows high efficiency, which seamlessly extends to support parallel sampling of weight distributions, enabling reliable confidence probability evaluations. Furthermore, the parameterization of BNN architectures with configuration files and employment of a layer-by-layer computing mode ensure that different BNNs can be accelerated without reprogramming the FPGA, offering excellent scalability. The entire system, implemented with the OpenCL heterogeneous computing library, leverages parallel processing units and pipeline channels to achieve high acceleration performance and efficient data transfer. The experiment results demonstrate that the system achieves a data processing throughput of 1.002 milliseconds per image, exceeding CPU performance by 1000-fold and GPU performance by nearly 500-fold. © The Author(s), under exclusive licence to Spring

关键词： Gaussian distribution

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TPE for JPEG images with Dynamic M-ary Decomposition and Adaptive Threshold Constraints

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Ma, Yakun Chai, Xiuli Long, Guoqiang Gan, Zhihua Zhang, Yushu Henan University School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of Things Zhengzhou450046 China Henan University School of Software Intelligent Data Processing Engineering Research Center of Henan Province Institute of Intelligent Network System Kaifeng475004 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security. © 1991-2012 IEEE.

关键词： Ciphertext

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Web access as a service for CSA matter protocol: Bridging matter and the W3C Web of Things framework for smart home interoperability

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Internet of Things (The Netherlands) 2025年 32卷

作者： Nguyen, Huy-Trung Pham, Van-Ha Vu, Viet-Thang Nguyen, Thanh-Trung Research Institute of Posts and Telecommunications Posts and Telecommunication Institute of Technology Hanoi Viet Nam Data Governance Laboratory Posts and Telecommunication Institute of Technology Hanoi Viet Nam Hanoi University of Industry Hanoi Viet Nam Intelligence Data Processing System Research Group Dong Nai Technology University Bien Hoa City Viet Nam Faculty of Information Technology Dong Nai Technology University Bien Hoa City Viet Nam

The rapid proliferation of smart home appliances has highlighted the growing need for enhanced interoperability across diverse IoT ecosystems. The Matter protocol, emerging as a global standard for smart home devices, provides a unified communication framework, while the W3C Web of Things (WoT) framework facilitates the seamless integration of IoT devices through web technologies. Despite the potential benefits, the interworking between Matter and WoT remains an underexplored area, which, if addressed, could substantially improve the accessibility and interoperability of smart home systems. This paper introduces a novel approach for integrating Matter-enabled devices with the W3C Web of Things, ultimately enabling Web Access as a Service for Matter devices. We propose a comprehensive solution that makes Matter devices discoverable and interactable within the WoT ecosystem. The proposed solution comprises: (1) a Thing model for Matter and an adaptive translation algorithm that maps Matter device specifications to WoT concepts, (2) a Matter controller that exposes Matter devices as WoT Thing Descriptions, enabling bi-directional communication between Matter devices and WoT-based applications, and (3) an extensive evaluation to assess the effectiveness of the integration. The results indicate that the proposed solution is both feasible and practical for deployment in real-world settings. This approach lays the groundwork for a more unified and user-friendly smart home experience, thereby advancing the potential for smart home ecosystems in broader, cross-domain contexts. © 2025 Elsevier B.V.

关键词： Matter Matter controller Matter-woT integration Web access a service Web of things

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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

作者： Jinwei Dong Xinsheng Wang Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy.

关键词： Training Adaptation models Technological innovation Source separation databases Stochastic processes Differential equations Speech enhancement Diffusion models Mathematical models

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

arXiv

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arXiv 2025年

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Building 3 levels of security for traceability using Blockchain 3.0 Generation

Building 3 levels of security for traceability using Blockch...

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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Thanh-Trung Nguyen, Tuan A. Nguyen, Hien Huynh, Qui Phu Research Group of Intelligent Data Processing System Ho Chi Minh City University of Foreign Languages - Information Technology Viet Nam

ISBN: (纸本)9781665461665

Nowadays, tracking the origin of a product as it goes from raw material to finished product is an essential need of consumers, which determines whether consumers choose to buy that product or not. At present, the most common method of product traceability is to scan the QR code stamp on the goods. These stamps, however, are still being counterfeited, resulting in inaccurate product information, not ensuring product integrity and transparency for consumers. Therefore, this research suggests a variety of blockchain-based solutions with different security levels to improve the security, integrity, and transparency of product data. In this paper, we propose to use blockchain technology 3.0 which has more advanced features than blockchain 1.0 and 2.0, including a transaction processing time of just roughly 6 seconds. © 2022 IEEE.

关键词： Blockchain

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Building an Electronic Medical Records system using Blockchain 3.0 technology and new data Mechanism

Building an Electronic Medical Records System using Blockcha...

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2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022

作者： Nguyen, Tuan A. Nguyen, Thanh-Trung Nguyen, Hien Huynh, Qui Phu Ho Chi Minh City University of Foreign Languages - Information Technology Research Group of Intelligent Data Processing System Viet Nam

ISBN: (纸本)9781665461665

Nowadays, access to electronic medical records is an essential requirement. However, in the current healthcare system, patient records are created by the healthcare provider and stored locally in the provider's data system. This is particularly detrimental when a service user needs to query their profile from different providers or providers desire to query a service user profile from other providers as needed. Hence, blockchain is effectively background technology to resolve this problem because of its existing features. Currently, there are many models proposed based on blockchain technology 2.0. However, these systems still take a long time for a transaction (from 6 to 10 minutes). Another obvious problem is that healthcare providers participating in the system must use the SQL database because the integrity of data is required. Meantime, the NoSQL database is primary care on the decentralized environments. Therefore, the problem of converting big data from SQL to NoSQL is also important in the healthcare system. In this article, we propose to realize an electronic medical records system by using a third-generation version of blockchain technology with many advantages. Especially the time for a transaction is only about 6 seconds based on the new data mechanism. Besides, there is a solution to convert big data from SQL to NoSQL safely and effectively. © 2022 IEEE.

关键词： Big data

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