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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Meng, Lei Ye, Zhonglin Zhao, Haixing Yang, Yanlin Chen, Yang Wang, Zhaoyang College of Computer Qinghai Normal University Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China

ISBN: (纸本)9798350319934

At present, as an emerging direction of artificial intelligence, graph neural networks (GNNs) have developed rapidly and can solve many graph data problems efficiently. The real world is composed of a large number of networks. Compared with the widely studied single-relationship feature information networks, the real-world networks are mostly composed of multi-relationship feature information networks in which different types of objects are interconnected through complex relationships. Complex structural information and rich semantic information are included by multi-relationship feature information networks, thereby the real network can be restored more realistically. However, one type of relational feature of the network is merely considered by most of the existing GNNs, and the information of multi-relationship features and the hidden information between multi-relationship features are ignored in the network. In order to deal with these restrictions, a novel graph convolutional network (TCGCN) based on an attention mechanism is proposed, which aims to extract different relational features in multi-relationship feature networks and the hidden information between them more comprehensively, thereby improving the performance of GNNs in downstream tasks. In this paper, an improved attention mechanism is used to aggregate different types of single relational features, and the aggregated multiple single relational feature network information is fused with the mixed relational feature network information into the final node representation. The proposed TCGCN outperforms most other methods in extensive experiments conducted on three real-world datasets. © 2022 IEEE.

关键词： Graph neural networks

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Stream Attention Based U-Net for L3DAS23 Challenge 48

Stream Attention Based U-Net for L3DAS23 Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Honglong Fu, Yanjie Li, Junjie Ge, Meng Wang, Longbiao Qian, Xinyuan Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China The Chinese University of Hong Kong Shenzhen China Co. Ltd Tianjin China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9781728163277

Machine learning applications of 3D audio are gaining increasing interest in recent years. In this paper, we propose a stream attention based U-Net to remove background noise and reverberation based on ICASSP Signal Processing Grand Challenge 2023: L3DAS23 Challenge1 Audio-only track task1 3D Speech Enhancement. Results show that proposed method achieves superior performance than the official baseline model. © 2023 IEEE.

关键词： 3D Speech enhancement L3DAS23 Challenge

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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism

Multivariate Time Series Anomaly Detection based on Pre-trai...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Yongqian Sun Yang Guo Minghan Liang Xidao Wen Junhua Kuang Shenglin Zhang Hongbo Li Kaixu Xia Dan Pei Nankai University Tianjin Key Laboratory of Software Experience and Human Computer Interaction BizSeer Haihe Laboratory of Information Technology Application Innovation Tencent Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%.

关键词： Training Adaptation models Time series analysis Production Reliability engineering Data models Software reliability Servers Anomaly detection Monitoring

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Hey! You Photographed My Screen without Approved: A Practical Screen Content Protection Scheme Based on Visual Cryptography

Hey! You Photographed My Screen without Approved: A Practica...

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2022 International Conference on Networking and Network applications, NaNA 2022

作者： Liu, Kui Wang, Bin Shi, Ziwei Chen, Jiadong Zhang, Zhiwei School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Network and Information Security Laboratory Hangzhou Hikvision Digital Technology Co. LTD. Hangzhou China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Nowadays , with the continuous development of digital media, electronic screens have become the most direct tool for people to interact with information. Various styles of electronic screens provide vivid information exchange methods for people's lives, and at the same time make information leakage more likely to happen. Electronic screens are susceptible to stealing photographs when sharing information, which leads to information leakage and security issues. Under the unavoidable situation of such leaks, various protection schemes have been proposed before, but these schemes cannot effectively protect the screen content. Therefore, in this paper, we propose an effective a screen content protection scheme, based on Visual Cryptography(VC) for image encryption, combined with Scale-Invariant Feature Transform(SIFT) to deal with screen stealing scenes. In addition, our experiments show that our scheme is superior to existing schemes in terms of convenience and security. © 2022 IEEE.

关键词： Digital storage

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Multiple Attention-Guided Neural Architecture Search for Hyperspectral Image Classification 22

Multiple Attention-Guided Neural Architecture Search for Hyp...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Yingluo Song Aili Wang Shiyu Dai Haibin Wu Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin Universiy of Science and Technology China Department of Computer Science Chubu University Japan

ISBN: (纸本)9781450397810

Hyperspectral images(HSIs) have rich spatial and spectral information, which provide a strong basis for distinguishing different landcover objects. The HSI classification methods based on deep learning have achieved good performance. Recently, several Neural Architecture Search (NAS) algorithms have been proposed for HSI to further improve the accuracy to a new level. In this paper, the neural structure search method based onmultiple attention mechanism search space is proposed for hyperspectral images. In the search process, the network selects attention mechanism operation at an appropriate layer, which can help the network focus on useful features and discard useless features. In our experiments, we have verified the superiority of this method on two hyperspectral datasets.

关键词： image classification Hyperspectral images Neural Architecture Search attention mechanism

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Augmenting Short Enrollment Speech via Synthesis for Target Speaker Extraction

Augmenting Short Enrollment Speech via Synthesis for Target ...

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

作者： Huang, Zikang Lin, Jingru Ge, Meng Jiang, Yu Wang, Xiaobao Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Shenzhen China Department of Electrical and Computer and Engineering National University of Singapore Singapore Co. Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9798350368741

A high-quality enrollment speech is crucial to target speaker extraction (TSE), since it provides essential cues for identifying the target speaker in the mixture. However, real applications usually only permit a short enrollment speech, e.g. a wakeup word for a mobile device, that provides limited cues. To address this issue, we propose an enrollment augmentation strategy that allows us to enrich the limited enrollment speech with massive text data through speech synthesis. By doing so, the extended enrollment speech contains enhanced speaker timbre and phonetic content which leads to better extraction quality. Furthermore, we propose a training data augmentation strategy to improve the model's robustness and generalization in short enrollment speech scenarios. Experiments on Libri2Mix demonstrate that our proposed strategies bring a significant improvement in extreme scenarios where only 0.5s and 1-word enrollment speech is provided. We also release our code at https://***/HuangZikang-TJU/Aug4TSE. © 2025 IEEE.

关键词： continue speaking data augmentation short enrollment speech synthesis Target speaker extraction

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Research on Tibetan-Chinese Neural Machine Translation Integrating Statistical Method 6

Research on Tibetan-Chinese Neural Machine Translation Integ...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Zhou, Maoxian The Computer College of Qinghai Normal University Qinghai Xining810016 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Xining810008 China Qinghai Engineering and Technology Research Center for Tibetan Information Processing Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9798400709241

In recent years, with the emergence of deep learning methods, Neural Machine Translation has become a new research direction of machine translation. Due to the scarcity of digital resources in Tibetan, there is only a small-scale Tibetan-Chinese bilingual parallel corpus to train translation models. In the case of insufficient resources, the performance of Neural Machine Translation decreases significantly. In this regard, considering that the word alignment results in Tibetan-Chinese Statistical Machine Translation are better, and the alignment information in Tibetan-Chinese Neural Machine Translation model is significantly different from that in Tibetan-Chinese Statistical Machine Translation, this paper proposes a Tibetan-Chinese Neural Machine Translation method that integrates statistical methods. We using Statistical Machine Translation method to generate word alignment information of Tibetan-Chinese parallel corpus to supervise the training process of Tibetan-Chinese Neural Machine Translation model to make the model achieve more accurate translation and alignment effect. Experimental results show that this method can effectively improve translation quality in low resource environment. © 2023 ACM.

关键词： Neural machine translation

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A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

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Intelligent Automation & Soft Computing 2023年第9期37卷 2865-2882页

作者： Jianfeng Lu Xinyi Liu Mengtao Shi Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Ceramic tiles are one of the most indispensable materials for interior *** ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural *** this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile *** proposed method consists of four ***,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on ***,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated ***,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training ***,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images *** measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)*** experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

关键词： Ceramic tile pattern design cross-domain learning deep learning GAN generative adversarial networks ResNet Block

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BREAKING SPEAKER RECOGNITION WITH PADDINGBACK

arXiv

引用

arXiv 2023年

作者： Ye, Zhe Yan, Diqun Dong, Li Shen, Kailai Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China

Machine Learning as a Service (MLaaS) has gained popularity due to advancements in Deep Neural Networks (DNNs). However, untrusted third-party platforms have raised concerns about AI security, particularly in backdoor attacks. Recent research has shown that speech backdoors can utilize transformations as triggers, similar to image backdoors. However, human ears can easily be aware of these transformations, leading to suspicion. In this paper, we propose PaddingBack, an inaudible backdoor attack that utilizes malicious operations to generate poisoned samples, rendering them indistinguishable from clean ones. Instead of using external perturbations as triggers, we exploit the widely-used speech signal operation, padding, to break speaker recognition systems. Experimental results demonstrate the effectiveness of our method, achieving a significant attack success rate while retaining benign accuracy. Furthermore, PaddingBack demonstrates the ability to resist defense methods and maintain its stealthiness against human perception. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Investigation of Using Large-Scale Swarm Optimizers to Optimize Sub-Problems in Cooperative Co-Evolution

Investigation of Using Large-Scale Swarm Optimizers to Optim...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lu, Ming-Yuan Yang, Qiang Liu, Dong Ma, Yuan-Yuan Li, Tao Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Zhejiang Normal University Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Jinhua China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350337020

Cooperative co-evolutionary algorithms (CCEAs) have witnessed giant success in solving large-scale optimization problems (LSOPs). However, most existing CCEAs use low-dimensional EAs to optimize the decomposed sub-problems. Such utilization of low-dimensional EAs may limit the effectiveness of CCEAs because some of the decomposed sub-problems may still be high-dimensional. Since there exist many non-decomposition based large-scale EAs, it is interesting to investigate the optimization effectiveness of CCEAs by using these non-decomposition based large-scale EAs to solve the decomposed sub-problems. To this end, this paper incorporates two state-of-the-art large-scale swarm optimizers into CCEAs with five state-of-the-art decomposition strategies to solve LSOPs. Experiments conducted on the CEC'2010 and CEC'2013 LSOP benchmark sets have shown that the two large-scale swarm optimizers help CCEAs with the five decomposition strategies achieve much better performance than the most widely used low-dimensional EA. © 2023 IEEE.

关键词： Cooperative Co-evolution Evolutionary Algorithms High-Dimensional Optimization Large-Scale Optimization Problems Large-Scale Swarm Optimizers

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