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9th International Conference on intelligent computing and Signal Processing, ICSP 2024

作者： Cheng, Yinqing Zhao, Lasheng Wang, Ling Wang, Han Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China

ISBN: (纸本)9798350376548

Automated speaker verification systems face a higher risk of replay attacks in practice. However, the existing studies face the problem of limited detection capabilities and insufficient use of shallow fine-grained information. To address these issues, we propose the cross-stage mutual distillation(CS-MD) framework, which involves two models learning from a deep network output of each other in different stages of training. This mutual learning approach enhances the ability of shallow networks to capture fine-grained speech information. Additionally, we use an attentional feature fusion module to integrate shallow information more effectively. The multi-scale attention mechanisms in this module can combine local and global speech features while preserving detailed information. Experimental results on the ASVspoof 2019 physical access dataset demonstrate that our proposed method outperforms state-of-the-art methods in terms of EER and min t-DCF metrics, validating the effectiveness of our CS-MD framework. © 2024 IEEE.

关键词： Speech enhancement

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Robust keyword Spotting Based on Long-Term Feature Fusion and Band-Weighted Normalization 7

Robust Keyword Spotting Based on Long-Term Feature Fusion an...

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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Qiu, Zepeng Zhao, Lasheng Wang, Ling Zhang, Tingting Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian China

ISBN: (纸本)9798350375107

Currently, convolution-based models for keyword spotting focus predominantly on research conducted in clean speech environments. However, their recognition accuracy is lower in low signal-to-noise ratio conditions. To enhance model robustness, we propose the long-term feature fusion module. By extracting and fusing long-term features in both the time and frequency domains, the model's perception of long-term features is strengthened, improving its recognition performance in noisy environments. At the same time, we propose a band-weighted normalization method, allowing the model to adjust the importance of different frequency bands during the normalization process, further enhancing the robustness of the model. Experimental results indicate that, on the Google Speech Command V2 dataset, the proposed model achieves higher recognition accuracy under various signal-to-noise ratios compared to the comparative models, with a lower parameter count. Moreover, the proposed model exhibits superior generalization to signal-to-noise ratios not covered during the training phase. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Adaptive Fixed-Time Synchronization of Delayed Memristor-Based Neural Networks with Discontinuous Activations

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 221-239页

作者： Tianyuan Jia Xiangyong Chen Xiurong Yao Feng Zhao Jianlong Qiu School of Automation and Electrical Engineering and Key Laboratory of Complex Systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyi276005China The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and School of AutomationChina University of GeosciencesWuhan430074China

Fixed-time synchronization(FTS)of delayed memristor-based neural networks(MNNs)with discontinuous activations is studied in this *** continuous and discontinuous activations are considered *** the mixed delays which are closer to reality are taken into the ***,two kinds of control schemes are proposed,including feedback and adaptive control *** on some lemmas,mathematical inequalities and the designed controllers,a few synchronization criteria are ***,the upper bound of settling time(ST)which is independent of the initial values is ***,the feasibility of our theory is attested by simulation examples.

关键词： Memristor-based neural networks fixed-time synchronization mixed delays discontinuous activations adaptive control

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A Color Image Encryption Algorithm Based on Complementary Map and Iterative Convolutional Code 24

A Color Image Encryption Algorithm Based on Complementary Ma...

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8th International Conference on Machine Learning and Soft computing, ICMLSC 2024

作者： Xie, Yanlu Zhou, Shihua Lv, Hui Wang, Bin Che, Chao Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9798400716546

Encryption is a valid means to safeguard the safety of images, and for color images, encryption should be performed considering the intrinsic correlation between R, G, and B components. In this paper, we propose an image encryption algorithm based on a complementary map and an iterative convolutional code. Firstly, the plain image is input into the convolutional encoders for iteration to generate the correctional secret key. Secondly, we design a new complementary map. From the test data, the new chaotic map has passed the NIST testing, exhibits a good chaotic characteristic, and has a wider range of chaotic parameters. Thirdly, global scrambling is performed on the color image to disrupt the distribution between R, G, and B. Then, a row-layer and a column-layer are randomly selected to form a set of elements to be encrypted. Finally, performing global diffusion on the image further increases the safety of our scheme. Experimental results show that our algorithm has a preferable encryption effect and elevated safety. © 2024 ACM.

关键词： Convolutional codes

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Acoustic Word Embedding Model with Transformer Encoder and Multivariate Joint Loss 11

Acoustic Word Embedding Model with Transformer Encoder and M...

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11th International Conference on intelligent computing and Wireless Optical Communications, ICWOC 2023

作者： Gao, Yunyun Zhang, Qiang Zhao, Lasheng Dalian University Ministry of Education Key Laboratory of Advanced Design and Intelligent Computing Dalian China

ISBN: (纸本)9798350321791

As a representation of speech information, acoustic word embedding can enable query-by-example keyword search with low-resource speech data. An acoustic word embedding model with Transformer encoder and multivariate joint loss is presented in this article. First of all, on the basis of BLSTM, the model can extract richer information by adding a Transformer encoder, which improves representation ability of the embedding vector. Then, the contrast loss is improved according to the multiple negative samples, and combined with the hinge loss with distance characteristics, the model generates a more compact vector representation and improves the identification of features. Finally, considering the unbalanced characteristics of samples, the anti-focal loss is introduced to form a multivariate joint loss together with the above two losses, so as to make the model performance better. The model has achieved certain results in word recognition tasks and has certain competitiveness compared with other acoustic word models. © 2023 IEEE.

关键词： Embeddings

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Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

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Big Data Mining and Analytics 2024年第3期7卷 781-793页

作者： Ruimin Ma Ruitao Xie Yanlin Wang Jintao Meng Yanjie Wei Yunpeng Cai Wenhui Xi Yi Pan Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Shenzhen Key Laboratory of Intelligent Bioinformatics and with Center for High Performance ComputingShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD *** the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack *** this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature *** ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared *** case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential ***,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.

关键词： Autism Spectrum Disorder(ASD)classification Contrastive Variational AutoEncoder(CVAE) transfer learning neuroanatomical interpretation

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Leveraging Slot-Aware Enhancements for Improved Zero-Shot Dialogue State Tracking 6

Leveraging Slot-Aware Enhancements for Improved Zero-Shot Di...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Song, Tianhang Hu, Haolei Chen, Xinxin Zi, Kangli Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Task-oriented dialogue systems (TOD) aim to help users complete specific tasks through multiple rounds of dialogue, in which Dialogue State Tracking (DST) is a key component. The training of DST models typically necessitates the availability of a substantial corpus of dialogue data with turn-level annotations. However, the collection of such data is both costly and inefficient. Despite the impressive performance of systems based on advanced large language models (LLMs), their closed-source nature restricts the potential for local deployment. To address this challenge, this paper proposes a new zero-shot slot-aware enhanced model (SAE), which introduces a novel coded prompting framework to enhance the DST capability of small parameter models in new domains and task scenarios. In particular, we have devised a prompting framework that employs code to refine task instructions. Furthermore, we have proposed a slot-aware question generation mechanism that generates slot-aware questions based on contextual details in accordance with the dialogue context, thereby enhancing the model's perception and matching capabilities for slots. In order to verify the effectiveness of the model, we conducted experiments on the MultiWOZ dataset. The results show that the SAE model has achieved significant performance improvements in zero-shot DST tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Blockchain-Based Privacy-Preserving and Accountable Mobile Edge Outsourcing computing Framework for the Metaverse

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 711-724页

作者： Li, Chunpei Liu, Chen Liu, Peng Li, Xianxian Qiu, Wangjie Lei, Lei Jin, Yanli Guangxi Normal University Key Laboratory of Educational Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Zhongguancun Laboratory China Beihang University Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

Mobile Edge computing (MEC) enables Metaverse Terminal Devices (MTD) to perform complex tasks, including graphic rendering and physical simulation, by leveraging low-latency outsourced computing. However, existing research has not fully addressed the challenge of establishing an efficient outsourced computing service within an open and dynamic MEC environment that simultaneously ensures privacy and accountability. To address this, we proposes a blockchain-based privacy-preserving and accountable mobile edge outsourcing computing framework for the Metaverse, termed Meta-BMEOC. Specifically, we have designed an outsourcing computing protocol based on smart contracts and threshold secret sharing, enabling MTD to outsource tasks to multiple edge servers while preserving privacy. Furthermore, we have developed an off-chain smart contract protocol based on a Trusted Execution Environment. This protocol is designed to reduce the risk of malicious edge servers colluding to reconstruct the computational tasks of MTD, and it enables accountability for servers that return erroneous results. Additionally, we designed an incentive mechanism to resist malicious attacks and ensure system security and stability. Security analysis and experimental evaluation show that Meta-BMEOC not only ensures the privacy and accountability of outsourced computing but also provides outsourced computing services with lower computational latency. © 2017 IEEE.

关键词： Differential privacy

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 16th

SCFNet: A Spatial-Channel Features Network Based on Heteroc...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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MEVTR: A Multilingual Model Enhanced With Visual Text Representations 30

MEVTR: A Multilingual Model Enhanced With Visual Text Repres...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Xiaohua Fei, Wenlong Hu, Min Zhang, Qingyu Zhu, Aoqiang School of Computer Science and Information Engineering HeFei University of Technology China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China

ISBN: (纸本)9782493814104

The goal of multilingual modelling is to generate multilingual text representations for various downstream tasks in different languages. However, some state-of-the-art pre-trained multilingual models perform poorly on many low-resource languages due to the lack of representation space and model capacity. To alleviate this issue, we propose a Multilingual model Enhanced with Visual Text Representations (MEVTR), which complements textual representations and extends the multilingual representation space with visual text representations. First, the visual encoder focuses on the glyphs and structure of the text to obtain visual text representations, and the textual encoder obtains textual representations. Then, multilingual representations are enhanced by aligning and fusing visual text representations and textual representations. Moreover, we propose similarity constraint, a self-supervised task to prompt the visual encoder to focus on more additional information. Prefix alignment and multi-head bilinear module are designed to acquire an improved integration effect of visual text representations and textual representations. Experimental results indicate that MEVTR benefits from visual text representations and achieves significant performance gains in downstream tasks. In particular, in the zero-shot cross-lingual transfer task, MEVTR achieves results that outperform the state-of-the-art adapter-based framework without the target language adapter. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Signal encoding

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