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IEEE International Conference on data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks data mining Task analysis

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A Pitch-aware Speaker Extraction Serial Network

A Pitch-aware Speaker Extraction Serial Network

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Yu Jiang Meng Ge Longbiao Wang Jianwu Dang Kiyoshi Honda Sulin Zhang Bo Yu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Japan Automotive Data of China Co. Ltd

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Despite deep learning has an excellent performance in monaural speaker extraction, it's still a challenge to extract speakers when facing the same gender, i.e., male-male and female-female. On the other hand, it has been proved that pitch tracking is effective for same-gender speech separation. In this study, we proposed a pitch-aware speaker extraction serial network (PSESNet) to improve extraction performance. We designed a serial system and compared it with multi-task learning, we tried to use the target speaker's pitch information to optimize the loss function rather than as input to the extraction network. The extraction part uses SpeakerBeam-FE (SBF) with magnitude and temporal spectrum approximation loss (MTSAL) and speaker embedding concatenation. After extracting the spectrogram of the target speaker, we connected the spectrogram to predict the pitch information to do further optimization. Experimental results show that serial system performs better than multi-task learning and proposed method improves performance in both same and opposite gender conditions. On average, PSESNet achieves 4.7% and 3.8% relative improvements on WSJ0 dataset over the SBF-MTSAL-Concat baseline on signal-to-distortion ratio (SDR) under both closed and open condition.

关键词： data mining Spectrogram Estimation Training Target tracking Optimization Acceleration

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USEV: Universal Speaker Extraction with Visual Cue

arXiv

引用

arXiv 2021年

作者： Pan, Zexu Ge, Meng Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300072 China The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The University of Bremen 28359 Germany Kriston AI Xiamen China

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly overlapped multi-talker speech mixture. However, the target-interference speaker overlapping ratios could vary over a wide range from 0% to 100% in natural speech communication, furthermore, the target speaker could be absent in the speech mixture, the speech mixtures in such universal multi-talker scenarios are described as general speech mixtures. The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker. We advocate that a visual cue, i.e., lip movement, is more informative than an audio cue, i.e., pre-recorded speech, to serve as the auxiliary reference for speaker extraction in disentangling the target speaker from a general speech mixture. In this paper, we propose a universal speaker extraction network with a visual cue, that works for all multi-talker scenarios. In addition, we propose a scenario-aware differentiated loss function for network training, to balance the network performance over different target-interference speaker pairing scenarios. The experimental results show that our proposed method outperforms various competitive baselines for general speech mixtures in terms of signal fidelity. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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SPECT bone imaging thyroid lesion segmentation

引用

Journal of Physics: Conference Series 2021年第1期1994卷

作者： Mingyang-Luo Qiang-Lin Tongtong-Li Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Lanzhou 730030 Key Laboratory of Streaming Data Computing and Application Northwest Minzu University Lanzhou 730124 School of Mathematics and Computer Science Northwest Minzu University Lanzhou 730030

Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Wu, Xuke Gou, Hang Wu, Hao Wang, Juan Chen, Minzhi Lai, Siyu Computer School China West Normal University Nanchong Sichuan637002 China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China Department of Medical Imaging North Sichuan Medical College Nanchong Sichuan637000 China

ISBN: (纸本)9781728168531

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy. © 2020 IEEE.

关键词： Factorization

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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Fractional Modeling and Dynamic Analysis of Two Kinds of Viscoelastic Thin Plates with Gaussian White Noise

SSRN

引用

SSRN 2023年

作者： Deng, Jinna Liu, Qiumei Yang, Aimin Chen, Yiming College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Tangshan063210 China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Tangshan063210 China School of Science Yanshan University Qinhuangdao066004 China LE STUDIUM Loire Valley Institute for Advanced Studies Orléans & Tours Orléans45000 France

According to fractional order model, the governing equation of viscoelastic plate is established. Using the properties of the shifted Bernstein polynomials, the fractional order equation of viscoelastic plate is resolved, the conversion of fractional differential equations to algebraic equations, and operator matrices represented by Kronecker products are solved in the domain. The convergence study validates the algorithm's efficacy and precision in solving differential equations. The displacement of viscoelastic metal thin plate under different external loads (uniform distribution and linear distribution), the effect of white Gaussian noise on the thin metal plate displacement, the effect of different boundary conditions on thin metal plate displacement under harmonic load are studied, the displacements of metal and polymer plates under different loads are contrasted and stress and strain relationship of two kinds of plates are discussed. Based on the outcomes, it can be concluded that the shift Bernstein polynomial approach is useful for numerically simulating viscoelastic plates. © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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Ed-Tgan: A Methodology on Temporal Generative Gan for Imbalanced data for Predicting Academic Performance of Students

SSRN

引用

SSRN 2022年

作者： Waheed, Hajra Zhou, Jia Aljohani, Naif R. Tuarob, Suppawong Nawaz, Raheel Herrera, Francisco Hassan, Saeed-Ul Information Technology University 346-B Ferozepur Road Lahore Pakistan Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Faculty of Information and Communication Technology Mahidol University Thailand Staffordshire University Stoke-on-Trent United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Manchester Metropolitan University Manchester United Kingdom

With the increase in the self-paced learning phenomenon, class imbalance in the learning analytics community has become more prevalent, with lesser students dropping out than students who intend to pursue. Similarly, an imbalance in the behavioral performances of students is also observed. This imbalance problem becomes more complicated for temporal data, where each time sequence represents a particular student, and several sequences formulate the overall engagement of students throughout a course. The conventional methods for generating synthetic data do not account for this association of sequences, where each synthetically generated student is associated with its previous sequence. Generative Adversarial Networks (GANs) have been rapidly adopted in the machine learning discipline for synthetic data generation to reduce the class imbalance. In this study, we propose a deep learning-powered methodology called Educational Temporal Generative Adversarial Networks (Ed-TGANs) methodology comprising the following steps: a) Smart data Processing for Actionable data, b) GANs for Temporal data Generation, and c) Learning Approach. Furthermore, the Open University Learning Analytics dataset was deployed as a case study comprising 69% pass and 31% fail students to validate the effectiveness of our proposed methodology, encompassing discrete and continuous features. The results are compared with GAN-based and non-GAN upsampling approaches to demonstrate the proposed methodology's effectiveness, which shows an increase in accuracy of up to 5.33% compared to classic GANs, resulting in improved understating of student behavior. We believe that the presented methodology can be used as a starting point for various areas for upsampling jobs with temporal longitudinal data. © 2022, The Authors. All rights reserved.

关键词： Students

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