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Hc-Apnet: Harmonic Compensate Auditory Perception Network for Low-Complexity Speech Enhancement

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SSRN

SSRN 2024年

作者： Li, Nan Ge, Meng Wang, Longbiao Zhou, Yang-Hao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Co. Ltd Tianjin China Saw Swee Hock School of Public Health National University of Singapore Singapore Singapore School of Computer Science Beijing Institute of Technology Beijing China

Speech enhancement is critical for improving speech quality and intelligibility in a variety of noisy environments. While neural network-based methods have shown promising results in speech enhancement, they often suffer from performance degradation in scenarios with limited computational resources. This paper presents HC-APNet (Harmonic Compensate Auditory Perception Network), a novel lightweight approach tailored to exploit the perceptual capabilities of the human auditory system for efficient and effective speech enhancement, with a focus on harmonic compensation. Inspired by human auditory reception mechanisms, we first segment audio into subbands using an auditory filterbank for speech enhancement. The use of subbands helps to reduce the number of parameters and the computational load, while the use of an auditory filterbank effectively preserves high-quality speech enhancement. In addition, inspired by the perception of human auditory context, we have developed an auditory perception network to capture gain information for different subbands. Furthermore, considering that subband processing only applies gain to the spectral envelope, which may introduce harmonic distortion, we design a learnable multi-subband comb-filter inspired by human fundamental frequency perception to mitigate harmonic distortion. Finally, by incorporating an auditory perception network architecture, our proposed HC-APNet model outperforms state-of-the-art methods. At the same time, our approach significantly compresses both parameters and computational load, with the number of parameters approximately 1/21 of DCCRN's and the computational load approximately 1/31. © 2024, The Authors. All rights reserved.

关键词： Speech enhancement

Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm

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arXiv 2023年

作者： Li, Jiatong Liu, Qi Wang, Fei Liu, Jiayu Huang, Zhenya Yao, Fangzhou Zhu, Linbo Su, Yu Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Hefei Normal University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners’ cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow the proficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners’ cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novel response-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://***/CSLiJT/ID-CDF. Copyright © 2023, The Authors. All rights reserved.

关键词： HTTP

A Consensus Algorithm Based on Risk Assessment Model for Permissioned Blockchain

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arXiv 2022年

作者： Zhang, Xiaohui Xue, Mingying Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Computer Technology Application Key Laboratory of Yunnan Province Kunming China Faculty of Management and Economics Kunming University of Science and Technology Kunming China

Blockchain is characterized by privacy, traceability, and security features as a novel framework of distributed ledger technologies. Blockchain technology enables stakeholders to conduct trusted data sharing and exchange without a trusted centralized institution. These features make blockchain applications attractive to enhance trustworthiness in very different contexts. Due to unique design concepts and outstanding performance, blockchain has become a popular research topic in industry and academia in recent years. Every participant is anonymous in a permissionless blockchain represented by cryptocurrency applications such as Bitcoin. In this situation, some special incentive mechanisms are applied to permissionless blockchain, such as "mined" native cryptocurrency to solve the trust issues of permissionless blockchain. In many use cases, permissionless blockchain has bottlenecks in transaction throughput performance, which restricts further application in the real world. A permissioned blockchain can reach a consensus among a group of entities that do not establish an entire trust relationship. Unlike permissionless blockchains, the participants must be identified in permissioned blockchains. By relying on the traditional crash fault-tolerant consensus protocols, permissioned blockchains can achieve high transaction throughput and low latency without sacrificing security. However, how to balance the security and consensus efficiency is still the issue that needs to be solved urgently in permissioned blockchains. As the core module of blockchain technology, the consensus algorithm plays a vital role in the performance of the blockchain system. Thus, this paper proposes a new consensus algorithm for permissioned blockchain, the Risk Assessment-based Consensus protocol (RAC), combined with the decentralized design concept and the risk-node assessment mechanism to address the unbalance issues of performance in speed, scalability, and security. Copyright © 2022, The Au

关键词： Blockchain

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning

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arXiv 2024年

作者： Xu, Zhuo Bai, Lu Cui, Lixin Li, Ming Wang, Yue Hancock, Edwin R. School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China The Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Computer Science University of York York United Kingdom

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

A CPK-Based Identity Authentication Scheme for IoT

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computer Systems Science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

Bone metastasis segmentation based on Improved U-NET algorithm 4

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Bone metastasis segmentation based on Improved U-NET algorit...

2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Zhang, Jingyi Huang, Mengge Deng, Tao Cao, Yongchun Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

Structured information extraction for bone scan image report texts 4

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Structured information extraction for bone scan image report...

2021 4th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2021

作者： Huang, Mengge Zhang, Jingyi Deng, Tao Man, Zhengxing Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Lanzhou730030 China

Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. This study summarized the structure and characteristics of 3608 bone scan image report text using dictionary-based information extraction method, including data cleaning, entity recognition, building dictionary and extraction rules. This method was used to obtain the structured data of bone scan image report text required for clinical research, and the effect evaluation was carried out on 1000 randomly selected report texts, with the precision rate and recall rate higher than 90%. The method proposed in this study is practical and could have good effect on structured results for bone scan imaging report text. © Published under licence by IOP Publishing Ltd.

关键词： Clinical research

ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks

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arXiv 2024年

作者： Zhao, Zhehan Bai, Lu Cui, Lixin Li, Ming Wang, Yue Xu, Lixiang Hancock, Edwin R. School of Artificial Intelligence Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China The Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Jinhua China Department of Computer Science University of York York United Kingdom

Graph Neural Networks (GNNs) are powerful tools for graph classification. One important operation for GNNs is the downsampling or pooling that can learn effective embeddings from the node representations. In this paper, we propose a new hierarchical pooling operation, namely the Edge-Node Attention-based Differentiable Pooling (ENADPool), for GNNs to learn effective graph representations. Unlike the classical hierarchical pooling operation that is based on the unclear node assignment and simply computes the averaged feature over the nodes of each cluster, the proposed ENADPool not only employs a hard clustering strategy to assign each node into an unique cluster, but also compress the node features as well as their edge connectivity strengths into the resulting hierarchical structure based on the attention mechanism after each pooling step. As a result, the proposed ENADPool simultaneously identifies the importance of different nodes within each separated cluster and edges between corresponding clusters, that significantly addresses the shortcomings of the uniform edge-node based structure information aggregation arising in the classical hierarchical pooling operation. Moreover, to mitigate the over-smoothing problem arising in existing GNNs, we propose a Multi-distance GNN (MD-GNN) model associated with the proposed ENADPool operation, allowing the nodes to actively and directly receive the feature information from neighbors at different random walk steps. Experiments demonstrate the effectiveness of the MD-GNN associated with the proposed ENADPool. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

Electrical Transport Properties of Few-Layer SnS2 Field-effect Transistors

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Electrical Transport Properties of Few-Layer SnS2 Field-effe...

2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Zhang, Lu Li, Tuo Yao, Peng Zou, Xiaofeng Ji, Chao Zhan, Yongzheng He, Jiaqi Zhang, Qing State Key Laboratory of High-end Server & Storage Technology Jinan250013 China Inspur Electronic Information Industry Co. Ltd. Jinan250013 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Jinan250013 China Beijing Institute of Computer Technology and Application Beijing100854 China College of Mathematics and Physics Beijing University of Chemical Technology Beijing100029 China

After the discovery of graphene in 2004, two dimensional (2D) materials have fascinated a lot of view due to the excellent properties. Nowadays, the research on 2D materials has spread to other graphene-like layer structured materials, especially transition metal dichalcogenides (TMDCs). Tin disulfide (SnS2) is a kind of TMDCs with a sizable bandgap. Here we introduce few-layer SnS2 field-effect transistors (FETs) fabricated using micromechanical exfoliation method. The FETs show n-type behavior, the on/off ratio exceeding 0.54×104 and the carrier mobility is 0.61 cm2V-1s-1. The electronic and optical characteristics of SnS2 flakes with a finite bandgap illustrate their potential applications in optoelectronics device. © Published under licence by IOP Publishing Ltd.

关键词： Field effect transistors