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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Huang, Guimin Li, Ying Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

With online education rapidly developing, it is a significant issue to evaluate students' grasp of knowledge more accurately. Knowledge tracing models are good at it, in which convolutional knowledge tracing (CKT) based on convolutional neural networks(CNN) gets better performance. However, we found that the CKT model ignores the relation between the knowledge points, which we believe has an important impact on the correctness of students' answers. Therefore, we put forward a graph embedding based convolutional knowledge tracing model (CKT-GE), which feeds the relation between the knowledge points with other inputs into model for improving the effectiveness of knowledge tracing. Our model outperforms existing models on two classic datasets, and improves the performance over the original CKT model, allowing for more accurate assessment of student performance. © 2023 IEEE.

关键词： Students

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation 9

Multi-Scale and Improved Skip Connections Network for Skin L...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Botao Chen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350376548

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's 3× 3 convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation. © 2024 IEEE.

关键词： Problem solving

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A Low Cost Triple-Node-Upset Self-Recoverable Latch Design for Nanoscale CMOS

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IEEE Transactions on Device and Materials Reliability 2025年第2期25卷 296-307页

作者： Bai, Na Zhang, Pengshuai Xu, Yaohua Guo, Yunhui Li, Hang Wang, Yi Wen, Xiaoqing Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Hefei230601 China National University of Defense Technology College of Computers Hunan Changsha410073 China Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Fukuoka8208502 Japan

Due to the constant reduction of semiconductor feature sizes, the charge sharing effect is getting worse. The occurrence probability of triple-node upset (TNU), which has a significant impact on the circuit’s reliability, is rising in nanoscale digital circuits. This paper proposes a low overhead and TNU self-recovery latch (TNU-SR) based on the 28 nm CMOS technology to efficiently tolerate TNU and reduce the power dissipation and latency of the latch. The latch design proposed in this article adopts stacked transistors to reduce sensitive nodes and area overhead. The TNU-SR latch uses clock gating and high-speed transfer path technology, resulting in low power dissipation and low delay. For simulation results, compared with existing latches, it can be known that the TNU-SR latch performs well in terms of delay, power consumption, and area, so it is a good choice for radiation-hardened design for space applications. In addition, compared with the latest TNU self-recovery latch (HLTNURL), the TNU-SR latch proposed in this paper reduces the Power-Area-Delay Product (PADP) metric by 111.13%. © 2001-2011 IEEE.

关键词： CMOS integrated circuits

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An Assisting Education System of Electronic Building Blocks Based on Deep Learning 24

An Assisting Education System of Electronic Building Blocks ...

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2024 7th International Conference on Computer information Science and Artificial Intelligence, CISAI 2024

作者： Shi, Zhuo Xu, Ziquan Qiu, Xia He, Haoen Luo, Xiaonan School of Art and Design Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of lmage and Graphic Intelligent Processing Guangxi Guilin China Department of Computer and Information Security University of Guilin Electronic Technology Guangxi Guilin China Department of Guangxi Key Laboratory of Image and Graphic Intelligent Processing University of Guilin Electronic Technology Guangxi Guilin China

ISBN: (纸本)9798400707254

It is difficult for children to build electronic building blocks. In order to facilitate users to build building blocks, this paper designs and implements an electronic building block auxiliary system based on machine vision. The system uses the deep learning object detection model PP-YOLOv2 to detect the building block pictures taken by the user, obtain the location information of all the two-dimensional code in the pictures, and then decode the two-dimensional code, perform perspective transformation on the pictures, and obtain the final two-dimensional code decoding information and location information. Finally, the position of the QR code identified by the building blocks on the user’s picture is crossed and compared with the position of the standard building blocks, so as to determine whether the building blocks placed by the user are correct and give the user a prompt. The system test shows that the system is not only easy to use, but also easy to maintain, and can correctly handle most of the small and medium difficulty examples in the electronic building blocks course. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Design Of A Weakly Coupled 19-Core 5-Mode-Group Fiber For Dense Space-Division Multiplexing

Design Of A Weakly Coupled 19-Core 5-Mode-Group Fiber For De...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Chen, Kaihui Tu, Jiajing Gao, Shecheng Li, Zhaohui Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China

ISBN: (纸本)9798350379266

Few-mode multi-core fiber is a space-division multiplexing backup solution to address the communication capacity crunch which is limited by the capacity of single-mode fiber. In this paper, we propose a weakly coupled 19-core 5-mode-group trench-assisted ring-core fiber with ultra-high spatial channel number, which can support up to 380 usable spatial mode channels (19 cores × 5 radial first-order OAM mode groups × 4 degenerate modes). To our knowledge, this is the highest channel count reported for few-mode multi-core fibers. The fiber can stably transmit 5 OAM mode groups in the C+L band. The minimum effective index difference between adjacent radial first-order mode groups is 1.35 × 10-3. With limited cladding diameter, when the core pitch is 44 µm, the inter-core crosstalk for the high-order OAM5,1 mode can reach -60.13 dB/100km. © 2024 IEEE.

关键词： Crosstalk

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HCU-Net: An Efficient Fully Convolutional Neural Network for Thyroid Nodules Ultrasound Image Segmentation

HCU-Net: An Efficient Fully Convolutional Neural Network for...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Ren, Mengxi Liu, Jun School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9781665410205

Thyroid nodules, which can manifest as solitary or multiple growths, are among the most prevalent diseases with a high incidence rate. When examining thyroid nodules, ultrasound is typically the first step. Ultrasound image segmentation of thyroid nodules can assist physicians in diagnosis. However, the following three factors limit the development of thyroid nodule segmentation: (1) most of the existing studies optimize the segmentation of single nodules, and the incidence of multiple nodules is higher, so the segmentation of multiple nodules is also very important;(2) the ultrasound image of thyroid nodules is difficult to segment because of its low resolution and fuzzy boundary;(3) Due to the small data set of thyroid nodule ultrasound, in such a small dataset, attention mechanisms may be prone to overfitting, resulting in poorer generalization performance. Therefore, the attention-based model may not be suitable for the segmentation of thyroid nodules. Hence, we propose Hybrid Convolutional U-Net (HCU-Net), a simple and strong fully convolutional neural network for the segmentation of single or multiple thyroid nodules. HCU-Net incorporates a large receptive field to ensure accurate and stable segmentation. Specifically, we design Hybrid Branch Block to enhance the network's feature extraction capability for single or multiple nodules in ultrasound images by integrating multi-scale features. Furthermore, we add Enhanced ConvNeXt Blocks to the U-Net's final layer, enabling the extraction of global contextual information for segmentation performance. The Experimental results show that our proposed method can effectively improve these issues on two publicly available datasets. In addition, compared to attention-based models, our network is simpler and more suitable for small datasets like ultrasound images. © 2024 IEEE.

关键词： Ultrasonic imaging

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Towards Efficient and Secure Verifiable Aggregation for Federated Learning 9

Towards Efficient and Secure Verifiable Aggregation for Fede...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Wang, Chang-Ji Jiang, Yu-Ning Gan, Qing-Qing Liu, Ning Yin, Yu-Chen Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou Key Laboratory of Intelligent Processing of Non-Common Languages Guangzhou China

ISBN: (纸本)9798350317251

Federated learning is an advanced technique that outperforms traditional centralized approaches in machine learning. It allows participants to keep their data locally and obtain a global model through aggregation. However, this technique introduces parameter exchange processes, increasing privacy risks. In this study, we investigate two privacy concerns: protecting users' data during training and verifying the accuracy of aggregation results from the server. To address these issues, we propose a verifiable Federated Learning aggregation scheme based on the RSA accumulator. We employ double-masking techniques for secure federated learning and to accommodate disconnected users. Using the RSA accumulator and homomorphic hash function, our scheme offers efficient correctness verification with minimal storage requirements and constant verification time, even with more users. Through experiments and analysis, we demonstrate that our scheme reduces computation and communication costs while ensuring lightweight verifiability. © 2023 IEEE.

关键词： Digital storage

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A Deep Knowledge Tracking Model Based on Forgetting Features and Self-Attention Mechanism 4

A Deep Knowledge Tracking Model Based on Forgetting Features...

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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Huang, Guimin Wei, Xiong Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

The integration of large education and artificial intelligence technologies is gradually deepening, and how to provide personalized user profiling services for learners is an important research problem. In response to the long-term dependency problem and lack of learning features of today's deep knowledge tracking models, a deep knowledge tracking model (FSA-MuLSTM) based on forgetting features and the self-attentiveness mechanism is proposed. Firstly, the model combines the learner's historical learning interaction tuple sequences with learning times and memory residuals to obtain the input vector based on the LSTM;secondly, the correlation weights between tuples are captured using the self-attention mechanism;finally, the deep information between tuple sequences is captured using the stacking of multilayer LSTMs. The probability of correctly answering the current question is derived from the hidden vectors and the self-attentive weights of the output of the multilayer LSTM. Two public datasets are used for the experiments, and the combined analysis outperforms existing knowledge-tracking models in terms of performance and improves the accuracy of prediction. © 2023 IEEE.

关键词： Multilayers

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Facile intercalation of alkali ions in WO_(3)for modulated electronic and optical properties:Implications for artificial synapses and chromogenic application

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 149-160页

作者： Changmeng Huan Zihan Lu Silin Tang Yongqing Cai Qingqing Ke School of Microelectronics Science and Technology Sun Yat-sen UniversityZhuhai 519082China Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityZhuhai 519082China Institute of Applied Physics and Materials Engineering University of MacaoTaipaMacao 999078China

Tungsten oxides(WO_(3))are widely recognized as multifunctional systems owing to the existence of rich *** diverse phases exhibit distinct octahedra-tilting patterns,generating substantial tunnels that are ideally suited for ***,a quantitative comprehension regarding the impact of distinct phases on the kinetics of intercalated conducting ions remains ***,we employ first-principles calculations to explore the spatial and orientational correlations of ion transport inγ-and h-WO_(3),shedding light on the relationship between diffusion barriers and the size of the conducting *** findings reveal that different types and concentrations of alkali-metals induce distinct and continuous lattice distortions in WO_(3)***,γ-WO_(3)is more appropriate to accommodate Li+ions,exhibiting a diffusion barrier and coefficient of 0.25 eV and 9.31×10^(-8)cm^(2)s^(-1),***,h-WO_(3)features unidirectional and sizeable tunnels that facilitate the transport of K+ions with an even lower barrier and a high coefficient of 0.11 e V and 2.12×10^(-5)cm^(2)s^(-1),***,the introduction of alkali-metal into WO_(3)tunnels tends to introduce n-type conductivity by contributing s-electrons to the unoccupied W 5d states,resulting in enhanced conductivity and tunable electronic *** alkali metals in WO_(3)tunnels are prone to charge transfer,forming small polaronic states and modulating the light absorption in the visible and nearinfrared *** tunable electronic and optical properties,combined with the high diffusion coefficient,underscore the potential of WO_(3)in applications such as artificial synapses and chromogenic devices.

关键词： WO_(3)polymorphs ion insertion kinetic activity tunable electronic and optical properties iontronics

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship 4

An Analysis Model of English Text Coherence Based on RST Dep...

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4th International Conference on information Science, Parallel and Distributed systems, ISPDS 2023

作者： Huang, Guimin Shuai, Wanying Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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