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6th International Conference on intelligent computing and signal processing (ICSP)

作者： Xin Jiang Peng Zhou Shu Zhao Yufeng Guo School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China

Technological innovation is becoming one of the critical factors in promoting social development all over the world. The vigorous development of patent applications in recent years provides an opportunity to reveal the inherent laws of innovation, but it also puts forward higher requirements for patent mining technology. An essential step in patent text mining is to build a technical portrait for each patent, that is, to identify the technical phrases involved, which can summarize and represent the patent from a technical perspective. Previous technical phrase extraction methods thoroughly used technical phrases' characteristics and the relationship between technical phrases. Regarding our observations, the relationship between patent texts and technical phrases is also essential. Specifically, critical technical phrases are more relevant to the patent text and can be discovered by the attention mechanism. Motivated by this, we propose an unsupervised technical phrase extraction method based on the attention mechanism named UTESC. Self-attention captures the importance of technical phrases in sentences, and cross-attention captures the relevance between technical phrases and patents. Extensive experiments and algorithm comparisons on patent datasets have proven the effectiveness of our algorithm.

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TransCoop: Cooperation of Transformers and CNNs for Camouflaged Object Segmentation

TransCoop: Cooperation of Transformers and CNNs for Camoufla...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wu, Fucai Li, Xuanya Zhang, Yuan Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artifical Intelligence of Hunan Province Chenzhou423000 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Camouflaged object segmentation (COS) is a challenging task due to the existence of high intrinsic similarities between the object and background. To overcome this challenge, we pro-pose a new framework, called TransCoop, for COS through the cooperation of Transformers and convolutional neural net-works (CNNs). Specifically, Transformer is used to model global context and structural information for accurately positioning potential target objects. Meanwhile, a well-designed texture feature fusion module (TFFM) is used to fuse the features encoded by CNN with the shallow Transformer to fully mine the low-level features in the scene. Furthermore, we propose a noise removal module (NRM), which can eliminate the background noise of low-level features with the guidance of precise target location. Notably, our TFFM and NRM can effectively realize the interaction between Transformer and CNN features. Extensive experiments on four benchmark datasets demonstrate the superiority of our TransCoop against existing state-of-the-art methods. © 2022 IEEE.

关键词： Convolutional neural networks

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Transwnet: Integrating Transformers into CNNS via Row and Column Attention for Abdominal Multi-Organ Segmentation 48

Transwnet: Integrating Transformers into CNNS via Row and Co...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xie, Yazhen Huang, Yanglin Zhang, Yuan Li, Xuanya Ye, Xiongjun Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China Chinese Academy of Medical Sciences and Peking Union Medical College National Cancer Center Cancer Hospital Beijing100021 China

ISBN: (纸本)9781728163277

Learning how to model global relationships and extract local details is crucial in improving the performance of multi-organ segmentation. Most existing U-shaped structure methods use feature fusion to address these two challenges, but still lack the ability to balance capturing global relationships and local details. To address these issues, we propose a novel multi-organ segmentation framework called TransWnet to mine global relationships and local details from both intra-and inter-scale perspectives. To achieve this, we innovatively design a Row and Column Swin Transformer (RCST) module that can efficiently capture global contextual features and construct local information. Specifically, we design a parallel structure of Row and Column Attention to model the global relationships of multi-scale encoded features, and further mine local information from the global relationships through a local window mechanism. Extensive experiments on the Synapse dataset show that our method outperforms state-of-The-Art approaches and achieves accurate segmentation of abdominal multi-organs. © 2023 IEEE.

关键词： Computerized tomography

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Investigation on ULA Fitting Promoting Low Coupling Sparse Arrays

Investigation on ULA Fitting Promoting Low Coupling Sparse A...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Yao, Yichang Shi, Wanlu Li, Yingsong Huang, Zhixiang Yang, Guohui Zhang, Kuang Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150086 China

ISBN: (纸本)9781665452366

A low coupling sparse array (LCSA) is presented, analyzed and discussed on the basis of the recent proposed uniform linear array (ULA) fitting scheme with close-expressions, and its performance with different uniform subarrays in the basic layer is investigated via parameter estimation like direction-of-arrival(DOA). The results gives a superior performance for the presented LCSA. © 2022 IEEE.

关键词： Low mutual coupling sparse array uniform linear array fitting

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Design of a Broadband MIMO Antenna with Shared Radiator

Design of a Broadband MIMO Antenna with Shared Radiator

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Rongzhen Fang Xianliang Wu Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China Department of Education of Anhui Province Anhui University Hefei China

In this paper, a broadband 5G MIMO mobile antenna with a shared radiator is proposed. The working band range is 3.3-6.0GHz, covering n77, n78, n79 and WLAN 5G working bands, which can meet a large number of application scenarios of 5G mobile phones. In addition, the antenna is applied to self-decoupling, defect ground, direction diversity and other technologies and principles in the decoupling way, without the use of complex decoupling structure, to ensure that its structure is simple and easy to process, small size is suitable for today's 5G mobile phones.

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A Unified Framework Integrating Knowledge and Data for Collaborative Root Cause Identification

A Unified Framework Integrating Knowledge and Data for Colla...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jiefei Yu Zicheng Cao Siyi He Zuyi Gu Yingcheng Xu Kai Zhong Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Anhui China Data Science and Big Data Technology School of Big Data and Statistics Anhui University Anhui China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller.

关键词： Fault diagnosis Knowledge engineering Reactive power Analytical models Delay effects Redundancy Collaboration Cause effect analysis Vectors Safety

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HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection 24

HD-KT: Advancing Robust Knowledge Tracing via Anomalous Lear...

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33rd ACM Web Conference, WWW 2024

作者： Ma, Haiping Yang, Yong Qin, Chuan Yu, Xiaoshan Yang, Shangshang Zhang, Xingyi Zhu, Hengshu Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Institutes of Physical Science and Information Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Beijing China School of Artificial Intelligence Anhui University Anhui Hefei China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui Hefei China Career Science Lab BOSS Zhipin Beijing China

ISBN: (纸本)9798400701719

Knowledge tracing (KT) is a crucial task in online learning, aimed at tracing and predicting each student's knowledge states throughout their learning process. Over the past decade, it has garnered widespread attention due to it provides the potential for more tailored and adaptive online learning experiences. Although most current KT methodologies emphasize optimizing network structures to enhance predictive accuracy for future student performance, they often neglect anomalous interactions in students' learning processes, which may arise from low data quality (i.e., inferior question quality) and abnormal student behaviors (i.e., guessing and mistakes). To this end, in this paper, we propose a novel framework, termed HD-KT, designed to enhance the robustness of existing KT methodologies with Hybrid learning interactions Denoising approach. Specifically, we introduce two detectors for anomalous learning interactions, namely knowledge state-guided anomaly detector and student profile-guided anomaly detector. In the first detection module, we design a sequential autoencoder to identify anomalous learning interactions by detecting atypical student knowledge states. In the second module, we incorporate an attention mechanism by modeling a student's long-term profile to capture irregular interactions. Extensive experiments on four real-world benchmark datasets have decisively shown our HD-KT markedly boosts the robustness of numerous prevailing KT models, consequently increasing the accuracy of future student performance predictions. Additionally, our case studies highlight the versatility of HD-KT in addressing diverse downstream tasks, such as exercise quality analysis and learning behavior-based student clustering. © 2024 ACM.

关键词： Students

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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion

arXiv

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arXiv 2024年

作者： Cao, Yice Liu, Chenchen Wu, Zhenhua Yao, Wenxin Xiong, Liu Chen, Jie Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China The 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

As remote sensing imaging technology continues to advance and evolve, processing high-resolution and diversified satellite imagery to improve segmentation accuracy and enhance interpretation efficiency emerg as a pivotal area of investigation within the realm of remote sensing. Although segmentation algorithms based on CNNs and Transformers achieve significant progress in performance, balancing segmentation accuracy and computational complexity remains challenging, limiting their wide application in practical tasks. To address this, this paper introduces state space model (SSM) and proposes a novel hybrid semantic segmentation network based on vision Mamba (CVMH-UNet). This method designs a cross-scanning visual state space block (CVSSBlock) that uses cross 2D scanning (CS2D) to fully capture global information from multiple directions, while by incorporating convolutional neural network branches to overcome the constraints of Vision Mamba (VMamba) in acquiring local information, this approach facilitates a comprehensive analysis of both global and local features. Furthermore, to address the issue of limited discriminative power and the difficulty in achieving detailed fusion with direct skip connections, a multi-frequency multi-scale feature fusion block (MFMSBlock) is designed. This module introduces multi-frequency information through 2D discrete cosine transform (2D DCT) to enhance information utilization and provides additional scale local detail information through point-wise convolution branches. Finally, it aggregates multi-scale information along the channel dimension, achieving refined feature fusion. Findings from experiments conducted on renowned datasets of remote sensing imagery demonstrate that proposed CVMH-UNet achieves superior segmentation performance while maintaining low computational complexity, outperforming surpassing current leading-edge segmentation algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A Time-Aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua Science and Technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

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The Application of the ADI-FDTD method in the 3D hybrid subgrid technique

The Application of the ADI-FDTD method in the 3D hybrid subg...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Song, Ziheng Xie, Guoda Huang, Zhixiang Hou, Guilin Pan, Pan Anhui University School of Electronic and Information Engineering Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9781665452366

A hybrid subgrid scheme based on the conventional finite-difference time-domain (FDTD) schemes are proposed. The alternating-direction-implicit FDTD (ADI-FDTD) is used to calculate electromagnetic fields in fine grid regions subgrids and the FDTD method is applied to the coarse grid regions. Numerical results demonstrate that the drastically reduced computation time and memory can be achieved by using the proposed 3D hybrid algorithm. © 2022 IEEE.

关键词： Finite difference time domain method

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