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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Bi, Chenchong Li, Yingsong Li, Liping Zlatanov, Nikola Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

A kernel Weibull M-transform maximum-versoria-criterion (KWMMVC) algorithm is constructed, which is to use for nonlinear system identification. The KWMMVC algorithm ingeniously integrates the Weibull M-transform scheme into the maximum-versoria-criterion to formulate a novel cost function. The ability of KWMMVC algorithm to identify nonlinear channels under various impulse noise environments is tested in the kernel adaptive filters, and the results proved its superior performance. © 2024 IEEE.

关键词： Adaptive filtering

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Robust DOA Estimation Against Outliers Based on Bias-compensated Adaptive Filtering 7

Robust DOA Estimation Against Outliers Based on Bias-compens...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Xiao, Wudang Yu, Canping Li, Yingsong Li, Liping Zou, Dingcheng Zlatanov, Nikola Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Innopolis University Faculty of Computer and Engineering Sciences Innopolis Russia

ISBN: (纸本)9798350384437

The direction-of-arrival (DOA) estimation approach utilizing the adaptive nulling array framework has gained widespread attention due to its low complexity. However, in impulsive noise environments, traditional least mean squares (LMS) algorithm may experience a sharp decline in performance or even fail to work properly. To address this challenge, we propose a DOA estimation approach designed for impulsive noise situations based on a robust nonlinear function. In addition, the algorithm can mitigate bias caused by auxiliary array noise and uses the variable step-size strategy to enhance its performance. Furthermore, the mean stability of the proposed approach is also assessed. Simulation results demonstrate its superior performance in impulsive noise environments. © 2024 IEEE.

关键词： Adaptive filtering

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Calibrating the Confidence of Large Language Models by Eliciting Fidelity

Calibrating the Confidence of Large Language Models by Elici...

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2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhang, Mozhi Huang, Mianqiu Shi, Rundong Guo, Linsen Peng, Chong Yan, Peng Zhou, Yaqian Qiu, Xipeng School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Meituan China

ISBN: (纸本)9798891761643

Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. However, post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. In this paper, we decompose the language model confidence into the Uncertainty about the question and the Fidelity to the answer generated by language models. Then, we propose a plug-and-play method, UF Calibration, to estimate the confidence of language models. Our method has shown good calibration performance by conducting experiments with 6 RLHF-LMs on four MCQA datasets. Moreover, we propose two novel metrics, IPR and CE, to evaluate the calibration of the model, and we have conducted a detailed discussion on Truly Well-Calibrated Confidence for large language models. Our method could serve as a strong baseline, and we hope that this work will provide some insights into the model confidence calibration. © 2024 Association for Computational Linguistics.

关键词： Calibration

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A Novel Transformed Nested Array with Enhanced DOFs Based on Sum-Difference Coarray 6

A Novel Transformed Nested Array with Enhanced DOFs Based on...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Xin, Wei Li, Yingsong Harbin Engineering University China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350399059

Sum-difference driven coarray (SDCA) was paid great attention in array-signal-processing (ASP). By considering SDCA, the degrees of freedom (DOFs) for sparse arrays can be further improved. Here, a new transformed nested-array (TNA) is constructed, which reduces the element redundancy via rearranging the density of sub-arrays of the constructed new TNA. Compared to former TNAs, the constructed TNA gets higher DOFs and maximization signals, numerical simulation are given for getting its superior behaviors. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation 30th

GDTNet: A Synergistic Dilated Transformer and CNN by Gate ...

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30th International Conference on MultiMedia Modeling, MMM 2024

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

ISBN: (纸本)9783031533013

As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer aided analysis

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Adaptive Spatial Modeling and Multi-Scale Attention Aggregation for Semantic Segmentation of Remote Sensing Images 2

Adaptive Spatial Modeling and Multi-Scale Attention Aggregat...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Yang, Wenying Zhang, Yuchuan Jin, Yuxuan Wang, Yupei Chen, Liang Beijing Institute of Technology Chongqing Innovation Center National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing China Jianghan University School of Artificial Intelligence Wuhan China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

As remote sensing technology advances and high-resolution sensors are deployed, the analysis of high-resolution remote sensing images encounters challenging issues, such as intra-class variability, and inter-class similarity, due to the unobvious appearance and complicated background. The high-resolution remote sensing images pose critical issues for precise semantic segmentation due to these fundamental issues. To this end, we propose an adaptive hierarchical aggregation network. Different from the previous approach of modeling semantic interdependencies solely within a single feature map in the channel dimension, we introduce multi-scale channel-wise feature aggregation to capture global contextual dependencies. Meanwhile, we design an adaptive detail-aware module to model long-range spatial dependencies, adaptively extracting effective details from different levels as compensation. Additionally, this module utilizes depth-wise separable convolution to selectively filter out irrelevant spatial details from lower-level feature maps. The outputs of the two modules are fused layer by layer to achieve refined feature representation, thereby generating precise pixel-level segmentation results. Our experiments are conducted on the Vaihingen dataset and the Potsdam dataset. The experimental results demonstrate that the proposed algorithm surpasses several existing state-of-the-art approaches. © 2024 IEEE.

关键词： Semantic Segmentation

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Numerical‐discrete‐scheme‐incorporated recurrent neural network for tasks in natural language processing

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1415-1424页

作者： Mei Liu Wendi Luo Zangtai Cai Xiujuan Du Jiliang Zhang Shuai Li School of Information Science and Engineering Lanzhou UniversityLanzhouChina The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Normal UniversityXiningChina Department of Electronic and Electrical Engineering The University of SheffieldSheffieldUK

A variety of neural networks have been presented to deal with issues in deep learning in the last *** the prominent success achieved by the neural network,it still lacks theoretical guidance to design an efficient neural network model,and verifying the performance of a model needs excessive *** research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential *** connection sheds light on designing an effective recurrent neural network(RNN)by resorting to numerical *** RNN is regarded as a discretisation of the forward Euler *** the limited solution accuracy of the forward Euler methods,a Taylor‐type discrete scheme is presented with lower truncation error and a Taylor‐type RNN(T‐RNN)is designed with its *** experiments are conducted to evaluate its performance on statistical language models and emotion analysis *** noticeable gains obtained by T‐RNN present its superiority and the feasibility of designing the neural network model using numerical methods.

关键词： deep learning natural language processing neural network text analysis

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A RIS-Assisted Multiuser MIMO Communication Method Based on Deep Reinforcement Learning 7

A RIS-Assisted Multiuser MIMO Communication Method Based on ...

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7th International Conference on Communication and information Systems, ICCIS 2023

作者： Tian, Changlin Zhi, Hui Tie, Kui Anhui University School of Electronic and Information Engineering Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei China CO. Ltd Bengbu China

ISBN: (纸本)9798350330540

In this paper, a RIS-assisted multiuser MIMO communication method based on deep reinforcement learning (RMMC-DRL) is proposed for multiuser scenarios. Our objective is to find the optimal transmit beamforming matrix of BS and optimal phase shift matrix of reflective intelligent surface (RIS) to maximize the sum rate of multiuser, this problem is reduced into a constrained optimization problem. It is a non-convex optimization problem, so we solve it through deep reinforcement learning (DRL) and then use the results for communication. In the DRL, a deep deterministic policy gradient (DDPG) framework that can handle continuous states and actions is designed, reward is set as optimization goal, and the transmit beamforming matrix and the phase shift matrix of RIS are obtained through the interaction with environment. Unlike the alternating optimization (AO) method, which solve the transmit beamforming matrix and the RIS phase shift matrix alternatively, the RMMC-DRL can obtain both transmit beamforming matrix and RIS phase shift matrix simultaneously as the output of DRL. Simulation results show that RMMC-DRL can learn and improve its behavior by interacting with the environment. Compared with AO method, RMMC-DRL can obtain higher sum rate and lower computational complexity. © 2023 IEEE.

关键词： beamforming matrix deep reinforcement learning phase shift matrix reconfigurable intelligent surface

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Generalized Self-Adaption Network for Domain Adaptation 2

Generalized Self-Adaption Network for Domain Adaptation

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2nd International Conference on Artificial Intelligence and intelligent information processing, AIIIP 2023

作者： Bian, Yongheng Ke, Xiao College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

ISBN: (纸本)9798350371451

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods. © 2023 IEEE.

关键词： Computer vision

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Exploring Frontier Technologies in Video-Based Person Re-Identification:A Survey on Deep Learning Approach

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Computers, Materials & Continua 2024年第10期81卷 25-51页

作者： Jiahe Wang Xizhan Gao Fa Zhu Xingchi Chen Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan250022China College of Information Science and Technology&College of Artificial Intelligence Nanjing Forestry UniversityNanjing210037China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen518066China

Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence *** advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the *** order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function ***,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these ***,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance ***,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person *** comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain.

关键词： Video-based person Re-ID deep learning survey of video Re-ID loss function

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