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Graph-Based Information Block Detection in Infographic With Gestalt Organization Principles

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023年第3期29卷 1705-1718页

作者： Lin, Jie Cai, Yi Wu, Xin Lu, Jianwei South China Univ Technol Sch Software Engn Guangzhou 510641 Guangdong Peoples R China SouthChina Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510641 Guangdong Peoples R China

An infographic is a type of visualization chart that displays pieces of information through information blocks. Existing information block detection work utilizes spatial proximity to group elements into several information blocks. However, prior studies ignore the chromatic and structural features of the infographic, resulting in incorrect omissions when detecting information blocks. To alleviate this kind of error, we use a scene graph to represent an infographic and propose a graph-based information block detection model to group elements based on Gestalt Organization Principles (spatial proximity, chromatic similarity, and structural similarity principle). We also construct a new dataset for information block detection. Quantitative and qualitative experiments show that our model can detect the information blocks in the infographic more effectively compared with the spatial proximity-based method.

关键词： Feature extraction Visualization data visualization Semantics Organizations Layout Task analysis Infographic deep learning graph-based approach information block detection

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Modularized Mutuality Network for Emotion-Cause Pair Extraction

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 539-549页

作者： Shang, Xichen Chen, Chuxin Chen, Zipeng Ma, Qianli South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Minist Educ Guangzhou 510006 Peoples R China

Emotion-cause pair extraction (ECPE) is an emerging task born out of Emotion cause extraction (ECE), which aims to extract the emotion clause and the corresponding cause clause simultaneously. Previous methods decompose ECPE into multiple sub-tasks, namely emotion clause extraction, cause clause extraction, and emotion-cause pair extraction, and employ different modules to address them separately. However, these methods fail to effectively capture the mutuality within the three sub-tasks, which may hinder the information interaction between emotion and cause. In this paper, we revisit and analyze the mutuality between emotion and cause clauses from a linguistic perspective and further propose a novel Modularized Mutuality Network (MMN) to capture the mutuality explicitly. Specifically, the mutuality can be divided into the following categories, including position bias, sentiment consistency, and natural duality. To this end, we design three modules wrapped with various simple but effective mechanisms to address the mutuality, respectively. Extensive experiments demonstrate that MMN achieves state-of-the-art performances on the ECPE task and detailed analyzed the effect of the three modules for capturing the mutuality within sub-tasks.

关键词： Mutuality attention mechanism dual regularized predictor emotion-cause pair extraction

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EXISTENCE AND UNIQUENESS OF SOLUTIONS FOR UNCERTAIN NONLINEAR SWITCHED SYSTEMS WITH V-N JUMPS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 129-140页

作者： Jia, Zhifu Li, Cunlin School of Mathematics and Physics Suqian University Suqian223800 China [Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and Social Management Research Center of Northwest Ethnic Regions North Minzu University Yinchuan China

In this paper, an uncertain nonlinear switched system with V-n jumps, characterized by its sensitivity to subjective uncertainties, is modeled using uncertain differential equations with V-n jumps. To account for the discontinuous jump behavior in each subsystem, a V-n jumps process associated with an uncertain Z-n jumps variable, defined by a jump uncertainty distribution, is introduced. Under the assumptions of linear growth and Lipschitz conditions, an existence and uniqueness theorem for the solutions of uncertain nonlinear switched systems with V-n jumps is established and rigorously proven. An illustrative example is provided to demonstrate the effectiveness and applicability of the theoretical results. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Uncertain systems

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An Answer Summarization Scheme Based on Multilayer Attention Model 26

An Answer Summarization Scheme Based on Multilayer Attention...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Dong, Yihao Song, Jian Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China

ISBN: (纸本)9798350331684

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence"of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system. © 2023 IEEE.

关键词： Semantics

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LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micro Base Stations 5

LDMOS-Based Doherty Power Amplifier Design in 5G Mobile Micr...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Gao, Mingming Wang, Xinyu Wang, Congying Zhang, Zhongxiang Shan, Shiquan Song, Yonghui College of Electronics and Information Engineering Liaoning Technicial University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Huludao China

ISBN: (纸本)9798331533694

A Doherty Power Amplifier (DPA) has been designed and optimized specifically for compact mobile base station deployment, operating within a frequency range of 3.3 GHz to 3.6 GHz. The amplifier utilizes the proprietary DS10-2092 LDMOS transistors developed by the company for both the carrier and peak power amplifiers. Physical test results indicate that the DPA achieves saturated output power meeting the specified requirements, with a maximum saturated drain efficiency of 41%. The drain efficiency remains stable between 26.3% and 30.7% over a 6 dB power back-off range, demonstrating its superior performance in high-efficiency and broadband applications. © 2025 IEEE.

关键词： Doherty amplifiers

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A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving

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Neural Computing and Applications 2025年第3期37卷 1651-1672页

作者： Xu, Junjie Chen, Yuzhong Xiao, Lingsheng Liao, Hongmiao Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD 11

TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD

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11th International Conference on Learning Representations, ICLR 2023

作者： Niu, Shuaicheng Wu, Jiaxiang Zhang, Yifan Wen, Zhiquan Chen, Yaofo Zhao, Peilin Tan, Mingkui South China University of Technology China Tencent AI Lab China National University of Singapore Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Pazhou Laboratory China

Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world. Specifically, TTA may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, and 3) online imbalanced label distribution shifts, which are quite common in practice. In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability. Conversely, TTA can perform more stably with batch-agnostic norm layers, i.e., group or layer norm. However, we observe that TTA with group and layer norms does not always succeed and still suffers many failure cases. By digging into the failure cases, we find that certain noisy test samples with large gradients may disturb the model adaption and result in collapsed trivial solutions, i.e., assigning the same class label for all samples. To address the above collapse issue, we propose a sharpness-aware and reliable entropy minimization method, called SAR, for further stabilizing TTA from two aspects: 1) remove partial noisy samples with large gradients, 2) encourage model weights to go to a flat minimum so that the model is robust to the remaining noisy samples. Promising results demonstrate that SAR performs more stably over prior methods and is computationally efficient under the above wild test scenarios. The source code is available at https://***/mr-eggplant/SAR. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Entropy

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MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning 26

MDPFL:A Multiple Differential Privacy Protection Method base...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Xinyu Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China

ISBN: (纸本)9798350331684

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users' original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved. © 2023 IEEE.

关键词： Learning algorithms

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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain Science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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Image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 IEEE.

关键词： Image matching

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