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SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1717-1719页

作者： Jiufang Chen Ye Yuan Xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

关键词： representation preserving undirected

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DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE OPTIMIZATION FOR MAXIMUM MEAN DISCREPANCY 12

DETECTING MACHINE-GENERATED TEXTS BY MULTI-POPULATION AWARE ...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Shuhai Song, Yiliao Yang, Jiahao Li, Yuanqing Han, Bo Tan, Mingkui South China University of Technology China Pazhou Laboratory China The University of Adelaide Australia Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong

Large language models (LLMs) such as ChatGPT have exhibited remarkable performance in generating human-like texts. However, machine-generated texts (MGTs) may carry critical risks, such as plagiarism issues, misleading information, or hallucination issues. Therefore, it is very urgent and important to detect MGTs in many situations. Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs. In this paper, we seek to exploit maximum mean discrepancy (MMD) to address this issue in the sense that MMD can well identify distributional discrepancies. However, directly training a detector with MMD using diverse MGTs will incur a significantly increased variance of MMD since MGTs may contain multiple text populations due to various LLMs. This will severely impair MMD's ability to measure the difference between two samples. To tackle this, we propose a novel multi-population aware optimization method for MMD called MMD-MP, which can avoid variance increases and thus improve the stability to measure the distributional discrepancy. Relying on MMD-MP, we develop two methods for paragraph-based and sentence-based detection, respectively. Extensive experiments on various LLMs, e.g., GPT2 and ChatGPT, show superior detection performance of our MMD-MP. The source code is available at https://***/ZSHsh98/MMD-MP. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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

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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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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing 400065China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing400065China Chongqing Innovation Center of Industrial Big-Data Co.Lt Chongqing400700China

The lack of facial features caused by wearing masks degrades the performance of facial recognition *** occluded face recognition methods cannot integrate the computational resources of the edge layer and the device ***,previous research fails to consider the facial characteristics including occluded and unoccluded *** solve the above problems,we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature ***,the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face ***,a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain ***,a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load,computational power,bandwidth,and delay tolerance constraints of the *** method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition *** experimental results show that the proposed method achieves an average gain of about 21%in recognition latency,while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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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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