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MSMAE-Net: multi-semantic and multi-attention enhanced network for image forgery localization

Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Liao, Jianjun Su, Lichao Lu, Menghan Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

The popularization of modern digital image technology has brought convenience to us, but it also poses many risks. The advancement of image editing software allows anyone to modify image content effortlessly. If these modified images are abused, they can severely impact societal safety and stability. To address these risks, we propose an end-to-end multi-scale collaborative enhancement image forgery localization network, termed MSMAE-Net. The method first employs a multi-branch feature extractor to initially capture both global and local information, with each branch optimized for different contextual information and features of the image. Next, to enhance the model’s ability to capture forged traces at various levels while maintaining the correlation between different spatial hierarchies, a boundary information aggregation module is designed. By constructing multiple branches with different receptive fields and collaborative learning with each other, a nested collaborative enhancement branch is designed to make the backbone network pay more attention to key features, so as to obtain better feature representation capability. Furthermore, to further fuse different semantic information, a novel strongly compatible semantic attention fusion module is proposed in this paper. Finally, an attention enhancement module is introduced in the localization of forgery regions to adaptively concentrate on key forged areas in the image and effectively capture the relationships between different pixels. In the experiments, we demonstrate that MSMAE-Net exhibits significant advantages in both localization accuracy and robustness in complex forgery scenarios when compared to other SOTA image forgery detection methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Photointerpretation

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A Simple Graph Contrastive Learning Framework for Short Text Classification

arXiv

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arXiv 2025年

作者： Liu, Yonghao Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China University of Trento Italy

Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short text classification. However, existing models have certain limitations. They rely on explicit data augmentation techniques to generate contrastive views, resulting in semantic corruption and noise. Additionally, these models only focus on learning the intrinsic consistency between the generated views, neglecting valuable discriminative information from other potential views. To address these issues, we propose a Simple graph contrastive learning framework for Short Text Classification (SimSTC). Our approach involves performing graph learning on multiple text-related component graphs to obtain multi-view text embeddings. Subsequently, we directly apply contrastive learning on these embeddings. Notably, our method eliminates the need for data augmentation operations to generate contrastive views while still leveraging the benefits of multi-view contrastive learning. Despite its simplicity, our model achieves outstanding performance, surpassing large language models on various datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Improving Visual Reinforcement Learning via Spatial-Temporal data Augmentation

Improving Visual Reinforcement Learning via Spatial-Temporal...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiabao Li Qingwen Hou Jie Zhang Xianzhong Chen Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Sample efficiency has been a challenging problem in visual reinforcement learning, where agents not only learn polices but also extract meaningful state representations for decision making from images. Reinforcement Learning methods that adopt data augmentation have significantly improved sample efficiency. However, prior studies have primarily focused on spatial augmentation, which enhances data diversity but insufficiently exploits temporal information, such as historical states which is critical for time-series-dependent reinforcement learning tasks. To address this gap, we introduce a Spatial-Temporal data Augmentation (STDA) framework designed to enhance the agent's ability to learn task-relevant state representations by effectively leveraging historical state information. We evaluate our approach using the DeepMind Control Suite, and empirical results demonstrate that STDA significantly improves performance, surpassing state-of-the-art methods across a range of tasks.

关键词： Visualization Q-learning Law enforcement Decision making Training data Reinforcement learning Benchmark testing data augmentation Consumer electronics

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LSDK-LegalSum: improving legal judgment summarization using logical structure and domain knowledge

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-15页

作者： Gao, Wei Yu, Shuai Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Engineering Research Center of Text Computing & CognitiveIntelligence Ministry of Education Guizhou University Guizhou Huaxi Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Huaxi Guiyang 550025 China College of Computer Science and Technology Guizhou University Guizhou Huaxi Guiyang 550025 China

Legal judgment summarization enhances the efficiency and accuracy of processing and retrieving similar cases by distilling key information from legal judgment documents. Legal judgments, which record the trial process and outcome, tend to have lengthy texts. However, most existing methods for legal judgment summarization struggle to effectively handle long texts and focus only on the serialized text information, neglecting the auxiliary role of the document’s logical structure in generating summaries. Additionally, legal judgment documents contain extensive domain knowledge formed by professional legal terminology, which is crucial for accurately understanding and analyzing the content of the documents. We propose a method to improve the quality of legal judgment summaries by utilizing logical structures and domain knowledge (LSDK-LegalSum). Specifically, we first use a multi-label document classification approach to divide the legal judgment into four parts: ‘Type’, ‘Claim’, ‘Fact’ and ‘Result’, then generate a summary of the ‘Type’ part, and combine it with the ‘Claim’ part to create a new summary, and so forth until the final summary is produced. Moreover, the case descriptions in legal judgments cause substantial textual noise;therefore, we employ a text-denoising method that integrates domain knowledge to further enhance the performance of the summaries. Extensive experiments on the CAIL2020 legal summarization dataset have shown that our method achieves improvements of 8.37, 16.64, and 13.07 percentage points in Rouge-1, Rouge-2, and Rouge-L scores respectively over the state-of-the-art models. This demonstrates that our method not only produces concise and semantically complete summaries but also ensures that the content is logically rigorous and well-organized, meeting practical needs more effectively. © The Author(s) 2025.

关键词： Abstractive summarization Domain knowledge Legal judgment summarization Logical structure Low-source summarization

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Steel Defect Detection Algorithm Based on Multiobjective ENAS Sharing Strategy 24

Steel Defect Detection Algorithm Based on Multiobjective ENA...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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Temperature distribution and damage of thrombus induced by pulsed laser

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Infrared Physics and Technology 2025年 148卷

作者： Zhao, Xiaoli Qiu, Pengtianyu Xie, Enci Zhang, Chenxi Wang, Guangyu Fu, Bo Key Laboratory of Precision Opto-Mechatronics Technology of Education Ministry School of Instrumentation and Optoelectronic Engineering Beihang University Beijing100191 China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology School of Engineering Medicine Beihang University Beijing100191 China

In the process of laser thermal ablation, real-time observation of temperature distribution and damage is helpful to guide the laser thrombolysis process. However, in actual operation, it is difficult to predict the effect of laser parameters on the thermal effect of thrombus. It is of great significance to use finite element simulation method to study the influence of different laser parameters, such as power, time, spot size, pulse width, pulse interval, absorption coefficient, and laser mode on the surface temperature and damage of thrombus. The influence of power and time on temperature and damage in the simulation are verified by experiment, and the trend of experimental variation is consistent with the simulation results. The simulation results will provide theoretical guidance for selecting the optimal laser parameters in subsequent experiments. © 2025 Elsevier B.V.

关键词： Temperature distribution

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A knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 506-517页

作者： Zhuoming Zheng Yi Cai Liuwu Li School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying Misc aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.

关键词： Reviews Contrastive learning Vectors Feature extraction knowledge graphs Speech processing Sentiment analysis knowledge engineering Hardware Touch sensitive screens

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Enhancing diabetes complications prediction through knowledge graphs and convolutional networks

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engineering Applications of Artificial Intelligence 2025年 153卷

作者： Cheng, Haitao Zheng, Qunli Li, Peng Xu, He School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Early prediction of diabetes complications is crucial for timely intervention and effective disease management. However, current deep learning approaches often lack sufficient representation of diabetes knowledge and rely on large-scale annotated data. To address this, we propose a knowledge Graphs-enhanced Diabetes Complications Prediction (KGDCP) model, which integrates a physical examination knowledge graph and an improved Diabetes knowledge Graph (DiaKG) using self-attention-based convolutional neural networks. We construct the physical examination knowledge graph based on normal reference ranges of examination indicators and enhance DiaKG through knowledge deduplication and coreference resolution, improving its accuracy and comprehensiveness. Furthermore, we employ the embeddings of these two knowledge graphs to represent examination data and diagnostic information. A token-to-token (token2token) self-attention is utilized to explore dependencies among examination indicators, and convolutional neural networks extract local features to generate representation vectors. Additionally, source-to-token (source2token) self-attention assesses dependencies between diagnostic entities and the entire entity set, with entity-to-Physical Examination (entity2PE) attention gauging entity relevance to examination data representation vectors. Our model combines these features to enhance diabetes complications prediction, outperforming traditional models in predicting cerebral infarction and peripheral neuropathy. © 2025

关键词： knowledge graph

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Development of a high-resolution near-surface meteorological forcing dataset for the Third Pole region

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Science China Earth Sciences 2025年第4期68卷 1274-1290页

作者： Yaozhi JIANG Wenjun TANG Kun YANG Jie HE Changkun SHAO Xu ZHOU Hui LU Yingying CHEN Xin LI Jiancheng SHI School of Resources and Environment University of Electronic Science and Technology of ChinaChengdu611731China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing100084China National Space Science Center Chinese Academy of SciencesBeijing100190China

Recently,the Third Pole(TP)region has experienced rapid environmental *** data are essential for hydrometeorological and ecological applications but still have large uncertainties on the TP owing to the heterogeneous land surface,complex terrain,and sparse weather *** this study,a long-term(1979–2020)high-resolution(1/30°)meteorological forcing dataset for the TP(TPMFD)was developed,as a sister to the widely used China Meteorological Forcing dataset(CMFD).The TPMFD comprises seven components necessary for driving land surface *** have previously contributed precipitation and downward shortwave radiation data for the TPMFD,and this study presents the development of five other components and focuses on validations for all ***,2-meter air temperature,2-meter specific humidity,10-meter wind speed,and surface air pressure were generated by combining the fifth-generation atmospheric reanalysis for European Center for Medium-Range Weather Forecasts(ERA5),a short-term high-resolution atmospheric simulation,and in situ observations,and the downward longwave radiation was calculated using semi-physical *** cross-validation and independent-validation demonstrated that most variables in the developed dataset outperformed those in widely used reanalysis datasets,including ERA5,ERA5-Land,and the Global Land data Assimilation System(GLDAS).This dataset is expected to be beneficial for climate analyses and modeling applications of land-surface processes on the TP.

关键词： Meteorological data Third Pole High-resolution data downscaling and merging

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UAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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