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arXiv 2023年

作者： Shi, Zenan Chen, Haipeng Chen, Long Zhang, Dong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of CSE The Hong Kong University of Science and Technology Hong Kong

In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (e.g., noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction

arXiv

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arXiv 2025年

作者： Zhang, Ruochi Yang, Qian Wang, Xiaoyang Wu, Haoran Zhou, Qiong Wang, Yu Li, Kewei Wang, Yueying Fan, Yusi Zhang, Jiale Huang, Lan Liu, Chang Zhou, Fengfeng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Beijing Life Science Academy Beijing102209 China Syneron Technology Guangzhou510700 China

The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven effective, they often lack robust representation learning and depend heavily on expert-crafted features. Although deep learning offers powerful solutions, it is often criticized for its lack of interpretability. To address these challenges, we propose DeepSelective, a novel end to end deep learning framework for predicting patient prognosis using EHR data, with a strong emphasis on enhancing model interpretability. DeepSelective combines data compression techniques with an innovative feature selection approach, integrating custom-designed modules that work together to improve both accuracy and interpretability. Our experiments demonstrate that DeepSelective not only enhances predictive accuracy but also significantly improves interpretability, making it a valuable tool for clinical decision-making. The source code is freely available at http://***/supp/*** . © 2025, CC BY-NC-ND.

关键词： Electronic health record

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END-TO-END ENTITY DETECTION WITH PROPOSER AND REGRESSOR

arXiv

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arXiv 2022年

作者： Wen, Xueru Zhou, Changjiang Tang, Haotian Liang, Luguang Jiang, Yu Qi, Hong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Sfmfold: A Multi-Modal Contrast Learning Model for Rna Secondary Structure Prediction

SSRN

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SSRN 2025年

作者： Li, Ang Liu, Yuanning Zhang, Zhaoyang Li, Shuwang Dong, Hao Lv, Meng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China College of Information Engineering Chang’an University Xi’an710064 China

The secondary structure of RNA can regulate the interaction between RNA and protein and perform various biological tasks through specific folding shapes, which plays an important role in revealing biomolecular mechanisms and developing new vaccines and therapeutic drugs. However, existing RNA secondary structure prediction methods based on thermodynamics and traditional neural network models usually lack the ability to model long-distance dependencies of RNA molecules and cannot handle complex non-nested structures in RNA molecules. In this paper, we propose a multi-modal contrastive learning method SFMFold based on state space model, which captures long-distance interactions in RNA sequences through RNA sequence representation learning part, and models the structure of RNA molecules through RNA structure representation learning part. Finally, the features of the two modalities are aligned and deeply fused to obtain the final RNA molecule prediction map. We test SFMFold on several datasets, and the experiments show that our proposed model shows excellent performance in cross-family prediction and long sequence prediction tasks. In addition, SFMFold can also effectively predict non-nested structures such as pseudoknot with better accuracy than the SOTA methods. © 2025, The Authors. All rights reserved.

关键词： Prediction models

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ON A NOVEL UCP RESULT AND ITS APPLICATION TO INVERSE CONDUCTIVE SCATTERING

arXiv

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arXiv 2024年

作者： Diao, Huaian Fei, Xiaoxu Liu, Hongyu School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Mathematics and Statistics Central South University Changsha410083 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong

In this paper, we derive a novel Unique Continuation Principle (UCP) for a system of second-order elliptic PDEs and apply it to investigate inverse problems in conductive scattering. The UCP relaxes the typical assumptions imposed on the domain or boundary with certain interior transmission conditions. This is motivated by the study of the associated inverse scattering problem and enables us to establish several novel unique identifiability results for the determination of generalized conductive scatterers using a single far-field pattern, significantly extending the results in [15, 21]. A key technical advancement in our work is the combination of Complex Geometric Optics (CGO) techniques from [15, 21] with the Fourier expansion method to microlocally analyze corner singularities and their implications for inverse problems. We believe that the methods developed can have broader applications in other contexts. © 2024, CC BY.

关键词： Inverse problems

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Multi-Task Self-Supervised Learning for Medical Image Segmentation

Multi-Task Self-Supervised Learning for Medical Image Segmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Wang Hang Zhao Xiongfei Li Mingjie Tian Bo Huang Feiyang Yang Intelligent Information Processing Laboratory Yanbian University Yanji China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) strategy and the dynamic multi-task loss weight adjustment method (DWL) to relieve potential early convergence and diverse learning difficulties of self-supervised learning. Our method deeply integrates the prior knowledge inherent in medical images, facilitates collaborative learning among tasks, and delves further into the latent information within the images. Experimental results demonstrate that regardless of the proportion of labeled data, our method outperforms baseline models on the KiTS19 and LiTS datasets, achieving state-of-the-art performance on the KiTS19 dataset.

关键词：

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Efficient Heuristics for Learning Scalable Bayesian Network Classifier from Labeled and Unlabeled Data

SSRN

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SSRN 2023年

作者： Wang, Limin Wang, Junjie Guo, Lu Li, Qilong Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China College of Instrumentation and Electrical Engineering Jilin University Changchun130012 China

Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood Bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 UCI benchmark datasets reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Structure- and Logic-Aware Heterogeneous Graph Learning for Recommendation

Structure- and Logic-Aware Heterogeneous Graph Learning for ...

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International Conference on Data engineering

作者： Anchen Li Bo Yang Huan Huo Farookh Khadeer Hussain Guandong Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure- and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets.

关键词： Geometry Semantics Contrastive learning Data engineering Boosting Cognition Graph neural networks

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Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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Chinese Journal of Electronics 2020年第6期29卷 1153-1164页

作者： XU Qianyi QIN Guihe SUN Minghui YAN Jie JIANG Huiming ZHANG Zhonghan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Jilin University College of Software Jilin University

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional ***,video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture *** that if the displays are more relevant,the results will more accurate,so ResNext,a kind of compact and efficient neural network,is firstly used as feature extractor,then an improved weighted frame unification method is adopted to obtain the key frame samples,finally the Discriminant correlation analysis(DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch(FEB) on static *** public dataset named Depth based gesture recognition database(DGRD) is used in this paper,but the dataset is a little small and the class distribution is largely imbalance,and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training *** order to conquer the disadvantages of limited dataset,a special loss function scheme combining the soft max loss and dice loss is *** of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

关键词： feature extraction gesture recognition learning (artificial intelligence) neural nets Depth based gesture recognition database Feature embedding branch dynamic data static data key frame samples improved weighted frame unification method feature extractor efficient neural network compact network uncorrelated gesture factors video gestures recognition depth videos automotive interfaces Feature fusion based hand gesture recognition method Weighted frame unification Discriminant correlation analysis (DCA) Feature embedding branch (FEB) Gesture recognition Loss function

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UAV-enabled Wireless Powered Communication Networks: A Joint Scheduling and Trajectory Optimization Approach

UAV-enabled Wireless Powered Communication Networks: A Joint...

引用

IEEE Symposium on Computers and Communications (ISCC)

作者： Ziwen An Yanheng Liu Geng Sun Hongyang Pan Aimin Wang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781665497923

ISBN: (纸本)9781665497930

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCN) are promising technologies in Internet of Things (IoTs). However, energy-constrained devices and connectivity in complex environments are two major challenges for IoTs. We consider a UAV-enabled WPCN scenario that a UAV can connect with the ground IoT devices (IoTDs). To connect and fly faster, UAV needs to be scheduled reasonably and the corresponding trajectory should be optimized. Thus, we formulate a UAV scheduling and trajectory optimization problem (USTOP) to minimize the total time so that improving the charging and transmission efficiency. Since conventional methods are difficult to solve USTOP, we propose an improved simulated annealing (ISA) with the variable size changing mechanism, the conflict resolution mechanism and the hybrid evolution method to solve it. Simulation results verify the effectiveness and performance of ISA under different scales of the network, and the stability of the proposed algorithm is verified.

关键词： Wireless communication Computers Annealing Simulation Simulated annealing Autonomous aerial vehicles Stability analysis

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