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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： software testing

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An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

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Journal of Bionic engineering 2021年第6期18卷 1452-1462页

作者： Yingda Lyu Yunqi Zhang Haipeng Chen Public Computer Education and Research Center Jilin UniversityChangchun130012China College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during *** this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse *** two-scale image decomposition,the input image is decomposed into base layer and detail *** the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge ***,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization *** the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency *** sum of the above base and detail layers is as the final fused *** results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.

关键词： Improved pigeon-inspired optimization Convolutional sparse representation Noisy image fusion Bionic algorithm

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3DSEAVNet: 3D-Squeeze-and-Excitation Networks for Audio-Visual Saliency Prediction

3DSEAVNet: 3D-Squeeze-and-Excitation Networks for Audio-Visu...

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International Joint Conference on Neural Networks (IJCNN)

作者： Silong Liang Chunxiao Li Naying Cui Minghui Sun Hao Xue College of Software Engineering JiLin University Changchun 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

Video saliency prediction is an important task in the field of computer vision. Most of the existing video saliency prediction methods only focus on image information, and the audio information is often ignored. This leads to an incomplete perception mode, which makes it difficult to achieve optimal performance. SENet is an excellent attention mechanism-based network. It significantly enhances the performance of 2D convolutional networks. However, whether the 3D convolutional network can be applied to this attention mechanism network remains to be studied. In order to solve the above problems, we propose a saliency prediction network for audio-visual fusion to extract and predict various information in videos. At the same time, we improve the traditional SENet to make it applicable in 3D convolutional neural networks and discuss its role. Compared with the state-of-the-art methods, our model has strong competitiveness in multiple data sets.

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Few-Shot Fine-Grained Classification of Histological Images

Few-Shot Fine-Grained Classification of Histological Images

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Yingdong Jiang Jing Huang Zihe Jin Leqi Shen Ziyi Zhang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Software Jilin University Jilin University ChangChun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin University ChangChun China College of Computer Science and Technology Jilin University ChangChun China

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named “Category-Aware Feature Map Reconstruction Network.” This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a

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Research on Liver Fat Sequencing Measurement Method Combined with Super-pixel Segmentation 3

Research on Liver Fat Sequencing Measurement Method Combined...

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： Jiao, Xu Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

The number of patients with nonalcoholic fatty liver is increasing. Liver fat quantification is the most reliable indicator for the diagnosis of non-alcoholic fatty liver disease, and it is of great value for the early diagnosis and condition monitoring of non-alcoholic fatty liver disease. Therefore, it is of great significance to quantitatively analyze liver fat. In this work, we utilize the superpixel segmentation method based on depth neural network to preprocess the MRI image, we can simplify the representation of the image and obtain good organ boundary information. In addition, we also understand the popular involution operator and improve it through combining the medical image segmentation model u-net to obtain a better region of interest. The segmentation accuracy of the proposed method is 1.82 percentage points higher than that of u-net model. On this basis, the proton density and fat fraction of liver are calculated by medical formula, achieving the sequencing measurement of liver fat. The experimental results show that the proposed method is efficient and feasible. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Research on Liver Segmentation of CT Image Based on Deep Active Contour Model 3

Research on Liver Segmentation of CT Image Based on Deep Act...

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer engineering and Applications, CVIDL and ICCEA 2022

作者： He, Shanshan Song, Jinfang Zhang, Yonggang Tian, Xin Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China Jilin University College of Software Changchun China Second Hospital of Jilin University Department of Pediatrics Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9781665459112

Liver cancer is one of the liver diseases with a high morbidity and mortality. At present, surgery is still the main treatment method for liver cancer. Therefore, it is particularly important to accurately segment the liver on CT imaging. Deep learning-based algorithms struggle to derive shape priors due to the irregular shape of the liver and blurring organ borders in CT images. We present a two-stage 3D deep active contour model 3DAC based on the V-net and CV models, in which the neural network is guided to learn the global features by regressing the signed distance map. The signed distance function can also be used as an initialization level set function. Then, the active contour model is extended to three dimensions to enable evolution of the overall liver surface. The advantages of the proposed method for liver segmentation were validated on the AbdomenCT-lK dataset. Compared with the DALS method, the Dice coefficient and the Jaccard coefficient are improved by 4.29% and 4.58%, the ASD and 95HD indicators are improved by 5. 96mm and 10. 04mm, which proves the effectiveness and robustness of the proposed method. © 2022 IEEE.

关键词： Image segmentation

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Toward Moiré-Free and Detail-Preserving Demosaicking

arXiv

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arXiv 2023年

作者： Li, Xuanchen Niu, Yan Zhao, Bo Shi, Haoyuan An, Zitong State Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Ministry of Education Jilin University Changchun China The College of Software Jilin University Changchun130012 China

3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spectra from exchanging complementary information, resulting in spectral-inconsistent inference of the local spatial high frequency components. As a consequence, shallow 3D convolution networks suffer the Moiré artifacts, but deep 3D convolutions cause over-smoothness. We analyze the fundamental difference between demosaicking and other problems that predict lost pixels between available ones (e.g., super-resolution reconstruction), and present the underlying reasons for the confliction between Moiré-free and detail-preserving. From the new perspective, our work decouples the common standard convolution procedure to spectral and spatial feature aggregations, which allow strengthening global communication in the spectral dimension while respecting local contrast in the spatial dimension. We apply our demosaicking model to two tasks: Joint Demosaicking-Denoising and Independently Demosaicking. In both applications, our model substantially alleviates artifacts such as Moiré and over-smoothness at similar or lower computational cost to currently top-performing models, as validated by diverse evaluations. Source code will be released along with paper publication. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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Application of Monocular Direct Vision Odometry in Augmented Reality 5

Application of Monocular Direct Vision Odometry in Augmented...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Zixuan Wang, Hanwen Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this technique, this paper introduces camera exposure parameters and puts the data association and pose estimation into a unified nonlinear optimization problem. Moreover, the direct monocular vision odometer is transplanted into the augmented reality system through the position adjustment module. We compare it with the traditional visual odometry method that matches the feature points. The results show that this improved method can be used to track more quickly and build a more visual semi-dense point cloud map, which can be used to support the registration and tracking of virtual objects in augmented reality. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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Application of Hybrid Monocular SLAM Method in Augmented Reality 5

Application of Hybrid Monocular SLAM Method in Augmented Rea...

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2020 5th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Zhang, Zuoming Wang, Hanwen Shu, Man Wang, Xin College of Software Engineering Jilin University Changchun Jilin130000 China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun Jilin130000 China

In this paper, we design a hybrid (semi-direct) approach to simultaneous localization and mapping (SLAM) for monocular cameras and apply it to augmented reality (AR) for monocular cameras. We combine the advantagesof the direct method and the feature point method. We use both photometric bundle adjustment which is robust to camera exposure time and motion bundle adjustment which is geometrically robust based on feature points to do tracking process. This approach can maintain an intuitive direct local map as well as a reusable global sparse feature point map. Through the processing of point clouds, such as PCA plane detection and grid reconstruction, we greatly improve the effect of the augmented reality system. © Published under licence by IOP Publishing Ltd.

关键词： Augmented reality

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NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli

arXiv

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arXiv 2024年

作者： Wang, Xu Li, Cheng Chang, Yi Wang, Jindong Wu, Yuan School of Artificial Intelligence Jilin University China Institute of Software CAS China Microsoft Research Asia China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Large Language Models (LLMs) have become integral to a wide spectrum of applications, ranging from traditional computing tasks to advanced artificial intelligence (AI) applications. This widespread adoption has spurred extensive research into LLMs across various disciplines, including the social sciences. Notably, studies have revealed that LLMs possess emotional intelligence, which can be further developed through positive emotional stimuli. This discovery raises an intriguing question: can negative emotions similarly influence LLMs, potentially enhancing their performance? In response to this question, we introduce NegativePrompt, a novel approach underpinned by psychological principles, involving ten specifically designed negative emotional stimuli. We embark on rigorous experimental evaluations of five LLMs including Flan-T5-Large, Vicuna, Llama 2, ChatGPT, and GPT-4, across a set of 45 tasks. The results are revealing: NegativePrompt markedly enhances the performance of LLMs, evidenced by relative improvements of 12.89% in Instruction Induction tasks and 46.25% in BIG-Bench tasks. Moreover, we conduct attention visualization experiments to decipher the underlying mechanisms of NegativePrompt’s influence. Our research contributes significantly to the understanding of LLMs and emotion interaction, demonstrating the practical efficacy of NegativePrompt as an emotion-driven method and offering novel insights for the enhancement of LLMs in real-world applications. The code is available at https://***/wangxu0820/NegativePrompt. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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