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arXiv 2025年

作者： Chang, Laibin Wang, Yunke Du, Bo Xu, Chang The School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The School of Computer Science The University of Sydney Australia

Underwater imaging often suffers from significant visual degradation, which limits its suitability for subsequent applications. While recent underwater image enhancement (UIE) methods rely on the current advances in deep neural network architecture designs, there is still considerable room for improvement in terms of cross-scene robustness and computational efficiency. Diffusion models have shown great success in image generation, prompting us to consider their application to UIE tasks. However, directly applying them to UIE tasks will pose two challenges, i.e., high computational budget and color unbalanced perturbations. To tackle these issues, we propose DiffColor, a distribution-aware diffusion and cross-spectral refinement model for efficient UIE. Instead of diffusing in the raw pixel space, we transfer the image into the wavelet domain to obtain such low-frequency and high-frequency spectra, it inherently reduces the image spatial dimensions by half after each transformation. Unlike single-noise image restoration tasks, underwater imaging exhibits unbalanced channel distributions due to the selective absorption of light by water. To address this, we design the Global Color Correction (GCC) module to handle the diverse color shifts, thereby avoiding potential global degradation disturbances during the denoising process. For the sacrificed image details caused by underwater scattering, we further present the Cross-Spectral Detail Refinement (CSDR) to enhance the high-frequency details, which are integrated with the low-frequency signal as input conditions for guiding the diffusion. This way not only ensures the high-fidelity of sampled content but also compensates for the sacrificed details. Comprehensive experiments demonstrate the superior performance of DiffColor over state-of-the-art methods in both quantitative and qualitative evaluations. © 2025, CC BY.

关键词： Budget control

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Mixed Color Channels (MCC): A Universal Module for Mixed Sample Data Augmentation Methods

Mixed Color Channels (MCC): A Universal Module for Mixed Sam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Yang Ma, Jianfeng Jiang, Zhongyuan Xiao, Bin School of Cyber Engineering Xidian University China Shaanxi Key Laboratory of Network and System Security China School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility. © 2022 IEEE.

关键词： Color

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence Technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

Wrong-of-Thought: An Integrated Reasoning Framework with Mul...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Yongheng Chen, Qiguang Zhou, Jingxuan Wang, Peng Si, Jiasheng Wang, Jin Lu, Wenpeng Qin, Libo School of Computer Science and Engineering Central South University China Research Center for SCIR Harbin Institute of Technology Harbin China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China Yunnan University Kunming China

ISBN: (纸本)9798891761681

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Single verification method: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: The traditional paradigm directly ignores wrong information during reasoning and refines the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks. © 2024 Association for Computational Linguistics.

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MICABAC: Multidimensional Industrial Control Attribute-Based Access Control Model

MICABAC: Multidimensional Industrial Control Attribute-Based...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Hangyu Lv, Fei Chen, Yuqi Si, Shuaizong Pan, Zhiwen Sun, Degang Sun, Limin Institute of Information Engineering Cas Beijing Key Laboratory of Iot Information Security Technology Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Shanghai Tech University Shanghai China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665410205

As the Industrial Control System (ICS) increasingly merges with the Internet, the security threats have been increasing from internal users and external hackers. These challenges are further intensified by the facts: industrial control devices and protocols, leading to the inadequacy of traditional access control models in tackling the intricacies of ICS. We identify attributes that are optimally aligned with the specific needs of the ICS environment and propose the Multidimensional Industrial Control Attribute-Based Access Control Model (MICABAC) as a customized solution. MICABAC model significantly improves access control security and is finer granularity by selecting and evaluating required attributes within various ICS. We have been validated in two real-world ICS environments: the Gas Pipe network System (GPNS) and the computer Numerical Control (CNC) machine tool. Experiments indicate that by integrating MICABAC into the existing system, the maximum delay for access requests is 63.83 ms. In terms of accuracy in defending against malicious attacks, the GPNS achieves 96.49% and the CNC reaches 94.86%. Finally, we discuss the advantages and limitations of MICABAC and explore potential directions for future research. © 2024 IEEE.

关键词： ABAC Access Control Industrial Control System PLC

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Application of Improved Canny Algorithm in Image Edge Detection

Application of Improved Canny Algorithm in Image Edge Detect...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Siyang Xu Peng Zhang Xin Song Runfeng Zhang Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

To solve the problems of poor denoising effect and limited threshold accuracy in traditional Canny algorithm, an improved Canny algorithm is proposed in this paper. Firstly, an image preprocessing method based on hybrid filtering is developed. On the basis of Gaussian filtering, the adaptive median filtering is adopted to denoise the image, and homomorphic filtering is used to enhance the high frequency information of image in the frequency domain. Besides, Sobel operator is used instead of Roberts operator to calculate the gradient amplitude and direction of the image. Finally, an iterative threshold selection method combining MEM and Otsu is proposed. After obtaining the high and low thresholds by MEM and Otsu respectively, the optimal threshold is calculated using iterative algorithm, which can improve the accuracy and adaptability of threshold. It is proved that the proposed algorithm can preserve the image details while ensuring the denoising effect, and the detected edges have better connectivity and smoothness.

关键词： Micromechanical devices Accuracy Filtering Image edge detection Noise reduction Diversity reception Noise

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Entity Disambiguation Based on UCL Knowledge Space

Entity Disambiguation Based on UCL Knowledge Space

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Xinchen, Chang Peng, Yang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China

ISBN: (纸本)9780738110837

Named entity disambiguation is an important task in the process of knowledge space construction and improvement, which builds a bridge between entities in text and entities in knowledge space. However, the existing entity disambiguation methods mainly rely on the similar or related entities in the context. For the short text widely appeared in the practical application scenarios, the performance of the existing methods drops sharply. In this paper, we propose an entity disambiguation method based on Uniform Content Label knowledge space (UCLKS-ED). This method uses UCL knowledge space to provide additional knowledge information for the matching task of candidate entities and the source entity, and extends the representation of the source entity by conceptualizing. The experimental results on a large manually annotated short-text dataset and another standard dataset demonstrate that the proposed method achieves significant improvement over the state-of-the-art baselines. © 2021 IEEE.

关键词： Large dataset

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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Low Light RGB and IR Image Fusion with Selective CNN-Transformer network

Low Light RGB and IR Image Fusion with Selective CNN-Transfo...

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IEEE International Conference on Image Processing

作者： Haiyan Jin Yue Yang Haonan Su Zhaolin Xiao Bin Wang School of Computer Science and Engineering Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

In low-light images, contrast and brightness are corrupted, making it difficult to accurately percept detail and edge information with the naked eye. Because of the development of multi sensor imaging, RGB-IR image fusion can enhance the imaging quality in low light conditions. However, the existing fusion algorithms have insufficient enhancement, distorted detail and low contrast, which make it difficult to generate high-quality fusion results. In this paper, we propose a Transformer-CNN image fusion method which considers global-local features fusion for low light image enhancement. The ConvGRU module is developed to alternatively select the global and local features with Transformer and CNN network. To effectively improve the network performance, a learnable weight adaptive loss function is proposed to adjust the weight of loss functions during training. Numerous experiments prove that our method can enrich fusion image information, improve image contrast and edge in low-light scenes compared to state of the art methods.

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YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algorithm

YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algori...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yi Ding Naixuan Guo Haitao Meng School of Information Engineering Yancheng Institute of Technology Yancheng China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Gesture recognition has been widely used in many situations, such as human-computer interaction, virtual reality and smart home. To meet the current demands for accuracy and speed in gesture recognition, this paper proposes an improved algorithm based on YOLOv8, referred to as YOLOv8-GR. By replacing the original YOLOv8 detection head with a Dynamic Head, we integrate scale-aware, spatial-aware, and task-aware attention mechanisms within a unified framework. To enhance context information capture, the SPPF (Spatial Pyramid Pooling - Fast) module is optimized to a modified SPPF-LSKA (Large Separable Kernel Attention) module. In addition, we employ the EIoU loss function for more accurate evaluation of object detection results. To evaluate our algorithm, we integrate a gesture dataset in complex and diverse scenarios through network and field shooting annotations. Compared to the original YOLOv8, the proposed YOLOv8-GR achieves a 22.5% improvement in mAP50 and a 10.0% improvement in mAP50-95 on our dataset. We also compare YOLOv8-GR with the MobileNetv2-based SSD, which is a kind of one-stage object detection algorithm. While maintaining similar recognition accuracy, the computational cost of YOLOv8-GR is 9.9 GFLOPs, which is 15.8% of SSD, and our algorithm can be deployed on Raspberry Pi 4B.

关键词： Human computer interaction Solid modeling Accuracy Convolution Heuristic algorithms Gesture recognition Object detection Virtual reality Smart homes Kernel

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