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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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An Improved Honey Badger Algorithm for Electric Vehicle Charge Orderly Planning

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 332-346页

作者： Chen, Ran-Fan Luo, Hao Huang, Kuan-Chun Nguyen, Trong-The Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China School of Aeronautics and Astronautics Zhejiang University HangZhou310000 China Department of Industrial and Systems Engineering Chung Yuan University Taoyuan320-33 Taiwan Multimedia Communications Lab VNUHCM-University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Multiple electric vehicles simultaneously connected to a distribution load network will significantly impact the stability of the power grid. This paper proposes an improved Honey-badger algorithm (IHBA) through elite reverse learning, spiral update, and wild dog survival strategies to optimize the orderly charging of electric vehicles (EV). Objective functions of EV charge orderly planning are taken by using load fluctuation satisfaction, user cost satisfaction, and user convenience satisfaction to achieve the high efficiency of orderly charging. The benchmark test function and the electric vehicle se-quential charging problem were employed to evaluate the proposed IHBA approach. The obtained results are compared with the other algorithms in the literature, which indicate that the IHBA algorithm does not only provides more accurate outcome but better con-vergence speed than the other competitors. © 2022;Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Food products

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A Class Imbalance Monitoring Model for Fetal Heart Contractions Based on Gradient Boosting Decision Tree Ensemble Learning 6th

A Class Imbalance Monitoring Model for Fetal Heart Contracti...

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6th International Conference on data Mining and Big data, DMBD 2021

作者： Qin, Chen Liu, Shaopeng Lin, Shengxiang Li, Guangzhe Hong, Jiaming School of Medical Information Engineering Guangzhou University of Chinese Medicine Guangzhou China Department of Computer Science Guangdong Polytechnic Normal University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9789811675010

Aiming at the imbalance and cost-sensitive problem of sample categories in actual fetal monitoring, as well as actual needs, we proposed a category imbalance fetal contraction monitoring model based on GBDT (Gradient Boosting Decision Tree) combined learning. Subsets with balanced category were generated by random under-sampling and applied to train several GBDT base classifiers using the method of feature selection. We integrated the base classifiers by the simple average method and calculated the final prediction probability. In this study, AUC and cost-sensitive error rate were used as evaluation indicators to compare with the commonly used single learning models such as Decision Tree, Logistic Regression and combined learning models like Random Forest to verify the effectiveness of the model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning 29

Parsing Natural Language into Propositional and First-Order ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Lu, Xuantao Liu, Jingping Gu, Zhouhong Tong, Hanwen Xie, Chenhao Huang, Junyang Xiao, Yanghua Wang, Wenguang Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. Beijing China School of Information Science and Engineering East China University of Science and Technology China Fudan-Aishu Cognitive Intelligence Joint Research Center China DataGrand Inc. Shanghai China

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Reinforcement learning

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Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment

arXiv

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arXiv 2023年

作者： Li, Gongyang Liu, Zhi Zhang, Xinpeng Lin, Weisi Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288×288 inputs. Our code and results are available at https://***/MathLee/SeaNet. © 2023, CC BY-NC-SA.

关键词： Optical remote sensing

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Multi-scale Graph Convolutional Neural Network for Object Recognition from Point Cloud data 2nd

Multi-scale Graph Convolutional Neural Network for Object Re...

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2nd International Conference on Urban Intelligence and Applications, ICUIA 2020

作者： Lu, Qiang Chen, Chao Teng, Jinfeng Zhang, Chunyuan Huang, Yi Xuan, Shanli Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China School of Computer and Information Hefei University of Technology Hefei230009 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230009 China

ISBN: (纸本)9789813346000

How to make robots understand the point cloud data which is collected from the 3D sensor and complete the recognition has become a hot research direction in recent years. In this paper, we propose a new approach to improve the critical robotic capability, semantic understanding of the environment (i.e., 3D object recognition). The convolutional neural network (CNN) method has a very good recognition result in the 2D image domain, but it has certain difficulty in applying irregular and unordered 3D point clouds data. The network for point cloud data generally uses the convolution to realize the extraction of point cloud features by finding the neighborhood features on the point set. Due to the different neighborhood scales caused by the irregularity of 3D point cloud data, we propose a CNN structure that combines multi-scale features. By finding multiple neighborhoods of the point set and establishing local graph extraction features, the stable expression of the local neighborhood is obtained. At the same time, the key point calibration method is added, so that the network can dynamically focus on key point features to improve the recognition result. In a series of analytical experiments, we demonstrate competing results that demonstrate the effectiveness of the network structure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations

arXiv

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arXiv 2024年

作者： Zhang, Yi Cheng, Chun-Wun He, Junyi He, Zhihai Schönlieb, Carola-Bibiane Chen, Yuyan Aviles-Rivero, Angelica I. Harbin Institute of Technology China Department of Electrical and Electronic Engineering Southern University of Science and Technology China Department of Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Pengcheng Laboratory China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Yau Mathematical Sciences Center Tsinghua University China

We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant challenge of overfitting, which is common in few-shot scenarios due to limited training examples. Our second-order approach can approximate a broader class of functions, enhancing the model’s expressive power and feature generalization capabilities. We initialize our cross-modal classifier with text embeddings derived from class-relevant prompts, streamlining training efficiency by avoiding the need for frequent text encoder processing. Additionally, we utilize text-based image augmentation, exploiting CLIP’s robust image-text correlation to enrich training data significantly. Extensive experiments across multiple datasets demonstrate that SONO outperforms existing state-of-the-art methods in few-shot learning performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Event Prediction Model Combining Ordinary Differential Equation and Hypernetworks

SSRN

引用

SSRN 2023年

作者： Zhang, Junhe Ma, Fuyuan Wang, Xin Yang, Bo Wang, Ying College of Computer Science and Technology Jilin University Jilin Changchun130012 China College of Artificial Intelligence Jilin University Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Jilin Changchun130012 China

Modeling the temporal evolution patterns of network events is crucial for enhancing user experience and creating commercial value. Existing methods combine graph neural networks and time-series models to capture spatial and temporal features, but they assume non-interaction among nodes when the network structure is stable, implying that node states do not undergo updates. Moreover, utilizing a single model to fit all events in these methods may lead to an excessive emphasis on global evolutionary patterns, potentially neglecting the individual heterogeneity among events. In this paper, we propose an event prediction model combining Ordinary Differential Equations and Hypernetworks, named ODEH. Firstly, to model the continuous evolution process of network structures under stability, we utilize ordinary differential equations based on graph neural networks to capture the continuous information dissemination among nodes and the interaction between node representation channels. Then, we use a message passing mechanism to model the direct impact of network topology changes on network entities. Finally, a hypernetwork is used to capture the individual characteristics of events and fine-tune the prediction model. Experimental results on multiple datasets demonstrate ODEH's superiority over baseline methods, highlighting its effectiveness in event prediction. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

引用

Annual IEEE Symposium on Computer-Based Medical Systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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