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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： He, Zhuoxin Mao, Yu Zeng, Yixiang Yu, Xiehua Lin, Yaojin Lab of Data Science and Intelligence Application Minnan Normal University School of Computer Science Zhangzhou China Minnan Science and Technology University School of Computer and Information Quanzhou China

ISBN: (纸本)9798350310153

In many practical classification scenarios, the data label space has a hierarchical structure, these data often have high-dimensional features, and there are irrelevant and redundant features. Thus, a variety of hierarchical classification feature selection methods are proposed. However, most of the existing algorithms ignore the temporal uncertainty and unknown of the feature space, and can only deal with static feature space data. Therefore, a new hierarchical streaming feature selection method is proposed. First, the algorithm uses the sibling strategy and the exclusion strategy in the hierarchical relationship to consider the relationship between homogeneous and heterogeneous samples. Then, the FDAF-score algorithm is integrated into the hierarchical classification streaming feature selection and used as a criterion for evaluating feature importance. Second, the improved Gaussian kernel function is used to judge the degree of redundancy between features, and the redundant features are removed. Finally, experiments on six hierarchical classification data sets show the effectiveness of the proposed algorithm. © 2022 IEEE.

关键词： Feature Selection

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Reversible data Hiding Using Convolutional Neural Network and Digital Signal Processing Techniques 12

Reversible Data Hiding Using Convolutional Neural Network an...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Qingmei, Peng Shaozi, Li Yaojin, Lin Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. Based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development. © 2022 IEEE.

关键词： Authentication

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Sustainable Self-evolution Adversarial Training 24

Sustainable Self-evolution Adversarial Training

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Wenxuan Wang, Chenglei Qi, Huihui Ye, Menghao Qian, Xuelin Wang, Peng Zhang, Yanning School of Computer Science Northwestern Polytechnical University China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi'an China School of Automation Northwestern Polytechnical University The BRain and Artificial INtelligence Lab Shaanxi Xi'an China

ISBN: (纸本)9798400706868

With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a one-time learning process, struggle to adapt to the dynamic and evolving nature of attack methods. Therefore, to achieve defense performance improvements for models in long-term applications, we propose a novel Sustainable Self-Evolution Adversarial Training (SSEAT) framework. Specifically, we introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples across multiple stages. Additionally, to address the issue of model catastrophic forgetting caused by continual learning from ongoing novel attacks, we propose an adversarial data replay module to better select more diverse and key relearning data. Furthermore, we design a consistency regularization strategy to encourage current defense models to learn more from previously trained ones, guiding them to retain more past knowledge and maintain accuracy on clean samples. Extensive experiments have been conducted to verify the efficacy of the proposed SSEAT defense method, which demonstrates superior defense performance and classification accuracy compared to competitors. © 2024 ACM.

关键词： Adversarial machine learning

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Communication-Efficient Distributed Learning with Local Immediate Error Compensation

arXiv

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arXiv 2024年

作者： Cheng, Yifei Shen, Li Xu, Linli Qian, Xun Wu, Shiwei Zhou, Yiming Zhang, Tie Tao, Dacheng Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science School of Computer Science University of Science and Technology China The JD Explore Academy China The Shanghai Artificial Intelligence Laboratory China

Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate. In this work, we propose the Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm to break the above bottlenecks based on bidirectional compression and carefully designed compensation approaches. Specifically, the bidirectional compression technique is to reduce the communication cost, and the compensation technique compensates the local compression error to the model update immediately while only maintaining the global error variable on the server throughout the iterations to boost its efficacy. Theoretically, we prove that LIEC-SGD is superior to previous works in either the convergence rate or the communication cost, which indicates that LIEC-SGD could inherit the dual advantages from unidirectional compression and bidirectional compression. Finally, experiments of training deep neural networks validate the effectiveness of the proposed LIEC-SGD algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Error compensation

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Hierarchical online streaming feature selection based on adaptive ReliefF

Hierarchical online streaming feature selection based on ada...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chenxi Wang Mengli Ren Chen E Xiehua Yu Yaojin Lin Shaozi Li School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China School of Computer Science Lab of Data Science and Intelligence Application Minnan Science and Technology University Quanzhou China School of Computer Science Lab of Data Science and Intelligence Application Xiamen University Xiamen China

In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm.

关键词： Correlation Heuristic algorithms Redundancy Education Feature extraction Classification algorithms Information technology

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Multi-label Feature Selection Based on Fuzzy Neighborhood Mutual Discrimination Index

Multi-label Feature Selection Based on Fuzzy Neighborhood Mu...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Chenxi Wang E Chen Mengli Ren Xiehua Yu Yaojin Lin Shaozi Li School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China School of Computer Science Lab of Data Science and Intelligence Application Minnan Science and Technology University Quanzhou China School of Computer Science Lab of Data Science and Intelligence Application Xiamen University Xiamen China

Multi-label feature selection advances the performance of multi-label classification models by eliminating irrelevant and redundant features. Most multi-label feature selection algorithms assume that the training set containing logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances of labels. To solve the problem, we proposes a multi-label feature selection method based on fuzzy neighborhood mutual discriminant index. Firstly, logical labels are transformed into label distribution by label enhancement. Secondly, the information entropy is referenced to the label distribution learning, the label neighborhood similarity matrix is constructed to describe the ambiguity between labels. Finally, linking the fuzzy neighborhood discrimination index under the selected feature subset with label neighborhood similarity matrix is proposed, which is used for judging the distinguishing ability of the feature subset to label. Comprehensive experiments on 6 multi-label datasets show that the algorithm outperforms other algorithms in classification performance.

关键词： Training Rough sets Feature extraction Classification algorithms Indexes Information technology Information entropy

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Teaching resource recommendation for OBE based on kernel canonical correlation analysis 12

Teaching resource recommendation for OBE based on kernel can...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Changqian, Wu Shaozi, Li Yaojin, Lin Xiehua, Yu MinNan Science and Technology Key Laboratory of Big Data and Artificial Intelligence University José Rizal University University of Fujian Province Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province Quanzhou China

ISBN: (纸本)9798350310153

data mining is used to extract information from OBE teaching resources, then the teaching resources and user personality database are established to improve the effectiveness of resources recommendation on the online learning platform. The properties of teaching resources and user personality traits are analyzed using the kernel canonical correlation analysis algorithm, and teaching materials with a high correlation coefficient are chosen to recommend to users. The simulation results demonstrate that this proposed method has higher accuracy and more accurate recommended resources when compared to the widely used teaching resource recommendation techniques. © 2022 IEEE.

关键词： data mining

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MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTURAL MOTIFS 11

MOLECULE GENERATION FOR TARGET PROTEIN BINDING WITH STRUCTUR...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhang, Zaixi Min, Yaosen Zheng, Shuxin Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Institute of Interdisciplinary Information Sciences Tsinghua University China Microsoft Research United States

Designing ligand molecules that bind to specific protein binding sites is a fundamental problem in structure-based drug design. Although deep generative models and geometric deep learning have made great progress in drug design, existing works either sample in the 2D graph space or fail to generate valid molecules with realistic substructures. To tackle these problems, we propose a Fragment-based LigAnd Generation framework (FLAG), to generate 3D molecules with valid and realistic substructures fragment-by-fragment. In FLAG, a motif vocabulary is constructed by extracting common molecular fragments (i.e., motif) in the dataset. At each generation step, a 3D graph neural network is first employed to encode the intermediate context information. Then, our model selects the focal motif, predicts the next motif type, and attaches the new motif. The bond lengths/angles can be quickly and accurately determined by cheminformatics tools. Finally, the molecular geometry is further adjusted according to the predicted rotation angle and the structure refinement. Our model not only achieves competitive performances on conventional metrics such as binding affinity, QED, and SA, but also outperforms baselines by a large margin in generating molecules with realistic substructures. Our code is publicly available at https://***/zaixizhang/FLAG. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Molecules

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Multi-source Multi-label Causal Feature Selection 11

Multi-source Multi-label Causal Feature Selection

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11th International Conference on Information Technology in Medicine and Education, ITME 2021

作者： Wang, Yun-An Lin, Yaojin Wang, Jie Wei, Zhisen Chen, Jinkun Li, Shaozi Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China Xiamen University Department of Artificial Intelligence Xiamen China

ISBN: (纸本)9781665406796

Multi-label causal feature selection plays a vital role in dealing with high-dimensional multi-label data, since the selected causal features signify the causal mechanism with respect to the class attribute. At present, the descriptive information of data is collected from different data sources in many practical applications. Therefore, there is a need of developing multi-source multi-label causal feature selection algorithms. In addition, existing multi-label causal feature selection algorithms cannot directly address multi-source heterogeneous distribution feature spaces. To address these problems, in this paper, we utilize the concept of causal invariance in causal inference. Firstly, we formulate the multi-label causal feature selection problem for multiple data sources as an invariant set search problem across the data sources. Secondly, we present the upper and lower bounds of the causal invariant set. Finally, we design a novel Multi-source Multi-label Causal Feature Selection (MMCFS) algorithm. Extensive experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods on synthetic datasets. © 2021 IEEE.

关键词： Feature Selection

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