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A data representation method using distance correlation

Frontiers of computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Recognition of carrier-based aircraft flight deck operations based on dynamic graph

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Chinese Journal of Aeronautics 2025年第3期38卷 474-490页

作者： Xingyu GUO Jiaxin LI Hua WANG Junnan LIU Yafei LI Mingliang XU School of Computer Science and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou 450001China National Supercomputing Center in Zhengzhou Zhengzhou 450001China School of Geo-Science&Technology Zhengzhou UniversityZhengzhou 450001China

Accurate recognition of flight deck operations for carrier-based aircraft, based on operation trajectories, is critical for optimizing carrier-based aircraft performance. This recognition involves understanding short-term and long-term spatial collaborative relationships among support agents and positions from long spatial–temporal trajectories. While the existing methods excel at recognizing collaborative behaviors from short trajectories, they often struggle with long spatial–temporal trajectories. To address this challenge, this paper introduces a dynamic graph method to enhance flight deck operation recognition. First, spatial–temporal collaborative relationships are modeled as a dynamic graph. Second, a discretized and compressed method is proposed to assign values to the states of this dynamic graph. To extract features that represent diverse collaborative relationships among agents and account for the duration of these relationships, a biased random walk is then conducted. Subsequently, the Swin Transformer is employed to comprehend spatial–temporal collaborative relationships, and a fully connected layer is applied to deck operation recognition. Finally, to address the scarcity of real datasets, a simulation pipeline is introduced to generate deck operations in virtual flight deck scenarios. Experimental results on the simulation dataset demonstrate the superior performance of the proposed method.

关键词： Carrier-based aircraft Flight deck operation Operation recognition Long spatial-temporal trajectories Dynamic graph Biased random walk Graph embeddings

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Deep supervised fused similarity hashing for cross-modal retrieval

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Multimedia Tools and Applications 2024年第39期83卷 86537-86555页

作者： Ng, Wing W. Y. Xu, Yongzhi Tian, Xing Wang, Hui Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering South China University of Technology Guangzhou China School of Artificial Intelligence South China Normal University Guangzhou China School of Electronics Electrical Engineering and Computer Science Queens University Belfast Belfast United Kingdom

The need for cross-modal retrieval increases significantly with the rapid growth of multimedia information on the Internet. However, most of existing cross-modal retrieval methods neglect the correlation between label similarity and intra-modality similarity in common semantic subspace training, which makes the trained common semantic subspace unable to preserve semantic similarity of original data effectively. Therefore, a novel cross-modal hashing method is proposed in this paper, namely, Deep Supervised Fused Similarity Hashing (DSFSH). The DSFSH mainly consists of two parts. Firstly, a fused similarity method is proposed to exploit the intrinsic inter-modality correlation of data while preserving the intra-modality relationship of data at the same time. Secondly, a novel quantization max-margin loss is proposed. The gap between cosine similarity and Hamming similarity is closed by minimizing this loss. Extensive experimental results on three benchmark datasets show that the proposed method yields better retrieval performance comparing to state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Unveiling factuality and injecting knowledge for LLMs via reinforcement learning and data proportion

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science China(Information sciences) 2024年第10期67卷 389-390页

作者： Wenjun KE Ziyu SHANG Zhizhao LUO Peng WANG Yikai GUO Qi LIU Yuxuan CHEN School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Beijing Institute of Technology Zhuhai Beijing Institute of Computer Technology and Application

Large language models(LLMs) have demonstrated remarkable effectiveness across various natural language processing(NLP) tasks, as evidenced by recent studies [1, 2]. However, these models often produce responses that conflict with reality due to the unreliable distribution of facts within their training data, which is particularly critical for applications requiring high credibility and accuracy [3].

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M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenes

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IEEE Transactions on Pattern Analysis and Machine intelligence 2025年第7期PP卷 PP页

作者： Yan, Sixu Zhang, Zeyu Han, Muzhi Wang, Zaijin Xie, Qi Li, Zhitian Li, Zhehan Liu, Hangxin Wang, Xinggang Zhu, Song-Chun Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China State Key Laboratory of General Artificial Intelligence Beijing100080 China Department of Mechanical and Aerospace Engineering United States Beihang University School of Automation Science and Electrical Engineering Beijing100191 China BIGAI China Xidian University School of Artificial Intelligence Xi'an710126 China Peking University School of Artificial Intelligence Institute for Artificial Intelligence Beijing100871 China

Recent advances in diffusion models have opened new avenues for research into embodied AI agents and robotics. Despite significant achievements in complex robotic locomotion and skills, mobile manipulation–a capability that requires the coordination of navigation and manipulation–remains a challenge for generative AI techniques. This is primarily due to the high-dimensional action space, extended motion trajectories, and interactions with the surrounding environment. In this paper, we introduce M2 Diffuser, a diffusion-based, scene-conditioned generative model that directly generates coordinated and efficient whole-body motion trajectories for mobile manipulation based on robot-centric 3D scans. M2 Diffuser first learns trajectory-level distributions from mobile manipulation trajectories provided by an expert planner. Crucially, it incorporates an optimization module that can flexibly accommodate physical constraints and task objectives, modeled as cost and energy functions, during the inference process. This enables the reduction of physical violations and execution errors at each denoising step in a fully differentiable manner. Through benchmarking on three types of mobile manipulation tasks across over 20 scenes, we demonstrate that M2 Diffuser outperforms state-of-the-art neural planners and successfully transfers the generated trajectories to a real-world robot. Our evaluations underscore the potential of generative AI to enhance the generalization of traditional planning and learning-based robotic methods, while also highlighting the critical role of enforcing physical constraints for safe and robust execution. Videos, code and more details are available at https://***. © 2025 IEEE. All rights reserved.

关键词： Trajectories

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Predictive Modeling of Engineering Placement Outcomes Using Genetic Algorithms

Predictive Modeling of Engineering Placement Outcomes Using ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Pandya, Aashish Harshini, Perala Sharma, Puneet Dwivedi, Anshu Kumar SR University School of Computer Science & Artificial Intelligence Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350385199

It is difficult to predict where engineering graduates will be employed due to various factors including market needs, technical skills, personal qualities, and academic achievement. Traditional techniques often produce less-than-ideal results because they are unable to fully capture the complex interactions between these factors. In this paper, we provide a novel method to tackle this problem by using genetic algorithm (GA) placement prediction capabilities. Genetic algorithms mimic the principles of evolution to find optimal solutions in a large solution space. The process of natural selection inspires them. GA effectively searches the solution space by listing possible solutions as chromosomes and performing selection, crossover, and mutation operations to identify optimal solutions. © 2024 IEEE.

关键词： Decision trees

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Prediction of Monkeypox Disease Using Biogeography Based Optimization Methodology

Prediction of Monkeypox Disease Using Biogeography Based Opt...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Kalapaka, Abhinay Chamakuri, Bhararath Koduru, Pavan Raj Gudas, Suraj Dwivedi, Anshu Kumar SR University School of Computer Science and Artificial Intelligence Warangal India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350385199

Monkeypox, a rare viral illness predominantly found in Central and West Africa, presents significant public health concerns. Detecting and predicting monkeypox outbreaks early is essential for effective disease management. In this study, we propose a new method for predicting monkeypox using Biogeography-Based Optimization (BBO) alongside machine learning techniques. We used clinical features and demographic data dataset to train and evaluate our predictive model. BBO was employed to select the most relevant subset of features from the dataset, improving the model's performance. Our findings demonstrate the efficacy of our approach, achieving high accuracy in predicting monkeypox outbreaks. Moreover, we emphasize the importance of feature selection in enhancing the interpretability and applicability of predictive models for infectious diseases. This research contributes to advancing predictive analytics in epidemiology and offers valuable insights for disease surveillance and public health interventions. © 2024 IEEE.

关键词： Adversarial machine learning

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Learning and Application of Different Machine Learning Methods (KNN, SVM, Decision Tree) in Different datasets 5

Learning and Application of Different Machine Learning Metho...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Zhu, Chaoqun School of Computer Science and Technology School of Artificial Intelligence Zhejiang Normal University Zhejiang Jinhua321004 China

ISBN: (纸本)9798331530334

With the continuous development of artificial intelligence, machine learning has shown great ability in classification and regression problems. For example, logistic regression, SVM methods, and neural networks are widely used in credit evaluation, image recognition, and sales forecasting. In this paper, KNN method, SVM method and decision tree method are used for iris data set, aircraft delay data set and diabetes dataset was tested by linear regression and Lasso regression ridge regression. We can conclude that different machine learning methods have different sensitivities to the partitioning of the dataset (controlled by random_state and test-size). When selecting the model parameters, the influence of these factors on the model performance should be considered. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data

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International Journal of Intelligent Computing and Cybernetics 2024年第4期17卷 671-704页

作者： Yifeng Zheng Xianlong Zeng Wenjie Zhang Baoya Wei Weishuo Ren Depeng Qing School of Computer Science Minnan Normal UniversityZhangzhouChina and Key Laboratory of Data Science and Intelligence ApplicationFujian Province UniversityZhangzhouChina

Purpose-As intelligent technology advances,practical applications often involve data with multiple ***,multi-label feature selection methods have attracted much attention to extract valuable ***,current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal ***/methodology/approach-To address the above problems,we propose an ensemble causal feature selection method based on mutual information and group fusion strategy(CMIFS)for multi-label ***,the causal relationship between labels and features is analyzed by local causal structure learning,respectively,to obtain a causal feature ***,we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset ***,we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the ***-Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different ***,the statistical analyses further validate the effectiveness of our ***/value-The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multilabel ***,our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

关键词： Multi-label learning Feature selection Causal relationship Mutual information Group fusion strategy

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