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Multi-Scale Feature Fusion and Advanced Representation Learning for Multi Label Image Classification

Computers, Materials & Continua 2025年第3期82卷 5285-5306页

作者： Naikang Zhong Xiao Lin Wen Du Jin Shi Institute of Artificial Intelligence on Education Research College of InformationMechanical and Electrical EngineeringShanghai Normal UniversityShanghai200234China Lab for Educational Big Data and Policymaking Ministry of EducationShanghai Normal UniversityShanghai200234China Shanghai Intelligent Education Big Data Engineering Technology Research Center Shanghai Normal UniversityShanghai200234China Shanghai Online Education Research Base for Primary and Secondary Schools Shanghai200234China DS Information Technology Co. Ltd.Shanghai200032China Faculty of Innovation Engineering Macao university of Science and TechnologyMacao999078China

Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in *** class-specific precise representations at different scales is a key aspect of feature ***,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same *** some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different *** address these issues,we propose a two-stage,three-branch Transformer-based *** first stage incorporates multi-scale image feature extraction and hierarchical scale *** design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object *** second stage includes a global feature enhancement module and a region selection *** global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and ***,these components enable the efficient acquisition of class-specific precise feature *** experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed *** approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,*** results validate the reliability and superiority of our approach for multi-label image classification.

关键词： Image classification multi-label multi scale attention mechanisms feature fusion

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High Dynamic Collaborative Team Query via Multi-Fuzzy-Constrained Graph Pattern Matching 9

High Dynamic Collaborative Team Query via Multi-Fuzzy-Constr...

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9th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2024

作者： Hu, Tao Zhang, Zan Bu, Chenyang Li, Lei Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798350373554

Graph pattern matching is a technique widely used in various fields such as protein structure analysis, social group querying, and expert localization. This technique involves finding matching subgraphs in large social networks that align with the patterns specified in the pattern graph. In this paper, we focus on a specific sub-problem in social group querying, known as the cooperative team query, which arises from practical applications, where the nodes in the pattern graph and the data graph represent team member entities, while the edges represent their social relationships. We note that the requirements of many teams in the real world are dynamic, necessitating iterative computation for graph pattern matching using traditional methods. To address this challenge in highly dynamic systems, we propose a graph pattern matching method based on core pattern graph matching cache. This approach involves extracting the core pattern graph, and comprising core team members based on the characteristics of cooperative teams. The core graph-based matching cache enables the second half of the algorithm to operate on an order-of-magnitude smaller graph, significantly improving efficiency. Additionally, the multi-threaded approach fully leverages hardware resources, synchronizing multiple matching result of the core pattern graph to reduce matching time. Experimental results on three real social network datasets demonstrate that our proposed algorithm, Core Pattern Graph Matching Cache-based Multi-threaded Exploration (CCMTE), significantly outperforms existing methods in terms of efficiency. © 2024 IEEE.

关键词： Efficiency

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Multivariable financial time series forecasting based on phase space reconstruction compensation

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Neural Computing and Applications 2025年第3期37卷 1389-1402页

作者： Li, Jincheng Zhou, Linli Li, Xuefei Wu, Di Xiong, Jianqiao Song, Liangtu School of Big Data and Statistics Anhui University Hefei230039 China Intelligent Agriculture Engineering Laboratory of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

Accurate financial time series forecasting is an important challenge in the financial field due to varying levels of interaction among multiple financial time series, complicating the extraction of valid information from these variables. This study introduces an effective and efficient multivariable financial time series forecasting model based on phase space reconstruction compensation: Phase Space Reconstruction Compensation Long Short-Term Memory (PSR-LSTM). The PSR-LSTM model leverages the long short-term memory (LSTM) network to analyze short-term data behaviors, reconstructs multiple long-term variables by phase space reconstruction method, and utilizes multivariable trend attention to capture trend information in correlated variables. This trend information is finally used to correct the LSTM network’s predictive result. Experimental results demonstrate that the PSR-LSTM outperforms existing multivariable forecasting models by effectively mitigating noise interference while achieving optimal forecasting performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phase space methods

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Resilient multi-objective mission planning for UAV formation:A unified framework integrating task pre-and re-assignment

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Defence technology(防务技术) 2025年第3期45卷 203-226页

作者： Xinwei Wang Xiaohua Gao Lei Wang Xichao Su Junhong Jin Xuanbo Liu Zhilong Deng Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalianLiaoning 116024China School of Mathematics and Big Data Anhui University of Science and TechnologyHuainanAnhui 232001China School of Mathematical Science Dalian University of TechnologyDalianLiaoning 116024China Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical UniversityYantai 264001China

Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.

关键词： Cooperative mission planning UAV formation Mission reliability Evolutionary algorithm Contract net protocol

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A Blockchain-Based Federated Learning for Smart Homes 4

A Blockchain-Based Federated Learning for Smart Homes

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4th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2023

作者： Zhu, Boqi Lu, Kezhong Tao, Tao School of Computer Science and Technology Anhui University of Technology Maanshan China School of Big Data and Artificial Chizhou University Chizhou China

ISBN: (纸本)9798350337181

Smart home devices generate a substantial amount of local data, and finding effective ways to utilize this data while ensuring privacy has become an increasingly pressing concern. Technologies such as Smart Homes, Federated Learning and Blockchain offer promising solutions to address this challenge. We introduce a blockchain-based federated learning approach that leverages edge nodes to maintain a decentralized blockchain, thus mitigating the risks associated with single points of failure. Furthermore, this method utilizes local data from home IoT devices for model training, ensuring efficient learning while preserving data privacy. To address the challenges posed by non-independent and homogeneous data distribution, we propose a clustering method. This strategy effectively tackles the issues arising from non-homogeneous data distribution, consequently improving model accuracy. Finally, experimental results demonstrate that our proposed approach significantly enhances model accuracy and generalization while safeguarding user privacy. © 2023 IEEE.

关键词： Blockchain

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Upconversion luminescence and optical thermometry behaviors of Yb^(3+)and Ho^(3+)co‑doped GYTO crystal

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Frontiers of Optoelectronics 2023年第3期16卷 179-189页

作者： Chuancheng Zhang Shoujun Ding Miaomiao Wang Hao Ren Xubing Tang Yong Zou Renqin Dou Wenpeng Liu School of Microelectronics and Data Science Anhui University of TechnologyMaanshan 243002China Anhui Institute of Optics and Fine Mechanics Chinese Academy of SciencesHefei 230031China Advanced Laser Technology Laboratory of Anhui Province Hefei 230037China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002China

Optical thermometry based on the upconversion(UC)luminescence intensity ratio(LIR)has attracted considerable attention because of its feasibility for achievement of accurate non-contact temperature *** with traditional UC phosphors,optical thermometry based on UC single crystals can achieve faster response and higher sensitivity due to the stability and high thermal conductivity of the single *** this study,a high-quality 5 at%Yb^(3+)and 1 at%Ho^(3+)co-doped Gd_(0.74)Y_(0.2)TaO_(4)single crystal was grown by the Czochralski(Cz)method,and the structure of the as-grown crystal was ***,the UC luminescent properties and optical thermometry behaviors of this crystal were *** 980 nm wavelength excitation,green and red UC luminescence lines at 550 and 650 nm and corresponding to the^(5)F_(4)/^(5)S_(2)→^(5)I_(8)and^(5)F_(5)→^(5)I_(8)transitions of Ho^(3+),respectively,were *** green and red UC emissions involved a two-photon mechanism,as evidenced by the analysis of power-dependent UC emission *** temperature-dependent UC emission spectra were measured in the temperature range of 330–660 K to assess the optical temperature sensing *** 660 K,the maximum relative sensing sensitivity(S_(r))was determined to be 0.0037 K^(−1).These results highlight the signifcant potential of Yb,Ho:GYTO single crystal for optical temperature sensors.

关键词： Yb,Ho:GYTO Optical temperature sensor Luminescence intensity ratio Upconversion luminescence

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

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Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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Multi-Label MambaOut for Quality Assessment of Low-Field Pediatric Brain MR Images 1st

Multi-Label MambaOut for Quality Assessment of Low-Field P...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2024

作者： Zhu, Yueyue Jiang, Haotian Cai, Rongqing Chen, Geng National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9783031830105

Magnetic Resonance Imaging (MRI) can be utilized to study the structure of pediatric brains non-invasively. In practice, low-field MRI scanners are widely adopted for pediatric brain imaging. However, the corresponding acquired MRI data usually suffers from severe artifacts, such as noise and motion. Therefore, an effective Quality Assessment (QA) method is essential. To this end, we design a Multi-Label MambaOut (MLMambaOut) model for the low-field pediatric brain MRI QA challenge. Specifically, we view this challenge as a multi-label classification task, utilizing four stages of gated convolution neural network blocks and ML-Decoder to finish the classification with class balance loss. Furthermore, we explore the performance of Mamba and some advanced models for this challenge. We performed extensive experiments on the challenge data, which is low-field and corrupted with seven kinds of artifacts. The results show that our MLMambaOut achieves superior classification results compared with other methods. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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