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Weak Multi-Label data Stream Classification Under Distribution Changes in Labels

IEEE Transactions on big data 2025年第3期11卷 1369-1380页

作者： Zou, Yizhang Hu, Xuegang Li, Peipei Hu, Jun Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei230002 China National University of Singapore School of Computing 119077 Singapore

Multi-label stream classification aims to address the challenge of dynamically assigning multiple labels to sequentially arrived instances. In real situations, only partial labels of instances can be observed due to the expensive human annotations, and the problem of label distribution changes arises from multiple labels in a streaming mode, but few existing works jointly consider such challenges. Motivated by this, we propose the problem of weak multi-label stream classification (WMSC) and an online classification algorithm robust to weak labels. Specifically, we incrementally update the margin-based model using information from both the past model and the current incoming instance with partially observed labels. To increase the robustness to weak labels, we first adjust the classification margin of negative labels using the label causality matrix, which is constructed by the conditional probability of label pairs. Second, we introduce the label prototype matrix to regulate the margin by controlling the weighting parameter of the slack term. Additionally, to handle the potential distribution changes in labels, we utilize the instance-specific threshold via online thresholding to perform binary classification, which is formulated as a regression problem. Finally, theoretical analysis and empirical experimental results are presented to demonstrate the effectiveness of WMSC in classifying unobserved streaming instances. © 2015 IEEE.

关键词： Labeled data

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A novel thermally-stable Ca2GaTaO6:Sm3+ phosphor and its application in WLEDs and anti-counterfeit inks

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Journal of Rare Earths 2025年第6期43卷 1120-1132页

作者： Zhang, Yanzhi Ling, Yi Cui, Ruirui Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

In this study, a novel Ca2GaTaO6:Sm3+ phosphor was developed using the conventional high-temperature solid-phase method. The phase structure and morphology test results of phosphor indicate that the Ca2GaTaO6 material was successfully synthesized and the Sm3+ ions were successfully doped into the host lattice. When utilizing 406 nm excitation, the Ca2GaTaO6:xSm3+ phosphor has the strongest emission intensity at 599 nm and shows orange-red emission, which is mainly owing to the 4G5/2 → 6H7/2 jump of Sm3+ ions. For the performance of different concentrations of Sm3+ ions, 3 mol% performs the best. At this time, concentration quenching occurs, which is most predominantly induced by dipole–dipole (d–d) interactions. In terms of thermal stability, the Ca2GaTaO6:0.03Sm3+ phosphor shows good properties, with the luminescence intensity at 423 K exhibiting 88.17% of that at 298 K. The white light-emitting diodes (WLEDs) devices prepared using Ca2GaTaO6:0.03Sm3+ phosphor shows warm white light with excellent performance in terms of correlated color temperature and color rendering index (CCT = 3642 K, CRI, Ra = 93.5). In terms of anticounterfeit inks, the Ca2GaTaO6:0.03Sm3+ phosphor also shows good potential. These research results show that Ca2GaTaO6: Sm3+ phosphors have great performance for application in WLEDs and anti-counterfeit inks. © 2024 Chinese Society of Rare Earths

关键词： Phosphors

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

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YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios

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IEEE Access 2025年 13卷 76609-76619页

作者： Suo, Peichun Zhu, Jincan Zhou, Qingyang Kou, Weili Wang, Xiuyan Suo, Wen Yunnan Medical Health Vocational College Information Center Kunming650033 China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming650224 China Key Laboratory of State Forestry and Grassland Administration on Forestry Ecological Big Data Kunming650224 China Yunnan College of Business Management Kunming650106 China

The ability to distinguish between birds and drones is essential for applications in wildlife preservation, aviation security, and defense operations. Reliable identification not only reduces the risk of bird strikes and monitors potential drone threats but also fosters the development of a secure and intelligent ecological environment. However, achieving high precision in recognition under complex and dynamic conditions remains a significant hurdle. To overcome this, we present an advanced detection framework, YOLOv9-AKConv-AFF-GSConv(YOLOv9-AAG), which substantially improves the accuracy of distinguishing birds from drones, even in varied poses, and enhances generalization capabilities in both infrared and visible light scenarios. The framework incorporates several key advancements. First, the GSConv is embedded into the head of the RepNSCPELAN4-AKConv module, enabling efficient extraction of edge and contour information without introducing additional computational cost. Second, the inclusion of the Attentional Feature Fusion (AFF) mechanism within AKConv amplifies the representation of shape features, particularly under challenging environments and multimodal scenarios. Lastly, the optimized AKConv module is effectively integrated with the original RepNSCPELAN4 model, facilitating more accurate and robust feature extraction for small targets, including birds and drones. Experimental evaluations underscore the effectiveness of the proposed framework. On an optical dataset containing multiple bird and drone targets, YOLOv9-AAG achieved recognition accuracy rates of 78.2%, while on an infrared dataset, it reached 86.1%. These results reflect improvements of 7.4% and 6.0%, respectively, over the baseline YOLOv9 model. Additionally, the F1 scores increased by 5.9% and 2.8%. On the publicly available CUB-200-2011 dataset and a combined dataset of visible and infrared images, the model attained mean Average Precision (mAP) scores of 81.9% and 81.6%, respectively, outper

关键词： Birds

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DRL-Enabled Computation Offloading for AIGC Services in IIoT-Assisted Edge Computing Networks

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IEEE Internet of Things Journal 2025年第9期12卷 12829-12844页

作者： Zhang, Xingxing Li, Shaobo Tang, Jianhang Zhu, keyu Zhang, Yang Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China National University of Singapore Department of Electrical and Computer Engineering Queenstown Singapore Guizhou Institute of Technology School of Big Data Guiyang550003 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China

The widespread application of AI-generated content (AIGC) services has driven demand for efficient computational resources, making effective task scheduling and computation offloading in edge computing (EC) environments a critical research topic. However, the high computational requirements and low latency demands of AIGC services, combined with the limitations of EC, present challenges for existing offloading methods, such as unstable decision making in dynamic task environments and resource overloading. Here, we propose a decentralized AIGC task offloading architecture within an IoT-assisted EC network to optimize the quality of AIGC services. In this architecture, we define a multiobjective joint optimization problem for AIGC task offloading, aiming to simultaneously optimize key performance metrics, such as task latency, energy efficiency, and load balancing. To address this problem, we introduce an improved proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm, named TOPPO. By incorporating a policy update step size constraint and a clipping mechanism, TOPPO significantly enhances the stability of the training process and reduces fluctuations during policy updates. Additionally, the algorithm integrates an LSTM model to improve its ability to handle temporal dependencies. Through continuous interaction between the model and the environment, the offloading strategy is iteratively updated to ensure that diverse AIGC tasks are efficiently executed on IoT devices or edge servers. Extensive simulations and performance evaluations demonstrate that the proposed method achieves significant improvements in task latency, energy consumption, and load management during AIGC task processing. © 2014 IEEE.

关键词： Computation offloading

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Attribute-Based Access Control Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2227-2242页

作者： Wang, Xiuhua Yu, Mengyang Wang, Yaohui Pi, Yinjia Xu, Peng Wang, Shuai Jin, Hai Han, Min Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan430074 China State Key Laboratory of Cryptology Beijing100878 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Huazhong University of Science and Technology 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 Wuhan430074 China

The burgeoning complexity of communication necessitates a high demand for security. Access control encryption is a promising primitive to meet the security demand but the bulk of its constructions rely on formulating the access control policy with identities. Attribute-based access control policy in attribute-based encryption (ABE) is known to be more expressive without relying on enumerating identities. We propose a generic framework to build attribute-based access control encryption from ciphertext-policy ABE. Our instantiations prioritize different emphases on expressiveness and efficiency. The first instantiation supports multi-valued AND-gate access control structures, while the second supports the linear-secret-sharing access structure. Both are prototyped with efficiency validated empirically. © 2024 IEEE.

关键词： Ciphertext

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Evolution of vegetation phenology and its response to urbanisation in Shaanxi province, China: A fine-scale perspective

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Journal of Geographical Sciences 2025年第5期35卷 1024-1048页

作者： GAO Meiling MA Jingjing LI Zhenhong YANG Guijun College of Geological Engineering and Geomatics Chang'an University State Key Laboratory of Loess Science Key Laboratory of Ecological Geology and Disaster Prevention Ministry of Natural Resources Big Data Center for Geosciences and Satellites

Shaanxi province in China is rich in vegetation resources, making the study of vegetation phenology crucial for understanding climate change and ecological dynamics. Previous studies on Shaanxi have primarily focused on coarse-scale phenology, which results in the loss of essential spatial details due to the lack of finer-scale analyses. Moreover, the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear. To address these gaps, we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence. Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed, and the responses of different vegetation types to urbanisation were explored. Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution, which is more precise than conventional coarse-scale investigations. The Start of Season(SOS) and End of Season(EOS) in the province advanced by-0.56 and-0.08 day/year, respectively, from 2000 to 2020. In general, the SOS gradually delays, and the EOS progressively advances with increasing latitude. However, anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N, which are primarily due to the effects of high elevation and rapid urbanisation, respectively. Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS, while grasslands present the latest. In addition, a significant negative correlation was found between the SOS difference and air temperature difference along the urban–rural gradient, with urbanisation having the most significant impact on cropland phenology. This study provides valuable insights into vegetation phenology and its response to urbanisation. These insights offer crucial information for ecological conservation and management strategies.

关键词： gap filling and Savitzky–Golay filtering Google Earth Engine spatiotemporal changes urbanisation vegetation phenology

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CASSNet: Cross-Attention Enhanced Spectral–Spatial Interaction Network for Hyperspectral Image Super-Resolution

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11716-11730页

作者： Zhang, Zhanxu Yang, Linzi Zhang, Guanglian Deng, Jiangwei Bian, Lifeng Yang, Chen Guizhou University Power Systems Engineering Research Center Ministry of Education College of Big Data and Information Engineering Guiyang550025 China Fudan University Frontier Institute of Chip and System Shanghai200433 China Guizhou University China State Key Laboratory of Public Big Data Guiyang550025 China

Deep-learning-based super-resolution (SR) methods for a single hyperspectral image have made significant progress in recent years and become an important research direction in remote sensing. Existing methods perform well in extracting spatial features, but challenges remain in integrating spectral and spatial features when modeling global relationships. In order to take full advantage of the higher spectral resolution of hyperspectral images, this article proposes a novel hyperspectral image SR method (CASSNet), which integrates convolutional neural networks and cross-attention mechanisms into a unified framework. This approach achieves comprehensive integration of spectral and spatial information, with extensive exploration at both local and global levels. In the local feature extraction stage, parallel 3-D/2-D convolutions work in tandem to efficiently capture detail information from both spectral and spatial dimensions. In addition, a spectral–spatial dual-branch module employing the cross-attention mechanism is designed to capture the global dependencies within the features, where the reconstructed spectral–spatial module and the spectral–spatial interaction unit can effectively promote the interaction and complementarity of spectral–spatial features. The experiments on three publicly available datasets demonstrated that the proposed method obtained superior SR results, outperforming state-of-the-art SR algorithms. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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