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Five-level anti-counterfeiting based on versatile luminescence of tri-doped double perovskites

Nano Research 2024年第11期17卷 9971-9979页

作者： Xingru Yang Yuhang Sheng Linglong Zhang Lun Yang Fangjian Xing Yunsong Di Cihui Liu Fengrui Hu Xifeng Yang Guofeng Yang Yushen Liu Zhixing Gan Center for Future Optoelectronic Functional Materials School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing 210023 China College of Physics Nanjing University of Aeronautics and Astronautics Key Laboratory of Aerospace Information Materials and Physics (NUAA) MIIT Nanjing 211106 China Hubei Key Laboratory of Photoelectric Materials and Devices School of Materials Science and Engineering Hubei Normal University Huangshi 435002 China College of Engineering and Applied Sciences and MOE Key Laboratory of Intelligent Optical Sensing and Manipulation Nanjing University Nanjing 210093 China College of Electronic and Information Engineering Changshu Institute of Technology Suzhou 215500 China School of Science Jiangsu Provincial Research Center of Light Industrial Optoelectronic Engineering and Technology Jiangnan University Wuxi 214122 China Yancheng Polytechnic College Yancheng 224005 China

Luminescent materials with multi-emission features are difficult to be replicated, which are highly desirable for advanced anti-counterfeiting. Here, we report the pioneering synthesis of Mn^(2+)/Yb^(3+)/Er^(3+) tri-doped Cs2Ag0.8Na0.2InCl6 double perovskites (MYE-DP), which exhibit photoluminescence (PL) covering from visible to near-infrared (NIR). The PL colors under excitations of 254 and 365 nm are notably different due to the changed relative emission intensities of self-trapped excitons (STEs) and Mn^(2+) d–d transition. Moreover, under the excitation of a NIR laser, the MYE-DP exhibits upconversion (UC) emissions of Mn^(2+) and Er^(3+). After ceasing the excitation, the long-lived trapped electrons can be thermally released to Mn^(2+) and Er^(3+) ions, resulting in both visible and NIR afterglow. Based on multi-modal emissions of the MYE-DP, we demonstrate a five-level anti-counterfeiting strategy, which significantly increases the anti-counterfeiting security. In addition, this work provides valuable insights into the energy transfer between STEs, Mn^(2+), Ln^(3+), and traps, laying a solid foundation for future development of new lead-free perovskites.

关键词： anti-counterfeiting photoluminescence self-trapped excitons upconversion

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Towards Task-Free Privacy-Preserving Data Collection

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China Communications 2022年第7期19卷 310-323页

作者： Zhibo Wang Wei Yuan Xiaoyi Pang Jingxin Li Huajie Shao School of Cyber Science and Engineering Wuhan University430072 China School of Cyber Science and Technology Zhejiang University310027 China State Key Laboratory of Integrated Services Networks Xidian University710071 China Department of Computer Science College of William and MaryWilliamsburgVA23186 USA

With the rapid developments of Internet of Things(IoT)and proliferation of embedded devices,large volume of personal data are collected,which however,might carry massive private information about attributes that users do not want to *** privacy-preserving methods have been proposed to prevent privacy leakage by perturbing raw data or extracting task-oriented features at local ***,they would suffer from significant privacy leakage and accuracy drop when applied to other tasks as they are designed and optimized for predefined *** this paper,we propose a novel task-free privacy-preserving data collection method via adversarial representation learning,called TF-ARL,to protect private attributes specified by users while maintaining data utility for unknown downstream *** this end,we first propose a privacy adversarial learning mechanism(PAL)to protect private attributes by optimizing the feature extractor to maximize the adversary’s prediction uncertainty on private attributes,and then design a conditional decoding mechanism(ConDec)to maintain data utility for downstream tasks by minimizing the conditional reconstruction error from the sanitized *** the joint learning of PAL and ConDec,we can learn a privacy-aware feature extractor where the sanitized features maintain the discriminative information except *** experimental results on real-world datasets demonstrate the effectiveness of TF-ARL.

关键词： IoT privacy protection data collection adversarial learning deep learning

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TF-DTA: A Deep Learning Approach Using Transformer Encoder to Predict Drug-Target Binding Affinity

TF-DTA: A Deep Learning Approach Using Transformer Encoder t...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Li, Wenjun Zhou, Yiqiang Tang, Xiwei Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Hunan Changsha China Hunan First Normal University School of Computer Science Hunan Changsha China

ISBN: (纸本)9798350337488

Bioinformatics is a rapidly growing field that involves the application of computational methods to analyze and interpret biological data. One important task in bioinformatics is predicting the drug-target affinity (DTA), which plays a significant role in drug discovery through virtual screening. Effectively predicting the association between drug molecules and target molecules can speed up the drug discovery process. The DTA can be quantitatively measured. This quantifiable affinity is more precise than a simple binary relationship. In this study, we propose a deep learning model for DTA prediction that utilizes the encoder module of the Transformer architecture. Our proposed model utilizes Convolutional Neural networks (CNNs) and the encoder module of Transformer to characterize protein and drug sequences. The model outperforms some methods such as KronRLS, SimBoost and DeepDTA as evidenced by superior evaluation metrics such as Mean Squared Error (MSE), Concordance Index (CI), and Regression toward the Mean Index (rm2). These results demonstrate the effectiveness of the Transformer's encoder in extracting meaningful representations from sequences, thereby improving the accuracy of DTA prediction. Deep learning models for DTA prediction can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, the use of machine learning technology enables more effective and efficient drug discovery. © 2023 IEEE.

关键词： deep learning drug-target affinity multi-head attention mechanism

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Local and Global Feature-Aware Dual-Branch networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information

Extractive Medical Entity Disambiguation with Memory Mechani...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Guobiao Peng, Xueping Shen, Tao Long, Guodong Si, Jiasheng Qin, Libo Lu, Wenpeng China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Computer Science and Engineering Central South University Changsha China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing China

ISBN: (纸本)9798891761681

Medical entity disambiguation (MED) aims to ground medical mentions in text with ontological entities in knowledge bases (KBs). A notable challenge of MED is the long medical text usually contains multiple entities' mentions with intricate correlations. However, limited by computation overhead, many existing methods consider only a single candidate entity mention during the disambiguation process. As such, they focus only on local MED optimal while ignoring the sole-mention disambiguation possibly boosted by richer context from other mentions' disambiguating processes - missing global optimal on entity combination in the text. Motivated by this, we propose a new approach called Extractive Medical Entity Disambiguation with Memory Mechanism and Memorized Entity Information (M3E). Specifically, we reformulate MED as a text extraction task, which simultaneously accepts the context of medical mentions, all possible candidate entities, and entity definitions, and it is then trained to extract the text span corresponding to the correct entity. Upon our new formulation, 1) to alleviate the computation overhead from the enriched context, we devise a memory mechanism module that performs memory caching, retrieval, fusion and cross-network residual;and 2) to utilize the disambiguation clues from other mentions, we design an auxiliary disambiguation module that employs a gating mechanism to assist the disambiguation of remaining mentions. Extensive experiments on two benchmark datasets demonstrate the superiority of M3E over the state-of-the-art MED methods on all metrics. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Efficient production of hydrogen-rich syngas by pyrolysis coupled with catalytic steam reforming of degradable polylactic acid waste plastics over high-entropy oxide catalyst

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International Journal of Hydrogen Energy 2025年 142卷 1102-1112页

作者： Wu, Jiaxuan He, Xi Xu, Liujie Song, Qingbin Tian, Zhipeng Wang, Chao Zhao, Ming Zhou, Guopeng Gao, Yajing National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR 999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR 999078 China Guangdong Provincial Key Laboratory on Functional Soft Condensed Matter School of Materials and Energy Guangdong University of Technology Guangzhou510006 China Key Laboratory for Solid Waste Management and Environment Safety Ministry of Education School of Environment Tsinghua University Beijing100084 China China Huaneng Group Energy Research Institute Beijing100031 China

To mitigate the solid waste crisis, converting waste plastics into valuable products through thermochemical processes, such as through pyrolysis, is emerging. Polylactic acid (PLA) plastics are difficult to separate from other plastics in solid waste, and mixed waste is usually directly landfilled or incinerated. Moreover, studies focused solely on the pyrolysis of PLA plastic remain relatively scarce. The large amount of tar produced during plastic pyrolysis can reduce the high-value production of hydrogen, and high-entropy oxide (HEO) catalyst exhibit excellent performance in tar conversion and anti-carbon deposition. In this research, HEO catalyst was synthesized by the sol-gel method for the pyrolysis of waste PLA plastic volatiles, aiming to generate H2-rich syngas in a two-stage integrated reactor system. During catalytic reforming, the highest hydrogen production was 124.1 mmol∙gPLA−1, and the gas yield based on plastic mass reached 259.8 wt%. The superiority of HEO catalyst stems from the synergistic interaction of various elements, enhancing the structural stability of the high-entropy system and increasing oxygen vacancies, resulting in strong C–C and C–H bond-breaking capabilities in the Ce, Mg, Fe, Co, and Ni HEO catalyst. In addition, this study investigated the mechanism of PLA's simple pyrolysis and the role of the HEO catalyst in the catalytic steam reforming of PLA. This study developed a novel high-entropy aluminate catalyst demonstrating efficient hydrogen-rich syngas production, providing an innovative approach for generating hydrogen-rich syngas from high-molecular-weight polymer plastics through pyrolysis coupled with catalytic steam reforming. © 2025 Hydrogen Energy Publications LLC

关键词： Sol-gel process

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System Combining Deep and Broad Representations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5075-5089页

作者： Lei, Chunyu Guo, Jifeng Chen, C. L. Philip South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou510335 China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou510006 China

Broad learning system (BLS) has to undergo a vectorization operation before modeling image data, which makes it challenging for BLS to learn local semantic features. Thus, various convolutional-based broad learning systems (C-BLSs) have been introduced to address these challenges. Regrettably, the existing C-BLS variants either lack an efficient training algorithm and incremental learning capability or suffer from poor performance. To this end, we propose a novel convolutional broad learning system (ConvBLS) based on the spherical K-means (SKM) algorithm and two-stage multiscale (TSMS) feature fusion, which consists of the convolutional feature layer (CFL), convolutional enhancement layer (CEL), TSMS feature fusion layer, and output layer. First, unlike the current C-BLS, the simple yet efficient SKM algorithm is utilized to learn the weights of CFLs. Compared with random filters, the SKM algorithm enables the CFL to learn more comprehensive spatial features. Second, to further mine the local semantic features, CELs are established to expand the feature space. Third, the TSMS feature fusion layer is proposed to extract more effective multiscale features by integrating deep and broad representations. Thanks to the above elaborate design and the pseudoinverse calculation of the output layer weights, our proposed ConvBLS method is unprecedentedly efficient and effective. Finally, the corresponding incremental learning algorithms are presented for rapid remodeling if the model deems to expand. Experiments and comparisons demonstrate the superiority of our method. © 2024 IEEE.

关键词： Image classification

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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Object detection algorithm based on cosine similarity IoU

Object detection algorithm based on cosine similarity IoU

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Sugang, Ma Ningbo, Li Guansheng, Peng Yanping, Chen Ying, Wang Zhiqiang, Hou School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China School of Computer Science and Technology Xidian University Shaanxi Xi'an China Xi'an University of Posts and Telecommunications Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Aiming at the problem that the traditional IoU-NMS algorithm has poor filtering of redundant boxes with high confidence scores in RetinaNet, an object detection algorithm based on cosine similarity IoU is proposed. Based on the original IoU calculation method, the cosine similarity between the detection boxes is calculated by the vector to better evaluate the similarity between them, which removes redundant boxes with high confidence scores and retain more accurate detection boxes. Meanwhile, CBAM is added to the ResNet-50 network to extract richer semantic information. the detection accuracy of the PASCAL VOC dataset reaches 80.6%, which is 2.1% higher than the benchmark algorithm. © 2022 IEEE.

关键词： Object detection

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