[Objectives]To develop an HPLC fingerprint analysis method for the medicinal material of Lysimachia foenum-graecum Hance,thereby providing a foundation for its quality control.[Methods]Samples of L.foenum-graecum coll...[Objectives]To develop an HPLC fingerprint analysis method for the medicinal material of Lysimachia foenum-graecum Hance,thereby providing a foundation for its quality control.[Methods]Samples of L.foenum-graecum collected from 10 distinct locations in Guangxi were analyzed using HPLC,and chromatographic fingerprints were established.The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(2012 Edition)was employed for common peak calibration and similarity evaluation.Additionally,principal component analysis was performed on the common peak area data.[Results]An HPLC fingerprint of L.foenum-graecum was developed,identifying a total of 13 common peaks.Among these,four characteristic components were specifically identified:chlorogenic acid,myricetin,quercetin,and kaempferol.The kaempferol chromatographic peak,exhibiting good resolution and a stable peak shape,was selected as the reference peak.The similarity indices between the fingerprints of the 10 sample batches and the reference fingerprint ranged from 0.954 to 0.995,indicating a relatively high consistency in the chemical composition of L.foenum-graecum from different origins.Principal component analysis identified two principal components,which together accounted for 89.45%of the cumulative variance,effectively capturing the primary chemical differences among the samples.[Conclusions]The established HPLC fingerprint method is straightforward to implement,stable,reliable,and exhibits high specificity.When combined with similarity evaluation and principal component analysis,it offers a scientific basis for developing quality standards for L.foenum-graecum medicinal materials.展开更多
The radio frequency(RF)fingerprint technique is a robust method for security enhancement of the physical layer by leveraging the unique RF imperfections inherent in various wireless devices.Among these imperfections,t...The radio frequency(RF)fingerprint technique is a robust method for security enhancement of the physical layer by leveraging the unique RF imperfections inherent in various wireless devices.Among these imperfections,the carrier frequency offset(CFO)stands out as a primary RF fingerprint(RFF)of the transmitter,offering the potential to distinguish among different transmitters.However,accurately estimating CFO in time-varying channels poses significant challenges due to multipath effects and Doppler shifts.In this paper,we focus on estimating CFO for wireless device identification in the orthogonal frequency division multiplexing(OFDM)communication system.To achieve precise CFO estimation under time-varying channels,we propose a frequency domain correlation and spline interpolation(FCSI)algorithm.This approach utilizes pilots distributed across different subcarriers to correlate with prior local sequences,facilitating accurate CFO estimation.Classification is then performed based on the Euclidean distance between the prior RFF and the tested RFF dataset.Simulation results demonstrate that the proposed Mconsecutive average method effectively reduces the classification error rate in the challenging high-frequency(HF)skywave channel environment.展开更多
Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users t...Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users throughwebsite fingerprinting(WF)attacks.ConventionalWFmethodologies demonstrate optimal performance in scenarios involving single-tab browsing.Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing.However,in real-world network environments,users often engage in multitab browsing,which generates overlapping traffic patterns from different websites.This overlap has been shown to significantly degrade the performance of classifiers that rely on the single-tab assumption.To address this challenge,this paper proposes a Transformer-basedmulti-tab website fingerprinting(MT-WF)attack framework.Themodel employs an adaptive sliding windowmechanism to capture fine-grained features of traffic direction.Additionally,it incorporates a label-aware attention mechanism designed to dynamically separate and refine entangled traffic representations,enhancing the model’s ability to distinguish between overlapping traffic patterns.Furthermore,the model leverages global traffic patterns through multi-segment feature fusion and incorporates an incremental learning(IL)strategy to adapt to the continuously evolving website categories in open-world environments.Experimental results demonstrate that the proposedmethod achieves a top-2 precision of 0.78 in the closed-world setting.In the open-world scenario,the model attains an F1 score of 0.904,outperforming most existing baselines.The proposed method maintains superior performance even under challenging conditions,including WF defenses and concept drift.展开更多
Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel ...Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.展开更多
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.展开更多
[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phas...[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phase microextraction method to separate and detect volatile components and construct chromatographic fingerprint.[Result] The honey was preheated for 15 min in water bath at 40 ℃ and solid-phase microextraction 85 μmPA was used to extract in overhead air about 30 min,then put it into the injector and desorpted 3 min,which is in 230 ℃.The Supelco WaxTM10 30 m×0.25 mm×0.25 μm column and gradient heating program was the best method to separate volatile components from honey.83 fingerprint peaks were constructed,among which 17 common fingerprint peaks were comprised of chromatographic fingerprint of volatile components of acacia honey.[Conclusion] The chromatographic fingerprint could provide reference for quality control of acacia honey.展开更多
To further advance the development of the fluorescent dyes for latent fingerprint imaging,two triphenylamine-based Schiff base compounds containing a benzimidazole group(TPA-BZI)and a phenyl unit(TPA-Ph)were designed ...To further advance the development of the fluorescent dyes for latent fingerprint imaging,two triphenylamine-based Schiff base compounds containing a benzimidazole group(TPA-BZI)and a phenyl unit(TPA-Ph)were designed and synthesized.Photoluminescence experiments revealed that both compounds exhibited solvatochromism and intramolecular charge transfer(ICT)characteristics in six organic solvents.Additionally,they showed aggregation-induced emission(AIE)in CH_(3)OH/water mixtures and solid-state fluorescence.These phenomena were further elucidated through time-dependent density functional theory(TD-DFT)calculations.It was also found that the two compounds could be used for latent fingerprints imaging,and could easily distinguish the details of fingerprints from Ⅰ to Ⅲ levels,which could provide the preliminary evidence to match personal identification.展开更多
A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be...A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be showcased at this year’s ITMA Asia+CITME exhibition,which takes place in Singapore from October 28-31.“Many of our members are currently developing new technologies,either in-house or increasingly through joint projects,and there will be much to reveal by the time of ITMA Asia in Singapore,”says BTMA CEO Jason Kent.“Some of the most recent developments are really going beyond what has previously been possible.”展开更多
This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passi...This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.展开更多
The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinti...The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach.This study explores the dynamic intersection of phytochemistry and forensic science,highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites,serve as distinctive markers for forensic investigations.By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography(UHPLC)and High-Resolution Mass Spectrometry(HRMS),the detection and quantification of plant-derived are made more accurate in forensic contexts.Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings.The challenges,potential,and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed.This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses,offering new insights into the complex stories embedded in plant toxins.展开更多
Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudul...Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudulent websites in batches.Accurate identification of fraudulent website will effectively re-duce the risk of public victimization.Therefore,this study developed a fraudulent website template iden-tification method based on DOM structure extraction of website fingerprint features,which solves the prob-lems of single-dimension identification,low accuracy,and the insufficient generalization ability of current fraudulent website templates.This method uses an im-proved SimHash algorithm to traverse the DOM tree of a webpage,extract website node features,calcu-late the weight of each node,and obtain the finger-print feature vector of the website through dimension-ality reduction.Finally,the random forest algorithm is used to optimize the training features for the best combination of parameters.This method automati-cally extracts fingerprint features from websites and identifies website template ownership based on these features.An experimental analysis showed that this method achieves a classification accuracy of 89.8%and demonstrates superior recognition.展开更多
With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.Th...With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.展开更多
Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classificati...Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.展开更多
Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemome...Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemometric analysis.Additionally,network pharmacology was employed to investigate the possible mechanisms of action of L.japonicum in the urinary calculi(UC)treatment.Methods:A fingerprinting method was established to identify components through UHPLC-tandem mass spectrometry.Chemometric techniques were used to compare the L.japonicum extraction methods.Furthermore,various network pharmacological approaches were used to identify and analyze the potential targets of the identified components in relation to UC.Results:The W and S extracts were distributed into two distinct clusters.Significant differences in the levels of protocatechuic aldehyde,caffeic acid,and p-coumaric acid were observed between S and W.Network pharmacology analysis revealed that the primary targets of L.japonicum in the UC treatment were serum albumin and epidermal growth factor receptors,with potential active components including protocatechuic acid and caffeic acid.Conclusion:This study comprehensively examined the therapeutic components of L.japonicum before and after boiling,shedding light on its potential mechanisms of action in UC treatment.These findings offer valuable insights into the development and utilization of L.japonicum resources.展开更多
Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose signifi...Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.展开更多
In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative pro...In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.展开更多
Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial fea...Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.展开更多
基金Supported by 2023 Self-funded Research Project of the Guangxi Zhuang Autonomous Region Administration of Traditional Chinese Medicine(GXZYL20230369)2022 Internal Talent Research Project of Affiliated Hospital of Youjiang Medical University for Nationalities(Y202210319).
文摘[Objectives]To develop an HPLC fingerprint analysis method for the medicinal material of Lysimachia foenum-graecum Hance,thereby providing a foundation for its quality control.[Methods]Samples of L.foenum-graecum collected from 10 distinct locations in Guangxi were analyzed using HPLC,and chromatographic fingerprints were established.The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(2012 Edition)was employed for common peak calibration and similarity evaluation.Additionally,principal component analysis was performed on the common peak area data.[Results]An HPLC fingerprint of L.foenum-graecum was developed,identifying a total of 13 common peaks.Among these,four characteristic components were specifically identified:chlorogenic acid,myricetin,quercetin,and kaempferol.The kaempferol chromatographic peak,exhibiting good resolution and a stable peak shape,was selected as the reference peak.The similarity indices between the fingerprints of the 10 sample batches and the reference fingerprint ranged from 0.954 to 0.995,indicating a relatively high consistency in the chemical composition of L.foenum-graecum from different origins.Principal component analysis identified two principal components,which together accounted for 89.45%of the cumulative variance,effectively capturing the primary chemical differences among the samples.[Conclusions]The established HPLC fingerprint method is straightforward to implement,stable,reliable,and exhibits high specificity.When combined with similarity evaluation and principal component analysis,it offers a scientific basis for developing quality standards for L.foenum-graecum medicinal materials.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240723011National Natural Science Foundation of China under Grant No.62371123+1 种基金Young Elite Scientists Sponsorship Program of the Beijing High Innovation Plan under Grant No.20251077Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2023A03。
文摘The radio frequency(RF)fingerprint technique is a robust method for security enhancement of the physical layer by leveraging the unique RF imperfections inherent in various wireless devices.Among these imperfections,the carrier frequency offset(CFO)stands out as a primary RF fingerprint(RFF)of the transmitter,offering the potential to distinguish among different transmitters.However,accurately estimating CFO in time-varying channels poses significant challenges due to multipath effects and Doppler shifts.In this paper,we focus on estimating CFO for wireless device identification in the orthogonal frequency division multiplexing(OFDM)communication system.To achieve precise CFO estimation under time-varying channels,we propose a frequency domain correlation and spline interpolation(FCSI)algorithm.This approach utilizes pilots distributed across different subcarriers to correlate with prior local sequences,facilitating accurate CFO estimation.Classification is then performed based on the Euclidean distance between the prior RFF and the tested RFF dataset.Simulation results demonstrate that the proposed Mconsecutive average method effectively reduces the classification error rate in the challenging high-frequency(HF)skywave channel environment.
文摘Despite the ability of the anonymous communication system The Onion Router(Tor)to obscure the content of communications,prior studies have shown that passive adversaries can still infer the websites visited by users throughwebsite fingerprinting(WF)attacks.ConventionalWFmethodologies demonstrate optimal performance in scenarios involving single-tab browsing.Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing.However,in real-world network environments,users often engage in multitab browsing,which generates overlapping traffic patterns from different websites.This overlap has been shown to significantly degrade the performance of classifiers that rely on the single-tab assumption.To address this challenge,this paper proposes a Transformer-basedmulti-tab website fingerprinting(MT-WF)attack framework.Themodel employs an adaptive sliding windowmechanism to capture fine-grained features of traffic direction.Additionally,it incorporates a label-aware attention mechanism designed to dynamically separate and refine entangled traffic representations,enhancing the model’s ability to distinguish between overlapping traffic patterns.Furthermore,the model leverages global traffic patterns through multi-segment feature fusion and incorporates an incremental learning(IL)strategy to adapt to the continuously evolving website categories in open-world environments.Experimental results demonstrate that the proposedmethod achieves a top-2 precision of 0.78 in the closed-world setting.In the open-world scenario,the model attains an F1 score of 0.904,outperforming most existing baselines.The proposed method maintains superior performance even under challenging conditions,including WF defenses and concept drift.
文摘Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
基金Support by Department of Education Science and Technology Research Projects of Hebei Province(2008310)the National Special Fund for the Commonweal Industry(200810345)~~
文摘[Objective] The experiment aimed to study chromatographic fingerprint in volatile components of acacia honey and provide scientific evaluation and effective control on quality of acacia honey.[Method] Using solid-phase microextraction method to separate and detect volatile components and construct chromatographic fingerprint.[Result] The honey was preheated for 15 min in water bath at 40 ℃ and solid-phase microextraction 85 μmPA was used to extract in overhead air about 30 min,then put it into the injector and desorpted 3 min,which is in 230 ℃.The Supelco WaxTM10 30 m×0.25 mm×0.25 μm column and gradient heating program was the best method to separate volatile components from honey.83 fingerprint peaks were constructed,among which 17 common fingerprint peaks were comprised of chromatographic fingerprint of volatile components of acacia honey.[Conclusion] The chromatographic fingerprint could provide reference for quality control of acacia honey.
文摘To further advance the development of the fluorescent dyes for latent fingerprint imaging,two triphenylamine-based Schiff base compounds containing a benzimidazole group(TPA-BZI)and a phenyl unit(TPA-Ph)were designed and synthesized.Photoluminescence experiments revealed that both compounds exhibited solvatochromism and intramolecular charge transfer(ICT)characteristics in six organic solvents.Additionally,they showed aggregation-induced emission(AIE)in CH_(3)OH/water mixtures and solid-state fluorescence.These phenomena were further elucidated through time-dependent density functional theory(TD-DFT)calculations.It was also found that the two compounds could be used for latent fingerprints imaging,and could easily distinguish the details of fingerprints from Ⅰ to Ⅲ levels,which could provide the preliminary evidence to match personal identification.
文摘A leading position in the areas of testing,instrumentation and machine control has been established by members of the British Textile Machinery Association(BTMA)and a number of new developments in these fields will be showcased at this year’s ITMA Asia+CITME exhibition,which takes place in Singapore from October 28-31.“Many of our members are currently developing new technologies,either in-house or increasingly through joint projects,and there will be much to reveal by the time of ITMA Asia in Singapore,”says BTMA CEO Jason Kent.“Some of the most recent developments are really going beyond what has previously been possible.”
基金supported in part by the National Key Research and Development Program under Grant(2021YFB2900300)by the National Natural Science Foundation of China(NSFC)under Grants 61971127,61871122by the Southeast University-China Mobile Research Institute Joint Innovation Center,and by the Major Key Project of PCL(PCL2021A01-2).
文摘This paper investigates how to achieve integrated sensing and communication(ISAC)based on a cell-free radio access network(CF-RAN)architecture with a minimum footprint of communication resources.We propose a new passive sensing scheme.The scheme is based on the radio frequency(RF)fingerprint learning of the RF radio unit(RRU)to build an RF fingerprint library of RRUs.The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side.The receiver extracts the channel parameters from the signal and estimates the channel environment,thus locating the reflectors in the environment.The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance.
文摘The integration of phytochemistry into forensic science has emerged as a groundbreaking frontier,providing unprecedented insights into nature's secrets through the precise application of phytochemical fingerprinting of phytotoxins as a cutting-edge approach.This study explores the dynamic intersection of phytochemistry and forensic science,highlighting how the unique phytochemical profiles of toxic plants and their secondary metabolites,serve as distinctive markers for forensic investigations.By utilizing advanced techniques such as Ultra-High-Performance Liquid Chromatography(UHPLC)and High-Resolution Mass Spectrometry(HRMS),the detection and quantification of plant-derived are made more accurate in forensic contexts.Real-world case studies are presented to demonstrate the critical role of plant toxins in forensic outcomes and legal proceedings.The challenges,potential,and future prospects of integrating phytochemical fingerprinting of plant toxins into forensic science were discussed.This review aims to illuminate phytochemical fingerprinting of plant toxins as a promising tool to enhance the precision and depth of forensic analyses,offering new insights into the complex stories embedded in plant toxins.
基金This research is a phased achievement of The National Social Science Fund of China(23BGL272).
文摘Fraudulent website is an important car-rier tool for telecom fraud.At present,criminals can use artificial intelligence generative content technol-ogy to quickly generate fraudulent website templates and build fraudulent websites in batches.Accurate identification of fraudulent website will effectively re-duce the risk of public victimization.Therefore,this study developed a fraudulent website template iden-tification method based on DOM structure extraction of website fingerprint features,which solves the prob-lems of single-dimension identification,low accuracy,and the insufficient generalization ability of current fraudulent website templates.This method uses an im-proved SimHash algorithm to traverse the DOM tree of a webpage,extract website node features,calcu-late the weight of each node,and obtain the finger-print feature vector of the website through dimension-ality reduction.Finally,the random forest algorithm is used to optimize the training features for the best combination of parameters.This method automati-cally extracts fingerprint features from websites and identifies website template ownership based on these features.An experimental analysis showed that this method achieves a classification accuracy of 89.8%and demonstrates superior recognition.
文摘With the rapid development of Internet of things technology,the efficiency of data transmission between devices has been significantly improved.However,the open network environment also poses serious security risks.This paper proposes an innovative fingerprint template protection scheme,which generates key streams through an improved fourdimensional superchaotic system(4CSCS),uses the space-filling property of Hilbert curves to achieve pixel scrambling,and introduces dynamic DNA encoding to improve encryption.Experimental results show that this scheme has a large key space 2^(528),encrypts image information entropy of more than 7.9970,and shows excellent performance in defending against statistical attacks and differential attacks.Compared with existing methods,this scheme has significant advantages in terms of encryption performance and security,and provides a reliable protection mechanism for fingerprint authentication systems in the Internet of things environment.
文摘Fingerprint classification is a biometric method for crime prevention.For the successful completion of various tasks,such as official attendance,banking transactions,andmembership requirements,fingerprint classification methods require improvement in terms of accuracy,speed,and the interpretability of non-linear demographic features.Researchers have introduced several CNN-based fingerprint classification models with improved accuracy,but these models often lack effective feature extractionmechanisms and complex multineural architectures.In addition,existing literature primarily focuses on gender classification rather than accurately,efficiently,and confidently classifying hands and fingers through the interpretability of prominent features.This research seeks to improve a compact,robust,explainable,and non-linear feature extraction-based CNN model for robust fingerprint pattern analysis and accurate yet efficient fingerprint classification.The proposed model(a)recognizes gender,hands,and fingers correctly through an advanced channel-wise attention-based feature extraction procedure,(b)accelerates the fingerprints identification process by applying an innovative fractional optimizer within a simple,but effective classification architecture,and(c)interprets prominent features through an explainable artificial intelligence technique.The encapsulated dependencies among distinct complex features are captured through a non-linear activation operation within a customized CNN model.The proposed fractionally optimized convolutional neural network(FOCNN)model demonstrates improved performance compared to some existing models,achieving high accuracies of 97.85%,99.10%,and 99.29%for finger,gender,and hand classification,respectively,utilizing the benchmark Sokoto Coventry Fingerprint Dataset.
基金supported by Ministry of Industry and Information Technology of the People's Republic of China 2022 Industrial Technology Basic Public Service Platform Project-Traditional Chinese Medicine Whole Industry Chain Quality and Technology Service Platform(2022-230-221)Foshan Nanhai District Key Area Science and Technology Research Project[Nanke(2023)20-18].
文摘Objective:To evaluate the quality of Lygodium japonicum(Thunb.)Sw.(L.japonicum,Hai Jin Sha)by comparing its components without stewed(W)and stewed(S)using ultra-high-performance liquid chromatography(UHPLC)and chemometric analysis.Additionally,network pharmacology was employed to investigate the possible mechanisms of action of L.japonicum in the urinary calculi(UC)treatment.Methods:A fingerprinting method was established to identify components through UHPLC-tandem mass spectrometry.Chemometric techniques were used to compare the L.japonicum extraction methods.Furthermore,various network pharmacological approaches were used to identify and analyze the potential targets of the identified components in relation to UC.Results:The W and S extracts were distributed into two distinct clusters.Significant differences in the levels of protocatechuic aldehyde,caffeic acid,and p-coumaric acid were observed between S and W.Network pharmacology analysis revealed that the primary targets of L.japonicum in the UC treatment were serum albumin and epidermal growth factor receptors,with potential active components including protocatechuic acid and caffeic acid.Conclusion:This study comprehensively examined the therapeutic components of L.japonicum before and after boiling,shedding light on its potential mechanisms of action in UC treatment.These findings offer valuable insights into the development and utilization of L.japonicum resources.
基金supported by the National Natural Science Foundation of China(Nos.62002100,61902237)Key Research and Promotion Projects of Henan Province(Nos.232102240023,232102210063,222102210040).
文摘Fingerprint features,as unique and stable biometric identifiers,are crucial for identity verification.However,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data leakage.Federated Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security concerns.However,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across clients.This heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse distributions.To address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated Learning.The algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature characteristics.Additionally,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model updates.Experiments conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.
基金Project supported by the Scientific Research Project of Jilin Provincial Department of Education(JJKH20230821KJ,JJKH20230823KJ,JJKH20230822KJ)。
文摘In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.
基金supported by the National Defense Basic Scientific Research Program of China(No.JCKY2023602C026)the funding of Key Laboratory of Mobile Application Innovation and Governance Technology,Ministry of Industry and Information Technology(2023IFS080601-K).
文摘Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.
