Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digita...Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digital rightsmanagement.ABE schemes rely on hard cryptographic assumptions such as pairings and others(pairingfree)to ensure their security against external and internal attacks.Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent.One common internal attack is the attribute collusion attack,in which users with different attribute keys collaborate to decrypt data they could not individually access.This paper focuses on the ciphertext-policy ABE(CP-ABE),a type of ABE where ciphertexts are produced with access policies.Our firstwork is to carry out the attribute collusion attack against several existing pairingfree CP-ABE schemes.As a main contribution,we introduce a novel attack,termed the anonymous key-leakage attack,concerning the context in which users could anonymously publish their secret keys associated with certain attributes on public platforms without the risk of detection.This kind of internal attack has not been defined or investigated in the literature.We then show that several prominent pairing-based CP-ABE schemes are vulnerable to this attack.We believe that this work will contribute to helping the community evaluate suitable CP-ABE schemes for secure deployment in real-life applications.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosph...Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosphere microbiome and functioning as influenced by these cropping methods is essential for sustaining rice productivity.The objective of this study was to assess the impact of three different rice cropping methods(wetland rice,SRI,and aerobic rice)on the biochemical properties and bacterial communities within the rice rhizosphere across three key rice growth stages:tillering,flowering,and maturity.Soil organic carbon(SOC),microbial biomass carbon(MBC),dehydrogenase activity,substrate-induced respiration(SIR),and metabolic quotient(MQ)were assessed along with high-throughput 16S rRNA sequencing of rice rhizosphere soils.The rice rhizosphere soil registered the highest SOC,MBC,and dehydrogenase activity in SRI followed by wetland rice and then aerobic rice.Cropping method had a minimal impact on SIR and MQ.Along with cropping method,growth stage also significantly altered these biological attributes of rice rhizosphere.The trends of the highest SOC content and dehydrogenase activity at the flowering stage and the highest MBC content and SIR at the tillering stage of rice were observed in all three rice cropping methods.The analysis of bacterial communities,based on 16S rRNA gene sequencing,revealed that both cropping method and growth stage significantly impacted the composition of rhizosphere microbiomes.However,the influence of cropping method was less pronounced compared to growth stage.Cropping method caused notable shifts in the abundances of Proteobacteria,Bacteroidetes,and Chloroflexi,while growth stage affected the abundances of Proteobacteria,Actinobacteria,Cyanobacteria,Firmicutes,Chloroflexi,and Bacteroidetes.Based on these results,the SRI method led to higher diversification to the rhizosphere bacteriobiota,as well as greater incorporation of carbon into the soil and increased dehydrogenase activity compared to wetland rice and aerobic rice.This study deepens our understanding of how different cropping methods influence plant-microbe interaction and the implications for overall rice productivity and soil health.展开更多
Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribu...Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.展开更多
Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,th...Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.展开更多
The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogene...The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.展开更多
Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rul...Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.展开更多
Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this st...Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this study employs the RFECV method for seismic attribute selection,inputting the optimized attributes into a LightGBM model to enhance spatial delineation of sandbody identification.By constructing training datasets based on optimized seismic attributes and well logs,followed by class imbalance correction as input variables for machine learning models,with sandbody probability as the output variable,and employing grid search to optimize model parameters,a high-precision sandbody prediction model was established.Taking the 3D seismic data of Block F3 in the North Sea of Holland as an example,this method successfully depicted the three-dimensional spatial distribution of target formation sandstones.The results indicate that even under strong noise conditions,the multi-attribute sandbody identification method based on LightGBM effectively characterizes the distribution features of sandbodies.Compared to unselected attributes,the prediction results using selected attributes have higher vertical resolution and inter-well conformity,with the prediction accuracy for single wells reaching 80.77%,significantly improving the accuracy of sandbody boundary delineation.展开更多
Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management pl...Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.展开更多
Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distribu...Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.展开更多
This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increase...This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.展开更多
Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in t...Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.展开更多
Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself disc...Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.展开更多
Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,20...Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,2011).Since then,the number of reported SFTS cases has rapidly increased in China,South Korea,and Japan(Li,et al.,2018;Takahashi et al.,2014;Kim et al.,2018).Sporadic SFTS cases have also been identified in several other Asian countries,such as Vietnam,Pakistan,Myanmar,and Thailand(Takahashi et al.,2014;Tran et al.,2019;Li et al.,2021).This disease is recognized as a highly lethal viral hemorrhagic fever with a mortality rate ranging from 12%to 50%(Yu et al.,2011;Li et al.,2018;Takahashi et al.,2014;Kim et al.,2013).SFTS primarily spreads to humans through bites from ticks infected with SFTSV,with the Haemaphysalis longicornis tick acting as the predominant vector for SFTSV(Zhuang et al.,2018).展开更多
The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attr...The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.展开更多
Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biom...Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.展开更多
Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing me...Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.展开更多
Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosi...Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators.To address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and wear.Correspondingly,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures.In particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators.With this model,the lubricant conditions can be accurately assessed through rule-based reasoning.Furthermore,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring data.For verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines.Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing methods.While for the engine lubricant test,a high accuracy of 90%was achieved.展开更多
文摘Attribute-based encryption(ABE)is a cryptographic framework that provides flexible access control by allowing encryption based on user attributes.ABE is widely applied in cloud storage,file sharing,e-Health,and digital rightsmanagement.ABE schemes rely on hard cryptographic assumptions such as pairings and others(pairingfree)to ensure their security against external and internal attacks.Internal attacks are carried out by authorized users who misuse their access to compromise security with potentially malicious intent.One common internal attack is the attribute collusion attack,in which users with different attribute keys collaborate to decrypt data they could not individually access.This paper focuses on the ciphertext-policy ABE(CP-ABE),a type of ABE where ciphertexts are produced with access policies.Our firstwork is to carry out the attribute collusion attack against several existing pairingfree CP-ABE schemes.As a main contribution,we introduce a novel attack,termed the anonymous key-leakage attack,concerning the context in which users could anonymously publish their secret keys associated with certain attributes on public platforms without the risk of detection.This kind of internal attack has not been defined or investigated in the literature.We then show that several prominent pairing-based CP-ABE schemes are vulnerable to this attack.We believe that this work will contribute to helping the community evaluate suitable CP-ABE schemes for secure deployment in real-life applications.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
基金support from the Indian Council of Agricultural Research through the All India Network Project(AINP)on Soil Biodiversity and Biofertilizers to conduct this study is acknowledged。
文摘Rice cropping method is primarily decided by soil moisture regime.System of rice intensification(SRI)and direct-seeded aerobic rice are two primary modifications of traditional wetland rice.Understanding rice rhizosphere microbiome and functioning as influenced by these cropping methods is essential for sustaining rice productivity.The objective of this study was to assess the impact of three different rice cropping methods(wetland rice,SRI,and aerobic rice)on the biochemical properties and bacterial communities within the rice rhizosphere across three key rice growth stages:tillering,flowering,and maturity.Soil organic carbon(SOC),microbial biomass carbon(MBC),dehydrogenase activity,substrate-induced respiration(SIR),and metabolic quotient(MQ)were assessed along with high-throughput 16S rRNA sequencing of rice rhizosphere soils.The rice rhizosphere soil registered the highest SOC,MBC,and dehydrogenase activity in SRI followed by wetland rice and then aerobic rice.Cropping method had a minimal impact on SIR and MQ.Along with cropping method,growth stage also significantly altered these biological attributes of rice rhizosphere.The trends of the highest SOC content and dehydrogenase activity at the flowering stage and the highest MBC content and SIR at the tillering stage of rice were observed in all three rice cropping methods.The analysis of bacterial communities,based on 16S rRNA gene sequencing,revealed that both cropping method and growth stage significantly impacted the composition of rhizosphere microbiomes.However,the influence of cropping method was less pronounced compared to growth stage.Cropping method caused notable shifts in the abundances of Proteobacteria,Bacteroidetes,and Chloroflexi,while growth stage affected the abundances of Proteobacteria,Actinobacteria,Cyanobacteria,Firmicutes,Chloroflexi,and Bacteroidetes.Based on these results,the SRI method led to higher diversification to the rhizosphere bacteriobiota,as well as greater incorporation of carbon into the soil and increased dehydrogenase activity compared to wetland rice and aerobic rice.This study deepens our understanding of how different cropping methods influence plant-microbe interaction and the implications for overall rice productivity and soil health.
基金Programs for Science and Technology Development of Henan Province,grant number 242102210152The Fundamental Research Funds for the Universities of Henan Province,grant number NSFRF240620+1 种基金Key Scientific Research Project of Henan Higher Education Institutions,grant number 24A520015Henan Key Laboratory of Network Cryptography Technology,grant number LNCT2022-A11.
文摘Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.
基金supported in part by the National Natural Science Foundation of China(Nos.62202234,62401270)the China Postdoctoral Science Foundation(No.2023M741778)the Natural Science Foundation of Jiangsu Province(Nos.BK20240706,BK20240694).
文摘Generative image steganography is a technique that directly generates stego images from secret infor-mation.Unlike traditional methods,it theoretically resists steganalysis because there is no cover image.Currently,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction.Therefore,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule.Firstly,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,respectively.Then,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute features.This noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference image.Additionally,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed model.Experimental results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden information.Furthermore,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.
文摘The information from sparsely logged wellbores is currently under-utilized in reservoir simulation models and their proxies using deep and machine learning (DL/ML).This is particularly problematic for large heterogeneous gas/oil reservoirs being considered for repurposing as gas storage reservoirs for CH_(4),CO_(2) or H_(2) and/or enhanced oil recovery technologies.Lack of well-log data leads to inadequate spatial definition of complex models due to the large uncertainties associated with the extrapolation of petrophysical rock types (PRT) calibrated with limited core data across heterogeneous and/or anisotropic reservoirs.Extracting well-log attributes from the few well logs available in many wells and tying PRT predictions based on them to seismic data has the potential to substantially improve the confidence in PRT 3D-mapping across such reservoirs.That process becomes more efficient when coupled with DL/ML models incorporating feature importance and optimized,dual-objective feature selection techniques.
基金supported by the National Natural Science Foundation of China under Grant 61972057Hunan Provincial Natural Science Foundation of China under Grant 2022JJ30623.
文摘Linguistic steganography(LS)aims to embed secret information into normal natural text for covert communication.It includes modification-based(MLS)and generation-based(GLS)methods.MLS often relies on limited manual rules,resulting in low embedding capacity,while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency.To address this,we propose a sentence attribute encodingbased MLS method that enhances extraction efficiency while maintaining strong performance.The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information.When the attribute values of the cover sentence differ from the secret information to be embedded,a semantic attribute adjuster based on paraphrasing is used to automatically generate paraphrase sentences of the target attribute,thereby improving the problem of insufficient manual rules.During the extraction,secret information can be extracted solely by employing the semantic attribute analyzer,thereby eliminating the dependence on the paraphrasing generation model.Experimental results show that thismethod achieves an extraction speed of 1141.54 bits/sec,compared with the existing methods,it has remarkable advantages regarding extraction speed.Meanwhile,the stego text generated by thismethod respectively reaches 68.53,39.88,and 80.77 on BLEU,△PPL,and BERTScore.Compared with the existing methods,the text quality is effectively improved.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
基金co-funded by the China National Nuclear Corporation-State Key Laboratory of Nuclear Resources and Environment(East ChinaUniversity of Technology)Joint Innovation Fund Project(No.2023NRE-LH-08)the Natural Science Foundation of Jiangxi Province,China(No.20252BAC240270)+1 种基金the Funding of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing(2025QZ-YZZ-08)the National Major Science and Technology Project on Deep Earth of China(No.2024ZD 1003300)。
文摘Seismic attributes encapsulate substantial reservoir characterization information and can effectively support reservoir prediction.Given the high-dimensional nonlinear between sandbodies and seismic attributes,this study employs the RFECV method for seismic attribute selection,inputting the optimized attributes into a LightGBM model to enhance spatial delineation of sandbody identification.By constructing training datasets based on optimized seismic attributes and well logs,followed by class imbalance correction as input variables for machine learning models,with sandbody probability as the output variable,and employing grid search to optimize model parameters,a high-precision sandbody prediction model was established.Taking the 3D seismic data of Block F3 in the North Sea of Holland as an example,this method successfully depicted the three-dimensional spatial distribution of target formation sandstones.The results indicate that even under strong noise conditions,the multi-attribute sandbody identification method based on LightGBM effectively characterizes the distribution features of sandbodies.Compared to unselected attributes,the prediction results using selected attributes have higher vertical resolution and inter-well conformity,with the prediction accuracy for single wells reaching 80.77%,significantly improving the accuracy of sandbody boundary delineation.
基金funded by NARI Group’s Independent Project of China(Grant No.524609230125)the Foundation of NARI-TECH Nanjing Control System Ltd.of China(Grant No.0914202403120020).
文摘Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and environmental variables is presented. Then, a resampling process is applied to the initial data set to generate three other subsets of data. All the subsets were evaluated to deduce the adequate granularity for the prediction of the energy demand. Then, a cloud-assisted deep neural network model is designed to forecast short-term energy consumption in a residential area while preserving user privacy. The solution is applied to the consumption data of four appliances elected from a set of real household power data. The experiment results show that the proposed framework is effective for estimating consumption with convincing accuracy.
基金supported by the Spanish Ministry of Science and Innovation project GREEN-RISK(Evaluation of past changes in ecosystem services and biodiversity in forests and restoration priorities under global change impacts-PID2020-119933RB-C21)A.C.received a pre-doctoral fellowship funded by the Spanish Ministry of Science and Innovation(PRE2021-099642).
文摘Conservation and enhancement of old-growth forests are key in forest planning and policies.In order to do so,more knowledge is needed on how the attributes traditionally associated with old-growth forests are distributed in space,what differences exist across distinct forest types and what natural or anthropic conditions are affecting the distribution of these old-growthness attributes.Using data from the Third Spanish National Forest Inventory(1997–2007),we calculated six indicators commonly associated with forest old-growthness for the plots in the territory of Peninsular Spain and Balearic Islands,and then combined them into an aggregated index.We then assessed their spatial distribution and the differences across five forest functional types,as well as the effects of ten climate,topographic,landscape,and anthropic variables in their distribution.Relevant geographical patterns were apparent,with climate factors,namely temperature and precipitation,playing a crucial role in the distribution of these attributes.The distribution of the indicators also varied across different forest types,while the effects of recent anthropic impacts were weaker but still relevant.Aridity seemed to be one of the main impediments for the development of old-growthness attributes,coupled with a negative impact of recent human pressure.However,these effects seemed to be mediated by other factors,specially the legacies imposed by the complex history of forest management practices,land use changes and natural disturbances that have shaped the forests of Spain.The results of this exploratory analysis highlight on one hand the importance of climate in the dynamic of forests towards old-growthness,which is relevant in a context of Climate Change,and on the other hand,the need for more insights on the history of our forests in order to understand their present and future.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00238866)Korea government(MOE)(2024 government collaboration type training project[Information security field],No.2024 personal information protection-002).
文摘This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.
文摘Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.
基金supported by the National Natural Science Foundation of China(Nos.62006001,62372001)the Natural Science Foundation of Chongqing City(Grant No.CSTC2021JCYJ-MSXMX0002).
文摘Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many scholars.Inspired by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network privacy.Therefore,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called AttNetNRI.Specifically,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private.Moreover,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social networks.To evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks.The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts.The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.
基金funded by the National Nature Science Foundation of China(81,825,019 and 82,330,103)performed with approval from the Ethical Committee of Beijing Institute of Microbiology and Epidemiology(AF/SC-08/02.128).
文摘Dear Editor,Severe fever with thrombocytopenia syndrome(SFTS)is an emerging tick-borne infectious disease caused by a novel bunyavirus called SFTS virus(SFTSV).It was initially identified in China in 2009(Yu et al.,2011).Since then,the number of reported SFTS cases has rapidly increased in China,South Korea,and Japan(Li,et al.,2018;Takahashi et al.,2014;Kim et al.,2018).Sporadic SFTS cases have also been identified in several other Asian countries,such as Vietnam,Pakistan,Myanmar,and Thailand(Takahashi et al.,2014;Tran et al.,2019;Li et al.,2021).This disease is recognized as a highly lethal viral hemorrhagic fever with a mortality rate ranging from 12%to 50%(Yu et al.,2011;Li et al.,2018;Takahashi et al.,2014;Kim et al.,2013).SFTS primarily spreads to humans through bites from ticks infected with SFTSV,with the Haemaphysalis longicornis tick acting as the predominant vector for SFTSV(Zhuang et al.,2018).
基金Anhui Province Natural Science Research Project of Colleges and Universities(2023AH040321)Excellent Scientific Research and Innovation Team of Anhui Colleges(2022AH010098).
文摘The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.
基金supported by the Guangxi Key R&D Program (project No. AB16380254)a research project of Guangxi Forestry Department (Guilinkezi [2015] No.5)supported a grant for Bagui Senior Fellow (C33600992001)。
文摘Forests,the largest terrestrial carbon sinks,play an important role in carbon sequestration and climate change mitigation.Although forest attributes and environmental factors have been shown to impact aboveground biomass,their influence on biomass stocks in species-rich forests in southern China,a biodiversity hotspot,has rarely been investigated.In this study,we characterized the effects of environmental factors,forest structure,and species diversity on aboveground biomass stocks of 30 plots(1 ha each) in natural forests located within seven nature reserves distributed across subtropical and marginal tropical zones in Guangxi,China.Our results indicate that forest aboveground biomass stocks in this region are lower than those in mature tropical and subtropical forests in other regions.Furthermore,we found that aboveground biomass was positively correlated with stand age,mean annual precipitation,elevation,structural attributes and species richness,although not with species evenness.When we compared stands with the same basal area,we found that aboveground biomass stock was higher in communities with a higher coefficient of variation of diameter at breast height.These findings highlight the importance of maintaining forest structural diversity and species richness to promote aboveground biomass accumulation and reveal the potential impacts of precipitation changes resulting from climate warming on the ecosystem services of subtropical and northern tropical forests in China.Notably,many natural forests in southern China are not fully stocked.Therefore,their continued growth will increase their carbon storage over time.
基金National Natural Science Foundation of China(No.61971121)。
文摘Clothing attribute recognition has become an essential technology,which enables users to automatically identify the characteristics of clothes and search for clothing images with similar attributes.However,existing methods cannot recognize newly added attributes and may fail to capture region-level visual features.To address the aforementioned issues,a region-aware fashion contrastive language-image pre-training(RaF-CLIP)model was proposed.This model aligned cropped and segmented images with category and multiple fine-grained attribute texts,achieving the matching of fashion region and corresponding texts through contrastive learning.Clothing retrieval found suitable clothing based on the user-specified clothing categories and attributes,and to further improve the accuracy of retrieval,an attribute-guided composed network(AGCN)as an additional component on RaF-CLIP was introduced,specifically designed for composed image retrieval.This task aimed to modify the reference image based on textual expressions to retrieve the expected target.By adopting a transformer-based bidirectional attention and gating mechanism,it realized the fusion and selection of image features and attribute text features.Experimental results show that the proposed model achieves a mean precision of 0.6633 for attribute recognition tasks and a recall@10(recall@k is defined as the percentage of correct samples appearing in the top k retrieval results)of 39.18 for composed image retrieval task,satisfying user needs for freely searching for clothing through images and texts.
基金supported in part by the National Natural Science Foundation of China(Nos.52275126 and 52105159)the Science and Technology Planning Project of Shaanxi Province,China(No.2024GX-YBXM-292).
文摘Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators.To address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and wear.Correspondingly,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures.In particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators.With this model,the lubricant conditions can be accurately assessed through rule-based reasoning.Furthermore,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring data.For verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines.Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing methods.While for the engine lubricant test,a high accuracy of 90%was achieved.
