Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

A privacy-preserving method for publishing data with multiple sensitive attributes

The overgeneralisation may happen because most studies on data publishing for multiple sensitive attributes(SAs)have not considered the personalised privacy requirement.Furthermore,sensitive information disclosure may also be caused by these personalised requirements.To address the matter,this article develops a personalised data publishing method for multiple SAs.According to the requirements of individuals,the new method partitions SAs values into two categories:private values and public values,and breaks the association between them for privacy guarantees.For the private values,this paper takes the process of anonymisation,while the public values are released without this process.An algorithm is designed to achieve the privacy mode,where the selectivity is determined by the sensitive value frequency and undesirable objects.The experimental results show that the proposed method can provide more information utility when compared with previous methods.The theoretic analyses and experiments also indicate that the privacy can be guaranteed even though the public values are known to an adversary.The overgeneralisation and privacy breach caused by the personalised requirement can be avoided by the new method.

Fatigue Life Prediction for SiC/Al Materials Based on Path Planning Algorithm Considering Residual Stress

To explore the influence of path deflection on crack propagation,a path planning algorithm is presented to calculate the crack growth length.The fatigue crack growth life of metal matrix composites(MMCs)is estimated based on an improved Paris formula.Considering the different expansion coefficient of different materials,the unequal shrinkage will lead to residual stress when the composite is molded and cooled.The crack growth model is improved by the modified stress ratio based on residual stress.The Dijkstra algorithm is introduced to avoid the cracks passing through the strengthening base and the characteristics of crack steps.This model can be extended to predict crack growth length for other similarly-structured composite materials.The shortest path of crack growth is simulated by using path planning algorithm,and the fatigue life of composites is calculated based on the shortest path and improved model.And the residual stress caused by temperature change is considered to improve the fatigue crack growth model in the material.The improved model can well predict the fatigue life curve of composites.By analyzing the fatigue life of composites,it is found that there is a certain regularity based on metal materials,and the new fatigue prediction model can also reflect this regularity.

Transcriptome Analysis and Genes Function Verification of Root Development of Paeonia suffruticosa under Sandy Loam Cultivation

Relatively poor in vitro rooting has limited the large-scale commercial production of tree peony.In this study,on the basis of transcriptome sequencing,differentially expressed genes and the associated metabolic pathways were identified in tree peony roots at different stages of root formation under sandy loam cultivation.A total of 31.63 Gb raw data were generated and 120,188 unigenes(mean length of 911.98 bp)were annotated according to six databases(NR,NT,GO,KEGG,COG,and Swiss-Prot).Analyses of the ungerminated root primordium period,induced root primordium period,and root formation period detected 8,232,6,907,and 10,687 differentially expressed genes related to 133,132,and 133 metabolic pathways,respectively.Two significantly differentially expressed genes(Unigene13430_All and CL10096.Contig1_All)were associated with the auxin pathway.The full-length Unigene13430_All coding sequence(843 bp)encoded 280 amino acids,whereas the full-length CL10096.Contig1_All coding sequence(1,470 bp)encoded 489 amino acids.Unigene13430_All and CL10096.Contig1_All were identified as IAA gene family members and were respectively named PsIAA27 and PsARF19.The qRT-PCR analysis and functional verification indicated that the expressions of PsARF19 and PsIAA27 in tree peony seedlings,cuttings and grafted seedlings were significant different.PsARF19 promoted root development,it might be a regulatory gene related to the formation of tree peony roots,while PsIAA27 inhibited lateral root development,and it might be involved in controlling auxin sensitivity during root formation.The results of this study may form the basis of future investigations on the mechanism mediating peony root formation.The transcriptome data will be an excellent resource for researchers interested in characterizing the rooting-related tree peony genes.

A multiple sensitive attributes data publishing method with guaranteed information utility

Data publishing methods can provide available information for analysis while preserving privacy.The multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression ratio.Another category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for analysis.Hence,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information utility.To acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information loss.The experimental results verify the practice of the proposed data publishing method with multiple sensitive attributes.The proposed method can guarantee information utility when compared with previous ones.

A new media content trusted dissemination architecture based on AV-blockchain and ChinaDRM

The diffusion of all-media content plays a vital role in guiding public opinion and ideology.However,at present,most of the media content exists on all kinds of mainstream media platforms,which poses great challenges to the effective supervision of relevant departments and society.This has led to arbitrary charges,chaotic media content,difficulties in supervision and evidence collection,and infringements of the rights and interests of original content creators.To address these problems,this paper constructs a trustworthy propagation architecture that supports multi-platform media content sharing.This architecture collaboratively builds an audio-visual blockchain through public and consortium blockchains,coupled with an improved ChinaDRM to provide digital rights management and content encryption.Simultaneously,we employ an enhanced Diffie-Hellman key agreement protocol to offer distributed encryption and decryption for media content.Within this model,various media platforms and national regulatory authorities are responsible for content storage and distribution as consortium nodes and public blockchain nodes,respectively.At the same time,users,as light nodes of public chain or service consumers of consortium blockchain,can consume and comment on content.Analysis shows that the trusted communication framework of media content based on the audio-visual blockchain has certain expansibility and practicability.It can facilitate the supervision of mainstream media platforms by national authorities and society through inter-blockchain technology,offering a novel solution for multi-platform trustworthy cooperative information sharing.