Versatile core-shell magnetic fluorescent mesoporous microspheres for multilevel latent fingerprints magneto-optic information recognition

Latent fingerprints are extremely vital for personal identification and criminalinvestigation,and potential information recognition techniques have been widelyused in the fields of information and communication electronics.Although physicalpowder dusting methods have been frequently employed to develop latent fingerprintsand most of them are carried out by using single component powders ofmicron-sized fluorescent particles,magnetic powders,or metal particles,there isstill an enormous challenge in producing high-resolution image of latent fingerprintsat different backgrounds or substrates.Herein,a novel and effectivenanoimpregnation method is developed to synthesize bifunctional magnetic fluorescentmesoporous microspheres for latent fingerprints visualization by growthof mesoporous silica(mesoSiO_(2))on magical Fe_(3)O_(4) core and then deposition offluorescent YVO4:Eu^(3+)nanoparticles in the mesopores.The obtainedFe_(3)O_(4)@mesoSiO_(2)@YVO4:Eu^(3+)microspheres possess spatially isolated magneticcore and fluorescent shell which were insulated by mesoporous silica layer.Dueto their small particle size of submicrometer scale,high magnetization and lowmagnetic remanence as well as the combined magnetic and fluorescent properties,the microspheres show superior performance in visual latent fingerprint recognitionwith high contrast,high anti-interference,and sensitivity as well as goodretention on multifarious substrates regardless of surface permeability,roughness,refraction,colorfulness,and background fluorescence interference,and it makesthem ideal candidates for practical application in fingerprint visualization andeven magneto-optic information storage.

Research on Fine-Grained Recognition Method for Sensitive Information in Social Networks Based on CLIP

With the emergence and development of social networks,people can stay in touch with friends,family,and colleagues more quickly and conveniently,regardless of their location.This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy.Due to the complexity and subtlety of sensitive information,traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data,thus weakening the deep connections between text and images.In this context,this paper adopts the CLIP model as a modality discriminator.By using comparative learning between sensitive image descriptions and images,the similarity between the images and the sensitive descriptions is obtained to determine whether the images contain sensitive information.This provides the basis for identifying sensitive information using different modalities.Specifically,if the original data does not contain sensitive information,only single-modality text-sensitive information identification is performed;if the original data contains sensitive information,multimodality sensitive information identification is conducted.This approach allows for differentiated processing of each piece of data,thereby achieving more accurate sensitive information identification.The aforementioned modality discriminator can address the limitations of existing sensitive information identification technologies,making the identification of sensitive information from the original data more appropriate and precise.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information （MI） , a feature selection algorithm were proposed based on improved mutual information. The improved mutual information algorithm, which is on the basis of traditional improved mutual information methods that enbance the MI value of negative characteristics and feature＇ s frequency, supports the concept of concentration degree and dispersion degree. In accordance with the concept of concentration degree and dispersion degree, formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these. In this paper, the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods. The experimental results showed that the improved mutu- al information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain. Through the introduction of the concept of concentration degree and dispersion degree, the improved mutual information feature selection method greatly improves the performance of text classification system.