SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems

Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination. While such a paradigm holds the promise of making wireless communications more intelligent, it also suffers from severe semantic security issues, such as eavesdropping, privacy leaking, and spoofing, due to the open nature of wireless channels and the fragility of neural modules. Previous works focus more on the robustness of SC via offline adversarial training of the whole system, while online semantic protection, a more practical setting in the real world, is still largely under-explored. To this end, we present SemProtector, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements, facilitating the practical deployment in real-world SC systems. Experiments on two public datasets show the effectiveness of our proposed SemProtector, offering some insights of how we reach the goal of secrecy, privacy and integrity of an SC system. Finally, we discuss some future directions for the semantic protection.Comment: Accepted by Communications Magazin

The Immungenicity and Cross-Neutralizing Activity of Enterovirus 71 Vaccine Candidate Strains

This study aimed to evaluate enterovirus 71 (EV-A71) vaccine candidate strains, including their genotypes, immunogenicity and cross-neutralization capacity. From clinical samples, EV-A71 strains were separated by using Vero cells. Six strains were chosen for vaccine candidates, and the sequences were analyzed. To detect the immunogenicity of the strains, we used them to immunize NIH mice at 0 and 14 days. Cytopathic effects (CPE) were examined to determine the EV-A71 neutralizing antibody (NTAb) titer 14 d after the first and second inoculations. To evaluate the cross-neutralizing capacity of the EV-A71 vaccine candidate strains, we tested serum immunized mice with ten EV-A71 genotype strains. Six EV-A71 vaccine candidate strains were identified, all belonging to sub-genotype C4, the prevalent genotype in China. The sequence similarity of the VP1 regions of the six candidate vaccine strains and three approved inactivated vaccines was 97.58%–97.77%, and the VP1 amino acid similarity was 98.65%–99.33%. Experiments were performed to evaluate the immunogenicity and cross-neutralizing activity of the EV-A71 vaccine candidate strains. The strains had good immunogenicity 14 d after two immunizations, inducing an NTAb titer ranging from 1:94 to 1:346. The NTAb seroconversion rates 14 d after one immunization were above 80% (except HB0007), and significantly increased immunogenicity of EV-A71 strains was observed post-inoculation. Furthermore, our candidate vaccine strains had broad cross-neutralizing activity after challenge with ten sub-genotypes of EV-A71. The highest NTAb titer/lowest NTAb titer ratios of sera against EV-A71 sub-genotypes were 8.0 (JS0002), 8.0 (JS0005), 21.3 (HB0005), 21.3 (HB0007), 10.7 (HB0040) and 8.0 (GD0002), respectively. Our EV-A71 strains had good immunogenicity and cross-neutralization activity, and have the potential to serve as vaccine strains for multivalent hand, foot and mouth disease vaccines

Ontology similarity computing based on stochastic primal dual coordinate technique

With the extensive application of ontology in the fields of information retrieval and artificial intelligence, the ontology-based conceptual similarity calculation becomes a hot topic in ontology research. The essence of ontology learning is to obtain the ontology function through the learning of ontology samples, so as to map the vertices in each ontology graph into real numbers, and finally determine the similarity between corresponding concepts by the difference between real numbers. The essence of ontology mapping is to calculate concepts from different ontologies. In this paper, we introduce new ontology similarity computing in view of stochastic primal dual coordinate method, and two experiments show the effectiveness of our proposed ontology algorithm

Study on model of aroma quality evaluation for flue-cured tobacco based on principal component analysis

To establish a new method for quality evaluation of flue-cured tobacco, aroma components in thirteen flue-cured tobacco samples were identified and determined by the simultaneous distillation extraction-gas chromatography-mass spectrometry (SDE-GC-MS), and an evaluation model of aroma quality was established based on principal component analysis. Results indicated that the highest integrated score occurred in the sample T10, followed by T13, T1, T8, T12, T5, T3, T4, T9, T7, T11, T2 and T6, which were grossly consistent with the results of the traditional sensory smoking evaluation. Therefore, this model established in our study was feasible. Compared with the appearance, sensory, and traditional chemical components quality evaluation methods, our evaluation model based on aroma quality and principal component analysis can reflect the aroma quality of flue-cured tobacco more objectively, directly and accurately, and clear the position of flue-cured tobacco leaves in the cigarette blend

Realization of atomic-level smooth surface of sapphire (0001) by chemical-mechanical planarization with nano colloidal silica abrasives

In this paper, an innovative study is presented to characterize the chemical-mechanical planarization (CMP) performance on hexagonal sapphire (0001) wafer surface by using colloidal silica (SiO 2 ) abrasives based slurry with two different particle sizes, which indicates that the value of abrasive size is an important factor to determine the efficiency of CMP and the final planarization quality of wafer surface. The nano SiO 2 abrasives used in this study could perfectly optimize the quality of surface roughness. Furthermore, the authors put forward some suggestions to optimize the CMP efficiency and planarization quality of sapphire wafer

Atomically smooth gallium nitride surfaces generated by chemical mechanical polishing with non-noble metal catalyst(Fe-Nx/C) in acid solution

In this paper, a novel method for preparing atomically smooth gallium nitride (GaN) wafer surfaces which involves chemical mechanical polishing with a non-noble metal catalyst (Fe-N x ) in acidic slurry is presented. It was confirmed that non-noble metal catalyst based slurry could be used for gallium face of GaN. Atomic force microscope images of the processed surface indicate that an atomically flat surface with Ra=0.0518 nm was achieved after planarization and the processed surface has an atomic step-terrace structure. Besides, the rate of removal of the GaN surface was measured to be approximately 66.9 nm/h, more than triple times higher than that nothing was used as catalyst

CMP of GaN using sulfate radicals generated by metal catalyst

A method for preparing atomically smooth gallium nitride (GaN) surface with high material removal rate that involves chemical mechanical polishing with sulfate radical (SO4 center dot-) oxidizer and Fe2+ activator in slurry is presented. The results indicate that complexing agent with Fe2+ activator is the key point to obtain atomically smooth GaN surface and higher removal rate of GaN. Atomic force microscope (AFM) shows that the average surface roughness (Ra) is 0.0601nm

CMP of GaN using sulfate radicals generated by metal catalyst

A method for preparing atomically smooth gallium nitride (GaN) surface with high material removal rate that involves chemical mechanical polishing with sulfate radical (SO 4 - ) oxidizer and Fe 2+ activator in slurry is presented. The results indicate that complexing agent with Fe 2+ activator is the key point to obtain atomically smooth GaN surface and higher removal rate of GaN. Atomic force microscope (AFM) shows that the average surface roughness (Ra) is 0.0601nm

Transcriptome sequencing reveals the effect of biochar improvement on the development of tobacco plants before and after topping.

The application of biochar is one of the most useful methods for improving soil quality, which is of the utmost significance for the continuous production of crops. As there are no conclusive studies on the specific effects of biochar application on tobacco quality, this study aimed to improve the yield and quality of tobacco as a model crop for economic and genetic research in southern China, by such application. We used transcriptome sequencing to reveal the effects of applied biochar on tobacco development before and after topping. Our results showed that topping affected carbon and nitrogen metabolism, photosynthesis and secondary metabolism in the tobacco plants, while straw biochar-application to the soil resulted in amino acid and lipid synthesis; additionally, it affected secondary metabolism of the tobacco plants through carbon restoration and hormonal action, before and after topping. In addition to the new insights into the impact of biochar on crops, our findings provide a basis for biochar application measures in tobacco and other crops