Deep Joint Encryption and Source-Channel Coding: An Image Visual Protection Approach

Joint source and channel coding (JSCC) has achieved great success due to the introduction of deep learning. Compared with traditional separate source channel coding (SSCC) schemes, the advantages of DL based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and the relief of "cliff effect". However, it is difficult to couple encryption-decryption mechanisms with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of the emerging technology. To this end, our paper proposes a novel method called DL based joint encryption and source-channel coding (DJESCC) for images that can successfully protect the visual information of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is using a neural network to conduct image encryption, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJESCC method learns: 1) deep neural networks for image encryption and image decryption, and 2) an effective DJSCC network for image transmission in encrypted domain. Compared with the perceptual image encryption methods with DJSCC transmission, the DJESCC method achieves much better reconstruction performance and is more robust to ciphertext-only attacks.Comment: 12 pages, 13 figure

Deep Joint Source-Channel Coding for Image Transmission With Visual Protection

Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL). Compared to traditional separate source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and relief of “cliff effect”. However, it is difficult to couple existing secure communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of this emerging technology. To this end, our paper proposes a novel method called DL-based joint protection and source-channel coding (DJPSCC) for images that can successfully protect the visual content of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is to use a neural network to conduct visual protection, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJPSCC method learns: 1) deep neural networks for image protection and image deprotection, and 2) an effective DJSCC network for image transmission in the protected domain. Compared to existing source protection methods applied with DJSCC transmission, the DJPSCC method achieves much better reconstruction performance

Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.Comment: 13 pages, 13 figures, journal pape

Fungiculture in termites is associated with a mycolytic gut bacterial community

ABSTRACT Termites forage on a range of substrates, and it has been suggested that diet shapes the composition and function of termite gut bacterial communities. Through comparative analyses of gut metagenomes in nine termite species with distinct diets, we characterize bacterial community compositions and use peptide-based functional annotation method to determine biomass-degrading enzymes and the bacterial taxa that encode them. We find that fungus-growing termite guts have relatively more fungal cell wall-degrading enzyme genes, while wood-feeding termite gut communities have relatively more plant cell wall-degrading enzyme genes. Interestingly, wood-feeding termite gut bacterial genes code for abundant chitinolytic enzymes, suggesting that fungal biomass within the decaying wood likely contributes to gut bacterial or termite host nutrition. Across diets, the dominant biomass-degrading enzymes are predominantly coded for by the most abundant bacterial taxa, suggesting tight links between diet and gut community composition, with the most marked difference being the communities coding for the mycolytic capacity of the fungus-growing termite gut. IMPORTANCE Understanding functional capacities of gut microbiomes is important to improve our understanding of symbiotic associations. Here, we use peptide-based functional annotation to show that the gut microbiomes of fungus-farming termites code for a wealth of enzymes that likely target the fungal diet the termites eat. Comparisons to other termites showed that fungus-growing termite guts have relatively more fungal cell wall-degrading enzyme genes, whereas wood-feeding termite gut communities have relatively more plant cell wall-degrading enzyme genes. Across termites with different diets, the dominant biomass-degrading enzymes are predominantly coded for by the most abundant bacterial taxa, suggesting tight links between diet and gut community compositions

Exploiting Linked Data in Financial Engineering

Part 3: Finance and Service ScienceInternational audienceIn this paper, we report on a recent initiative that exploiting Linked Data for financial data integration. Financial data present high heterogeneity. Linked Data helps to reveal the true data semantics and “hidden” connection, upon which meaningful mappings can be constructed. The work reported in this paper has been well-accepted at several public events and conferences, including the 26th XBRL conference, involving the realisation of the XBRL (eXtensible Business Reporting Language) prototype called HIKAKU, which means “comparison” in Japanese. It demonstrates our approach to exploit the power of Linked Data in enhancing flexibility for data integration in the financial domain

The Effect of 50 000 IU Vitamin A with BCG Vaccine at Birth on Growth in the First Year of Life

Vitamin A supplements may interact with diphtheria-tetanus-pertussis (DTP) vaccine causing increased female mortality. In a randomised trial of neonatal vitamin A supplementation (VAS), we examined growth during the first year of life in 808 children, pursuing the hypothesis that a negative interaction between VAS and DTP in girls would be reflected in growth. Length and weight were measured at 6 weekly visits and WHO-growth-reference z-scores derived. Neonatal VAS had no effect on anthropometric measures at 12 months, but may interact sex differentially with routine vaccines. While BCG was the most recent vaccine, neonatal VAS benefitted growth (difference in weight-for-length z-score (dWFL: 0.31(95% CI: 0.03–0.59)). While DTP was the most recent vaccine, VAS tended to affect growth adversely in girls (dWFL = −0.21 (−0.48–0.06)). After measles vaccine (MV) there was no overall effect of neonatal VAS. The VAS effect differed significantly between the BCG and DTP windows (P = 0.03), and the difference was borderline significant between the DTP and MV windows for girls (P = 0.09)

Inhibition and Substrate Specificity Properties of FKBP22 from a Psychrotrophic Bacterium, Shewanella sp. SIB1

SIB1 FKBP22 is a peptidyl prolyl cis–trans isomerase (PPIase) member from a psychrotrophic bacterium, Shewanella sp. SIB1, consisting of N- and C-domains responsible for dimerization and catalytic PPIase activity, respectively. This protein was assumed to be involved in cold adaptation of SIB1 cells through its dual activity of PPIase activity and chaperone like function. Nevertheless, the catalytic inhibition by FK506 and its substrate specificity remain unknown. Besides, ability of SIB1 FKBP22 to inhibit phosphatase activity of calcinuerin is also interesting to be studied since it may reflect wider cellular functions of SIB1 FKBP22. In this study, we found that wild type (WT) SIB1 FKBP22 bound to FK506 with IC50 of 77.55 nM. This value is comparable to that of monomeric mutants (NNC-FKBP22, C-domain+ and V37R/L41R mutants), yet significantly higher than that of active site mutant (R142A). In addition, WT SIB1 FKBP22 and monomeric variants were found to prefer hydrophobic residues preceding proline. Meanwhile, R142A mutant has wider preferences on bulkier hydrophobic residues due to increasing hydrophobicity and binding pocket space. Surprisingly, in the absence of FK506, SIB1 FKBP22 and its variants inhibited, with the exception of N-domain, calcineurin phosphatase activity, albeit low. The inhibition of SIB1 FKBP22 by FK506 is dramatically increased in the presence of FK506. Altogether, we proposed that local structure at substrate binding pocket of C-domain plays crucial role for the binding of FK506 and peptide substrate preferences. In addition, C-domain is essential for inhibition, while dimerization state is important for optimum inhibition through efficient binding to calcineurin