An Information- Theoretical Model for Streaming Media Based Stegosystems

Steganography in streaming media differs from steganography in images or audio files because of the continuous embedding process and the necessary synchronization of sender and receiver due to packet loss in streaming media. The conventional theoretical model for image steganography is not appropriate for explaining the security scenarios for streaming media based stegosystems. In this paper, we propose a new information-theoretical model with two pseudo-random sequences imitating the continuous embedding and synchronization characteristics of streaming media based stegosystems. We also discuss the statistical properties of Voice over Internet Protocol (VoIP) speech streams through theoretical analysis and experimental testing. The experimental results show the bit stream consisting of fixed codebook parameters in speech frames is similar in statistical characteristics to a white-noise sequence. The relative entropy between the VoIP speech stream and the embedded secret message has been found to be zero. This leads us to conclude that the proposed streaming media based stegosystem is secure against statistical detection; in other words, the statistical measures cannot detect the existence of the secret message embedded in VoIP speech streams

On finite-dimensional irreducible modules for the universal Askey-Wilson algebra

Let $\Delta_q$ be the universal Askey-Wilson algebra. If $q$ is not a root of unity, it is shown in the Huang's earlier paper that an $(n+1)$-dimensional irreducible $\Delta_q$-module is a quotient $V_n(a, b, c)$ of a $\Delta_q$-Verma module with ${\textbf{ Condition A: }} \; abc, a^{-1}bc, ab^{-1}c, abc^{-1} \notin \left \{q^{n-2i+1}| 1 \leq i \leq n\right \}.$ The aim of this paper is to discuss the structures of $(n+1)$-dimensional $\Delta_q$-modules $V_n(a, b, c)$ when the given triples $(a, b, c)$ do not satisfy Condition A

RTP timestamp steganography detection method

A histogram cosine similarity matching method for real-time transport protocol (RTP) timestamp difference vectors and a clustering method of the area between the best-fit curves of 2 RTP timestamp difference sequences are presented. These 2 methods realize timestamp-based least significant bit (LSB) steganography detection respectively. A clustering analysis of the area between the 5th-degree polynomial best-fit curves with message windows w of 20, 50, 100, and 200 was conducted. The results indicated that when the message window w was 100, the result was the best when the characteristic extraction time was shortest, and the initial clustering accuracy was 84.5%. Through further analysis, the clustering accuracy was increased to 100% in the 2nd round of clustering based on whether the mean distance from a data point in an initial cluster to each cluster center was changed

Cotton disease identification method based on pruning

Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms

Markov bidirectional transfer matrix for detecting LSB speech steganography with low embedding rates

Steganalysis with low embedding rates is still a challenge in the field of information hiding. Speech signals are typically processed by wavelet packet decomposition, which is capable of depicting the details of signals with high accuracy. A steganography detection algorithm based on the Markov bidirectional transition matrix (MBTM) of the wavelet packet coefficient (WPC) of the second-order derivative-based speech signal is proposed. On basis of the MBTM feature, which can better express the correlation of WPC, a Support Vector Machine (SVM) classifier is trained by a large number of Least Significant Bit (LSB) hidden data with embedding rates of 1%, 3%, 5%, 8%,10%, 30%, 50%, and 80%. LSB matching steganalysis of speech signals with low embedding rates is achieved. The experimental results show that the proposed method has obvious superiorities in steganalysis with low embedding rates compared with the classic method using histogram moment features in the frequency domain (HMIFD) of the second-order derivative-based WPC and the second-order derivative-based Mel-frequency cepstral coefficients (MFCC). Especially when the embedding rate is only 3%, the accuracy rate improves by 17.8%, reaching 68.5%, in comparison with the method using HMIFD features of the second derivative WPC. The detection accuracy improves as the embedding rate increases

Yeast Probiotics Shape the Gut Microbiome and Improve the Health of Early-Weaned Piglets

Weaning is one of the most stressful challenges in the pig’s life, which contributes to dysfunctions of intestinal and immune system, disrupts the gut microbial ecosystem, and therefore compromises the growth performance and health of piglets. To mitigate the negative impact of the stress on early-weaned piglets, effective measures are needed to promote gut health. Toward this end, we tamed a Saccharomyces cerevisiae strain and developed a probiotic Duan-Nai-An, which is a yeast culture of the tamed S. cerevisiae on egg white. In this study, we tested the performance of Duan-Nai-An on growth and health of early-weaned piglets and analyzed its impact on fecal microbiota. The results showed that Duan-Nai-An significantly improved weight gain and feed intake, and reduced diarrhea and death of early-weaned piglets. Analysis of the gut microbiota showed that the bacterial community was shaped by Duan-Nai-An and maintained as a relatively stable structure, represented by a higher core OTU number and lower unweighted UniFrac distances across the early weaned period. However, fungal community was not significantly shaped by the yeast probiotics. Notably, 13 bacterial genera were found to be associated with Duan-Nai-An feeding, including Enterococcus, Succinivibrio, Ruminococcus, Sharpea, Desulfovibrio, RFN20, Sphaerochaeta, Peptococcus, Anaeroplasma, and four other undefined genera. These findings suggest that Duan-Nai-An has the potential to be used as a feed supplement in swine production

Loss-of-function mutations in TNFAIP3 leading to A20 haploinsufficiency cause an early-onset autoinflammatory disease

Systemic autoinflammatory diseases are driven by abnormal activation of innate immunity. Herein we describe a new disease caused by high-penetrance heterozygous germline mutations in TNFAIP3, which encodes the NF-B regulatory protein A20, in six unrelated families with early-onset systemic inflammation. The disorder resembles Behçet\u27s disease, which is typically considered a polygenic disorder with onset in early adulthood. A20 is a potent inhibitor of the NF-B signaling pathway. Mutant, truncated A20 proteins are likely to act through haploinsufficiency because they do not exert a dominant-negative effect in overexpression experiments. Patient-derived cells show increased degradation of IBα and nuclear translocation of the NF-B p65 subunit together with increased expression of NF-B-mediated proinflammatory cytokines. A20 restricts NF-B signals via its deubiquitinase activity. In cells expressing mutant A20 protein, there is defective removal of Lys63-linked ubiquitin from TRAF6, NEMO and RIP1 after stimulation with tumor necrosis factor (TNF). NF-B-dependent proinflammatory cytokines are potential therapeutic targets for the patients with this disease

VPS35 haploinsufficiency increases Alzheimer’s disease neuropathology

The retromer complex component VPS35 prevents activation of the BACE1 and Aβ production and thus plays an essential role in limiting Alzheimer’s disease neuropathology

Early-Onset Stroke and Vasculopathy Associated with Mutations in ADA2

We observed a syndrome of intermittent fevers, early-onset lacunar strokes and other neurovascular manifestations, livedoid rash, hepatosplenomegaly, and systemic vasculopathy in three unrelated patients. We suspected a genetic cause because the disorder presented in early childhood

Steganalysis of low embedding rates LSB speech based on histogram moments in frequency domain

LSB speech steganography with low embedding rate is an effective method to confront speech steganalysis. It is still a big challenging issue to detect LSB speech steganography with low embedding rate. Based on wavelet packet transform focusing micro change of the signal, this study proposes a statistical analysis method to extract the high order histogram moments in frequency domain which are extremely sensitive to LSB speech steganography, and then train the Support Vector Machine (SVM) classifier, which is used to comprehensively analyze in depth the LSB matching steganography with different lower embedding rates, such as 5% or 10% and so on. Experimental results show that statistical moments of histogram and moments in frequency domain can be used to detect the LSB matching steganography, the detection performance of moments in frequency domain and combined moments is superior to that of statistical moments of histogram; The detection accuracy of the histogram features by Wavelet Packet Decomposition (WPD) is higher than that of the corresponding features by Wavelet Decomposition (WD). The moments in frequency domain features by WPD are particularly prominent in detecting LSB speech steganography with low embedding rates, and the accuracy rate can achieve 60.8% when the embedding rate is only 3%