Steganography integration into a low-bit rate speech codec

Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits / frame (133.3 bits / second)

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not

Alcohol Intake and Abnormal Expression of Brf1 in Breast Cancer.

Breast cancer is the most common malignant disease of females. Overall, one woman in every nine will get breast cancer at some time in her life. Epidemiological studies have indicated that alcohol consumption has most consistently been associated with breast cancer risk. However, the mechanism of alcohol-associated breast cancer remains to be addressed. Little is known about the effects of alcohol consumption on Brf1 (TFIIIB-related factor 1) expression and RNA Pol III gene (RNA polymerase III-dependent gene) transcription, which are responsible for protein synthesis and tightly linked to cell proliferation, cell transformation, and tumor development. Emerging evidences have indicated that alcohol induces deregulation of Brf1 and Pol III genes to cause the alterations of cell phenotypes and tumor formation. In this paper, we summarize the progresses regarding alcohol-caused increase in the expression of Brf1 and Pol III genes and analysis of its molecular mechanism of breast cancer. As the earlier and accurate diagnosis approach of breast cancer is not available yet, exploring the molecular mechanism and identifying the biomarker of alcohol-associated breast cancer are especially important. Recent studies have demonstrated that Brf1 is overexpressed in most ER+ (estrogen receptor positive) cases of breast cancer and the change in cellular levels of Brf1 reflects the therapeutic efficacy and prognosis of this disease. It suggests that Brf1 may be a potential diagnosis biomarker and a therapeutic target of alcohol-associated breast cancer