Randomness Recoverable Secret Sharing Schemes

It is well-known that randomness is essential for secure cryptography. The randomness used in cryptographic primitives is not necessarily recoverable even by the party who can, e.g., decrypt or recover the underlying secret/message. Several cryptographic primitives that support randomness recovery have turned out useful in various applications. In this paper, we study randomness recoverable secret sharing schemes (RR-SSS), in both information-theoretic and computational settings and provide two results. First, we show that while every access structure admits a perfect RR-SSS, there are very simple access structures (e.g., in monotone AC?) that do not admit efficient perfect (or even statistical) RR-SSS. Second, we show that the existence of efficient computational RR-SSS for certain access structures in monotone AC? implies the existence of one-way functions. This stands in sharp contrast to (non-RR) SSS schemes for which no such results are known. RR-SSS plays a key role in making advanced attributed-based encryption schemes randomness recoverable, which in turn have applications in the context of designated-verifier non-interactive zero knowledge

Deep-based conditional probability density function forecasting of residential loads

This paper proposes a direct model for conditional probability density forecasting of residential loads, based on a deep mixture network. Probabilistic residential load forecasting can provide comprehensive information about future uncertain-ties in demand. An end-to-end composite model comprising convolution neural networks (CNNs) and gated recurrent unit (GRU) is designed for probabilistic residential load forecasting. Then, the designed deep model is merged into a mixture density network (MDN) to directly predict probability density functions (PDFs). In addition, several techniques, including adversarial training, are presented to formulate a new loss function in the direct probabilistic residential load forecasting (PRLF) model. Several state-of-the-art deep and shallow forecasting models are also presented in order to compare the results. Furthermore, the effectiveness of the proposed deep mixture model in characterizing predicted PDFs is demonstrated through comparison with kernel density estimation, Monte Carlo dropout, a combined probabilistic load forecasting method and the proposed MDN without adversarial trainin

Measuring the organizational intelligence of the experts and managers of the cement factory in Sistan

This study has been carried out aiming at measuring the organizational intelligence of the experts and managers of the cement factory in Sistan. The research is practical-descriptive and the statistical population includes all 50 experts and managers of the cement factory in Sistan. Sampling method was consensus. Therefore, all the population was considered, yet 42 questionnaires were returned for analysis. The instrumentation included the 49-question Albrecht’s organizational intelligence questionnaire. The content validity and its reliability (using Cronbach index = 0.81) were calculated. The results show that the organizational intelligence of the experts and managers of the Sistan cement factory is unacceptably below average. The findings also revealed that the parameters of strategic vision, shared fate, appetite for change, and alignment and congruence were below average and the ones of heart, knowledge deployment, and performance pressure were above average

Measuring the organizational intelligence of the experts and managers of the cement factory in Sistan

This study has been carried out aiming at measuring the organizational intelligence of the experts and managers of the cement factory in Sistan. The research is practical-descriptive and the statistical population includes all 50 experts and managers of the cement factory in Sistan. Sampling method was consensus. Therefore, all the population was considered, yet 42 questionnaires were returned for analysis. The instrumentation included the 49-question Albrecht’s organizational intelligence questionnaire. The content validity and its reliability (using Cronbach index = 0.81) were calculated. The results show that the organizational intelligence of the experts and managers of the Sistan cement factory is unacceptably below average. The findings also revealed that the parameters of strategic vision, shared fate, appetite for change, and alignment and congruence were below average and the ones of heart, knowledge deployment, and performance pressure were above average