In today’s highly competitive retail industry, offline stores face increasing pressure on profitability. They hope to improve their ability in shelf management with the help of big data technology. For this, on-shelf availability is an essential indicator of shelf data management and closely relates to customer purchase behavior. RFM (recency, frequency, and monetary) pattern mining is a powerful tool to evaluate the value of customer behavior. However, the existing RFM pattern mining algorithms do not consider the quarterly nature of goods, resulting in unreasonable shelf availability and difficulty in profit-making. To solve this problem, we propose a quarterly RFM mining algorithm for On-shelf products named OS-RFM. Our algorithm mines the high recency, high frequency, and high monetary patterns and considers the period of the on-shelf goods in quarterly units. We conducted experiments using two real datasets for numerical and graphical analysis to prove the algorithm’s effectiveness. Compared with the state-of-the-art RFM mining algorithm, our algorithm can identify more patterns and performs well in terms of precision, recall, and F1-score, with the recall rate nearing 100%. Also, the novel algorithm operates with significantly shorter running times and more stable memory usage than existing mining algorithms. Additionally, we analyze the sales trends of products in different quarters and seasonal variations. The analysis assists businesses in maintaining reasonable on-shelf availability and achieving greater profitability.

Multivariate Public Key Cryptography (MPKC) has intensively and rapidly developed during the past three decades. MPKC is a promising candidate for post-quantum cryptography. However, designing it is universally regarded as a difficult task to design a secure MPKC foundation scheme, such as an encryption scheme and key exchange scheme. In this work, we investigate the security of a new public key cryptosystem that is based on the Morphism of Polynomials (MP). The public key cryptosystem proposed by Wang et al. (Wuhan University, China) comprises a key exchange scheme and encryption scheme. Its security can be provably reduced to the hardness of solving a new difficult problem, namely, the Decisional Multivariate Diffie Hellman (DMDH) problem. This problem is a variant of the MP problem, which is difficult to solve by random systems. We present a proposition that reduces the DMDH problem to an easy example of the MP problem. Then, we propose an efficient algorithm for the Key Recover Attack (KRA) on the schemes of the public key cryptosystem. In practice, we are able to entirely break the cryptosystem’s claimed parameter of 96 security levels in less than 17.252 s. Furthermore, we show that finding parameters that yield a secure and practical scheme is impossible.