Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This paper reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method.

Standardization is used to ensure that the variables in a similarity calculation make an equal contribution to the computed similarity value. This paper compares the use of seven different methods that have been suggested previously for the standardization of integer-valued or real-valued data, comparing the results with unstandardized data. Sets of structures from the MDL Drug Data Report and IDAlert databases and represented by Pipeline Pilot physicochemical parameters, molecular holograms and Molconn-Z parameters are clustered using the k-means and Ward's clustering methods. The resulting classifications are evaluated in terms of the degree of clustering of active compounds selected from eleven different biological activity classes, with these classes also being used in similarity searches. It is shown that there is no consistent pattern when the various standardization methods are ranked in order of decreasing effectiveness and that there is no obvious performance benefit (when compared to unstandardized data) that is likely to be obtained from the use of any particular standardization method.

Since entering the 21st century, “economic globalization” has become a hot topic. Under the impact of “economic globalization”, the competition of the Chinese domestic market continues to intensify, and Chinese enterprises are facing enormous pressure for survival and development. Among them, there are many cases of poor business operation caused by financial crisis which have directly put these companies in trouble, even causing them to go bankrupt. Therefore, it is very practical to establish a scientific data model to analyze and predict the financial data of enterprises. It can not only monitor the financial status of the enterprise in real time, but also play an effective financial early warning role. This research focuses on using the combined forecasting method to establish a more comprehensive financial early warning model to solve the related financial crisis forecasting problem. Specifically, two different forecasting methods are first adopted in this study to conduct financial early warning research. The first is time series forecasting. It is a dynamic data processing statistical method, which is often used in forecasting research in the business field. The second is the BP neural network algorithm (referred to as BP), which is an error back-propagation learning algorithm, which is often used in the field of artificial intelligence. Then, the prediction error values of the two methods are compared and they are applied to the combined prediction method. Finally, a new error prediction formula is obtained. The result shows that the BP method provides the best performance over others, while the combinational forecasting method offers better performance than any single method.

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A blockchain-based continuous micropayment is a crucial component of the digital economy as it facilitates transactions and promotes small purchases. However, due to the throughput limitations of blockchain, payment channels (PC) are often used to process a large volume of transactions through an off-chain mode. Nevertheless, the introduction of PC requires a trusted third party to ensure transaction security, which creates an additional security assumption since only the first and last transactions can be witnessed by other system users. To address this issue, we propose a novel micropayment scheme based on lockable signatures. All transactions in the PC form a continuous microtransaction hash-chain (CMHC) to prevent malicious users from obtaining transaction information during the process. Furthermore, all locks in the CMHC are invisible during the entire transaction process, and all information is transferred in a tamper-resistant manner. We provide corresponding security analysis and conduct a series of evaluations. The results show that the proposed scheme performs better than the state-of-the-art solutions in terms of transaction time and verification costs. This lockable signature-based micropayment scheme not only guarantees security but also improves transaction speed and efficiency, thereby promoting the development of the digital economy.

There is a strong demand for multi-attribute auctions in real-world scenarios for non-price attributes that allow participants to express their preferences and the item’s value. However, this also makes it difficult to perform calculations with incomplete information, as a single attribute—price—no longer determines the revenue. At the same time, the mechanism must satisfy individual rationality (IR) and incentive compatibility (IC). This paper proposes an innovative dual network to solve these problems. A shared MLP module is constructed to extract bidder features, and multiple-scale loss is used to determine network status and update. The method was tested on real and extended cases, showing that the approach effectively improves the auctioneer’s revenue without compromising the bidder.

A blockchain-oriented continuous micropayment system forms an integral element of the digital economy, enabling seamless transactions and encouraging minor purchases. However, due to the inherent throughput constraints of blockchain, payment channels (PCs) are customarily deployed for managing high-volume transactions in an off-chain mode. Despite this, the integration of a PC necessitates a trusted intermediary to safeguard transactional security, thereby imposing an extra security assumption as only the initial and concluding transactions are visible to other system participants. To circumvent this limitation, we introduce an innovative micropayment structure utilizing lockable signatures. Each transaction within the PC coalesces into a continuous microtransaction hash-chain (CMHC), precluding unscrupulous users from accessing transactional data during the process. Additionally, all locks within the CMHC remain concealed throughout the transaction, with all information relayed in a tamper-proof manner. We provide a comprehensive security analysis and perform a gamut of evaluations. Empirical evidence indicates that our proposed system outperforms existing state-of-the-art solutions in transaction time and verification expenses. This lockable signature-dependent micropayment system not only ensures robust security but also enhances transactional speed and efficiency, thereby catalyzing the growth of the digital economy.

In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.