Jimmy Ming-Tai Wu's research while affiliated with Shandong University of Science and Technology and other places

Frequent itemsetmining and high-utility itemsetmining have been widely applied to the extraction of useful information from databases. However, with the proliferation of the Internet of Things, smart devices are generating vast amounts of data daily, and studies focusing on individual dimensions are increasingly unable to support decision-making. Hence, the concept of a skyline query considering frequency and utility (which returns a set of points that are not dominated by other points) was introduced. However, in most cases, firms are concerned about not only the frequency of purchases but also quantities. The skyline quantity-utility pattern (SQUP) considers both the quantity and utility of items. This paper proposes two algorithms, FSKYQUP-Miner and FSKYQUP, to efficiently mine SQUPs. The algorithms are based on the utility-quantity list structure and include an effective pruning strategy which calculates the minimum utility of SQUPs after one scan of the database and prunes undesired items in advance, which greatly reduces the number of concatenation operations. Furthermore, this paper proposes an array structure superior to utilmax for storing the maximum utility of quantities, which further improves the efficiency of pruning. Extensive comparison experiments on different datasets show that the proposed algorithms find all SQUPs accurately and efficiently.

Money management, also known as asset allocation, is constantly at the forefront of research in the trading and investing fields. Since Markowitz established the current portfolio theory in 1952, it has drawn many experts to this intriguing topic. The Kelly criteria are one of the brightest stars among these new techniques. It provides an elegant solution for players and investors to get the best bidding fraction and maximize their logarithm worth over time. However, it ignores the fact that each investor has a different risk tolerance, and the proportion was calculated using the Kelly criterion without taking into account the downside risk. This paper attempts to develop a risk prediction model using a probability-based method and adjust the reward function of deep reinforcement learning to account for the downside risk. To summarize, rather than a naive reward function that solely optimizes the return, the improved deep reinforcement learning may consider an investor’s risk tolerance. Finally, we solely analyze the scenario of a single asset and employ DXY, GBP/USD, and EUR/USD as the underlying training and validation data sets. The outcome demonstrates that the adjustment to the reward mechanism produces an interesting performance. When the required MDD (Maximum draw-down) is greater than 3%, the likelihood is on average greater than 70%.

The Internet of Things (IoT) has revolutionized practically every industry, including agriculture, due to its fast expansion and integration into other industries. The application of IoT in agriculture motivates farmers to use their resources wisely and allows for better field monitoring and decision-making, resulting in increased agricultural productivity. Because IoT-enabled agriculture systems need the use of various types of sensors that collect data (such as soil moisture and humidity) and then transmit it over the network. IoT-based agriculture systems, on the other hand, are always vulnerable to security threats. Authentication is one of the assured options for addressing the security concern, since it only enables an authorized party to access the data. Existing authentication schemes typically use the Rivest-Shamir-Adleman (RSA) algorithm and elliptic curve cryptography (ECC), which has a greater computational and communication cost. Furthermore, the security of the majority of existing authentication schemes is not verified using any security tool. As a result, we propose an identity-based authentication scheme for IoT-enabled agriculture in this article. To ensure that our scheme is cost-effective, we employ hyperelliptic curve cryptography (HECC). Our scheme surpasses existing authentication schemes in terms of computational cost and communication overhead while providing better security, according to a thorough investigation of performance and security.

The Smart Grid-Enabled Industrial Internet of Things (SG-IIoT) is a hybrid data communications network connected with the power grid that collects and analyses data from transmission lines, distribution substations, and consumers. In the IIoT setting, SG provides predictive information to its supplier and customers on how to effectively manage the power based on this aggregated data. To achieve this goal, every virtual or physical entity in the SG-IIoT must be linked and accessible over the Internet, which can be susceptible to numerous cyber-attacks. In this paper, we propose a multi-documents blind signcryption scheme to simultaneously resolve the security and efficiency issues. The proposed scheme performs the blind signature and encryption operation on multiple digital documents in one step because SG-IIoT outputs a large amount of data that needs to be blind signed and encrypted in a batch. The proposed scheme employs the concept of Hyper Elliptic Curve Cryptography (HECC), which is lightweight owing to the smaller key size. The comparative analysis in both security and efficiency with the relevant existing scheme authenticates the viability of the proposed scheme.

The Industrial Internet of Things (IIoT) is a technology that uses the Internet of Things (IoT) infrastructure to sense, process, and communicate real-time events in the industrial system to cut down on unnecessary operating costs and to speed up industrial automation of internal and external working processes. Since the IIoT system inherits the same cyber-physical vulnerabilities that the IoT system already encounters, it requires additional work to address security concerns owing to its heterogeneous nature. As a result, an efficient security mechanism is essential to protect against various and unknown cyber-attacks. In this article, we propose a certificate-based signature scheme based on Hyperelliptic Curve Cryptography (HECC), with the aim of improving security while reducing computational and communication costs in the IIoT environment. The proposed scheme outperforms existing schemes in terms of both computational and communication costs, as well as offering better security.