Wireless Communications and Mobile Computing

As the blockchain platform is widely used as a new trading way, both participants and transaction volume in the blockchain projects have been growing by leaps and bounds. The generic mechanisms of ranking transaction priorities are heavily dependent on the transaction fees the users append into each transaction; then, all transactions are ranked in the nonincreasing order according to the transaction fee amounts, and the selected transactions will be packed into a new created block in order based on the ranking results. However, more complex influence factors more than transaction fees on transaction priority ranking results are not taken into consideration in the generic transaction priority ranking mechanisms, and a single user is the objective to create transactions in these mechanisms. In order to optimize the generic transaction priority ranking mechanisms and enrich transaction creation modes, a novel user-coalition-based transaction pricing mechanism (UCTPM) is proposed, and the user coalition quality score, user coalition contribution degree, and the transaction type demand degree are formulated and introduced into the UCTPM mechanism. Our research findings indicate that the UCTPM mechanism satisfies the economic attributes of budget balanced, individual rationality, and incentive compatibility when the user coalition contribution degree increases through theoretical proof and experimental analysis. Moreover, the UCTPM mechanism allows all the transactions to be processed more efficiently by experimental analysis.

Under the background of mass entrepreneurship, academic entrepreneurship activities in universities are booming. DEA model is used to analyze the input-output data of academic entrepreneurship in colleges and universities in 2012 and 2016. According to the validity of the input-output data of academic entrepreneurship in different regions, the differences in efficiency between regions are compared and the reasons are analyzed. The research shows that the economic service function of colleges and universities to regional economic and social development is becoming increasingly prominent, resulting in a certain scale effect; the overall development of academic entrepreneurship efficiency, pure technical efficiency, and scale of colleges and universities in various regions is good, showing an upward trend; academic entrepreneurship activities of colleges and universities in different regions show different development trends. The scale efficiency of each region is at a higher level, but we should also actively pay attention to the utilization efficiency of academic entrepreneurship resources in colleges and universities, improve the allocation of resources, and prevent excessive investment in human and financial resources to produce redundancy, so as to achieve scale expansion while improving efficiency.

To date, the Medical Internet of Things (MIoT) technology has been recognized and widely applied due to its convenience and practicality. The MIoT enables the application of machine learning to predict diseases of various kinds automatically and accurately, assisting and facilitating effective and efficient medical treatment. However, the MIoT are vulnerable to cyberattacks which have been constantly advancing. In this paper, we establish a MIoT platform and demonstrate a scenario where a trained Convolutional Neural Network (CNN) model for predicting lung cancer complicated with pulmonary embolism can be attacked. First, we use CNN to build a model to predict lung cancer complicated with pulmonary embolism and obtain high detection accuracy. Then, we build a copycat model using only a small amount of data labeled by the target network, aiming to steal the established prediction model. Experimental results prove that the stolen model can also achieve a relatively high prediction outcome, revealing that the copycat network could successfully copy the prediction performance from the target network to a large extent. This also shows that such a prediction model deployed on MIoT devices can be stolen by attackers, and effective prevention strategies are open questions for researchers.

-shape acceleration and deceleration are the most widely used flexible acceleration and deceleration method in the current CNC system, but its velocity solution equation contains irrational terms, which create a more complicated solution process. When analyzing the solution process of the -shape acceleration and deceleration directly, using a traditional numerical solution method, the phenomenon of “solving the interval jump” arises, which is the main reason for low efficiency and poor stability of the solution. According to the -curve profile and solution, the concept of separating the curve profile recognition from the velocity solution was proposed, and a method of quickly identifying the interval of the solution location was introduced. Through the method mentioned above, the complete acceleration and deceleration curve parameters can be obtained through a one-time plan and a one-time solution, and the solution efficiency and stability are guaranteed; solving the Newton problem depends too much on the initial value of Newton velocity, which not only retains the speed advantage of the Newton method but also uses the downhill factor to ensure its convergence. Through the simulation comparison and analysis, the efficiency, stability, and universality of the method are verified.

With the advance of optimization and merger colleges and universities, a university often contains more than one campus. The traditional centralized education management system has been unable to meet the needs of use. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of object to be detected in each cluster and the weighted overlapping distance of and each centre point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental results show that the model established in this paper has certain theoretical value.

In order to improve the utilization rate of agricultural big data and solve the security issues problem of multisource and heterogeneous agricultural big data, an improved agricultural big data ant colony optimization algorithm (BigDataACO) is proposed to complete the multisource agricultural big data information in the feature layer and decision-making, and the problem of multisource data fusion was solved. The swarm intelligence algorithm is a process of simulating the complex problem of populations in nature through the mutual cooperation between individuals. The algorithm has potential parallelism and strong robustness, and the algorithm does not depend on specific problems. The definition, principle, and implementation method of agricultural big data fusion problem are studied. Then, the insufficiency of big data fusion modeling algorithm is analyzed. Finally, the source and core steps of the ant colony big data fusion algorithm are studied. The experimental results show that the improved BigDataACO algorithm is verified by the measured data. Compared with K-means, D-S evidence theory, and Bayesian algorithm, the uncertainty of data fusion is greatly reduced by the improved algorithm proposed in this paper.