With the advent of the data age, the public has been facing the problem of information overload. Recommendation algorithms are an effective way to solve this problem. At present, a large number of recommended algorithms adopt the following two ideas: content-based text similarity algorithm and user-based collaborative filtering algorithm. Researchers have developed a distributed collaborative recommendation protocol based on blockchain. However, these algorithms ignore the characteristics of the news industry itself. Just adopting the above ideas will inevitably lead to many internet public opinion problems. Therefore, this paper proposes an improved N-TF-IDF algorithm, which is more suitable for the news industry, and can control the outbreak of negative public opinion, and has a positive effect on stabilizing internet public opinion. Through the verification of the experimental data set, the algorithm is superior to the traditional information retrieval and text mining technology TF-IDF in both the time dimension and the emotional dimension, and this algorithm is not affected by citizens' privacy rights.
The widespread application of blockchain technology and artificial intelligence in the field of accounting will replace many manual tasks, which will have a great impact on the training of accounting professionals in colleges and universities, mainly focused on training skilled personnel. Therefore, it is of great significance to study the impact of blockchain and artificial intelligence on the accounting professionals in universities and their corresponding ability structure. Defining the application stage of artificial intelligence and blockchain in the field of finance and accounting from the perspective of technology implementation, we can clarify the functional characteristics of artificial intelligence and blockchain in this field; By analyzing the development process of artificial intelligence and blockchain in the field of finance and accounting with non-technical factors, it can be judged that it has an achievable development stage. And on this basis, the author put forward specific suggestions for the ability structure training of future accounting professionals.
Physical layer security has recently emerged as a promising approach to provide secure wireless communications. Physical layer security technology is the key means to achieve security and communication integration. The built-in security mechanism based on the characteristics of wireless channel provides a feasible idea for the realization of “one secret at a time”. It has important application prospects in integrity protection of service data, Internet of things (IoT) lightweight encryption and blockchain wireless security technology. In this paper, we consider the scenario of a unicast multiuser multiple-input single-output (MISO) downlink system in the presence of multiple single-antenna passive eavesdroppers. Instead of assuming perfect channel state information (CSI), we investigate the artificial noise (AN)-aided robust transmit beamforming scheme with imperfect CSI on legitimate receivers and no CSI regarding eavesdroppers at the transmitter. Under the fixed total transmit power, the information symbol and the AN are transmitted concurrently. We aim to guarantee each legitimate receiver's signal-to-interference-and-noise ratio(SINR) outage probability below a preset level with minimum power. Hence, as much power as possible can be allocated to AN to confuse the eavesdroppers. In order to reduce the negative impact on the legitimate receivers, the AN is transmitted in the null space of the estimated legitimate channels. To deal with this nonconvex optimization problem, a rank relaxed formulation is obtained by employing semidefinite relaxation (SDR). Then we propose two approaches by using Bernstein-type inequality and S-procedure, respectively, to transform the probabilistic constraints into deterministic constraints. The optimization problem is reformulated as a convex semidefinite program (SDP). Simulation results show that the proposed approaches have better performances than the non-robust approach.
In this paper, aiming at the shortcomings of the Artificial Bee Colony (ABC) algorithm in guiding bees and observing bees in the search mechanism, the author integrated the idea of self-regulation of antibody concentration in the immune algorithm, regarded the optimal solution as antigen, and the search bee as antibody for target search. In this paper, the differences between the optimal solution and the initial solution are compared by setting parameters when initializing the nectar source, so as to enhance the memory of antibodies, avoid falling into the local optimal when maintaining the diversity of the population, accelerate the convergence speed, and increase the global search ability of the algorithm. Simulation results of six classical test functions show that the proposed algorithm has obvious advantages over ABC algorithm in terms of optimization accuracy, convergence, accuracy and stability. We will combine the proposed algorithm with Blockchain techniques in our future work.
Since railway traffic has come into people's life, it has been acting as a very important role in people's daily living. The major content of this research is to build a railway accident analysis system based on Artificial Intelligence (aliased as AI) and blockchain technology, which have the function of railway accident reproduction and traffic data analysis, taking the train crash happened on June 29, 2009 in Chenzhou for case study. This research reproduces the environment scenes simulating the accident happened in Chenzhou with the help of MSTS, including the longitude, the latitude, the landform, the buildings, the platforms, the traffic and the track installment of that time, and represents the real situation taking a driver's field of view. Besides, the research also builds a railway traffic data collecting and analyzing system based on C#, with the help of Cheat Engine, Microsoft Access and Visual Studio 2010. The system can automatically track and analyze the traffic data from Microsoft Train Simulator. This reproduction procedure is with great significance for analyzing the real accident in an Artificial Intelligence way, as well as providing a broad prospect for the practical application of blockchain technology.
