X. Wang

This paper presents a theoretical framework of the circular shift network coding system through the study of nonmultiple clustered interval music performance style conversion and the analysis of music conversion by using circular shift topology, and a series of basic research results of circular shift network coding is obtained under this framework. It reveals the essential connection between scalar network coding based on finite domain and cyclic shift network coding, designs a solution construction algorithm for cyclic shift network coding under multicast network, and portrays the multicast capacity of cyclic shift network coding. It overcomes the problem that the piano roll-curtain representation cannot distinguish between a single long note and multiple consecutive notes of the same pitch, describes musical information more comprehensively, extracts musical implicit style from the note matrix based on autoencoder, and better eliminates the potential influence of musical content on musical performance style. A two-way recurrent neural network based on the gated recurrent unit is used to extract a sequence of note feature vectors of different styles, and a one-dimensional convolutional neural network is used to predict the intensity of the extracted note feature vector sequence for a specific style, which better learns the intensity variation of different styles of MIDI music.

Online social network services have brought a kind of new lifestyle to the world that is parallel to people’s daily offline activities. Social network analysis provides a useful perspective on a range of social computing applications. Social interaction on the Web includes both positive and negative relationships, which is certainly important to social networks. The authors of this article found that the accuracy of the signs of links in the underlying social networks can be predicted. The trust that other users impart on a node is an important attribute of networks. In this article, the authors present a model to compute the prestige of nodes in a trust-based network. The model is based on the idea that trustworthy nodes weigh more. To fulfill this task, the authors first attempt to infer the attitude of one user toward another by predicting signed edges in networks. Then, the authors propose an algorithm to compute the prestige and trustworthiness where the edge weight denotes the trust score. To prove the algorithm’s effectiveness, the authors conducted experiments on the public dataset. Theoretical analysis and experimental results show that this method is efficient and effective.

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation.

Large amount of data are exchanged and the internet is turning into twenty-first century Silk Road for data. Machine learning (ML) is the new area for the applications. The artificial intelligence (AI) is the field providing machines with intelligence. In the last decades, more developments have been made in the field of ML and deep learning. The technology and other advanced algorithms are implemented into more computational constrained devices. The online English test system based on ML breaks the shackles of the traditional paper English test and improves the efficiency of the English test. At the same time, it also maintains the fairness of English test and improves the marking speed. In order to realize an online English test system based on ML and facilitate the assessment of students’ college English courses, this paper mainly adopts relevant research and design on the main functional modules, key technologies, and functional realization of the online English test. The brand-new powerful teaching software and the online examination system can help schools to conduct more systematic and scientific management. The conclusion shows that as brand-new and powerful teaching software, the online examination system can help schools to conduct more systematic and scientific management.

Evidence accumulation has been the core component in recent development of perceptual and value-based decision-making theories. Most studies have focused on the evaluation of evidence between alternative options. What remains largely unknown is the process that prepares evidence: how may the decision-maker sample different sources of information sequentially, if they can only sample one source at a time? Here we propose a normative framework in prescribing how different sources of information should be sampled proactively to facilitate the decision process: beliefs for different noisy sources are updated in a Bayesian manner and participants can proactively allocate resource for sampling (i.e. saccades) among different sources to maximize the information gain in such process. We show that our framework can account for human participants’ actual choice and saccade behaviors in a two-alternative value-based decision-making task. Moreover, our framework makes novel predictions about the empirical eye movement patterns.

For the first time, this study uses the sports event—tennis as an intermediate carrier to build a bridge of interaction between wealth management characteristic talents and wealth management objects, so that the training of wealth management characteristic talents and employers can be seamlessly connected and integrated naturally. It has broadened the employment channels for talents with characteristics of wealth management, shortened the time process for the integration of wealth management-related college graduates into the society, and improved the recognition of wealth management-related students by employers. At the same time, this study proposes specific measures for wealth management-related employers to feed back the quality of wealth management talent training in colleges and universities.

This paper expands upon the work by Wang (Proceedings of TARK, pp. 493–512, 2017) who proposes a new framework based on quantifier-free predicate language extended by a new bundled modality \(\exists x\Box \) and axiomatizes the logic over S5 frames. This paper first gives complete axiomatizations of the logics over K, D, T, 4, S4 frames with increasing domains and constant domains, respectively. The systems w.r.t. constant domains feature infinitely many additional rules defined inductively than systems w.r.t. increasing domains. In the second part of the paper, we show that these rules are admissible in the systems without them by providing a general strategy for proving completeness theorems for the logics without these rules over constant domain models (also increasing domain models).