The wide application of information technology and network technology in automobiles has made great changes in the Human-computer interaction. This paper studies the influence of Human-computer interaction modes on driving safety, comfort and efficiency based on physical interaction, touch screen control interaction, augmented reality, speech interaction and somatosensory interaction. The future Human-com-puter interaction modes such as multi-channel Human-computer interaction mode and Human-computer interaction mode based on biometrics and perception techno-logy are also discussed. At last, the method of automobile Human-computer interaction design based on the existing technology is proposed, which has certain guiding significance for the current automobile Human-computer interaction interface design.

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In education, the use of smart-phone apps is very helpful in the process of teaching and learning. Therefore, this situation inspires and opportunities for the development of a smart-phone app namely the Electrical e-Wiring Module (called, MePE). The MePE app is a smart-phone app or mobile apps developed to help students who are taking electrical wiring courses to refer how to learn electrical wiring via smart-phone applications. In addition, this application can also be used as a teaching tool in the process of learning electrical wiring. The study was conducted to identify the requirements for MePE app, develops and tests the functionality of MePE app. There are four main sections in this application. This application is developed in accordance with the evolutionary prototype methodology. As a result of the functionality testing performed on the nine functions available on the MePE app all the functions passed the tests that have been performed. In addition, 24 of 31 respondents agreed that the MePE application is helpful and effective in learning electrical wiring. It is hoped that with this application it will help students that study electrical wiring courses to make reference to electrical wiring through this application.

As the country implements the big data strategy and accelerates the construction of a digital China, data science has entered a new and dynamic era, and the demand for data science talents in all walks of life is increasing. Many talent training departments have added undergraduates or degrees to data science talents, but it is still unclear whether they can meet social and economic development needs. This article aims to improve the quality and adaptability of data science talent training and conduct an in-depth analysis of the demand for data science talents. The technology used in this article is data mining technology. The data information of data science talents is crawled out of the demand information of data science talents on the recruitment website. The core content of network relationship visualization is proposed and analyzed through machine learning methods and text subject word extraction models. Achieve a comprehensive exploration of the demand for data science talents and provide a reference for talent training units to formulate data science talent training models.

Drowsiness is one of the main factors causing traffic accidents. Research on drowsiness can effectively reduce the traffic accident rate. According to the existing literature, this paper divides the current measurement techniques into subjective and objective ones. Among them, invasive detection and non-invasive detection based on vehicles or drivers are the main objective detection methods.Then, this paper studies the characteristics of drowsiness, and analyzes the advantages and disadvantages of each detection method in practical application. Finally, the development of detection technology is prospected, and provides ideas for the follow-up development of fatigue driving detection technology.