Virtual reality technology has been widely used in education scenarios. Among them, the immersive virtual multimedia classroom can mimic the physical classroom, so as to facilitate the performance of on-line learning. However, there are still gaps between the virtual and physical classroom, especially the lighting environment which plays an important role in the visual experience of participants. In this paper, we aim to study the relationship between the lighting parameters and the visual comfort. We firstly establish a virtual classroom with adjustable lighting parameters. Then we conduct user cases which mimic the natural and indoor lighting respectively, investigating the effect of the two different lighting environments upon the visual comfort. Particularly, we establish the empirical fitting models from the collected subjective ratings, which can provide the perception threshold and the optimal lighting conditions. The proposed method can benefit the design and implementation of virtual multimedia classroom.
In recent years, with the increase of world trade volume, multimodal transport has become the main mode of international trade transportation, and its development is facing a new stage. As an important part of multimodal transport, invoice affects the efficiency of multimodal transport. Electronic invoices are replacing traditional paper invoices as the main form of bill of lading. But due to the characteristics of paperless and intangible electronic, the electronic invoices, unlike traditional paper invoices, cannot be actually possessed. Considering the block chain technology possesses the characteristics of unchanged and traceability, it will apply into the electronic invoice. It provides shippers and carriers with freight information and pre-agreed service contract rates, enables shippers and carriers to conduct secure and transparent transactions, improves electronic invoice processing efficiency, and reduces shipper and carrier costs.
The advent of blockchain technology has transformed traditional business processes from centralized to decentralized. By eliminating the unnecessary intervention of middlemen, it can reduce the overall cost of patient medication by turning the drug supply chain entirely into a point-to-point decentralized business. This paper presents a reliable and encouraging P2P drug trading block chain technology and four related smart contracts. These contracts include consumer contracts and the supply, bidding and trading of drugs, which have been deployed on the Ethereum blockchain for the decentralized trading of drugs. We will use the Approach of Real cost descending (RCD) to achieve incentive transactions for suppliers and patients. This method provides P2P transactions and ensures the safety and transparency of drug data, as well as the anonymity of users in the transaction process. Finally, according to the requirements of Good Supplying Practice(GSP), the effectiveness of the proposed model is evaluated and analyzed.
Micro-blog is a platform for users to get information and convey their own ideas. In recent years, the emotional analysis of micro-blog has gradually become a hot topic. The publication of micro blog not only includes text, but also emoticons are a part that cannot be ignored. Traditional research methods ignore the importance of emoticons to the emotional polarity of text when preprocessing the micro blog. This paper proposes a research method of text emotion analysis based on the fusion of emoticons. By micro-blog to crawl the data preprocessing, selected text in the emoticons, using emotional dictionary gives corresponding weights and calculate the score, then transform text into the corresponding word vector sequence, using Bidirectional Gated Recurrent Unit network context information text emotion tendency, finally selects the Conditional Random Field polarity judgment of text. The experimental results show that the accuracy of the proposed method is up to 89%.
Cross-domain text classification has broad application prospects in the field of data mining. Since transfer learning can help target domain data to achieve the sharing and transfer of semantic information with the help of existing knowledge domains, transfer learning are generally used to achieve cross-domain text processing. Based on this, we propose a cross-domain text classification algorithm -MTrA. The algorithm is based on TrAdaBoost, taking into account the distribution differences between the source domain and the target domain. It uses the Maximum Mean Discrepancy(MMD) as the initial weight parameter of the two domain. MTrA adds a weight backfill factor that considers the accuracy of the source domain classification and balances the weight update method of the source domain data. Through the verification in the dataset 20 Newsgroups, Compared with the traditional TrAdaBoost algorithm, it improves the classification accuracy by 9.4% on average. it proves the effectiveness and advantages of the algorithm.
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