Coronavirus disease 2019 (COVID-19) poses massive challenges for the world. Public sentiment analysis during the outbreak provides insightful information in making appropriate public health responses. On Sina Weibo, a popular Chinese social media, posts with negative sentiment are valuable in analyzing public concerns. 999,978 randomly selected COVID-19 related Weibo posts from 1 January 2020 to 18 February 2020 are analyzed. Specifically, the unsupervised BERT (Bidirectional Encoder Representations from Transformers) model is adopted to classify sentiment categories (positive, neutral, and negative) and TF-IDF (term frequency-inverse document frequency) model is used to summarize the topics of posts. Trend analysis and thematic analysis are conducted to identify characteristics of negative sentiment. In general, the fine-tuned BERT conducts sentiment classification with considerable accuracy. Besides, topics extracted by TF-IDF precisely convey characteristics of posts regarding COVID-19. As a result, we observed that people concern four aspects regarding COVID-19, the virus Origin (Gamey Food, 3.08%; Bat, 2.70%; Conspiracy Theory, 1.43%), Symptom (Fever, 2.13%; Cough, 1.19%), Production Activity (Go to Work, 1.94%; Resume Work, 1.12%; School New Semester Beginning, 1.06%) and Public Health Control (Temperature Taking, 1.39%; Coronavirus Cover-up, 1.26%; City Shutdown, 1.09%). Results from Weibo posts provide constructive instructions on public health responses, that transparent information sharing and scientific guidance might help alleviate public concerns.
We test the joint efficiency of the bitcoin options and perpetual futures markets, and likewise for ether, and identify the frequency and magnitude of arbitrage opportunities. Our novel fiat-currency-free put-call parity relationship leads to several new arbitrage tests. Bitcoin and ether derivatives markets are evolving to a more efficient state. Price efficiency for longer-dated options (with maturity >= 15 days) has improved very significantly over time. Bitcoin markets are more efficient than ether, for arbitrage strategies can still be highly profitable, even under conservative transaction cost scenarios, especially during periods of extreme volatility.
Object detection has always been one of the hot tasks in the computer vision community, whose goal is to accurately and efficiently identify and locate a large number of predefined categories of object instances from the image. With the widespread application of deep learning, the accuracy and efficiency of object detection have been greatly improved, and thousands of methods have be proposed to improve the detection accuracy and speed. In this paper, we first introduce and analyze the representative methods from the 2D object detection to 3D object detection, and then give an exhaustive introduction to common datasets and conduct a comparative performance analysis of the core ideas of different algorithms. Also, we summarize the existing problems in the detection research field and predict the solutions to these problems in the future.
从人与情境互动的角度出发，探讨人——组织匹配对员工创新行为的影响机制。通过对350名在职员工的调查，发现人——组织匹配对员工创新行为、创造力自我效能感均有显著的正向影响，其中价值观匹配和要求——能力匹配对员工创新行为、创造力自我效能感具有显著的正向影响，而需求——供给匹配对员工创新行为和创造力自我效能感没有影响；创造力自我效能感对员工创新行为具有正向影响；创造力自我效能感在人——组织匹配与员工创新行为之间起到部分中介作用。文章基于社会交换等理论对研究结果进行了讨论,存在的局限性是文中只研究了创造力自我效能感作为中介变量，在未来的相关研究中，希望加入其他中介和调节变量，以建立更加完善的理论模型。 From the perspective of interaction between people and the environment, to explore the impact of Person—organization on employee’s Innovative behavior. Through the survey of 350 employees, it was found that Person—organization fit has a significant positive impact on employee’s innovative behavior and creative self—efficacy. The value congruence and the demands—abilities had significant positive impact on the employees' Innovative behavior, creative self—efficacy. The needs—supplies has no impact on the employee's Innovative behavior and the creative self—efficacy; the creative self—efficacy has positive impact on the employee's Innovative behavior; the creative self-efficacy play a part of the intermediary role between Person—organization and Employee’s Innovative behavior. This paper discusses the research results based on the theory of social exchange and other other related theories, the limitations of this paper are that only the Creative self-efficacy as the mediator variable, in the future related research, we hope to add other mediating and regulating variables to establish more perfect of the theoretical model.
We test the joint efficiency of the bitcoin and ether options and perpetual futures markets and identify the determinants of arbitrage opportunities. Our novel fiat-currency-free put–call parity relationship motivates new arbitrage tests for options-only and option–perpetual cross-markets. Bitcoin and ether derivatives markets are becoming more efficient, especially for options of maturity ≥ 15 days. Bitcoin derivative markets are generally more efficient than ether derivative markets, but arbitrage strategies can still be highly profitable even under conservative transaction cost scenarios, which include slippage for large orders, especially during periods of high trading volumes or when the blockchain traffic becomes more congested.
At present, the reverse confusion cases of trademark right often appear in the justice practice in China, most of which are judged in accordance with the relevant provisions of the "Trademark Law of the People's Republic of China"; however it is difficult to avoid some problems.This paper aims to illuminate the development and overview of the reverse confusion theory of trademark law through literature research method, historical analysis method, comparative analysis method and case analysis method, starts with a typical case of " trademark case of If You are the One " and combines with the scholar's researches on the reverse confusion of trademark.This paper analyzes the shortcomings and insufficiencies of trademark legislation in China, and combines the current situation of Trademark Act to propose corresponding improvement suggestions from the aspect of legislation, hoping to enrich the theoretical research of reverse confusion of trademark right and promote the continuous improvement of trademark law in China.
Numerous new broadband and multimedia applications have emerged in recent years, in which streaming video services are particularly of interest. Unlike data communication, streaming video requires a subjective measure of quality referred to as the Quality of Experience (QoE), but the subjective evaluation is very time consuming and complex. We are proposing an objective approach to provide mapping between QoE and Quality of Service (QoS), in which Video Quality Metric (VQM) is utilized to indicate QoE. We have analyzed the emulation results and derived a simple formula to estimate QoE from QoS parameters for streaming video under certain conditions. This method is non-intrusive and quick to use.
To monitor and analyze students' classroom attention, an approach of discriminating attention of the students based on the recognition of head position is presented.. The facial feature points of classroom monitoring images are detected using convolutional neural network, the face is tracked using ( pose from orthography and scaling with iterations, POSIT) algorithm, the rotation angle of the face is calculated, and the student's attention is analyzed based on the tilt angle of his or her head. The proposed model has an accuracy of 88.7% for attention detection, which allows for effective analysis of students' attention and provide a basis for evaluating course teaching.
Delay Tolerant Networks are a special kind of ad hoc networks that consist of a number of mobile devices. In DTNs, network topology frequently changes and source-to-destination communication paths can hardly be sustained. However, some social features have been observed in DTNs by analyzing the real mobile trajectories of DTNs. To utilize these social features to facilitate data routing, we present a community-based routing protocol that efficiently detects the community structure by using proposed time-varying community partitioning algorithm. For intracommunity routing, according to the remaining time-to-live of the message, the minimum number of message copies necessary to achieve a given delivery probability is calculated, which is the number of message copies utilized to spray in the current community. To facilitate intercommunity routing, we choose the gateway node that connects the next community frequently as relay. The evaluation of our strategy is conducted by extensive traced-driven simulations. The results of simulations confirm the effectiveness of our routing strategy.
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