Nguyenmcknight0378

5 days (4-48·5-; P less then  .001] times were strikingly shorter for COVID-19. Almost all COVID-19 (n = 396; 99.5%) and MERS (n = 55; 100%) studies were open-access. Data sharing was infrequent, with original data available for 104 (26.1%) COVID-19 and 10 (18.2%) MERS studies (P = .203). The early academic response was characterized by investigators aiming to define the disease. Studies were made rapidly and openly available. Only one-in-four were published alongside original data, which is a key target for improvement.Like previous educational technologies, artificial intelligence in education (AIEd) threatens to disrupt the status quo, with proponents highlighting the potential for efficiency and democratization, and skeptics warning of industrialization and alienation. However, unlike frequently discussed applications of AI in autonomous vehicles, military and cybersecurity concerns, and healthcare, AI's impacts on education policy and practice have not yet captured the public's attention. This paper, therefore, evaluates the status of AIEd, with special attention to intelligent tutoring systems and anthropomorphized artificial educational agents. I discuss AIEd's purported capacities, including the abilities to simulate teachers, provide robust student differentiation, and even foster socio-emotional engagement. Next, to situate developmental pathways for AIEd going forward, I contrast sociotechnical possibilities and risks through two idealized futures. Finally, I consider a recent proposal to use peer review as a gatekeeping strategy to prevent harmful research. This proposal serves as a jumping off point for recommendations to AIEd stakeholders towards improving their engagement with socially responsible research and implementation of AI in educational systems.Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient dimensions of a culture is critically important for producing relevant and accurate evaluations of social policy when using multi-agent artificial intelligence models and simulations.In the past few years, the subject of AI rights-the thesis that AIs, robots, and other artefacts (hereafter, simply 'AIs') ought to be included in the sphere of moral concern-has started to receive serious attention from scholars. In this paper, I argue that the AI rights research program is beset by an epistemic problem that threatens to impede its progress-namely, a lack of a solution to the 'Hard Problem' of consciousness the problem of explaining why certain brain states give rise to experience. To motivate this claim, I consider three ways in which to ground AI rights-namely superintelligence, empathy, and a capacity for consciousness. I argue that appeals to superintelligence and empathy are problematic, and that consciousness should be our central focus, as in the case of animal rights. However, I also argue that AI rights is disanalogous from animal rights in an important respect animal rights can proceed without a solution to the 'Hard Problem' of consciousness. Not so with AI rights, I argue. There we cannot make the same kinds of assumptions that we do about animal consciousness, since we still do not understand why brain states give rise to conscious mental states in humans.At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible-Exposed-Infected-Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. 2-DG cost In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000.Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh-ninetieth-day forecast.