亚洲男人的天堂2018av,欧美草比,久久久久久免费视频精选,国色天香在线看免费,久久久久亚洲av成人片仓井空

One of the most widely known and important applications of probability and statistics is scientific polling to forecast election results. In 1936, Gallup predicted correctly the victory of Roosevelt over Landon in the US presidential election, using scientific sampling of 3,000 persons, whereas the Literary Digest failed using 2.4 million answers to 10 million mailed questionnaires to automobile and telephone owners. Since then, polls have grown to be a flourishing and very influential and important industry, spreading around the world. Polls have mostly been accurate in the US presidential elections, with a few exceptions. Their two most notable failures were their wrong predictions of the US 1948 and 2016 presidential elections. Most polls failed too in the 2016 UK Referendum, in the 2014 and 2019 India Lok Sabha elections, and in the US 2020 presidential election, even though in the latter three they did predict the winner. We discuss these polls in the present paper. The failure in 1948 was due to non-random sampling. In 2016 and 2020 it was mainly due to the problem of non-response and possible biases of the pollsters. In 2014 and 2019 it was due to non-response and political biases of the polling agencies and news outlets that produced the polls.

Hybrid systems theorem proving provides strong correctness guarantees about the interacting discrete and continuous dynamics of cyber-physical systems. The trustworthiness of proofs rests on the soundness of the proof calculus and its correct implementation in a theorem prover. Correctness is easier to achieve with a soundness-critical core that is stripped to the bare minimum, but, as a consequence, proof convenience has to be regained outside the soundness-critical core with proof management techniques. We present modeling and proof management techniques that are built on top of the soundness-critical core of KeYmaera X to enable expanding definitions, parametric proofs, lemmas, and other useful proof techniques in hybrid systems proofs. Our techniques steer the uniform substitution implementation of the differential dynamic logic proof calculus in KeYmaera X to allow users choose when and how in a proof abstract formulas, terms, or programs become expanded to their concrete definitions, and when and how lemmas and sub-proofs are combined to a full proof. The same techniques are exploited in implicit sub-proofs (without making such sub-proofs explicit to the user) to provide proof features, such as temporarily hiding formulas, which are notoriously difficult to get right when implemented in the prover core, but become trustworthy as proof management techniques outside the core. We illustrate our approach with several useful proof techniques and discuss their presentation on the KeYmaera X user interface.

The Institute of Materials and Processes, IMP, of the University of Applied Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher Ingenieure e.V, AEN Automotive Engineering Network and their cooperation partners present their competences of AI-based solution approaches in the production engineering field. The online congress KI 4 Industry on November 12 and 13, 2020, showed what opportunities the use of artificial intelligence offers for medium-sized manufacturing companies, SMEs, and where potential fields of application lie. The main purpose of KI 4 Industry is to increase the transfer of knowledge, research and technology from universities to small and medium-sized enterprises, to demystify the term AI and to encourage companies to use AI-based solutions in their own value chain or in their products.

Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework proposal. The paper also contributes the Cybonto ontology, formally documenting 108 constructs and thousands of cognitive-related paths based on 20 time-tested psychology theories. Finally, the paper applied 20 network centrality algorithms in analyzing the 108 constructs. The identified top 10 constructs call for extensions of current digital cognitive architectures in preparation for the DT future.

The spread of COVID-19 has been a major disruptive force in people's everyday lives and mobility behavior. The demand for on-demand ride services, such as taxis and ridehailing has been specifically impacted, given both restrictions in service operations and user's concerns about virus transmission in shared vehicles. During the pandemic, demand for these modes have decreased by as much as 80%. This study examines intentions to use on-demand ride services in a period of drastic changes in lifestyles and daily routines coupled with unprecedented mobility reductions. Specifically, we examine the determinants for the shift of intentions to use these on-demand modes of travel in the early stages of the pandemic. Using data from a survey disseminated in June 2020 to 700 respondents from contiguous United States, ordinal regression modeling is applied to analyze the shift in consideration. The results indicate that political orientation and health-related experiences during the pandemic are significant sources of variation for individual changes in intentions to use ridehailing. Additionally, characteristics such as age and income result in consideration shifts that contradict the typical ridership profiles found in the ridehailing literature. Specifically, on-demand ride consideration decreases as a function of age and income. Moreover, transit-users are more willing to consider on-demand rides than private vehicle users, suggesting that shared vehicle modes have a similar risk-profile. We discuss the role of on-demand ride services in the pandemic era, and the need to investigate political orientation and evolving pandemic experiences to pinpoint their role in future mobility systems.

The onset of the Coronavirus disease 2019 (COVID-19) pandemic instigated a global infodemic that has brought unprecedented challenges for society as a whole. During this time, a number of manual fact-checking initiatives have emerged to alleviate the spread of dis/mis-information. This study is about COVID-19 debunks published in multiple languages by different fact-checking organisations, sometimes as far as several months apart, despite the fact that the claim has already been fact-checked before. The spatiotemporal analysis reveals that similar or nearly duplicate false COVID-19 narratives have been spreading in multifarious modalities on various social media platforms in different countries. We also find that misinformation involving general medical advice has spread across multiple countries and hence has the highest proportion of false COVID-19 narratives that keep being debunked. Furthermore, as manual fact-checking is an onerous task in itself, therefore debunking similar claims recurrently is leading to a waste of resources. To this end, we propound the idea of the inclusion of multilingual debunk search in the fact-checking pipeline.

There is a need for systems to dynamically interact with ageing populations to gather information, monitor health condition and provide support, especially after hospital discharge or at-home settings. Several smart devices have been delivered by digital health, bundled with telemedicine systems, smartphone and other digital services. While such solutions offer personalised data and suggestions, the real disruptive step comes from the interaction of new digital ecosystem, represented by chatbots. Chatbots will play a leading role by embodying the function of a virtual assistant and bridging the gap between patients and clinicians. Powered by AI and machine learning algorithms, chatbots are forecasted to save healthcare costs when used in place of a human or assist them as a preliminary step of helping to assess a condition and providing self-care recommendations. This paper describes integrating chatbots into telemedicine systems intended for elderly patient after their hospital discharge. The paper discusses possible ways to utilise chatbots to assist healthcare providers and support patients with their condition.

Internet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. The evaluation and analysis herein clarified the nature of users' requests for recommendations based on their location, and describes subject areas of the library for which users request recommendations. The results indicated that users of IoT-based recommendations are interested in a broad distribution of subjects, with a short-head distribution from this collection in American and English Literature. A long-tail finding showed a diversity of topics that are recommended to users in the library book stacks with IoT-powered recommendations.

Effective task management is essential to successful team collaboration. While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat. Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency. To address this problem, we explore how bots might be used to mediate task management for individuals and teams. We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel. We derived seven insights for the design of future bots for coordinating work.

Conversational systems have come a long way after decades of research and development, from Eliza and Parry in the 60's and 70's, to task-completion systems as in the ATIS project, to intelligent personal assistants such as Siri, and to today's social chatbots like XiaoIce. Social chatbots' appeal lies in not only their ability to respond to users' diverse requests, but also in being able to establish an emotional connection with users. The latter is done by satisfying the users' essential needs for communication, affection, and social belonging. The design of social chatbots must focus on user engagement and take both intellectual quotient (IQ) and emotional quotient (EQ) into account. Users should want to engage with the social chatbot; as such, we define the success metric for social chatbots as conversation-turns per session (CPS). Using XiaoIce as an illustrative example, we discuss key technologies in building social chatbots from core chat to visual sense to skills. We also show how XiaoIce can dynamically recognize emotion and engage the user throughout long conversations with appropriate interpersonal responses. As we become the first generation of humans ever living with AI, social chatbots that are well-designed to be both useful and empathic will soon be ubiquitous.