Cloud Data google
The Difference Between Big Data and Cloud Data: New technologies are required for the emergence and standardization of cloud data to take hold. Big data was meant as a holding cell for large amounts of data that could be sorted effectively only by specialized data scientists (this is becoming easier with OLAP on Hadoop type tools). The protocols for big data rely upon simple, standard protocols and can’t be adjusted easily to meet the demands of complex operations. Big data takes time to sort through and analyze, whereas cloud data is immediate and happens in the background using the tremendous resources of cloud servers. Cloud data requires a significantly higher number of resources since it must connect to databases in several geographically distributed services. Since cloud data must flexibly interact with several unique interfaces and security models, the mechanisms used for big data won’t work for cloud data. …

Overconfidence Effect google
The overconfidence effect is a well-established bias in which someone’s subjective confidence in their judgments is reliably greater than their objective accuracy, especially when confidence is relatively high. For example, in some quizzes, people rate their answers as “99% certain” but are wrong 40% of the time. It has been proposed that a metacognitive trait mediates the accuracy of confidence judgments, but this trait’s relationship to variations in cognitive ability and personality remains uncertain. Overconfidence is one example of a miscalibration of subjective probabilities. …

Emotional Chatting Machine (EMC) google
Emotional intelligence is one of the key factors to the success of dialogue systems or conversational agents. In this paper, we propose Emotional Chatting Machine (ECM) which generates responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor in three ways: modeling high-level abstraction of emotion expression by embedding emotion categories, changing of implicit internal emotion states, and using explicit emotion expressions with an external emotion vocabulary. Experiments show that our model can generate responses appropriate not only in content but also in emotion. …

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