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marjorie taylor greene - AzetSar - 2022/05/08
But secrets can breed suspicion. What if the search engine company has made a deal with another organization. Ranks that organization's Web sites higher than its competitors as a result? Wikia Search was meant to remove the mystery behind search engine rankings. Cuil's home page looks like the Bizarro version of Google. In the summer of 2008, a new search engine emerged onto the scene and began to make headlines. Headed by Web veterans -- including former Google employees -- this new search engine seemed poised to take on Google in a head-to-head competition. The launch of Cuil wasn't exactly an example of smooth sailing. The problem was that, despite claims that Cuil would search far more sites than Google or Microsoft, results came back incomplete or just plain wrong. Google's strategy is to search sites for keywords. Then rank the sites based upon popularity. The more popular a Web site is, the higher it will rank on a Google results page. Fujitsu's Fabric PC concept. See more computer pictures. Imagine walking to school or work with a brand-new type of laptop computer in hand. You walk casually, swinging the laptop back and forth between your arms, which is easy, since it weighs well under one pound (0.45 kg) and isn't much thicker than a checkbook. Although it has no carrying case, you hardly blink after dropping it onto the concrete sidewalk. Instead you pick it up, dust it off, and continue on your way. When you arrive at your desk, you toss the laptop down on the table and open it up. The screen immediately unfolds, spreading out into an enormous display! While this scenario sounds very futuristic, it actually isn't that far from reality, thanks in part to a concept design called a Fabric PC (personal computer), produced by Fujistu, Inc. Amazingly, a Fabric PC won't be encased within a tough metal shell like the PCs that have been around up to this point.|Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important. Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions. This paper presents a new method based on a deep learning model to evaluate these systems. The model is based on BERT, which is a language model that has been shown to work well in multiple NLP tasks. The aim is for the model to learn to perform an evaluation similar to that of a human. To do so, we use a dataset that contains human evaluations of system generated captions. These annotations will be made publicly available11footnotemark: 1. BERTHA obtain favourable results, outperforming the commonly used metrics in some setups. world news
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