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The Electrum development team also warned them that they had nothing to do with a project called Electrum Dark: they used our name without our permission. Be careful with the altcoin version of Electrum, as they are sometimes used as vectors to install malware against your real Bitcoin wallet.

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On December 27, Reddit user u/normal_rc reported that Electrum's wallet had been hacked and that nearly 250 bitcoins (243.6 BTCs, nearly $1 million) had been maliciously stolen, coinelegraph reported. Electrum then confirmed that the attack included creating a fake version of the wallet to trick users into providing password information. Electrum responded on Twitter that "this is a persistent phishing attack on Electrum users" and warned users not to download Electrum from any source other than the official website.

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It's not hard to run your own Electrum server and point your wallet to just use it. This restores Electrum to the point where it has the same privacy and security attributes as the full node, where no one else can see the address or transaction that the wallet is interested in. Electrum then becomes an all-node wallet.

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The light cone LCC currency did not rise or fall, and by the end of December, prices had nearly doubled. On December 27th, Wang Jingjing sold 3,500 light cones of LCC coins to Qunyou for 11.15 yuan each. Count, excluding 10% of the handling fee, less than a month, Wang Jingjing earned about 6000 yuan.

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Word2vec is a way to convert words into embedded vectors. Similar words should have similar embeddings. Word2vec trains with a skip-gram neural network with only one hidden layer. The goal of training is to predict adjacent words of the current word in a sentence. Because such tasks only occur during the training phase, skip-gram context word prediction is also known as pseudo-tasks. The input of the network is a word that maximizes the probability of the word being adjacent to the word in the sentence by optimization. The following image shows this task, marked with green input words, which are predicted over the network before and after the two words. With this training, two words with similar meanings are likely to have similar neighborhood words, resulting in similar embedded meanings.