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sudo parted /dev/sdc --script mklabel gpt mkpart xfspart xfs 0% 100% | |
sudo mkfs.xfs /dev/sdc1 | |
sudo partprobe /dev/sdc1 | |
mkdir /home/azureuser/.injectived | |
sudo mount /dev/sdc1 /home/azureuser/.injectived | |
sudo chown -R azureuser:azureuser /home/azureuser/.injectived/ | |
sudo apt update | |
sudo apt install -y unzip wget |
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var publicKeyCredentialCreationOptions = { | |
challenge: Uint8Array.from( | |
"randomStringFromServer", c => c.charCodeAt(0)), | |
rp: { | |
name: "dev", | |
id: "developers.yubico.com", | |
}, | |
user: { | |
id: Uint8Array.from( | |
"UZSL85T9AFC", c => c.charCodeAt(0)), |
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import asyncio | |
import websockets | |
import hashlib | |
import hmac | |
import base64 | |
import time | |
import json | |
key = '' | |
secret = '' | |
host = 'wss://demo.dvchain.co/websocket' |
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:8001 { | |
proxy /graphql :8080 { | |
without /graphql | |
websocket | |
} | |
proxy / :3001 { | |
} | |
} |
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events { | |
worker_connections 1024; | |
} | |
http { | |
include /etc/nginx/mime.types; | |
types { | |
application/wasm wasm; | |
} |
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import { PolymerElement, html } from 'https://unpkg.com/@polymer/[email protected]/polymer-element.js'; | |
const styleElement = document.createElement('dom-module'); | |
styleElement.innerHTML = | |
`<template> | |
<style> | |
paper-button { | |
--paper-button: { | |
color : var(--theme-primary-text-color); | |
}; |
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SHOBJ_CFLAGS ?= -fno-common -g -ggdb | |
SHOBJ_LDFLAGS ?= -shared -Bsymbolic | |
CFLAGS = -Wall -g -fPIC -lc -lm -Og -std=gnu99 | |
CC=gcc | |
all: example.so | |
example.so: example.o | |
$(LD) -o $@ example.o $(SHOBJ_LDFLAGS) $(LIBS) -lc |
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04b219b7c8482b9652f3ba0124b4d245fe4eec64c40eece7d9362bff05d09cb243fed8a320d97518e09f4049206ec73f29aa8f8195fd23ac5b91204fcd8105d423 |
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#!/bin/bash | |
# You must accept the Oracle Binary Code License | |
# http://www.oracle.com/technetwork/java/javase/terms/license/index.html | |
# usage: get_jdk.sh <ext> <jdk_version> | |
# ext: rpm | |
# jdk_version: default 8 | |
ext=rpm | |
jdk_version=8 |
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#Actaul inference | |
square_sum = tf.reduce_sum(tf.square(x), axis=1, keep_dims=True) | |
sqrt = tf.sqrt(square_sum) | |
inv_sqrt = tf.rsqrt(square_sum) | |
normalized = tf.multiply(x, inv_sqrt) | |
infer_pred = BiRNN(normalized, weights, biases) | |
trend = tf.multiply(infer, sqrt) |
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