Tag: parameter

用 uBlock Origin 過濾 URL 裡面的 tracking parameter

在「ClearURLs – automatically remove tracking elements from URLs (github.com/clearurls)」這邊的討論裡面看到 gorhill (uBlock Origin 的作者) 的回文，裡面提到了 uBlock Origin 目前也有支援 removeparam 了，而且有對應的 filter list 在維護這個表格：

不過他也有提到 CleanURLs 可以清更多東西：

Addendum: to be clear, this is not a replacement for ClearURLs. ClearURLs has more capabilities then just removing query parameters from the URLs of outgoing network requests.

但這樣起來也不錯了 (尤其是對於只裝 uBlock Origin 的情況下)，可以訂起來...

AWS CodeBuild 可以管 Secret 了...

AWS CodeBuild 可以管理 secret 了：「AWS CodeBuild Now Provides Ability To Manage Secrets」。

AWS CodeBuild now further enhances securing your build environment. CodeBuild can now store sensitive information as secrets, which can now get directly passed to your build jobs. This can be achieved by modifying the parameter store directly in your buildspec.yml, or via the CodeBuild console.

在文件裡提到：

We strongly discourage using environment variables to store sensitive values, especially AWS access key IDs and secret access keys. Environment variables can be displayed in plain text using tools such as the AWS CodeBuild console and the AWS CLI. For sensitive values, we recommend you use the parameter-store mapping instead, as described later in this section.

這次算是補上其他家已經有蠻久的功能...

不過在找資料的時候，發現 AWS CodeBuild 提供了每個月一百分鐘的 free quota，不論是新帳號還是現有帳號都一直有？(這點是之前沒注意到的...)

The AWS CodeBuild free tier includes 100 build minutes of build.general1.small per month. The CodeBuild free tier does not expire automatically at the end of your 12-month AWS Free Tier term. It is available to new and existing AWS customers.

對於按讚數排名的方法

前幾天看到一篇 2009 年的老文章，在討論使用者透過「喜歡」以及「不喜歡」投票後，要怎麼排名的方法：「How Not To Sort By Average Rating」。

基本的概念是當使用者投票數愈多時就會愈準確，透過統計方法可以算一個信賴區間，再用區間的下限來排... 但沒想到公式「看起來」這麼複雜 XDDD

Score = Lower bound of Wilson score confidence interval for a Bernoulli parameter

但實際的運算其實沒那麼複雜，像是 Ruby 的程式碼可以看出大多都是系統內的運算就可以算出來。其中的 z 在大多數的情況下是常數。

require 'statistics2'

def ci_lower_bound(pos, n, confidence)
    if n == 0
        return 0
    end
    z = Statistics2.pnormaldist(1-(1-confidence)/2)
    phat = 1.0*pos/n
    (phat + z*z/(2*n) - z * Math.sqrt((phat*(1-phat)+z*z/(4*n))/n))/(1+z*z/n)
end

The z-score in this function never changes, so if you don't have a statistics package handy or if performance is an issue you can always hard-code a value here for z. (Use 1.96 for a confidence level of 0.95.)

作者後來在 2012 年與 2016 年也分別給了 SQL 以及 Excel 的範例程式碼出來，裡面 hard-code 了 95% 信賴區間的部份：

SELECT widget_id, ((positive + 1.9208) / (positive + negative) - 
                   1.96 * SQRT((positive * negative) / (positive + negative) + 0.9604) / 
                          (positive + negative)) / (1 + 3.8416 / (positive + negative)) 
       AS ci_lower_bound FROM widgets WHERE positive + negative > 0 
       ORDER BY ci_lower_bound DESC;
=IFERROR((([@[Up Votes]] + 1.9208) / ([@[Up Votes]] + [@[Down Votes]]) - 1.96 * 
    SQRT(([@[Up Votes]] *  [@[Down Votes]]) / ([@[Up Votes]] +  [@[Down Votes]]) + 0.9604) / 
    ([@[Up Votes]] +  [@[Down Votes]])) / (1 + 3.8416 / ([@[Up Votes]] +  [@[Down Votes]])),0)

而更多的說明在維基百科的「Binomial proportion confidence interval」可以翻到，裡面也有其他的方法可以用。