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This six-part documentary series tells the history of Industrial Light & Magic. It features interviews with famous filmmakers and the people behind George Lucas’ esteemed visual effects studio, and explores how they created some of the most memorable movie scenes of all time. Arrives on Disney+ 7.27.22.
The Awesomer
July 1st, 2022
CipherSweet is a backend library developed by Paragon Initiative Enterprises for implementing searchable field-level encryption. It can encrypt and decrypt values in a very secure way. It is also able to create blind indexes. A blind index can be used to perform some targeted searches on the encrypted data. The indexes themselves are unreadable by humans.
We’ve just released laravel-ciphersweet. This package is a wrapper over CipherSweet, which allows you to easily use it with Laravel’s Eloquent models.
Add the CipherSweetEncrypted interface and UsesCipherSweet trait to the model that you want to add encrypted fields to.
You’ll need to implement the configureCipherSweet method to configure CipherSweet.
use Spatie\LaravelCipherSweet\Contracts\CipherSweetEncrypted;
use Spatie\LaravelCipherSweet\Concerns\UsesCipherSweet;
use ParagonIE\CipherSweet\EncryptedRow;
use Illuminate\Database\Eloquent\Model;
class User extends Model implements CipherSweetEncrypted
{
use UsesCipherSweet;
public static function configureCipherSweet(EncryptedRow $encryptedRow): void
{
$encryptedRow
->addField('email')
->addBlindIndex('email', new BlindIndex('email_index'));
}
}
We’ve also added a small helper command to the package that allows you to generate a new key in a way that is suggested by Paragon Initiative Enterprises.
This encryption key is used to encrypt your values.
php artisan ciphersweet:generate-key
Once everything is set up, you can start encrypting your model values:
php artisan ciphersweet:encrypt <your-model-class> <generated-key>
The command will update all the encrypted fields and blind indexes of the model.
If you have a lot of rows, this process can take a long time since encryption is a resource intensive operation. Don’t worry if it times out for some reason, the command is always restartable and only tries to encrypt models when it’s needed.
After the fields have been encrypted, you should add the generated CipherSweet key to your .env file.
CIPHERSWEET_KEY=<YOUR-KEY>
The key will be used by your application to read encrypted values.
Even though values are encrypted, you can still search them using a blind index. The blind indexes will have been built up when you ran the command to encrypt the model values.
Our package provides a whereBlind and orWhereBlind scope to search on blind indexes.
The first parameter is the column, the second the index name you set up when calling ->addBlindIndex, the third is the raw value, the package will automatically apply any transformations and hash the value to search on the blind index.
CipherSweet is a very powerful library, and our package makes it very approachable. Of course, be very sure about what columns actually need encrypting, and don’t overdo it, encryption is resource intensive and comes with some downsides.
We’ll be using this package sparingly in the development of our upcoming SaaS mailcoach.cloud to protect the personal information that will be stored by our users.
Of course, laravel-ciphersweet isn’t the first package that our team has built. On our company website, check out all of our open source packages in this long list. If you want to support us, consider picking up any of our paid products.
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Mega Process takes us on a tour of Zion Industrial Co., which apparently is the last shovel factory in South Korea. The laborious process starts with freshly-cut oak trees, milling the timber into boards, sanding until round, trimming, then splitting, steaming, and bending the wood to hold the shape for its handle.
The Awesomer
https://theawesomer.com/photos/2022/07/how_shovels_are_made_t.jpg
Mega Process takes us on a tour of Zion Industrial Co., which apparently is the last shovel factory in South Korea. The laborious process starts with freshly-cut oak trees, milling the timber into boards, sanding until round, trimming, then splitting, steaming, and bending the wood to hold the shape for its handle.
The Awesomer
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U.S.A. –-(AmmoLand.com)-— Former Japanese Prime Minister Shinzo Abe was assassinated with a homemade gun on July 8th, 2022, in the Japanese city of Nara.
From video of the event, it appears the assassin fired his weapon at Abe from a distance of 20 to 30 feet. The two distinct shots were fired about a second apart and produced copious amounts of white smoke.
Early reports were of wounds to the neck and chest. It is unknown what sort of projectiles were used in the homemade weapon.
This correspondent made several similar homemade, electrically actuated, multishot, muzzleloading black powder firearms 40 years ago. Electrical firing mechanisms are much easier to make than percussion systems used in modern firearms. A simple switch is a trigger for each barrel. Batteries supply the power to ignite each charge. If you want multiple shots, multiple barrels are used. A nine-volt battery worked well in my 40-year-old experiments, as did a four-pack of AA batteries.
A simple galvanized water pipe is plenty strong to withstand black powder pressures for a few shots. The firearm used in the assassination appears to be close to a 12 gauge bore size. Larger bores and projectiles create sufficient energies at the lower velocities on such firearms.
The propellants for such guns are easily made at home. Many high school students successfully made black powder in my youth. Fireworks can be disassembled as another source of propellant. It has been reported that people in prisons and the former Soviet Union used ground-up match heads as a propellant. Making propellant is much more dangerous than making the guns.
This correspondent disagrees with professor Daniel Foote of Tokyo.
“This actually shows the extent that Japan gun laws are working,” said Daniel Foote, a professor at the University of Tokyo specializing in law and society. “Very few people have the ability to create such a weapon.”
It is unlikely the professor has ever done the experimental work to make such weapons. Forty years ago, this correspondent was able to make a four-shot repeater from 12 dollars of materials obtained from a hardware store and Radio Shack, using about 12 hours’ worth of labor. In order to be effective with such weapons, testing and practice is necessary. It appears the former Naval officer did the preparation required.
Police found several possible explosives during a raid on Yamagami’s home, and the suspect confessed that he had “manufactured multiple pistols and explosives so far,” NHK said.
Other local reports said that he told cops he initially planned to blow up Abe, but decided to instead build a gun because he feared a bomb was less reliable.
Some sources are saying the assassination occurred at 10 feet. In the video referenced above, the distance appears to be 20 to 30 feet. There did not appear to be any sights on the homemade firearm. Today, some sort of laser pointer would be easy to install and bore sight. But NO mention of a laser has been made to this correspondent’s knowledge.
The successful assassination of former Japanese PM Abe, with a homemade firearm in a country with some of the most extreme firearms restrictions on the planet, shows how difficult it is to regulate what is essentially a 15th-century technology.
About Dean Weingarten:
Dean Weingarten has been a peace officer, a military officer, was on the University of Wisconsin Pistol Team for four years, and was first certified to teach firearms safety in 1973. He taught the Arizona concealed carry course for fifteen years until the goal of Constitutional Carry was attained. He has degrees in meteorology and mining engineering, and retired from the Department of Defense after a 30 year career in Army Research, Development, Testing, and Evaluation.
AmmoLand Shooting Sports News
In response to numerous inquiries from local law enforcement departments, the FBI undertook a comprehensive evaluation of the sidearms available in 1987 (most of which are still made today) to determine which pistol was the best. Thirteen of the most talented instructors that the Federal Bureau of Investigation had to offer all met at a […]
The post The FBI Said This is the Best Handgun (And Why They’re Wrong) appeared first on The Firearm Blog.
The Firearm Blog
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Laravel Ciphersweet is a package by Spatie to integrate searchable field-level encryption in Laravel applications. The package’s readme explains the problem Ciphersweet can help solve as follows:
In your project, you might store sensitive personal data in your database. Should an unauthorised person get access to your DB, all sensitive can be read which is obviously not good.
To solve this problem, you can encrypt the personal data. This way, unauthorized persons cannot read it, but your application can still decrypt it when you need to display or work with the data.
This package is a wrapper for Ciphersweet to integrate its features into Laravel models easily. Here’s an example of a model from the readme’s setup instructions that illustrates what a model looks like using Ciphersweet:
1use Spatie\LaravelCipherSweet\Contracts\CipherSweetEncrypted;
2use Spatie\LaravelCipherSweet\Concerns\UsesCipherSweet;
3use ParagonIE\CipherSweet\EncryptedRow;
4use Illuminate\Database\Eloquent\Model;
5
6class User extends Model implements CipherSweetEncrypted
7{
8 use UsesCipherSweet;
9
10 public static function configureCipherSweet(EncryptedRow $encryptedRow): void
11 {
12 $encryptedRow
13 ->addField('email')
14 ->addBlindIndex('email', new BlindIndex('email_index'));
15 }
16}
This allows you the encrypt a user’s email to keep it safe from unauthorized people reading the data, but give you the ability to decrypt the data to display it or work with it.
Once you have configured this package and set up a model, you can search encrypted data in the database using blind indexes:
1$user = User::whereBlind('email', 'email_index', 'rias@spatie.be');
This package also aids in generating encrypting keys and encrypting model attributes to speed up integration with Ciphersweet.
I want to point out that you should not use this package blindly without understanding the ins and outs of the use case you are trying to solve. You can learn more about CipherSweet on this page, which has many linked resources.
CipherSweet also has PHP-specific documentation to help get you up to speed with the underlying PHP package.
I would also recommend reading Rias’ post, Encrypting Laravel Eloquent models with CipherSweet.
To get started with this package, check it out on GitHub at spatie/laravel-ciphersweet.
Laravel News
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Do you have any idea what this crazy thing is?
So that’s a vehicle drainage rack (!) made by Seda, an Austrian company that makes machines used to dismantle automobiles. You’ve probably seen a video of a car being crushed. What you may not have realized is that prior to crushing it, the cars must be drained of all fluids (gas, oil, brake fluid, radiator fluid, etc.). Dismantling technicians cut into hoses, then the vehicle is lifted and tilted by the rack to drain every last drop of toxic goodness.
I wanted to find video of this in action, and got more than that: Here Seda demonstrates, in about 90 seconds, how to dismantle and recycle an entire car using their machines, including the draining rack:
Seeing the wiring alone was staggering. I’d heard that modern cars have nearly one mile of wiring in them, but seeing it all pulled out and placed in that bin was really something.
Core77
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To analyze a dataset, you first need to understand the data. Sometimes, you might have no forehand knowledge of a dataset, preventing you from getting the most out of it. As a data analyst, you can use Exploratory data analysis (EDA) to gain knowledge of your dataset before in-depth analysis.
Exploratory data analysis (EDA) investigates a dataset to gain meaningful insights. The process of performing EDA involves querying information about the structure and contents of a dataset.
The Gota package is the most popular for data analysis in Go; it’s like the Python Pandas package but for Go. The Gota package contains many methods for analyzing datasets and reading JSON, CSV, and HTML formats.
Run this command on your terminal in the directory where you have initialized a Go module file:
go get -u github.com/go-gota/gota
The command will install Gota in the local directory, ready for you to import the package to use it.
Just like Pandas, Gota supports series and dataframes operations. There are two sub-packages in the Gota package: the series, and the dataframe package. You can import either one or both, depending on your needs.
import (
"github.com/go-gota/gota/series"
"github.com/go-gota/gota/dataframe"
)
You can use any CSV file you like, but the following examples show results from a Kaggle dataset, containing laptop price data.
Gota lets you read CSV, JSON, and HTML file formats to create dataframes using the ReadCSV, ReadJSON, and ReadHTML methods. Here’s how you load a CSV file into a dataframe object:
file, err := os.Open("/path/to/csv-file.csv")
if err != nil {
fmt.Println("file open error")
}
dataFrame := dataframe.ReadCSV(file)
fmt.Println(dataFrame)
You can use the Open method of the os package to open a CSV file. The ReadCSV method reads the file object and returns a dataframe object.
When you print this object, the output is in a tabular format. You can further manipulate the dataframe object using the various methods Gota provides.
The object will only print some of the columns if a dataset has more than a set value.
The dimensions of a dataframe are the number of rows and columns it contains. You can fetch these dimensions using the Dims method of the dataframe object.
var rows, columns = dataFrame.Dims()
Replace one of the variables with an underscore to fetch the other dimension only. You can also query the number of rows and columns individually, using the Nrow and Ncol methods.
var rows = dataFrame.Nrow()
var columns = dataFrame.Ncol()
You’ll need to know the composite data types in a dataset’s columns to analyze it. You can fetch these using the Types method of your dataframe object:
var types = dataFrame.Types()
fmt.Println(types)
The Types method returns a slice containing the column’s data types:
You’ll need the column names to select specific columns for operations. You can use the Names method to fetch them.
var columnNames := dataFrame.Names()
fmt.Println(columnNames)The Names method returns a slice of the column names.
You might have a dataset that contains null or non-numeric values. You can check for such values using the HasNaN and IsNaN methods of a series object:
aCol := dataFrame.Col("display_size")
var hasNull = aCol.HasNaN()
var isNotNumber = aCol.IsNaN()
HasNan checks if a column contains null elements. IsNaN returns a slice of booleans representing whether each value in the column is a number.
Descriptive statistical analysis helps you understand the distribution of numerical columns. Using the Describe method, you can generate a descriptive statistical analysis of your dataset:
description := dataFrame.Describe()
fmt.Println(description)
The Describe method returns metrics like the mean, standard deviation, and maximum values of columns in a dataset. It summarizes these in a tabular format.
You can also be specific and focus on columns and metrics by selecting a particular column, then querying for the metric you want. You should first fetch the series representing a specific column, then use its methods like so:
aCol := dataFrame.Col("display_size")
var mean = aCol.Mean()
var median = aCol.Median()
var minimum = aCol.Min()
var standardDeviation = aCol.StdDev()
var maximum = aCol.Max()
var quantiles25 = aCol.Quantile(25.0)
These methods mirror the results from the descriptive statistical analysis that Describe performs.
One of the final tasks you’ll want to perform is to check the values in a column for a general overview. You can use the Records method to view the values of a column.
aCol := dataFrame.Col("brand")
fmt.Println(aCol.Records())
This method returns a slice of strings containing the values in your selected column:
If you choose to go further and use the Gota package for full data analysis, you’ll need to save data in files. You can use the WriteCSV and WriteJSON methods of dataframe to export files. The methods take in a file that you’ll create using the os package’s Create method.
Here’s how you can export a dataframe using the Gota package.
dataFrame := dataframe.ReadCSV(file)
outputFile, err := os.Create("output.csv")
if err != nil {
log.Fatal(err)
}
err = dataFrame.WriteCSV(outputFile)
if err != nil {
log.Fatalln("There was an error writing the dataframe contents to the file")
}
The dataFrame variable is a representation of the dataframe. When you use the Create method of the os package, it creates a new, empty file with the specified name and returns the file. The WriteCSV method takes in the file instance and returns an error or nil if there’s no error.
An understanding of data and datasets is essential for data analysts and machine learning specialists. It is a critical operation in their work cycle, and exploratory data analysis is one of the techniques they use to achieve that.
There’s more to the Gota package. You can use it for various data wrangling functions in the same way that you’d use the Python Pandas library for data analysis. However, Gota doesn’t support quite as much functionality as Pandas.
MUO – Feed
http://img.youtube.com/vi/F2VG3H1IR60/0.jpgIf your DB structure seems quite large and unpredictable, maybe it’s worth storing the data as a JSON column? Let’s take a look at a few examples.Laravel News Links