The first think to do is to make sure that the USB device is recognized by the operating system.

lsusb

Which produced a lot of listings, the most relevant one being:

Bus 001 Device 002: ID 1058:0748 Western Digital Technologies, Inc. My Passport (WDBKXH, WDBY8L)

Which is the drive I wanted to unencrypt and mount. The next thing is to find the file name (in this case I know the name of the device - "My Passport" - so I used grep, otherwise I'd use less).

sudo fdisk -l | grep "My Passport" -B 1

Which currently gives this:

Partition 2 does not start on physical sector boundary.
Disk /dev/sdb: 931.49 GiB, 1000170586112 bytes, 1953458176 sectors
Disk model: My Passport 0748

It might have looked a little different when I originally ran it since the drive is already mounted but whatever is in that second line is what we want.

That is the name we need for the drive, but we're going to mount a partition so you need to know the partition name. lsblk will show it to us.

lsblk -e7

Which gave me the output:

NAME                        MAJ:MIN RM   SIZE RO TYPE  MOUNTPOINT
sda                           8:0    0 931.5G  0 disk  
├─sda1                        8:1    0     1M  0 part  
├─sda2                        8:2    0     1G  0 part  /boot
└─sda3                        8:3    0 930.5G  0 part  
  └─dm_crypt-0              253:0    0 930.5G  0 crypt 
    └─ubuntu--vg-ubuntu--lv 253:1    0   200G  0 lvm   /
sdb                           8:16   0 931.5G  0 disk  
└─sdb1                        8:17   0 931.5G  0 part

Now you can see that the partion for our disk is sdb1 (the last row where it's shown to be a child of sdb and that its TYPE is a partition).

Note: This works, but there's an alternative way to do it with cryptsetup that I find a little easier (but not much). I documented that command as if it continued from this point in this post.

Next unlock the drive. When you do this it will create a file in /dev/mapper/ that you'll need so it would be a good idea to see what's there before you run it.

ls /dev/mapper/

And then do the decrypting, remembering that the partition is sdb1 and like our disk the file is in the /dev directory.

udisksctl unlock -b /dev/sdb1

This will bring up two prompts for you to fill out which are (confusingly) "Passphrase:" and "Password:". The first prompt ("Passphrase") is what you entered when the disk was encrypted so you need to enter whatever you normally enter to decrypt the disk. The second prompt ("Password:") is your admin password so that the program can run as root (assuming you have the right privileges).

If the last command went okay you now need to mount it. There's going to be a file in /dev/mapper that you need to know. When I did it there was only one new file (luks-3eea956c-e684-4bcb-a640-97d0c8c5a700) so I didn't have to do anything special to get it.

udisksctl mount -b /dev/mapper/luks-3eea956c-e684-4bcb-a640-97d0c8c5a700

If you run the command lsblk -e7 it will show you a tree with the /dev/mapper/ file mapped to the mount point where you can access it.

sdb                                             8:16   0 931.5G  0 disk  
└─sdb1                                          8:17   0 931.5G  0 part  
  └─luks-3eea956c-e684-4bcb-a640-97d0c8c5a700 253:3    0 931.5G  0 crypt /media/hades/WDData

So in this case the drive is accessible at /media/hades/WDData (it's always the same place but I wanted to document the lsblk -e7 command).

• sourcedigit.com - "How To List USB Devices On Ubuntu – Find USB Device Name On Linux Ubuntu"
• Stack Overflow - "Mount encrypted volumes from command line?"

First some terminology. We're going to assume that we want to label data as either being something or not being that thing. e.g. guilty or not guilty, duck or not a duck, etc. The label for things that are the thing is called Positive and the label for things that aren't the thing is Negative.

This is sometimes represented using a matrix.

Okay, I said we aren't going to use accuracy, but just to be complete… accuracy asks what fraction of the anwsers did you get correct?

\[ \textrm{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \]

This is probably what most of us are familiar with from being graded in school.

How much of what was predicted positive was really positive?

\[ \textrm{Precision} = \frac{TP}{TP+FP} \]

Since we have the count of false-positives in the denominator, your score will go down the more negatives you label positive (e.g. the more innocents you convict, the lower the score).

How many of the positives did you catch?

\[ \textrm{Recall} = \frac{TP}{TP + FN} \]

Here your score will go down the more positives you miss (the more guilty you find innocent).

So, in some cases you might want to favor Precision over Recall and vice-versa, but what if you don't really want one over the other? The F-Measure allows us to combine them into one metric.

\[ F_{\beta} = \frac{(\beta^2 + 1) Precision \times Recall}{\beta^2 Precision + Recall} \]

To make it simpler I'll just use P for precision and R for recall from here on.

\(\beta\) in the equation is a parameter that we can tune to favor precision or recall. If you'll notice, \(\beta\) in the numerator affects both precision and recall equally, while it only affects precision in the denominator, so the larger it is, the more precision diminishes the score.

If you look at the inequalities for the effects of \(\beta\) on the F-Measure you might notice that they don't include 1. That's because when \(\beta\) is 1 it doesn't favor either precision or recall, giving a case that combines both of them and treating them equally.

\[ F_1 = \frac{2PR}{P + R} \]

People often discount evidence that contradicts their firmly held beliefs.

However, little is known about the neural mechanisms that govern this behavior.

Paul R. Ehrlich predicted that population increases would cause scarcity and starvation and pushed for regulations to protect the environment and resources. Julian Simon predicted that technological innovation would solve the scarcity problem and clean up the environment. They placed a bet based on the price of five metals which they used as a proxy for resource scarcity. Ehrlich lost the bet but doubled-down on his predictions, saying the timing was off. Later studies showed that the price of metals didn't reflect scarcity and Simon just had the luck of the market. Ehrlich was wrong on scarcity but right on the need for regulation to protect the environment. Simon was right that technology would prevent the catastrophe Ehrlich predicted but couldn't concede that it was regulation and not technology that helped the environment.

Philip Tetlock set up a study where he collected the predictions of experts on political science for twenty years. At the end of it he found that they were generally horrible at forecasting and that even when shown their predictions, the experts wouldn't concede that they were wrong. The experts tended to make predictions based on their political identifications but one group took ideas from multiple camps and were more successful at making predictions. Tetlock named the two types of experts Hedgehogs and Foxes.

The more experienced Hedgehogs became the more they tended to use their added knowledge to bend reality to fit their view.

See this wikipedia page - the Hedgehog and the Fox - for the source of those terms.

Multa novit vulpes, verum echinus unum magnum - "a fox knows many things, but a hedgehog one important thing".

IARPA saw Tetlock's study and set up a prediction tournament to test expert teams. Tetlock entered using volunteers identified as Foxes and won. The Foxes tended to see teammates as sources of information to learn from, while Hedgehogs saw teammates as adversaries who needed to be convinced of their opinions. Both Foxes and Hedgehogs saw successes as reinforcing what they believed, but when information came up that conflicted with those beliefs the Foxes updated their positions while the Hedgehogs doubled-down on their prior beliefs instead.

In his paper on The Believing Game Peter Elbow proposes that in addition to the traditional "Doubting Game" (skeptical thinking) we should also engage in the "Believing Game" which involves understanding an argument by accepting it as true. He argues that while the skeptical, scientific, method (searching for flaws) is valuable it needs to be augmented by an accepting method (searching for virtues) as well. The Doubting Game has dominated because of its usefulness but it can lead us to nurture blind spots that are protected by our skepticism. By accepting the arguments of someone whose position we dislike we can, potentially find flaws in our own thinking.

An important point that Elbow makes is that there are two levels at which we have to look at arguments - the logic behind the arguments and the actual thing that is being argued for or against. It is possible to make a flawed argument for a good idea and a sound argument for a bad idea. The Doubting Game is a search for flaws in the argument while the Believing Game is a way to examine the underlying position that the argument is trying to make.

Within the sciences you can see many cases where adopting a believing mindset ultimately proved useful, leading to paradigm shifts - the switch from the Earth as the center of the cosmos to having the Earth orbit the Sun, miasma theory switching to germ theory, and the acceptance of Plate Tectonics, among other examples - but Elbow says that it is also helpful to play the Believing Game even if you ultimately don't accept the underlying premise, because without the suspension of disbelief you won't fully understand what's being proposed and ultimately "They may seem wrong or crazy–they may be wrong or crazy–but nevertheless they may still be able to see something that none of us can see."