It's transparent to the client, so if the server adds support, every client gains 2FA support. The server needs to check if 2FA is enabled, and if so, try the last 6 characters of the user provided text as the OTP and the rest as the password.
It's a pretty commonly used, and works very well, but requires user education on how to fill in their combined password. A proper API with distinct fields for password and OTP is cleaner, but requires protocol support.
Isn't that horrible UI-wise? The UI will ask for password and show '******'. The user then has to remove the last 6 stars and put in the OTP.
A dedicated question for the OTP would be much better. Also, the password manager would know to not save the password+OTP every time as a new password.
It is, but think of why you'd build this. You own the backend and need to add 2FA support. The various client software isn't written by you so you can't change them. This approach allows the client software to add an OTP field (concat the fields for the user) but doesn't require it (user must concat OTP on password manually).
Many of the places I've seen this used don't integrate well with software password managers. OS login screen, console apps, etc; typically not web apps. But this is a good criticism.
> Of course, this won’t be anywhere near as efficient as just implementing the decoder in C
> It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.
Then why are you talking about this instead of extension modules?
> quiet preloading of an attacker-selected state while the machine is off.
That attacker with physical access can chose from multiple other attacks, so why bother making this one more secure. They can sniff keyboard strokes, install malware into the UEFI, etc. pp.
> To give you an example, fsck.ext4 will happily carve a working filesystem out of random data
What application does this have for recovering user data? Would it not be more interesting to see how much of the user data can be recovered? Or are you implying that the random data can symbolize the user data here and much of it would be recovered?
You'd think Norton, McAfee et al would be interested in selling this kind of a product. Not that I trust them much nowadays, but it seems like an obvious opportunity that fits well with their other business.
1. Rule of probability: The concept of statistics is being used to predict outcomes based on previous observations or experiences, such as the likelihood of an object falling when released.
2. Rule of inherent knowledge: Some understanding or knowledge, like objects falling when not supported, can be known without explicit study or exposure to the specific terminology (e.g., gravity).
3. Rule of generalization: Observations or experiences with one type of object (e.g., a smartphone) can be generalized to other objects or situations, as long as they share similar characteristics (e.g., not being supported).
4. Rule of causality: There is an implied cause-and-effect relationship between an action (releasing the grip) and an outcome (the smartphone falling to the floor).
5. Rule of experiential learning: Knowledge can be gained through direct experiences, even if the specific terms or scientific concepts are not known.
A missing part that AI needs is a experimental way to verify its results. In some contexts, like math, this is entirely possible because it does not involve manipulation of real world objects. For example, i asked GTP-4, In the context of optimization, does the generalized assignment problem satisfy total unimodularity. Then experimental way would be to look up the formulas of the integer program on the internet or in its training data, write down the constraint matrix of the given problem, fill it with values, and check if the matrix is unimodular. Instead, the way gpt-4 answered this question, correctly, albeit with a weak reasoning, is by saying that researchers worked on heuristics to solve the problem, thus it is probably not unimodular as that would allow a quick solution without resorting to heuristics.
This is the answer I got. with this system prompt "You are an intelligent PhD processor explaining topics to your PhD students."
In the context of optimization, does the generalized assignment problem satisfy total unimodularity?
Assistant:
The generalized assignment problem (GAP) is an NP-hard optimization problem that can be formulated as an integer linear program. The problem involves assigning a set of tasks to a set of agents, subject to capacity constraints, with the goal of maximizing the total profit or minimizing the total cost. The integer linear programming formulation of GAP is as follows:
Objective function:
Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij
Subject to:
1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent)
2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent)
3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables)
Total unimodularity (TU) is a property of a matrix that guarantees that the linear programming relaxation of an integer linear program will result in integer optimal solutions. If the constraint matrix is totally unimodular, you can solve the linear programming relaxation and obtain an integer solution without using specialized integer programming algorithms.
In the case of the generalized assignment problem, the constraint matrix does not satisfy
I prompted it some more to see if it got any better. I also increased the number of tokens so it could have time to answer. I cut it off before.
USER
In the context of optimization, does the generalized assignment problem satisfy total unimodularity?
Assistant:
The generalized assignment problem (GAP) is an NP-hard optimization problem that can be formulated as an integer linear program. The problem involves assigning a set of tasks to a set of agents, subject to capacity constraints, with the goal of maximizing the total profit or minimizing the total cost. The integer linear programming formulation of GAP is as follows:
Objective function:
Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij
Subject to:
1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent)
2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent)
3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables)
Total unimodularity (TU) is a property of a matrix that guarantees that the linear programming relaxation of an integer linear program will result in integer optimal solutions. If the constraint matrix is totally unimodular, you can solve the linear programming relaxation and obtain an integer solution without using specialized integer programming algorithms.
ASSISTANT
what are some weaknesses to the this argument?
ASSISTANT
In the context of the original answer, the main weakness is that it does not directly answer the question of whether the generalized assignment problem (GAP) satisfies total unimodularity. The answer provides a description of GAP and the concept of total unimodularity, but it does not provide a conclusion on whether the constraint matrix of GAP is totally unimodular or not.
Another weakness is that the answer does not provide any evidence or examples to support the conclusion. To strengthen the argument, one could analyze the constraint matrix of GAP and determine whether it satisfies the conditions for total unimodularity, such as all subdeterminants being 0, 1, or -1. Additionally, providing examples or counterexamples of instances where GAP exhibits total unimodularity or not would help to clarify the relationship between the problem and this property.
In general, the answer could be improved by directly addressing the question and providing evidence or reasoning to support the conclusion.
USER
Please craft a better argument.
ASSISTANT
In order to determine whether the generalized assignment problem (GAP) satisfies total unimodularity, we need to analyze its constraint matrix and check if it satisfies the conditions for total unimodularity.
Recall the integer linear programming formulation of GAP:
Objective function:
Maximize ∑_(i=1)^m ∑_(j=1)^n c_ij * x_ij
Subject to:
1. ∑_(j=1)^n a_ij * x_ij ≤ b_i, for all i (capacity constraints for each agent)
2. ∑_(i=1)^m x_ij = 1, for all j (each task must be assigned to exactly one agent)
3. x_ij ∈ {0, 1}, for all i and j (binary assignment variables)
The matrix A is not totally unimodular. To see why, consider a submatrix formed by the first m rows and n columns:
[ a_11 a_12 ... a_1n ]
[ a_21 a_22 ... a_2n ]
A' = [ ... ... ... ... ]
[ a_m1 a_m2 ... a_mn ]
The determinant of A' can be an arbitrary integer, depending on the values of a_ij. Therefore, the condition for total unimodularity, which requires all subdeterminants to be 0, 1, or -1, is not satisfied.
This implies that the constraint matrix of GAP does not satisfy total unimodularity, and as a result, the linear programming relaxation of GAP does not guarantee integer optimal solutions. Solving GAP requires specialized integer programming algorithms or approximation algorithms to obtain optimal or near-optimal solutions.
42^3 = 138 mod 493
It should read
42^3 mod 493 = 138