Real value of MAC models in Linux

I have read about MAC vs. DAC in the Internet, but I still fail to understand, what kind of attack it is impossible to protect against if one only uses DAC+capabilities in comparison to MAC+DAC+capabilities. If a process does not run as root and lacks CAP_DAC_OVERRIDE, CAP_FOWNER and other dangerous capabilities, it cannot overcome the already assigned ownership and ACL’s of the resources it uses. On the other hand, if a process runs as root and has CAP_MAC_ADMIN, it can overwrite the security context enforced by MAC.

So is MAC "just additional layer of protection" without any real advantage on modern Linux system?

Hidden Markov Models for Hand Gestures

I am completing a final year project for hand gesture recognition using Hidden Markov Models

I have a fair understanding of Hidden Markov Models and how they work using simple examples such as the Unfair Casino and some Weather examples.

I am looking to implement multiple Hidden markov models where each model corresponds to a single gesture, similarly to this paper where the observed states are the angles between the coordinates of different points. This would create a sequence of numbers from 0 to 18 as seen in Figure 3 and Figure 4. .

What would the hidden states be in terms of this scenario?

The weather example has the observations ‘Walk’, ‘Shop’ and ‘Clean’ which would be the numbers 0-18 in the hand gesture case, however I do not know what the states ‘Rainy’ and ‘Sunny’ would correspond to in the hand gesture scenario.

Terms for different models of sum types

There seem to be at least a couple different possible ways of modeling sum types in a type system, but I haven’t been able to find consistent terms for referring to them:

  1. A sum type is formed from a set of "data constructors", which are function-like entities that notionally map values of a summand type to values of the sum type. This is the model adopted by e.g. Haskell and the various flavors of ML.

  2. A sum type is formed directly from the underlying summand types, with no data constructors, and as a consequence the sum type is a supertype of the summands (or at least behaves very much like one). This model seems to be much less common, but it’s the model adopted by Ceylon, and by C++’s std::variant.

Note that this is separate from the distinction between discriminated and non-discriminated unions: both models permit the sum type to be discriminated (although only if the summands are disjoint, in the case of #2).

Are there settled terms for distinguishing these two models?

Exporting models from Crocotile3D to Unreal Engine 4 (.obj). Texures are always blurry. I want crisp, clean textures

Can anyone help me? I am exporting models with textures (.obj) from Crocotile3D to Unreal Engine 4. I want them to be crisp and clear, as in the left image (Crocotile editor), but only get blurry, as in the right image (Unreal editor). Whether I export the model at scale x 1 or greater doesn’t make any difference. My textures are 16×16 pixels.

I have watched endless videos and tinkered with settings but been stuck for two days. Any help or advice will be much appreciated!

Crocotile3D,, Unreal Editor, Crocotile Eport Settings

land-cover classification (matlab) (maximum likelihood) Gaussian models [closed]

Remotely sensed data are provided as 6 images showing an urban area, with the ground-truth information. These images have already been registered. You are required to implement the Maximum Likelihood (ML) algorithm to classify the given data into four classes, 1 – building; 2 – vegetation; 3 – car; 4 – ground. By doing so, each pixel in the images will be assigned a class. There are four objectives to achieve the final classification goal.

To select training samples from given source data based on information in the given ground truth (at least 20 training samples for each class)

To establish Gaussian models for each class with the training samples;

To apply maximum likelihood to the testing data (measured data) and classify each pixel into a class;

To evaluate the classification accuracy by using a confusion matrix and visual aid (colour coded figures).

Do the high-fashion armour clothings from Run & Gun that are tagged as ‘Newest Models’ lose armour rating over time?

Relevant rules snippets from Run & Gun, p. 59:

NEWEST MODEL

These items are the most recent incarnations of their corporate creators. That means they lose a little more when purchased as Lightly Worn, namely a 20 percent loss of Armor Rating (round adjusted Rating up) when buying older models of the clothes.

LIGHTLY WORN

The Lightly Worn option provides runners with a chance to buy some primo gear at a discount rate, with a few catches. Buying from the Lightly Worn section requires the character to have Armand as a contact with a Loyalty of at least 2. When gear is purchased Lightly Worn, the character gets a price discount of 25 percent, but they only get the Armor rating; they do not get any of the Features of the armor. The Lightly Worn feature can be bought off by having the piece of Armor refit. This requires an Armorer + Logic [Mental] (10, 1 hour) Extended Test and costs 10 percent of the original armor cost for each Feature the character is trying to have restored.

Do I need to refit a Newest Model every so often to avoid having it degrade into Lightly Worn and lose 20% of its armour rating? Or is Newest Model only meant to imply that purchasing the used version gets you an older model that isn’t as good, and applies the -20% armour rating in addition to Lightly Worn’s lack of armour features?

What could be the bound of the number of elements of a models of a given first order sentence?

Sorry for the weird title.

The Problem:

Consider the first-order logic sentence

φ≡∃s∃t∃u∀v∀w∀x∀yψ(s,t,u,v,w,x,y) where ψ(s,t,u,v,w,x,y,) is a quantifier-free first-order logic formula using only predicate symbols, and possibly equality, but no function symbols. Suppose φ has a model with a universe containing 7 elements.

Which one of the following statements is necessarily true?

  1. There exists at least one model of φ with universe of size less than or equal to 3
  2. There exists no model of φ with universe of size less than or equal to 3
  3. There exists no model of φ with universe size of greater than 7
  4. Every model of φ has a universe of size equal to 7

My attempt:
There exist at least one s,t, and u in some domain. It is also given that there exist a model with 7 elements. i.e there is at least one instance of v,w,x and y as well, together making 7 elements with s,t,u. So any other model of φ must have at least these 7 elements as well. Any model cannot have more than seven elements because there are only seven given. i.e every model of φ will have exactly seven elements. So Option 4 seems to be the right one.

I wish to develop intuition to solve these problems. Also I want to know your thought process and how you solve this problem. Thanks.