Paranoid project checks for well known weaknesses on cryptographic artifacts such as public keys, digital signatures and general pseudorandom numbers. This library contains implementations and optimizations of existing work found in the literature. The existing work showed that the generation of these artifacts was flawed in some cases. The following are some examples of publications the library is based on.
The goal is to increase the confidence in cryptography use cases inside and outside Google.
When dealing with asymmetric encryption, crypto artifacts usually are:
* Generated by one of our own tools (e.g., at Google we use for example boringssl or tink); or,
* Generated by third party tools that we have access to (so these tools can be, for example, checked for vulnerabilities using wycheproof); or,
* Generated by third party tools and/or hardware or software black boxes that we do not have access to.
With Paranoid, any cryptographic artifact can be tested, but its primary motivation is to detect the usage of weak third party hardware or software black boxes. Hence, Paranoid can be used even if we are not able to inspect the source code (situation 3. listed above).
The project aims to detect known vulnerabilities as well as unknown ones. E.g., it tries to identify vulnerabilities caused by programming errors or the use of weak proprietary random number generators. Detecting new vulnerabilities is of course much more difficult than detecting known ones. Such detections may require large sets of artifacts or find weak ones only with a low probability.
> The Constructive Cost Model (COCOMO) is a procedural software cost estimation model developed by Barry W. Boehm. The model parameters are derived from fitting a regression formula using data from historical projects.
> Intermediate COCOMO computes software development effort as function of program size and a set of "cost drivers" that include subjective assessment of product, hardware, personnel and project attributes. This extension considers a set of four "cost drivers", each with a number of subsidiary attributes.
The biggest difference from other backend solutions like Firebase, Supabase, Nhost, etc., is that *PocketBase actually could be used as a Go framework that enables you to build your own custom app specific business logic and still have a single portable executable at the end.*
Dr. Yan Wong, an evolutionary geneticist at the Big Data Institute, and one of the principal authors, explained: “We have basically built a huge family tree, a genealogy for all of humanity that models as exactly as we can the history that generated all the genetic variation we find in humans today. This genealogy allows us to see how every person’s genetic sequence relates to every other, along all the points of the genome.”
Since individual genomic regions are only inherited from one parent, either the mother or the father, the ancestry of each point on the genome can be thought of as a tree. The set of trees, known as a “tree sequence” or “ancestral recombination graph,” links genetic regions back through time to ancestors where the genetic variation first appeared.
Lead author Dr. Anthony Wilder Wohns, who undertook the research as part of his PhD at the Big Data Institute and is now a postdoctoral researcher at the Broad Institute of MIT and Harvard, said: “Essentially, we are reconstructing the genomes of our ancestors and using them to form a vast network of relationships. We can then estimate when and where these ancestors lived. The power of our approach is that it makes very few assumptions about the underlying data and can also include both modern and ancient DNA samples.”
The study integrated data on modern and ancient human genomes from eight different databases and included a total of 3,609 individual genome sequences from 215 populations. The ancient genomes included samples found across the world with ages ranging from 1,000s to over 100,000 years. The algorithms predicted where common ancestors must be present in the evolutionary trees to explain the patterns of genetic variation. The resulting network contained almost 27 million ancestors.
After adding location data on these sample genomes, the authors used the network to estimate where the predicted common ancestors had lived. The results successfully recaptured key events in human evolutionary history, including the migration out of Africa.
> Called with no arguments, `:Git` opens a summary window with dirty files and unpushed and unpulled commits. Press `g?` to bring up a list of maps for numerous operations including diffing, staging, committing, rebasing, and stashing. (This is the successor to the old `:Gstatus`.) [1]
To open file in split: <C-w>f
2) fugitive works with it too. See this thread [2]
3) I use fzf [3] too. My pattern is a bit shorter: '\r' (mnemonic from '' for search the word under cursor). With the mapping and command:
```
nnoremap <silent> <Leader>r* :Rgw <C-R><C-W><CR> "" <C-R><C-W> - to paste word under cursor in command prompt
https://hn.algolia.com/?q=firefox%20address%20bar