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# A Package for Fast Evaluation Of Multivariate Polynomials

## FixedPolynomials

FixedPolynomials.jl is a library for really fast evaluation of multivariate polynomials. Here are the latest benchmark results.

Since `FixedPolynomials` polynomials are optimised for fast evaluation they are not suited for construction of polynomials. It is recommended to construct a polynomial with an implementation of MultivariatePolynomials.jl, e.g. DynamicPolynomials.jl, and to convert it then into a `FixedPolynomials.Polynomial` for further computations.

## Getting started

Here is an example on how to create a `Polynomial` with `Float64` coefficients:

``````using FixedPolynomials
import DynamicPolynomials: @polyvar

@polyvar x y z

f = Polynomial{Float64}(x^2+y^3*z-2x*y)
``````

To evaluate `f` you simply have to pass in a `Vector{Float64}`

``````x = rand(3)
f(x) # alternatively evaluate(f, x)
``````

But this is not the fastest way possible. In order to achieve the best performance we need to precompute some things and also preallocate intermediate storage. For this we have `GradientConfig` and `JacobianConfig`. For single polynomial the API is as follows

``````cfg = GradientConfig(f) # this can be reused!
f(x) == evaluate(f, x, cfg)
# We can also compute the gradient of f at x
map(g -> g(x), ∇f) == gradient(f, x, cfg)
``````

We also have support for systems of polynomials:

``````cfg = JacobianConfig([f, f]) # this can be reused!
[f(x), f(x)] == evaluate([f, f] x, cfg)
# We can also compute the jacobian of [f, f] at x
jacobian(f, x, cfg)``````

Author: JuliaAlgebra
Source Code: https://github.com/JuliaAlgebra/FixedPolynomials.jl

1666121220

## FixedPolynomials

FixedPolynomials.jl is a library for really fast evaluation of multivariate polynomials. Here are the latest benchmark results.

Since `FixedPolynomials` polynomials are optimised for fast evaluation they are not suited for construction of polynomials. It is recommended to construct a polynomial with an implementation of MultivariatePolynomials.jl, e.g. DynamicPolynomials.jl, and to convert it then into a `FixedPolynomials.Polynomial` for further computations.

## Getting started

Here is an example on how to create a `Polynomial` with `Float64` coefficients:

``````using FixedPolynomials
import DynamicPolynomials: @polyvar

@polyvar x y z

f = Polynomial{Float64}(x^2+y^3*z-2x*y)
``````

To evaluate `f` you simply have to pass in a `Vector{Float64}`

``````x = rand(3)
f(x) # alternatively evaluate(f, x)
``````

But this is not the fastest way possible. In order to achieve the best performance we need to precompute some things and also preallocate intermediate storage. For this we have `GradientConfig` and `JacobianConfig`. For single polynomial the API is as follows

``````cfg = GradientConfig(f) # this can be reused!
f(x) == evaluate(f, x, cfg)
# We can also compute the gradient of f at x
map(g -> g(x), ∇f) == gradient(f, x, cfg)
``````

We also have support for systems of polynomials:

``````cfg = JacobianConfig([f, f]) # this can be reused!
[f(x), f(x)] == evaluate([f, f] x, cfg)
# We can also compute the jacobian of [f, f] at x
jacobian(f, x, cfg)``````

Author: JuliaAlgebra
Source Code: https://github.com/JuliaAlgebra/FixedPolynomials.jl

1657356960

## Oyente

An Analysis Tool for Smart Contracts

This repository is currently maintained by Xiao Liang Yu (@yxliang01). If you encounter any bugs or usage issues, please feel free to create an issue on our issue tracker.

## Quick Start

A container with required dependencies configured can be found here. The image is however outdated. We are working on pushing the latest image to dockerhub for your convenience. If you experience any issue with this image, please try to build a new docker image by pulling this codebase before open an issue.

To open the container, install docker and run:

``````docker pull luongnguyen/oyente && docker run -i -t luongnguyen/oyente
``````

To evaluate the greeter contract inside the container, run:

``````cd /oyente/oyente && python oyente.py -s greeter.sol
``````

and you are done!

Note - If need the version of Oyente referred to in the paper, run the container from here

To run the web interface, execute `docker run -w /oyente/web -p 3000:3000 oyente:latest ./bin/rails server`

## Custom Docker image build

``````docker build -t oyente .
docker run -it -p 3000:3000 -e "OYENTE=/oyente/oyente" oyente:latest
``````

Open a web browser to `http://localhost:3000` for the graphical interface.

## Installation

Execute a python virtualenv

``````python -m virtualenv env
source env/bin/activate
``````

Install Oyente via pip:

``````\$ pip2 install oyente
``````

Dependencies:

The following require a Linux system to fufill. macOS instructions forthcoming.

## Full installation

### Install the following dependencies

#### solc

``````\$ sudo add-apt-repository ppa:ethereum/ethereum
\$ sudo apt-get update
\$ sudo apt-get install solc
``````

#### evm from go-ethereum

2. By from PPA if your using Ubuntu

#### z3 Theorem Prover version 4.5.0.

Install z3 using Python bindings

``````\$ python scripts/mk_make.py --python
\$ cd build
\$ make
\$ sudo make install
``````

#### Requests library

``````pip install requests
``````

#### web3 library

``````pip install web3
``````

### Evaluating Ethereum Contracts

``````#evaluate a local solidity contract
python oyente.py -s <contract filename>

#evaluate a local solidity with option -a to verify assertions in the contract
python oyente.py -a -s <contract filename>

#evaluate a local evm contract
python oyente.py -s <contract filename> -b

#evaluate a remote contract
python oyente.py -ru https://gist.githubusercontent.com/loiluu/d0eb34d473e421df12b38c12a7423a61/raw/2415b3fb782f5d286777e0bcebc57812ce3786da/puzzle.sol
``````

And that's it! Run `python oyente.py --help` for a list of options.

## Paper

The accompanying paper explaining the bugs detected by the tool can be found here.

## Miscellaneous Utilities

A collection of the utilities that were developed for the paper are in `misc_utils`. Use them at your own risk - they have mostly been disposable.

1. `generate-graphs.py` - Contains a number of functions to get statistics from contracts.
2. `get_source.py` - The get_contract_code function can be used to retrieve contract source from EtherScan
3. `transaction_scrape.py` - Contains functions to retrieve up-to-date transaction information for a particular contract.

## Benchmarks

Note: This is an improved version of the tool used for the paper. Benchmarks are not for direct comparison.

To run the benchmarks, it is best to use the docker container as it includes the blockchain snapshot necessary. In the container, run `batch_run.py` after activating the virtualenv. Results are in `results.json` once the benchmark completes.

The benchmarks take a long time and a lot of RAM in any but the largest of clusters, beware.

Some analytics regarding the number of contracts tested, number of contracts analysed etc. is collected when running this benchmark.

## Contributing

Checkout out our contribution guide and the code structure here.

``````\$ sudo apt-get install software-properties-common
\$ sudo apt-get update
\$ sudo apt-get install ethereum``````

Author: enzymefinance
Source Code: https://github.com/enzymefinance/oyente

#blockchain #smartcontract #ethereum

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## How to Find All the Classes of a Package in Java

#### In this article let’s take a look at how to find all classes of a package in Java

To find all classes of a package in Java we can use the ClassHunter of Burningwave Core library. So we start by adding the following dependency to our pom.xml:

XML

1

``````<dependency>2
<groupId>org.burningwave</groupId>3
<artifactId>core</artifactId>4
<version>8.4.0</version>5
</dependency>
``````

The next steps are the following:

• retrieving the ClassHunter through the ComponentContainer
• defining a regular expression that we must pass to the **ClassCriteria **object that will be injected into the **SearchConfig **object
• calling the **loadInCache **method that loads in the cache all loadable classes of the indicated paths, then applies the criteria filter and then returns the **SearchResult **object which contains the classes that match the criteria

#java #classes #class #packages #package #how to find all the classes of a package in java

1596709222

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## How to Install Pyenv on Ubuntu 18.04

What is Pyenv?
Pyenv is a fantastic tool for installing and managing multiple Python versions. It enables a developer to quickly gain access to newer versions of Python and keeps the system clean and free of unnecessary package bloat. It also offers the ability to quickly switch from one version of Python to another, as well as specify the version of Python a given project uses and can automatically switch to that version. This tutorial covers how to install pyenv on Ubuntu 18.04.

#tutorials #apt #debian #environment #git #github #linux #package #package management #package manager #personal package archive #ppa #pyenv #python #python 3 #python support #python-pip #repository #smb #software #source install #ubuntu #ubuntu 18.04 #venv #virtualenv #web application development