1596806280
Control flow basics with Julia
Let’s continue our exploration of Julia basics. Previously I talked about for loops and vectorization. Here, we will talk about how to use control flow operators inside Julia.
What are control flow operators?
As the name suggests control flow operators help us shape the flow of the program. You can return from a function, you can break from a loop, you can skip an iteration of the loop with continue.
A simple task
To understand these concepts, we’ll attempt to solve a problem. Nothing better than some hands-on experience, right? Our challenge is as follows:
Given 2 integers (a, b) print the smallest (up to) 5 integers between a and b such that we’re not printing numbers divisible by 3.
For example if a=5 and b=23 we should print the following numbers:
5
7
8
10
11
If a and b are closer to each other we print everything up to b. Here’s another example with a=2 and b=4:
2
4
If you’re a complete newcomer to programming, you might want to check out my FizzBuzz article [LINK] where I explain for loops and the modulo function.
First, we print
In the spirit of doing this step by step, let’s build up our function:
All this does is print every number between a and b inclusive.
Then, we continue
#programming #python #data-science #code #julia
What is GEEK
Buddha Community
1621251999
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1593396660
Intel Adds Anti-Malware Protection in Tiger Lake CPUs
Intel’s Tiger Lake CPUs will come with Control-flow Enforcement Technology (CET), aimed at battling common control-flow hijacking attacks.
Intel’s upcoming class of mobile CPUs, code named “Tiger Lake,” will feature a long anticipated security layer, called Control-flow Enforcement Technology (CET), which aims to protect against common malware attacks.
CET protects against attacks on processors’ control flow, which refers to the order in which different functions calls are executed. Previously, attackers have targeted control flow in attacks where they hijack the processes and modify the instructions. This could potentially allow them to execute arbitrary code on victims’ systems.
“Intel CET delivers CPU-level security capabilities to help protect against common malware attack methods that have been a challenge to mitigate with software alone,” said Tom Garrison, vice president and general manager of Client Security Strategy and Initiatives with Intel, in a Monday post. “These types of attack methods are part of a class of malware referred to as memory-safety issues, and include tactics such as the corruption of stack buffer overflow and use-after-free.”
Intel’s upcoming Tiger Lake CPUs (which were first announced in January) are the first to come equipped with Intel CET, which will battle control-flow hijacking attacks by adding two types of protection.
The first is Indirect Branch Tracking (IBT), which defends against attacks called call-oriented programming or jump-oriented programming (COP and JOP). These types of code-reuse attacks occur when short code sequences that end in specific call and jump instructions are located and chained into a specific order, in order to execute attackers’ payloads. IBT prevents this by creating a new instruction, ENDBRANCH, which tracks all indirect call and jump instructions to detect any control-flow violations.
The second protection is shadow stack (SS). Shadow stack helps to defend against return-oriented programming (ROP) attacks. These types of attacks center around return instructions in a control flow, which are intended to fetch the address of the next instruction from the stack, and execute instructions from that address. In ROP attacks, an attacker abuses these return instructions to stitch together a malicious code flow.
Shadow stack (separate from the data stack) prevents this by adding return address protection. When shadow stacks are enabled, the CALL instruction on a processor pushes the return address on both the data stack and shadow stack and make sure that they match.
“JOP or ROP attacks can be particularly hard to detect or prevent because the attacker uses existing code running from executable memory in a creative way to change program behavior,” Baiju Patel, fellow with Intel’s client computing group, said. “What makes it hard to detect or prevent ROP/JOP is the fact that attacker uses existing code running from executable memory. Many software-based detection and prevention techniques have been developed and deployed with limited success.”
Intel published the first specification of CET in 2016. Various software makers have added support for the technology into their products, including Microsoft in its Hardware-enforced Stack Protection for Windows.
While CET is launching for Intel’s mobile lineup, the technology will soon be available on desktop and server platforms, according to Garrison. The chip giant is now preparing for volume production of its Tiger Lake chipset, and expects to being shipping the processors to OEMs mid-year.
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#malware #mobile security #anti-malware #buffer overflow #cet #chip level security #control flow #control flow enforcement technology #control flow hijacking #cop #cpu #hardware #intel #intel tiger lake #jop #rop
1596806280
Control flow basics with Julia
Let’s continue our exploration of Julia basics. Previously I talked about for loops and vectorization. Here, we will talk about how to use control flow operators inside Julia.
What are control flow operators?
As the name suggests control flow operators help us shape the flow of the program. You can return from a function, you can break from a loop, you can skip an iteration of the loop with continue.
A simple task
To understand these concepts, we’ll attempt to solve a problem. Nothing better than some hands-on experience, right? Our challenge is as follows:
Given 2 integers (a, b) print the smallest (up to) 5 integers between a and b such that we’re not printing numbers divisible by 3.
For example if a=5 and b=23 we should print the following numbers:
5
7
8
10
11
If a and b are closer to each other we print everything up to b. Here’s another example with a=2 and b=4:
2
4
If you’re a complete newcomer to programming, you might want to check out my FizzBuzz article [LINK] where I explain for loops and the modulo function.
First, we print
In the spirit of doing this step by step, let’s build up our function:
All this does is print every number between a and b inclusive.
Then, we continue
#programming #python #data-science #code #julia
1666801446
GeoAlg.jl: A Basic Geometric Algebra Library in Julia
Geometric Algebra for Julia
This is work-in-progress straight port of Fontijne's reference implementation of geometric algebra utilities to Julia. The code is right now mostly unperformant and non-idiomatic, but I try to improve it whenever I can.
This project follows the license of the original implementation, GPL2.
Things to do
A list of things to do, in no particular order:
- Rewrite in idiomatic Julia
- Implement general_inverse and utility functions for Multivectors
- Support named basis vectors
- Maybe a builtin support for popular geometry models (conformal, hyperbolic, homogeneous etc)
- A comprehensive test suite
- cos() and sin()
- Multivector types
- meet and join (possibly using the new method described here
- A comprehensive benchmark to track performance improvements/regressions
- Travis-ci support?
- Write documentation
- Use BitArray for bitmaps instead of integers?
Download Details:
Author: Andrioni
Source Code: https://github.com/andrioni/GeoAlg.jl
License: GPL-2.0 license
1666757580
Seep.jl: A Tensor Flow Like Library for Julia
Seep.jl
Because not everybody's data is big.
Basics: ANodes, Arrays, and Instances
Seep builds and evaluates computational flow graphs in julia. A computational flow graph (CFG) is a directed acyclic graph where nodes represent values and edges represent data dependencies. It is similar to the Single Static Assignment form of a program used by compilers, but there is no control flow. All nodes are evaluated each time the graph is executed.
The CFG is first defined as an abstract graph using ANodes. The abstract graph specifies the size and shape of each variable, how they are connected, and how each value will be computed. It does not allocate storage or define the order of operations.
One or more instances of may be constructed from the abstract graph. Each instance is a julia function that evaluates the CFG described by the abstract graph. Storage for the values of each ANode is allocated statically when the instance is constructed. In addition to being callable as a function, the instance provides access to the Arrays on which it operates.
Creating The Abstract Graph
Abstract graphs are built from ANodes. The first ANode defined is always an input ANode (the constructors of all of the other types require ANodes as arguments). Input ANodes are build by calling the ANode(name::String, dims::Int...) constructor. The name is optional, but it helps to make any code you will write using the instance a bit cleaner and sometimes also faster.
To get started, let's create an ANode to hold a single element, and call it x.
julia> x = ANode("x", 1)
Since it's often useful to create a ANode and assign it to a variable of the same name, there is a macro called @named to do exactly that. Let's create another ANode named y using the @named macro.
julia> @named y = ANode(1)
ANodes can be treated as if they were arrays in many cases. You can't index them (i.e. x[1] won't work) since they're abstract, but you can perform arithmetic on them. The result of operating on ANodes is always a new ANode that represents the computed value.
Let's create a couple more ANodes with compute simple functions of x and y.
julia> @named begin
a = x + y
b = 2a - log(y)
end
This creates 4 new ANodes: a, 2a, log(y), and b. Only a and b are named and assigned to variables in the workspace. 2a and log(y) are referenced by b, but not otherwise visible.
Instantiating the Graph
So far, we've created six ANodes. Let's instantiate the graph so we can use them.
julia> graph = instance(a, b)
Each ANode passed to the instance constructor will be evaluated exactly once when the instance is evaluated. All of the nodes they reference (e.g. 2a and log(y)) will also be evaluated as necessary.
Evaluating the Graph
To use the graph, we first have to provide values for the input nodes. The arrays that were allocated when the instance was created are available as fields of the instance. Let's populate the input ANodes' backing arrays with some data.
julia> graph.x[1] = 1
julia> graph.y[1] = 2
Now that the inputs are populated, we can evaluate the instance by calling it as a function.
julia> graph()
Finally, we can use the results by inspecting the arrays where the results are stored.
julia> println(graph.a)
julia> println(graph.b)
More Information
This has been a short introduction that shows only the most basic features of Seep. For mor information, see the doc/ directory in the root of the source tree.
- Supported Operations on
ANodes - Automatic Differentiation (and Gradient Descent)
- Memory Pools
- Convolution and Pooling
Disclaimer.
DISTRIBUTION STATEMENT A. Approved for public release: distribution unlimited.
© 2017 MASSACHUSETTS INSTITUTE OF TECHNOLOGY.
- Subject to FAR 52.227-11 – Patent Rights – Ownership by the Contractor (May 2014).
- SPDX-License-Identifier: MIT.
This material is based upon work supported by the Undersecretary of Defense for Research and Engineering under Air Force Contract No. FA8721-05-C-0002. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of USD(R&E).
The software/firmware is provided to you on an As-Is basis.
Download Details:
Author: mit-ll
Source Code: https://github.com/mit-ll/Seep.jl
License: MIT license