by Scott Muniz | Feb 21, 2023 | Security, Technology
This article is contributed. See the original author and article here.
CISA has added three new vulnerabilities to its Known Exploited Vulnerabilities Catalog, based on evidence of active exploitation.
These types of vulnerabilities are frequent attack vectors for malicious cyber actors and pose significant risks to the federal enterprise. Note: To view other newly added vulnerabilities in the catalog, click on the arrow in the “Date Added to Catalog” column—which will sort by descending dates.
Binding Operational Directive (BOD) 22-01: Reducing the Significant Risk of Known Exploited Vulnerabilities established the Known Exploited Vulnerabilities Catalog as a living list of known Common Vulnerabilities and Exposures (CVEs) that carry significant risk to the federal enterprise. BOD 22-01 requires Federal Civilian Executive Branch (FCEB) agencies to remediate identified vulnerabilities by the due date to protect FCEB networks against active threats. See the BOD 22-01 Fact Sheet for more information.
Although BOD 22-01 only applies to FCEB agencies, CISA strongly urges all organizations to reduce their exposure to cyberattacks by prioritizing timely remediation of Catalog vulnerabilities as part of their vulnerability management practice. CISA will continue to add vulnerabilities to the catalog that meet the specified criteria.
by Contributed | Feb 21, 2023 | Technology
This article is contributed. See the original author and article here.
We hope you will join us on Tuesday, March 7th to learn how to build intelligent, scalable apps faster and easier at this deep dive into open source and Azure. See the latest open-source technology in action—while connecting with the community of industry leaders, innovators, and open-source enthusiasts.
Register now for this free digital event to:
- See app-building demos using Azure and the latest in open-source technologies, cloud-native architectures, and microservices.
- Get tips and best practices for open source from industry experts at companies like HashiCorp, GitHub, and Redis.
- Learn to build cloud-native apps for relational and nonrelational data with Azure Cosmos DB, now supporting native PostgreSQL.
- Discover new capabilities in IaaS, PaaS, containers, and serverless computing, including Azure Kubernetes Service (AKS).
- Explore practical ways to optimize your open-source investments and gain more time for innovation.
- Learn how to protect your data and business assets by building on a highly secure cloud platform designed to meet your open-source security and compliance needs.
Plus, ask your questions during the live chat Q&A.
Azure Open Source Day
Tuesday, March 7, 2023
9:00 AM–10:30 AM Pacific Time
by Scott Muniz | Feb 21, 2023 | Security, Technology
This article is contributed. See the original author and article here.
CISA released two (2) Industrial Control Systems (ICS) advisories on February 21, 2023. These advisories provide timely information about current security issues, vulnerabilities, and exploits surrounding ICS.
CISA encourages users and administrators to review the newly released ICS advisories for technical details and mitigations:
by Contributed | Feb 20, 2023 | Technology
This article is contributed. See the original author and article here.
Have you heard about AI or specific terms like ChatGPT or OpenAI?
You might even have formed an opinion whether to allow it or not for your students.
> But have you thought about it for your own part, what you could use it for?
In this article, we explore a specific use case, generating a summary for a course, something that takes time to do as you need to write the text, find proper links and other things. Let’s be honest, you love teaching, and this is just boring “admin” work.
References:
– What is Azure OpenAI Service? – Azure Cognitive Services | Microsoft Learn
– Introduction to Azure OpenAI Service – Training | Microsoft Learn
– OpenAI API playground
Create the course summary
Let’s pick a subject to teach, “causation algorithms” and next let’s decide on some reasonable prompts that we will ask our Open AI about:
Prompt: describe why causation algorithms is so interesting compared to correlation-based ones
OpenAI output:
Causation algorithms are gaining more attention due to their ability to identify the causal relationships between two or more variables. Unlike correlation-based algorithms, which merely measure the strength of the association between two variables, causation algorithms can determine the cause-and-effect relationship between those variables. This is a powerful tool for predictive analysis, as it allows for the identification of factors that lead to a certain outcome and the development of strategies to influence that outcome. In other words, causation algorithms can provide a much more sophisticated level of insight into a data set than correlation-based algorithms.
Prompt: write me a 500 word article explaining causation algorithms for beginners
OpenAI Output:
Causation algorithms are a type of machine learning algorithm that uses statistical techniques to evaluate causal relationships between two or more variables. They are becoming increasingly popular in the world of data science because of their ability to make predictions about the future based on past data. Causal algorithms are used to identify patterns in datasets and then use those patterns to make predictions about future events.
In order to understand how causation algorithms work, it is important to understand the concept of causality. Causality is the relationship between two events, where one event causes the other to happen. For example, if the weather is hot, then the ice cream sales in the store will likely increase. In this case, the hot weather is the cause and the increased ice cream sales is the effect.
Causation algorithms use advanced statistical techniques to identify these causal relationships in datasets. They can look at data from a variety of sources such as weather data, economic data, and customer data to identify patterns and then use those patterns to make predictions. For example, a causation algorithm could look at past sales data and weather data and then make a prediction about future sales based on the relationship between the two datasets.
Causation algorithms can be used for a variety of purposes. They can be used to identify patterns in customer data to better understand consumer behavior. They can also be used to predict future trends in the stock market or in the economy. They can even be used to predict the success of a new product before it is released.
In order for causation algorithms to make accurate predictions, they need to be trained with large amounts of data. This is where machine learning comes in. Machine learning is a type of artificial intelligence that uses algorithms to learn from data and then use that data to make predictions. By training causation algorithms with large datasets, they can become more accurate in their predictions.
Causation algorithms are a powerful tool that can be used to make predictions about the future.
prompt: suggest me some good exercises on causation algorithms
OpenAI Output:
- Create a program that simulates a decision tree.
- Create a program that implements a Naive Bayes classifier.
- Create a program that implements a Markov Decision Process.
- Develop a program that implements a logistic regression classifier.
- Create a program that implements a reinforcement learning algorithm.
- Develop a program that uses Bayesian networks to make predictions.
- Create a program that implements a Hidden Markov Model.
- Build a program that uses a Monte Carlo simulation to predict outcomes.
- Develop a program that uses a Genetic Algorithm to optimize a solution.
- Create a program that uses a neural network to solve a problem.
prompt: show me some references on causation algorithms either research papers or books
OpenAI Output:
- Causality: Models, Reasoning, and Inference, Judea Pearl (2000)
- Algorithmic Learning Theory, Eyke Hüllermeier (2006)
- Causal Inference in Statistics: A Primer, Judea Pearl (2009)
- Causality: Philosophical Theory Meets Scientific Practice, Kevin Hoover (2009)
- Causality and Prediction in Economics, Marc R. Reinganum (2011)
- Understanding Causality: From Probabilistic Graphical Models to Causal Networks, Judith M. Rolfe and David C. Hogg (2013)
- Causal Inference in Statistics: A Primer, Pearl, J. (2016)
- Causality in Statistics, Thomas S. Richardson and Vincent Dorie (2017)
- Causality: A Very Short Introduction, Stephen Mumford (2018)
Great, we have some raw data at this point that we can assess. However, we still need to check for correctness and that is sounds good, but this was a lot of help in just seconds right?
Editorial
Next, let’s do some editorial by assigning some reasonable headers and see if we want to adjust the text somewhat.
Why Causation algorithms is such an interesting area right now
Causation algorithms are gaining more attention due to their ability to identify the causal relationships between two or more variables. Unlike correlation-based algorithms, which merely measure the strength of the association between two variables, causation algorithms can determine the cause-and-effect relationship between those variables. This is a powerful tool for predictive analysis, as it allows for the identification of factors that lead to a certain outcome and the development of strategies to influence that outcome. In other words, causation algorithms can provide a much more sophisticated level of insight into a data set than correlation-based algorithms.
What is Causation algorithms
Causation algorithms are a type of machine learning algorithm that uses statistical techniques to evaluate causal relationships between two or more variables. They are becoming increasingly popular in the world of data science because of their ability to make predictions about the future based on past data. Causal algorithms are used to identify patterns in datasets and then use those patterns to make predictions about future events.
In order to understand how causation algorithms work, it is important to understand the concept of causality. Causality is the relationship between two events, where one event causes the other to happen. For example, if the weather is hot, then the ice cream sales in the store will likely increase. In this case, the hot weather is the cause and the increased ice cream sales is the effect.
Causation algorithms use advanced statistical techniques to identify these causal relationships in datasets. They can look at data from a variety of sources such as weather data, economic data, and customer data to identify patterns and then use those patterns to make predictions. For example, a causation algorithm could look at past sales data and weather data and then make a prediction about future sales based on the relationship between the two datasets.
Causation algorithms can be used for a variety of purposes. They can be used to identify patterns in customer data to better understand consumer behavior. They can also be used to predict future trends in the stock market or in the economy. They can even be used to predict the success of a new product before it is released.
In order for causation algorithms to make accurate predictions, they need to be trained with large amounts of data. This is where machine learning comes in. Machine learning is a type of artificial intelligence that uses algorithms to learn from data and then use that data to make predictions. By training causation algorithms with large datasets, they can become more accurate in their predictions.
Causation algorithms are a powerful tool that can be used to make predictions about the future.
Some problems we will study throughout the course
- Create a program that simulates a decision tree.
- Create a program that implements a Naive Bayes classifier.
- Create a program that implements a Markov Decision Process.
- Develop a program that implements a logistic regression classifier.
- Create a program that implements a reinforcement learning algorithm.
- Develop a program that uses Bayesian networks to make predictions.
- Create a program that implements a Hidden Markov Model.
- Build a program that uses a Monte Carlo simulation to predict outcomes.
- Develop a program that uses a Genetic Algorithm to optimize a solution.
- Create a program that uses a neural network to solve a problem.
Syllabus
- Causality: Models, Reasoning, and Inference, Judea Pearl (2000)
- Algorithmic Learning Theory, Eyke Hüllermeier (2006)
- Causal Inference in Statistics: A Primer, Judea Pearl (2009)
- Causality: Philosophical Theory Meets Scientific Practice, Kevin Hoover (2009)
- Causality and Prediction in Economics, Marc R. Reinganum (2011)
- Understanding Causality: From Probabilistic Graphical Models to Causal Networks, Judith M. Rolfe and David C. Hogg (2013)
- Causal Inference in Statistics: A Primer, Pearl, J. (2016)
- Causality in Statistics, Thomas S. Richardson and Vincent Dorie (2017)
- Causality: A Very Short Introduction, Stephen Mumford (2018)
There you have it, a course summary. Depending on what you’re trying to create, you might need to undertake some verification on the output and some additional editorial. Hopefully, you’ve seen the potential of this powerful tool, what you as an educator can use it for.
by Contributed | Feb 18, 2023 | Technology
This article is contributed. See the original author and article here.
Pair programming is a software development technique in which two developers work together on the same code at the same time, with one typing and the other reviewing and offering suggestions. This technique can increase productivity and promote knowledge sharing, as well as improve code quality through increased collaboration and code review.
Test-driven development (TDD) is a software development process in which tests are written before writing the actual code. The tests serve as a guide for the development process and help ensure that the code meets the requirements and behaves as expected. The TDD cycle consists of writing a test, running it to see if it fails, writing the minimum amount of code needed to make the test pass, and then repeating this process until all tests pass and the code is complete.
GitHub Copilot is a tool that can be used to support pair programming and TDD. It allows developers to collaborate in real-time on the same codebase and provides an environment for running tests and code review. This can help teams work more efficiently and effectively, especially for remote teams who may be working from different locations. Additionally, Copilot can also assist with automated code suggestions and recommendations based on best practices, further improving code quality and efficiency.
In summary, pair programming and TDD are valuable software development techniques that can enhance the development process, and GitHub Copilot can help teams effectively utilize these practices to produce high-quality code.has context menu
Getting Started
To get started with activating your FREE GitHub Copilot
Download GitHub Copilot for VS Code
Download GitHub Copilot Labs for VS Code
GitHub Copilot is a simple, powerful way to use GitHub for automated, test-driven development. It provides an easy-to-use framework for writing, running, and monitoring tests, as well as an integrated interface for managing the entire development process.
To get started with GitHub Copilot, first create your GitHub account and activate your copilot subscription. Create a new repository in GitHub and add the Copilot extension, then, create your test files and add code to run the tests many academic institutions introduce test driven development in classes which use pair programming.
Pair programming is a popular Agile technique that involves two developers working on the same piece of functionality together. In many cases this is used in lab exercises to help student identify bugs and gain understanding of reading and documenting codes bases.
The benefits of pair programming include:
- Quality of produced code should be higher as two developers have been involved and verified code.
- Increased awareness and understanding of the codebase across the team which leads to improved experiences and documentation.
- Skilling and sharing of best practices which lead to higher quality and greater collaboration.
During COVID many courses lost the opportunity of students being with other students in the same room looking at the same screen, thankfully tools like VSCode LiveShare faciliated students to keep pair programming and allowed students to communicate not only screen share but also via voice.
Allowing students to give peer feedback on code, discover underlying issues, and support the coder/driver in successful development.
Many benefits of pair programming are only seen when it’s done as a collaborative team effort. On the other hand, artificial intelligence may potentially give you a real virtual tutor or assistant. We have found GitHub Copilot works really well at increasing your productivity, and also helping.
How to generate tests and test data
Students often need to think of sample data for your tests, documentation, or default settings. In many cases this results in a burden for the course tutor or academic to validate the data or in some case even produce the sample data sets.
GitHub Copilot is an amazing support asset. For example, your student need a set of personnal data record for there application development.
The task set has a Person class:
from dataclasses import dataclass
from datetime import date
@dataclass
class Customers:
first_name: string
last_name: string
birth_date: date
def age(self):
return (date.today() – self.birth_date).days // 365
@property
def full_name(self):
return f“{self.first_name} {self.last_name}“
What you now need is a selection of data for this class did you know you can used GitHub Copilot to populate a list of Sales details.
So if your starting from nothing you can simply ask copilot to create a data set.
// create me a list of people based on first_name:string, last_name:string, birth_date:date
Sales = [
{ “first_name”: “John”, “last_name”: “Doe”, “birth_date”: “1970-01-01” },
{ “first_name”: “Jane”, “last_name”: “Doe”, “birth_date”: “1970-01-02” },
{ “first_name”: “John”, “last_name”: “Smith”, “birth_date”: “1970-01-03” },
{ “first_name”: “Jane”, “last_name”: “Smith”, “birth_date”: “1970-01-04” },
{ “first_name”: “John”, “last_name”: “Doe”, “birthdate”: “1970-01-05” },
{ “first_name”: “Jane”, “last_name”: “Doe”, “birthdate”: “1970-01-06” },
{ “first_name”: “John”, “last_name”: “Smith”, “birthdate”: “1970-01-07” },
]
GitHub Copilot successfully figured out to use your Person class for the Sales list elements.
If you all ready have the reference code, you simply need to Declare an empty Python list using an expressive variable name, and hit Enter just after the opening square bracket ([) to trigger the suggestions:
As you keep hitting Tab after each suggested line, you might end up with the following list of employees: and then close ]
Creating a Test Driven Development
You can use that list as a common test fixture for a group of test cases that require it. GitHub Copilot can be an excellent help in the testing process itself by suggesting both tests and the code under test.
Exercise Test-Driven Development (TDD)
Briefly, the TDD process:
- Write a failing test case that you have too satisfy
- Implement the smallest amount of code to make your test case pass
- Optionally, refactor the code while all your test cases are still passing
- Then, rinse and repeat!
- As long as you’re disciplined enough to stay in this perpetual cycle, you’ll write testable code that has high test coverage and documents itself.
So an example we want to create and test a HashTable implementation, using GitHub Copilot as your virtual pair programmer?
Step 1. Create two empty Python files next to each other in the same folder:
src/
├── hashtable.py
└── test_hashtable.py
The hashtable.py code under test. The test_hashtable.py is the home of your test cases driving the implementation.
Automation of TDD
Once you have set up your tests, you can use the Copilot interface to manage and monitor your testing process. This includes running tests, viewing results, and tracking progress. GitHub Copilot also integrates with popular continuous integration (CI) tools like Jenkins, Travis CI, and CircleCI, allowing you to easily integrate tests into your development workflow.
To help ensure that your tests are up-to-date, Copilot can be configured to run automatically on a regular basis, such as after each commit or nightly. This helps keep your tests and code in sync and ensures that any bug fixes are applied quickly.
Conclusion
There are several reasons why students should use GitHub Copilot for TDD and virtual pair programming:
- Improved Code Quality: By incorporating TDD and pair programming into the development process, students can ensure that their code meets the requirements and behaves as expected. They can also catch and fix any bugs or issues early on in the development process.
- Enhanced Collaboration: GitHub Copilot provides an environment for real-time collaboration, allowing students to work together on the same codebase and share ideas and knowledge with each other. This can lead to a more efficient development process and can help students learn from each other.
- Remote Work Support: With the increasing popularity of remote work, virtual pair programming has become an important tool for development teams. GitHub Copilot allows students to work together, even when they are in different locations, which can help them collaborate effectively and build a better understanding of each other’s strengths and weaknesses.
- Automated Code Suggestions: GitHub Copilot provides automated code suggestions and recommendations based on best practices, which can help students write better code and improve their coding skills.
- Real-World Experience: Using GitHub Copilot for TDD and virtual pair programming gives students a taste of what it’s like to work on real-world software development projects. Students can apply the skills they’ve learned in the classroom to real-world situations and gain hands-on experience.
GitHub Copilot is a great tool for developers who want to take advantage of test-driven development and automated testing. It provides an easy-to-use interface and powerful features to help developers quickly and easily create, run, and monitor tests. Copilot is FREE for students and educators and enables educators and students to practice and improve their TDD and pair programming skills, which can help them build better software and enhance their career prospects.
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