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Wednesday: A look back at the Port Chicago disaster, 75 years later. Also: Another Elon Musk venture, and California State Fair food.

The House is expected to vote Wednesday evening to hold Attorney General William P. Barr and Commerce Secretary Wilbur Ross in criminal contempt of Congress.

On the Supreme Court, John Paul Stevens transformed from a Republican antitrust lawyer into the leader of the court’s liberal wing.

For decades, smaller metropolitan areas closed the income gap with bigger, richer ones, but no longer. So places like Winston-Salem, N.C., are trying to lay a new foundation for prosperity.

The investigator, Neil David Kimball, pleaded guilty to a charge of unlawful sexual intercourse with a teenager he met when she reported a sexual assault to the authorities.

President Trump spent much of his campaign essentially disparaging America. In office, however, he has wrapped himself in the flag.

Here’s what you need to know.

Joomla 3.9.10 is now available. This is a bug fix release for the 3.x series of Joomla which addresses one bug introduced into 3.9.9, affecting template styles of multilingual web sites.

Joomla 3.9.9 is now available. This is a security fix release for the 3.x series of Joomla which addresses one security vulnerability and contains over 30 bug fixes and improvements.

The YOYOW Foundation has become a Platinum Global Sponsor of Joomla. 

Joomla 3.9.8 is now available. This is a bug fix release for the 3.x series of Joomla which addresses one bug introduced into 3.9.7 which affects web sites using the French Help Server.

Joomla 3.9.7 is now available. This is a security fix release for the 3.x series of Joomla which addresses three security vulnerabilities and contains over 40 bug fixes and improvements.

Joomla 3.9.6 is now available. This is a security fix release for the 3.x series of Joomla which addresses two security vulnerabilities and contains over 25 bug fixes and improvements.

Joomla 3.9.5 is now available. This is a security fix release for the 3.x series of Joomla which addresses three security vulnerabilities and contains over 20 bug fixes and improvements.

In an effort likely aimed at boosting family memberships, Spotify this morning announced a new partnership with Disney on the creation of a Disney Hub on its streaming service. Here, Disney fans in select markets including the U.S. will find a selection of Disney playlists like soundtracks from Disney, Pixar and Marvel movies, Star Wars […]

Fifth Wall Ventures, an investor in Clutter, b8ta, ClassPass, Lime and others, has raised $503 million for its sophomore venture capital fund. The firm, which closed on $212 million for its debut fund in May 2017, prefers to invest in the burgeoning real estate industry but has made opportunistic investments in other sectors. The Los […]

FaceApp, the AI-powered selfie-editing app that’s been having another viral moment of late, has now responded to a privacy controversy that we covered earlier here. We’ve pasted the company’s full statement at the bottom of this post. The tl;dr here is that concerns had been raised that FaceApp, a Russian startup, uploads users’ photos to […]

The need-to-know takeaways from VidCon 2019 Extra Crunch media columnist Eric Peckham took part in all the craziness that has become VidCon this past week, where 75,000 influencers, YouTubers, advertisers, and more congregate in one place to see how many likes they all can generate (and I guess to discuss business strategy). This year, there […]

Amid worker protests and antitrust investigations, Amazon’s Prime Day sales event carried on as usual — and that means it again set new records for the online retailer. This time, Amazon says Prime Day 2019 was bigger than both Black Friday and Cyber Monday combined, as Prime members purchased more than 175 million items during […]

10x Ascend is a new firm that helps software development, cybersecurity and data science professionals negotiate for better deals. Founders Michael Solomon and Rishon Blumberg started out in talent management for the music industry (their clients still include musicians like Vanessa Carlton), then moved into representing tech freelancers with their firm 10x Management. More recently, […]

ClassPass has set up yet another revenue stream, signing on partners like Facebook, Glossier, Google, Morgan Stanley, Under Armour, Etsy, Southwest Airlines and Gatorade to a corporate wellness program. The program will give employees at these companies access to the ClassPass network of more than 22,000 studio partners across 2500 cities around the world, which […]

Introduction Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been used to win many Kaggle data science competitions. The Python machine learning library, Scikit-Learn, supports different implementations of gradient boosting classifiers, including XGBoost. In this article we'll go over the theory behind gradient boosting models/classifiers, and look at two different ways of carrying out classification with gradient boosting classifiers in Scikit-Learn. Defining Terms Let's start by defining some terms in relation to machine learning and gradient boosting classifiers. To begin with, what is classification? In machine learning, there are two types of supervised learning problems: classification and regression. Classification refers to the task of giving a machine learning algorithm features, and having the algorithm put the instances/data points into one of many discrete classes. Classes are categorical in nature, it isn't possible for an instance to be classified as partially one class and partially another. A classic example of a classification task is classifying emails as either "spam" or "not spam" - there's no "a bit spammy" email. Regressions are done when the output of the machine learning model is a real value or a continuous value. Such an example of these continuous values would be "weight" or "length". An example of a regression task is predicting the age of a person based off of features like height, weight, income, etc. Gradient boosting classifiers are specific types of algorithms that are used for classification tasks, as the name suggests. Features are the inputs that are given to the machine learning algorithm, the inputs that will be used to calculate an output value. In a mathematical sense, the features of the dataset are the variables used to solve the equation. The other part of the equation is the label or target, which are the classes the instances will be categorized into. Because the labels contain the target values for the machine learning classifier, when training a classifier you should split up the data into training and testing sets. The training set will have targets/labels, while the testing set won't contain these values. Scikit-Learn, or "sklearn", is a machine learning library created for Python, intended to expedite machine learning tasks by making it easier to implement machine learning algorithms. It has easy-to-use functions to assist with splitting data into training and testing sets, as well as training a model, making predictions, and evaluating the model. How Gradient Boosting Came to Be The idea behind "gradient boosting" is to take a weak hypothesis or weak learning algorithm and make a series of tweaks to it that will improve the strength of the hypothesis/learner. This type of Hypothesis Boosting is based on the idea of Probability Approximately Correct Learning (PAC). This PAC learning method investigates machine learning problems to interpret how complex they are, and a similar method is applied to Hypothesis Boosting. In hypothesis boosting, you look at all the observations that the machine learning algorithm is trained on, and you leave only the observations that the machine learning method successfully classified behind, stripping out the other observations. A new weak learner is created and tested on the set of data that was poorly classified, and then just the examples that were successfully classified are kept. This idea was realized in the Adaptive Boosting (AdaBoost) algorithm. For AdaBoost, many weak learners are created by initializing many decision tree algorithms that only have a single split, such as the "stump" in the image below. The instances/observations in the training set are weighted by the algorithm, and more weight is assigned to instances which are difficult to classify. More weak learners are added into the system sequentially, and they are assigned to the most difficult training instances. In AdaBoost, the predictions are made through majority vote, with the instances being classified according to which class receives the most votes from the weak learners. Gradient boosting classifiers are the AdaBoosting method combined with weighted minimization, after which the classifiers and weighted inputs are recalculated. The objective of Gradient Boosting classifiers is to minimize the loss, or the difference between the actual class value of the training example and the predicted class value. It isn't required to understand the process for reducing the classifier's loss, but it operates similarly to gradient descent in a neural network. Refinements to this process were made and Gradient Boosting Machines were created. In the case of Gradient Boosting Machines, every time a new weak learner is added to the model, the weights of the previous learners are frozen or cemented in place, left unchanged as the new layers are introduced. This is distinct from the approaches used in AdaBoosting where the values are adjusted when new learners are added. The power of gradient boosting machines comes from the fact that they can be used on more than binary classification problems, they can be used on multi-class classification problems and even regression problems. Theory Behind Gradient Boost The Gradient Boosting Classifier depends on a loss function. A custom loss function can be used, and many standardized loss functions are supported by gradient boosting classifiers, but the loss function has to be differentiable. Classification algorithms frequently use logarithmic loss, while regression algorithms can use squared errors. Gradient boosting systems don't have to derive a new loss function every time the boosting algorithm is added, rather any differentiable loss function can be applied to the system. Gradient boosting systems have two other necessary parts: a weak learner and an additive component. Gradient boosting systems use decision trees as their weak learners. Regression trees are used for the weak learners, and these regression trees output real values. Because the outputs are real values, as new learners are added into the model the output of the regression trees can be added together to correct for errors in the predictions. The additive component of a gradient boosting model comes from the fact that trees are added to the model over time, and when this occurs the existing trees aren't manipulated, their values remain fixed. A procedure similar to gradient descent is used to minimize the error between given parameters. This is done by taking the calculated loss and performing gradient descent to reduce that loss. Afterwards, the parameters of the tree are modified to reduce the residual loss. The new tree's output is then appended to the output of the previous trees used in the model. This process is repeated until a previously specified number of trees is reached, or the loss is reduced below a certain threshold. Steps to Gradient Boosting In order to implement a gradient boosting classifier, we'll need to carry out a number of different steps. We'll need to: Fit the model Tune the model's parameters and Hyperparameters Make predictions Interpret the results Fitting models with Scikit-Learn is fairly easy, as we typically just have to call the fit() command after setting up the model. However, tuning the model's hyperparameters requires some active decision making on our part. There are various arguments/hyperparameters we can tune to try and get the best accuracy for the model. One of the ways we can do this is by altering the learning rate of the model. We'll want to check the performance of the model on the training set at different learning rates, and then use the best learning rate to make predictions. Predictions can be made in Scikit-Learn very simply by using the predict() function after fitting the classifier. You'll want to predict on the features of the testing dataset, and then compare the predictions to the actual labels. The process of evaluating a classifier typically involves checking the accuracy of the classifier and then tweaking the parameters/hyperparameters of the model until the classifier has an accuracy that the user is satisfied with. Different Improved Gradient Boosting Classifiers Because of the fact that grading boosting algorithms can easily overfit on a training data set, different constraints or regularization methods can be utilized to enhance the algorithm's performance and combat overfitting. Penalized learning, tree constraints, randomized sampling, and shrinkage can be utilized to combat overfitting. Penalized Learning Certain constraints can be utilized to prevent overfitting, depending on the structure of the decision tree. The type of decision tree used in gradient boosting is a regression tree, which has numeric values as leaves or weights. These weight values can be regularized using the different regularization methods, like L1 or L2 regularization weights, which penalizes the radiant boosting algorithm. Tree Constraints The decision tree can be constrained in numerous ways, such as limiting the tree depth, imposing a limit on the number of leaves or nodes of the tree, limiting the number of observations per split, and limiting the number of observations trained on. In general, the more constraints you use when creating trees, the more trees the model will need to properly fit the data. Random Sampling/Stochastic Boosting Taking random subsamples of the training data set, a technique referred to as stochastic gradient boosting, can also help prevent overfitting. This technique essentially reduces the strength of the correlation between trees. There are multiple ways to subsample the data set, such as subsampling columns before each split, subsampling columns before creating a tree, as subsampling rows before creating a tree. In general, subsampling at large rates not exceeding 50% of the data seems to be beneficial to the model. Shrinkage/Weighted Updates Because the predictions of each tree are summed together, the contributions of the trees can be inhibited or slowed down using a technique called shrinkage. A "learning rate" is adjusted, and when the learning rate is reduced more trees must be added to the model. This makes it so that the model needs longer to train. There's a trade-off between the learning rate and the number of trees needed, so you'll have to experiment to find the best values for each of the parameters, but small values less than 0.1 or values between 0.1 and 0.3 often work well. XGBoost XGBoost is a refined and customized version of a gradient boosting decision tree system, created with performance and speed in mind. XGBoost actually stands for "eXtreme Gradient Boosting", and it refers to the fact that the algorithms and methods have been customized to push the limit of what is possible for gradient boosting algorithms. We'll be comparing a regular boosting classifier and an XGBoost classifier in the following section. Implementing A Gradient Boosting Classifier We'll now go over the implementation of a simple gradient boosting classifier and an XGBoost classifier. We'll begin with the simple boosting classifier. Regular Boosting Classifier To start with, we need to choose a dataset to work on, and for this example we'll be using the Titanic Dataset. You can download the data here. Let's start by importing all our libraries: import pandas as pd from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, confusion_matrix from sklearn.ensemble import GradientBoostingClassifier Now let's load in our training data: train_data = pd.read_csv("train.csv") test_data = pd.read_csv("test.csv") We may need to do some preprocessing of the data. Let's set the index as the PassengerId and then select our features and labels. Our label data, the y data is the Survived column. So we'll make that it's own dataframe and then remove it from the features: y_train = train_data["Survived"] train_data.drop(labels="Survived", axis=1, inplace=True) Now we have to create a concatenated new data set: full_data = train_data.append(test_data) Let's drop any columns that aren't necessary or helpful for training, although you could leave them in and see how they affect things: drop_columns = ["Name", "Age", "SibSp", "Ticket", "Cabin", "Parch", "Embarked"] full_data.drop(labels=drop_columns, axis=1, inplace=True) Any text data needs to be converted into numbers that our model can use, so let's change that now. We'll also fill any empty cells with 0: full_data = pd.get_dummies(full_data, columns=["Sex"]) full_data.fillna(value=0.0, inplace=True) Let's split the data into training and testing sets: X_train = full_data.values[0:891] X_test = full_data.values[891:] We'll now scale our data by creating an instance of the scaler and scaling it: scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) Now we can split the data into training and testing sets. Let's also set a seed (so you can replicate the results) and select the percentage of the data for testing on: state = 12 test_size = 0.30 X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=test_size, random_state=state) Now we can try setting different learning rates, so that we can compare the performance of the classifier's performance at different learning rates. lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1] for learning_rate in lr_list: gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=2, max_depth=2, random_state=0) gb_clf.fit(X_train, y_train) print("Learning rate: ", learning_rate) print("Accuracy score (training): {0:.3f}".format(gb_clf.score(X_train, y_train))) print("Accuracy score (validation): {0:.3f}".format(gb_clf.score(X_val, y_val))) Let's see what the performance was for different learning rates: Learning rate: 0.05 Accuracy score (training): 0.801 Accuracy score (validation): 0.731 Learning rate: 0.075 Accuracy score (training): 0.814 Accuracy score (validation): 0.731 Learning rate: 0.1 Accuracy score (training): 0.812 Accuracy score (validation): 0.724 Learning rate: 0.25 Accuracy score (training): 0.835 Accuracy score (validation): 0.750 Learning rate: 0.5 Accuracy score (training): 0.864 Accuracy score (validation): 0.772 Learning rate: 0.75 Accuracy score (training): 0.875 Accuracy score (validation): 0.754 Learning rate: 1 Accuracy score (training): 0.875 Accuracy score (validation): 0.739 We're mainly interested in the classifier's accuracy on the validation set, but it looks like a learning rate of 0.5 gives us the best performance on the validation set and good performance on the training set. Now we can evaluate the classifier by checking its accuracy and creating a confusion matrix. Let's create a new classifier and specify the best learning rate we discovered. gb_clf2 = GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=2, max_depth=2, random_state=0) gb_clf2.fit(X_train, y_train) predictions = gb_clf2.predict(X_val) print("Confusion Matrix:") print(confusion_matrix(y_val, predictions)) print("Classification Report") print(classification_report(y_val, predictions)) Here's the output of our tuned classifier: Confusion Matrix: [[142 19] [ 42 65]] Classification Report precision recall f1-score support 0 0.77 0.88 0.82 161 1 0.77 0.61 0.68 107 accuracy 0.77 268 macro avg 0.77 0.74 0.75 268 weighted avg 0.77 0.77 0.77 268 XGBoost Classifier Now we'll experiment with the XGBoost classifier. As before, let's start by importing the libraries we need. from xgboost import XGBClassifier Since our data is already prepared, we just need to fit the classifier with the training data: xgb_clf = XGBClassifier() xgb_clf.fit(X_train, y_train) Now that the classifier has been fit and trained, we can check the score it achieves on the validation set by using the score command. score = xgb_clf.score(X_val, y_val) print(score) Here's the output: 0.7761194029850746 Alternatively, you could predict the X_val data and then check the accuracy against the y_val by using accuracy_score. It should give you the same kind of result. Comparing the accuracy of XGboost to the accuracy of a regular gradient classifier shows that, in this case, the results were very similar. However, this won't always be the case and in different circumstances, one of the classifiers could easily perform better than the other. Try varying the arguments in this model to see how the result differ. Conclusion Gradient boosting models are powerful algorithms which can be used for both classification and regression tasks. Gradient boosting models can perform incredibly well on very complex datasets, but they are also prone to overfitting, which can be combated with several of the methods described above. Gradient boosting classifiers are also easy to implement in Scikit-Learn. Now that we've implemented both a regular boosting classifier and an XGBoost classifier, try implementing them both on the same dataset and see how the performance of the two classifiers compares. If you'd like to learn more about the theory behind Gradient Boosting, you can read more about that here. You may also want to know more about the other classifiers that Scikit-Learn supports, so you can compare their performance. Learn more about Scikit-Learn's classifiers here. If you'd like to play around with the code, it's up on GitHub!

I’ve released another alpha of coverage.py 5.0: coverage.py 5.0a6. There are some design decisions ahead that I could use feedback on.Important backstory: The big feature in 5.0 is “contexts”: recording data for varying execution context, also known as Who Tests What. The idea is to record not just that a line was executed, but also which tests ran each line. Some of the changes in alpha 6 were driven by a hackathon project at work: using who-tests-what on the large Open edX codebase. We wanted to collect context information, and then for each new pull request, run only the subset of tests that touched the lines you changed. Initial experiments indicate this could be a huge time-savings. Big changes in this alpha: Support for contexts when reporting. The --show-contexts option annotates lines with the names of contexts recorded for the line. The --contexts option lets you filter the report to only certain contexts. Big thanks to Stephan Richter and Albertas Agejevas for the contribution. Our largest test suite at work has 29k tests. The .coverage SQLite data file was 659Mb, which was too large to work with. I changed the database format to use a compact bitmap representation for line numbers, which reduced the data file to 69Mb, a huge win. The API to the CoverageData object has changed. Some implications of these changes: The HTML reporting on contexts is good for small test suites, but very quickly becomes unwieldy if you have more than 100 tests. Please try using it and let me know what kind of reporting would be helpful. The new more-compact data file is harder to query. The larger data file has a schema designed to be useful for ad-hoc querying. It was a classic third-normal form representation of the data. Now I consider the database schema to be a private implementation detail. Should we have a new “coverage sql” report command that exports the data to a convenient SQLite file? Because CoverageData has changed, you will need an updated version of pytest-cov if you use that plugin. The future of the plugin is somewhat up in the air. If you would like to help maintain it, get in touch. You can install the up-to-date code with: pip install git+https://github.com/nedbat/pytest-cov.git@nedbat/cov5-combine#egg=pytest-cov==0.0 To support our hackathon project, we wrote a new pytest plugin: it uses pytest hooks to indicate the test boundaries, and can read the database and the code diff to choose the subset of tests to run. This plugin is in very rough shape (as in, it hasn’t yet fully worked), but if you are interested in participating in this experiment, get in touch. The code is here nedbat/coverage_pytest_plugin. I don’t think this will remain as an independent plugin, so again, if you want to help with future maintenance or direction, let me know. All of our experimentation (and improvements) for contexts involve line coverage. Branch coverage only complicates the problems of storage and reporting. I’ve mused about how to store branch data more compactly in the past, but nothing has been done. I know this is a lot, and the 5.0 alpha series has been going on for a while. The features are shaping up to be powerful and useful. All of your feedback has been very helpful, keep it coming.

Get to grips with one of the fundamental building blocks of functional programming in Python: map.

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In the last episode, we talked about going from script to supported package. I worked on a project called subark and did the packaging with flit. Today's episode is a continuation where we add new features to a supported package and how to develop and test a flit based package. Covered: viewing stages of a project with git tags flit support for editable installs flit description entry in pyproject.toml to put README on pypi. development dependencies in pyproject.toml editor layout for optimal TDD-ing test case grouping modifications to traditional TDD that helps me develop faster. code and command snippets from episode: For git checkout of versions: $ git clone https://github.com/okken/submark.git $ cd submark $ python3 -m venv venv --prompt submark $ source ./bin/activate (submark) $ git checkout v0.1 ... etc ... (submark) $ git checkout v0.7 To grab the latest again: (submark) $ git checkout master pyproject.toml change for README to show up on pypi: [tool.flit.metadata] ... description-file = "README.md" ... Adding dev dependencies to pyproject.toml: [tool.flit.metadata.requires-extra] test = ["pytest", "pytest-cov", "tox"] Installing in editable mode (in top level repo directory). works in mac, linux, windows: (submark) $ flit install --pth-file or for mac/linux: (submark) $ flit install -s Sponsored By:PyCharm Professional: PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development.Support Test & Code - Python Testing & DevelopmentLinks:submark · PyPIsubmark on GitHubThe pyproject.toml config file — Flit 1.3 documentationFlit install — Flit 1.3 documentationTest & Code 80: From Python script to Maintainable Package<p>In the last episode, we talked about going from script to supported package. <br> I worked on a project called subark and did the packaging with flit.</p> <p>Today&#39;s episode is a continuation where we add new features to a supported package and how to develop and test a flit based package.</p> <p>Covered:</p> <ul> <li>viewing stages of a project with git tags</li> <li>flit support for editable installs</li> <li>flit description entry in pyproject.toml to put README on pypi.</li> <li>development dependencies in pyproject.toml</li> <li>editor layout for optimal TDD-ing</li> <li>test case grouping</li> <li>modifications to traditional TDD that helps me develop faster.</li> </ul> <hr> <p>code and command snippets from episode:</p> <hr> <p>For git checkout of versions:</p> <pre><code>$ git clone https://github.com/okken/submark.git $ cd submark $ python3 -m venv venv --prompt submark $ source ./bin/activate (submark) $ git checkout v0.1 ... etc ... (submark) $ git checkout v0.7 </code></pre> <p>To grab the latest again:</p> <pre><code>(submark) $ git checkout master </code></pre> <hr> <p>pyproject.toml change for README to show up on pypi:</p> <pre><code>[tool.flit.metadata] ... description-file = &quot;README.md&quot; ... </code></pre> <p>Adding dev dependencies to pyproject.toml:</p> <pre><code>[tool.flit.metadata.requires-extra] test = [&quot;pytest&quot;, &quot;pytest-cov&quot;, &quot;tox&quot;] </code></pre> <hr> <p>Installing in editable mode (in top level repo directory). works in mac, linux, windows:</p> <pre><code>(submark) $ flit install --pth-file </code></pre> <p>or for mac/linux:</p> <pre><code>(submark) $ flit install -s </code></pre> <hr><p>Sponsored By:</p><ul><li><a href="https://testandcode.com/pycharm" rel="nofollow">PyCharm Professional</a>: <a href="https://testandcode.com/pycharm" rel="nofollow">PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development.</a></li></ul><p><a href="https://www.patreon.com/testpodcast" rel="payment">Support Test & Code - Python Testing & Development</a></p><p>Links:</p><ul><li><a href="https://pypi.org/project/submark/" title="submark · PyPI" rel="nofollow">submark · PyPI</a></li><li><a href="https://github.com/okken/submark" title="submark on GitHub" rel="nofollow">submark on GitHub</a></li><li><a href="https://flit.readthedocs.io/en/latest/pyproject_toml.html" title="The pyproject.toml config file — Flit 1.3 documentation" rel="nofollow">The pyproject.toml config file — Flit 1.3 documentation</a></li><li><a href="https://flit.readthedocs.io/en/latest/cmdline.html#flit-install" title="Flit install — Flit 1.3 documentation" rel="nofollow">Flit install — Flit 1.3 documentation</a></li><li><a href="https://testandcode.com/80" title="Test & Code 80: From Python script to Maintainable Package" rel="nofollow">Test & Code 80: From Python script to Maintainable Package</a></li></ul>

Hello everyone! This is 7th week of GSoC 2019, just one week away from 2nd Evaluations. Here's what I did this week. What did I do this week? This week was a bit relaxing. I didn't do much except making changes in my old PR for improvement of Icons Picker API. Added some features like zipping folders, providing a separate route for download of files and improved file structure. There were a few changes my mentors told me to make in that PR so that the code matches all the other repos. Except that I started working on the current version of EOS-icons while the new version is still in development. What is coming up next week? In one or two days the code base for new version of EOS-icons will be ready which brings some major changes like moving to pug from HTML templates, using Gulp instead of Grunt and a lot more. Once that is done, I'll make the icons-picker and connect it with API. Did I get stuck anywhere? Yes, I am still stuck on some error with heroku deployment. I tested it locally and the solution seems to work. Once my PR is merged I'll try to make the changes in it. Till next time, Cheers!

How to setup Raspberry Pi, Starting with Raspberry Pi programming with Python, Sending IP address to Telegram channel on Raspberry Pi Boot up, SSH using Raspberry Pi, Raspberry Pi and python

Stock Analysis: Focusing on Shares of Restoration Hardware Holdings (RH)  Hermann HeraldChecking in on the 50-day moving average vs price signal for Restoration Hardware Holdings (RH), we have noted that the current reading is Buy. We have ...

BEMS Hardware Market – Major Technology Giants in Buzz Again | Commercial Buildings, Manufacturing Facilities, Educational Institutions, Hospitals, Government Institutes  MildMintThe latest business intelligence report on BEMS Hardware Market Research: Global Status & Forecast by Geography, Type & Application (2015-2025) is ...

Global Hardware OTP Token Authentication Market 2019 Technology, Outlook and Significance 2025|VASCO, Authenex, Deepnet Security  Market News ReportsA latest survey on Global Hardware OTP Token Authentication Market is conducted covering various organizations of the industry from different geographies to ...

AMD Arcturus Is Probably a Vega-Based Professional GPU (Updated)  Tom's HardwareAMD starts rolling out patches for Arcturus for the AMDGPU Linux graphics driver.

Xiaomi Mi A3 unveiled with Snapdragon 665 chipset, triple rear cameras  Android CentralXiaomi's latest Android One smartphone is just a global version of the Mi CC9e. It packs a Qualcomm Snapdragon 665 chipset and has a triple camera setup on ...

Hardware Support Services Market 2019 Global Analysis by Key Players, Share, Trend, Segmentation and Forecast to 2024  The Market PlanHardware Support Services Market 2019. This report is a product of an exhaustive analysis of the latest trends prevalent in the industry. It contains a brief but ...

Hardware Support Services Market 2019 Global Key Players, Size, Applications & Growth Opportunities – Analysis to 2024  Market ReportsHardware Support Services Market 2019. This report is a product of an exhaustive analysis of the latest trends prevalent in the industry. It contains a brief but ...

Joomla 3.9.10 is now available. This is a bug fix release for the 3.x series of Joomla which addresses one bug introduced into 3.9.9, affecting template styles of multilingual web sites.

Joomla 3.9.9 is now available. This is a security fix release for the 3.x series of Joomla which addresses one security vulnerability and contains over 30 bug fixes and improvements.

The YOYOW Foundation has become a Platinum Global Sponsor of Joomla. 

Joomla 3.9.8 is now available. This is a bug fix release for the 3.x series of Joomla which addresses one bug introduced into 3.9.7 which affects web sites using the French Help Server.

Joomla 3.9.7 is now available. This is a security fix release for the 3.x series of Joomla which addresses three security vulnerabilities and contains over 40 bug fixes and improvements.

Joomla 3.9.6 is now available. This is a security fix release for the 3.x series of Joomla which addresses two security vulnerabilities and contains over 25 bug fixes and improvements.

Joomla 3.9.5 is now available. This is a security fix release for the 3.x series of Joomla which addresses three security vulnerabilities and contains over 20 bug fixes and improvements.

An easy-to-use Joomla Plugin to add custom CSS styles that override Extensions and Theme default styles. Features it has : Works with any templates. Options here to optimize inline CSS You can enable or disable Gzip Cross browser support. Simple and Friendly user interface. Ease to use. Supported Platform: Joomla 3.x and Joomla 4.X Note : Any issue let me know. Joomla latest version (3.x and 4.0) tested and working great ? How to use : 1) Just install plugin and do configuration 2) Then just publish it and it will start working. Details : Simple configuration. Working fine. Tutorial https://www.youtube.com/watch?v=P2zn09DRRUA

A powerful email marketing and automation component for Joomla. You can create users, lists, profile fields, newsletters, campaigns. Before sending emails, you can apply filters and add tags in newsletters based on various user profile fields. And, all this can be automated using cron job.

Elastic Email Sender allows you to connect your Joomla website with the powerful, low-cost Elastic Email API and send unlimited emails per month! Please follow the information below and find out more about how we can help you send your emails in a more efficient way. In case you have any questions or concerns, feel free to contact us anytime. What is Elastic Email Sender plugin? Elastic Email Sender is an easy way to maintain all the aspects related to your email campaigns. From creating and sending your emails to monitoring and managing campaigns stats. Elastic Email Sender uses an API integration with Elastic Email which enables you to send outgoing email straight from your Joomla installation. Because of this, you can track all the parameters of your delivery, use Private IP addresses to get full control over your sending, maintain reputation and delivery and secure your data better than ever. You can also use your own domain and analyze your data with ease. How to get started? Just sign into your Elastic Email account, copy the API Key and log into your Joomla dashboard. Add the Elastic Email Sender plugin and paste the API Key from your Elastic Email account.

Main Features: Create amazing pricing tables without any programming skills on your website in a matter of minutes. Simple and graphic visual representation with comprehensive purchase information are the main components of a pricing and compare table that sells..  Google Fonts Support.  Ease of use.  Customizable columns to fit your website.  Support Font Awesome Icons.  Support to visible to all or registered users only (Access Level).  Very flexible in Settings.  Intuitive and user-friendly interface.  Customizable font size and weight. Changelog VERSION 1.0 - Released on June 17, 2019 First release.  

It divides a circle into multiple slices that are proportional to their contribution towards the total sum. It is useful in comparing the share or proportion of various items. It is interactive, responsive, cross-browser compatible, supports animation. Key Features  Google Fonts Support.  Unlimited Pie Chart Elements.  Support to arrange Pie Chart Elements.  Support Font Awesome Icons.  Support to visible to all or registered users only (Access Level).  Very flexible in Settings.  Unlimited Color.  CSS3 Animation Support for the module in front-end. Changelog VERSION 1.0 - Released on June 5, 2019 First release.

This module shows contact info for your website. Everything is clickable. Features it has : Works with any templates. Change background Responsive option Cross browser support. Simple and Friendly user interface. Ease to use. Supported Platform: Joomla 3.x and Joomla 4.X Note : Any issue let me know. Joomla latest version (3.x and 4.0) tested and working great ? How to use : 1) Just install module and do configuration 2) Then just publish it and it will start working. Details : Simple configuration. Working fine.

Gaode map module is a gaode map module developed by sixe team for Joomla system. You can add the functions of displaying a certain geographic location, map coordinates and other functions in the pages of your website such as "contact us". Multiple maps can be inserted into the same page, and the map supports the selection of language, type and style. Simple configuration, easy to use.

PHP team is glad to announce the release of the third PHP 7.4.0 version, PHP 7.4.0 Alpha 3. This continues the PHP 7.4 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 7.4.0 Alpha 3 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release would be Beta 1, planned for July 25th. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP development team announces the immediate availability of PHP 7.2.20. This is a bugfix release.All PHP 7.2 users are encouraged to upgrade to this version.For source downloads of PHP 7.2.20 please visit our downloads page, Windows source and binaries can be found on windows.php.net/download/. The list of changes is recorded in the ChangeLog.

The PHP development team announces the immediate availability of PHP 7.3.7. This is a bug fix release.All PHP 7.3 users are encouraged to upgrade to this version.For source downloads of PHP 7.3.7 please visit our downloads page, Windows source and binaries can be found on windows.php.net/download/. The list of changes is recorded in the ChangeLog.

PHP team is glad to announce the release of the second PHP 7.4.0 version, PHP 7.4.0 Alpha 2. This continues the PHP 7.4 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 7.4.0 Alpha 2 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release would be Alpha 3, planned for July 11th. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

PHP team is glad to announce the release of the first PHP 7.4.0 version, PHP 7.4.0 Alpha 1. This starts the PHP 7.4 release cycle, the rough outline of which is specified in the PHP Wiki. For source downloads of PHP 7.4.0 Alpha 1 please visit the download page. Please carefully test this version and report any issues found in the bug reporting system. Please DO NOT use this version in production, it is an early test version. For more information on the new features and other changes, you can read the NEWS file, or the UPGRADING file for a complete list of upgrading notes. These files can also be found in the release archive. The next release would be Alpha 2, planned for June 27. The signatures for the release can be found in the manifest or on the QA site. Thank you for helping us make PHP better.

The PHP development team announces the immediate availability of PHP 7.3.6. This is a security release which also contains several bug fixes.All PHP 7.3 users are encouraged to upgrade to this version.For source downloads of PHP 7.3.6 please visit our downloads page, Windows source and binaries can be found on windows.php.net/download/. The list of changes is recorded in the ChangeLog.

The PHP development team announces the immediate availability of PHP 7.2.19. This is a security release which also contains several minor bug fixes.All PHP 7.2 users are encouraged to upgrade to this version.For source downloads of PHP 7.2.19 please visit our downloads page, Windows source and binaries can be found on windows.php.net/download/. The list of changes is recorded in the ChangeLog.

PSH highlights and indents source code using highlight configuration files which are similar to Ultraedit highlighting files As such it currently supports C C Java Perl PHP Pascal Lisp Mumps The post PHP Syntax Highlighter appeared first on PHPBuilder.

404 redirect not found The post Redirecting on Page Not Found (404) appeared first on PHPBuilder.

This page will validate the structure of a string to your specifications This script will verify a password to make sure it contains a lower and upper case alpha character The post Password Structure Validation appeared first on PHPBuilder.

The Zend Framework's Zend_Cache component is an easy way to implement PHP caching, a sure site performance The post Boost PHP Site Performance with Zend Framework Data Caching appeared first on PHPBuilder.

By Ying Zhang on July 30, 2000 This document describes how to display safely formatted output from user input. We will discuss the dangers of displaying unfiltered output and then provide a safe means of displaying formatted output. Download the attachment and extract it into your web documents directory. Dangers... The post Displaying Formatted User Input appeared first on PHPBuilder.

This script will display a progress bar. It's beneficial when you are performing many tasks on the back end that take a while to complete. In these The post Displaying Dynamic Progress Bars appeared first on PHPBuilder.

All web developers will at some point need to program their own form. Whether it is for database submission, The post PHP Form Validation System: An Object-Oriented Approach appeared first on PHPBuilder.

The Netcraft Anti-Phishing Extension is now available for Microsoft Edge. The Netcraft Anti-Phishing Extension provides phishing and XSS protection as well as informing you about the websites you visit. It was first made available for Internet Explorer in December 2004, followed by Firefox in 2005, Chrome in 2012 and Opera in 2013. Microsoft Edge is [...]

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 GoDaddy.com Inc Linux 0:00:00 0.000 0.423 0.004 0.018 0.018 2 Rackspace Linux 0:00:00 0.000 0.644 0.005 0.013 0.013 3 Webair Linux 0:00:00 0.000 0.327 0.074 0.150 0.151 4 EveryCity SmartOS 0:00:00 0.000 0.238 0.075 0.375 0.375 5 CWCS Managed Hosting Linux 0:00:00 0.000 0.308 [...]

In the June 2019 survey we received responses from 1,333,421,275 sites across 236,991,999 unique domain names and 8,840,331 web-facing computers. This reflects gains of 6.76 million sites, 1.98 million domains, and 113,000 computers. nginx has further extended its lead in the hostnames metric, with 52.2 million additional sites pushing its total up to 440 million. [...]

Rank Performance Graph OS Outagehh:mm:ss FailedReq% DNS Connect Firstbyte Total 1 Webair Linux 0:00:00 0.000 0.341 0.078 0.156 0.157 2 Swishmail FreeBSD 0:00:00 0.000 0.257 0.090 0.181 0.183 3 Pair Networks Linux 0:00:00 0.000 0.351 0.092 0.185 0.185 4 One.com Linux 0:00:00 0.000 0.416 0.094 0.189 0.189 5 www.choopa.com Linux 0:00:00 0.005 0.287 0.006 0.023 [...]

Read this article in English La bijouterie en ligne française CLEOR, active dans 136 bijouteries, a été infectée d’un skimmer JavaScript. Le code malveillant injecté est conçu pour envoyer les données bancaires aux criminels sur le formulaire de paiement en ligne légitime de Cleor. Skimmers sur les boutiques en ligne Cette attaque compte de nombreuses [...]

Presently, the most impersonated UK institution is not a bank nor a Government department, but the Daily Mirror, which is used to promote cryptocurrency scams. The scale of these cryptocurrency scams is substantial, such that there are currently more fake Daily Mirror front pages than PayPal phishing login forms. An example is an article on [...]

Lisez cet article en français Netcraft recently discovered that French jewellery retailer Cleor—which operates 136 boutiques across France—was infected with a malicious JavaScript skimmer. The injected code sent the fraudster a copy of the credit card details entered by unsuspecting Cleor customers during the legitimate checkout process. Shopping Site Skimmers This attack has many of [...]

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