antelope | Starter webapp framework that scaffolds out a front-end web | Style Language library

I have a class containing a mixture of @abstractmethods and normal implementation methods, and I'm wondering how I should go about testing the normal implementations.

Quick Example: I'd like to test the zoo_str method, even though it depends on the abstract description method. If I have 100 animals, it seems like overkill to write a test in the Lion class, the Antelope class, the Hippo class, etc. What's the best way to do this -- my intuition says I should try to mock description, but I can't instatntiate the class and this falls apart if the abstract method is private (_description).

I have a wordpress+nginx in a docker container that is working perfectly through the browser, but when I try to send an http request via curl without headers the response is always empty

I have compiled CLIPS 6.4 into a shared library (compiled as C++) so that I can use in a C++ application.

I want to now write a simple test C++ application that allows me to:

1. Start up the CLIPS engine
2. Load a CLIPS program (see animal.clp)
3. Assert a fact from the C++ program to the CLIPS engine and receive responses back from CLIPS in my C++ program
4. Safely terminate the CLIPS engine and cleanup when nothing on the agenda (all rules fired) - i.e. program completed

I am new to Typescript, Node as well as Express. I setup my project exactly as described here: https://www.digitalocean.com/community/tutorials/setting-up-a-node-project-with-typescript

This is the code, I am trying to run which I got from that link:

I want to install MOODLE 3.10.1 in the local server (xampp windows 10 pc) for my own learning. According to the installation guide I need to Compile the following

"Your database uses Antelope as the file format. Full UTF-8 support in MySQL and MariaDB requires the Barracuda file format. Please switch to the Barracuda file format. See the documentation MySQL full Unicode support for details."

According to the moodle installation guide (https://docs.moodle.org/310/en/MySQL_full_unicode_support), I ran the following SQL command and I am getting the following error.

#1238 - Variable 'innodb_file_format' is a read only variable

Other details of PHP MYADMIN

• PHP Version 7.3.26
• System Windows NT DESKTOP-LTTS1D7 10.0 build 18363 Windows 10) AMD64
• Build Date Jan 5 2021 15:04:15
• Compiler MSVC15(Visual C++ 2017)

I already tried uninstalling and reinstalling but it does not work. In PHP 7.1 version this problem doesn't occur but moodle installation required PHP 7.2 or above.

Thank you in advance.

Say for example I have this text:

hello world **ant*** lorem **cat** opposum** *** ***antelope*** *rabbit __dog__

I would like to match strings that only have ** and __ as its preceding and concluding characters. So in the case above, the matches that I would only want are "cat" and "dog". This means that I have to cancel or negate the match if there are extra surrounding characters. For example, ***dog** or __dog___ should fail.

I've tried to solve this using a negative look around http://www.regular-expressions.info/lookaround.html to no avail.

Here's the current pattern I have

I have the following dataset:

I'm struggling to reorder the bars in the plot and the legend, following the data structure (SD,MB,FI,FO,NP). Here is my data;

I have a query regarding the extraction of VGG16/VGG19 features for my experiments.

The pre-trained VGG16 and VGG19 models have been trained on ImageNet dataset having 1000 classes (say c1,c2, ... c1000) and normally we extract the features from first and second fully connected layers designated ('FC1' and 'FC2'); these 4096 dimensional feature vectors are then used for computer vision tasks.

My question is that can we use these networks to extract features of an image that does not belong to any of the above 1000 classes ? In other words, can we use these networks to extract features of an image with label c1001 ? Remember that c1001 does not belong to the Imagenet classes on which these networks were initially trained on.

In the article available on https://www.pyimagesearch.com/2019/05/20/transfer-learning-with-keras-and-deep-learning/, I am quoting the following -

When performing feature extraction, we treat the pre-trained network as an arbitrary feature extractor, allowing the input image to propagate forward, stopping at pre-specified layer, and taking the outputs of that layer as our features

From the above text, there is no restriction to whether the image must necessarily belong to one of the Imagenet classes.

Kindly spare some time to uncover this mystery.

In the research papers, the authors simply state that they have used features extracted from VGG16/VGG19 network pre-trained on Imagenet dataset without giving any further details.

I am giving a case study for reference:

Animal with Attribute dataset (see https://cvml.ist.ac.at/AwA2/) is a very popular dataset with 50 animal classes for image recognition task. The authors have extracted ILSVRC-pretrained ResNet101 features for the above dataset images. This ResNet 101 network has been pre-trained on 1000 imagenet classes (different imagenet classes are available at https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a#file-imagenet1000_clsidx_to_labels-txt).

Also, the AWA classes are put as follows: