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EMEA Delivery Lead

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XML is used in many aspects of web development and often used to separate data from presentation. Many desktop & web applications export data under this format. Therefore this code snippet allows you to navigate through the hierarchy until reaches the leafs. By default the output is written in txt file but this can be easily change with another type of Append activity like Append CSV, Excel and so on. 

**NOTE**: Installation Guidelines for **Snippets** can be found [**here**](https://dmtyylqvwgyxw.cloudfront.net/instances/214/assets/help-guides/UiPath-Go-Installation-Guide-Snippets.pdf)**.**

Extract XPATHs for all leaf elements in a given XML file

Compare Text activity implements [Myer's diff algorithm](https://neil.fraser.name/writing/diff/myers.pdf) which is generally considered to be the best general-purpose diff.

  

**Example:**

  

Input:

  

*   Text1: "Hello World."
*   Text2 "Goodbye World."

  

Output:

*   List&lt;KeyValuePair&gt; of differences is computed which describe the transformation of text1 into text2.
*   The first element specifies if it is an insertion (1), a deletion (-1) or an equality (0). The second element specifies the affected text.
*   Therefore the result for the above Text1 and Text2 is: \[(DELETE, "Hello"), (INSERT, "Goodbye"), (EQUAL, " World.")\]

Compare two blocks of plain text and efficiently return a list of differences.

Compare Text activity implements [Myer's diff algorithm](https://neil.fraser.name/writing/diff/myers.pdf) which is generally considered to be the best general-purpose diff.

  

**Example:**

  

Input:

*   Text1: "Hello World."
*   Text2 "Goodbye World."

  

Output:

*   List&lt;KeyValuePair&gt; of differences is computed which describe the transformation of text1 into text2.
*   The first element specifies if it is an insertion (1), a deletion (-1) or an equality (0). The second element specifies the affected text.
*   Therefore the result for the above Text1 and Text2 is: \[(DELETE, "Hello"), (INSERT, "Goodbye"), (EQUAL, " World.")\]

In UiPath Attended Robot Framework you can find a TensorFlow implementation of the face recognizer described in the paper [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832). The project also uses ideas from the paper [Deep Face Recognition](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) from the [Visual Geometry Group](http://www.robots.ox.ac.uk/~vgg/) at Oxford.

  

The framework allows you to add an extra layer of security in attended scenarios. In order to do that the robot will ask your name and to take few photos. 

The model needs to be trained once, for each user that is allowed to kick start the digital assistent for sensitive processes. 

Face Recognition Framework is splited in two major segments:

1.  Training: When the robot gets train with new photos for an existing user or a new one.
2.  Execution of a business process by having in front of the computer a known user.

The recognition is completely based on deep learning neural network and implanted using Tensorflow framework

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