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articles/active-directory/saas-apps/kantegassoforbamboo-tutorial.md

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title: 'Tutorial: Azure Active Directory integration with Kantega SSO for Bamboo | Microsoft Docs'
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description: Learn how to configure single sign-on between Azure Active Directory and Kantega SSO for Bamboo.
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services: active-directory
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In the **Sign-on URL** textbox, type a URL using the following pattern: `https://<server-base-url>/plugins/servlet/no.kantega.saml/sp/<uniqueid>/login`
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> [!NOTE]
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are recieved during the configuration of Bamboo plugin which is explained later in the tutorial.
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are received during the configuration of Bamboo plugin which is explained later in the tutorial.
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1. On the **SAML Signing Certificate** section, click **Metadata XML** and then save the metadata file on your computer.
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articles/active-directory/saas-apps/kantegassoforbitbucket-tutorial.md

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@@ -120,7 +120,7 @@ In this section, you enable Azure AD single sign-on in the Azure portal and conf
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In the **Sign-on URL** textbox, type a URL using the following pattern: `https://<server-base-url>/plugins/servlet/no.kantega.saml/sp/<uniqueid>/login`
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> [!NOTE]
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are recieved during the configuration of Bitbucket plugin which is explained later in the tutorial.
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are received during the configuration of Bitbucket plugin which is explained later in the tutorial.
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1. On the **SAML Signing Certificate** section, click **Metadata XML** and then save the metadata file on your computer.
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articles/active-directory/saas-apps/kantegassoforfisheyecrucible-tutorial.md

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title: 'Tutorial: Azure Active Directory integration with Kantega SSO for FishEye/Crucible | Microsoft Docs'
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description: Learn how to configure single sign-on between Azure Active Directory and Kantega SSO for FishEye/Crucible.
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services: active-directory
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In the **Sign-on URL** textbox, type a URL using the following pattern: `https://<server-base-url>/plugins/servlet/no.kantega.saml/sp/<uniqueid>/login`
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> [!NOTE]
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are recieved during the configuration of FishEye/Crucible plugin which is explained later in the tutorial.
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> These values are not real. Update these values with the actual Identifier, Reply URL, and Sign-On URL. These values are received during the configuration of FishEye/Crucible plugin which is explained later in the tutorial.
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1. On the **SAML Signing Certificate** section, click **Metadata XML** and then save the metadata file on your computer.
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articles/active-directory/saas-apps/leapsome-tutorial.md

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title: 'Tutorial: Azure Active Directory integration with Leapsome | Microsoft Docs'
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description: Learn how to configure single sign-on between Azure Active Directory and Leapsome.
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services: active-directory
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b. Copy the **Login URL (point your users here to start login)** value and paste it into the **Sign-on URL** textbox in **Leapsome Domain and URLs** section on Azure portal.
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c. Copy the **Reply URL (recieves response from your identity provider)** value and paste it into the **Reply URL** textbox in **Leapsome Domain and URLs** section on Azure portal.
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c. Copy the **Reply URL (receives response from your identity provider)** value and paste it into the **Reply URL** textbox in **Leapsome Domain and URLs** section on Azure portal.
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d. In the **SSO Login URL (provided by identity provider)** textbox, paste the value of **SAML Single Sign-On Service URL**, which you copied from the Azure portal.
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articles/databox/data-box-portal-ui-admin.md

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## Edit notification details
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You may need to change the users whom you want to recieve the order status emails. For instance, a user needs to be informed when the disk is delivered or picked up. Another user may need to be informed when the data copy is complete so he can verify the data is in the Azure storage account before deleting it from the source. In these instances, you can edit the notification details.
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You may need to change the users whom you want to receive the order status emails. For instance, a user needs to be informed when the disk is delivered or picked up. Another user may need to be informed when the data copy is complete so he can verify the data is in the Azure storage account before deleting it from the source. In these instances, you can edit the notification details.
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Perform the following steps to edit notification details.
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|Processed | Order processing is complete. <br> During order processing, following actions occur:<li>Disks are encrypted using AES-128 BitLocker encryption. </li> <li>The Data Box Disk are locked to prevent any unauthorized access.</li><li>The passkey that unlocks the disks is generated during this process.</li> |
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|Dispatched | Order has shipped. You should receive the order in 1-2 days. |
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|Delivered | Order was delivered to the address specified in the order. |
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|Picked up |Your return shipment was picked up. <br> Once the shipment is recieved at Azure datacenter, data will be automatically uploaded to Azure. |
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|Picked up |Your return shipment was picked up. <br> Once the shipment is received at Azure datacenter, data will be automatically uploaded to Azure. |
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|Received | Your disks were received at the Azure datacenter. Data copy will start soon. |
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|Data copied |Data copy is in progress.<br> Wait until the data copy is complete. |
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|Completed |Successfully completed the order.<br> Verfiy your data is in Azure before you delete the on-premises data from servers. |

articles/iot-hub/iot-hub-java-java-file-upload.md

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public void run() {
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try {
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while (true) {
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System.out.println("Recieve file upload notifications...");
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System.out.println("Receive file upload notifications...");
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FileUploadNotification fileUploadNotification = fileUploadNotificationReceiver.receive();
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if (fileUploadNotification != null) {
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System.out.println("File Upload notification received");

articles/iot-hub/iot-hub-python-python-c2d.md

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print ( iothub_client_error )
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```
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1. Add the following code to initialize the client and wait to recieve the cloud-to-device message:
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1. Add the following code to initialize the client and wait to receive the cloud-to-device message:
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```python
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def iothub_client_init():
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![Run the app to send the cloud-to-device command][img-send-command]
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1. Note the message recieved by the device.
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1. Note the message received by the device.
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![Message received][img-message-recieved]
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![Message received][img-message-received]
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## Next steps
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<!-- Images -->
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[img-simulated-device]: media/iot-hub-python-python-c2d/simulated-device.png
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[img-send-command]: media/iot-hub-python-python-c2d/send-command.png
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[img-message-recieved]: media/iot-hub-python-python-c2d/message-recieved.png
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[img-message-received]: media/iot-hub-python-python-c2d/message-received.png
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<!-- Links -->
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[lnk-python-download]: https://www.python.org/downloads/

articles/log-analytics/log-analytics-agent-linux-support.md

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### Resolution
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* Verify the configuration in the Log Analytics workspace for Syslog has all the facilities and the correct log levels. Review [configure Syslog collection in the Azure portal](log-analytics-data-sources-syslog.md#configure-syslog-in-the-azure-portal)
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* Verify the native syslog messaging daemons (`rsyslog`, `syslog-ng`) are able to recieve the forwarded messages
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* Verify the native syslog messaging daemons (`rsyslog`, `syslog-ng`) are able to receive the forwarded messages
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* Check firewall settings on the Syslog server to ensure that messages are not being blocked
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* Simulate a Syslog message to Log Analytics using `logger` command
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* `logger -p local0.err "This is my test message"`

articles/machine-learning/service/how-to-configure-auto-train.md

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`max_time_sec` | Limits the amount of time (seconds) a particular iteration takes. If an iteration exceeds the specified amount, that iteration gets canceled. If not set, then the iteration continues to run until it is finished. | None
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`n_cross_validations` |Number of cross validation splits| None
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`validation_size` |Size of validation set as percentage of all training sample.| None
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`preprocess` | True/False <br/>True enables experiment to perform preprocessing on the input. Following is a subset of preprocessing<li>Missing Data: Imputes the missing data- Numberical with Average, Text with most occurrence </li><li>Categorical Values: If data type is numeric and number of unique values is less than 5 percent, Converts into one-hot encoding </li><li>Etc. for complete list check [the GitHub repository](https://aka.ms/aml-notebooks)</li><br/>Note : if data is sparse you cannot use preprocess = true | False |
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`preprocess` | True/False <br/>True enables experiment to perform preprocessing on the input. Following is a subset of preprocessing<li>Missing Data: Imputes the missing data- Numerical with Average, Text with most occurrence </li><li>Categorical Values: If data type is numeric and number of unique values is less than 5 percent, Converts into one-hot encoding </li><li>Etc. for complete list check [the GitHub repository](https://aka.ms/aml-notebooks)</li><br/>Note : if data is sparse you cannot use preprocess = true | False |
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`blacklist_algos` | Automated ML experiment has many different algorithms that it tries. Configure Automated ML to exclude certain algorithms from the experiment. Useful if you are aware that algorithm(s) do not work well for your dataset. Excluding algorithms can save you compute resources and training time.<br/>Allowed values for Classification<br/><li>logistic regression</li><li>SGD classifier</li><li>MultinomialNB</li><li>BernoulliNB</li><li>SVM</li><li>LinearSVM</li><li>kNN</li><li>DT</li><li>RF</li><li>extra trees</li><li>gradient boosting</li><li>lgbm_classifier</li><br/>Allowed values for Regression<br/><li>Elastic net</li><li>Gradient boosting regressor</li><li>DT regressor</li><li>kNN regressor</li><li>Lasso lars</li><li>SGD regressor</li><li>RF regressor</li><li>extra trees regressor</li>| None
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`verbosity` |Controls the level of logging with INFO being the most verbose and CRITICAL being the least.<br/>Allowed values are:<br/><li>logging.INFO</li><li>logging.WARNING</li><li>logging.ERROR</li><li>logging.CRITICAL</li> | logging.INFO</li>
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`X` | All features to train with | None

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