vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Section 5.2 Pod Security Policies benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.2.1 Minimize the admission of privileged containers. Not Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.2 Minimize the admission of containers wanting to share the host process ID namespace. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.3 Minimize the admission of containers wanting to share the host IPC namespace. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.4 Minimize the admission of containers wanting to share the host network namespace. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.5 Minimize the admission of containers with allowPrivilegeEscalation. Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.6 Minimize the admission of root containers. Not Scored 2 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.7 Minimize the admission of containers with the NET_RAW capability. Not Scored 1 [Pass](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-120cis-benchmark-remediations-120) Shared 
 5.2.8 Minimize the admission of containers with added capabilities.
Analyzing machine learning model performance 

Review the annotations that were added by the trained model to determine whether any adjustments must be made to the model to improve its ability to find valid entity mentions, relation mentions, and coreferences in the documents.



 About this task 

You can analyze performance by viewing a summary of statistics for entity types, relation types, and coreferenced mentions. You can also analyze statistics that are presented in a confusion matrix. The confusion matrix helps you compare the annotations added by the machine learning model to the annotations in ground truth.

The model statistics provide the following metrics:



* F1 score

A measurement that considers both precision and recall to compute the score. The F1 score can be interpreted as a weighted average of the precision and recall values, where an F1 score reaches its best value at 1 and worst value at 0. See [Analyzing low F1 scores](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-evaluate-mlevaluate-mllowf1).
* Precision

A measurement that specifies what fraction of the machine learning model's output was accurate when compared to the human annotator output. Precision is determined by the number of correctly labeled annotations divided by the total number of annotations added by the machine learning model. A precision score of 1.0 for entity type A means that every mention that was labeled as entity type A does indeed belong to that classification. A low precision score helps you identify places where the machine learning model created incorrect annotations. The score says nothing about how many other mentions that were labeled as entity type A by the human annotator were missed by the machine learning model; the recall score reflects that information. See [Analyzing low precision scores](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-evaluate-mlevaluate-mllowp).
* Recall

A measurement that specifies how many mentions that should have been annotated by a given label were actually annotated with that label - the right mentions being those that human annotators identified in the same documents.
So the service returns a single result_index field with a value of 0, which is always the initial index.



If the input audio is more complex or the request includes additional parameters, the results can contain much more information.



 The alternatives field 

The alternatives field provides an array of transcription results. For this request, the array includes just one element.



* The transcript field provides the results of the transcription.
* The confidence field is a score that indicates the service's confidence in the transcript, which for this example exceeds 90 percent.



The final and result_index fields qualify the meaning of these fields.

Internal changes and improvements to the service can affect transcripts and confidence scores. For example, speech recognition may be improved to return more precise transcription results. Similarly, transcript and word confidence scores might change slightly as a result of improved speech recognition. Such changes are expected to be modest, but do not expect transcripts and confidence scores to remain unchanged over time.





 The final field 

The final field indicates whether the transcript shows final transcription results:



* The field is true for final results, which are guaranteed not to change. The service sends no further updates for final results.
* The field is false for interim results, which are subject to change. If you use the interim_results parameter with the WebSocket interface, the service returns evolving hypotheses in the form of multiple results fields as it transcribes the audio. For interim results, the final field is always false and the confidence field is always omitted.



For more information about using the WebSocket interface to obtain interim results with previous- and next-generation models, see the following topics:



* [Interim results](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-results)
* [Requesting interim results and low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interiminterim-low-latency)
4.2.6 Ensure that the --protect-kernel-defaults option is set to true. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) IBM 
 4.2.7 Ensure that the --make-iptables-util-chains option is set to true. Scored 1 Pass IBM 
 4.2.8 Ensure that the --hostname-override option is not set. Not Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) IBM 
 4.2.9 Ensure that the --event-qps option is set to 0 or a level which ensures appropriate event capture. Not Scored 2 Pass IBM 
 4.2.10 Ensure that the --tls-cert-file and --tls-private-key-file options are set as appropriate. Scored 1 Pass IBM 
 4.2.11 Ensure that the --rotate-certificates option is not set to false. Scored 1 Pass IBM 
 4.2.12 Ensure that the RotateKubeletServerCertificate option is set to true. Scored 1 Pass IBM 
 4.2.13 Ensure that the kubelet makes use of only Strong Cryptographic Ciphers. Not Scored 1 Pass IBM 









 5 Kubernetes policies 

Review the Kubernetes Policies results of the version 1.5 CIS Kubernetes benchmark.



 5.1 RBAC and service accounts 



Section 5.1 RBAC and Service Accounts benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 5.1.1 Ensure that the cluster-admin role is only used where required. Not Scored 1 Pass Shared 
 5.1.2 Minimize access to secrets. Not Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared
1.2.16 Ensure that the admission control plug-in PodSecurityPolicy is set. Scored 1 [Pass](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-46cis-benchmark-remediations-46) IBM 
 1.2.17 Ensure that the admission control plug-in NodeRestriction is set. Scored 1 Pass IBM 
 1.2.18 Ensure that the --insecure-bind-address option is not set. Scored 1 Pass IBM 
 1.2.19 Ensure that the --insecure-port option is set to 0. Scored 1 Pass IBM 
 1.2.20 Ensure that the --secure-port option is not set to 0. Scored 1 Pass IBM 
 1.2.21 Ensure that the --profiling option is set to false. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-46cis-benchmark-remediations-46) IBM 
 1.2.22 Ensure that the --audit-log-path option is set. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-46cis-benchmark-remediations-46) Shared 
 1.2.23 Ensure that the --audit-log-maxage option is set to 30 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-46cis-benchmark-remediations-46) Shared 
 1.2.24 Ensure that the --audit-log-maxbackup option is set to 10 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-46cis-benchmark-remediations-46) Shared 
 1.2.25 Ensure that the --audit-log-maxsize option is set to 100 or as appropriate.
{"intent":"eCommerce_Cancel_Product_Order","confidence":0.46903514862060547},
{"intent":"Customer_Care_Appointments","confidence":0.29033891558647157},
{"intent":"General_Greetings","confidence":0.2894785046577454},
In fact, if you test from the "Try it out" pane, you can hover over the eye icon to see the top three intents that were recognized in the test input.!]Your assistant is 0.5618281841278076 (56%) confident that the user goal matches the Customer_Care_Cancel_Account intent. If another intent has a confidence score that is close to the score of this top intent, then disambiguation is triggered. In this example, the eCommerce_Cancel_Product_Order intent has a close confidence score of 46%.As a result, when the user input is i must cancel it today, both dialog nodes are likely to be considered viable candidates to respond. To determine which dialog node to process, the assistant asks the user to pick one. And to help the user choose between them, the assistant provides a short summary of what each node does. The summary text is extracted directly from the node's name field. If present and if a description is added to it, then the text is taken from the external node name field instead.The description that is displayed in the disambiguation list comes from the name (or external node name) of the last node that is processed in the branch where the intent match occurs. For more information, see Disable jumped-to utility nodes]Choosing nodes to disable] Service prompts the user to choose from a list of dialog options, including Cancel an account, Cancel a product order, and None of the above.Service prompts the user to choose from a list of dialog options_ including Cancel an account_ Cancel a product order_ and None of the above_.]
The response also includes the top 10 other intents that are defined in the skill regardless of their confidence score. (In this example, its confidence in the other intents is set to 0 because the first intent is an exact match.) The top 10 intents are returned because the "Try it out" pane sends the alternate_intents:true parameter with its request. If you are using the API directly and want to see the top 10 results, be sure to specify this parameter in your call. If alternate_intents is false, which is the default value, only intents with a confidence above 0.2 are returned in the array.

[!{"intent":"greeting","confidence":1},
{"intent":"yes","confidence":0},
{"intent":"pizza-order","confidence":0}]

If you want to include text in the response, use the toJson() method in the expression to cast the returned intents list into a JSON object. For example:

Recognized intents are: <? intents.toJson() ?>

The following examples show how to check for an intent value:



* intents[0] == 'Help'
* intent == 'Help'



intent == 'help' differs from intents[0] == 'help' because intent == 'help' does not throw an exception if no intent is detected. It is evaluated as true only if the intent confidence exceeds a threshold. If you want to, you can specify a custom confidence level for a condition, for example, intents.size() > 0 && intents[0] == 'help' && intents[0].confidence > 0.1





 Accessing input 

The input JSON object contains one property only: the text property. The text property represents the text of the user input.



 Input property usage examples 

The following example shows how to access input:



* To execute a node if the user input is "Yes", add this expression to the node condition: input.text == 'Yes'
{"begin": 0.001, "end": 0.01, "count": 0},
{"begin": 0.01, "end": 0.1, "count": 0},
{"begin": 0.1, "end": 1.0, "count": 0}
],
"speech_level": [
{"begin": 0.0, "end": 0.1, "count": 37},
{"begin": 0.1, "end": 0.2, "count": 0},
{"begin": 0.2, "end": 0.3, "count": 0},
{"begin": 0.3, "end": 0.4, "count": 0},
{"begin": 0.4, "end": 0.5, "count": 0},
{"begin": 0.5, "end": 0.6, "count": 0},
{"begin": 0.6, "end": 0.7, "count": 0},
{"begin": 0.7, "end": 0.8, "count": 0},
{"begin": 0.8, "end": 0.9, "count": 0},
{"begin": 0.9, "end": 1.0, "count": 0}
],
"non_speech_level": [
{"begin": 0.0, "end": 0.1, "count": 33},
{"begin": 0.1, "end": 0.2, "count": 0},
{"begin": 0.2, "end": 0.3, "count": 0},
{"begin": 0.3, "end": 0.4, "count": 0},
{"begin": 0.4, "end": 0.5, "count": 0},
"speaker": 1,
"confidence": 0.80,
"final": true
}
]
}
Show more

Instead, the service returns final results like the following. Note that the object for the last speaker label, which begins at 1.76 and ends at 2.50, is returned twice. The "final": true indication is associated with the second instance of the label.

{
"speaker_labels": [
. . .
{
"from": 1.01,
"to": 1.75,
"speaker": 0,
"confidence": 0.75,
"final": false
},
{
"from": 1.76,
"to": 2.50,
"speaker": 1,
"confidence": 0.80,
"final": false
}
]
}{
"from": 1.76,
"to": 2.50,
"speaker": 1,
"confidence": 0.80,
"final": true
}
Show more

For more information about using speaker labels with interim results, see [Requesting interim results for speaker labels](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-speaker-labelsspeaker-labels-interim).





 Issue: Supported features for speaker labels is always true 

6 August 2020: The GET /v1/models and GET /v1/models/{model_id} methods list information about language models. Under supported_features, the speaker_labels field indicates whether you can use the speaker_labels parameter with a model. At this time, the field returns true for all models.

However, speaker labels are supported as beta functionality only for US English, Australian English, German, Japanese, Korean, and Spanish (both broadband and narrowband models) and UK English (narrowband model only). Speaker labels are not supported for any other models. Do not rely on the field to identify which models support speaker labels.
1.2.16 Ensure that the admission control plug-in PodSecurityPolicy is set. Scored 1 [Pass](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) IBM 
 1.2.17 Ensure that the admission control plug-in NodeRestriction is set. Scored 1 Pass IBM 
 1.2.18 Ensure that the --insecure-bind-address option is not set. Scored 1 Pass IBM 
 1.2.19 Ensure that the --insecure-port option is set to 0. Scored 1 Pass IBM 
 1.2.20 Ensure that the --secure-port option is not set to 0. Scored 1 Pass IBM 
 1.2.21 Ensure that the --profiling option is set to false. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) IBM 
 1.2.22 Ensure that the --audit-log-path option is set. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.23 Ensure that the --audit-log-maxage option is set to 30 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.24 Ensure that the --audit-log-maxbackup option is set to 10 or as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/openshift?topic=openshift-cis-benchmark-47cis-benchmark-remediations-47) Shared 
 1.2.25 Ensure that the --audit-log-maxsize option is set to 100 or as appropriate.
1.2.27 Ensure that the --service-account-lookup option is set to true. Scored 1 Pass IBM 
 1.2.28 Ensure that the --service-account-key-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.29 Ensure that the --etcd-certfile and --etcd-keyfile options are set as appropriate. Scored 1 Pass IBM 
 1.2.30 Ensure that the --tls-cert-file and --tls-private-key-file options are set as appropriate. Scored 1 Pass IBM 
 1.2.31 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.32 Ensure that the --etcd-cafile option is set as appropriate. Scored 1 Pass IBM 
 1.2.33 Ensure that the --encryption-provider-config option is set as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-122cis-benchmark-remediations-122) Shared 
 1.2.34 Ensure that encryption providers are appropriately configured. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-122cis-benchmark-remediations-122) Shared 
 1.2.35 Ensure that the API server makes use of only Strong Cryptographic Ciphers. Not Scored 1 Pass IBM 







 1.3 Controller manager 



Section 1.3 Controller manager benchmark results

 Section Recommendation Scored/Not Scored Level Result Responsibility 

 1.3.1 Ensure that the --terminated-pod-gc-threshold option is set as appropriate. Scored 1 Pass IBM 
 1.3.2 Ensure that the --profiling option is set to false. Scored 1 Pass IBM 
 1.3.3 Ensure that the --use-service-account-credentials option is set to true. Scored 1 Pass IBM 
 1.3.4 Ensure that the --service-account-private-key-file option is set as appropriate. Scored 1 Pass IBM
1.2.27 Ensure that the --service-account-lookup option is set to true. Scored 1 Pass IBM 
 1.2.28 Ensure that the --service-account-key-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.29 Ensure that the --etcd-certfile and --etcd-keyfile options are set as appropriate. Scored 1 Pass IBM 
 1.2.30 Ensure that the --tls-cert-file and --tls-private-key-file options are set as appropriate. Scored 1 Pass IBM 
 1.2.31 Ensure that the --client-ca-file option is set as appropriate. Scored 1 Pass IBM 
 1.2.32 Ensure that the --etcd-cafile option is set as appropriate. Scored 1 Pass IBM 
 1.2.33 Ensure that the --encryption-provider-config option is set as appropriate. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-124cis-benchmark-remediations-124) Shared 
 1.2.34 Ensure that encryption providers are appropriately configured. Scored 1 [Fail](https://cloud.ibm.com/docs/containers?topic=containers-cis-benchmark-124cis-benchmark-remediations-124) Shared 
 1.2.35 Ensure that the API server makes use of only Strong Cryptographic Ciphers. Not Scored 1 Pass IBM 







 1.3 Controller manager 



Section 1.3 Controller manager benchmark results

 Section Recommendation Scored? Level Result Responsibility 

 1.3.1 Ensure that the --terminated-pod-gc-threshold option is set as appropriate. Scored 1 Pass IBM 
 1.3.2 Ensure that the --profiling option is set to false. Scored 1 Pass IBM 
 1.3.3 Ensure that the --use-service-account-credentials option is set to true. Scored 1 Pass IBM 
 1.3.4 Ensure that the --service-account-private-key-file option is set as appropriate. Scored 1 Pass IBM
* Percentage of corpus density (by the number of words)

A measurement of ground truth that shows the number of words that were annotated with a given entity type or relation type out of the total number of words, whether annotated or unannotated. This statistic is not available for coreferenced mentions. This value can help you to see how prevalent mentions of this type are compared to all of the other words in your domain documents.
* Percentage of documents that contain the type

A measurement of ground truth that shows how many documents contain a given entity type or relation type. This statistic is not available for coreferenced mentions. This value can help you to assess whether the documents in the set represent the domain sufficiently. If the percentage is low for key entity types, then you might want to add more documents with mentions of under-represented types.







 Procedure 

To view performance statistics for how well the model was trained:



1. Log in as a Knowledge Studio administrator or project manager, and select your workspace.
2. Select Machine Learning Model > Performance.
3. For the mentions, relations, or coreferences, select the Detailed Statistics link.
4. In the Summary view, specify whether you want to evaluate test data or training data, and then specify the type of annotations you want to see statistics for: entity types, relation types, or coreferenced mentions. As you scroll through the data, items that have low scores are flagged and highlighted to indicate that they require investigation and improvement. The triangle warning icon indicates that the F1 value is less than the fixed value, 0.5.

For example, the F1 score for some entity types might be high because the document was annotated through pre-annotation as well as by a human annotator. But the F1 score for other entity types might be low because differences in phrasing, and differences in how human annotators interpret the text or annotation guidelines, make it more difficult for the machine learning model to recognize the pattern and apply the correct annotation.
5. In the Confusion Matrix view for test data, specify the type of annotations that you want to see statistics for: entity types or relation types. For each entity type or relation type:
var wsURI = '{ws_url}/v1/synthesize'
+ '?access_token=' + access_token
+ '&voice=en-US_AllisonV3Voice'
+ '&rate_percentage=-5';
var websocket = new WebSocket(wsURI);







 Modifying the speaking pitch 

The pitch_percentage parameter is beta functionality.

The speaking pitch represents the pitch, or tone, of the speech that the service synthesizes, which also affects the intonation. It represents how high or low the tone of the voice is perceived by the listener. A higher pitch results in speech that is spoken at a higher tone and is perceived as a higher voice; a lower pitch results in speech that is spoken in a lower tone and is perceived as a lower, deeper voice.

You can use the optional pitch_percentage query parameter to modify the pitch of the synthesized speech for a voice. Each voice has a default, baseline pitch that reflects the intended tone of that voice. The parameter accepts an integer that represents the percentage change from the voice's default:



* Specify a signed negative integer to reduce the pitch by that percentage. For example, -10 reduces the pitch by ten percent.
* Specify an unsigned or signed positive integer to increase the pitch by that percentage. For example, 10 and +10 increase the pitch by ten percent.
* Specify 0 or omit the parameter to get the default pitch for the voice.



The best means of determining how the parameter affects the pitch of a given voice is to experiment with different values. Increase or decrease the rate incrementally, by five or ten percent, before trying more drastic changes. When decreasing the pitch, a value of -50 halves the baseline pitch; lower values might not yield appreciable or positive results. Similarly, when increasing the pitch, a value of 100 doubles the pitch; higher values might produce results that are not discernibly different from 100.

Table 2 provides examples of how the service changes the speaking pitch with different values for the pitch_percentage parameter. The examples use the en-US_AllisonV3Voice.



Table 2. Using the pitch_percentage parameter