Customizable ExpansionPanel indicator icon

Description
Introduces expansionIndicator parameter, allowing for a custom icon to be used for expansion instead of ExpandIcon. Thanks to @welstone for their initial work on this effort.
Edit: I made a change to how ExpansionPanelList.expansionCallback is used in the radio case -- in it's current state, it's very difficult to use correctly.
Originally, if a radio panel was being opened and another panel had to be closed, it would:

call expansionCallback for the opening panel with it's previous state, and then
call expansionCallback for the closing panel with it's new state

This made it impossible to write logic that properly sets state, since expansionPanelListState[index] = !currentState would work for one and not the other, and vice versa.
This change makes it a breaking change, but I believe it's necessary for proper use of the API.

Related Issues
Continues the work of https://github.com/flutter/flutter/pull/32887
Fixes https://github.com/flutter/flutter/issues/32802
Tests
I added the following tests:

One test each for ExpansionPanelList and ExpansionPanelList.radio verifying that a custom indicator was used instead of ExpandIcon.

Checklist
Before you create this PR confirm that it meets all requirements listed below by checking the relevant checkboxes ([x]). This will ensure a smooth and quick review process.

[x] I read the Contributor Guide and followed the process outlined there for submitting PRs.
[x] I signed the CLA.
[x] I read and followed the Flutter Style Guide, including Features we expect every widget to implement.
[x] I updated/added relevant documentation (doc comments with ///).
[x] All existing and new tests are passing.
[x] The analyzer (flutter analyze --flutter-repo) does not report any problems on my PR.
[x] I am willing to follow-up on review comments in a timely manner.

Breaking Change
Does your PR require Flutter developers to manually update their apps to accommodate your change?

[x] Yes, this is a breaking change (Please read Handling breaking changes). Replace this with a link to the e-mail where you asked for input on this proposed change.
[ ] No, this is not a breaking change.

Should be ready for review once more. @chunhtai If expandIconBuilder is not null, the developer has to provide their own gesture detection and state update. This is reflected in the API documentation.

I'm going to close this out for now, since I have not had the opportunity to come back to this. If anybody is interested in taking over this PR, please feel free to do so.


Unable to use folders/file present locally from non-package

Background & motivation
Currently upload_files allows to pass the GitHub URL, I have a usecase where I wish to upload files from my local machine into kurtosis enclave. The files are present in the same repository where the kurtosis package is present, but not within the package.
I went through the other issues that are raised, couldn't find how to do it. Let me know if there's a workaround for this. Can we have this feature in the roadmap pls.
https://github.com/kurtosis-tech/kurtosis/issues/1700 has been closed with the GitHub PR, seems local folder stuff is not yet supported.
Desired behaviour
Should be able to specify any local file path & turn into a files artifact to use for the services inside their enclaves, without needing to have the desired data to be part of a Kurtosis package.
How important is this to you?
Critical; Kurtosis is unusable for me without it.
What area of the product does this pertain to?
CLI: the Command Line Interface

Yes, I understand that. The thing is I have a usecase where I wish to upload files from my local machine into kurtosis enclave. The files are present in the same repository where the kurtosis package is present, but not within the package.


JDL - Deployment with Istio

Overview of the issue

I am having problem when configure deployment with istio in jdl document.
Motivation for or Use Case

error: Error message:
MismatchedTokenException: Expecting --> '}' <-- but found --> 'istioRoute' <--
at line: 61, column: 3
SyntaxError: Error: MismatchedTokenException: Expecting --> '}' <-- but found --> 'istioRoute' <--
at line: 61, column: 3
ERROR! Error while parsing applications and entities from the JDL SyntaxError: Error: MismatchedTokenException: Expecting --> '}' <-- but found --> 'istioRoute' <--
at line: 61, column: 3
Reproduce the error

application {
config {
baseName bankingGateway
applicationType gateway
packageName com.newway.banking.gateway
serviceDiscoveryType no
authenticationType jwt
prodDatabaseType mysql
cacheProvider hazelcast
buildTool gradle
clientFramework react
useSass true
testFrameworks [protractor]
}
entities *
}
application {
config {
baseName banking
applicationType microservice
packageName com.newway.banking.microservice
serviceDiscoveryType no
authenticationType jwt
prodDatabaseType mysql
buildTool gradle
serverPort 8082
}
entities Bank, Branch
}
entity Bank {
name String required
fullName String required
number String required
numberVerifier String required
priority Boolean required
}
entity Branch{
name String required
number String required
numberVerifier String required
}
relationship ManyToOne {
Branch{bank(number) required} to Bank
}
service Bank, Branch with serviceClass
paginate Bank, Branch with pagination
microservice Bank, Branch with banking
deployment {
deploymentType kubernetes,
appsFolders [banking, bankingGateway],
dockerRepositoryName "paulossjunior",
serviceDiscoveryType no,
kubernetesServiceType Ingress,
istio autoInjection,
istioRoute true,
ingressDomain "<IP_ADDRESS>.nip.io"
}
Related issues

Suggest a Fix

JHipster Version(s)

JHipster configuration

Entity configuration(s) entityName.json files generated in the .jhipster directory

Browsers and Operating System

[ ] Checking this box is mandatory (this is just to show you read everything)

istioRoute is no longer supported


operator: GC nodes from existing CNPs

:rotating_light:  :warning:  :warning: :rotating_light:  this PR is going to be merge against https://github.com/cilium/cilium/pull/7675 and not master. :rotating_light:  :warning:  :warning: :rotating_light:
As nodes can be removed from the cluster their status in the CNP can be
left behind. The operator will be checking if nodes are still running
and if not, they will be removed from the CNP Node Status.
Signed-off-by: André Martins<EMAIL_ADDRESS>Should we backport this to v1.4 and v1.3?

This change is

While this is a bug, I don't think this is needed for v1.4 and v1.3 since it doesn't cause end-user disruption.

test-me-please

Unit tests aren't getting triggered for some reason, so I'm running them locally. If they pass, will merge.

Unit tests passed locally, merging


Data license?

I was wondering what is the license of the data you provide?
I could not find something explicit.
This is to possibly import these in https://github.com/nexB/vulnerablecode/issues/341 as we were alredy planning to import oss-fuzz data otherwise.
Thanks

As part of #44 the source of truth for our data will live in various repos.
If that repo happens to be part of OSV / OSS-Fuzz / Something else controlled by us, it'll be whatever license that repo is licensed under, which is most likely Apache 2.0.
Would that work for you?

As part of #44 the source of truth for our data will live in various repos.
If that repo happens to be part of OSV / OSS-Fuzz / Something else controlled by us, it'll be whatever license that repo is licensed under, which is most likely Apache 2.0.
Would that work for you?

Thanks. Any license is better than no license, Apache is fine, although not a license designed for data and therefore likely not the best pick. As for the API-accessible data today, what would the license be?
BTW, your API requires an API key but your web site does not. So why an extra layer of auth? Implicitly you are encouraging users (me) to build a web scraper rather than to use your API to avoid the hassle of API key management.

Thanks. Any license is better than no license, Apache is fine, although not a license designed for data and therefore likely not the best pick. As for the API-accessible data today, what would the license be?

I don't know the answer for the API-accessible data. I can find out and get back to you. I don't expect this to be an issue.

BTW, your API requires an API key but your web site does not. So why an extra layer of auth? Implicitly you are encouraging users (me) to build a web scraper rather than to use your API to avoid the hassle of API key management.

Thanks for the feedback.
The API key requirement is an unfortunate requirement but it's necessary for the higher QPS allowed by the API and to prevent abuse. The web UI has a lower QPS and serves a different purpose. The web UI primary serves the pagination/search and viewing individual Vulnerability data while the API is mostly for querying by commit or version.

Thanks. Any license is better than no license, Apache is fine, although not a license designed for data and therefore likely not the best pick. As for the API-accessible data today, what would the license be?

I don't know the answer for the API-accessible data. I can find out and get back to you. I don't expect this to be an issue.

Following up on this, may I recommend just waiting for our officially supported data dump (#44) to starting dumping OSS-Fuzz vulnerabilities? The license will also likely be something that's friendly for datasets. This will be available very soon.

Following up on this, may I recommend just waiting for our officially supported data dump (#44) to starting dumping OSS-Fuzz vulnerabilities? The license will also likely be something that's friendly for datasets. This will be available very soon.

@oliverchang sure thing and thank you for the reply. We have had a pending ticket to include and scrape OSS-Fuzz data for about 1.5 years at https://github.com/nexB/vulnerablecode/issues/117 so we can wait alright and there is no rush!
We will not write a scraper for now.

Hi @pombredanne , just following up on this issue.
Our data is now available at https://github.com/google/oss-fuzz-vulns under the CC-BY-4.0 License.
Users can also submit changes to these files and have them reflect in OSV (i.e. they are the source of truth).


Create a feature for Bun dependencies

This feature adds the devcontainer dependencies needed for a Rails app using Bun.
The devcontainer created when currently running rails new -j bun test runs into the following error
bin/setup:7:in `system': No such file or directory - bun (Errno::ENOENT)
        from bin/setup:7:in `system!'
        from bin/setup:19:in `block in <main>'
        from /home/vscode/.rbenv/versions/3.3.6/lib/ruby/3.3.0/fileutils.rb:240:in `chdir'
        from /home/vscode/.rbenv/versions/3.3.6/lib/ruby/3.3.0/fileutils.rb:240:in `cd'
        from bin/setup:10:in `<main>'

There are a few bun features maintained by individuals but none I can find maintained by any organizations so I felt it best to create one in the rails set to ensure reliability.
A PR adding this to https://github.com/rails/rails/blob/main/railties/lib/rails/generators/rails/devcontainer/devcontainer_generator.rb will follow.

For future reference (It would be appreciated if it became "official", but in current policies it is not yet accepted):
https://github.com/devcontainers/features/pull/949


Bot state visualizer now defaults to JSON viewer.

Part of #1443
===
Behavior is that on first inspect of a BotState trace, the inspector will default to the JSON viewer mode, however, on every subsequent inspect, it will use whatever the previous mode was.
Ex:
Inspect BotState A  ----> JSON view
Inspect BotState B ------> JSON view
Switch to Graph view
Inspect BotState C ------> Graph view

Coverage remained the same at 56.94% when pulling ef17e83c6ddce4d09740a92c2a441cfebbc0255d on toanzian/default-json into 3f358d392e4190b09142180295c2c3d727d83e20 on master.


[Swag] T-Issue is extremely exciting - for Majid!

Hola @aeiras
I'm so excited for creating T-Issue ;)))

Your mpSwag was sent yesterday!
If you would like the international tracker, please let us know. Next week, we can share it internally with you. :)

Hola @majidmostafavi,
Your MP swag, was re-sent today. Should arrive within 10 to 30 days. :)


Installation instructions

For installing this it says...
To use and deploy this component (with the other 'out of the box' components) please follow the overall README
...which doesn't make mention of the SMART auth.
I do notice a branch called authz-smart. Are the instructions to switch to that branch then run the install? Any other things to keep in mind?

This package and the SMART feature is still a work in progress. Documentation has not been worked on yet - We aim to have documentation complete in the coming month.

This package and the SMART feature is still a work in progress. Documentation has not been worked on yet - We aim to have documentation complete in the coming month.

Updated!
To use and deploy this component please follow the overall smart-mainline branch README


Revert "Fix idempotency issue when ensuring absent of multiple databa…

…ses in one Puppet run"
This reverts commit 6ca39ad103766de798f0e2f4b705a963ffda3f1e.

Why revert this?

@raphink I suspect it to be the cause of acceptance tests failure.

Ah ok. Yet they're failing in this PR.


Missing Microsoft.RecoveryServices/vaults provider

Hello,
The provider Microsoft.RecoveryServices/vault is missing in this page.
Based on my experience, we have at least tests about :

Inactivity : No Backup or restore operations in the last 24hours

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 9b92884e-4bca-47ca-9d8e-4c29c8169b36
Version Independent ID: db3ee172-6788-dc57-c56e-95156023373b
Content: Supported Resource Types through Azure Resource Health - Azure Service Health
Content Source: articles/service-health/resource-health-checks-resource-types.md
Service: service-health
GitHub Login: @stephbaron
Microsoft Alias: stbaron

@wilfriedwoivre Thanks for your comment. We will review the issue and get back to you shortly.

Assigning to content author @stephbaron for review

Any news for this update ?

This resource provider has been added at this point. #please-close


Install sccache

Part of servo/servo#17106.

This change is

r? @larsbergstrom
cc @luser

:umbrella: The latest upstream changes (presumably #694) made this pull request unmergeable. Please resolve the merge conflicts.

Does this just need a rebase now that all the Android SDK build stuff has been updated?

@bors-servo retry
I've updated this with a workaround for the failing Android SDK test; I think there is a slightly better way possible but it requires a newer Salt and/or a bunch of backporting, so this is simpler.
This should clean up the builds that failed due to force pushing and being unable to find the previous SHA.
Looking at the saltfs build history, we also seem to be hitting https://github.com/travis-ci/travis-ci/issues/8363. I can look for a temporary workaround for that if we don't want to wait.

:broken_heart: Test failed - status-travis

hrm:
$ export GHTOKENLINE=[secure]

The previous command failed, possibly due to a malformed secure environment variable.

Please be sure to escape special characters such as ' ' and '$'.

For more information, see https://docs.travis-ci.com/user/encryption-keys.

Does that look familiar?

@larsbergstrom that's a separate problem; I thought I had an issue tracking that, but not yet it seems. We're not using this variable (I think it was for a PR we decided not to merge), so it should be unset at some point, but that isn't causing the test failure.
The actual test failure is caused by travis-ci/travis-ci#8363; we want to get Python 3.5 but are getting a weird Python 3.4 instead, causing the test failure. If you want sccache now we can add a workaround, or we can wait for Travis to fix this.

@bors-servo r=larsbergstrom

:pushpin: Commit 63f02a6 has been approved by larsbergstrom

:bulb: This pull request was already approved, no need to approve it again.

This pull request previously failed. You should add more commits to fix the bug, or use retry to trigger a build again.
There's another pull request that is currently being tested, blocking this pull request: #716

:hourglass: Testing commit 63f02a6dcf0ff74f945944b04c66f9d73109599f with merge e4a1d01330a5ecc1b41423e1a4e27560f9cca1d3...

:sunny: Test successful - status-travis
Approved by: larsbergstrom
Pushing e4a1d01330a5ecc1b41423e1a4e27560f9cca1d3 to master...


python311Packages.gentools: switch to pyproject; fix build

build broken due to missing setup.py. switch to pyproject, add missing build dependency poetry-core, prefer pytestCheckHook over pytest, add pythonImportCheck.
ZHF: #265948
https://hydra.nixos.org/build/241256836
Result of nixpkgs-review run on x86_64-linux 1

4 packages built:

python310Packages.gentools
python310Packages.gentools.dist
python311Packages.gentools
python311Packages.gentools.dist

Result of nixpkgs-review run on x86_64-darwin 1

4 packages built:

python310Packages.gentools
python310Packages.gentools.dist
python311Packages.gentools
python311Packages.gentools.dist

Description of changes

Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[x] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[x] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[ ] Tested basic functionality of all binary files (usually in ./result/bin/)
23.11 Release Notes (or backporting 23.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[ ] Fits CONTRIBUTING.md.

Priorities
Add a :+1: reaction to pull requests you find important.

Result of nixpkgs-review pr 270590 run on x86_64-linux 1

4 packages built:

python310Packages.gentools
python310Packages.gentools.dist
python311Packages.gentools
python311Packages.gentools.dist


Cannot install Airconnect on my Synology NAS DS720+

Is your question related to a problem? Please describe
I tried to install Airconnect on my Synology NAS DS720+ (DSM-Version: 7.2.1-69057) with the following packages:
First, I tried: AirConnect-dsm7-x86_64-static-1.7.0-20240115.spk
Second, I tried: AirConnect-dsm7-x86_64-static-1.7.0-20240115.spk
I installed it with manual installation in the paket-manager
At the end of both installation-attempts a window was shown with "Airconnect konnte nicht installiert werden"
The AirConnect application logfile shows: (you find the complete logfile as an attachment):
2024/07/10 15:24:48	start AirConnect 1.7.0-20240115 Begin start-stop-status start
2024/07/10 15:24:48	start AirConnect 1.7.0-20240115 End start-stop-status start ret=[0]
The AirConnect Service logfile shows: (you find the complete logfile as an attachment) shows the same.
What can I do?
Thank you  for your support.
Regards,
Peter
AirConnect-Application-Logfile.log
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.
AirConnect-Application-Logfile.log
AirConnect-Service-Log-File.log

Could you please try the newest non static version?
https://github.com/eizedev/AirConnect-Synology/releases/download/1.8.3-20240403/AirConnect-dsm7-x86_64-1.8.3-20240403.spk

Thank you for your answer.
I tried to install version 1.8.3
Now the error message is:
"Das Paket konnte nicht installiert werden. Freigegebener Ordner mit dme Namen "airconnect" kann nicht erstellt werden."
What can I do?

Please, can you help me?

sorry for the late reply @Ped68
Have you ever run an old version of AirConnect? Possibly also under DSM 6? The specified error appears when it tries to create the “airconnect” folder (you can find it in File Explorer on your Synology) but it already exists. This can only happen if you are upgrading from a very old version or if AirConnect was previously installed but not uninstalled properly (this was a problem in the early days years years ago).
Anyway, you can fix this by manually cleanup the old installation
Could you please cleanup your old installation as mentionend in the readme? https://github.com/eizedev/AirConnect-Synology?tab=readme-ov-file#cannot-be-installed-or-upgrade-from-an-older-version
Then please try to reinstall the newest version, this version should work on your device: https://github.com/eizedev/AirConnect-Synology/releases/download/1.8.3-20240403/AirConnect-dsm7-x86_64-1.8.3-20240403.spk

Thank you very much @eizedev
Today, I tried your solution.
I cleanup my installation as mentioned in the readme.
Then I installed the version AirConnect-dsm7-x86_64-1.8.3-20240403.spk
At the end occurs the error "AirConnect" konnte nicht installiert werden
I post the Logfiles and the screenshots from installation
I have noticed the following - the airconnect directory is not displayed in my FileStation, although it exists
Do you have an idea?

airconnect-2.log
airconnect-1.log


refactor(avatar): remove cursor style

No need to cursor to be on the Avatar component itself.

LGTM


fix(RingTheory/Finiteness): stablizes -> stabilizes

This was also spelt incorrectly in Lean 3.

maintainer merge

bors r+
Thanks!


Add free-text search extension

Description:
Adding the free-text search extension. Related to https://github.com/stac-utils/stac-fastapi/pull/655
PR Checklist:

[x] Code is formatted and linted (run pre-commit run --all-files)
[x] Tests pass (run make test)
[x] Documentation has been updated to reflect changes, if applicable
[x] Changes are added to the changelog

I've not included FreeTextAdvancedExtension as I don't think it works in it's current form and expect it will be removed in the future.

@jamesfisher-geo yes I'll make a new pull request to add those fields. Alternately it could just search all fields instead?


fix: Datasource edit icon is different from platform

Description
Edit icon was different in edit datasource button, this PR fixes that issue. Also, changed to sentense case in authentication tab.
PR fixes following issue(s)
Fixes #8057
Media

Type of change

Bug fix (non-breaking change which fixes an issue)

Testing
How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Also list any relevant details for your test configuration.
Delete anything that is not relevant

[ ] Manual
[ ] Jest
[ ] Cypress

Test Plan

Add Testsmith test cases links that relate to this PR

Issues raised during DP testing

Link issues raised during DP testing for better visiblity and tracking (copy link from comments dropped on this PR)

Checklist:
Dev activity

[x] My code follows the style guidelines of this project
[x] I have performed a self-review of my own code
[ ] I have commented my code, particularly in hard-to-understand areas
[ ] I have made corresponding changes to the documentation
[x] My changes generate no new warnings
[ ] I have added tests that prove my fix is effective or that my feature works
[x] New and existing unit tests pass locally with my changes
[ ] PR is being merged under a feature flag

QA activity:

[ ] Speedbreak features have been covered
[ ] Test plan covers all impacted features and areas of interest
[ ] Test plan has been peer reviewed by project stakeholders and other QA members
[ ] Manually tested functionality on DP
[ ] We had an implementation alignment call with stakeholders post QA Round 2
[ ] Cypress test cases have been added and approved by SDET/manual QA
[ ] Added Test Plan Approved label after Cypress tests were reviewed
[ ] Added Test Plan Approved label after JUnit tests were reviewed

/ok-to-test

/build-deploy-preview env=release

Tested this PR. Looks good.

New icon for Edit Datasource button.
Sentence case applied in auth page.

/ok-to-test


Error at start up

Issue Type: Bug
three errors -
"Couldn't start client AzureResourceProvider"
"Couldn't start client SerializationProvider"
"Failed to start SQL Tools Service"
Furthermore I can't add an account (asking to enable cloud, although it is enabled). I believe it is because of above three errors at the startup
Azure Data Studio version: azuredatastudio 1.30.0 (59c4b8e90cf2d3a24bed72623197f10f8d090fdc, 2021-06-16T19:04:08.858Z)
OS version: Windows_NT x64 10.0.19042

System Info

Item
Value

CPUs
Intel(R) Core(TM) i7-8650U CPU @ 1.90GHz (8 x 2112)

GPU Status
2d_canvas: enabledgpu_compositing: enabledmultiple_raster_threads: enabled_onoop_rasterization: enabledopengl: enabled_onprotected_video_decode: enabledrasterization: enabledskia_renderer: enabled_onvideo_decode: enabledvulkan: disabled_offwebgl: enabledwebgl2: enabled

Load (avg)
undefined

Memory (System)
15.88GB (10.43GB free)

Process Argv

Screen Reader
no

VM
0%

Extensions: none

Attaching the console.log and zipped log folder.
console.log
log.zip

It looks like you have some issues running the SQL Tools Service executable. Do you have any antivirus or other software installed on your machine that might be blocking this?
I would also suggest trying out the archive (zip) version of ADS to see if you're seeing the same issue there.

It works from archive(zip) version.
Thanks very much. Appreciate your help.


Add Saving To Build Tool And Update Path Arg

I've added A Property Settings File that'll allow users to save the engine path to their computer. I also am checking for the updatepath argument, so that users can easily change the engine path if they desire.

New PR adds these changes


Storing the keys as sha256 fingerprints limits functionality

Hi, interesting project –
Storing user keys in the database as sha256 limits what could be done with keys in the future. For example, if one wanted to add a get authorized_keys <email> command, you are not able to generate an authorized_keys entry for a key from the sha256. I would suggest storing it in the DB as gossh.MarshalAuthorizedKey(s.PublicKey()).
I know this uses more storage space, but it one-to-one with the information available in PublicKey, which can be fully reconstructed using gossh.ParseAuthorizedKey. I'm not sure what your plans are for this project & I would not write a PR that obsoletes your current user data without a clear migration path.
Beyond simply writing authorized_keys, it would be possible to use this as CA – but that's getting a little ahead of myself.

Thanks for the thoughtful feedback! I agree that storing the full public key data would enable more functionality like generating authorized_keys entries or potential CA features.
I think we can implement this without obsoleting existing user data. New commands that require the full key data could fail gracefully when encountering users who only have the sha256, displaying an error message that explains the missing data. We could also add commands allowing users to upload their full public key if it's not present in the database.
These kinds of growing pains are expected as the project evolves, and graceful degradation would let us add new features while maintaining compatibility with existing users.


[3.x] Backport locale selection improvements.

Backport of #52969 for 3.x, fixes #61828.
Note: this is not a breaking change, but it affects a lot of stuff, so should not be merged into 3.5 in a last minute. Better to keep it for 3.6.

Thanks!


Add @EntityListeners [moved]

This is Issue 43 moved from a Google Code project.
Added by 2010-05-09T17:41:36.000Z by scotthernandez.
Please review that bug for more context and additional comments, but update this bug.
Closed (Fixed).
Original labels: Type-Enhancement, Priority-Low, Milestone-Release0.93
Original description
Add @EntityListener support; works just like lifecycle methods but for
another class.

@Entity
@EntityListeners(com.acme.AlertMonitor.class)
public class Account {
@Id long accountId;
}

public class AlertMonitor {
@PostPersist
public void newAccountAlert(Account acct) {
Alerts.sendMarketingInfo(acct.getAccountId(), acct.getBal-
ance());
}
}

Hi,
I created a interface
public interface ListnerInterface {}
and provide this interface to the one of my entity class like @EntityListeners(ListnerInterface .class)
and given the implementation of this interface to other folder eg:
public class ListnerUtilService implements ListnerInterface {}.
But it is not invoking this class.  why? please help me.


C++ SD v1.5 pipeline

Could we move this to different path?
@Wovchena , what would be correct format? ImageGeneration/StableDiffusion1_5?

Following the style proposed in https://github.com/openvinotoolkit/openvino.genai/pull/9 it should be image_generation/stable_diffusion_1_5/cpp

updated with "image_generation/stable_diffusion_1_5/cpp" and use new trapezoidal implementation without Boost

@xipingyan to take a look at the code and provide comments if there is any. Thanks!

It is a good example. It contains model convert, compress, independent C++ reference codes.


bin/magento is assumed to be a php script (not sh) by test framework

The integration test framework calls bin/magento multiple times to uninstall and install Magento in the testing database.
It does so with a command like
/usr/local/bin/php -f '/var/www/html/bin/magento' setup:install -vvv --db-host='docker_db_1' --db-user='magento2_test' --db-password='123123q' --db-name='magento_integration_tests' --backend-frontname='backend' --admin-user='user' --admin-password='password1'<EMAIL_ADDRESS>--admin-firstname='firstname' --admin-lastname='lastname' --magento-init-params='MAGE_DIRS[etc][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/etc&MAGE_DIRS[var][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var&MAGE_DIRS[media][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/pub/media&MAGE_DIRS[static][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/pub/static&MAGE_DIRS[generation][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/generation&MAGE_DIRS[cache][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/cache&MAGE_DIRS[log][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/log&MAGE_DIRS[session][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/session&MAGE_DIRS[tmp][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/tmp&MAGE_DIRS[upload][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/var/upload&MAGE_DIRS[pub][path]=/var/www/html/dev/tests/integration/tmp/sandbox-0-a06c5218c942e8428d1719573e5cf3bc/pub&MAGE_MODE=developer' 2>&1

Because the mageinferno docker image replaces bin/magento with a bash script (assumably to change the UID) this fails, but doesn't even register as a failure since PHP processes that file as a plain text file without <?php tags. It just outputs it's contents to stdout.
In order to be able to use the docker images to run integration tests bin/magento has to be a php script. Depending on why the UID is changed, the same effect might be achieved using by setting the suid or sgid bits.

Replacing the shell script with this PHP wrapper instead does the trick:
#!/usr/bin/env php
<?php

$args = array_reduce(array_slice($argv, 1), function ($acc, $arg) {
    return $acc . ' ' . escapeshellarg($arg);
}, '');

$cmd='su -c "/usr/local/bin/php /var/www/html/bin/magento-php ' . $args . '" -s /bin/sh www-data';

passthru($cmd, $result);
exit($result);

Thanks @Vinai. I'll try to test this out in the next day or two and merge it in, but it looks good.

Oops. Apparently me moving lost about 6 months or so of my life, and it all started right around this ticket submission ;)
I trust your changeset and made the update to https://github.com/mageinferno/docker-magento2-php/blob/master/bin/magento without testing it myself. I'll test this shortly.

Jup, that change takes care of the issue.


Project maintainance

Hello,
your plugin was a great start in getting DataTables to work with CakePHP 3. It seems you don't find the time anymore to maintain it.
I am maintaining my fork at https://github.com/ypnos-web/cakephp-datatables
I implemented several new features and also did some cleanup work. I am committed to keeping the plugin maintained.
I would like to invite you to reference my fork in your project or work together to keep the plugin maintained in the future.
Thanks again for your great work,
Johannes

Hi ypnos-web, i have a simple question regarding your fork.
Can i ask you here ?
it looks like nothing is drawn , i'm using the basic example with users, everything looks fine but nothing is drawn on the table.
If i'm not in the correct section please just tell me.
Thank you,
Stefan.

Please use issues in my fork to report your problem at https://github.com/ypnos-web/cakephp-datatables/issues
Sorry I didn't realize this was not enabled in Github before.


Improve error handling

You have a few approaches to error handling:

console.log() a message
throw and err object

I would improve this a bit.  What you do in 1. is good, but would be better done on stderr vs. stdout.  Also, I would consider exiting the program with an error code vs. crashing with a stack trace:
if(err) {
  console.error('Helpful error message here...');
  process.exit(-1);
}

I cleaned up error handling following the code template above in a recent commit.


Jobs list: incorrect counts for job status

Neal,
There are inconsistencies in the job status in the job list and the job status counts displayed above the list. Look at the screenshot below from one of the users. He has five failed jobs. However, above the job lists it says 5 completed and 0 failed. The same goes for the job status in the bottom right corner.
Please see if you can resolve this for March Release.
-Maulik

This is a duplicate of https://github.com/PATRIC3/patric3_website/issues/2060 .  @olsonanl any chance of fixing the count returned by AppService.query_task_summary?  It's been reported a a few times now.

Neal: I think AppService.query_task_summary is returning awe_status counts instead of status counts.
@olsonanl, is there a way to get job status?

It requires fiddling. If it's a big problem I can look into it; it's a problem that will likely go away entirely with the new scheduler.

Given the issue has been open for a while now, this is not urgent or critical.
I would wait until we have the new scheduler.
On Apr 16, 2019, at 10:25 AM, olsonanl <notifications<EMAIL_ADDRESS>wrote:
It requires fiddling. If it's a big problem I can look into it; it's a problem that will likely go away entirely with the new scheduler.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHubhttps://github.com/PATRIC3/patric3_website/issues/2235#issuecomment-483710062, or mute the threadhttps://github.com/notifications/unsubscribe-auth/AAJN3344YWTP3KM75IMXBDDPQXVXTANCNFSM4HDO43DQ.

I believe @olsonanl has fixed this last year as part of the new scheduler.  Closing.


user problem with last update

Hello, I had this working with two users without any trouble, with the last update now it looks like the sensors are switching. I cannot make it work with two users.
If I setup the first user it works ok, but when I add the second user, the firs user gets the data from the second user and viceversa.
I hope it can be fixed since it is an excellent integratio.

I got the same issue.

I got the same issue.


Assignments for session 5

1 Read and comment
Read Paper:
Sanders, Abraham, Rachael White, Lauren Severson, Rufeng Ma, Richard McQueen, Haniel Campos Alcanatara Paulo, Yucheng Zhang, John S Erickson, und Kristin P Bennett. „Unmasking the Conversation on Masks: Natural Language Processing for Topical Sentiment Analysis of COVID-19 Twitter Discourse“. Preprint. Health Informatics, 1. September 2020. https://doi.org/10.1101/2<IP_ADDRESS>83863.
Answer the following questions in a summary of 150 words:

In which ‘mode of inquiry’ (Marres and Moats 2015) is the research project and paper operating?
What are the issues that are silenced / cannot be grasped by this mode of inquiry?

2 Set up pipeline and comment
In the folder 'Pipeline/':

Setup the Jupyter notebook. A guide on how do this is provided in Setup.txt.
Preprocess and embed your data with the Language Model
Pick some comments you found interesting in your prior analysis. Get similar comments.
Answer the following questions in a summary of 150 words:

What can the model do well? When does it fail?
How can you use it in your project?

Add the folders 'data/' and 'output/' to .gitignore, so they are excluded from the repository
Commit+Push your Notebook with outputs to GitHub: create a new folder named '[your_name]_assignment4'.

3 Submit assignments on Github (reply to this issue) until 2 Dec 12h00 (noon)
Commentaries to 1 and 2 + URL link to your notebook

Reading Assignment
I think the research was done in precautious mode. They mostly focused on the content of the discussion while mostly disregarding the platform features at hand. They did not try to weed out any bot accounts or other platform-specific content, but on the other hand they did not try to analyze the platform specific dynamics at their core, disregarding retweets and not investigating how the platform dynamics play into the evolution of the discussion about masks. This also shows the first issue that is silenced by this mode of inquiry. The research analyzes the content of the discussion but pays no specific attention towards how the characteristic twitter dynamics can or actually do change the discussion. There is no look into how for example retweeting into different circles can influence the sentiment in a cluster or how likes and trending hashtags for some of the cluster keywords may influence the discussion by putting some tweets in the spotlight.
Pipeline Assignment
The model does well in finding similar or neighbouring comments. It fails seemingly in printing these as a heat map, as in my results similar comments were found but the heatmap showed no similarity between comments. In my project, I can use the model to quickly find similar comments to comments that are relevant to my research topic or that spark discussion about this. It can also be helpful for finding similar tweets or other sorts I guess. It can also be a valuable tool to show how diverse the discussion in the comments/tweets/etc is, as many similar comments may indicate that the comment section is more of a “conformist” section, with many people just agreeing to the video contents. It may also help finding similarities across different video comment sections, matching discussions to the same topic together, which helps finding relevant data for my research project and showing relations between these comments and discussions.

Paper analysis:

In which ‘mode of inquiry’ (Marres and Moats 2015) is the research project and paper operating?
What are the issues that are silenced / cannot be grasped by this mode of inquiry?

In this study, I could not identify any activity correlated to bot detection. On the other hand, non meaningful data points that were not related with the debates were filtered out by selecting those tweets that contain at least one keyphrase in both categories: COVID-19 and mask-wearing. For this reason I assume that this research operates in Precautionary mode.
Therefore I could not identify any form of affirmative and empirical approach.
The fact that the researchers were not focussing on platform specific features or effects belonging to the media technologies, could in my opinion compromise the representativeness of the data.
One aspect not taken into consideration by the approach used, is that the twitter API may introduce BIAS due to manipulation:  “... it has been shown that samples of tweets obtained via the API reflect the general user content generation patterns of the complete Twittersphere accurately”. We know from the previous discussed research, that this may not be true.
Model analysis:

What can the model do well? When does it fail?
How can you use it in your project?

I could test the algorithm with some comments regarding different debates in the video and the results are surprising.
The model is able to recognize similar comments quite well. For example, comments regarding the interviewee's abilities (“Who gave this man a degree”) or comments on the reliability of the television broadcast (“CBS is BS News”) are very well mapped in the vector space and produce very reliable results.
Since my dataset is very small (around 70 comments) I could not find any comment that can be miss-clustered due to the discrepancy between the data used for pretraining and my data.
The model obviously cannot recognize which idea is prevalent and also cannot gain a deeper understanding of the debate.
That’s why I would rather use the model only to perform some explorative analysis in order to brainstorm which kind of debates are present in the dataset, and then also to find and filter relevant comments related to my research goal.
LINK TO THE JUPYTER NOTEBOOK

Link to the notebook

Reading assignment:
Sanders, White, et al (2020) used Twitter to garner data on public opinions on wearing masks during the pandemic, an issue relevant to all tiers of society. While that would suggest they view social media as a relevant place of interaction, a few decisions in the way they approached the data reveal their mode of inquiry to be ‘precautious’.
They discarded elements of a common twitter controversy, such as retweets, the number of likes, and threads as not relevant. They also chose to not analyze hashtags but keyphrases. While that makes sense considering the number of tweets, it suggests the researchers were not looking for platform specific interactions. A question aiming at interrogating the role of Twitter in the controversy could not be answered this way. Discarding the retweet, while keeping the original tweet is telling of approach to social media and more specifically Twitter. Quoting a retweet is often done is a way that reveals the users’ attitude towards the issue at hand. The quoting is done in a mocking, agreeing or ambivalent manner.

1. Read and comment
I suppose the paper by A. C. Sanders et al. adopts a precautionary approach. The method is well suited for deploying social media analysis for social research and that’s what the authors do: they focus on content by building clusters (and subclusters) representing discussion themes and specific topics within each theme. In doing so, they filter tweets by keywords (not hashtags though), clean tweets by removing URLs, and discard retweets. They do not take into account the number of likes or followers, to whom tweets were shown. In other words, in the article it is not considered how platform-specific features may influence digital issue analysis (which is one of the main characteristics of the affirmative approach). The chosen mode of inquiry has its flaws, e.g. it cannot investigate connections between content and users, relations between different users, or the difference between knowledge claims and the political/social positions of users.
2. Set up pipeline and comment
The Transformers model is good at finding similar comments. It works better on shorter sentences, especially if the comments refer to the guest or host (it’s a podcast). The longer the initial comment, the more diverse the similar ones are. It’s not a flaw in the pipeline, but a research issue, since the problem of sentence similarity is not yet as well studied as the problem of the semantic similarity between two words or longer texts. However, I could use this method to cluster comments on topics. Although the heat map is not very practical/convenient for exploring a large number of comments, it is quite interesting to see it and get an idea of the dynamics of comments, e.g. how similar the nearby comments are. For example, I could select comments from one discussion and see how similar the first comments are to the last ones or find out if the top comments relate to the same topic etc.
3. URL link to the notebook
Jupyter Notebook

Pipeline assignment
In general, the Universal Sentence Encoder performs well for natural language processing tasks such as language translation, semantic analysis, outlier detection or fact-checking. It works by encoding the linguistic structures in the form of vector embeddings which represent the dependencies between individual words and sentences mathematically. The resulting embeddings allow for numerous further analytics. In the given assignment, we computed a similarity matrix for YouTube comments using angular similarity distance. However, there is a lot more that one can do using the embeddings. One exemplary application is sentiment-analysis or a hate-speech detector. As good as the model is for some tasks, it also has its drawbacks. First of all, it can only be as good as the data is, meaning that noisy data will make it more difficult to obtain meaningful results. Second, it has no deeper understanding of language. Therefore, it won't understand irony or other literal techniques often used in YouTube comments.

Reading Assignment:
In the light of the work of "Marres and Moats 2015" and since the paper "Unmasking the Conversation on Masks" is interested in deploying social media analysis for social research, it is operating in the precautionary inquiry.
The data was collected using API, filtered just according to the content to be strongly related to COVID-19 and mask-wearing and cleaned by removing URLs and non-punctuation characters.
In addition, there was no study of the impact of relations between users, number of likes, or bot detection.
It shows that the project was done irrespective of analyzing platform-specific dynamics in order to detect issue activity (which can be explained by the affirmative mode).
We can also talk about the biasness of API sampling we have read about last week and the researchers did not take it into consideration which increases the potentiality of skewness in the results.
Model analysis:
The model had a bad performance when I tried it with a large number of comments (about 22000), therefore I applied it for 1000 comments that present of course just a part of the debates. The model is very good at getting the most similar comments of a comment especially when the central comment is not long, otherwise, the results were less precise. The heatmap presents only one topic (related to water issues) although it seems reasonable. However, it did not work well for other samples.
The model can be used in many applications of natural language processing such as sentiment analysis and topic modeling. So, in my project, I can make use of the model when the aim is clustering the comments according to their similarity, detecting the most similar comments to one comment as we did, or to achieve the previous applications.
URL link to the notebook
https://github.com/Rahaf66/seminar_critical-social-media-analysis/tree/master/Pipeline/rahaf_assignment_4

Reading Assignment:
The research in this paper seems to operate in a "Precautionary mode" as the initial data obtained from "Twitter's API 1%" is filtered by keywords to reduce the data on which the analysis was based. However, it did not carry out an exhaustive cleaning of the data, which is the core idea of this mode of inquiry. Authors appeared to omit the treatment of bots or any other platform artifact to deal with the issue of platform bias. Moreover, limiting the extraction of the final data to data filtering by keywords does not exclude advertisement Tweets which may influence the analysis. The analysis did not follow an "Affirmative mode" of inquiry as they did not consider the platform-specific dynamic by discarding retweets, that would reveal interesting insights for the analysis.
Pipeline and comment:
Executing transformations on texts (sentences, words) to convert them into numerical vectors (Text embeddings) so that they can be easily analyzed is an important task in Natural Language Processing. However, the Universal Sentence encoder does quite well the job of finding semantic similarity among comments, disregarding the type of sentence encoding model (DAN and Transformer) chosen when trying with different comments. Although, in this case, the encoder performed well, I can imagine that sentences with considerable grammatically incorrect syntax, as usually seen on Youtube or other platforms interactions, as well as other literary figures such as sarcasm, satire, etc., will negatively influence the outcome of the analysis.
This technique, already available and implemented in the TensorFlow-hub, would be useful in the final project, leveraging it for the same purpose of this assignment, that is, finding and clustering semantically similar comments.
Link to the notebook

1. Reading assignment
I believe that the researchers applied in this scientific paper a precautionary approach mode to analyze data extracted from tweets about the topic of mask usage during the COVID-19 pandemic in the first five months of 2020. Their analysis focused mainly on the content of the extracted tweets and paid no attention to possible data manipulation and data bias due to bot activity, over-represented Twitter accounts or other platform related technical vulnerabilities (the latter aspects were discussed in the paper by Pfeffer, Mayer & Morstatter, 2018).

The tweets were collected from the Twitter streaming API in a “semi-selective approach”: they were filtered two times, the first time directly after collection by keywords “loosely associated to COVID-19” (Sanders et al., 2020) and the second time using Elasticsearch by “the criteria that a tweet must include at least one keyword indicating it is strongly associated to COVID-19 and at least one keyword indicating it is strongly associated to mask-wearing” (Sanders et al., 2020).
From my point of view, the filtered data doesn’t manage to paint the whole picture, so to say, because other important factors were completely discarded from the analysis. For example, the retweets of the collected tweets and their role in the overall discussion were not taken into consideration into the analysis; nor were the platform specific dynamics (for examples, hashtags or likes) when analyzing how the discussion about the topic of mask usage evolved within the five months or how certain sentiments might have been instigated or activated.

1. Reading
TODO
2. Pipeline
Well, I think it depends on the definition of 'similarity'. For me, similar comments to a comment, that critizises the AfD and those people who say that climate change is a lie, would not be AfD supporting comments. But of course, they are talking on a similar topic, so one could say that they are similar. The comment was also rather long I have to admit and I think the model works best for short statements. It was pretty good in finding the Claudia Roth related comments.
For my project I think it that the model could be helpful as a preprocessing step but I would have to manually look again at the comments that were found and sort them.
Notebook

1 Read and comment
• In which ‘mode of inquiry’ (Marres and Moats 2015) is the research project and paper operating?
• What are the issues that are silenced / cannot be grasped by this mode of inquiry?

The given Paper has to be written in precautionary mode since there is no discussion about platform specific subjects. Platform specific topics are also the issues that were not tackled by the paper. The authors just discuss the mechanism of the Twitter API. Unlike an earlier paper (Pfeffer, Jürgen, Katja Mayer, und Fred Morstatter. „Tampering with Twitter’s Sample API“. EPJ Data Science 7, Nr. 1 (Dezember 2018): 1–21.)  the 1% sample of the Twitter-Data is presented as accurate when it comes to represent the worldwide Twitter-Data.
While the methodological process of the analysis of the content is very cleary discribed, the platform-specific issues are not mentioned in a word. When dealing with social-media data it is crucial to keep in mind that there is an effect on the content of the data as well as the sampled data itself.
2 Set up pipeline and comment
    ◦ What can the model do well? When does it fail?
    ◦ How can you use it in your project?

The modell does really well when it comes to compare comments, that are written in a fairly simple structure. Also short sentences are handled better than long ones. If a sentence is short, the modell compares it as it should by content of the message. If the sentence gets too long, it basically finds other comments, that are also long but does only pay little attention to the content.
In the upcoming project this modell is good to get a feeling for how widely spread the opinions of the commenters are. Since our project is about single individuals it could be used to track other users that have similar opinions as the observed one. Other than that this modell might not be the perfect solution to use in our project.
Jupyter Notebook

Reading Assignment:
The authors of this paper clearly focus on the content of the data while discarding platform specific attributes like retweets and are not making use of features such as likes. This brings me to the conclusion that they do operate  with the precautionary approach.
To my understanding liking a tweed expresses confirmation and agreement with the tweet. Therefore using another approach likes could had been used for the trend analysis part of this research (complementary to the approach which is already used). Another issue which had not been investigated is detecting possible data bias. The data which is used in this work derives from twitters Sample API which is prone to data manipulation.
With further pre-analysis of the data, such biases for example from bots could had been removed from the data. This could had been done for the dataset which was used out of which the authors filtered samples containing the two keywords related to covid and mask to make sure that the data is from humans.
Pipeline and comment:
As I had a video in german language I first tried the model comments in germany language. By viewing the Semantic textual similarity matrix I was a bit confused as the similarity for all words was quite high. By taking a closer look I was capture for cell which were a bit darker I was able to spot same words patterns wich ware appearing in the two compared sentences like:
"liest du eigentlich auch mal was du schreibts ?"
"niemals, schonmal mal was von wahlfälschung  gehört ?"
By taking a look on the top similar comments I was not able to find the comments are sorted based on their length. Is I had only around 700 comments I viewed all and noticed that on the top comments with one or 2 full sentences appeared and towards the bottom of the list very long comments and only one or two-word comments were grouped.
Because I was curious If the model performs better for english language I tested the model also with comments on a english video.
For the Sematic textual similarity matrix it showed a clear difference. I assume the model was trained with english words and is less confused/ more certain with english sentences.
I think we can used this model as a kind of pre-filtering and sorting of the comments.

Read and comment
This paper seems to be in precautionary mode as the team does not make use of platform specific features such as retweets and likes and also performs some cleaning on the data before further processing.
Things that were not grasped or mentioned are things like bot manipulation or even manipulation of Twitter's sampling that we read about in a previous assignment. This mode of inquiry also misses further networks of agreement trough disregarding the like and retweet mechanisms that make it relatively easy to gauge how many people agree with something
Pipeline and comment:
The model seems to not be useful for smaller comment-chains. My video only had around 400 comments so there were very little similarites, as is evident by one comment being "Shame. There is no asteroid, it is man made agenda to sow fear among the
populace. Yet many believe it 😔" and the most similar comment being "Dude, are you kidding yourself? The planet is HUGE compared to your body. Your body will succumb to this cancer at a rate faster than the planet. Think about
the obvious" with seemingly no similarities.
The model could be quite useful in the project for the influence of the alt-right to determine the amount of use of alt-right dogwhistles, since these can be presumed to appear relatively often and then use this to classify the users accordingly.
link to notebook

In which ‘mode of inquiry’ (Marres and Moats 2015) is the research project and paper operating?
What are the issues that are silenced / cannot be grasped by this mode of inquiry?
The mode is most likely the precautious mode. The researchers used the sample data set (1% free api) from Twitter and filtered Posts by keywords. Firstly they filtered all posts loosely connected to covid-19 that were collected over a 5 month period. Afterwards those were again filtered for keywords related to covid-19 and mask-wearing. But while they cleaned the data of URLS and unrelevant characters for easier processing, they did not filter again for bots or possible bias (corporate advertisements, possible manipulation of the sample set) in the data-set. This filter method excludes all posts that do not mention covid-19. In the designated timeframe from the researches it is highly likely that the mentioning of facemasks alone is related to covid-19. A great number of relevant comments is likely lost in the analysis due to this filtering method.
Furthermore they discarded all platform specific elements like retweets, followers or likes, despite this being integral features of Twitter. Therefore any influence those features have on the discussion is lost. This is a major issue that is being silenced, since the number of retweets and the likes of such tweets influence the sentiment of the followers who receive them.
What can the model do well? When does it fail?
The model does well in finding similar comments, but the heat-map feature does not show much usefulness. The labels are only readable when using truly small sample sizes, at the same time, only by using large sample sizes does the heat-map show if there are large clusters of similar comments or if the comments are mostly individual statements. Sadly due to the labeling issue one can't determine which comment has many similar comments and which comments do not.
How can you use it in your project?
It makes it easier to find how many people write similar comments, which helps to determine if a specific comment is a prevalent opinion distributed over the comments or just a minority, one that might even have gamed the sites algorithm to be represented more strongly in the topmost comments. In my test, both with random comments and with specifically selected ones, the selected similar comments made sense and I could not find any explicit false positives.
Jupyter Notepad

Was done by Alexey Khrustalev and Anastasiia Todoshchuk
1
From reading the paper “Unmasking the Conversation on Masks: NLP for Topical Sentiment Analysis of COVID-19 Twitter Discourse” it was hard to define certainly, which of the approaches mentioned in the Marres and Moats paper were applied to the current research. The easier way is to say, which approaches were not applied and to confirm this position. Firstly, according to the paper “Tampering with Twitter’s Sample API” by Pfeffer, Mayer, and Morstatter, Twitter API provides researchers with unrepresentative data sampling, which obviously can easily lead to wrong results of an analysis, and it can be treated as the feature of the mentioned API and Twitter itself. There were no mentions of this feature in the current research and these characteristics were not taken into account. That’s being said, we can’t say for sure that the researchers used a truly precautionary approach or, at least, to try to use such an approach was not perfect.
2
The model shows good results in finding similar comments. However, it has some problems with the heat map, as the model fails to plot the similarities in this way. I can imagine using this model to find similar comments and to analyze them. Since our goal is to detect bots, it may be possible to find the bot’s comment and to find similar comments using the model. It could limit human interaction, as it would be sufficient to find only 1 bot-comment using a human. Then, the process could be repeated iteratively and all the similar comments could be treated as the bot’s comments. There can be another way to use the model. It may be not that easy to say for sure that a comment was written by a bot. To prove the assumption the model can be used: if there are a lot of super (open question: how to define the border?) similar comments - they could be written by the bots.
https://github.com/Einnmann/khrustalev_todoshchuk_assignment4/blob/main/Assignment-4_Language_model.ipynb

The paper „Unmasking the Conversation on Masks: Natural Language Processing for Topical Sentiment Analysis of COVID-19 Twitter Discourse“ is operating in the precautionary mode of inquiry (Marres and Moates). Such conclusion is clear from the methodological approach the research is following: they directly strive into the data, without paying attention to possible platform-based specifics (likes, RT, Quoted RT) or ‘irregularities’ such as bots (both human or programmable). This way of research is good in a sense that it takes only the pure data into account, but it also has some flaws - meaning that the selected tweets could be poisoned by bots or company accounts (in this case big public health institutions for example). Also the lack of platform specific points of view could affect the results. Another interesting thing is that in this paper Twitter’s API is praised as objective, while in paper (Pfeffer, Jürgen, Mayer, Morstatter) from 2018. we could see totally different thing. It could be that Twitter improved the algorithm in these two years.

When it comes to the work at the notebook, it is worth mentioning that the pipeline clearly has some space for improvement. The code would be useful in a research project as a broad view of comments topics, especially in videos with high number of comments (thousands). The heatmap did not produce what I expected.  Btw, the video being observed has almost 5000 comments.

1. My classmates above have found the consensus that the paper was conducted in the precautionary mode, which is hard to for to argue against and by the principle of exclusion I, too, end up at this conclusion. Clearly the authors did not mention anything about platform specific influence on the controversy and they did in fact heavily filter their data in advance. This should exclude the empiricist and affirmative approach. However, what they did filter out were retweets as well as alleged off-topic data, which is not connected to detecting bot activities. So there is no compelling argument to be made for a precautionary mode as defined in Marres and Moats 2015.
But maybe I am still lacking a clear understanding for what the different approaches stand for and whether they define a cover for all possible approaches.
2. As one would expect the model works well if there are similar words in two different comments and it works best if those words are arranged in a similar order. This is the case e.g. when people are referencing quotes from the video, so in such cases the pipeline usually found highly related comments.
On the other hand when I tried it for some random comment containing the word "you", I received a ton of other comments with the word "you" in them, but naturally the content of the comments was not related at all.
I haven't decided on a project topic yet, so I am yet to find out where these tool will come in handy.
Notebook in my fork repository

1. Reading Assignment
I suppose that the authors did the research mainly from a precautionary mode since the consideration of platforms features and bot detections are not undertaken by researchers (as far as I recognized in this paper).
The researchers mainly focus on key phrases regarding "COVID 19" and "wearing masks" but ignore user interactions focused on certain topics (clusters and sub clusters in this paper). Therefore, the researchers although discovered the concentrated discussion around certain topics, but failed to discover the dynamics of discourses around them. The platform features of twitter which may influence the dynamics of discussion stays currently unresearched. For example, retweets without replying or number of likes show somehow agreement on certain comment, but when this features/interactions get ignored, the cluster outcomes maybe inaccurate/in-appropriate for user-trend discovery.
Additionally, the clustering method applied in this research seems to be hard clustering which ignore the interaction/fuzzy areas between different clusters. Some comments may belong simultaneously to disjoint clusters and may display links/relationships between different discussion clusters. Furthermore, the analytical biases from different sources (data bias, sampling bias etc.) can also rise problems in research.
2. Comment Analysis Assignment
The most problems I encountered with was the unexpected long execution time and I faced the ResourceExaustedError serveral times.   It is horrible when dealing with large set of data and get my laptop crashed (But maybe, it is not the problem of the algorithm, rather of my computer capacity).
The model works well for explicit/short sentences but seem how misunderstood/misinterpreted long sentences with complicated structure. However, it did well in discovering neighbors (find similar comments) if the given comment is clear in its wording and meaning (not emotionally with ironic factors or so).
I think this model can be well used to find similar comments if we want to research on single comment and explore the discussion around similar opinions, but how and whether it can be used into our project, depends totally on the research question and research design.
3. Link to Pipeline
xinyu_Assignment_4

1. Reading
The aim of this analysis was to study COVID-19 and mask related Tweets and detect specific topics and sentimentality trends within this content over time.
But the authors in this paper only focus on the content of the tweets: tweets about COVID-19 and wearing masks, but don't differentiate between the two topics.  Retweets, number of likes or any other interactions were not taken into account, which are typical features of this platform. Thus, I think the precautionary approach describes it the best. In addition they use the 1% Twitter API which can result in bias due to data manipulation (as we previously learned).
2.Pipeline
I already worked a bit with similar methods previously, hence I know that this can be computationally expensive. My laptops memory is definitely not good enough to perform a large analysis (this is why I could only use 10.000 out of 40.000 comments and reduced the batch size). But in general the model performs well and in case of my comment example it could find a lot of similar comments. A common procedure also is to fine tune a model based on the data, but that is beyond the scope of the course. I like the idea of working with embeddings, but for an analysis with large datasets it's computationally impossible for me to use this. But if for example a topic modeling is performed beforehand (or some additional filtering of the text) this could reduce the memory consumption. OR I have to use my work laptop :D
3. Link
Assignment Link

1. Reading
The research paper uses a precautionary mode of inquiry. Social media and platform-specific artefacts such as URLs and non-punctuation characters are removed from the dataset to analyse the content with the use of natural language processing, clustering and sentiment analysis.
In a precautionary approach, the social media platform's influence on the controversy itself is not taken into account. Contextual information is deleted from the dataset, to make it more substantial. Nevertheless, especially this contextual information could give information on the nature of the controversy and its dynamics. Do people in real-life express the same level of polarization as found in the data set or is it just a social media phenomena that take place on Twitter solely? Who are the main actors in spreading content on COVID on Twitter and how do they influence the discourse?
2. Pipeline
The model does well in finding similar comments that are based on the same topic and contain the same buzzwords. The expressed opinion is not distinguished as easily and you find comments that deal with the same topic but express totally different views on the subject of climate change. Furthermore, the model works much better with short statements like „I think this guy is getting paid by oil companies".
The project that I will work is the analysis of discourse dynamics in social media. The model can help to identify topics or themes that are reoccurring and have a high potential to spark a specific outcome of a discourse. It would be interesting to find out what topics are most hazardous. Furthermore, it could be interesting to use sentiment analysis to analyze what feelings are expressed for videos that are scientifically grounded or based on conspiracies and compare those two.
3. Link
Notebook

Reading assignment:
Sanders et al. drew conclusions about public attitudes towards the preventative measure of mask usage during the COVID-19 pandemic. Since the study was focused on topic clustering and sentiment analysis, a detection of possible non-human tweets produced by bots was not performed. The authors of the study payed most attention on the content of the discussion itself and not on the plattform-specific dynamics and features of Twitter. Retweets, the number of likes and threads were not considered, which are key features of twitter that can definitely influence the course of debate. The general approach of Sander et al. Can best be assigned to the precautionary mode of inquiry.
The lack in the analysis of platform-specific effects can be seen as an issue of the approach, since it decreased the representativeness of the overall findings.
Model analysis:
The model in general seem to work well. However, comparing a small data set of only ~500 comments (that also tend to be very long) made it very difficult to find similarities in the comments. Therefore the heatmap could not present very meaningful results, which should change when investigating a larger amount of comments. Here, I only tried a test video and didn’t want to redo the entire analysis due to the time consumption regarding the word embeddings. If I will use the pipeline in the final project or not very much depends on the actual task. In any case, the pipeline is a good starting, that can be expanded depending on the planned analysis.
Notebook Link

1 Reading assignment

Unlike other fellow students, I would like to assume that the paper of Sanders et al. features an empiricist approach. Though the scholars cleaned the data before data analysis, the paper focuses on conversations on mask usage during the COVID-19 pandemic. It did not examine the negative participation of digital platforms through bot and influentials detection (precautionary mode) or take platform-specific aspects into account (affirmative mode).
The findings are limited to the content of the related discourse on Twitter. The users are neglected in this context. The possibility of unauthentic behaviors caused by bots or political campaigning during the covid is not considered.

2 Language Model

The model did well in finding similar comments with related keywords. Yet, it did not offer satisfactory performance when dealing with lengthy comments with multiple arguments and ideas.
The methods in this paper is highly relevant for the project discourse dynamics, from clustering to sentiment analysis; all these parts are widely used in the field of related research.

3 Link
https://github.com/FUB-HCC/seminar_critical-social-media-analysis/blob/master/Pipeline/xixuan_zhang_assignment_4/Assignment-4_Language_model.ipynb

Assignment 1
The most fitting mode is the precautionary mode, since twitter platform specifics, such as the use of likes, retweets, hashtags were not (directly) analysed in this paper.  The main focus is only on the meaning/intention/sentiment of the text in the tweets that were processed.
The tweets are collected through the 1% API, which we know not to be a representative sampling method for social media analysis, which might overly represent bot accounts or people exploiting the techniques shown in last weeks assignment. There was also no filtering done to prevent parts of the data set (around 1.000.000 samples) to be from users which may spam posts (bots) with the filtered out keywords.
Assignment 2
Because of an automatic python update to 3.9 on my system conda broke down completely for me today. I can't activate an environment anymore and tensorflow isn't available for 3.9 yet. I will address this in our meeting tomorrow.


Fix for decimals in update

Unlike bootstrap, during prepare-update, decimals set to 1 , not 1

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

1 of 1 (100.0%)  changed or added relevant line in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 53.722%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
2035

Relevant Lines:
3788

💛  - Coveralls

Should probably merge https://github.com/ergoplatform/oracle-core/pull/311 before we merge this, to test that CI is actually working.


Add R-L-A-P to QC html report

Orientations (R-L, A-P) does not display on QC html report. Please add them.

Now fixed :-)


Compare with (void *)0 instead of casting to uintptr_t

Such casts otherwise pessimize optimization

What is this change trying to address? This seems like a no-op.

It aligns with the definition of NULL better

Well firstly, _dispatch_needs_to_return_to_kernel is reading an integer value out of a TSD slot, in order to determine if we need to return to the kernel to pump events. So a comparison to NULL is not valid here - nor is (void *)0 because we're not reading pointers.
This change seems like a change for the sake of it without understanding how this code is used in the larger context. Please refer to the guidelines document on how to identify and make useful changes to the project.


getting the _id inside of autoValue

Hello Eric,
I just had the use case inside an AutoForm where I wanted to get the _id of the document inside of the autoValuefunction. But even in isUpdatecontext the field _id is not accessible. The this context has a docIdbut it is undefined.
Shouldn't be the docIdset?
Cheers

@aldeed I'd also lke to have docId available to the client. I'm using a custom validator to verify that field A and field B are a unique pair. I find it easiest if I can do this via the docId, so right now I'm limisted to putting this on a server side check.


FQ: Markdown support

Hey @Tarpsvo , cool package thanks for that :)
Wanted to throw in the mix: It would actually be cool if the NotesField would (maybe optionally) use the MarkDown field.
Thoughts?

We already have the option of using the Trix field, but we currently do not offer the option of markdown field. However, it would indeed be useful. ;)

Hey @madsem
We are happy to review pull requests and merge them after testing!


No B64 Password file

Ive run all of the .bat files, starting with the startup one first, then it said that there was no encrypted password file, so then I went to the b64 password batch and it said it was stored somewhere but it wasn't. ( i figured it out, my bad)

It starts a connection to your nextcloud instance via webdav and creates a net drive in windows explorer, so you have to setup/edit these lines in the file:
set "_HOST=example.com/nextcloud/remote.php/dav/files/USERNAME/"
set "_USER=example_username"


[Auto-conversion]-Implement full screen processing overlay (including header) with ability to stop and go back to form, cancel process, and open a new simularium instance in a new tab

Overlay while the file is being converted
link to design

Design question. @lynwilhelm
Design here shows semi-transparent overlay on top of viewer screen. It is also shown in sequence of previous modal based designs.
In the current design the input form is full screen, not modal, so I assume that the transparent processing overlay should reveal the input form page behind it and not the viewer screen. Please correct me if that's wrong.


Use inside of env:

Hi, i want to rename the env variables (select only a few) returned from aws secret manager but it looks like its not possible to use the values inside of env.
jobs:
  deploy:
    name: deploy
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@master
    - name: Configure AWS Credentials
      uses: aws-actions/configure-aws-credentials@v1
      with:
        aws-region: us-east-2
    - name: Read secrets from AWS Secrets Manager into environment variables
      uses<EMAIL_ADDRESS>      with:
        secrets: |
          prod-rds
        parse-json: true
        disable-warnings: true
    - uses: actions/setup-node@v1
      with:
        node-version: '14.x'
    - name: deploy
      working-directory: example-lambda
      run: |
        npm i -g serverless
        npm install
        echo $DATABASE_NAME
        serverless config credentials --provider aws --key $AWS_ACCESS_KEY_ID --secret $AWS_SECRET_ACCESS_KEY
        sls deploy --stage production
      env:
        DATABASE_USERNAME: $PROD_RDS_MYSQL_USERNAME
        DATABASE_PASSWORD: $PROD_RDS_MYSQL_PASSWORD
        DATABASE_NAME: $PROD_RDS_DATABASE_NAME

The echo $DATABASE_NAME comes back as just "$PROD_RDS_MYSQL_USERNAME"
Any ideas how i might do this?

Should be like this instead
 DATABASE_NAME: ${{ env.PROD_RDS_DATABASE_NAME }}


Security fixes

Even if not directly exploitable, the source code contained unsecure operations..
In Image_lib.php escape file paths before giving them to the shell, otherwise an attacker that controls the file names / dir names can inject arbitrary commands.
Note that CodeIgniter already fixed this Image_lib.php
function get_remote_data is not called with user provided url but I applied a simple fix to prevent future exploitation

Spot on! Thanks for the PR. Please direct it to the develop branch instead. 👍🏾


GH-120 Attributes Autodoc

Add links in API documentation to the sections in the specification for each STIX Object type.
Automatically add properties for each STIX Object type to their documentation.
Fixes #120.

Codecov Report

Merging #128 into master will decrease coverage by 0.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #128      +/-   ##
==========================================
- Coverage    97.7%   97.68%   -0.02%
==========================================
  Files          56       56
  Lines        5453     5453
==========================================
- Hits         5328     5327       -1
- Misses        125      126       +1

Impacted Files
Coverage Δ

stix2/v20/sro.py
100% <ø> (ø)
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stix2/v20/observables.py
100% <ø> (ø)
:arrow_up:

stix2/core.py
100% <ø> (ø)
:arrow_up:

stix2/v20/common.py
98.66% <ø> (ø)
:arrow_up:

stix2/v20/sdo.py
100% <ø> (ø)
:arrow_up:

stix2/properties.py
99.54% <0%> (-0.46%)
:arrow_down:

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Joining tables, selection of all fields not working

$db_select = CLASS_DB::getInstance()->select(
	$this->tables['fields'].'(f)',
	array(
		'[>]'.$this->tables['profiles'].'(p)' => array('field_id')
	),
	array(
		'f.*',
		'p.'.$this->idfields['profiles'].'(in_profile)'
	),
	array(
		'AND' => array(
			'f.deleted' => 0,
			'f.active' => 1,
			'f.isuserdata' => 0,
			'f.template_id' => $node_info['template_id']
		)
	)
);

Expected:
SELECT "f".*,"p"."user_id" AS "in_profile" FROM "portal_userlist_fields" AS "f" LEFT JOIN "portal_userlist_profile" AS "p" USING ("field_id") WHERE "f"."deleted" = 0 AND "f"."active" = 1 AND "f"."isuserdata" = 0 AND "f"."template_id" = '1'
Generated:
SELECT "f"."","p"."user_id" AS "in_profile" FROM "portal_userlist_fields" AS "f" LEFT JOIN "portal_userlist_profile" AS "p" USING ("field_id") WHERE "f"."deleted" = 0 AND "f"."active" = 1 AND "f"."isuserdata" = 0 AND "f"."template_id" = '1'
Note that "f"."" is not "f".*

@tds4u Wildcards in table names are not supported. Its a bad practice to do so.

If you have user field tables with dynamic fields for data than it's equal whether to join with * or list all table fields. Getting user preferences in this case needs all fields. So I can't select only needed fields. And first getting field names will result in additional query which also can be slow on MySQl.

@tds4u Okay then, I guess a little change could be made:
Change
https://github.com/catfan/Medoo/blob/6798b9698ee60ee98a4d342485904322922b2b3b/src/Medoo.php#L356-L366
To
	protected function columnQuote($string)
	{
		preg_match('/(^#)?([a-zA-Z0-9_]*)\.([a-zA-Z0-9_]*)(\s*\[JSON\]$)?/', $string, $column_match);
		if (isset($column_match[ 2 ], $column_match[ 3 ]))
		{
			if ($column_match[ 3 ] !== '*')  $column_match[ 3 ] = '"' .  $column_match[ 3 ] . '"';
			return '"' . $this->prefix . $column_match[ 2 ] . '".' . $column_match[ 3 ]';
		}
		return '"' . $string . '"';
	}

Doesn't work. Problem is around line 386:
$match:
Array(
  [0] => f.
  [column] => f.
  [1] => f.
)

preg_match('/(?<column>[a-zA-Z0-9_\.]+)(?:\s*\((?<alias>[a-zA-Z0-9_]+)\)|\s*\[(?<type>(String|Bool|Int|Number|Object|JSON))\])?/i', $value, $match);

@tds4u Then change the regex to
preg_match('/(?[a-zA-Z0-9_.*]+)(?:\s*((?[a-zA-Z0-9_]+))|\s*[(?(String|Bool|Int|Number|Object|JSON))])?/i', $value, $match);

No. You can not do that while you joining two or more tables. Because it will be conflicted if those table with same column name. Database engine did not know which column should be output. This will be error.
So, you may have to specify all column name while you joining tables, or find the way to get or cache the targeted column name at first.
Additionally, there too much article talking about that using * for getting all column will be slow down the query speed. You will get more unexpected data and using more memory.

It works on phpmyadmin. Please resolve


Extreme slowdown at specific execution timesteps

Hi all,
I am releasing particles in an open 3D domain with velocities only in the [x,z]-plane.
I am running into a sudden extreme increase in execution time at specific moments for specific combinations of dt and outputdt. At different starttimes, fieldsets, and both in forwards in time and backwards in time execution parcels takes extremely long at the same specific timestep.
Decreasing dt seems to make this moment come earlier, while decreasing outputdt seems to do the opposite. However, some values for dt work significantly better, i.e. no difference between 0.005 and 0.01 but large difference between 0.005 and 0.004.
A number of combinations I have found:
DT [s]                      OutputDT [s]                     Timestep [s] where parcels starts taking very long
0.002                      0.1                                     0.6
0.002                      0.08                                   0.64
0.002                      0.01                                   5.42
0.004                      0.1                                     0.9999
0.004                      0.08                                   1.28
0.005                      0.1                                     44.8
0.005                      0.08                                   44.8
0.01                        0.1                                     44.8
0.01                        0.08                                   44.8
Since the bug is irrespective of starttime or fieldset, I do not think anything is happening to the particles. My memory looks fine. I get no errors. The problem is exactly reproducible.
ExtremeSlowdown.txt
Any ideas what this might be?

Hi, as a quick fix for the bug, we've tried increasing the __tol variable. This seems to increase the time it takes before the extreme slowdown occurs and perhaps even speed up the continuation. It doesn't get rid of the problem altogether however.
Now, I have found that for __tol == 1e-4, outputdt = 0.1 and dt = 0.001 I can get Parcels to execute for 44 seconds, with a slowdown at 21.3 seconds. After the slowdown, it seems something changes in the outputdt, as you can see in these figures:

Up until 21.3 seconds, each timestep takes 0.1 seconds, so the line is nicely 1:1. After the slowdown, the dt becomes variable. You can see this in this figure:

The outputdt starts at 100 ms = 0.1 s, but after 21.3 seconds it hops around from 100 to 400 to 100 to 300.
Christian mentioned this bug might be related to the pull-request  #751 .

I have been looking at it some more and I have two things that I'd like to check:

For now, we have only changed something in the JIT-version of the execution loop, should we also change something in the python-version?

The tolerance variable is compared to dt_pos in the python-version and __dt in the JIT-version. These are calculated by taking the minimum of p.dt and the absolute difference between endtime and p.time. Is it possible that the bug is caused by p.time overshooting endtime by more than the tolerance, creating an infinite while-loop? And if so, could we not change the definition of dt_pos so that the while-loop also stops when p.time is on the 'wrong' side of endtime?

Feel free to ask me for clarification of my thoughts

Actually, let's look at line 942 - 944:
        dt_pos = c.Assign("__dt", "fmin(fabs(particles[p].dt), fabs(endtime - particles[p].time))")
        pdt_eq_dt_pos = c.Assign("__pdt_prekernels", "__dt * sign_dt")
        partdt = c.Assign("particles[p].dt", "__pdt_prekernels")

Problem: p.dt is not always pset.dt, because it is not reset for erroneous loop executions, and this offset error accumulates in p.time.

What I'm thinking here (and first test in scipy and then here): semantic problem of the ErrorCode.Success - the code expresses (a) all particles that still need to be computed per time step; (b) all particles that are (already) successfully calculated; (c) all particles that need to be re-calculated. If we would actually split that up, we could (in the particle for-loop and as pre-condition to the while-loop for kernel execution) actually say to only (re-)calculate particles that need to be calculated - and ignore additional kernel evaluations. That would, semantically to me, also be the expected norm.
It does not prevent re-calculation in the future ... if you bind particles in groups and, for some user-defined kernel behaviour and reason, particles need to be re-evaluated, then the user kernel could manually reset this 'ErrorCode' (which, at that point, is more of a 'StatusCode') at will.
Still, the norm (and expectation, I guess) is: If a particle is, for one dt time iteration, evaluated correctly (without error), do not re-evaluate it.

Yes, I think that implementing this semantic change is a very good idea. Feel free to implement a proof of concept?

fixed by #762

Hey, @cjongedijk and I have done some more testing, since we've figured out that this problem hasn't really been solved after all. First we found out that p.dt becomes really small at the extreme slowdown (1E-4 -> 1E-8). Then, we figured out that the slowdown is caused by the fact that this dt stays 1E-8 throughout the rest of the simulation and therefore 1E4 more computations have to be carried out.
Next we tested how the p.dt resets after finishing the kernel.execute loop. I.e. we ran a simulation with dt=0.03 and outputdt=0.1. For the first three timesteps p.dt=0.03 and then to end up on p.time=0.1 (for writing) p.dt becomes 0.01. We would expect p.dt to return to 0.03 in the following kernel.execute loop. It seems the function update_next_dt is written for this purpose. However, p.dt remains 0.01 throughout the rest of the simulation, so we suspect this function does not work as intended.
In our original simulation the outputdt is a multiple of dt, so this behaviour is not expected, but it might still happen due to rounding errors. We will now further investigate why the p.dt does not get updated and how the p.dt becomes 1E-8 all of the sudden late in a run.

Hey, @cjongedijk and I have done some more testing, since we've figured out that this problem hasn't really been solved after all. First we found out that p.dt becomes really small at the extreme slowdown (1E-4 -> 1E-8). Then, we figured out that the slowdown is caused by the fact that this dt stays 1E-8 throughout the rest of the simulation and therefore 1E4 more computations have to be carried out.
Next we tested how the p.dt resets after finishing the kernel.execute loop. I.e. we ran a simulation with dt=0.03 and outputdt=0.1. For the first three timesteps p.dt=0.03 and then to end up on p.time=0.1 (for writing) p.dt becomes 0.01. We would expect p.dt to return to 0.03 in the following kernel.execute loop. It seems the function update_next_dt is written for this purpose. However, p.dt remains 0.01 throughout the rest of the simulation, so we suspect this function does not work as intended.
In our original simulation the outputdt is a multiple of dt, so this behaviour is not expected, but it might still happen due to rounding errors. We will now further investigate why the p.dt does not get updated and how the p.dt becomes 1E-8 all of the sudden late in a run.

is this now fixed, @cjongedijk , @SLYpma and @reint-fischer ? if so, we could close the issue.


add doc for CSIServiceAccountToken

enhancement issue: https://github.com/kubernetes/enhancements/issues/2047
KEP: https://github.com/kubernetes/enhancements/tree/master/keps/sig-storage/1855-csi-driver-service-account-token

/hold

/hold cancel

/hold
pending merge of https://github.com/kubernetes/kubernetes/pull/93130
/milestone 1.20
/assign @eagleusb
/sig auth storage

Hi @zshihang
The earlier you can provide some content for documenting this feature, the earlier that SIG Docs can get reviewing. We'd really appreciate a chance to get as much reviewing done as we can before KubeCon.

the doc will be added to this repo https://kubernetes-csi.github.io/docs. the doc change for kubernetes.io is just the feature gate. cc @msau42

It might be useful to add a task page about configuring service account tokens (and without making security slips). What do you think @zshihang ?
If that's true, it might well be OK to track that need as an issue rather than delay shipping the feature. But, let's still track it.

that's a good idea. this service account token is particularly for csi driver so the csi website would be a good place to start.

added the doc in csi website https://github.com/kubernetes-csi/docs/pull/383

Thanks @zshihang
/lgtm
/approve


Merge to live!

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For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Ui: Add colors to tag labels

The colors of the tag labels remain consistent between different runs of the program.

The latest pull request has been reviewed and merged. This the older version will be closed.


Modrinth

I would love to use this mod in my modrinth modpack (Better Experiences+).
Please make a modrinth version.

It's currently under review on Modrinth.


Add dependencies of assets to the list of watched assets for transition out of loading state

Here's an example:
I use a custom loader for ldtk map files which loads and parses the ldtk file, and provides it as an asset to Bevy. I also load all of the images used for sprites/backgrounds/tiles/etc when I do this, and use the LoadedAsset::with_dependencies(...) member to add them as dependencies to my ldtk asset.
When I register this asset using an AssetCollection, as described in your readme, it seems to be correctly waiting on the ldtk file itself to load. However, it does not wait on all of the referenced images.
I will look at creating a dynamic asset description when constructing the asset, or simply adding the images separately. It would be a nice feature if your crate could detect this, however, due to the fact that the ldtk user could change what images they utilize at any time.
I don't know of other examples of assets with dependencies, but I imagine they could take advantage of this feature as well.

It sounds very reasonable to wait for dependencies to load.
I shortly looked into this and it seems I do not have access to the dependencies where I would need to. The only place I could find that makes use of them is the asset server itself.

Honestly when compiled with --release there is very little time between when my custom ldtk loader finishes, and the images finish loading. In debug builds, it takes like five seconds though (on my system without dynamically linking bevy).
So I think it would be a great feature to have, and could also be argued to be the "correct" way. But, considering the unsupported and undocumented nature of custom asset loaders, I could totally understand if this was a low priority at this time.
Thanks for looking into it!

In my opinion the priority is pretty high. There is just little I can do in the plugin itself, since Bevy does not expose the dependencies.
Without bevyengine/bevy#5297 I can only think of ugly workarounds, that would require the asset loaders to somehow tell bevy_asset_loader about their dependencies. And those wouldn't even work with "standard" loaders like Bevy's GLTF loader.

This can be implemented as soon as bevyengine/bevy#8624 lands (Bevy 0.12 in around 3 months).

The latest version waits until a handle and all it's dependencies are loaded.


tests, xfail: Change XFail API to wrap the expected failure

What this PR does / why we need it:
The current XFail implementation incorrectly marked regular tests as
xfail. This has been observed when an xfail test passed but the following
one, which was not marked as xfail, failed as an xfail.
The original implementation reverted to the regular non-xfail handler
only if a failure occurred, which is obviously incorrect.
The XFail API has been adjusted to wrap a body of code that is expected
to fail and revert the fail handler when exiting the body.
This approach is also more accurate, as the area where the expected
failure is covered is limited and managed.
Which issue(s) this PR fixes (optional, in fixes #<issue number>(, fixes #<issue_number>, ...) format, will close the issue(s) when PR gets merged):
Fixes #
Special notes for your reviewer:
Release note:
NONE

/cc @AlonaKaplan @alonSadan

/cc @AlonaKaplan @alonSadan

/approve

/approve


Update README.md for text customization

This PR updates the README to clarify how change the title and subtitle text attributes by using .popupBarTitleTextAttributes() and .popupBarSubtitleTextAttributes() modifiers.

Updated to reflect the changes made in 1.7.5


New pipelines for the new vNextPrototype branch

Fixes #minor
Description
The new pipelines are vNext-BB-DotNet-CI-PR and vNext-BB-DotNet-CI-PR-(MacLinux).

Changes:
yaml files added to new folder build\yaml\vnextprototype
Comment fix in build/yaml/botbuilder-dotnet-ci.yml.

Pull Request Test Coverage Report for Build 271933

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 78.193%

Totals

Change from base Build 271871:
0.0%

Covered Lines:
23920

Relevant Lines:
30591

💛  - Coveralls