Analyzer: Built-in identifier as prefix is not detected as a compile-time error.

This is the analyzer specific sub-issue for #28811, which has the details.
The test built_in_identifier_prefix_test.dart implies that built-in identifiers should be accepted as the declared names of library prefixes, which is not the case.
The test should be adjusted accordingly, and the analyzer should detect and flag such prefix names as compile-time errors.
Cf. the spec change (#28817), usages as in #28811 should also be flagged as compile-time errors (it is not just a malformed type).

In this CL, built_in_identifier_prefix_test.dart has been split into several tests, and the new tests contain updated expectations.
There are still some issues: dynamic<int> is accepted, typedef Function() x = null; causes a crash, operator Function() x = null; is not a compile-time error (even though it uses a built-in identifier in a type annotation), and 19 other issues (marked as Issue 28813 in the status file).

Yes, I think these have all been handled now.


get_historical_lmp takes a market object, not the default string

The function for get_historical_lmp prescribes a string that represents the market (e.g."REAL_TIME_HOURLY") but the conditional in the function takes an object (e.g. self.REAL_TIME_HOURLY)
I got around this with the following:
date= "June 1, 2020"
rth=isodata.base.Markets.REAL_TIME_HOURLY
data=caiso.get_historical_lmp(date=date, market=rth, nodes=None)
I found this example for CAISO, but I'm not sure if it persists across the other ISOs.

"REAL_TIME_HOURLY" might be returning "REAL_TIME_15_MIN" instead.

I made a release yesterday (v0.6.0), that cleaned up this code a bit so you can use string or object
>>> from isodata import Markets
>>> caiso.get_historical_lmp(date=date, market=Markets.REAL_TIME_HOURLY, locations=None)

works the same as
>>> caiso.get_historical_lmp(date=date, market="REAL_TIME_HOURLY", locations=None)

Also, note that the nodes parameter changed to locations to make it more general purpose
if you need to upgrade  you can run
pip install --upgrade isodata

you can confirm it upgraded like this
>>> import isodata
>>> isodata.__version__
'0.6.0'

The change for the market/location parameter works great now. Thanks for addressing this so quickly!
The remaining (minor) issue is that "REAL_TIME_HOURLY" seems to be returning "REAL_TIME_15_MIN". I just changed the indexing in post.
>>> data = caiso.get_historical_lmp()[::4]

I looked into it more and "REAL_TIME_HOURLY" is probably the wrong name for the market.
It is actually returning the results for the HASP (hourly ahead scheduling process) market in CAISO. I'm not very familiar with it myself, so im looking for a good source that explains it to share
But, my understanding is that bids get placed every hour, but the actually settles every fifteen minutes which is why you get 4 prices an hour.


Upgrade dependencies

Submodule:
Direct:
github.com/pion/dtls/v2 v2.2.7
github.com/pion/transport/v2 v2.2.1
github.com/stretchr/testify v1.8.4
go.uber.org/atomic v1.11.0
golang.org/x/exp v0.0.0-20230522175609-2e198f4a06a1 golang.org/x/net v0.10.0
golang.org/x/sync v0.2.0
Indirect:
golang.org/x/crypto v0.9.0
golang.org/x/sys v0.8.0

Codecov Report

Merging #455 (72b7c52) into master (8f16feb) will decrease coverage by 0.25%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #455      +/-   ##
==========================================
- Coverage   72.16%   71.91%   -0.25%
==========================================
  Files          70       70
  Lines        5428     5448      +20
==========================================
+ Hits         3917     3918       +1
- Misses       1120     1136      +16
- Partials      391      394       +3

see 3 files with indirect coverage changes


Add support for Personal Package Archive (PPA) installation of teku

Description
Current available installation methods require downloading and installing packages from github or docker, but these installation methods require significant effort on behalf of the user. Ethereum maintains a PPA and geth is currently most-easily installed using this method. If other clients adopt the PPA installation method it will significantly reduce the effort required to install a Consensus client and enable people with limited technical ability to quickly and easily upgrade their consensus client using standard apt repositories.

Wrote up some (very rough) notes in https://hackmd.io/IeXF59JNQ4qTpP2-plln9A
The summary is that there's basically two ways we can go about this:
We could build an actual PPA that's deployed to Ubuntu LaunchPad. This would need to be a deb source package that LaunchPad could build for us.  The advantage is that it's simple for users to add as a source with just sudo add-apt-repository consensus/teku.  The downsides are that it's significantly more complex for us to build and maintain a suitable source package, and it would potentially violate our usual policy of compiling once then testing and shipping that same built distribution. A problem introduced in the building of the PPA (most likely because the PPA source package misconfigured the build) would not be picked up by any of our testing because it's being done right at the point of publishing.
Alternatively we could build a .deb package with fpm and publish it to a Debian repository using our existing artefacts repo with Cloudsmith. fpm makes building the package simple and we can reuse the existing distribution artefacts the same way we do when building the docker image. We also reuse the existing security infrastructure and access controls as we're already publishing to Cloudsmith.  The downside is that its more difficult for users to add the repository. Cloudsmith provide a script you can download and run as root that will add the package or users need to manually add the gpg key and the repo as a source. See the doc linked above for the full details.  That only needs to be done once though and then updates are simple apt update.
Given we're Ethereum core devs not Debian package maintainers I'm not sure we're well placed to try and maintain a PPA via LaunchPad and the lack of ability to test it also concerns me. So I'd lean towards either publishing the .deb file via Cloudsmith if it's likely to get enough usage despite the more complicated initial setup, or leave it to the community to provide Debian/Ubuntu packaging (which we'd eagerly support).


The Composer installer script was not successful [exit code 1].  OpenSSL failed with a 'certificate verify failed' error. This indicates a problem with the Certificate Authority file(s) on your system, which either cannot be found or may be out of date.  Script Output: The "https://getcomposer.org/versions" file could not be downloaded: SSL operation failed with code 1. OpenSSL Error messages: error:0A000086:SSL routines::certificate verify failed Failed to enable crypto Failed to open stream: operation failed

Duplicated #169


Add Axiom to Final Energy Consumption Value #1832

Summary of the discussion
As discussed in #1832 I added an axiom to final energy consumption and since it is a quantity value I adjusted the label and the definition, so that the distinction between quantity and quantity value becomes more clear.
Add

Added an axiom: Equivalent to 'energy consuption value' and 'is about' some (consumption and 'has participant' some 'final energy carrier')

Update

changed label: final energy consumption
changed definition: Final energy consumption value is an energy consumption value expressing the magnitude of the energy delivered to and consumed by end users.

Workflow checklist
Automation
Closes #1832
PR-Assignee

[x ] 🐙 Follow the Pull Request Workflow
[ ] 📝 Update the CHANGELOG.md
[ ] 📙 Add #'s to term tracker item

Reviewer

[ ] 🐙 Follow the Reviewer Guide
[ ] 🐙 Provided feedback and show sufficient appreciation for the work done

I just saw that energy consumption value is not related to consumption or energy use. I'd proposed to add that axiom and have it inherited by final energy consumption value, instead of adding it to the equivalent axiom. Do you agree?

I just saw that energy consumption value is not related to consumption or energy use. I'd proposed to add that axiom and have it inherited by final energy consumption value, instead of adding it to the equivalent axiom. Do you agree?

Yes I agree, we should add the axiom to energy consumption value. I will do that and adjust the axiom of final energy consumption value then, so that it inherits it. Should I add both 'energy use' and 'consumption' or only 'energy use' because it is a subclass of 'consumption'?

I've made the axiomatic change of primary energy consumption and changed the label. Since we were already adjusting the consumptions value, I changed the labels of the other to consumption values, added the old label as an alternative label and changed the axiom of gross inland energy consumption also to 'is about' some 'non-energy-use' instead of 'has part'. Do you agree with these changes?

Could you please add an axiom to primary energy carrier to primary energy consumption value. To harmonize it with final energy consumption value, also make it a defined class?!

Could you please add an axiom to primary energy carrier to primary energy consumption value.

Why? Primary energy consumption is not the consumption of primary energy carriers.

I am a bit unhappy about the axiomatisation of primary energy consumption value and gross inland energy consumption value. The axioms basically say, that any energy consumption value which excludes non-energy use is a PEC value and any energy consumption value which included non-energy use is a GIEC value. But this is not true.

Do you have an idea how to change that? If not, I will look into it next week and try to make a new suggestion.

I will change the axioms about the non-energy use of final energy consumption value and gross inland consumption value from EquivalentTo to SubclassOf.

@l-emele could you review this PR again? The Merging is still blocked, because you requested changes, which I have already implemented now.


st-mui-dialog

Heey @flucas96 !
Quick question, did you also create the st-mui-dialog?
Best regards
Chris

Hey ,
yes it is- I am no my sure how useful it's though :D when I created it I was just looking for something to create to learn about making components for streamlit


Merge Nightscout settings

A small request ( maybe? )
Can we put all of the Nightscout settings under Nightscout?
Allow Treatments Upload
Allow Glucose Upload
Allow Statistics Upload
I am working with someone on recovering after her phone blew up, and didn't realize how many menu that we needed to go through just to get Nightscout fully populated 😭
Its not often that we have to do something like this, but it would be nice if it was all in one place ...

Pushed to dev branch: https://github.com/Artificial-Pancreas/iAPS/commit/aacfc1325d21d07e700d7a04fea365cb6d05616c


doc: helm chart docs have hard-coded vsecm compoenent version

Put a placeholder instead. That’s better since the version changes all the time.
Or maybe populate the README dynamically adding the current version there too.

Done.


feat: Linear Classifier

Here's the operator description.
Here is the onnx python implementation.

Can I take it ? :)


Unable to remove the estimates from Azure devops

### Tasks
- [ ] https://github.com/microsoft/azure-boards-estimate/issues/258
- [ ] https://github.com/microsoft/azure-boards-estimate/pull/271

When i am trying to end session of estimates i am unable to do that Kindly Help

Hi, please read https://github.com/microsoft/azure-boards-estimate/issues/25.


Reconnect on page load

Summary
Problem
On reloading the page or returning from the Fission Auth Lobby, the connection with Webnative will be dropped.
Impact
Some events may be missed and developers will need to reconnect with Webnative.
Solution
Store a flag to indicate connection state in extension storage. Check the flag and reconnect as needed.

Note that we implemented this without storing state about the connection in storage. We removed the start and stop listening buttons which removed the need to track connections. Instead, we can simply listen when the devtools are open and not otherwise.


Avoid using Comparable(self) in reference types

Fixes #6547.
This is a workaround fix.
There seems to be an issue with type guessed ivars and generics and self that I'm trying to narrow it down. But maybe it also involves macros that lucky quite much since I wasn't able to frame it yet.
Meanwhile this PR removes include Comparable(self) from reference types (leaving it only in Enum and some specs).

I like this change anyway because it is more explicit.

FYI, related discussion in https://github.com/crystal-lang/crystal/pull/3972#discussion_r210792069 that leads to revert the lazy evaluation of self.

With #6557 merges this can be closed actually.


Must use tenant root group?

Firstly, the doc should make clear that security center does NOT support reporting at management group scopes. It only supports reporting across multiple subscriptions using the global subscription filter. There's a big difference.
Second, the doc implies that the only way to achieve multi-subscription reporting is to assign permissions at the tenant root scope (that's what it shows, and it doesn't give any alternative). In a deeper (more realistic) management group hierarchy, surely permissions could instead be granted at a intermediate management group instead, to just cover those subscriptions in that part of the hierarchy? This should be made clear, as it avoids the need for Azure AD Global Admin access / elevated access (which is a big deal).

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

ID: b82ebe00-d124-9555-df28-e7d7408e9809
Version Independent ID: 1e58db8f-7b41-cc5d-a97e-b1acc38981c3
Content: Gain tenant-wide visibility for Azure Security Center
Content Source: articles/security-center/security-center-management-groups.md
Service: security-center
GitHub Login: @memildin
Microsoft Alias: memildin

@jtuliani, Thank you for the query. We are looking into this and will update the docs after reviewing the same.

@memildin Could you please help on this further and have this verified with engineering ? The document will need to be updated accordingly.

Hi Jonathan, thanks for the feedback.
Below are our thoughts on the topics you raised.
Whilst it's true that secure score is not calculated at the Management Group level, Azure Security Center policies can be applied at the Management Group level. Consequently, a compliance report will cover all resources under that Management Group.
Regarding the point you raised about multi-subscription reporting, this document refers to tenant-wide visibility, which is achieved through the use of the root Management Group.
Thanks again.

#please-close

Hi @memildin. I still have a couple of questions.
You say that ASC policies can be applied at MG scope. But AFAIK this is never done by ASC itself. Are you saying I can modify the scope of ASC policy assignments to a higher scope myself to see compliance reports through Azure Policy rather than through ASC itself? Is this a supported approach, and if so where is it documented?


ContextMenu submenus are toggled through parent MenuItem hover

Closely related to #531; ideal fix should address that & this.
Moving the mouse off of a MenuItem with an active submenu will not remove that submenu:
https://user-images.githubusercontent.com/41480159/231719766-e65ae0d6-374e-4b53-83b1-d00e3c1f5bdb.mp4
Seems like an active submenu will only be removed from the DOM through one of the two actions:

toggling another submenu on the same "menu level" as the submenu's parent (e.g. hovering over the "workspaces" menuItem with active "add tab" submenu)
clicking a MenuItem (noted in #531)

Ideally, a submenu should be created on parent MenuItem mouseenter, and removed on mouseexit.
Alternatively, the entire menu structure could be present in the DOM upon creating root ContextMenu, and submenus would just be present through a CSS rule like .menu-item:has(.submenu):hover > .submenu (this may provide some nice hierarchy as well, instead of submenu being presented within the DOM as their own popover).

Would it be possible to implement some level of delay between a mouseexit-type event and the popover disappearing? Such an action seems to be the default in most context menu interactions I've seen (i.e. Windows)

To be honest I kind of like it this way. But if enough users are annoyed by it then a cooldown could be added.


BUG: Activate displaced detector padding in iterative FDK #499

Set displaced detector filter in iterative FDK reconstruction filter to true by default, to avoid dark hole in reconstructed image from displaced detector projections. #499

Thanks! Looks good to me, I'll merge when the CI is green. FYI, you could have simply amended your commit and force pushed instead of closing #500.

Thanks! Looks good to me, I'll merge when the CI is green. FYI, you could have simply amended your commit and force pushed instead of closing #500.

Thanks, I may do it this way in the future.


quantity in L2Event

referring to the code , the quantity in L2Event is base coin in both ask and bid order,  is it correct?

referring to the code , the quantity in L2Event is base coin in both ask and bid order, is it correct?

Yes, this is correct. This is inline with stock market, where the quantity is generally referring to the quantity of the base instrument - the stock you are buying/selling.


[SECURESIGN-1037] inject certificates if proxy is enabled

replaces PR #448

/retest

The proxy parameter in CRD is currently pointless. The operator should be proxy aware and automatically configure proxy for it's operands automatically. This means that it detects HTTP_PROXY, HTTPS_PROXY and NO_PROXY env variables above the operator deployment and use them accordingly.


ROS# over UDP

(confirm) I am at the right place and my issue is directly related to ROS#. General technical questions I would post e.g. at ROS Answers or Stack Overflow. For library-specific questions I would look for help in the corresponding library forums.
(confirm) I have thoroughly read the Contributing Guideline and writing this issue is the right thing to do in my case.

I have a question!

[x] I searched the Wiki, open and closed issues for an answer. I tried my best to find the answer by myself without success. I believe that the discussion we will have in this issue, and the solutions we might find, will help me, and likely other community members who have a similar problem.

Here is my question:
Hi everyone!
I am setting up a communication between Unity and ROS using two different machines over static IP (at least I believe I am trying to do that). So I set my IPv4 settings on the Ubuntu to a static address, set my Windows machine to other static ip, connected them with LAN and have attempted to run the tutorial 2.1 (sending urdf from ros to unity) and it failed in exactly same fashion as https://github.com/siemens/ros-sharp/issues/71 or https://github.com/siemens/ros-sharp/issues/149>, i.e. it procs connect/disconnect on both sides and nothing more.
I am aware that the tutorials were meant for VM but it does not suit my project. I have tried to run the code that was suggested in 71 but I guess the api changed since so I am trying to rewrite it now. Although judging from other issues the problem seems to be rooted in communications. I could not find other attempts to run the ros# with static ip, hence the new issue. Ideas?

My OS is: <Windows 10, Ubuntu 16.04 on 4.14.12 rt kernel>
My Unity Version is: <2018.3>
My ROS Distribution is: ROS Kinetic
My Build Target Platform is:

I want to do this:
I want to connect two different machines with ros#.
Recommended feature
I believe there is a need for a basic tutorial on how to setup simple subscriber publisher class in unity with ros# since the code suggested in 71 does not work and points to a bit of wiki that does not exist

I have disabled the firewall and the anti-virus, it did not help. I have echo'ed the connected clients on the ros side and saw that the windows machine was connected for short period (~100ms) and disconnected. In that little period nothing was published. (I had a pose publisher running from windows)

Is there a difference between enp0s31f6 and enp0s8?

Hi @bomarali ! I am sorry noone found time to support you with your issue for such a long period of time.
Have you already proceeded in solving your problem yourself in the meantime?

Is there a difference between enp0s31f6 and enp0s8?

Both seem to be Predictable Network Interface Names and just specify which network interface you are using on your machine.
Based on your problem descriptions above it seems to me like a pure LAN connection problem. RosbridgeClient connects to rosbridge_suite via a common WebSocket connection. These work on both LAN and WLAN connections with dynamic or static IPs, between virtual and real machines. Make sure that both machines are in the same network and can communicate.

Recommended feature
I believe there is a need for a basic tutorial on how to setup simple subscriber publisher class in unity with ros# since the code suggested in 71 does not work and points to a bit of wiki that does not exist

We removed the wiki page since we included the code here in the repo.
You find more console and test classes  that run without Unity in RosBridgeClientTest and UrdfTest.

Thank you for the reply. I did not manage to make it work, so I am currently using own communication setup. That said, I really want to make ros-sharp work.
It still fails to connect to the server, even in non-unity version. Also the VS solution jumps right into the urdf export to ros in the rosbridgeclienttest. I am new to c# VS framework. Could you tell me how to only run the RosSocketConsoleExample?

(Resolved) So it was hardware issue all along.
I have played with roslibjs around trying to find where connection is dropped. After a while a saw some inconsistencies on my server laptop. Switched it with my client VR PC. Everything works as intended. I am both relieved and incredible frustrated. I should have tested for hardware issues much earlier.


simulateTx while calling an method doesn't work

When passing simulateTx as true gets ignored while sending requests to engine , The transaction is suppose to just simulate but is executed instead.
Thanks to myk.eth(@mykcryptodev) for pointing this out.

Hi @Yash094
The simulateTx flag, will simulate the transaction during the request time and then add it to the queue if the simulation works otherwise if simulation fails, it sends an error back.
Once the request is queued the worker will pick it up and process the request and send transaction on chain.
Thus, you see the transaction being sent on-chain, but Engine does simulate the transaction to check its validity

Okay, that makes sense. Thank you so much for your prompt response


[Tailscale] Switch Account command is broken

Description
Tailscale extension Switch Account command is broken due to the changes to CLI output. Now it has:
ID    Tailnet              Account
1234                       Yukaii
5678  hhhhhhh.org.github   Yukaii@github
asdf  xxxxxxxx.org.github  Yukaii@github*

I fixed the command with the following changes:

Algorithm changes to deal with column whitespace padding output
Change the User interface. Match the CLI output property as much as possible
Output tailnet info as subtitle attribute
Use Icon.Checkmark to indicate active status

Screencast

Checklist

[x] I read the extension guidelines
[x] I read the documentation about publishing
[x] I ran npm run build and tested this distribution build in Raycast
[x] I checked that files in the assets folder are used by the extension itself
[x] I checked that assets used by the README are placed outside of the metadata folder

Closing in favor of #9917


Authentication Profiles UI

URL: https://api-services-portal-feature-auth-profiles-ui.apps.silver.devops.gov.bc.ca/manager/authorization-profiles
Create a Kong Profile Story:

Sign in
Select a namespace
Click Create New Profile
Enter a new profile name and click Continue
Select Kong API Key radio
Enter API Key in input and click Create
A success message should appear and a new profile should appear in the profiles page.
Click edit. A dialog should appear. Make a change and click Save. The changes should be reflected.

Change a name:

Click Edit on the newly created Kong profile.
Click the Edit button in the top of the editing modal beside the name of the profile.
Enter a new name and hit enter.
There should be a toast reporting success.
Click Cancel on the modal. The new name should be reflected in the auth profiles list view.

Create a Client Credential Flow

Sign in
Select a namespace
Click Create New Profile
Select any of the top 3 Client Credential radios and click Continue
The Authorization tab should be visible, with a large Automatic radio button selected by default
Enter values into the form. The radio buttons should toggle the mode. Most inputs are tag-inputs, that allow you to enter/remove multiple values in a text input.
Click Continue when done entering values. This should take you to the Client Management tab
There should be no environments and a prompt to make one. Click Add Environment
You should be able to enter in the Environment and idP Issuer URL. Select different Client Registration values and fill in the different conditional fields that change depending on which registration you pick.
Clicking Add Environment submit button should create a new Environment in the table below.
Click Create. A success message should appear and a new profile should appear in the profiles page.
Click Edit on the newly created profile. You should be able to edit the name and change the values. Clicking save on the last tab should update the profile.

Deleting a profile

Create a simple test profile.
Click the ••• menu and select delete.
The profile should be deleted

Global actions

All forms should have working validation. Entering a name in a URL input should have error feedback.
The escape button should only close the modal for the first step of the create process.
Clicking Cancel should always close the modal and clear the form.
The name column of the profiles table should have a popover appear on hover.
The administrator profile should appear in the modal

Format for name is a bit off

Placeholder texts cut off:

The last letter of Scope, Client, and UMA2 in the placeholder text get cut off too

"UMA2 Resource Type" is just a text box (no multi-value)
"Resource Access Scope " is just a text box (no multi-value) and I'm also not able to type into it
For "Audience" the placeholder should be something like "Enter the Audience" and it is a text box (no multi-value)
Under "Client Management" the "idP Issuer URL" should be "IdP Issuer URL"
Placeholder for Initial Access Token should be something like "Enter Initial Access Token"
On the hover of the username, the two fields in the popup are "Display Name" (or "Name" I suppose) and "Email".. not "username".


Issue-244: Added a trigger attribute to the onTagAdded

This work hopes to resolve Issue #244, allowing a simple indicator on where the tag is being added from: input based vs. auto-complete based.

@mbenford
If this PR is acceptable, would you be able to estimate when it would make it into a release?
Thanks.


Update control

Change the minimal version of cmake to 3.19.0 to allow presets.

The issue that I have is that we are using mainly presets. I don't expect other developers not to use presets and version 3.16 is pretty much useless for us.
I know snap has a more recent version of cmake. Can we integrate with snap?

The issue that I have is that we are using mainly presets. I don't expect other developers not to use presets and version 3.16 is pretty much useless for us.

True, true, but on the other side, the Debian build preset is pretty different from the rest of the other CMake development presets, so it's not too much trouble manually specify the -DMY_VAR=VALUE for it (I believe we need to do the same thing for OpenWRT buildroot).

I know snap has a more recent version of cmake. Can we integrate with snap?

snap is great for applications, but it's pretty awful to use for automated builds. The entire point of the Debian build is that it uses the Debian system to build, so that we can make our own https://help.launchpad.net/Packaging/PPA or even add our package to Debian/Ubuntu once it gets more stable.
Plus, I don't even know if snap supports armhf (e.g. old raspberry pis/Turris Omnia), since Ubuntu doesn't support them. However, the Debian project still supports them.
Plus, if we want to publish edgesec as a snap, we'd have to ask for Ubuntu's permission, there's no way to self-host a snap, as far as I'm aware.

@aloisklink What's the decision on this PR? Do we close it and later on we add the snap integration?

Do we close it and later on we add the snap integration?

That's my gut feeling. But I'll be honest, I still think we should keep deb integration too, for Debian/light-weight Ubuntu distros.
It would also be nice to set up an edgesec PPA too so that we can have a super easy way to install edgesec on Debian/Ubuntu (and do automatic updates).


Epoch format from now helper does not give a UNIX Timestamp

Using {{now format='epoch'}} in a templated response gives a timestamp with too much precision. A Unix timestamp should be represented in seconds since 1/1/1970 etc, the representation returned needs to be divided by 1000 it seems.
Right now using {{now format='epoch'}} gives<PHONE_NUMBER>111 which is 25th August 51245 at 8:45pm... in reality, to be a truly portable timestamp, it the result should be divided by 1000 (and rounded I guess), to give<PHONE_NUMBER> 11th April 2019 @ 1:29pm (the correct value).
Depending on opinions, this could either be a bug (Unix timestamp is output with too much precision), or a feature request (please add support for portable Unix timestamps) :grin: thanks!

I'd be happy to accept a PR to add {{now format='unix'}} if you feel like contributing one.

Seeing what I can do :+1:

Added PR in #1111


Install: option to skip current project package

Add --no-root option to the install command to avoid the installation of the project's root package.
Fixes #730.
Pull Request Check List
This is just a reminder about the most common mistakes. Please make sure that you tick all appropriate boxes.  But please read our contribution guide at least once, it will save you unnecessary review cycles!

[ ] Added tests for changed code.
[x] Updated documentation for changed code.

Note: If your Pull Request introduces a new feature or changes the current behavior, it should be based
on the develop branch. If it's a bug fix or only a documentation update, it should be based on the master branch.
If you have any questions to any of the points above, just submit and ask!  This checklist is here to help you, not to deter you from contributing!

@seleem1337 Thanks for addressing this issue.  Its a blocking us from using poetry in a large corporate environment.
I just tried this out this fix and it didn't seem to work. I got this error:
{path to my package} does not contain any element
I made the following change and then it worked as expected:
        return_code = installer.run()

        if return_code != 0:
            return return_code

        if self.option("no-root"):
            return 0

        try:
            builder = SdistBuilder(self.poetry, NullEnv(), NullIO())
        except ModuleOrPackageNotFound:
            # This is likely due to the fact that the project is an application
            # not following the structure expected by Poetry
            # If this is a true error it will be picked up later by build anyway.
            return 0

        self.line(
            "  - Installing <info>{}</info> (<comment>{}</comment>)".format(
                self.poetry.package.pretty_name, self.poetry.package.pretty_version
            )
        )

        if self.option("dry-run"):
            return 0

@brycedrennan @sdispater thanks for the feedback! I've applied the changes.

Thanks!


Fix yang get stats script

fixed an edge-case in yang_get_stats.py script, refactored this script, after running it for the first time I got this output:

it seems like everything is okay now (not sure about cannot stat errors in the end)
resolves #204

cp: cannot stat '/var/yang/ietf/draft-with-YANG-no-strictdraft-ietf-rats-network-device-subscription-02.txt': No such file or directory
There is missing / character between dir name (draft-with-YANG-no-strict) and filename (draft-ietf-rats-network-device-subscription-02.txt)

cp: cannot stat '/var/yang/ietf/draft-with-YANG-no-strictdraft-ietf-rats-network-device-subscription-02.txt': No such file or directory There is missing / character between dir name (draft-with-YANG-no-strict) and filename (draft-ietf-rats-network-device-subscription-02.txt)

fixed it, thanks


Fix failing tests with PyTorch nightly | fix(torchlib)

Follow up to fix https://github.com/microsoft/onnxscript/issues/1061. All pipelines should now be green.

Adjusted tolerance for float16 tests that are recently added in PyTorch
Fix some dtype issues in function implementations

Yes there are still a few but my vpn isn't working :(

win pipelines are also expected to be green?

Yes. There are some other issues happening


Fix IoT Models Repository Readme issues

Fix https://github.com/Azure/azure-sdk-for-net/issues/20215
@jongio for notification.

@jongio Who should we ping to review this PR?

It would probably help to change the title. There are no IoT Models Repository changes in this PR....and there is a label of Storage associated.

@drwill-ms - Could you help us merge this PR?

@drwill-ms - Could you help us merge this PR?

I think we should see sign-off from the owners of the other two files before we merge.

@drwill-ms  We have updated and removed the other two files. Please re-review.


Add .npmrc files for feed hardening

Overview
[MSFT compliance]
Fixes: "Node.js project(s) are missing feed configuration"
Also updates our packages-lock.json to the latest state of the project.

Does this cause issues for publishing to internal feeds?

Does this cause issues for publishing to internal feeds?

Since we publish from the packed tarballs instead of from the folders (and as far as I can tell, packing ignores this .npmrc), this should be okay! 🤞
https://github.com/microsoft/MixedRealityToolkit-Unity/blob/dbe8b8bc5cbdcfc9f3e980dc825ad43cd997c951/Pipelines/Scripts/publish-upm.ps1#L41-L45


starting client crashes

Following the tutorial trying to start a node locally.
docker run --name mina -d
-p 8302:8302
--restart=always
--mount "type=bind,source=pwd/keys,dst=/keys,readonly"
--mount "type=bind,source=pwd/.mina-config,dst=/root/.mina-config"
-e CODA_PRIVKEY_PASS="THISISNOTMYREALPASSWORD"
minaprotocol/mina-daemon-baked:1.1.3-48401e9
daemon
--block-producer-key /keys/my-wallet
--insecure-rest-server
--file-log-level Debug
--log-level Info
--peer-list-url https://storage.googleapis.com/mina-seed-lists/mainnet_seeds.txt
created local private key by using this https://minaprotocol.com/docs/keypair and the docker tool. No idea what is wrong, I just followed the tuto. Thanks in advance
mina-log.txt

Are you running it on mac? If so, make sure you allow docker for mac to use at least 8gb of ram, the default 2gb is not enough
https://docs.docker.com/docker-for-mac/#resources

Closing this one because of no activity for a long time and I hope that the most recent versions of Mina Daemon behave more stable for you.
Please feel free to report new issues should you face any.
Thank you.


UListener::on_error shoul provide message context

Currently UListener::on_error(UStatus) only provides error details in the UStatus struct but does not provide any information regarding the context in which the error occurred.
It seems reasonable to either pass in a full UMessage with the UStatusas payload or instead pass in both UStatus and UAttributes.

Personally, I would be in favor of passing in a UMessage, which would allow a UTransport implementation to do both, either pass in a UMessage returned from a service provider as is or manually create a UMessage in case of a transport error.
WDYT @PLeVasseur @AnotherDaniel ?

I'm not sure, but at least using UStatus is enough for me now. I can add some messages in the UStatus to describe what kind of error is.
For example, this is how it works in up-client-zenoh-rust:
https://github.com/ZettaScaleLabs/up-client-zenoh-rust/blob/680dc87ea334ae54e07a361a8d2138f7aac70d89/src/utransport.rs#L268
Indeed passing UMessage allows users to do more things in on_error, but it's a little cumbersome if we don't have UMessage and need to create an empty one to return. (Maybe using Option instead?)

Indeed passing UMessage allows users to do more things in on_error, but it's a little cumbersome if we don't have UMessage and need to create an empty one to return. (Maybe using Option instead?)

I think this is a good point. Could be that for some internal error reason there is no UMessage available.
Feels a little weird for the API to be UListener::on_error(Result<UMessage, UStatus>), but I suppose it could allow for handling cases where the implementor of UTransport is unable to provide the UMessage.

Feels a little weird for the API to be UListener::on_error(Result<UMessage, UStatus>), but I suppose it could allow for handling cases where the implementor of UTransport is unable to provide the UMessage.

How about this? UListener::on_error(UStatus, Option<UMessage>)
Users only need to provide UMessage if there is a valid UMessage

The idea would be to have
fn on_error(error: UMessage);

The message would either come directly from the service implementation or the transport would create it manually if something goes wrong, e.g. when forwarding a request message. In the latter case, the transport could copy the request message's UAttributes (to provide for context), swap the source and dest and set a commstatus other than OK. It could then add a UStatus as the message payload.
WDYT?

IMHO, I think extracting UStatus from UPayload is a little not intuitive and it would mix with the original UPayload.
It would be better to separate them.

IMHO, I think extracting UStatus from UPayload is a little not intuitive and it would mix with the original UPayload.
It would be better to separate them.

That is exactly what an application would need to do in case the response sent from the service contains an error, wouldn't it?

Do you mean we don't keep the origin UPayload and swap it with UStatus? I'm still not clear about how to put the original UPayload and UStatus together.

As part of implementing the L2 RpcClient we have removed the UListener::on_error function  which does not serve any purpose (anymore). See d49ee8e8e202a0f23579d33bfae36da699e7fd4b


Match failed on update status

Can't change the status of an error to 'fixing'.

Is anyone else experiencing this? It is only on one error. Others can be changed without problem.

can you please send me your appId or url related to this issue?

Gladly,
https://ui.kadira.io/apps/BeXRQTJM7cZzWr3uG/errors/overview?range=259200000&metric=count
~ Nick
On Fri, Oct 9, 2015 at 12:07 PM, Pahan Sarathchandra <
<EMAIL_ADDRESS>wrote:

can you please send me your appId or url related to this issue?
—
Reply to this email directly or view it on GitHub
https://github.com/meteorhacks/kadira/issues/190#issuecomment-146822155.

@nimerritt we added a fix for this two weeks ago, forgot to notify you. Can you please check and confirm  ?


Raise RateNotFoundError when an incorrect code is given

Hi there,
First of all thanks for making this Gem. It saved me a lot of time today.
I ran across this problem when I accidentally mistyped a currency code in my app. Unfortunately it would cause the Gem to blow up with errors like TypeError: nil can't be coerced into Fixnum etc.
This returns a RateNotFoundError when rate isn't found between the two given currencies, which allows the Gem user to add a begin..rescue RateNotFoundError.. end block to their code.
Cheers!

Very nice @seanarnold
Would you mind adding test that RateNotFoundError is raised when invalid currency code is used here https://github.com/seanarnold/open_exchange_rates/blob/master/test/rates_test.rb
Thanks!

@vlado the tests are there :) Just hiding away in the error_handling branch (which this is created off)
https://github.com/seanarnold/open_exchange_rates/blob/error_handling/test/rates_test.rb#L213

Damn, I got confused by houndci and did not notice them :innocent:
Merged!

Great! Any chance we can get this in a new version sometime soon? Hoping to use this in Production shortly

Sure, just released v0.4.1 :)


Pin results to Estuary after publishing to IPFS

Our Estuary publisher is disabled again because sending data to Estuary or retrieving again immediately afterwards is sometimes too slow.
Instead, Estuary has an alternative API that allows pinning an existing CID. So instead, we now use the normal IPFS publisher to publish results to our own IPFS node and then get Estuary to pin them afterwards. We do this out of band and ensure jobs remain fast.
Resolves #1001.

@simonwo please could you rebase this on main?

Yes – I am also now awaiting the results of https://github.com/application-research/estuary-clients/pull/1

looks like upstream merged!

Alas, the code that is generated is broken! So I'm talking with them – they either need to upgrade the code generator to v3 or modify a doc signature to refer to a proper type. Still working through it!


Vickel: Watch fascobill.pk

Vickel requests the watch of the watch_keyword fascobill\.pk. See the MS search here and the Stack Exchange search in text, in URLs, and in code.

fascobill\.pk has been seen in 1 true positive, 0 false positives, and 0 NAAs.

Approved by Spevacus in Charcoal HQ


Shouldn't getGroupsForUser accept an array of roles instead of a singular role?

What are your thoughts on this one?

getGroupsForUser was contributed by a community member and I guess their use case only needed a single role.
Sounds like your use case would benefit from being able to check for multiple roles at once.  Happy to accept a PR (needs to include tests).  Let me know if you'd like help doing the conversion.
Closing this issue for now but feel free to log your PR against it anyways if you want to put one together.

This was done as part of v2.0. getPartitionsForUser accepts now an array of roles.


How to configure token name received from OP?

How to configure token name received from OP? I'm using Azure B2C, and they returning id_token instead of access_token.
From the logs I saw this line:
oidc_proto_validate_code_response: requested flow is "code id_token" but no "access_token" parameter found in the code response,
This is response from OP /token endpoint:
{  "not_before":"1474599671",  "token_type":"Bearer",  **"id_token":"eyJ0eXAiOiJ...",**  "id_token_expires_in":"3600",  "profile_info":"eyJ2ZXIiOiIx...",  "refresh_token":"eyJraWQiOiJj...",  "refresh_token_expires_in":"1209600" }
Apparently Azure team changed something because from their guide specifically mentioned access_token instead of id_token.
https://azure.microsoft.com/en-gb/documentation/articles/active-directory-b2c-reference-oidc/#send-authentication-requests
Thank you

It may be that you're sending a different authentication request than documented. Can you show the part of the (debug) server log where it says:
oidc_proto_authorization_request: adding outgoing header: Location...

But as you mention: since the requested flow is code id_token and the id_token is returned already in the front-channel, the code must be used to obtain an access_token, not a second id_token.
If that is not the case then Azure B2C is not OpenID Connect compliant and that side should be fixed. But let's make sure that the authentication request is indeed a compliant OpenID Connect request and conforms to the settings that MS had documented wrt. response_mode  and the proprietary p parameter; the latter can be added according to: https://github.com/pingidentity/mod_auth_openidc/wiki#13-how-can-i-add-custom-parameters-to-the-authorization-request)

Thanks for quick response. Here is the outgoing header sent by the mod:
oidc_proto_authorization_request: adding outgoing header: Location: https://login.microsoftonline.com/test.onmicrosoft.com/oauth2/v2.0/authorize?p=b2c_1_apache_signin&response_type=code%20id_token&scope=openid%20offline_access&client_id=7dd4e373-1234-123b-b123-a944abab250b&state=ZBPcwV0PmnpTYSGAlUk4_pEB4u0&redirect_uri=https%3A%2F%2Flocalhost%2Ftest%2Flogin&nonce=95qitvDS1za2_nZNxiCBEu1wafa7ExnRh0_DVH71IDw&response_mode=form_post

That seems like a perfectly valid OIDC request to me and follows what is documented on that Azure B2C page. You'll have to talk to MS to find out why the behavior is different from what is documented. If you could report your findings here that would be great and I'll leave the ticket open for that.

Looking at the response some more, it seems totally off: there's a token_type of Bearer, and an *expires_in value (detached from the token itself) and a refresh_token; those are all access_token related values that don't apply to an id_token. So it looks like they've just swapped the token name by accident and what you're receiving is really an access_token. But as said, you should really take it up with MS.

I'm closing it here. If you have any followup from MS, feel free to add it to the ticket.


[FEATURE] one range algorithm common interface

We should expect that all of the one range algorithms will have similar interfaces.
We should have just one component to do it.

Went a different way


Add support for trace col grouping

Closes #83.

Thank you @grjte and @Al-Kindi-0 for the review. Made suggested changes.


RFM - PyCharm Professional

https://chocolatey.org/packages/pycharm-professional/2016.2.2

I'd like to become a new maintainer of this package.

@TakataSanshiro what is your Chocolatey.org username? Did you also submit a request to become maintainer via the "Contact Site Admins" on Chocolatey.org?

@dragon788 My Chocolatey.org username is "Sanshiro".
No, I didn't, but I have sent a message via the "Contact Site Admins" for other packages(discord, sigil, pinta, processing... etc) 7 days ago, haven't gotten an answer yet.

The moderation queue and the site admins list has a lot of activity so it can take a while for a response depending on how busy the volunteers monitoring them are at their day jobs.
If you can send a message over for this package (paper trail) I'll ping somebody and see if we can look at those others as well. One thing to note is that this package name should be deprecated (per the most recent moderation comments from ferventcoder) and https://chocolatey.org/packages/pycharm should be updated for the pro version, though I'm curious if that is the standard for other JetBrains packages like IntelliJ/RubyMine/WebStorm/etc.

@dragon788 Could you contact to the admins?
Though I've sent messages to the following packages admins via the "Contact Site Admins" on Chocolatey.org 3 weeks ago, I received a message only Hain package from ferventcoder.
Hain, Sigil, discord, Pinta, Processing, seer, yasm, lightshot, GoogleJapaneseInput, freemind, ghost, Monosnap, Atom, poedit, divvy, origin, MakeHuman, sugarsync, Pycharm, Emacs.
I'd like to become a new maintainer of those packages.
Please add my Chocolatey.org username "Sanshiro", then I'll update those packages.
Thanks.

I've messaged about this on gitter, but haven't gotten an answer yet.

Hi @TakataSanshiro - do keep in mind you sent messages about more than five packages to the admins. It's good that you want to help out, but all messages coming through at the same time begs the question if you started by reaching out to the maintainers first to co-maintain packages with them prior to reaching out to the admins.
Bringing on new maintainers is slower, as admins will also reach out to current maintainers and wait on a response, unless we know the maintainers are already no longer actively maintaining. HTH

Hi to the maintainer, I am new to this page and I am looking for chocolatey version of latest PyCharm Professional as I could find only the Community version present. Thanks!

@deva1003 I believe the package "pycharm" is also the Professional version, while pycharm-community is the free version.

@dragon788 I'm not sure about the functionality difference between the two but they install in machine with different names in ARP as well as the shortcut name itself. Can you refer me any formal document where it mentions that both's functionality are same except the licensing part?

I've submitted one and it has approved.
https://chocolatey.org/packages/Pycharm/2017.2.3
I've specified that the id "pycharm-professional" was deprecated.
These are the same.

we'll keep this open until the deprecated pycharm-professional package have been approved.


Use ryu for float serialization

... supposedly pulling in the advantages of ryu (speed, code size?) over core::fmt.
The bigger and likely more valuable task would be deserialization and replacing ::from_str by something else. As mentioned in #12, last time I tried, lexical_core's atof wasn't easy.

Ok, could you add an entry to the changelog, squash all commits into one and rebase to the master branch?


fix return code when readwrite image is not supported

This function (do_test) starts by testing write and read individually. Both of them can have errors.
When readwrite image is not supported, the function returns TEST_SKIPPED_ITSELF potentially masking errors leading to the test returning EXIT_SUCCESS even with errors along the way.

Merging as discussed in the January 16th teleconference.


CNN-LSTM with video frame sequence: InvalidArgumentError: You must feed a value for placeholder tensor

I'm building a CNN-LSTM network in Keras (v2.02) + Tensorflow (v1.0.1) using video frames as input. I'm setting up the network as shown below:
import tensorflow as tf
import keras
import cv2

video = keras.layers.Input(shape=(None, 299,299,3),name='video_input')

cnn = keras.applications.InceptionV3(weights='imagenet',
                                 include_top='False',
                                 pooling='avg')

cnn.trainable = False
encoded_frame = keras.layers.TimeDistributed(cnn)(video)
encoded_vid = keras.layers.LSTM(256)(encoded_frame)
outputs = keras.layers.Dense(128, activation='relu')(encoded_vid)

Some of the tensor properties are below:
video
<tf.Tensor 'video_input:0' shape=(?, ?, 299, 299, 3) dtype=float32>

cnn.input
<tf.Tensor 'input_1:0' shape=(?, 299, 299, 3) dtype=float32>

cnn.output
<tf.Tensor 'predictions/Softmax:0' shape=(?, 1000) dtype=float32>

encoded_frame
<tf.Tensor 'time_distributed_1/Reshape_1:0' shape=(?, ?, 1000) dtype=float32>

encoded_vid
<tf.Tensor 'lstm_1/TensorArrayReadV3:0' shape=(?, 256) dtype=float32>

outputs
<tf.Tensor 'dense_1/Relu:0' shape=(?, 128) dtype=float32>

Now I build the model and fit the data:
model = keras.models.Model(inputs=[video],outputs=outputs)
model.compile(optimizer='adam',
          loss='mean_squared_logarithmic_error')
# Generate random targets
y = np.random.random(size=(128,))
y = np.reshape(y,(-1,128))
model.fit(x=frame_sequence, y=y, validation_split=0.0,shuffle=False, batch_size=1)

where frame_sequence is a sequence of video frames from one video:
frame_sequence.shape
(1, 48, 299, 299, 3)

All seems well up to the training step model.fit, where I get an error attributed to the input_1 placeholder in the InceptionV3 model input:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'input_1' with dtype float
 [[Node: input_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Training works without error if I build my CNN from scratch instead of loading InceptionV3. For example, replacing InceptionV3 with:
cnn = Sequential()
cnn.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(229, 229, 3)))
cnn.add(Conv2D(64, (3, 3), activation='relu'))
cnn.add(MaxPooling2D((2, 2)))
cnn.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
cnn.add(Conv2D(128, (3, 3), activation='relu'))
cnn.add(MaxPooling2D((2, 2)))
cnn.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
cnn.add(Conv2D(256, (3, 3), activation='relu'))
cnn.add(Conv2D(256, (3, 3), activation='relu'))
cnn.add(MaxPooling2D((2, 2)))
cnn.add(Flatten())

Here is some minimal code to reproduce the issue.

I'm seeing the same error, though in a very different context.  I have an implementation of Bidirectional Attention Flow (BiDAF) in Keras.  I want to load that model, similar to how @Feynman27 is loading InceptionV3, then pull parts out of it for use in a model on a different, but similar task (see code here).  This worked in Keras 1, but is breaking when porting our code to Keras 2.  You can see a trace of the failure here.  I spent several hours trying to figure out what is going on here, and I'm at a wall.  I thought that the __call__ method might not be hooking up the inputs correctly, because there's an extra placeholder still lying around, but I made a minimal example trying to show that something is broken, and it actually works.  I'm at a total loss for why my example doesn't have the same crash that my real model has.
I very much want this bug to be fixed - I'm happy to help debug, if anyone has suggestions on what to do.  I've run out of ideas.

Why do u define the layers as ( keras.layers.Input ) etc?
Why don’t  u use this style:
from keras.models import Model
from keras.layers import Input, Dense
a = Input(shape=(32,))
b = Dense(32)(a)
model = Model(inputs=a, outputs=b)

@MuOtb they're functionally identical, some people just don't like having multiple imports at the top of their file.

I should mention that I'm using TF v1.0 and Keras v2.0.

One of my colleagues referred me to this issue encountered when using BN in a sub-model applied to a time-distributed layer: https://github.com/fchollet/keras/issues/2827. Looks like a similar issue, but at the moment it's still unclear to me how to apply that recommendation to the case above with Inception.

@Feynman27 Potential workaround: set learning phase to 0 and pass Lambda(lambda x: cnn(x)) to TimeDistributed.
Here's the full example: https://gist.github.com/alfiya400/9d3bf303966f87a3c2aa92a0a0a54662
I also checked that output from TimeDistributed and cnn.predict match each other.
The drawback of this approach: you can't use Dropouts and there might be other restrictions when learning_phase is set to 0.

@alfiya400, do you have any idea why that workaround works for the CNN?  I tried those in my model, and it does not solve the issue.

Yeah, I tried wrapping the TimeDistributed part of my model in a Lambda after your first comment, but it didn't work.  I just now tried also wrapping the other Model that I use in a Lambda, and that didn't work, either.  I still get the missing placeholder error (on an input tensor, not a batch norm tensor, so it's the second issue you mention, not the first).  If I knew why adding the Lambda helps in your case, maybe I could figure out what's going wrong in my case, because I'm pretty sure they're related...

I met the same error when applying TimeDistributed to InceptionV3.  I also think it due to the compatibility of TimeDistributed and BatchNormalization, because I didn't met this when using TimeDistributed to wrap VGG16 which does not have BN layer.
@alfiya400  Thanks for your solution!  It works at least for now in my project.

I got the same problem.
I'm agree with  @Wenbo93 .I think it due to the compatibility of TimeDistributed and BatchNormalization.This is my code.I used BN in a TimeDistributed CNN.
    convs= Sequential()
    convs.add(Convolution2D(int(hidden_size/(2**(nb_conv_layers-1))), 1, 1,input_shape=shape[1:], border_mode="same", bias=False,activation='relu'))
    convs.add(Convolution2D(int(hidden_size/(2**(nb_conv_layers-1))), 3, 3, border_mode="same", bias=False,activation='relu'))
    convs.add(BatchNormalization(axis=3))
    convs.add(MaxPooling2D((2,2),border_mode='same'))
    for l_cnn in range(1,nb_conv_layers):
        convs.add(Convolution2D(int(hidden_size/(2**(nb_conv_layers-l_cnn-1))), 1, 1, border_mode="same", bias=False,activation='relu'))
        convs.add(Convolution2D(int(hidden_size/(2**(nb_conv_layers-l_cnn-1))), 3, 3, border_mode="same", bias=False,activation='relu'))
        convs.add(Convolution2D(int(hidden_size/(2**(nb_conv_layers-l_cnn-1))), 3, 3, border_mode="same", bias=False,activation='relu'))
        convs.add(BatchNormalization(axis=3))
        convs.add(MaxPooling2D((2,2),border_mode='same'))
    convs.add(Flatten())
    #Warp the cnn and conect it with a rnn
    out=TimeDistributed(convs)(inputs)
    for l_rnn in range(nb_rnn_layers-1):
        out=LSTM(512,return_sequences=True,activation='relu',stateful=stateful)(out)
    out=LSTM(512,return_sequences=False,activation='relu',stateful=stateful)(out)
    out=Dropout(0.2)(out)
    out=Dense(1024,activation='relu')(out)
    out=Dropout(0.2)(out)
    out=Dense(1,activation=activation)(out)
    tdcnn=Model(input=[inputs],output=[out])

I have a small example that reproduces the problem.
nb_samples = 50
input_a_len = 50
X = np.ones((nb_samples, 2, input_a_len), dtype=np.float32)
Y = np.ones((nb_samples, 2, 1), dtype=np.float32)
input_a = Input(shape=(2, input_a_len), name='input_a', dtype='float32')
input_a_reshaped = Reshape((2, input_a_len, 1))(input_a)
pred = TimeDistributed(LSTM(1, recurrent_dropout=0.1))(input_a_reshaped)
model = Model([input_a], pred)
model.compile(loss='binary_crossentropy', optimizer='sgd')
hist = model.fit(x=X, y=Y)

This produces the error:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'time_distributed_1/keras_learning_phase' with dtype bool
         [[Node: time_distributed_1/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

In this case:

adding K.set_learning_phase(1) explicitly, or
removing the TimeDistributed (with dimensional adjustments) or
removing the recurrent_dropout option, solves the problem.

solve the problem.

`      self.model = Sequential()
self.model.add(Embedding(input_dim=vocab_size,
output_dim=embedding_size, input_length=max_len))
lstm_model = LSTM(num_units, dropout=dropout)
if useBiDirection:
lstm_model = Bidirectional(lstm_model)
self.model.add(lstm_model)
self.model.add(Dense(n_classes, activation='softmax'))
    self.model.summary()
    self.model.compile(loss='categorical_crossentropy',
                       optimizer=Adam(lr=learning_rate),
                       metrics=['accuracy'])`

I also see the same error if I try to fit this model.
I get the error
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'bidirectional_1/keras_learning_phase' with dtype bool
I set K.set_learning_phase(1) before fit but still not working.

@StefPac Yeah I have the same error. Adding set_learning_phase(1) solves it. What issues or consequences could arise from hard-coding this value when training (with validation) a model.

@abnera in that case Dropout will also drop out neurons during testing for example.

@gewoonrik Thanks for the explanation. Yeah, I am getting very poor validation results by hard-coding the learning_phase: set_learning_phase(1). What if I temporarily modify fit_generator to make set_learning_phase(1) at beginning of each epoch and set_learning_phase(0) at the validation of every epoch? Until bug fix?

@QuantumLiu  did you manage to find a workaround for the problem with the batchnorm layer? I am also encoutering problems when trying to make TimeDistributed(BatchNormalization())(input) which gives
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'time_distributed_1/keras_learning_phase' with dtype bool
	 [[Node: time_distributed_1/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=<unknown>, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
	 [[Node: Mean_3/_33 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_1296_Mean_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

@gewoonrik I've circumvented this behavior by recreating the model without the dropout layers and reloading the weights into it - it loads and the predictions are stable indicating that dropout is not applied. Batch norm layer is not so easy though - it has weights so if you drop the layers the weights won't be loaded due to layer mismatch between "training model" and "predictive" one. I am thinking this could be circumvented by creating another layer that follows the same structure as Batch Norm but returns the same value when in_train_phase is called.

I met the same issue in a different context. In my model I tried to produce the model to learn adaptively from Chinese character vectors to word vectors and further to word properties.
def HBLSTM4POS(maxword_per_sen=20,maxchar_per_word=8,word_vec_dim=52,pos_num=26):
    InputLayers = Input(shape=(maxword_per_sen,maxchar_per_word,word_vec_dim),name='InputTensor')
    Posmask = TimeDistributed(Masking(mask_value=0.0,input_shape=(8,52)),input_shape=(20,8,52))(InputLayers)
    WordLayer = TimeDistributed(Bidirectional(LSTM(52,return_sequences=False,dropout=0.1,input_shape=(8,52),name='WordVector')),input_shape=(20,8,52))(Posmask)
    POS_LSTM1 = Bidirectional(LSTM(52,return_sequences=True))(WordLayer)
    POS_LSTM2 = Bidirectional(LSTM(52,return_sequences=True))(POS_LSTM1)
    Dense1 = TimeDistributed(Dense(POS_NUM*3,activation='relu'))(POS_LSTM2)
    Dense2 = TimeDistributed(Dense(POS_NUM,activation='softmax',name='POS_Output'))(Dense1)
    model = Model(inputs=InputLayers, outputs=Dense2)
    model.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
    return model

train_data = np.random.rand(100,20,8,52)
train_y = np.random.randint(26,size=(100,20,26))
model = HBLSTM4POS()
model.fit(train_data,train_y,batch_size=10,epochs=2,validation_split=0.1)

Full error message:
2017-09-10 20:42:29.012323: W tensorflow/core/framework/op_kernel.cc:1158] Invalid argument: You must feed a value for placeholder tensor 'time_distributed_2/keras_learning_phase' with dtype bool
	 [[Node: time_distributed_2/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=<unknown>, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Traceback (most recent call last):
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1139, in _do_call
    return fn(*args)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
    status, run_metadata)
  File "/usr/lib/python3.5/contextlib.py", line 66, in __exit__
    next(self.gen)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'time_distributed_2/keras_learning_phase' with dtype bool
	 [[Node: time_distributed_2/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=<unknown>, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
	 [[Node: mul_1/_41 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_12859_mul_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "integrated_model.py", line 36, in <module>
    model.fit(train_data,train_y,batch_size=10,epochs=2,validation_split=0.1)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1507, in fit
    initial_epoch=initial_epoch)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1156, in _fit_loop
    outs = f(ins_batch)
  File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2269, in __call__
    **self.session_kwargs)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 997, in _run
    feed_dict_string, options, run_metadata)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1132, in _do_run
    target_list, options, run_metadata)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1152, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'time_distributed_2/keras_learning_phase' with dtype bool
	 [[Node: time_distributed_2/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=<unknown>, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
	 [[Node: mul_1/_41 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_12859_mul_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'time_distributed_2/keras_learning_phase', defined at:
  File "integrated_model.py", line 35, in <module>
    model = HBLSTM4POS()
  File "integrated_model.py", line 15, in HBLSTM4POS
    WordLayer = TimeDistributed(Bidirectional(LSTM(52,return_sequences=False,dropout=0.1,input_shape=(8,52),name='WordVector')),input_shape=(20,8,52))(Posmask)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 596, in __call__
    output = self.call(inputs, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/wrappers.py", line 177, in call
    y = self.layer.call(inputs)  # (num_samples * timesteps, ...)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/wrappers.py", line 263, in call
    y = self.forward_layer.call(inputs, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/recurrent.py", line 333, in call
    preprocessed_input = self.preprocess_input(inputs, training=None)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/recurrent.py", line 1077, in preprocess_input
    timesteps, training=training)
  File "/usr/local/lib/python3.5/dist-packages/keras/layers/recurrent.py", line 46, in _time_distributed_dense
    x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)
  File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2602, in in_train_phase
    training = learning_phase()
  File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 115, in learning_phase
    name='keras_learning_phase')
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/ops/array_ops.py", line 1530, in placeholder
    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1954, in _placeholder
    name=name)
  File "/home/ht/tensorflow3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/tensorflow3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/ht/tensorflow3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'time_distributed_2/keras_learning_phase' with dtype bool
	 [[Node: time_distributed_2/keras_learning_phase = Placeholder[dtype=DT_BOOL, shape=<unknown>, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
	 [[Node: mul_1/_41 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_12859_mul_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Current my get-around is to using Sequential(). It did manage to train and get around.

Try instead
encoded_frame = keras.layers.TimeDistributed(cnn)(video)
use
encoded_frame = keras.layers.TimeDistributed(cnn.outputs[0])(video)

raise ValueError("Tensor %s is not an element of this graph." % obj)

ValueError: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph.
in keras VGG16 model

InvalidArgumentError: indices[0,19] = 87890 is not in [0, 35173) [{{node embedding_2/embedding_lookup}}]]
COLAB https://colab.research.google.com/drive/1vXAswtbuCf-wXQNc3mUQROlFzUUssBs_?usp=sharing
DATASET [ner_dataset] 15MB https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus?select=ner_dataset.csv
BiLSTM-CRF Model
input = Input(shape=(MAX_LEN,))
model = Embedding(input_dim=n_words + 1, output_dim=20,
              input_length=MAX_LEN, mask_zero=True)(input)  # 20-dim embedding
model = Bidirectional(LSTM(units=50, return_sequences=True,
                       recurrent_dropout=0.1))(model)  # variational biLSTM
model = TimeDistributed(Dense(50, activation="relu"))(model)  # a dense layer as suggested by
neuralNer
crf = CRF(18)  # CRF layer
out = crf(model)  # output
model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()
history = model.fit(tr_inputs, np.array(tr_tags), batch_size=32, epochs=5,
                validation_split=0.1, verbose=1)

Was trying BERT embedding with BiLSTM-CRF model but couldn't fix this issue. Using bert-base-multilingual-uncased tokenizer I am having this InvalidArgumentError error. Have ran with bert-base-cased tokenizer with NULL error.
Train on 38846 samples, validate on 4317 samples
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-71-d239a56e5cc7> in <module>()
      1 history = model.fit(tr_inputs, np.array(tr_tags), batch_size=32, epochs=5,
----> 2                     validation_split=0.1, verbose=1)

4 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args,
**kwargs)
   1456         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1457                                                self._handle, args,
-> 1458                                                run_metadata_ptr)
   1459         if run_metadata:
   1460           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: indices[0,19] = 87890 is not in [0, 35173)
[[{{node embedding_2/embedding_lookup}}]]

@Feynman27  @alfiya400 @StefPac please help....

InvalidArgumentError: indices[0,19] = 87890 is not in [0, 35173) [{{node embedding_2/embedding_lookup}}]]
COLAB https://colab.research.google.com/drive/1vXAswtbuCf-wXQNc3mUQROlFzUUssBs_?usp=sharing
DATASET [ner_dataset] 15MB https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus?select=ner_dataset.csv
BiLSTM-CRF Model
input = Input(shape=(MAX_LEN,))
model = Embedding(input_dim=n_words + 1, output_dim=20,
              input_length=MAX_LEN, mask_zero=True)(input)  # 20-dim embedding
model = Bidirectional(LSTM(units=50, return_sequences=True,
                       recurrent_dropout=0.1))(model)  # variational biLSTM
model = TimeDistributed(Dense(50, activation="relu"))(model)  # a dense layer as suggested by
neuralNer
crf = CRF(18)  # CRF layer
out = crf(model)  # output
model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
model.summary()
history = model.fit(tr_inputs, np.array(tr_tags), batch_size=32, epochs=5,
                validation_split=0.1, verbose=1)

Was trying BERT embedding with BiLSTM-CRF model but couldn't fix this issue. Using bert-base-multilingual-uncased tokenizer I am having this InvalidArgumentError error. Have ran with bert-base-cased tokenizer with NULL error.
Train on 38846 samples, validate on 4317 samples
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-71-d239a56e5cc7> in <module>()
      1 history = model.fit(tr_inputs, np.array(tr_tags), batch_size=32, epochs=5,
----> 2                     validation_split=0.1, verbose=1)

4 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py in __call__(self, *args,
**kwargs)
   1456         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1457                                                self._handle, args,
-> 1458                                                run_metadata_ptr)
   1459         if run_metadata:
   1460           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

InvalidArgumentError: indices[0,19] = 87890 is not in [0, 35173)
[[{{node embedding_2/embedding_lookup}}]]

@Feynman27  @alfiya400 @StefPac please help....


Preview for algolia pages

Newsroom preview and Magazine Index preview do not work when opening in a new tab, and cannot be shared.

I like the refactoring, especially with the sameLang and noDrafts, and the preview for the magazine index and newsroom now work - but the magazine preview problem from #1344 is now back 😅

@SvSven, can you please check it again?

Issue still persists for unpublished magazine article with a slug. It is however now saying it needs a slug to preview when it doesn't have one yet
https://studio-global-development-equinor-web-sites-dev.c2.radix.equinor.com/desk/magazine;6c1c14fe-b728-4082-8a8d-dfa5e64a6c77%2Ctemplate%3Dmagazine

Found the error. Should be fixed now 😬 @SvSven

Perfect, that fixed it! Can't say I fully understand why that fixed it; but it works 😬

@meols can you review this one please?

@fernandolucchesi - preview without a slug doesn't seem to work for news or magazine. (tested at the Fornebu office)

Hi @meols, the magazine, news, and local news will need a slug to be previewed properly (no need to publish it)

@fernandolucchesi - then it's OK. Not crystal clear what should be tested and what the acceptance criteria were for this one (at least not to me).

@fernandolucchesi - then it's OK. Not crystal clear what should be tested and what the acceptance criteria were for this one (at least not to me).

this is the preview for misc > newsroom and magazineIndex

it didn't exist before


"setup.py sdist --dist-dir" command converts version numbers with _ to -

This seems to be the opposite of what the setup.py bdist_egg command does.
When setup.py sdist encounters a version with an _ character in it the resulting tar.gz file has the version converted to a -
So a version 'gradleBuild_2016111701.preview'
Creates a tar.gz with the name: $project.name-gradleBuild-2016111701.preview.tar.gz
The getSdistOutput method in SourceDistTask should handle this.

This obviously needs to be fixed for such version "numbers".
However, neither underscore, nor dash should be used in the version.
This is the output of python setup.py sdist command for both:
setuptools/dist.py:293: UserWarning: The version specified ('2.5.4-whatever') is an invalid version, this may not work as expected with newer versions of setuptools, pip, and PyPI. Please see PEP 440 for more details.

setuptools/dist.py:293: UserWarning: The version specified ('2.5.4-whatever') is an invalid version, this may not work as expected with newer versions of setuptools, pip, and PyPI. Please see PEP 440 for more details.

So, with a future update of setuptools, we can fully expect such version "numbers" to cause an error.


Change tractability response to include false entries and adjust order

The new target ETL and API response only returns true tractability assessments.
So that we can differentiate between targets where an assessment has been run and all are false versus a target where no assessment has been run, can we please update the ELT and API to return `false values?
Also, can we please return the assessments in the same order as the columns in the input file?

Sorry, because I was the one asking to remove the false entries. We have reassess and we think there is value on keeping them


ERROR in Cannot find module 'babel-core'

Hi!
Looks like you have installed 'babel-core' as global dependency on your machine.
Mb you should add it to package.json or add a little message to the video about
npm install babel-core -g?
Best regards

Added a pull request for the same, #8


wamr encountered an error during AOT execution

Test Case
filea8.zip
Result
==================================================
/home/exp/simd/filea8.wasm
-----------------wamr-----------------
Create AoT compiler with:
  target:        x86_64
  target cpu:    znver3
  target triple: x86_64-unknown-linux-gnu
  cpu features:
  opt level:     3
  size level:    3
  output format: AoT file
Compile success, file /home/exp/simd/filea8.wasm.aot was generated.
Aborted

-----------------wasmedge-----------------
1324035698926385749195016455103447040
1682754050
1324035698926385749195016455103447040
1324035698926385749195016455103447040
1324035698926385749195016455103447040
1324035698926385749195016455103447040
1324035698926385749195016455103447040

==================================================

I ran filea8.wasm with wamr and wasmedge, wamr aborted
Versions and Environment
root@2f9b6c2c3e26:/home/exp# wamrc --version
wamrc 1.2.3
root@2f9b6c2c3e26:/home/exp# iwasm --version
iwasm 1.2.3

Operating System: Ubuntu 22.04.3 LTS (GNU/Linux 5.15.0-79-generic x86_64)

Hi, thanks for reporting the issue, please try the patch and re-compile the wamrc:
diff --git a/core/iwasm/compilation/aot_emit_memory.c b/core/iwasm/compilation/aot_emit_memory.c
index 7484d4b5..8c35c3fe 100644
--- a/core/iwasm/compilation/aot_emit_memory.c
+++ b/core/iwasm/compilation/aot_emit_memory.c
@@ -157,7 +157,10 @@ aot_check_memory_overflow(AOTCompContext *comp_ctx, AOTFuncContext *func_ctx,

         if (mem_offset + bytes <= mem_data_size) {
             /* inside memory space */
-            offset1 = I32_CONST((uint32)mem_offset);
+            if (comp_ctx->pointer_size == sizeof(uint64))
+                offset1 = I64_CONST((uint32)mem_offset);
+            else
+                offset1 = I32_CONST((uint32)mem_offset);
             CHECK_LLVM_CONST(offset1);
             if (!enable_segue) {
                 if (!(maddr = LLVMBuildInBoundsGEP2(comp_ctx->builder,

https://github.com/bytecodealliance/wasm-micro-runtime/pull/2697

Hi, I used this patch and recompiled the iwasm.
The result is: iwasm successfully executed filea8.wasm without aborting. However, the execution results still differ from other runtimes.

Hi, not sure which tool you used to convert decimal v128 to hex 128? I used python, it seems ok:

BTW, had better apply patch https://github.com/bytecodealliance/wasm-micro-runtime/pull/2697 before testing, I fixed several issues with it.

Apologize for the basic mistake I made and I was using an online binary conversion tool .
And thanks very much for your reply and suggestion.

Close this issue as it was fixed.


Rijksoverheid (Independent Publisher)

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[x] I validated the swagger file, apiDefinition.swagger.json, by running paconn validate command.
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[ ] Within this PR markdown file, I have pasted screenshots that show: 3 unique operations (actions/triggers) working within a Flow. This can be in one flow or part of multiple flows. For each one of those flows, I have pasted in screenshots of the Flow succeeding.
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[ ] If the connector uses OAuth, I have provided detailed steps on how to create an app in the readme.md.

Tests of the new connector.

[[certify-connector]]

[[certify-connector]]

Hello @expiscornovus,
I hope you are doing well.
Your connector is in production deployment and queued up for the next deployment schedule. This process typically takes 3-4 weeks. Please note that the documentation for your connector will not be generated until the deployment process is in the final steps.
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Hi @Amjed-Ayoub, I am doing well. How are you? That is great news, thanks for certifying the connector! 😃

@expiscornovus Congratulations! The deployment of your connector and documentation page is now complete, We have a short survey on the connector certification process that we would like for you to complete. Your feedback is appreciated and will help us improve the program, Thanks.

@sriyen-msft, I have completed the survey. Apologies for the delayed response.


Question/possible bug in link event handling in forwarding.l2_multi, dart branch

Sorry if this may not be up to standards (this is my first time posting), but in the code of l2_multi.py (pox/forwarding/l2_multi.py, using the dart branch), I noticed this, in the function
  def _handle_openflow_discovery(self, event):

        # If we already consider these nodes connected, we can ignore this link up.
        # Otherwise, we might be interested...
        if adjacency[sw1][sw2] is None:
              # These previously weren't connected.  If the link
              # exists in both directions, we consider them connected now.
              if flip(l) in core.openflow_discovery.adjacency:
                    # Yup, link goes both ways -- connected!
                    adjacency[sw1][sw2] = l.port1
                    adjacency[sw2][sw1] = l.port2

But why is the link only added to the adjacency matrix if it goes both ways? Why not add a 1-way link already, no matter if it goes in both directions? So then it would be more like this:
  def _handle_openflow_discovery:

        # If we already consider these nodes connected, we can ignore this link up.
        # Otherwise, we might be interested...
        if adjacency[sw1][sw2] is None:
              # These previously weren't connected.  If the link
              # exists in both directions, we consider them connected now.
              adjacency[sw1][sw2] = l.port1

              if flip(l) in core.openflow_discovery.adjacency:
                    # Yup, link goes both ways -- connected!
                    adjacency[sw2][sw1] = l.port2
              else:
                    adjacency[sw2][sw1] = None  # The link in the other direction does not exist

Before 118edac42e4f78be6df358c8da1bc746ca54f776, it actually did both directions independently.  It was a couple years ago, so my memory is sketchy, but I don't remember there being a single big reason for that particular change.  I think it just made it more straightforward and easier to reason about.  Which is probably true in several ways.  If you Wireshark a link and see the traffic in one direction, you'll see the other.  If you see the SYNs getting to a host, you know there should be a path back for the SYNACKs.  Etc.
Is there any big advantage to doing it unidirectionally for common use cases?

I can't think of an advantage to doing it unidirectionally for common cases (other than the fact that links between switches rarely only go 1 way), but neither do I see any disadvantage in already adding one way of the link, and only adding the other part if it actually exists. I guess this is a matter of perspective.
I'm currently experimenting with mininet and writing my own POX module to do some multipath routing based on free bandwidth on links. If host A sends a flow of packets to host B, my method could use a different path from B to A than from A to B, based on some decision making. So monitoring one link on the path from A to B may not show the flow back to A, as that flow takes a different path. Then again, this is not really a common use case. Anyway, thanks for the quick clarification.


Not all data seems to be present on OpenNeuro

Here's what I've got from v1.1.0:
console
julien-macbook:~/code/coil-qc-code/data $ gl
9fe89d54644aca294e55ebb190917a248905e7b4 (HEAD -> main, tag: 1.1.0, origin/main, origin/HEAD) [OpenNeuro] Recorded changes
julien-macbook:~/code/coil-qc-code/data $ ls -1
[...]
sub-MGH1
sub-MGH2
sub-MGH3
sub-MGH4
sub-MNI1
sub-MNI2
sub-MNI3
sub-MNI4
sub-NYU1
sub-NYU2
sub-NYU3
```

Here's what @evaalonsoortiz shared with me on 2024-04-13 via Slack:
```console
julien-macbook:~/code/coil-qc-code/data $ ls -1 ~/Desktop/derivatives/labels/
sub-MGH1
sub-MGH2
sub-MGH3
sub-MNI1
sub-MNI2
sub-MNI3
sub-MPI1
sub-MPI2
sub-MPI3
sub-Marseille1
sub-Marseille2
sub-Marseille3
sub-NTNU1
sub-NTNU2
sub-NTNU3
sub-NYU1
sub-NYU2
sub-NYU3
sub-UCL1
sub-UCL2
sub-UCL3
```

This is because I hadn't created a new version on OpenNeuro in a while, and just left my changes in the draft version. v1.1.0 contains only MGH/MNI/NYU, while the draft version contains also MPI/Marseille/NTNU/UCL. I have just created v2.0.1, which contains the latest updates.

Downloaded 2.0.1 and confirmed the data are correct. We can close


Wiki: Reference SecConnEngine in SecConn*StateLimit sections

Hi,
I couldn't find a way to edit the wiki myself?  So a small request.
Could you add a small reference in these sections:
https://github.com/SpiderLabs/ModSecurity/wiki/Reference-Manual#SecConnReadStateLimit
https://github.com/SpiderLabs/ModSecurity/wiki/Reference-Manual#SecConnWriteStateLimit
To this:
https://github.com/SpiderLabs/ModSecurity/wiki/Reference-Manual#secconnengine
(i.e. that you should turn SecConnEngine On if you want requests actually dropped).
I spent a few hours trying to figure out why the log was correctly catching a simulated Slow Read attack while not dropping anything.  The log didn't obviously state that the attack was being detected but not blocked, so I couldn't figure out what was going on:
[date] [:warn] [pid n] [client i:p] ModSecurity: Access denied with code 400. Too
many threads [141] of 50 allowed in WRITE state from [ip] - Possible DoS
Consumption Attack [Rejected]

It wasn't until I stumbled upon SecConnEngine On that the attack was properly detected and connections were dropped.
The SecConnEngine wiki section does also not state what the default is (which in my distribution, appears to be DetectionOnly).
The SecConnEngine, SecConn*StateLimit sections in the wiki would also be useful to be next to each other in the wiki, since they should be alphabetically.
Thanks! (or happy to do this myself if there is a way for me to edit the wiki)

Thanks @nicjansma,
Documentation updated accordingly


upgrade 0.10.0 -> 0.12.1 img src=https://my.com/storage/...

Hi,
I updated Cockpit CMS (0.10.0 -> 0.12.1) and now when Insert an assets (image) the html img src have the full URL (with the domain). Before (0.10.0) the URL was img src="/storage/..." now (0.12.1) it's img src="https://my.com/storage/...".
How to change that?

I had to:
"convert_urls" : true
But what is the right way to add this option to tinymce, is it directly in:
./modules/Cockpit/assets/components/field-wysiwyg.tag (what about when upgrading)
Or I need to modify each collection, fieldtype wysiwyg (not fun, I have like 20 collections)
Anyone^


test result resolution

Is the resolution of the final output video image of the algorithm different from that of the input image? Same or bigger or smaller?how to adjust parameter for adapting my poor GPU memory(6G)？

The resolution of the final output video is the same as that of the input image. If you want to get the results as original resolution but the GPU memory is limited, I recommend to increase the parameter in https://github.com/hitachinsk/FGT/blob/6bfc5dcb1778f650735d5a070974ca8318f1866f/tool/video_inpainting.py#L649 or decrease the parameter in https://github.com/hitachinsk/FGT/blob/6bfc5dcb1778f650735d5a070974ca8318f1866f/tool/video_inpainting.py#L651
If you want to change the resolution, please adjust the parameters in https://github.com/hitachinsk/FGT/blob/6bfc5dcb1778f650735d5a070974ca8318f1866f/tool/video_inpainting.py#L641
and
https://github.com/hitachinsk/FGT/blob/6bfc5dcb1778f650735d5a070974ca8318f1866f/tool/video_inpainting.py#L642

very very thanks


Climacell weather provider

Just updated to the current version and it is now not showing in lovelace.
Ymal file is still there, but not deplay.
Please check.
Regards

hi, the component is changed. See the readme and edit your yaml file, then make the changes to your lovelace configuration.
Thanks,
Renato


Issue with guild serve in UI

Hi Team, I'm facing this issue with "gulp serve" in Hygieia/UI

I also got error when running gulp serve, with a bit different error msg.
Is there recommended version to be used? My env set up is:
CLI 2.2.0
gulp 3.9.1
java 1.8
node v12.16.2
npm 6.14.4
mvn 3.3.9

Node 12 is not supported, please try using node 8. The upgrade/rewrite is in progress and you may check the progress in https://www.github.com/Hygieia/UI

@rvema Thanks! Issue resolved after using node v8.1.4


Menu bar icon offset unfocused monitor

OS: macOS Mojave 10.14.6
On a multi-monitor setup, the menu bar icon moves to the side a fair amount when it becomes unfocused (i.e., when focusing on an application on a different monitor).
Before:

After:

The expected behavior is that the icon does not move side-to-side when becoming unfocused.

The problem persists even when I close it, indicating that this application is not the issue.


Use CompileResult where possible

First step towards addressing https://github.com/jyn514/rcc/issues/156

Codecov Report

Merging #159 into master will decrease coverage by 0.38%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #159      +/-   ##
==========================================
- Coverage   76.43%   76.05%   -0.39%
==========================================
  Files          23       23
  Lines        5542     5537       -5
==========================================
- Hits         4236     4211      -25
- Misses       1306     1326      +20

Impacted Files
Coverage Δ

src/parse/mod.rs
90.14% <ø> (-0.47%)
:arrow_down:

src/parse/stmt.rs
74.58% <100%> (ø)
:arrow_up:

src/ir/mod.rs
81.81% <100%> (-0.65%)
:arrow_down:

src/parse/decl.rs
81.91% <0%> (-0.85%)
:arrow_down:

src/parse/expr.rs
77.06% <0%> (-0.74%)
:arrow_down:

src/ir/static_init.rs
68.3% <0%> (-0.45%)
:arrow_down:

src/lex.rs
86.76% <0%> (-0.36%)
:arrow_down:

src/data/mod.rs
33.33% <0%> (-0.3%)
:arrow_down:

... and 4 more

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update e3a847e...c532455. Read the comment docs.


MSID: 90203 Version: 2 DOI: 10.1101/2<IP_ADDRESS>4876

MSID: 90203
Version: 2
Preprint DOI: https://doi.org/10.1101/2<IP_ADDRESS>4876
Step 1. Awaiting reviews
Editorial to post reviews via hypothesis
Useful links:

DocMap: https://data-hub-api.elifesciences.org/enhanced-preprints/docmaps/v2/by-publisher/elife/get-by-manuscript-id?manuscript_id=90203
New model tracking: https://docs.google.com/spreadsheets/d/1_fHaoOy7hjyocptKtVJRijeNpUY4hBS7Ck_aVmx6ZJk/
Reviews on sciety: https://sciety.org/articles/activity/10.1101/2<IP_ADDRESS>4876

For trouble shooting (e.g. no Docmaps available):

DocMap issue addressing: https://miro.com/app/board/uXjVNCwK6EI=/
Explore DataHub DocMaps API: https://lookerstudio.google.com/reporting/4c2f0368-babb-4beb-b5b3-497e7e7b0f08/page/ejphD
Unmatched submissions and preprints: https://lookerstudio.google.com/u/0/reporting/9f86204f-3bf7-477c-9b18-5c5ef141bf69/page/p_gxi57ha93c
Unmatched manuscripts spreadsheet: https://docs.google.com/spreadsheets/d/15QcK8w-ssB7109RQEDtFpJPZ0J5HTGxoHa_2TtpMBbg/edit#gid=1336081641

Step 2. Preview reviewed preprint
Production QC content ahead of publication
Instructions:

QC preview: https://prod--epp.elifesciences.org/previews/90203v2
Update ticket with any problems (add blocked label)
When QC OK, add QC OK label to ticket and add publication date and time to https://docs.google.com/spreadsheets/d/1amAlKvdLcaDp5W8Z8g77NmkwbMF5n_u89ArSqPMO8jg
Move card to next column
(At end of the day post link in #enhanced-preprint and ask for PDF to be generated)

Useful links:

Preprint DOI :  https://doi.org/10.1101/2<IP_ADDRESS>4876
Confirm reviews returned by EPP: https://prod--epp.elifesciences.org/api/reviewed-preprints/90203/v2/reviews
To update the MECA path in the docmap: https://docs.google.com/spreadsheets/d/1mctCQuNFBjSn97Lihy7_vBO6z7-N-oqyLv4clyi6zHg

Step 3: Awaiting search reindex
This step adds the reviewed preprint to the homepage: https://elifesciences.org
The search reindex is triggered once an hour. We need the reviewed preprint to be indexed as the search application serves the journal homepage.
Useful links:

Jenkins pipeline to reindex search can be triggered sooner or monitored here: https://alfred.elifesciences.org/job/process/job/process-reindex-reviewed-preprints/

Step 4: Published! PDF requested
Waiting for PDF to be generated
Useful links:

PDF tracking: https://docs.google.com/spreadsheets/d/106_XeDjmuBae7gexOTNzg60lapeqjl2aRn9DzupGyS8/

Step 5: Introduce PDF to data folder and git repo
Upload PDF to relevent folder in git repo https://github.com/elifesciences/enhanced-preprints-data/
Step 6: Done!

[ ] Kettle is on!

Typesetting error:


Needed automated updates

Some components need automation, including:

[x] Version check via GitHub API (e.g., latest version)
[x] Doxygen / API docs (with Breathe)
[ ] Citation count using Google Scholar
[ ] Download count using SourceForge and GitHub totals
[ ] Plugin list

Citation count:
from scholarly import scholarly
search_query = scholarly.search_pubs('Avogadro: an advanced semantic chemical editor, visualization, and analysis platform')
result = next(search_query)
print(result['num_citations'])

SourceForge download count:
https://sourceforge.net/projects/avogadro/files/stats/json?start_date=2006-04-14&end_date=2022-07-26
(Geographic information is interesting too..)

Plugin script is here:
https://github.com/Avogadro/plugins/blob/master/cachedetails.py


SLK-88407 Scanner additional cert fix

Secrets now being created:

Correct cert passing:
`additionalCerts:

createSecret: true
secretName: "proxy-server-certs"
certFile: |
-----BEGIN CERTIFICATE-----
1
-----END CERTIFICATE-----

createSecret: true
secretName: "another-cert"
certFile: "1"`


How to use in Java

'CallBackOfQuery()' has private access in 'com.simform.videooperations.CallBackOfQuery' in Java

@Appuniverse304 Let us know your issue fixed or not. Also, give us star if it's helpful to you