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784
| 3,265,987,041
|
IssuesEvent
|
2015-10-22 18:39:14
|
dita-ot/dita-ot
|
https://api.github.com/repos/dita-ot/dita-ot
|
closed
|
Copy-to usage with URI support does not properly work [DITA OT 2.x develop branch]
|
bug P2 preprocess
|
I'm publishing a DITA Map directly from an HTTP public server (no authentication).
At some point the DITA Map has a copy-to attribute:
```xml
<topicref href="topics/dita-pdf-select-processor.dita"
copy-to="topics/dita-pdf-select-processor-transform-documents.dita"/>
```
The XHTML publishing fails like:
>BUILD FAILED
>D:\projects\eXml\frameworks\dita\DITA-OT2.x\build.xml:41: The following error occurred while executing this line:
>D:\projects\eXml\frameworks\dita\DITA-OT2.x\plugins\org.dita.xhtml\build_general.xml:112: Failed to run pipeline: Failed to transform C:\Users\RADU_C~1\AppData\Local\Temp\OxygenXMLTemp\https___raw.githubusercontent.com_oxygenxml_userguide_master_DITA\temp\xhtml\topics\dita-pdf-select-processor-transform-documents.dita: Error reported by XML parser processing file:/C:/Users/RADU_C~1/AppData/Local/Temp/OxygenXMLTemp/https___raw.githubusercontent.com_oxygenxml_userguide_master_DITA/temp/xhtml/topics/dita-pdf-select-processor-transform-documents.dita: Premature end of file.
and indeed the file "pdf-select-processor-transform-documents.dita" in my temporary files folder is empty, it has no content.
Looking into the entire console output, here are some stages which deal with that copy-to:
>[filter] Processing https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor-transform-documents.dita
[filter] Recoverable error
[filter] I/O error reported by XML parser processing
so the filter stage attempts to download it directly from the website although it is a topic which does not exist there, it is artificially created in the temporary files folder.
In the same console I see at some point:
>[filter] Processing https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor.dita
[filter] [DOTX064W][WARN] The copy-to attribute [copy-to="topics\dita-map-edit-output-transform-documents.dita"] uses the name of a file that already exists, so this attribute is ignored.
which seems to indicate the file already exists, although it does not exist on the server. Trying to connect directly to the file `https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor-transform-documents.dita` should at some point throw a 404 not found exception which should be interpreted as a missing resource.
|
1.0
|
Copy-to usage with URI support does not properly work [DITA OT 2.x develop branch] - I'm publishing a DITA Map directly from an HTTP public server (no authentication).
At some point the DITA Map has a copy-to attribute:
```xml
<topicref href="topics/dita-pdf-select-processor.dita"
copy-to="topics/dita-pdf-select-processor-transform-documents.dita"/>
```
The XHTML publishing fails like:
>BUILD FAILED
>D:\projects\eXml\frameworks\dita\DITA-OT2.x\build.xml:41: The following error occurred while executing this line:
>D:\projects\eXml\frameworks\dita\DITA-OT2.x\plugins\org.dita.xhtml\build_general.xml:112: Failed to run pipeline: Failed to transform C:\Users\RADU_C~1\AppData\Local\Temp\OxygenXMLTemp\https___raw.githubusercontent.com_oxygenxml_userguide_master_DITA\temp\xhtml\topics\dita-pdf-select-processor-transform-documents.dita: Error reported by XML parser processing file:/C:/Users/RADU_C~1/AppData/Local/Temp/OxygenXMLTemp/https___raw.githubusercontent.com_oxygenxml_userguide_master_DITA/temp/xhtml/topics/dita-pdf-select-processor-transform-documents.dita: Premature end of file.
and indeed the file "pdf-select-processor-transform-documents.dita" in my temporary files folder is empty, it has no content.
Looking into the entire console output, here are some stages which deal with that copy-to:
>[filter] Processing https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor-transform-documents.dita
[filter] Recoverable error
[filter] I/O error reported by XML parser processing
so the filter stage attempts to download it directly from the website although it is a topic which does not exist there, it is artificially created in the temporary files folder.
In the same console I see at some point:
>[filter] Processing https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor.dita
[filter] [DOTX064W][WARN] The copy-to attribute [copy-to="topics\dita-map-edit-output-transform-documents.dita"] uses the name of a file that already exists, so this attribute is ignored.
which seems to indicate the file already exists, although it does not exist on the server. Trying to connect directly to the file `https://raw.githubusercontent.com/oxygenxml/userguide/master/DITA/topics/dita-pdf-select-processor-transform-documents.dita` should at some point throw a 404 not found exception which should be interpreted as a missing resource.
|
process
|
copy to usage with uri support does not properly work i m publishing a dita map directly from an http public server no authentication at some point the dita map has a copy to attribute xml topicref href topics dita pdf select processor dita copy to topics dita pdf select processor transform documents dita the xhtml publishing fails like build failed d projects exml frameworks dita dita x build xml the following error occurred while executing this line d projects exml frameworks dita dita x plugins org dita xhtml build general xml failed to run pipeline failed to transform c users radu c appdata local temp oxygenxmltemp https raw githubusercontent com oxygenxml userguide master dita temp xhtml topics dita pdf select processor transform documents dita error reported by xml parser processing file c users radu c appdata local temp oxygenxmltemp https raw githubusercontent com oxygenxml userguide master dita temp xhtml topics dita pdf select processor transform documents dita premature end of file and indeed the file pdf select processor transform documents dita in my temporary files folder is empty it has no content looking into the entire console output here are some stages which deal with that copy to processing recoverable error i o error reported by xml parser processing so the filter stage attempts to download it directly from the website although it is a topic which does not exist there it is artificially created in the temporary files folder in the same console i see at some point processing the copy to attribute uses the name of a file that already exists so this attribute is ignored which seems to indicate the file already exists although it does not exist on the server trying to connect directly to the file should at some point throw a not found exception which should be interpreted as a missing resource
| 1
|
18,473
| 3,691,462,389
|
IssuesEvent
|
2016-02-26 00:09:40
|
growcss/growcss
|
https://api.github.com/repos/growcss/growcss
|
opened
|
Add a responsive header for testing
|
Css Docs Enhancement Js Tests
|
- [ ] Phone
- [ ] Tablet
- [ ] Laptop
- [ ] Desktop
- [ ] Tv
- [ ] Full
- [ ] Immersive
- [ ] Open (new window)
|
1.0
|
Add a responsive header for testing - - [ ] Phone
- [ ] Tablet
- [ ] Laptop
- [ ] Desktop
- [ ] Tv
- [ ] Full
- [ ] Immersive
- [ ] Open (new window)
|
non_process
|
add a responsive header for testing phone tablet laptop desktop tv full immersive open new window
| 0
|
37,180
| 18,165,181,679
|
IssuesEvent
|
2021-09-27 13:58:40
|
Kotlin/kotlinx.coroutines
|
https://api.github.com/repos/Kotlin/kotlinx.coroutines
|
closed
|
Implement overloads for standard suspending functions with timeout
|
performance
|
This is a performance-related improvement. The goal is to have overloads for TBD standard suspending function with an optional timeout parameter for performance reasons to minimize the number of allocated objects and to ensure that no objects are allocated in fast path when no suspension happens as compared to using a generic `withTimeout(...) { ... }`.
|
True
|
Implement overloads for standard suspending functions with timeout - This is a performance-related improvement. The goal is to have overloads for TBD standard suspending function with an optional timeout parameter for performance reasons to minimize the number of allocated objects and to ensure that no objects are allocated in fast path when no suspension happens as compared to using a generic `withTimeout(...) { ... }`.
|
non_process
|
implement overloads for standard suspending functions with timeout this is a performance related improvement the goal is to have overloads for tbd standard suspending function with an optional timeout parameter for performance reasons to minimize the number of allocated objects and to ensure that no objects are allocated in fast path when no suspension happens as compared to using a generic withtimeout
| 0
|
530,382
| 15,422,071,200
|
IssuesEvent
|
2021-03-05 13:55:54
|
ScratchAddons/ScratchAddons
|
https://api.github.com/repos/ScratchAddons/ScratchAddons
|
closed
|
Cannot remove folder from sprite with normally reserved name
|
priority: 4 scope: addon type: bug
|
If a sprite is named `a//(reserved name e.g. _mouse_)`, you cannot delete folder "a" or remove `_mouse_` from the folder.
|
1.0
|
Cannot remove folder from sprite with normally reserved name - If a sprite is named `a//(reserved name e.g. _mouse_)`, you cannot delete folder "a" or remove `_mouse_` from the folder.
|
non_process
|
cannot remove folder from sprite with normally reserved name if a sprite is named a reserved name e g mouse you cannot delete folder a or remove mouse from the folder
| 0
|
286,951
| 31,800,780,802
|
IssuesEvent
|
2023-09-13 10:57:30
|
Trinadh465/external_tcpdump_CVE-2018-14879
|
https://api.github.com/repos/Trinadh465/external_tcpdump_CVE-2018-14879
|
opened
|
CVE-2018-14461 (High) detected in platform_external_tcpdumpandroid-mainline-12.0.0_r17
|
Mend: dependency security vulnerability
|
## CVE-2018-14461 - High Severity Vulnerability
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>platform_external_tcpdumpandroid-mainline-12.0.0_r17</b></p></summary>
<p>
<p>Library home page: <a href=https://github.com/aosp-mirror/platform_external_tcpdump.git>https://github.com/aosp-mirror/platform_external_tcpdump.git</a></p>
<p>Found in HEAD commit: <a href="https://github.com/Trinadh465/external_tcpdump_CVE-2018-14879/commit/5d281056614b8b3c7c812b3ac3ad7b4911424849">5d281056614b8b3c7c812b3ac3ad7b4911424849</a></p>
<p>Found in base branch: <b>main</b></p></p>
</details>
</p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Source Files (1)</summary>
<p></p>
<p>
<img src='https://s3.amazonaws.com/wss-public/bitbucketImages/xRedImage.png' width=19 height=20> <b>/print-ldp.c</b>
</p>
</details>
<p></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary>
<p>
The LDP parser in tcpdump before 4.9.3 has a buffer over-read in print-ldp.c:ldp_tlv_print().
<p>Publish Date: 2019-10-03
<p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2018-14461>CVE-2018-14461</a></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>7.5</b>)</summary>
<p>
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Network
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: High
</p>
For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>.
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary>
<p>
<p>Type: Upgrade version</p>
<p>Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-14461">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-14461</a></p>
<p>Release Date: 2019-10-03</p>
<p>Fix Resolution: 4.9.3</p>
</p>
</details>
<p></p>
***
Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
|
True
|
CVE-2018-14461 (High) detected in platform_external_tcpdumpandroid-mainline-12.0.0_r17 - ## CVE-2018-14461 - High Severity Vulnerability
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>platform_external_tcpdumpandroid-mainline-12.0.0_r17</b></p></summary>
<p>
<p>Library home page: <a href=https://github.com/aosp-mirror/platform_external_tcpdump.git>https://github.com/aosp-mirror/platform_external_tcpdump.git</a></p>
<p>Found in HEAD commit: <a href="https://github.com/Trinadh465/external_tcpdump_CVE-2018-14879/commit/5d281056614b8b3c7c812b3ac3ad7b4911424849">5d281056614b8b3c7c812b3ac3ad7b4911424849</a></p>
<p>Found in base branch: <b>main</b></p></p>
</details>
</p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Source Files (1)</summary>
<p></p>
<p>
<img src='https://s3.amazonaws.com/wss-public/bitbucketImages/xRedImage.png' width=19 height=20> <b>/print-ldp.c</b>
</p>
</details>
<p></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary>
<p>
The LDP parser in tcpdump before 4.9.3 has a buffer over-read in print-ldp.c:ldp_tlv_print().
<p>Publish Date: 2019-10-03
<p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2018-14461>CVE-2018-14461</a></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>7.5</b>)</summary>
<p>
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Network
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: None
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: High
</p>
For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>.
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary>
<p>
<p>Type: Upgrade version</p>
<p>Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-14461">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-14461</a></p>
<p>Release Date: 2019-10-03</p>
<p>Fix Resolution: 4.9.3</p>
</p>
</details>
<p></p>
***
Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
|
non_process
|
cve high detected in platform external tcpdumpandroid mainline cve high severity vulnerability vulnerable library platform external tcpdumpandroid mainline library home page a href found in head commit a href found in base branch main vulnerable source files print ldp c vulnerability details the ldp parser in tcpdump before has a buffer over read in print ldp c ldp tlv print publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact none integrity impact none availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution step up your open source security game with mend
| 0
|
6,602
| 9,683,631,553
|
IssuesEvent
|
2019-05-23 12:01:02
|
linnovate/root
|
https://api.github.com/repos/linnovate/root
|
opened
|
after filtering by all/archived and clicking on the favorite filter, the filter changes to status:active
|
2.0.7 Process bug
|
create new items
go to filter by status : all/ archived
click on the favorite filter
the status changes to status : active
**exiting filter by favorite mode also changes the status to active**
before pressing favorite filter
after pressing the favorite filter
|
1.0
|
after filtering by all/archived and clicking on the favorite filter, the filter changes to status:active - create new items
go to filter by status : all/ archived
click on the favorite filter
the status changes to status : active
**exiting filter by favorite mode also changes the status to active**
before pressing favorite filter
after pressing the favorite filter
|
process
|
after filtering by all archived and clicking on the favorite filter the filter changes to status active create new items go to filter by status all archived click on the favorite filter the status changes to status active exiting filter by favorite mode also changes the status to active before pressing favorite filter after pressing the favorite filter
| 1
|
124,217
| 4,893,575,665
|
IssuesEvent
|
2016-11-18 23:49:16
|
concrete5/concrete5
|
https://api.github.com/repos/concrete5/concrete5
|
closed
|
Public Registration: On - approve manually (removed?)
|
accepted:in progress priority:must have type:bug
|
This option no longer appears to be available in v8.
This was where visitors could register to be a member, but would only become an active member once an admin had received an email notification and they'd gone into the dashboard to actually activate the new user. The user would then receive a 'you've been approved' style email.
This is/was quite handy in certain circumstances and I'm using it on a few 5.7 sites - has this been removed completed, or was it just inadvertently left off as things have been reworked? The email template that it used is still in the concrete/mail directory.
|
1.0
|
Public Registration: On - approve manually (removed?) - This option no longer appears to be available in v8.
This was where visitors could register to be a member, but would only become an active member once an admin had received an email notification and they'd gone into the dashboard to actually activate the new user. The user would then receive a 'you've been approved' style email.
This is/was quite handy in certain circumstances and I'm using it on a few 5.7 sites - has this been removed completed, or was it just inadvertently left off as things have been reworked? The email template that it used is still in the concrete/mail directory.
|
non_process
|
public registration on approve manually removed this option no longer appears to be available in this was where visitors could register to be a member but would only become an active member once an admin had received an email notification and they d gone into the dashboard to actually activate the new user the user would then receive a you ve been approved style email this is was quite handy in certain circumstances and i m using it on a few sites has this been removed completed or was it just inadvertently left off as things have been reworked the email template that it used is still in the concrete mail directory
| 0
|
2,060
| 4,864,956,542
|
IssuesEvent
|
2016-11-14 19:27:09
|
Sage-Bionetworks/Genie
|
https://api.github.com/repos/Sage-Bionetworks/Genie
|
opened
|
maf entries lacking variant allele
|
data processing MSK
|
MSK says to ignore for now -- being fixed, will upload new maf when addressed.
|
1.0
|
maf entries lacking variant allele - MSK says to ignore for now -- being fixed, will upload new maf when addressed.
|
process
|
maf entries lacking variant allele msk says to ignore for now being fixed will upload new maf when addressed
| 1
|
9,409
| 12,406,704,368
|
IssuesEvent
|
2020-05-21 19:38:17
|
kubeflow/internal-acls
|
https://api.github.com/repos/kubeflow/internal-acls
|
closed
|
Add mxnet-operator to repos managed by project-maintainers team
|
area/engprod kind/process priority/p2
|
[mxnet-operator](https://github.com/kubeflow/mxnet-operator) is missing from the [list of repos managed by project-maintainers team](https://github.com/orgs/kubeflow/teams/project-maintainers/repositories).
/cc @abhi-g @jlewi @richardsliu
Could you add it since you are the maintainers of project-maintainers team?
|
1.0
|
Add mxnet-operator to repos managed by project-maintainers team - [mxnet-operator](https://github.com/kubeflow/mxnet-operator) is missing from the [list of repos managed by project-maintainers team](https://github.com/orgs/kubeflow/teams/project-maintainers/repositories).
/cc @abhi-g @jlewi @richardsliu
Could you add it since you are the maintainers of project-maintainers team?
|
process
|
add mxnet operator to repos managed by project maintainers team is missing from the cc abhi g jlewi richardsliu could you add it since you are the maintainers of project maintainers team
| 1
|
71,540
| 8,663,979,038
|
IssuesEvent
|
2018-11-28 18:50:46
|
Microsoft/vscode-azureappservice
|
https://api.github.com/repos/Microsoft/vscode-azureappservice
|
closed
|
Remove the action 'Connect to Log Stream…' when the log streaming service is already active
|
CTI bydesign
|
**Platform / OS Version:** All
**Node.js** : V8.12.0
**Build Version:** [20181121.1](https://dev.azure.com/ms-azuretools/AzCode/_build/results?buildId=798&view=logs)
**Repro Steps:**
1. Drill down one web app service -> Logs.
2. Click 'Connect to Log Stream...'.
3. After the log streaming service is active, check whether the action disappears or not.
**Expect Experiment:**
The action 'Connect to Log Stream...' disappears after the log streaming is already active.
**Actual Experiment:**
The action 'Connect to Log Stream...' still shows.
|
1.0
|
Remove the action 'Connect to Log Stream…' when the log streaming service is already active - **Platform / OS Version:** All
**Node.js** : V8.12.0
**Build Version:** [20181121.1](https://dev.azure.com/ms-azuretools/AzCode/_build/results?buildId=798&view=logs)
**Repro Steps:**
1. Drill down one web app service -> Logs.
2. Click 'Connect to Log Stream...'.
3. After the log streaming service is active, check whether the action disappears or not.
**Expect Experiment:**
The action 'Connect to Log Stream...' disappears after the log streaming is already active.
**Actual Experiment:**
The action 'Connect to Log Stream...' still shows.
|
non_process
|
remove the action connect to log stream… when the log streaming service is already active platform os version all node js build version repro steps drill down one web app service logs click connect to log stream after the log streaming service is active check whether the action disappears or not expect experiment the action connect to log stream disappears after the log streaming is already active actual experiment the action connect to log stream still shows
| 0
|
13,562
| 16,104,036,448
|
IssuesEvent
|
2021-04-27 13:03:01
|
prisma/prisma-engines
|
https://api.github.com/repos/prisma/prisma-engines
|
closed
|
Vitess test runs into mergeable state
|
engines/migration engine process/candidate team/migrations topic: testing
|
What we will do now:
- Instead of the big docker setup, run only the vttestserver that loses some of the vitess magic in between, but is easier to manage
- Four vttestservers: mysql8 and mysq57, and 2 shadow databases (mysql8 and mysql57)
- A way in ME tests to notice we're in vitess, and never ever try to drop/create databases, and always using a static shadow database which we define in the configuration
- All tests (QE, IE, ME) must run single-threaded
The whole big PR needs a thorough review in a Zoom call with @tomhoule.
|
1.0
|
Vitess test runs into mergeable state - What we will do now:
- Instead of the big docker setup, run only the vttestserver that loses some of the vitess magic in between, but is easier to manage
- Four vttestservers: mysql8 and mysq57, and 2 shadow databases (mysql8 and mysql57)
- A way in ME tests to notice we're in vitess, and never ever try to drop/create databases, and always using a static shadow database which we define in the configuration
- All tests (QE, IE, ME) must run single-threaded
The whole big PR needs a thorough review in a Zoom call with @tomhoule.
|
process
|
vitess test runs into mergeable state what we will do now instead of the big docker setup run only the vttestserver that loses some of the vitess magic in between but is easier to manage four vttestservers and and shadow databases and a way in me tests to notice we re in vitess and never ever try to drop create databases and always using a static shadow database which we define in the configuration all tests qe ie me must run single threaded the whole big pr needs a thorough review in a zoom call with tomhoule
| 1
|
306,285
| 9,383,320,502
|
IssuesEvent
|
2019-04-05 02:49:06
|
jaws/jaws
|
https://api.github.com/repos/jaws/jaws
|
closed
|
Standardize variable names across networks
|
medium priority
|
Create a table having most important variables with different names
|
1.0
|
Standardize variable names across networks - Create a table having most important variables with different names
|
non_process
|
standardize variable names across networks create a table having most important variables with different names
| 0
|
12,417
| 14,921,214,991
|
IssuesEvent
|
2021-01-23 09:01:32
|
threefoldfoundation/tft-stellar
|
https://api.github.com/repos/threefoldfoundation/tft-stellar
|
closed
|
One infoscript/function based on a hash
|
process_wontfix type_feature
|
Based on a hash, check if the transaction is a:
- [x] TFTA destruction transaction
- [ ] tfchain locking transaction
In the first case, output the amount and the address that sent it
In the second case ,output the amounts and possible locktimes (TFTA/TFT) ( check the https://github.com/threefoldfoundation/tft-stellar/tree/master/scripts/conversion code), don't create a tfchain wallet in js-sdk
|
1.0
|
One infoscript/function based on a hash - Based on a hash, check if the transaction is a:
- [x] TFTA destruction transaction
- [ ] tfchain locking transaction
In the first case, output the amount and the address that sent it
In the second case ,output the amounts and possible locktimes (TFTA/TFT) ( check the https://github.com/threefoldfoundation/tft-stellar/tree/master/scripts/conversion code), don't create a tfchain wallet in js-sdk
|
process
|
one infoscript function based on a hash based on a hash check if the transaction is a tfta destruction transaction tfchain locking transaction in the first case output the amount and the address that sent it in the second case output the amounts and possible locktimes tfta tft check the code don t create a tfchain wallet in js sdk
| 1
|
1,368
| 3,925,265,252
|
IssuesEvent
|
2016-04-22 18:17:48
|
e-government-ua/iBP
|
https://api.github.com/repos/e-government-ua/iBP
|
closed
|
Дніпропетровська область - Видача довідки про неотримання аліментів-розмір аліментів
|
In process of testing
|
[Послуга 3 Отримання довідки про аліменти.docx](https://github.com/e-government-ua/iBP/files/198059/3.docx)
[ЗАЯВКА ПРО ВИДАЧУ ДОВІДКИ ПРО РОЗМІР АЛІМЕНТІВ.docx](https://github.com/e-government-ua/iBP/files/198062/default.docx)
[ЗАЯВКА ПО ДОВІДКЕ ПО АЛІМЕНТАМ.docx](https://github.com/e-government-ua/iBP/files/198063/default.docx)
|
1.0
|
Дніпропетровська область - Видача довідки про неотримання аліментів-розмір аліментів - [Послуга 3 Отримання довідки про аліменти.docx](https://github.com/e-government-ua/iBP/files/198059/3.docx)
[ЗАЯВКА ПРО ВИДАЧУ ДОВІДКИ ПРО РОЗМІР АЛІМЕНТІВ.docx](https://github.com/e-government-ua/iBP/files/198062/default.docx)
[ЗАЯВКА ПО ДОВІДКЕ ПО АЛІМЕНТАМ.docx](https://github.com/e-government-ua/iBP/files/198063/default.docx)
|
process
|
дніпропетровська область видача довідки про неотримання аліментів розмір аліментів
| 1
|
14,659
| 17,783,930,821
|
IssuesEvent
|
2021-08-31 08:47:37
|
googleapis/gapic-generator-csharp
|
https://api.github.com/repos/googleapis/gapic-generator-csharp
|
closed
|
Dependency Dashboard
|
type: process
|
This issue provides visibility into Renovate updates and their statuses. [Learn more](https://docs.renovatebot.com/key-concepts/dashboard/)
## Open
These updates have all been created already. Click a checkbox below to force a retry/rebase of any.
- [ ] <!-- rebase-branch=renovate/gcr.io-gapic-images-api-common-protos-1.x -->[chore(deps): update gcr.io/gapic-images/api-common-protos docker tag to v1](../pull/363)
## Ignored or Blocked
These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below.
- [ ] <!-- recreate-branch=renovate/xunit.runner.visualstudio-2.x -->[chore(deps): update dependency xunit.runner.visualstudio to v2.4.3](../pull/353)
---
- [ ] <!-- manual job -->Check this box to trigger a request for Renovate to run again on this repository
|
1.0
|
Dependency Dashboard - This issue provides visibility into Renovate updates and their statuses. [Learn more](https://docs.renovatebot.com/key-concepts/dashboard/)
## Open
These updates have all been created already. Click a checkbox below to force a retry/rebase of any.
- [ ] <!-- rebase-branch=renovate/gcr.io-gapic-images-api-common-protos-1.x -->[chore(deps): update gcr.io/gapic-images/api-common-protos docker tag to v1](../pull/363)
## Ignored or Blocked
These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below.
- [ ] <!-- recreate-branch=renovate/xunit.runner.visualstudio-2.x -->[chore(deps): update dependency xunit.runner.visualstudio to v2.4.3](../pull/353)
---
- [ ] <!-- manual job -->Check this box to trigger a request for Renovate to run again on this repository
|
process
|
dependency dashboard this issue provides visibility into renovate updates and their statuses open these updates have all been created already click a checkbox below to force a retry rebase of any pull ignored or blocked these are blocked by an existing closed pr and will not be recreated unless you click a checkbox below pull check this box to trigger a request for renovate to run again on this repository
| 1
|
81,203
| 3,587,926,589
|
IssuesEvent
|
2016-01-30 17:34:40
|
PowerPointLabs/PowerPointLabs
|
https://api.github.com/repos/PowerPointLabs/PowerPointLabs
|
closed
|
Updating pptlabs may cause it to disappear
|
Feature.Deploy Priority.Medium type-enhancement
|
I guess it's caused by changes of certificate.
Our test certificate's only valid for 1 year. when certificate is changed and pptlabs is to update, the update system (by MS ClickOnce) will ask user whether to trust & install/update this add-in (because certificate is add-in's identifier, if it's changed, ClickOnce will ask user whether to trust the new identity).
At this moment, if user clicks NO (don't trust new certificate), then our add-in will disappear from the ribbon.
Solution:
1. provide instructions page to guide user how to make it appear again.
2. modify our certificate to make it valid for a longer time.
|
1.0
|
Updating pptlabs may cause it to disappear - I guess it's caused by changes of certificate.
Our test certificate's only valid for 1 year. when certificate is changed and pptlabs is to update, the update system (by MS ClickOnce) will ask user whether to trust & install/update this add-in (because certificate is add-in's identifier, if it's changed, ClickOnce will ask user whether to trust the new identity).
At this moment, if user clicks NO (don't trust new certificate), then our add-in will disappear from the ribbon.
Solution:
1. provide instructions page to guide user how to make it appear again.
2. modify our certificate to make it valid for a longer time.
|
non_process
|
updating pptlabs may cause it to disappear i guess it s caused by changes of certificate our test certificate s only valid for year when certificate is changed and pptlabs is to update the update system by ms clickonce will ask user whether to trust install update this add in because certificate is add in s identifier if it s changed clickonce will ask user whether to trust the new identity at this moment if user clicks no don t trust new certificate then our add in will disappear from the ribbon solution provide instructions page to guide user how to make it appear again modify our certificate to make it valid for a longer time
| 0
|
17,711
| 23,608,405,732
|
IssuesEvent
|
2022-08-24 10:13:25
|
bazelbuild/bazel
|
https://api.github.com/repos/bazelbuild/bazel
|
opened
|
Release 6.0.0 - August 2022
|
P1 type: process release team-OSS
|
# Status of Bazel 6.0.0
- Target baseline: 2022-09-12
- Expected release date: 2022-11-14
- [List of release blockers](https://github.com/bazelbuild/bazel/milestone/38)
To report a release-blocking bug, please add a comment with the text `@bazel-io flag` to the issue. A release manager will triage it and add it to the milestone.
<!-- uncomment this line when the release branch is open
To cherry-pick a mainline commit into 6.0.0, simply send a PR against the `release-6.0.0` branch. -->
Task list:
- [ ] Pick release baseline:
- [ ] Create release candidate:
- [ ] Check downstream projects:
- [ ] [Create draft release announcement](https://docs.google.com/document/d/1wDvulLlj4NAlPZamdlEVFORks3YXJonCjyuQMUQEmB0/edit)
- [ ] Send for review the release announcement PR:
- [ ] Push the release, notify package maintainers:
- [ ] Update the documentation
- [ ] Push the blog post
- [ ] Update the [release page](https://github.com/bazelbuild/bazel/releases/)
|
1.0
|
Release 6.0.0 - August 2022 - # Status of Bazel 6.0.0
- Target baseline: 2022-09-12
- Expected release date: 2022-11-14
- [List of release blockers](https://github.com/bazelbuild/bazel/milestone/38)
To report a release-blocking bug, please add a comment with the text `@bazel-io flag` to the issue. A release manager will triage it and add it to the milestone.
<!-- uncomment this line when the release branch is open
To cherry-pick a mainline commit into 6.0.0, simply send a PR against the `release-6.0.0` branch. -->
Task list:
- [ ] Pick release baseline:
- [ ] Create release candidate:
- [ ] Check downstream projects:
- [ ] [Create draft release announcement](https://docs.google.com/document/d/1wDvulLlj4NAlPZamdlEVFORks3YXJonCjyuQMUQEmB0/edit)
- [ ] Send for review the release announcement PR:
- [ ] Push the release, notify package maintainers:
- [ ] Update the documentation
- [ ] Push the blog post
- [ ] Update the [release page](https://github.com/bazelbuild/bazel/releases/)
|
process
|
release august status of bazel target baseline expected release date to report a release blocking bug please add a comment with the text bazel io flag to the issue a release manager will triage it and add it to the milestone uncomment this line when the release branch is open to cherry pick a mainline commit into simply send a pr against the release branch task list pick release baseline create release candidate check downstream projects send for review the release announcement pr push the release notify package maintainers update the documentation push the blog post update the
| 1
|
28,277
| 2,700,753,463
|
IssuesEvent
|
2015-04-04 14:43:57
|
cs2103jan2015-f10-3c/main
|
https://api.github.com/repos/cs2103jan2015-f10-3c/main
|
closed
|
Display task of the day and a brief description
|
priority.medium type.story
|
so that user can have a quick glimpse of the issues need attending of the day
|
1.0
|
Display task of the day and a brief description - so that user can have a quick glimpse of the issues need attending of the day
|
non_process
|
display task of the day and a brief description so that user can have a quick glimpse of the issues need attending of the day
| 0
|
278,124
| 21,058,090,201
|
IssuesEvent
|
2022-04-01 06:43:21
|
medajet/ped
|
https://api.github.com/repos/medajet/ped
|
opened
|
Not enough visuals in UG
|
severity.Medium type.DocumentationBug
|
The only visual in the UG is the one in the quick start guide. Perhaps more should be added to illustrate the use of commands.
<!--session: 1648792880809-a0d404c0-a5ce-4319-b3e0-6ef579fd4865-->
<!--Version: Web v3.4.2-->
|
1.0
|
Not enough visuals in UG - The only visual in the UG is the one in the quick start guide. Perhaps more should be added to illustrate the use of commands.
<!--session: 1648792880809-a0d404c0-a5ce-4319-b3e0-6ef579fd4865-->
<!--Version: Web v3.4.2-->
|
non_process
|
not enough visuals in ug the only visual in the ug is the one in the quick start guide perhaps more should be added to illustrate the use of commands
| 0
|
331,875
| 29,146,249,361
|
IssuesEvent
|
2023-05-18 03:22:35
|
kubernetes-sigs/kubespray
|
https://api.github.com/repos/kubernetes-sigs/kubespray
|
closed
|
CI: tf-elastx_cleanup is failed
|
kind/failing-test
|
<!-- Please only use this template for submitting reports about failing tests in Kubespray CI jobs -->
**Which jobs are failing**:
tf-elastx_cleanup
**Since when has it been failing**:
05/18/2023
**Testgrid link**:
https://gitlab.com/kargo-ci/kubernetes-sigs-kubespray/-/jobs/4300994179
**Reason for failure**:
```
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/generic/password.py", line 16, in <module>
from keystoneauth1.identity import v3
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/v3/__init__.py", line 27, in <module>
from keystoneauth1.identity.v3.oauth2_mtls_client_credential import * # noqa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/v3/oauth2_mtls_client_credential.py", line 17, in <module>
import six
ModuleNotFoundError: No module named 'six'
Cleaning up project directory and file based variables
00:01
ERROR: Job failed: command terminated with exit code 1
```
**Anything else we need to know**:
|
1.0
|
CI: tf-elastx_cleanup is failed - <!-- Please only use this template for submitting reports about failing tests in Kubespray CI jobs -->
**Which jobs are failing**:
tf-elastx_cleanup
**Since when has it been failing**:
05/18/2023
**Testgrid link**:
https://gitlab.com/kargo-ci/kubernetes-sigs-kubespray/-/jobs/4300994179
**Reason for failure**:
```
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/generic/password.py", line 16, in <module>
from keystoneauth1.identity import v3
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/v3/__init__.py", line 27, in <module>
from keystoneauth1.identity.v3.oauth2_mtls_client_credential import * # noqa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/keystoneauth1/identity/v3/oauth2_mtls_client_credential.py", line 17, in <module>
import six
ModuleNotFoundError: No module named 'six'
Cleaning up project directory and file based variables
00:01
ERROR: Job failed: command terminated with exit code 1
```
**Anything else we need to know**:
|
non_process
|
ci tf elastx cleanup is failed which jobs are failing tf elastx cleanup since when has it been failing testgrid link reason for failure file usr local lib site packages identity generic password py line in from identity import file usr local lib site packages identity init py line in from identity mtls client credential import noqa file usr local lib site packages identity mtls client credential py line in import six modulenotfounderror no module named six cleaning up project directory and file based variables error job failed command terminated with exit code anything else we need to know
| 0
|
450,466
| 31,925,817,216
|
IssuesEvent
|
2023-09-19 01:37:41
|
aaronthangnguyen/cen4010-swe1
|
https://api.github.com/repos/aaronthangnguyen/cen4010-swe1
|
closed
|
Schema Reference
|
documentation
|
### Overview
Document database schemas in wiki.
### Wiki
https://github.com/aaronthangnguyen/cen4010-swe1/wiki/Schema-Reference
### Tasks
- [x] `Author` schema @aaronthangnguyen
- [x] `Book` schema @aaronthangnguyen
|
1.0
|
Schema Reference - ### Overview
Document database schemas in wiki.
### Wiki
https://github.com/aaronthangnguyen/cen4010-swe1/wiki/Schema-Reference
### Tasks
- [x] `Author` schema @aaronthangnguyen
- [x] `Book` schema @aaronthangnguyen
|
non_process
|
schema reference overview document database schemas in wiki wiki tasks author schema aaronthangnguyen book schema aaronthangnguyen
| 0
|
54,772
| 6,403,509,473
|
IssuesEvent
|
2017-08-06 19:26:12
|
tgstation/tgstation
|
https://api.github.com/repos/tgstation/tgstation
|
closed
|
People can take comical amounts of tox damage from plasma gas for some reason
|
Bug Higgs-Bugson Needs Reproducing/Testing
|
Saw someone step into plasma gas for about two seconds and they ended up taking 167 tox damage.
|
1.0
|
People can take comical amounts of tox damage from plasma gas for some reason - Saw someone step into plasma gas for about two seconds and they ended up taking 167 tox damage.
|
non_process
|
people can take comical amounts of tox damage from plasma gas for some reason saw someone step into plasma gas for about two seconds and they ended up taking tox damage
| 0
|
10,521
| 13,304,255,987
|
IssuesEvent
|
2020-08-25 16:39:03
|
pystatgen/sgkit
|
https://api.github.com/repos/pystatgen/sgkit
|
closed
|
Mergify not merging
|
process + tools
|
Is it normal for mergify to take ~2 hours to try to merge a PR? This one seems stuck: https://github.com/pystatgen/sgkit/pull/114/checks?check_run_id=1021253465.
Do you typically wait these out @jeromekelleher or is it more likely that something has gone wrong?
|
1.0
|
Mergify not merging - Is it normal for mergify to take ~2 hours to try to merge a PR? This one seems stuck: https://github.com/pystatgen/sgkit/pull/114/checks?check_run_id=1021253465.
Do you typically wait these out @jeromekelleher or is it more likely that something has gone wrong?
|
process
|
mergify not merging is it normal for mergify to take hours to try to merge a pr this one seems stuck do you typically wait these out jeromekelleher or is it more likely that something has gone wrong
| 1
|
1,266
| 9,808,301,900
|
IssuesEvent
|
2019-06-12 15:21:14
|
MicrosoftDocs/azure-docs
|
https://api.github.com/repos/MicrosoftDocs/azure-docs
|
closed
|
Known issues should mention boolean is not supported.
|
assigned-to-author automation/svc doc-enhancement triaged
|
Under "passing parameters", object and array aren't the only types not supported. Boolean also causes the runbook to fail, and should be mentioned under known issues.
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 443146ec-fc40-657f-69fc-55c3b8ce3c8f
* Version Independent ID: 8c340541-5b88-b8a3-c547-791189d32c6b
* Content: [Configure pre and post scripts on your Update Management deployment in Azure](https://docs.microsoft.com/en-us/azure/automation/pre-post-scripts#passing-parameters)
* Content Source: [articles/automation/pre-post-scripts.md](https://github.com/Microsoft/azure-docs/blob/master/articles/automation/pre-post-scripts.md)
* Service: **automation**
* Sub-service: **update-management**
* GitHub Login: @georgewallace
* Microsoft Alias: **gwallace**
|
1.0
|
Known issues should mention boolean is not supported. - Under "passing parameters", object and array aren't the only types not supported. Boolean also causes the runbook to fail, and should be mentioned under known issues.
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 443146ec-fc40-657f-69fc-55c3b8ce3c8f
* Version Independent ID: 8c340541-5b88-b8a3-c547-791189d32c6b
* Content: [Configure pre and post scripts on your Update Management deployment in Azure](https://docs.microsoft.com/en-us/azure/automation/pre-post-scripts#passing-parameters)
* Content Source: [articles/automation/pre-post-scripts.md](https://github.com/Microsoft/azure-docs/blob/master/articles/automation/pre-post-scripts.md)
* Service: **automation**
* Sub-service: **update-management**
* GitHub Login: @georgewallace
* Microsoft Alias: **gwallace**
|
non_process
|
known issues should mention boolean is not supported under passing parameters object and array aren t the only types not supported boolean also causes the runbook to fail and should be mentioned under known issues document details ⚠ do not edit this section it is required for docs microsoft com ➟ github issue linking id version independent id content content source service automation sub service update management github login georgewallace microsoft alias gwallace
| 0
|
123,123
| 4,857,515,736
|
IssuesEvent
|
2016-11-12 17:05:00
|
TASVideos/BizHawk
|
https://api.github.com/repos/TASVideos/BizHawk
|
closed
|
GLideN64 support
|
auto-migrated Core-Mupen64Plus Priority-Medium Type-Enhancement
|
```
The new GLideN64 is shaping up to be the most accurate HLE N64 video plugin
there currently is. Whilst it's not without bugs and there may be later
updates, it's at public release. Would you be able to include it in Bizhawk?
```
Original issue reported on code.google.com by `video-ga...@pages.plusgoogle.com` on 5 May 2015 at 2:59
|
1.0
|
GLideN64 support - ```
The new GLideN64 is shaping up to be the most accurate HLE N64 video plugin
there currently is. Whilst it's not without bugs and there may be later
updates, it's at public release. Would you be able to include it in Bizhawk?
```
Original issue reported on code.google.com by `video-ga...@pages.plusgoogle.com` on 5 May 2015 at 2:59
|
non_process
|
support the new is shaping up to be the most accurate hle video plugin there currently is whilst it s not without bugs and there may be later updates it s at public release would you be able to include it in bizhawk original issue reported on code google com by video ga pages plusgoogle com on may at
| 0
|
111,517
| 11,735,209,849
|
IssuesEvent
|
2020-03-11 10:42:44
|
xtermjs/xterm.js
|
https://api.github.com/repos/xtermjs/xterm.js
|
opened
|
Move vtfeatures script over to website repo
|
type/documentation
|
Currently the script to extract vtfeatures and create the markdown file resides under this repo, which is unfortunate, as it creates PRs on both repos, if template changes are needed. The xterm repo should not be bothered with this at all, thus we should move the script to the website repo.
|
1.0
|
Move vtfeatures script over to website repo - Currently the script to extract vtfeatures and create the markdown file resides under this repo, which is unfortunate, as it creates PRs on both repos, if template changes are needed. The xterm repo should not be bothered with this at all, thus we should move the script to the website repo.
|
non_process
|
move vtfeatures script over to website repo currently the script to extract vtfeatures and create the markdown file resides under this repo which is unfortunate as it creates prs on both repos if template changes are needed the xterm repo should not be bothered with this at all thus we should move the script to the website repo
| 0
|
11,875
| 14,674,916,683
|
IssuesEvent
|
2020-12-30 16:22:19
|
amor71/LiuAlgoTrader
|
https://api.github.com/repos/amor71/LiuAlgoTrader
|
closed
|
Alphalens
|
enhancement in-process
|
**Is your feature request related to a problem? Please describe.**
Improve analysis capabilities
**Describe the solution you'd like**
https://github.com/quantopian/alphalens
**Additional context**
might be relevant for model automation re "towards ML" for Liu
|
1.0
|
Alphalens - **Is your feature request related to a problem? Please describe.**
Improve analysis capabilities
**Describe the solution you'd like**
https://github.com/quantopian/alphalens
**Additional context**
might be relevant for model automation re "towards ML" for Liu
|
process
|
alphalens is your feature request related to a problem please describe improve analysis capabilities describe the solution you d like additional context might be relevant for model automation re towards ml for liu
| 1
|
223,615
| 7,459,044,443
|
IssuesEvent
|
2018-03-30 13:37:15
|
webcompat/web-bugs
|
https://api.github.com/repos/webcompat/web-bugs
|
closed
|
m.tvtv.de - site is not usable
|
browser-firefox-mobile priority-normal
|
<!-- @browser: Firefox Mobile 58.0 -->
<!-- @ua_header: Mozilla/5.0 (Android 7.0; Mobile; rv:58.0) Gecko/58.0 Firefox/58.0 -->
<!-- @reported_with: mobile-reporter -->
**URL**: http://m.tvtv.de/sendungen/
**Browser / Version**: Firefox Mobile 58.0
**Operating System**: Android 7.0
**Tested Another Browser**: Yes
**Problem type**: Site is not usable
**Description**: design ist broken
**Steps to Reproduce**:
[](https://webcompat.com/uploads/2017/9/0846758e-6f12-421e-ae09-34d14d3dc8e6.jpg)
_From [webcompat.com](https://webcompat.com/) with ❤️_
|
1.0
|
m.tvtv.de - site is not usable - <!-- @browser: Firefox Mobile 58.0 -->
<!-- @ua_header: Mozilla/5.0 (Android 7.0; Mobile; rv:58.0) Gecko/58.0 Firefox/58.0 -->
<!-- @reported_with: mobile-reporter -->
**URL**: http://m.tvtv.de/sendungen/
**Browser / Version**: Firefox Mobile 58.0
**Operating System**: Android 7.0
**Tested Another Browser**: Yes
**Problem type**: Site is not usable
**Description**: design ist broken
**Steps to Reproduce**:
[](https://webcompat.com/uploads/2017/9/0846758e-6f12-421e-ae09-34d14d3dc8e6.jpg)
_From [webcompat.com](https://webcompat.com/) with ❤️_
|
non_process
|
m tvtv de site is not usable url browser version firefox mobile operating system android tested another browser yes problem type site is not usable description design ist broken steps to reproduce from with ❤️
| 0
|
13,269
| 15,732,307,395
|
IssuesEvent
|
2021-03-29 18:08:05
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department-of-veterans-affairs/notification-api
|
https://api.github.com/repos/department-of-veterans-affairs/notification-api
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reopened
|
Start ESECC process to get publicly routable VA URL for webhooks
|
Process Task
|
We will need a publicly routable VA url for webhooks for text/email providers.
We should follow steps outline here:
https://github.com/department-of-veterans-affairs/devops/blob/master/docs/ESECC-Public-URL-process.md
|
1.0
|
Start ESECC process to get publicly routable VA URL for webhooks - We will need a publicly routable VA url for webhooks for text/email providers.
We should follow steps outline here:
https://github.com/department-of-veterans-affairs/devops/blob/master/docs/ESECC-Public-URL-process.md
|
process
|
start esecc process to get publicly routable va url for webhooks we will need a publicly routable va url for webhooks for text email providers we should follow steps outline here
| 1
|
55,529
| 30,794,501,283
|
IssuesEvent
|
2023-07-31 18:44:22
|
subspace/subspace
|
https://api.github.com/repos/subspace/subspace
|
opened
|
Disable incremental archiving during sync
|
improvement node performance
|
We should make it possible to explicitly disable incremental archiving while sync is happening, it'll save us wall clock time.
|
True
|
Disable incremental archiving during sync - We should make it possible to explicitly disable incremental archiving while sync is happening, it'll save us wall clock time.
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non_process
|
disable incremental archiving during sync we should make it possible to explicitly disable incremental archiving while sync is happening it ll save us wall clock time
| 0
|
15,433
| 19,633,770,539
|
IssuesEvent
|
2022-01-08 00:27:39
|
mkikets99/react-datatable
|
https://api.github.com/repos/mkikets99/react-datatable
|
closed
|
Support for react 17.0.0 [BUG]
|
inProcess
|
The library is not working on react 17.0.0 and above. Would you mind upgrading it
|
1.0
|
Support for react 17.0.0 [BUG] - The library is not working on react 17.0.0 and above. Would you mind upgrading it
|
process
|
support for react the library is not working on react and above would you mind upgrading it
| 1
|
136,852
| 5,289,448,454
|
IssuesEvent
|
2017-02-08 17:24:53
|
SIU-CS/J-JAM-production
|
https://api.github.com/repos/SIU-CS/J-JAM-production
|
opened
|
Blog post scoring
|
Functional Priority -H Product Backlog
|
As a user, I would like to receive a score from my blog post so that I can get a sense of what my current mental state is, and know how the system is evaluating me.
Acceptance criteria:
Score is calculated after posting blog post.
The app uses machine learning to classify posts as healthy and unhealthy.
Score is available to the bot and visual representation for informing the user.
Story source: SRS - FR7
Estimate: 10 man-hours
Risk: High
Value: High
Priority: H
|
1.0
|
Blog post scoring - As a user, I would like to receive a score from my blog post so that I can get a sense of what my current mental state is, and know how the system is evaluating me.
Acceptance criteria:
Score is calculated after posting blog post.
The app uses machine learning to classify posts as healthy and unhealthy.
Score is available to the bot and visual representation for informing the user.
Story source: SRS - FR7
Estimate: 10 man-hours
Risk: High
Value: High
Priority: H
|
non_process
|
blog post scoring as a user i would like to receive a score from my blog post so that i can get a sense of what my current mental state is and know how the system is evaluating me acceptance criteria score is calculated after posting blog post the app uses machine learning to classify posts as healthy and unhealthy score is available to the bot and visual representation for informing the user story source srs estimate man hours risk high value high priority h
| 0
|
16,187
| 20,626,696,277
|
IssuesEvent
|
2022-03-07 23:34:52
|
GSA/EDX
|
https://api.github.com/repos/GSA/EDX
|
closed
|
Websites to consider for consolidation
|
running list process
|
[Websites](https://touchpoints.app.cloud.gov/admin/websites) are labeled as:
* candidate-for-decommission (for TTS sites)
* edx-candidate-for-decommission (for EDX sites)
#### References
* https://cpsearch.fas.gsa.gov/cpsearch/search.do more details: https://github.com/GSA/EDX/issues/95
* TTS [maintains a spreadsheet](https://docs.google.com/spreadsheets/d/1EGgQpwq8kc43TuTYxtQAhzEnhf_p7m_6T0Q8Dt9mqjo/edit#gid=1916305098)
* [TTS Decision tree for required sites](https://docs.google.com/drawings/d/1LxsHCXHBc09u-H6FDD_C4_pj8rtdRKqgXWZixvEbcD4/edit)
* [TTS Decision tree for non-required sites](https://docs.google.com/drawings/d/1Bi2LTO6ANzcoTd_16tFZq8rL6A3SSXSLS2zgtielGK4/edit)
|
1.0
|
Websites to consider for consolidation - [Websites](https://touchpoints.app.cloud.gov/admin/websites) are labeled as:
* candidate-for-decommission (for TTS sites)
* edx-candidate-for-decommission (for EDX sites)
#### References
* https://cpsearch.fas.gsa.gov/cpsearch/search.do more details: https://github.com/GSA/EDX/issues/95
* TTS [maintains a spreadsheet](https://docs.google.com/spreadsheets/d/1EGgQpwq8kc43TuTYxtQAhzEnhf_p7m_6T0Q8Dt9mqjo/edit#gid=1916305098)
* [TTS Decision tree for required sites](https://docs.google.com/drawings/d/1LxsHCXHBc09u-H6FDD_C4_pj8rtdRKqgXWZixvEbcD4/edit)
* [TTS Decision tree for non-required sites](https://docs.google.com/drawings/d/1Bi2LTO6ANzcoTd_16tFZq8rL6A3SSXSLS2zgtielGK4/edit)
|
process
|
websites to consider for consolidation are labeled as candidate for decommission for tts sites edx candidate for decommission for edx sites references more details tts
| 1
|
10,335
| 13,163,483,824
|
IssuesEvent
|
2020-08-11 00:34:37
|
bazelbuild/rules_swift
|
https://api.github.com/repos/bazelbuild/rules_swift
|
closed
|
Add proper unit tests
|
P1 type: process
|
Right now the only we have to test the rules is to build the contents of the `examples` directory. Our experience from `rules_apple` is that the integration shell tests are both slow and flaky, so we'd like to avoid using that approach (except, perhaps, in some isolated cases).
Instead, lets use the action-based analysis-time testing APIs in Skylark to verify the behavior of the rules without actually executing the actions. This should be significantly faster.
|
1.0
|
Add proper unit tests - Right now the only we have to test the rules is to build the contents of the `examples` directory. Our experience from `rules_apple` is that the integration shell tests are both slow and flaky, so we'd like to avoid using that approach (except, perhaps, in some isolated cases).
Instead, lets use the action-based analysis-time testing APIs in Skylark to verify the behavior of the rules without actually executing the actions. This should be significantly faster.
|
process
|
add proper unit tests right now the only we have to test the rules is to build the contents of the examples directory our experience from rules apple is that the integration shell tests are both slow and flaky so we d like to avoid using that approach except perhaps in some isolated cases instead lets use the action based analysis time testing apis in skylark to verify the behavior of the rules without actually executing the actions this should be significantly faster
| 1
|
15,443
| 19,657,179,868
|
IssuesEvent
|
2022-01-10 13:42:54
|
plazi/community
|
https://api.github.com/repos/plazi/community
|
opened
|
to be processed for new species 2021 news item
|
process request
|
@flsimoes here is the copy of the Ankylosaurus pape
[Maidment et al. 2021 - Spicomellus.pdf](https://github.com/plazi/community/files/7839219/Maidment.et.al.2021.-.Spicomellus.pdf)
r
|
1.0
|
to be processed for new species 2021 news item - @flsimoes here is the copy of the Ankylosaurus pape
[Maidment et al. 2021 - Spicomellus.pdf](https://github.com/plazi/community/files/7839219/Maidment.et.al.2021.-.Spicomellus.pdf)
r
|
process
|
to be processed for new species news item flsimoes here is the copy of the ankylosaurus pape r
| 1
|
2,682
| 2,756,937,210
|
IssuesEvent
|
2015-04-27 11:58:29
|
juliusHuelsmann/paint
|
https://api.github.com/repos/juliusHuelsmann/paint
|
closed
|
Usability: Copy paste adapt location of insertion
|
Codedesign effort_low
|
Adapt location of insertion; do not move the selection while scrolling!
|
1.0
|
Usability: Copy paste adapt location of insertion - Adapt location of insertion; do not move the selection while scrolling!
|
non_process
|
usability copy paste adapt location of insertion adapt location of insertion do not move the selection while scrolling
| 0
|
3,712
| 6,732,571,837
|
IssuesEvent
|
2017-10-18 12:03:20
|
lockedata/rcms
|
https://api.github.com/repos/lockedata/rcms
|
opened
|
Manage sponsors
|
conference team osem processes
|
## Detailed task
- Add sponsors to a page
- Change a sponsor's listing
## Assessing the task
Try to perform the task. Use google and the system documentation to help - part of what we're trying to assess how easy it is for people to work out how to do tasks.
Use a 👍 (`:+1:`) reaction to this task if you were able to perform the task. Use a 👎 (`:-1:`) reaction to the task if you could not complete it. Add a reply with any comments or feedback.
## Extra Info
- Site: [osem](https://intense-shore-93790.herokuapp.com/)
- System documentation: [osem docs](http://osem.io/)
- Role: Conference team
- Area: Processes
|
1.0
|
Manage sponsors - ## Detailed task
- Add sponsors to a page
- Change a sponsor's listing
## Assessing the task
Try to perform the task. Use google and the system documentation to help - part of what we're trying to assess how easy it is for people to work out how to do tasks.
Use a 👍 (`:+1:`) reaction to this task if you were able to perform the task. Use a 👎 (`:-1:`) reaction to the task if you could not complete it. Add a reply with any comments or feedback.
## Extra Info
- Site: [osem](https://intense-shore-93790.herokuapp.com/)
- System documentation: [osem docs](http://osem.io/)
- Role: Conference team
- Area: Processes
|
process
|
manage sponsors detailed task add sponsors to a page change a sponsor s listing assessing the task try to perform the task use google and the system documentation to help part of what we re trying to assess how easy it is for people to work out how to do tasks use a 👍 reaction to this task if you were able to perform the task use a 👎 reaction to the task if you could not complete it add a reply with any comments or feedback extra info site system documentation role conference team area processes
| 1
|
22,339
| 30,989,081,397
|
IssuesEvent
|
2023-08-09 02:00:09
|
lizhihao6/get-daily-arxiv-noti
|
https://api.github.com/repos/lizhihao6/get-daily-arxiv-noti
|
opened
|
New submissions for Wed, 9 Aug 23
|
event camera white balance isp compression image signal processing image signal process raw raw image events camera color contrast events AWB
|
## Keyword: events
### Enhancing image captioning with depth information using a Transformer-based framework
- **Authors:** Aya Mahmoud Ahmed, Mohamed Yousef, Khaled F. Hussain, Yousef Bassyouni Mahdy
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.03767
- **Pdf link:** https://arxiv.org/pdf/2308.03767
- **Abstract**
Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on generating a single sentence for 2D images. This paper investigates whether integrating depth information with RGB images can enhance the captioning task and generate better descriptions. For this purpose, we propose a Transformer-based encoder-decoder framework for generating a multi-sentence description of a 3D scene. The RGB image and its corresponding depth map are provided as inputs to our framework, which combines them to produce a better understanding of the input scene. Depth maps could be ground truth or estimated, which makes our framework widely applicable to any RGB captioning dataset. We explored different fusion approaches to fuse RGB and depth images. The experiments are performed on the NYU-v2 dataset and the Stanford image paragraph captioning dataset. During our work with the NYU-v2 dataset, we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task. The results were even worse than using RGB images only. As a result, we propose a cleaned version of the NYU-v2 dataset that is more consistent and informative. Our results on both datasets demonstrate that the proposed framework effectively benefits from depth information, whether it is ground truth or estimated, and generates better captions. Code, pre-trained models, and the cleaned version of the NYU-v2 dataset will be made publically available.
### SODFormer: Streaming Object Detection with Transformer Using Events and Frames
- **Authors:** Dianze Li, Jianing Li, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
- **Arxiv link:** https://arxiv.org/abs/2308.04047
- **Pdf link:** https://arxiv.org/pdf/2308.04047
- **Abstract**
DAVIS camera, streaming two complementary sensing modalities of asynchronous events and frames, has gradually been used to address major object detection challenges (e.g., fast motion blur and low-light). However, how to effectively leverage rich temporal cues and fuse two heterogeneous visual streams remains a challenging endeavor. To address this challenge, we propose a novel streaming object detector with Transformer, namely SODFormer, which first integrates events and frames to continuously detect objects in an asynchronous manner. Technically, we first build a large-scale multimodal neuromorphic object detection dataset (i.e., PKU-DAVIS-SOD) over 1080.1k manual labels. Then, we design a spatiotemporal Transformer architecture to detect objects via an end-to-end sequence prediction problem, where the novel temporal Transformer module leverages rich temporal cues from two visual streams to improve the detection performance. Finally, an asynchronous attention-based fusion module is proposed to integrate two heterogeneous sensing modalities and take complementary advantages from each end, which can be queried at any time to locate objects and break through the limited output frequency from synchronized frame-based fusion strategies. The results show that the proposed SODFormer outperforms four state-of-the-art methods and our eight baselines by a significant margin. We also show that our unifying framework works well even in cases where the conventional frame-based camera fails, e.g., high-speed motion and low-light conditions. Our dataset and code can be available at https://github.com/dianzl/SODFormer.
### D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
- **Authors:** Hanjun Li, Xiujun Shu, Sunan He, Ruizhi Qiao, Wei Wen, Taian Guo, Bei Gan, Xing Sun
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04197
- **Pdf link:** https://arxiv.org/pdf/2308.04197
- **Abstract**
Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a large performance gap compared to fully supervised ones, while the latter requires laborious timestamp annotations. In this study, we aim to reduce the annotation cost yet keep competitive performance for TSG task compared to fully supervised ones. To achieve this goal, we investigate a recently proposed glance-supervised temporal sentence grounding task, which requires only single frame annotation (referred to as glance annotation) for each query. Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples reliable positive moments from a 2D temporal map via jointly leveraging Gaussian prior and semantic consistency, which contributes to aligning the positive sentence-moment pairs in the joint embedding space. Moreover, to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events, we propose the DGA module, which adjusts the distribution dynamically to approximate the ground truth of target moments. Extensive experiments on three challenging benchmarks verify the effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods. Code is available at https://github.com/solicucu/D3G.
### Exploring Transformers for Open-world Instance Segmentation
- **Authors:** Jiannan Wu, Yi Jiang, Bin Yan, Huchuan Lu, Zehuan Yuan, Ping Luo
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04206
- **Pdf link:** https://arxiv.org/pdf/2308.04206
- **Abstract**
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challenging, as the number of unseen categories could be hundreds of times larger than that of seen categories. Recently, the DETR-like models have been extensively studied in the closed world while stay unexplored in the open world. In this paper, we utilize the Transformer for open-world instance segmentation and present SWORD. Firstly, we introduce to attach the stop-gradient operation before classification head and further add IoU heads for discovering novel objects. We demonstrate that a simple stop-gradient operation not only prevents the novel objects from being suppressed as background, but also allows the network to enjoy the merit of heuristic label assignment. Secondly, we propose a novel contrastive learning framework to enlarge the representations between objects and background. Specifically, we maintain a universal object queue to obtain the object center, and dynamically select positive and negative samples from the object queries for contrastive learning. While the previous works only focus on pursuing average recall and neglect average precision, we show the prominence of SWORD by giving consideration to both criteria. Our models achieve state-of-the-art performance in various open-world cross-category and cross-dataset generalizations. Particularly, in VOC to non-VOC setup, our method sets new state-of-the-art results of 40.0% on ARb100 and 34.9% on ARm100. For COCO to UVO generalization, SWORD significantly outperforms the previous best open-world model by 5.9% on APm and 8.1% on ARm100.
### Person Re-Identification without Identification via Event Anonymization
- **Authors:** Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04402
- **Pdf link:** https://arxiv.org/pdf/2308.04402
- **Abstract**
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.
## Keyword: event camera
### SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
- **Authors:** Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
- **Arxiv link:** https://arxiv.org/abs/2308.04369
- **Pdf link:** https://arxiv.org/pdf/2308.04369
- **Abstract**
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt deep neural networks for event-based classification. Although good performance can be achieved on simple event recognition datasets, however, their results may be still limited due to the following two issues. Firstly, they adopt spatial sparse event streams for recognition only, which may fail to capture the color and detailed texture information well. Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition. However, seldom of them consider achieving a balance between these two aspects. In this paper, we formally propose to recognize patterns by fusing RGB frames and event streams simultaneously and propose a new RGB frame-event recognition framework to address the aforementioned issues. The proposed method contains four main modules, i.e., memory support Transformer network for RGB frame encoding, spiking neural network for raw event stream encoding, multi-modal bottleneck fusion module for RGB-Event feature aggregation, and prediction head. Due to the scarce of RGB-Event based classification dataset, we also propose a large-scale PokerEvent dataset which contains 114 classes, and 27102 frame-event pairs recorded using a DVS346 event camera. Extensive experiments on two RGB-Event based classification datasets fully validated the effectiveness of our proposed framework. We hope this work will boost the development of pattern recognition by fusing RGB frames and event streams. Both our dataset and source code of this work will be released at https://github.com/Event-AHU/SSTFormer.
### Person Re-Identification without Identification via Event Anonymization
- **Authors:** Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04402
- **Pdf link:** https://arxiv.org/pdf/2308.04402
- **Abstract**
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.
## Keyword: events camera
There is no result
## Keyword: white balance
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
## Keyword: color contrast
There is no result
## Keyword: AWB
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
### Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
- **Authors:** Zixuan He (1) (2), Salik Ram Khana (1) (2), Xin Zhang (3), Manoj Karkee (1) (2), Qin Zhang (1) (2) ((1) Center for Precision and Automated Agricultural Systems, Washington State University, (2) Department of Biological Systems Engineering, Washington State University, (3) Department of Agricultural and Biological Engineering, Mississippi State University)
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03998
- **Pdf link:** https://arxiv.org/pdf/2308.03998
- **Abstract**
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacing the C3 module with the C2f module in the backbone network, which provided a better feature gradient flow. Secondly, the Spatial Pyramid Pooling Fast in the final layer of the backbone network of YOLOv5s was combined with Cross Stage Partial Net to improve the generalization ability over the strawberry dataset in this study. The proposed architecture was named YOLOv5s-Straw. The RGB images dataset of the strawberry canopy with three maturity classes (immature, nearly mature, and mature) was collected in open-field environment and augmented through a series of operations including brightness reduction, brightness increase, and noise adding. To verify the superiority of the proposed method for strawberry detection in open-field environment, four competitive detection models (YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s) were trained, and tested under the same computational environment and compared with YOLOv5s-Straw. The results showed that the highest mean average precision of 80.3% was achieved using the proposed architecture whereas the same was achieved with YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s were 73.4%, 77.8%, 79.8%, 79.3%, respectively. Specifically, the average precision of YOLOv5s-Straw was 82.1% in the immature class, 73.5% in the nearly mature class, and 86.6% in the mature class, which were 2.3% and 3.7%, respectively, higher than that of the latest YOLOv8s. The model included 8.6*10^6 network parameters with an inference speed of 18ms per image while the inference speed of YOLOv8s had a slower inference speed of 21.0ms and heavy parameters of 11.1*10^6, which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking.
## Keyword: ISP
### AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
- **Authors:** Anish Mall, Sanchit Kabra, Ankur Lhila, Pawan Ajmera
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.03766
- **Pdf link:** https://arxiv.org/pdf/2308.03766
- **Abstract**
This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.
### ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
- **Authors:** Milad Sikaroudi, Shahryar Rahnamayan, H.R. Tizhoosh
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
- **Arxiv link:** https://arxiv.org/abs/2308.03936
- **Pdf link:** https://arxiv.org/pdf/2308.03936
- **Abstract**
We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images.
### Under-Display Camera Image Restoration with Scattering Effect
- **Authors:** Binbin Song, Xiangyu Chen, Shuning Xu, Jiantao Zhou
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.04163
- **Pdf link:** https://arxiv.org/pdf/2308.04163
- **Abstract**
The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semi-transparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at \url{https://github.com/NamecantbeNULL/SRUDC}.
## Keyword: image signal processing
There is no result
## Keyword: image signal process
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
## Keyword: compression
### Lossy and Lossless (L$^2$) Post-training Model Size Compression
- **Authors:** Yumeng Shi, Shihao Bai, Xiuying Wei, Ruihao Gong, Jianlei Yang
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
- **Arxiv link:** https://arxiv.org/abs/2308.04269
- **Pdf link:** https://arxiv.org/pdf/2308.04269
- **Abstract**
Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmission and storage. Many previous studies have explored model size compression. However, these studies often approach various lossy and lossless compression methods in isolation, leading to challenges in achieving high compression ratios efficiently. This work proposes a post-training model size compression method that combines lossy and lossless compression in a unified way. We first propose a unified parametric weight transformation, which ensures different lossy compression methods can be performed jointly in a post-training manner. Then, a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression. Additionally, our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers. Finally, our method can achieve a stable $10\times$ compression ratio without sacrificing accuracy and a $20\times$ compression ratio with minor accuracy loss in a short time. Our code is available at https://github.com/ModelTC/L2_Compression .
## Keyword: RAW
### Developability Approximation for Neural Implicits through Rank Minimization
- **Authors:** Pratheba Selvaraju
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
- **Arxiv link:** https://arxiv.org/abs/2308.03900
- **Pdf link:** https://arxiv.org/pdf/2308.03900
- **Abstract**
Developability refers to the process of creating a surface without any tearing or shearing from a two-dimensional plane. It finds practical applications in the fabrication industry. An essential characteristic of a developable 3D surface is its zero Gaussian curvature, which means that either one or both of the principal curvatures are zero. This paper introduces a method for reconstructing an approximate developable surface from a neural implicit surface. The central idea of our method involves incorporating a regularization term that operates on the second-order derivatives of the neural implicits, effectively promoting zero Gaussian curvature. Implicit surfaces offer the advantage of smoother deformation with infinite resolution, overcoming the high polygonal constraints of state-of-the-art methods using discrete representations. We draw inspiration from the properties of surface curvature and employ rank minimization techniques derived from compressed sensing. Experimental results on both developable and non-developable surfaces, including those affected by noise, validate the generalizability of our method.
### Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
- **Authors:** Zixuan He (1) (2), Salik Ram Khana (1) (2), Xin Zhang (3), Manoj Karkee (1) (2), Qin Zhang (1) (2) ((1) Center for Precision and Automated Agricultural Systems, Washington State University, (2) Department of Biological Systems Engineering, Washington State University, (3) Department of Agricultural and Biological Engineering, Mississippi State University)
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03998
- **Pdf link:** https://arxiv.org/pdf/2308.03998
- **Abstract**
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacing the C3 module with the C2f module in the backbone network, which provided a better feature gradient flow. Secondly, the Spatial Pyramid Pooling Fast in the final layer of the backbone network of YOLOv5s was combined with Cross Stage Partial Net to improve the generalization ability over the strawberry dataset in this study. The proposed architecture was named YOLOv5s-Straw. The RGB images dataset of the strawberry canopy with three maturity classes (immature, nearly mature, and mature) was collected in open-field environment and augmented through a series of operations including brightness reduction, brightness increase, and noise adding. To verify the superiority of the proposed method for strawberry detection in open-field environment, four competitive detection models (YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s) were trained, and tested under the same computational environment and compared with YOLOv5s-Straw. The results showed that the highest mean average precision of 80.3% was achieved using the proposed architecture whereas the same was achieved with YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s were 73.4%, 77.8%, 79.8%, 79.3%, respectively. Specifically, the average precision of YOLOv5s-Straw was 82.1% in the immature class, 73.5% in the nearly mature class, and 86.6% in the mature class, which were 2.3% and 3.7%, respectively, higher than that of the latest YOLOv8s. The model included 8.6*10^6 network parameters with an inference speed of 18ms per image while the inference speed of YOLOv8s had a slower inference speed of 21.0ms and heavy parameters of 11.1*10^6, which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking.
### Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos
- **Authors:** Huibing Wang, Tianxiang Cui, Mingze Yao, Huijuan Pang, Yushan Du
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04322
- **Pdf link:** https://arxiv.org/pdf/2308.04322
- **Abstract**
Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic.
### SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
- **Authors:** Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
- **Arxiv link:** https://arxiv.org/abs/2308.04369
- **Pdf link:** https://arxiv.org/pdf/2308.04369
- **Abstract**
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt deep neural networks for event-based classification. Although good performance can be achieved on simple event recognition datasets, however, their results may be still limited due to the following two issues. Firstly, they adopt spatial sparse event streams for recognition only, which may fail to capture the color and detailed texture information well. Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition. However, seldom of them consider achieving a balance between these two aspects. In this paper, we formally propose to recognize patterns by fusing RGB frames and event streams simultaneously and propose a new RGB frame-event recognition framework to address the aforementioned issues. The proposed method contains four main modules, i.e., memory support Transformer network for RGB frame encoding, spiking neural network for raw event stream encoding, multi-modal bottleneck fusion module for RGB-Event feature aggregation, and prediction head. Due to the scarce of RGB-Event based classification dataset, we also propose a large-scale PokerEvent dataset which contains 114 classes, and 27102 frame-event pairs recorded using a DVS346 event camera. Extensive experiments on two RGB-Event based classification datasets fully validated the effectiveness of our proposed framework. We hope this work will boost the development of pattern recognition by fusing RGB frames and event streams. Both our dataset and source code of this work will be released at https://github.com/Event-AHU/SSTFormer.
### DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
- **Authors:** Chensheng Peng, Guangming Wang, Xian Wan Lo, Xinrui Wu, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04383
- **Pdf link:** https://arxiv.org/pdf/2308.04383
- **Abstract**
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset.
## Keyword: raw image
There is no result
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New submissions for Wed, 9 Aug 23 - ## Keyword: events
### Enhancing image captioning with depth information using a Transformer-based framework
- **Authors:** Aya Mahmoud Ahmed, Mohamed Yousef, Khaled F. Hussain, Yousef Bassyouni Mahdy
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.03767
- **Pdf link:** https://arxiv.org/pdf/2308.03767
- **Abstract**
Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on generating a single sentence for 2D images. This paper investigates whether integrating depth information with RGB images can enhance the captioning task and generate better descriptions. For this purpose, we propose a Transformer-based encoder-decoder framework for generating a multi-sentence description of a 3D scene. The RGB image and its corresponding depth map are provided as inputs to our framework, which combines them to produce a better understanding of the input scene. Depth maps could be ground truth or estimated, which makes our framework widely applicable to any RGB captioning dataset. We explored different fusion approaches to fuse RGB and depth images. The experiments are performed on the NYU-v2 dataset and the Stanford image paragraph captioning dataset. During our work with the NYU-v2 dataset, we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task. The results were even worse than using RGB images only. As a result, we propose a cleaned version of the NYU-v2 dataset that is more consistent and informative. Our results on both datasets demonstrate that the proposed framework effectively benefits from depth information, whether it is ground truth or estimated, and generates better captions. Code, pre-trained models, and the cleaned version of the NYU-v2 dataset will be made publically available.
### SODFormer: Streaming Object Detection with Transformer Using Events and Frames
- **Authors:** Dianze Li, Jianing Li, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
- **Arxiv link:** https://arxiv.org/abs/2308.04047
- **Pdf link:** https://arxiv.org/pdf/2308.04047
- **Abstract**
DAVIS camera, streaming two complementary sensing modalities of asynchronous events and frames, has gradually been used to address major object detection challenges (e.g., fast motion blur and low-light). However, how to effectively leverage rich temporal cues and fuse two heterogeneous visual streams remains a challenging endeavor. To address this challenge, we propose a novel streaming object detector with Transformer, namely SODFormer, which first integrates events and frames to continuously detect objects in an asynchronous manner. Technically, we first build a large-scale multimodal neuromorphic object detection dataset (i.e., PKU-DAVIS-SOD) over 1080.1k manual labels. Then, we design a spatiotemporal Transformer architecture to detect objects via an end-to-end sequence prediction problem, where the novel temporal Transformer module leverages rich temporal cues from two visual streams to improve the detection performance. Finally, an asynchronous attention-based fusion module is proposed to integrate two heterogeneous sensing modalities and take complementary advantages from each end, which can be queried at any time to locate objects and break through the limited output frequency from synchronized frame-based fusion strategies. The results show that the proposed SODFormer outperforms four state-of-the-art methods and our eight baselines by a significant margin. We also show that our unifying framework works well even in cases where the conventional frame-based camera fails, e.g., high-speed motion and low-light conditions. Our dataset and code can be available at https://github.com/dianzl/SODFormer.
### D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
- **Authors:** Hanjun Li, Xiujun Shu, Sunan He, Ruizhi Qiao, Wei Wen, Taian Guo, Bei Gan, Xing Sun
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04197
- **Pdf link:** https://arxiv.org/pdf/2308.04197
- **Abstract**
Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a large performance gap compared to fully supervised ones, while the latter requires laborious timestamp annotations. In this study, we aim to reduce the annotation cost yet keep competitive performance for TSG task compared to fully supervised ones. To achieve this goal, we investigate a recently proposed glance-supervised temporal sentence grounding task, which requires only single frame annotation (referred to as glance annotation) for each query. Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples reliable positive moments from a 2D temporal map via jointly leveraging Gaussian prior and semantic consistency, which contributes to aligning the positive sentence-moment pairs in the joint embedding space. Moreover, to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events, we propose the DGA module, which adjusts the distribution dynamically to approximate the ground truth of target moments. Extensive experiments on three challenging benchmarks verify the effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods. Code is available at https://github.com/solicucu/D3G.
### Exploring Transformers for Open-world Instance Segmentation
- **Authors:** Jiannan Wu, Yi Jiang, Bin Yan, Huchuan Lu, Zehuan Yuan, Ping Luo
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04206
- **Pdf link:** https://arxiv.org/pdf/2308.04206
- **Abstract**
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challenging, as the number of unseen categories could be hundreds of times larger than that of seen categories. Recently, the DETR-like models have been extensively studied in the closed world while stay unexplored in the open world. In this paper, we utilize the Transformer for open-world instance segmentation and present SWORD. Firstly, we introduce to attach the stop-gradient operation before classification head and further add IoU heads for discovering novel objects. We demonstrate that a simple stop-gradient operation not only prevents the novel objects from being suppressed as background, but also allows the network to enjoy the merit of heuristic label assignment. Secondly, we propose a novel contrastive learning framework to enlarge the representations between objects and background. Specifically, we maintain a universal object queue to obtain the object center, and dynamically select positive and negative samples from the object queries for contrastive learning. While the previous works only focus on pursuing average recall and neglect average precision, we show the prominence of SWORD by giving consideration to both criteria. Our models achieve state-of-the-art performance in various open-world cross-category and cross-dataset generalizations. Particularly, in VOC to non-VOC setup, our method sets new state-of-the-art results of 40.0% on ARb100 and 34.9% on ARm100. For COCO to UVO generalization, SWORD significantly outperforms the previous best open-world model by 5.9% on APm and 8.1% on ARm100.
### Person Re-Identification without Identification via Event Anonymization
- **Authors:** Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04402
- **Pdf link:** https://arxiv.org/pdf/2308.04402
- **Abstract**
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.
## Keyword: event camera
### SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
- **Authors:** Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
- **Arxiv link:** https://arxiv.org/abs/2308.04369
- **Pdf link:** https://arxiv.org/pdf/2308.04369
- **Abstract**
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt deep neural networks for event-based classification. Although good performance can be achieved on simple event recognition datasets, however, their results may be still limited due to the following two issues. Firstly, they adopt spatial sparse event streams for recognition only, which may fail to capture the color and detailed texture information well. Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition. However, seldom of them consider achieving a balance between these two aspects. In this paper, we formally propose to recognize patterns by fusing RGB frames and event streams simultaneously and propose a new RGB frame-event recognition framework to address the aforementioned issues. The proposed method contains four main modules, i.e., memory support Transformer network for RGB frame encoding, spiking neural network for raw event stream encoding, multi-modal bottleneck fusion module for RGB-Event feature aggregation, and prediction head. Due to the scarce of RGB-Event based classification dataset, we also propose a large-scale PokerEvent dataset which contains 114 classes, and 27102 frame-event pairs recorded using a DVS346 event camera. Extensive experiments on two RGB-Event based classification datasets fully validated the effectiveness of our proposed framework. We hope this work will boost the development of pattern recognition by fusing RGB frames and event streams. Both our dataset and source code of this work will be released at https://github.com/Event-AHU/SSTFormer.
### Person Re-Identification without Identification via Event Anonymization
- **Authors:** Shafiq Ahmad, Pietro Morerio, Alessio Del Bue
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04402
- **Pdf link:** https://arxiv.org/pdf/2308.04402
- **Abstract**
Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.
## Keyword: events camera
There is no result
## Keyword: white balance
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
## Keyword: color contrast
There is no result
## Keyword: AWB
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
### Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
- **Authors:** Zixuan He (1) (2), Salik Ram Khana (1) (2), Xin Zhang (3), Manoj Karkee (1) (2), Qin Zhang (1) (2) ((1) Center for Precision and Automated Agricultural Systems, Washington State University, (2) Department of Biological Systems Engineering, Washington State University, (3) Department of Agricultural and Biological Engineering, Mississippi State University)
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03998
- **Pdf link:** https://arxiv.org/pdf/2308.03998
- **Abstract**
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacing the C3 module with the C2f module in the backbone network, which provided a better feature gradient flow. Secondly, the Spatial Pyramid Pooling Fast in the final layer of the backbone network of YOLOv5s was combined with Cross Stage Partial Net to improve the generalization ability over the strawberry dataset in this study. The proposed architecture was named YOLOv5s-Straw. The RGB images dataset of the strawberry canopy with three maturity classes (immature, nearly mature, and mature) was collected in open-field environment and augmented through a series of operations including brightness reduction, brightness increase, and noise adding. To verify the superiority of the proposed method for strawberry detection in open-field environment, four competitive detection models (YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s) were trained, and tested under the same computational environment and compared with YOLOv5s-Straw. The results showed that the highest mean average precision of 80.3% was achieved using the proposed architecture whereas the same was achieved with YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s were 73.4%, 77.8%, 79.8%, 79.3%, respectively. Specifically, the average precision of YOLOv5s-Straw was 82.1% in the immature class, 73.5% in the nearly mature class, and 86.6% in the mature class, which were 2.3% and 3.7%, respectively, higher than that of the latest YOLOv8s. The model included 8.6*10^6 network parameters with an inference speed of 18ms per image while the inference speed of YOLOv8s had a slower inference speed of 21.0ms and heavy parameters of 11.1*10^6, which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking.
## Keyword: ISP
### AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection
- **Authors:** Anish Mall, Sanchit Kabra, Ankur Lhila, Pawan Ajmera
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.03766
- **Pdf link:** https://arxiv.org/pdf/2308.03766
- **Abstract**
This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.
### ALFA -- Leveraging All Levels of Feature Abstraction for Enhancing the Generalization of Histopathology Image Classification Across Unseen Hospitals
- **Authors:** Milad Sikaroudi, Shahryar Rahnamayan, H.R. Tizhoosh
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
- **Arxiv link:** https://arxiv.org/abs/2308.03936
- **Pdf link:** https://arxiv.org/pdf/2308.03936
- **Abstract**
We propose an exhaustive methodology that leverages all levels of feature abstraction, targeting an enhancement in the generalizability of image classification to unobserved hospitals. Our approach incorporates augmentation-based self-supervision with common distribution shifts in histopathology scenarios serving as the pretext task. This enables us to derive invariant features from training images without relying on training labels, thereby covering different abstraction levels. Moving onto the subsequent abstraction level, we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals. To represent the highly specific features of participating hospitals, an encoder is trained to classify hospital labels, independent of their diagnostic labels. The features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features. This representation, which spans a broad spectrum of semantic information, enables the development of a model demonstrating increased robustness to unseen images from disparate distributions. Experimental results from the PACS dataset (a domain generalization benchmark), a synthetic dataset created by applying histopathology-specific jitters to the MHIST dataset (defining different domains with varied distribution shifts), and a Renal Cell Carcinoma dataset derived from four image repositories from TCGA, collectively indicate that our proposed model is adept at managing varying levels of image granularity. Thus, it shows improved generalizability when faced with new, out-of-distribution hospital images.
### Under-Display Camera Image Restoration with Scattering Effect
- **Authors:** Binbin Song, Xiangyu Chen, Shuning Xu, Jiantao Zhou
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
- **Arxiv link:** https://arxiv.org/abs/2308.04163
- **Pdf link:** https://arxiv.org/pdf/2308.04163
- **Abstract**
The under-display camera (UDC) provides consumers with a full-screen visual experience without any obstruction due to notches or punched holes. However, the semi-transparent nature of the display inevitably introduces the severe degradation into UDC images. In this work, we address the UDC image restoration problem with the specific consideration of the scattering effect caused by the display. We explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium. With the physical model of the scattering effect, we improve the image formation pipeline for the image synthesis to construct a realistic UDC dataset with ground truths. To suppress the scattering effect for the eventual UDC image recovery, a two-branch restoration network is designed. More specifically, the scattering branch leverages global modeling capabilities of the channel-wise self-attention to estimate parameters of the scattering effect from degraded images. While the image branch exploits the local representation advantage of CNN to recover clear scenes, implicitly guided by the scattering branch. Extensive experiments are conducted on both real-world and synthesized data, demonstrating the superiority of the proposed method over the state-of-the-art UDC restoration techniques. The source code and dataset are available at \url{https://github.com/NamecantbeNULL/SRUDC}.
## Keyword: image signal processing
There is no result
## Keyword: image signal process
### Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
- **Authors:** Furkan Kınlı, Doğa Yılmaz, Barış Özcan, Furkan Kıraç
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03939
- **Pdf link:** https://arxiv.org/pdf/2308.03939
- **Abstract**
Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications. Source code is available at https://github.com/birdortyedi/DeNIM/
## Keyword: compression
### Lossy and Lossless (L$^2$) Post-training Model Size Compression
- **Authors:** Yumeng Shi, Shihao Bai, Xiuying Wei, Ruihao Gong, Jianlei Yang
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
- **Arxiv link:** https://arxiv.org/abs/2308.04269
- **Pdf link:** https://arxiv.org/pdf/2308.04269
- **Abstract**
Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmission and storage. Many previous studies have explored model size compression. However, these studies often approach various lossy and lossless compression methods in isolation, leading to challenges in achieving high compression ratios efficiently. This work proposes a post-training model size compression method that combines lossy and lossless compression in a unified way. We first propose a unified parametric weight transformation, which ensures different lossy compression methods can be performed jointly in a post-training manner. Then, a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression. Additionally, our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers. Finally, our method can achieve a stable $10\times$ compression ratio without sacrificing accuracy and a $20\times$ compression ratio with minor accuracy loss in a short time. Our code is available at https://github.com/ModelTC/L2_Compression .
## Keyword: RAW
### Developability Approximation for Neural Implicits through Rank Minimization
- **Authors:** Pratheba Selvaraju
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
- **Arxiv link:** https://arxiv.org/abs/2308.03900
- **Pdf link:** https://arxiv.org/pdf/2308.03900
- **Abstract**
Developability refers to the process of creating a surface without any tearing or shearing from a two-dimensional plane. It finds practical applications in the fabrication industry. An essential characteristic of a developable 3D surface is its zero Gaussian curvature, which means that either one or both of the principal curvatures are zero. This paper introduces a method for reconstructing an approximate developable surface from a neural implicit surface. The central idea of our method involves incorporating a regularization term that operates on the second-order derivatives of the neural implicits, effectively promoting zero Gaussian curvature. Implicit surfaces offer the advantage of smoother deformation with infinite resolution, overcoming the high polygonal constraints of state-of-the-art methods using discrete representations. We draw inspiration from the properties of surface curvature and employ rank minimization techniques derived from compressed sensing. Experimental results on both developable and non-developable surfaces, including those affected by noise, validate the generalizability of our method.
### Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
- **Authors:** Zixuan He (1) (2), Salik Ram Khana (1) (2), Xin Zhang (3), Manoj Karkee (1) (2), Qin Zhang (1) (2) ((1) Center for Precision and Automated Agricultural Systems, Washington State University, (2) Department of Biological Systems Engineering, Washington State University, (3) Department of Agricultural and Biological Engineering, Mississippi State University)
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.03998
- **Pdf link:** https://arxiv.org/pdf/2308.03998
- **Abstract**
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment. The original architecture of the YOLOv5s was modified by replacing the C3 module with the C2f module in the backbone network, which provided a better feature gradient flow. Secondly, the Spatial Pyramid Pooling Fast in the final layer of the backbone network of YOLOv5s was combined with Cross Stage Partial Net to improve the generalization ability over the strawberry dataset in this study. The proposed architecture was named YOLOv5s-Straw. The RGB images dataset of the strawberry canopy with three maturity classes (immature, nearly mature, and mature) was collected in open-field environment and augmented through a series of operations including brightness reduction, brightness increase, and noise adding. To verify the superiority of the proposed method for strawberry detection in open-field environment, four competitive detection models (YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s) were trained, and tested under the same computational environment and compared with YOLOv5s-Straw. The results showed that the highest mean average precision of 80.3% was achieved using the proposed architecture whereas the same was achieved with YOLOv3-tiny, YOLOv5s, YOLOv5s-C2f, and YOLOv8s were 73.4%, 77.8%, 79.8%, 79.3%, respectively. Specifically, the average precision of YOLOv5s-Straw was 82.1% in the immature class, 73.5% in the nearly mature class, and 86.6% in the mature class, which were 2.3% and 3.7%, respectively, higher than that of the latest YOLOv8s. The model included 8.6*10^6 network parameters with an inference speed of 18ms per image while the inference speed of YOLOv8s had a slower inference speed of 21.0ms and heavy parameters of 11.1*10^6, which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking.
### Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos
- **Authors:** Huibing Wang, Tianxiang Cui, Mingze Yao, Huijuan Pang, Yushan Du
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04322
- **Pdf link:** https://arxiv.org/pdf/2308.04322
- **Abstract**
Person search has recently been a challenging task in the computer vision domain, which aims to search specific pedestrians from real cameras.Nevertheless, most surveillance videos comprise only a handful of images of each pedestrian, which often feature identical backgrounds and clothing. Hence, it is difficult to learn more discriminative features for person search in real scenes. To tackle this challenge, we draw on Generative Adversarial Networks (GAN) to synthesize data from surveillance videos. GAN has thrived in computer vision problems because it produces high-quality images efficiently. We merely alter the popular Fast R-CNN model, which is capable of processing videos and yielding accurate detection outcomes. In order to appropriately relieve the pressure brought by the two-stage model, we design an Assisted-Identity Query Module (AIDQ) to provide positive images for the behind part. Besides, the proposed novel GAN-based Scene Synthesis model that can synthesize high-quality cross-id person images for person search tasks. In order to facilitate the feature learning of the GAN-based Scene Synthesis model, we adopt an online learning strategy that collaboratively learns the synthesized images and original images. Extensive experiments on two widely used person search benchmarks, CUHK-SYSU and PRW, have shown that our method has achieved great performance, and the extensive ablation study further justifies our GAN-synthetic data can effectively increase the variability of the datasets and be more realistic.
### SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
- **Authors:** Xiao Wang, Zongzhen Wu, Yao Rong, Lin Zhu, Bo Jiang, Jin Tang, Yonghong Tian
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Neural and Evolutionary Computing (cs.NE)
- **Arxiv link:** https://arxiv.org/abs/2308.04369
- **Pdf link:** https://arxiv.org/pdf/2308.04369
- **Abstract**
Event camera-based pattern recognition is a newly arising research topic in recent years. Current researchers usually transform the event streams into images, graphs, or voxels, and adopt deep neural networks for event-based classification. Although good performance can be achieved on simple event recognition datasets, however, their results may be still limited due to the following two issues. Firstly, they adopt spatial sparse event streams for recognition only, which may fail to capture the color and detailed texture information well. Secondly, they adopt either Spiking Neural Networks (SNN) for energy-efficient recognition with suboptimal results, or Artificial Neural Networks (ANN) for energy-intensive, high-performance recognition. However, seldom of them consider achieving a balance between these two aspects. In this paper, we formally propose to recognize patterns by fusing RGB frames and event streams simultaneously and propose a new RGB frame-event recognition framework to address the aforementioned issues. The proposed method contains four main modules, i.e., memory support Transformer network for RGB frame encoding, spiking neural network for raw event stream encoding, multi-modal bottleneck fusion module for RGB-Event feature aggregation, and prediction head. Due to the scarce of RGB-Event based classification dataset, we also propose a large-scale PokerEvent dataset which contains 114 classes, and 27102 frame-event pairs recorded using a DVS346 event camera. Extensive experiments on two RGB-Event based classification datasets fully validated the effectiveness of our proposed framework. We hope this work will boost the development of pattern recognition by fusing RGB frames and event streams. Both our dataset and source code of this work will be released at https://github.com/Event-AHU/SSTFormer.
### DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
- **Authors:** Chensheng Peng, Guangming Wang, Xian Wan Lo, Xinrui Wu, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang
- **Subjects:** Computer Vision and Pattern Recognition (cs.CV)
- **Arxiv link:** https://arxiv.org/abs/2308.04383
- **Pdf link:** https://arxiv.org/pdf/2308.04383
- **Abstract**
Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset.
## Keyword: raw image
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new submissions for wed aug keyword events enhancing image captioning with depth information using a transformer based framework authors aya mahmoud ahmed mohamed yousef khaled f hussain yousef bassyouni mahdy subjects computer vision and pattern recognition cs cv artificial intelligence cs ai computation and language cs cl image and video processing eess iv arxiv link pdf link abstract captioning images is a challenging scene understanding task that connects computer vision and natural language processing while image captioning models have been successful in producing excellent descriptions the field has primarily focused on generating a single sentence for images this paper investigates whether integrating depth information with rgb images can enhance the captioning task and generate better descriptions for this purpose we propose a transformer based encoder decoder framework for generating a multi sentence description of a scene the rgb image and its corresponding depth map are provided as inputs to our framework which combines them to produce a better understanding of the input scene depth maps could be ground truth or estimated which makes our framework widely applicable to any rgb captioning dataset we explored different fusion approaches to fuse rgb and depth images the experiments are performed on the nyu dataset and the stanford image paragraph captioning dataset during our work with the nyu dataset we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task the results were even worse than using rgb images only as a result we propose a cleaned version of the nyu dataset that is more consistent and informative our results on both datasets demonstrate that the proposed framework effectively benefits from depth information whether it is ground truth or estimated and generates better captions code pre trained models and the cleaned version of the nyu dataset will be made publically available sodformer streaming object detection with transformer using events and frames authors dianze li jianing li yonghong tian subjects computer vision and pattern recognition cs cv artificial intelligence cs ai robotics cs ro arxiv link pdf link abstract davis camera streaming two complementary sensing modalities of asynchronous events and frames has gradually been used to address major object detection challenges e g fast motion blur and low light however how to effectively leverage rich temporal cues and fuse two heterogeneous visual streams remains a challenging endeavor to address this challenge we propose a novel streaming object detector with transformer namely sodformer which first integrates events and frames to continuously detect objects in an asynchronous manner technically we first build a large scale multimodal neuromorphic object detection dataset i e pku davis sod over manual labels then we design a spatiotemporal transformer architecture to detect objects via an end to end sequence prediction problem where the novel temporal transformer module leverages rich temporal cues from two visual streams to improve the detection performance finally an asynchronous attention based fusion module is proposed to integrate two heterogeneous sensing modalities and take complementary advantages from each end which can be queried at any time to locate objects and break through the limited output frequency from synchronized frame based fusion strategies the results show that the proposed sodformer outperforms four state of the art methods and our eight baselines by a significant margin we also show that our unifying framework works well even in cases where the conventional frame based camera fails e g high speed motion and low light conditions our dataset and code can be available at exploring gaussian prior for temporal sentence grounding with glance annotation authors hanjun li xiujun shu sunan he ruizhi qiao wei wen taian guo bei gan xing sun subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract temporal sentence grounding tsg aims to locate a specific moment from an untrimmed video with a given natural language query recently weakly supervised methods still have a large performance gap compared to fully supervised ones while the latter requires laborious timestamp annotations in this study we aim to reduce the annotation cost yet keep competitive performance for tsg task compared to fully supervised ones to achieve this goal we investigate a recently proposed glance supervised temporal sentence grounding task which requires only single frame annotation referred to as glance annotation for each query under this setup we propose a dynamic gaussian prior based grounding framework with glance annotation which consists of a semantic alignment group contrastive learning module sa gcl and a dynamic gaussian prior adjustment module dga specifically sa gcl samples reliable positive moments from a temporal map via jointly leveraging gaussian prior and semantic consistency which contributes to aligning the positive sentence moment pairs in the joint embedding space moreover to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events we propose the dga module which adjusts the distribution dynamically to approximate the ground truth of target moments extensive experiments on three challenging benchmarks verify the effectiveness of the proposed it outperforms the state of the art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods code is available at exploring transformers for open world instance segmentation authors jiannan wu yi jiang bin yan huchuan lu zehuan yuan ping luo subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract open world instance segmentation is a rising task which aims to segment all objects in the image by learning from a limited number of base category objects this task is challenging as the number of unseen categories could be hundreds of times larger than that of seen categories recently the detr like models have been extensively studied in the closed world while stay unexplored in the open world in this paper we utilize the transformer for open world instance segmentation and present sword firstly we introduce to attach the stop gradient operation before classification head and further add iou heads for discovering novel objects we demonstrate that a simple stop gradient operation not only prevents the novel objects from being suppressed as background but also allows the network to enjoy the merit of heuristic label assignment secondly we propose a novel contrastive learning framework to enlarge the representations between objects and background specifically we maintain a universal object queue to obtain the object center and dynamically select positive and negative samples from the object queries for contrastive learning while the previous works only focus on pursuing average recall and neglect average precision we show the prominence of sword by giving consideration to both criteria our models achieve state of the art performance in various open world cross category and cross dataset generalizations particularly in voc to non voc setup our method sets new state of the art results of on and on for coco to uvo generalization sword significantly outperforms the previous best open world model by on apm and on person re identification without identification via event anonymization authors shafiq ahmad pietro morerio alessio del bue subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract wide scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption energy bandwidth and computation neuromorphic vision sensors event cameras have been recently considered a valid solution to the privacy issue because they do not capture detailed rgb visual information of the subjects in the scene however recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity reintroducing a potential threat to privacy for event based vision applications in this paper we aim to anonymize event streams to protect the identity of human subjects against such image reconstruction attacks to achieve this we propose an end to end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person reid our network learns to scramble events enforcing the degradation of images recovered from the privacy attacker in this work we also bring to the community the first ever event based person reid dataset gathered to evaluate the performance of our approach we validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available softbio dataset and our proposed event reid dataset keyword event camera sstformer bridging spiking neural network and memory support transformer for frame event based recognition authors xiao wang zongzhen wu yao rong lin zhu bo jiang jin tang yonghong tian subjects computer vision and pattern recognition cs cv multimedia cs mm neural and evolutionary computing cs ne arxiv link pdf link abstract event camera based pattern recognition is a newly arising research topic in recent years current researchers usually transform the event streams into images graphs or voxels and adopt deep neural networks for event based classification although good performance can be achieved on simple event recognition datasets however their results may be still limited due to the following two issues firstly they adopt spatial sparse event streams for recognition only which may fail to capture the color and detailed texture information well secondly they adopt either spiking neural networks snn for energy efficient recognition with suboptimal results or artificial neural networks ann for energy intensive high performance recognition however seldom of them consider achieving a balance between these two aspects in this paper we formally propose to recognize patterns by fusing rgb frames and event streams simultaneously and propose a new rgb frame event recognition framework to address the aforementioned issues the proposed method contains four main modules i e memory support transformer network for rgb frame encoding spiking neural network for raw event stream encoding multi modal bottleneck fusion module for rgb event feature aggregation and prediction head due to the scarce of rgb event based classification dataset we also propose a large scale pokerevent dataset which contains classes and frame event pairs recorded using a event camera extensive experiments on two rgb event based classification datasets fully validated the effectiveness of our proposed framework we hope this work will boost the development of pattern recognition by fusing rgb frames and event streams both our dataset and source code of this work will be released at person re identification without identification via event anonymization authors shafiq ahmad pietro morerio alessio del bue subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract wide scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption energy bandwidth and computation neuromorphic vision sensors event cameras have been recently considered a valid solution to the privacy issue because they do not capture detailed rgb visual information of the subjects in the scene however recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity reintroducing a potential threat to privacy for event based vision applications in this paper we aim to anonymize event streams to protect the identity of human subjects against such image reconstruction attacks to achieve this we propose an end to end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person reid our network learns to scramble events enforcing the degradation of images recovered from the privacy attacker in this work we also bring to the community the first ever event based person reid dataset gathered to evaluate the performance of our approach we validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available softbio dataset and our proposed event reid dataset keyword events camera there is no result keyword white balance deterministic neural illumination mapping for efficient auto white balance correction authors furkan kınlı doğa yılmaz barış özcan furkan kıraç subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract auto white balance awb correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios this paper presents a novel and efficient awb correction method that achieves at least times faster processing with equivalent or superior performance on high resolution images for the current state of the art methods inspired by deterministic color style transfer our approach introduces deterministic illumination color mapping leveraging learnable projection matrices for both canonical illumination form and awb corrected output it involves feeding high resolution images and corresponding latent representations into a mapping module to derive a canonical form followed by another mapping module that maps the pixel values to those for the corrected version this strategy is designed as resolution agnostic and also enables seamless integration of any pre trained awb network as the backbone experimental results confirm the effectiveness of our approach revealing significant performance improvements and reduced time complexity compared to state of the art methods our method provides an efficient deep learning based awb correction solution promising real time high quality color correction for digital imaging applications source code is available at keyword color contrast there is no result keyword awb deterministic neural illumination mapping for efficient auto white balance correction authors furkan kınlı doğa yılmaz barış özcan furkan kıraç subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract auto white balance awb correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios this paper presents a novel and efficient awb correction method that achieves at least times faster processing with equivalent or superior performance on high resolution images for the current state of the art methods inspired by deterministic color style transfer our approach introduces deterministic illumination color mapping leveraging learnable projection matrices for both canonical illumination form and awb corrected output it involves feeding high resolution images and corresponding latent representations into a mapping module to derive a canonical form followed by another mapping module that maps the pixel values to those for the corrected version this strategy is designed as resolution agnostic and also enables seamless integration of any pre trained awb network as the backbone experimental results confirm the effectiveness of our approach revealing significant performance improvements and reduced time complexity compared to state of the art methods our method provides an efficient deep learning based awb correction solution promising real time high quality color correction for digital imaging applications source code is available at real time strawberry detection based on improved architecture for robotic harvesting in open field environment authors zixuan he salik ram khana xin zhang manoj karkee qin zhang center for precision and automated agricultural systems washington state university department of biological systems engineering washington state university department of agricultural and biological engineering mississippi state university subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this study proposed a based custom object detection model to detect strawberries in an outdoor environment the original architecture of the was modified by replacing the module with the module in the backbone network which provided a better feature gradient flow secondly the spatial pyramid pooling fast in the final layer of the backbone network of was combined with cross stage partial net to improve the generalization ability over the strawberry dataset in this study the proposed architecture was named straw the rgb images dataset of the strawberry canopy with three maturity classes immature nearly mature and mature was collected in open field environment and augmented through a series of operations including brightness reduction brightness increase and noise adding to verify the superiority of the proposed method for strawberry detection in open field environment four competitive detection models tiny and were trained and tested under the same computational environment and compared with straw the results showed that the highest mean average precision of was achieved using the proposed architecture whereas the same was achieved with tiny and were respectively specifically the average precision of straw was in the immature class in the nearly mature class and in the mature class which were and respectively higher than that of the latest the model included network parameters with an inference speed of per image while the inference speed of had a slower inference speed of and heavy parameters of which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking keyword isp amaized an end to end pipeline for automatic maize disease detection authors anish mall sanchit kabra ankur lhila pawan ajmera subjects computer vision and pattern recognition cs cv artificial intelligence cs ai image and video processing eess iv arxiv link pdf link abstract this research paper presents amaized an end to end pipeline for automatic maize disease detection an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones a custom hand collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists the dataset encompasses a diverse range of maize varieties cultivation practices and environmental conditions capturing various stages of maize growth and disease progression by leveraging multispectral imagery the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health the proposed framework employs a combination of convolutional neural networks cnns as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases including powdery mildew anthracnose and leaf blight the framework achieves state of the art performance on the custom hand collected dataset and contributes to the field of automated disease detection in agriculture offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures alfa leveraging all levels of feature abstraction for enhancing the generalization of histopathology image classification across unseen hospitals authors milad sikaroudi shahryar rahnamayan h r tizhoosh subjects computer vision and pattern recognition cs cv artificial intelligence cs ai arxiv link pdf link abstract we propose an exhaustive methodology that leverages all levels of feature abstraction targeting an enhancement in the generalizability of image classification to unobserved hospitals our approach incorporates augmentation based self supervision with common distribution shifts in histopathology scenarios serving as the pretext task this enables us to derive invariant features from training images without relying on training labels thereby covering different abstraction levels moving onto the subsequent abstraction level we employ a domain alignment module to facilitate further extraction of invariant features across varying training hospitals to represent the highly specific features of participating hospitals an encoder is trained to classify hospital labels independent of their diagnostic labels the features from each of these encoders are subsequently disentangled to minimize redundancy and segregate the features this representation which spans a broad spectrum of semantic information enables the development of a model demonstrating increased robustness to unseen images from disparate distributions experimental results from the pacs dataset a domain generalization benchmark a synthetic dataset created by applying histopathology specific jitters to the mhist dataset defining different domains with varied distribution shifts and a renal cell carcinoma dataset derived from four image repositories from tcga collectively indicate that our proposed model is adept at managing varying levels of image granularity thus it shows improved generalizability when faced with new out of distribution hospital images under display camera image restoration with scattering effect authors binbin song xiangyu chen shuning xu jiantao zhou subjects computer vision and pattern recognition cs cv image and video processing eess iv arxiv link pdf link abstract the under display camera udc provides consumers with a full screen visual experience without any obstruction due to notches or punched holes however the semi transparent nature of the display inevitably introduces the severe degradation into udc images in this work we address the udc image restoration problem with the specific consideration of the scattering effect caused by the display we explicitly model the scattering effect by treating the display as a piece of homogeneous scattering medium with the physical model of the scattering effect we improve the image formation pipeline for the image synthesis to construct a realistic udc dataset with ground truths to suppress the scattering effect for the eventual udc image recovery a two branch restoration network is designed more specifically the scattering branch leverages global modeling capabilities of the channel wise self attention to estimate parameters of the scattering effect from degraded images while the image branch exploits the local representation advantage of cnn to recover clear scenes implicitly guided by the scattering branch extensive experiments are conducted on both real world and synthesized data demonstrating the superiority of the proposed method over the state of the art udc restoration techniques the source code and dataset are available at url keyword image signal processing there is no result keyword image signal process deterministic neural illumination mapping for efficient auto white balance correction authors furkan kınlı doğa yılmaz barış özcan furkan kıraç subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract auto white balance awb correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios this paper presents a novel and efficient awb correction method that achieves at least times faster processing with equivalent or superior performance on high resolution images for the current state of the art methods inspired by deterministic color style transfer our approach introduces deterministic illumination color mapping leveraging learnable projection matrices for both canonical illumination form and awb corrected output it involves feeding high resolution images and corresponding latent representations into a mapping module to derive a canonical form followed by another mapping module that maps the pixel values to those for the corrected version this strategy is designed as resolution agnostic and also enables seamless integration of any pre trained awb network as the backbone experimental results confirm the effectiveness of our approach revealing significant performance improvements and reduced time complexity compared to state of the art methods our method provides an efficient deep learning based awb correction solution promising real time high quality color correction for digital imaging applications source code is available at keyword compression lossy and lossless l post training model size compression authors yumeng shi shihao bai xiuying wei ruihao gong jianlei yang subjects computer vision and pattern recognition cs cv artificial intelligence cs ai arxiv link pdf link abstract deep neural networks have delivered remarkable performance and have been widely used in various visual tasks however their huge size causes significant inconvenience for transmission and storage many previous studies have explored model size compression however these studies often approach various lossy and lossless compression methods in isolation leading to challenges in achieving high compression ratios efficiently this work proposes a post training model size compression method that combines lossy and lossless compression in a unified way we first propose a unified parametric weight transformation which ensures different lossy compression methods can be performed jointly in a post training manner then a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression additionally our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers finally our method can achieve a stable times compression ratio without sacrificing accuracy and a times compression ratio with minor accuracy loss in a short time our code is available at keyword raw developability approximation for neural implicits through rank minimization authors pratheba selvaraju subjects computer vision and pattern recognition cs cv graphics cs gr arxiv link pdf link abstract developability refers to the process of creating a surface without any tearing or shearing from a two dimensional plane it finds practical applications in the fabrication industry an essential characteristic of a developable surface is its zero gaussian curvature which means that either one or both of the principal curvatures are zero this paper introduces a method for reconstructing an approximate developable surface from a neural implicit surface the central idea of our method involves incorporating a regularization term that operates on the second order derivatives of the neural implicits effectively promoting zero gaussian curvature implicit surfaces offer the advantage of smoother deformation with infinite resolution overcoming the high polygonal constraints of state of the art methods using discrete representations we draw inspiration from the properties of surface curvature and employ rank minimization techniques derived from compressed sensing experimental results on both developable and non developable surfaces including those affected by noise validate the generalizability of our method real time strawberry detection based on improved architecture for robotic harvesting in open field environment authors zixuan he salik ram khana xin zhang manoj karkee qin zhang center for precision and automated agricultural systems washington state university department of biological systems engineering washington state university department of agricultural and biological engineering mississippi state university subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this study proposed a based custom object detection model to detect strawberries in an outdoor environment the original architecture of the was modified by replacing the module with the module in the backbone network which provided a better feature gradient flow secondly the spatial pyramid pooling fast in the final layer of the backbone network of was combined with cross stage partial net to improve the generalization ability over the strawberry dataset in this study the proposed architecture was named straw the rgb images dataset of the strawberry canopy with three maturity classes immature nearly mature and mature was collected in open field environment and augmented through a series of operations including brightness reduction brightness increase and noise adding to verify the superiority of the proposed method for strawberry detection in open field environment four competitive detection models tiny and were trained and tested under the same computational environment and compared with straw the results showed that the highest mean average precision of was achieved using the proposed architecture whereas the same was achieved with tiny and were respectively specifically the average precision of straw was in the immature class in the nearly mature class and in the mature class which were and respectively higher than that of the latest the model included network parameters with an inference speed of per image while the inference speed of had a slower inference speed of and heavy parameters of which indicates that the proposed model is fast enough for real time strawberry detection and localization for the robotic picking domain adaptive person search via gan based scene synthesis for cross scene videos authors huibing wang tianxiang cui mingze yao huijuan pang yushan du subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract person search has recently been a challenging task in the computer vision domain which aims to search specific pedestrians from real cameras nevertheless most surveillance videos comprise only a handful of images of each pedestrian which often feature identical backgrounds and clothing hence it is difficult to learn more discriminative features for person search in real scenes to tackle this challenge we draw on generative adversarial networks gan to synthesize data from surveillance videos gan has thrived in computer vision problems because it produces high quality images efficiently we merely alter the popular fast r cnn model which is capable of processing videos and yielding accurate detection outcomes in order to appropriately relieve the pressure brought by the two stage model we design an assisted identity query module aidq to provide positive images for the behind part besides the proposed novel gan based scene synthesis model that can synthesize high quality cross id person images for person search tasks in order to facilitate the feature learning of the gan based scene synthesis model we adopt an online learning strategy that collaboratively learns the synthesized images and original images extensive experiments on two widely used person search benchmarks cuhk sysu and prw have shown that our method has achieved great performance and the extensive ablation study further justifies our gan synthetic data can effectively increase the variability of the datasets and be more realistic sstformer bridging spiking neural network and memory support transformer for frame event based recognition authors xiao wang zongzhen wu yao rong lin zhu bo jiang jin tang yonghong tian subjects computer vision and pattern recognition cs cv multimedia cs mm neural and evolutionary computing cs ne arxiv link pdf link abstract event camera based pattern recognition is a newly arising research topic in recent years current researchers usually transform the event streams into images graphs or voxels and adopt deep neural networks for event based classification although good performance can be achieved on simple event recognition datasets however their results may be still limited due to the following two issues firstly they adopt spatial sparse event streams for recognition only which may fail to capture the color and detailed texture information well secondly they adopt either spiking neural networks snn for energy efficient recognition with suboptimal results or artificial neural networks ann for energy intensive high performance recognition however seldom of them consider achieving a balance between these two aspects in this paper we formally propose to recognize patterns by fusing rgb frames and event streams simultaneously and propose a new rgb frame event recognition framework to address the aforementioned issues the proposed method contains four main modules i e memory support transformer network for rgb frame encoding spiking neural network for raw event stream encoding multi modal bottleneck fusion module for rgb event feature aggregation and prediction head due to the scarce of rgb event based classification dataset we also propose a large scale pokerevent dataset which contains classes and frame event pairs recorded using a event camera extensive experiments on two rgb event based classification datasets fully validated the effectiveness of our proposed framework we hope this work will boost the development of pattern recognition by fusing rgb frames and event streams both our dataset and source code of this work will be released at delflow dense efficient learning of scene flow for large scale point clouds authors chensheng peng guangming wang xian wan lo xinrui wu chenfeng xu masayoshi tomizuka wei zhan hesheng wang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract point clouds are naturally sparse while image pixels are dense the inconsistency limits feature fusion from both modalities for point wise scene flow estimation previous methods rarely predict scene flow from the entire point clouds of the scene with one time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling knn and ball query algorithms for local feature aggregation to mitigate these issues in scene flow learning we regularize raw points to a dense format by storing coordinates in grids unlike the sampling operation commonly used in existing works the dense representation preserves most points in the given scene brings in a significant boost of efficiency and eliminates the density gap between points and pixels allowing us to perform effective feature fusion we also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume sufficient experiments demonstrate the efficiency and effectiveness of our method outperforming the prior arts on the and kitti dataset keyword raw image there is no result
| 1
|
22,166
| 30,716,405,665
|
IssuesEvent
|
2023-07-27 13:18:29
|
NationalSecurityAgency/ghidra
|
https://api.github.com/repos/NationalSecurityAgency/ghidra
|
closed
|
The generated PcodeOp is incorrect
|
Type: Bug Reason: Duplicate Feature: Processor/x86
|
For example,x86 instruction
`pop dword ptr ss:[esp+0x24]`
Ghidra generate the following code:
`0x004d4bee:a5: u0x00002500(0x0000000c:a5) = #0x24 + ESP(0x0000000b:a3)`
`0x004d4bee:a6: u0x00007a00(0x0000000c:a6) = *(ram,ESP(0x0000000b:a3))`
`0x004d4bee:a7: *(ram,u0x00002500(0x0000000c:a5)) = u0x00007a00(0x0000000c:a6)`
`0x004d4bee:a8: ESP(0x0000000c:a8) = ESP(0x0000000b:a3) + #0x4`
The code can be simplified to:
[esp + 0x24] = [esp]
esp = esp + 0x4
However,When I try to execute this instruction in the debugger,the result is:
[esp + 0x28] = [esp]
esp = esp + 0x4
Here is the screenshot,before execute pop ins:
ESP = 0x0019FBF8
[0x0019FBF8] = 0x282
After execute pop ins:
ESP = 0x0019FBFC
[0x19FC20] = 0x282
|
1.0
|
The generated PcodeOp is incorrect - For example,x86 instruction
`pop dword ptr ss:[esp+0x24]`
Ghidra generate the following code:
`0x004d4bee:a5: u0x00002500(0x0000000c:a5) = #0x24 + ESP(0x0000000b:a3)`
`0x004d4bee:a6: u0x00007a00(0x0000000c:a6) = *(ram,ESP(0x0000000b:a3))`
`0x004d4bee:a7: *(ram,u0x00002500(0x0000000c:a5)) = u0x00007a00(0x0000000c:a6)`
`0x004d4bee:a8: ESP(0x0000000c:a8) = ESP(0x0000000b:a3) + #0x4`
The code can be simplified to:
[esp + 0x24] = [esp]
esp = esp + 0x4
However,When I try to execute this instruction in the debugger,the result is:
[esp + 0x28] = [esp]
esp = esp + 0x4
Here is the screenshot,before execute pop ins:
ESP = 0x0019FBF8
[0x0019FBF8] = 0x282
After execute pop ins:
ESP = 0x0019FBFC
[0x19FC20] = 0x282
|
process
|
the generated pcodeop is incorrect for example instruction pop dword ptr ss ghidra generate the following code esp ram esp ram esp esp the code can be simplified to esp esp however when i try to execute this instruction in the debugger the result is esp esp here is the screenshot before execute pop ins esp after execute pop ins esp
| 1
|
438
| 2,870,249,516
|
IssuesEvent
|
2015-06-07 00:32:53
|
cyanisaac/openterminalos
|
https://api.github.com/repos/cyanisaac/openterminalos
|
closed
|
OS Integrity Checking
|
Background Process Bootloader OTOS API
|
This is a rather important feature, files will be confirmed to exist in the background so that we can make sure that things are working properly. This will run as a background process using the Parallels API. If an error is found it will signal the OS to terminate to the Blue Screen ASAP, and will display "File Integrity Issue - Please Refresh ASAP"
Note that this will not check if there are issues with the files themselves - I will look into hashing those out soon.
|
1.0
|
OS Integrity Checking - This is a rather important feature, files will be confirmed to exist in the background so that we can make sure that things are working properly. This will run as a background process using the Parallels API. If an error is found it will signal the OS to terminate to the Blue Screen ASAP, and will display "File Integrity Issue - Please Refresh ASAP"
Note that this will not check if there are issues with the files themselves - I will look into hashing those out soon.
|
process
|
os integrity checking this is a rather important feature files will be confirmed to exist in the background so that we can make sure that things are working properly this will run as a background process using the parallels api if an error is found it will signal the os to terminate to the blue screen asap and will display file integrity issue please refresh asap note that this will not check if there are issues with the files themselves i will look into hashing those out soon
| 1
|
274,380
| 8,560,218,452
|
IssuesEvent
|
2018-11-09 00:09:30
|
RobotLocomotion/drake
|
https://api.github.com/repos/RobotLocomotion/drake
|
closed
|
Bindings shouldn't get added to build_components.bzl
|
configuration: python priority: low team: kitware
|
Currently running `build_components_refresh.py` adds new lines for `//bindings/pydrake/*`. It shouldn't (or so I'm told).
|
1.0
|
Bindings shouldn't get added to build_components.bzl - Currently running `build_components_refresh.py` adds new lines for `//bindings/pydrake/*`. It shouldn't (or so I'm told).
|
non_process
|
bindings shouldn t get added to build components bzl currently running build components refresh py adds new lines for bindings pydrake it shouldn t or so i m told
| 0
|
351,972
| 32,039,785,803
|
IssuesEvent
|
2023-09-22 18:17:12
|
cockroachdb/cockroach
|
https://api.github.com/repos/cockroachdb/cockroach
|
opened
|
roachtest: cluster_creation failed
|
O-robot O-roachtest X-infra-flake branch-release-23.1.11-rc
|
roachtest.cluster_creation [failed](https://teamcity.cockroachdb.com/buildConfiguration/Cockroach_Nightlies_RoachtestNightlyGceBazel/11880913?buildTab=log) with [artifacts](https://teamcity.cockroachdb.com/buildConfiguration/Cockroach_Nightlies_RoachtestNightlyGceBazel/11880913?buildTab=artifacts#/restore/tpce/400GB/aws/nodes=9/cpus=8/zones=us-east-2b,us-west-2b,eu-west-1b) on release-23.1.11-rc @ [1540b4c3c6faade6f9b8cd0da19c5877e7a97f4e](https://github.com/cockroachdb/cockroach/commits/1540b4c3c6faade6f9b8cd0da19c5877e7a97f4e):
```
test restore/tpce/400GB/aws/nodes=9/cpus=8/zones=us-east-2b,us-west-2b,eu-west-1b was skipped due to (test_runner.go:745).runWorker: in provider: gce: Command: gcloud [compute instances create --subnet default --scopes cloud-platform --image ubuntu-2004-focal-v20230817 --image-project ubuntu-os-cloud --boot-disk-type pd-ssd --service-account 21965078311-compute@developer.gserviceaccount.com --maintenance-policy MIGRATE --create-disk type=pd-ssd,size=1000GB,auto-delete=yes --machine-type n2-standard-8 --labels usage=roachtest,roachprod=true,cluster=teamcity-11880913-1695361516-153-n9cpu8-geo,lifetime=12h0m0s,arch=amd64,created=2023-09-22t18_16_36z --metadata-from-file startup-script=/tmp/gce-startup-script3591626828 --project cockroach-ephemeral --boot-disk-size=32GB --zone us-east-2b teamcity-11880913-1695361516-153-n9cpu8-geo-0001 teamcity-11880913-1695361516-153-n9cpu8-geo-0002 teamcity-11880913-1695361516-153-n9cpu8-geo-0003]
Output: ERROR: (gcloud.compute.instances.create) Could not fetch resource:
- Permission denied on 'locations/us-east-2b' (or it may not exist).: exit status 1
```
<p>Parameters: <code>ROACHTEST_cloud=gce</code>
, <code>ROACHTEST_cpu=8</code>
, <code>ROACHTEST_ssd=0</code>
</p>
<details><summary>Help</summary>
<p>
See: [roachtest README](https://github.com/cockroachdb/cockroach/blob/master/pkg/cmd/roachtest/README.md)
See: [How To Investigate \(internal\)](https://cockroachlabs.atlassian.net/l/c/SSSBr8c7)
</p>
</details>
<details><summary>Same failure on other branches</summary>
<p>
- #110990 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-23.1.11-rc.FROZEN]
- #109982 roachtest: cluster_creation failed [FROZEN.branch-release-23.1.11-rc O-roachtest O-robot T-testeng X-infra-flake]
- #108859 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-22.2]
- #108653 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng TODO-deprecate.branch-release-23.1.9-rc X-infra-flake]
- #108629 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-master]
- #108533 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-23.1]
</p>
</details>
/cc @cockroachdb/test-eng
<sub>
[This test on roachdash](https://roachdash.crdb.dev/?filter=status:open%20t:.*cluster_creation.*&sort=title+created&display=lastcommented+project) | [Improve this report!](https://github.com/cockroachdb/cockroach/tree/master/pkg/cmd/internal/issues)
</sub>
|
1.0
|
roachtest: cluster_creation failed - roachtest.cluster_creation [failed](https://teamcity.cockroachdb.com/buildConfiguration/Cockroach_Nightlies_RoachtestNightlyGceBazel/11880913?buildTab=log) with [artifacts](https://teamcity.cockroachdb.com/buildConfiguration/Cockroach_Nightlies_RoachtestNightlyGceBazel/11880913?buildTab=artifacts#/restore/tpce/400GB/aws/nodes=9/cpus=8/zones=us-east-2b,us-west-2b,eu-west-1b) on release-23.1.11-rc @ [1540b4c3c6faade6f9b8cd0da19c5877e7a97f4e](https://github.com/cockroachdb/cockroach/commits/1540b4c3c6faade6f9b8cd0da19c5877e7a97f4e):
```
test restore/tpce/400GB/aws/nodes=9/cpus=8/zones=us-east-2b,us-west-2b,eu-west-1b was skipped due to (test_runner.go:745).runWorker: in provider: gce: Command: gcloud [compute instances create --subnet default --scopes cloud-platform --image ubuntu-2004-focal-v20230817 --image-project ubuntu-os-cloud --boot-disk-type pd-ssd --service-account 21965078311-compute@developer.gserviceaccount.com --maintenance-policy MIGRATE --create-disk type=pd-ssd,size=1000GB,auto-delete=yes --machine-type n2-standard-8 --labels usage=roachtest,roachprod=true,cluster=teamcity-11880913-1695361516-153-n9cpu8-geo,lifetime=12h0m0s,arch=amd64,created=2023-09-22t18_16_36z --metadata-from-file startup-script=/tmp/gce-startup-script3591626828 --project cockroach-ephemeral --boot-disk-size=32GB --zone us-east-2b teamcity-11880913-1695361516-153-n9cpu8-geo-0001 teamcity-11880913-1695361516-153-n9cpu8-geo-0002 teamcity-11880913-1695361516-153-n9cpu8-geo-0003]
Output: ERROR: (gcloud.compute.instances.create) Could not fetch resource:
- Permission denied on 'locations/us-east-2b' (or it may not exist).: exit status 1
```
<p>Parameters: <code>ROACHTEST_cloud=gce</code>
, <code>ROACHTEST_cpu=8</code>
, <code>ROACHTEST_ssd=0</code>
</p>
<details><summary>Help</summary>
<p>
See: [roachtest README](https://github.com/cockroachdb/cockroach/blob/master/pkg/cmd/roachtest/README.md)
See: [How To Investigate \(internal\)](https://cockroachlabs.atlassian.net/l/c/SSSBr8c7)
</p>
</details>
<details><summary>Same failure on other branches</summary>
<p>
- #110990 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-23.1.11-rc.FROZEN]
- #109982 roachtest: cluster_creation failed [FROZEN.branch-release-23.1.11-rc O-roachtest O-robot T-testeng X-infra-flake]
- #108859 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-22.2]
- #108653 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng TODO-deprecate.branch-release-23.1.9-rc X-infra-flake]
- #108629 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-master]
- #108533 roachtest: cluster_creation failed [O-roachtest O-robot T-testeng X-infra-flake branch-release-23.1]
</p>
</details>
/cc @cockroachdb/test-eng
<sub>
[This test on roachdash](https://roachdash.crdb.dev/?filter=status:open%20t:.*cluster_creation.*&sort=title+created&display=lastcommented+project) | [Improve this report!](https://github.com/cockroachdb/cockroach/tree/master/pkg/cmd/internal/issues)
</sub>
|
non_process
|
roachtest cluster creation failed roachtest cluster creation with on release rc test restore tpce aws nodes cpus zones us east us west eu west was skipped due to test runner go runworker in provider gce command gcloud output error gcloud compute instances create could not fetch resource permission denied on locations us east or it may not exist exit status parameters roachtest cloud gce roachtest cpu roachtest ssd help see see same failure on other branches roachtest cluster creation failed roachtest cluster creation failed roachtest cluster creation failed roachtest cluster creation failed roachtest cluster creation failed roachtest cluster creation failed cc cockroachdb test eng
| 0
|
268,925
| 8,415,800,761
|
IssuesEvent
|
2018-10-13 18:20:02
|
seattleagainstslavery/seattleagainstslavery-theme
|
https://api.github.com/repos/seattleagainstslavery/seattleagainstslavery-theme
|
closed
|
Books on resource page do not have href set in link
|
bug high priority
|
For example, despite the URL being set in the admin for the item, the html is render as:
```
<a href="" target="_blank">
<span>Girls like Us
<small>By Rachel Lloyd</small>
</span>
</a>
```
|
1.0
|
Books on resource page do not have href set in link - For example, despite the URL being set in the admin for the item, the html is render as:
```
<a href="" target="_blank">
<span>Girls like Us
<small>By Rachel Lloyd</small>
</span>
</a>
```
|
non_process
|
books on resource page do not have href set in link for example despite the url being set in the admin for the item the html is render as girls like us by rachel lloyd
| 0
|
7,610
| 10,723,438,747
|
IssuesEvent
|
2019-10-27 18:48:22
|
akey7/organicml
|
https://api.github.com/repos/akey7/organicml
|
closed
|
Postprocess Convnet Images
|
Collection/Preprocessing
|
Do the following post processing steps to the images:
1. Convert to 256x256 resolution
1. Convert to grayscale
Do this with Image Magick.
|
1.0
|
Postprocess Convnet Images - Do the following post processing steps to the images:
1. Convert to 256x256 resolution
1. Convert to grayscale
Do this with Image Magick.
|
process
|
postprocess convnet images do the following post processing steps to the images convert to resolution convert to grayscale do this with image magick
| 1
|
15,989
| 20,188,202,395
|
IssuesEvent
|
2022-02-11 01:17:38
|
savitamittalmsft/WAS-SEC-TEST
|
https://api.github.com/repos/savitamittalmsft/WAS-SEC-TEST
|
opened
|
Implement a solution to configure unique local admin credentials
|
WARP-Import WAF FEB 2021 Security Performance and Scalability Capacity Management Processes Operational Procedures Patch & Update Process (PNU)
|
<a href="https://docs.microsoft.com/azure/automation/update-management/overview">Implement a solution to configure unique local admin credentials</a>
<p><b>Why Consider This?</b></p>
Use of consistent local administrator passwords leaves the organization susceptible to rapid lateral account movement as a compromised credential can be used on multiple hosts in attempt to escalate privilege.
<p><b>Context</b></p>
<p><span>"nbsp;</span></p><p><span>LAPS provides a solution to the issue of using a common local account with an identical password on every computer in a domain. LAPS resolves this issue by setting a different, random password for the common local administrator account on every computer in the domain. Domain administrators who use this solution can determine which users, such as helpdesk administrators, are authorized to read passwords.</span></p><p><span>Best practice to avoid common lateral attack techniques such as pass-the-hash is to configure unique local administrator credentials and change periodically. </span></p>
<p><b>Suggested Actions</b></p>
<p><span>Deploy Microsoft Local Administrator Password Solution (LAPS) or comparable solution to ensure no system uses the same local administrator credential.</span></p>
<p><b>Learn More</b></p>
<p><a href="https://support.microsoft.com/en-us/help/3062591/microsoft-security-advisory-local-administrator-password-solution-laps" target="_blank"><span>Microsoft security advisory: Local Administrator Password Solution</span></a><span /></p>
|
2.0
|
Implement a solution to configure unique local admin credentials - <a href="https://docs.microsoft.com/azure/automation/update-management/overview">Implement a solution to configure unique local admin credentials</a>
<p><b>Why Consider This?</b></p>
Use of consistent local administrator passwords leaves the organization susceptible to rapid lateral account movement as a compromised credential can be used on multiple hosts in attempt to escalate privilege.
<p><b>Context</b></p>
<p><span>"nbsp;</span></p><p><span>LAPS provides a solution to the issue of using a common local account with an identical password on every computer in a domain. LAPS resolves this issue by setting a different, random password for the common local administrator account on every computer in the domain. Domain administrators who use this solution can determine which users, such as helpdesk administrators, are authorized to read passwords.</span></p><p><span>Best practice to avoid common lateral attack techniques such as pass-the-hash is to configure unique local administrator credentials and change periodically. </span></p>
<p><b>Suggested Actions</b></p>
<p><span>Deploy Microsoft Local Administrator Password Solution (LAPS) or comparable solution to ensure no system uses the same local administrator credential.</span></p>
<p><b>Learn More</b></p>
<p><a href="https://support.microsoft.com/en-us/help/3062591/microsoft-security-advisory-local-administrator-password-solution-laps" target="_blank"><span>Microsoft security advisory: Local Administrator Password Solution</span></a><span /></p>
|
process
|
implement a solution to configure unique local admin credentials why consider this use of consistent local administrator passwords leaves the organization susceptible to rapid lateral account movement as a compromised credential can be used on multiple hosts in attempt to escalate privilege context nbsp laps provides a solution to the issue of using a common local account with an identical password on every computer in a domain laps resolves this issue by setting a different random password for the common local administrator account on every computer in the domain domain administrators who use this solution can determine which users such as helpdesk administrators are authorized to read passwords best practice to avoid common lateral attack techniques such as pass the hash is to configure unique local administrator credentials and change periodically suggested actions deploy microsoft local administrator password solution laps or comparable solution to ensure no system uses the same local administrator credential learn more microsoft security advisory local administrator password solution
| 1
|
1,530
| 4,118,790,670
|
IssuesEvent
|
2016-06-08 12:54:48
|
e-government-ua/iBP
|
https://api.github.com/repos/e-government-ua/iBP
|
closed
|
Видача ліцензії на право роздрібної торгівлі алкогольними напоями
|
In process of testing in work
|
Налоговая Николаева
vladrakipov@rambler.ru
Влад Ракипов
063 945 23 40
|
1.0
|
Видача ліцензії на право роздрібної торгівлі алкогольними напоями - Налоговая Николаева
vladrakipov@rambler.ru
Влад Ракипов
063 945 23 40
|
process
|
видача ліцензії на право роздрібної торгівлі алкогольними напоями налоговая николаева vladrakipov rambler ru влад ракипов
| 1
|
1,084
| 3,547,890,425
|
IssuesEvent
|
2016-01-20 11:54:57
|
symfony/symfony
|
https://api.github.com/repos/symfony/symfony
|
closed
|
[Process] currently not suitable for running processes with large output
|
Process
|
By default, [PHP uses a memory_limit of 128M](http://php.net/manual/en/ini.core.php#ini.sect.resource-limits).
Since Symfony/Process stores all output in ```private $stdout;``` and ```private $stderr;```, this means that as soon as the output hits 128MB, your PHP script using Symfony/Process will fatally error out with:
```
[15-Jan-2016 06:31:02 America/New_York] PHP Fatal error: Allowed memory size of 134217728 bytes exhausted (tried to allocate 131862121 bytes) in /app/myapp/vendor/symfony/process/Process.php on line 815
```
You can test this pretty quickly by just doing this (assuming you have GNU Coreutils installed):
```php
require("vendor/autoload.php");
use Symfony\Component\Process\Process;
$process = new Process('yes "Hello"');
$process->setTimeout(172800);
$process->run(function ($type, $buffer) {
// do nothing
});
```
Unfortunately, we assumed that Symfony/Process was already using a temporary buffer for closures, and saw this issue in production. To mitigate the issue for now, we bumped up our ```memory_limit```, but ideally this should be fixed in Symfony itself.
Since the upper limit on a PHP string itself is 2GB, this also means that Symfony/Process will currently die when the buffer reaches 2GB, no matter what. Here's a sample of that error
```
[15-Jan-2016 10:16:05 America/New_York] PHP Fatal error: String size overflow in /app/myapp/vendor/symfony/process/Process.php on line 799
```
In my opinion, registering closure to ```run``` should mean that the entire buffer should not be stored in memory.
Please share your thoughts.
|
1.0
|
[Process] currently not suitable for running processes with large output - By default, [PHP uses a memory_limit of 128M](http://php.net/manual/en/ini.core.php#ini.sect.resource-limits).
Since Symfony/Process stores all output in ```private $stdout;``` and ```private $stderr;```, this means that as soon as the output hits 128MB, your PHP script using Symfony/Process will fatally error out with:
```
[15-Jan-2016 06:31:02 America/New_York] PHP Fatal error: Allowed memory size of 134217728 bytes exhausted (tried to allocate 131862121 bytes) in /app/myapp/vendor/symfony/process/Process.php on line 815
```
You can test this pretty quickly by just doing this (assuming you have GNU Coreutils installed):
```php
require("vendor/autoload.php");
use Symfony\Component\Process\Process;
$process = new Process('yes "Hello"');
$process->setTimeout(172800);
$process->run(function ($type, $buffer) {
// do nothing
});
```
Unfortunately, we assumed that Symfony/Process was already using a temporary buffer for closures, and saw this issue in production. To mitigate the issue for now, we bumped up our ```memory_limit```, but ideally this should be fixed in Symfony itself.
Since the upper limit on a PHP string itself is 2GB, this also means that Symfony/Process will currently die when the buffer reaches 2GB, no matter what. Here's a sample of that error
```
[15-Jan-2016 10:16:05 America/New_York] PHP Fatal error: String size overflow in /app/myapp/vendor/symfony/process/Process.php on line 799
```
In my opinion, registering closure to ```run``` should mean that the entire buffer should not be stored in memory.
Please share your thoughts.
|
process
|
currently not suitable for running processes with large output by default since symfony process stores all output in private stdout and private stderr this means that as soon as the output hits your php script using symfony process will fatally error out with php fatal error allowed memory size of bytes exhausted tried to allocate bytes in app myapp vendor symfony process process php on line you can test this pretty quickly by just doing this assuming you have gnu coreutils installed php require vendor autoload php use symfony component process process process new process yes hello process settimeout process run function type buffer do nothing unfortunately we assumed that symfony process was already using a temporary buffer for closures and saw this issue in production to mitigate the issue for now we bumped up our memory limit but ideally this should be fixed in symfony itself since the upper limit on a php string itself is this also means that symfony process will currently die when the buffer reaches no matter what here s a sample of that error php fatal error string size overflow in app myapp vendor symfony process process php on line in my opinion registering closure to run should mean that the entire buffer should not be stored in memory please share your thoughts
| 1
|
275,556
| 30,262,483,431
|
IssuesEvent
|
2023-07-07 09:15:33
|
hal/berg
|
https://api.github.com/repos/hal/berg
|
closed
|
[Coverage]: Add tests for security manager subsystem configuration
|
configuration subsystem securitymanager coverage
|
### Description
Currently, we're missing coverage of the securitymanager subsystem configuration (Configuration => Subsystem => Security Manager).
Security Manager subsystem page contains "minimum permissions" and "maximum permissions" vertical navigation items, expecting two spec files to be created
Resources to cover (from `/subsystem=securitymanager:read-resource(recursive=true)`):
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/actions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/class`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/module`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/name`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/actions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/class`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/module`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/name`
You can get inspired by the [tests in the legacy testsuite](https://github.com/hal/testsuite.next/blob/master/tests-configuration-securitymanager/src/test/java/org/jboss/hal/testsuite/test/configuration/security_manager/SecurityManagerTest.java)
### Affects
configuration
### Subsystem
securitymanager
|
True
|
[Coverage]: Add tests for security manager subsystem configuration - ### Description
Currently, we're missing coverage of the securitymanager subsystem configuration (Configuration => Subsystem => Security Manager).
Security Manager subsystem page contains "minimum permissions" and "maximum permissions" vertical navigation items, expecting two spec files to be created
Resources to cover (from `/subsystem=securitymanager:read-resource(recursive=true)`):
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/actions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/class`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/module`
- [ ] `/subsystem=security-manager/deployment-permissions=default/minimum-permissions/name`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/actions`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/class`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/module`
- [ ] `/subsystem=security-manager/deployment-permissions=default/maximum-permissions/name`
You can get inspired by the [tests in the legacy testsuite](https://github.com/hal/testsuite.next/blob/master/tests-configuration-securitymanager/src/test/java/org/jboss/hal/testsuite/test/configuration/security_manager/SecurityManagerTest.java)
### Affects
configuration
### Subsystem
securitymanager
|
non_process
|
add tests for security manager subsystem configuration description currently we re missing coverage of the securitymanager subsystem configuration configuration subsystem security manager security manager subsystem page contains minimum permissions and maximum permissions vertical navigation items expecting two spec files to be created resources to cover from subsystem securitymanager read resource recursive true subsystem security manager deployment permissions default minimum permissions subsystem security manager deployment permissions default minimum permissions actions subsystem security manager deployment permissions default minimum permissions class subsystem security manager deployment permissions default minimum permissions module subsystem security manager deployment permissions default minimum permissions name subsystem security manager deployment permissions default maximum permissions subsystem security manager deployment permissions default maximum permissions actions subsystem security manager deployment permissions default maximum permissions class subsystem security manager deployment permissions default maximum permissions module subsystem security manager deployment permissions default maximum permissions name you can get inspired by the affects configuration subsystem securitymanager
| 0
|
4,475
| 7,341,359,218
|
IssuesEvent
|
2018-03-07 01:39:19
|
MicrosoftDocs/azure-docs
|
https://api.github.com/repos/MicrosoftDocs/azure-docs
|
closed
|
Removed?
|
cxp in-process product-question triaged virtual-machines-windows
|
Has Deep Security been removed? I've just followed the instructions above and tried to add it to VMs in North Europe and UK South and it's not listed as an extension!?
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 28479128-bb7b-1d74-0e40-a7e8f816fd38
* Version Independent ID: 5f788c5c-e3f7-8031-47d5-959d047b805a
* [Content](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/classic/install-trend)
* [Content Source](https://github.com/Microsoft/azure-docs/blob/master/articles/virtual-machines/windows/classic/install-trend.md)
* Service: virtual-machines-windows
|
1.0
|
Removed? - Has Deep Security been removed? I've just followed the instructions above and tried to add it to VMs in North Europe and UK South and it's not listed as an extension!?
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 28479128-bb7b-1d74-0e40-a7e8f816fd38
* Version Independent ID: 5f788c5c-e3f7-8031-47d5-959d047b805a
* [Content](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/classic/install-trend)
* [Content Source](https://github.com/Microsoft/azure-docs/blob/master/articles/virtual-machines/windows/classic/install-trend.md)
* Service: virtual-machines-windows
|
process
|
removed has deep security been removed i ve just followed the instructions above and tried to add it to vms in north europe and uk south and it s not listed as an extension document details ⚠ do not edit this section it is required for docs microsoft com ➟ github issue linking id version independent id service virtual machines windows
| 1
|
18,145
| 24,186,818,492
|
IssuesEvent
|
2022-09-23 13:58:14
|
geneontology/go-ontology
|
https://api.github.com/repos/geneontology/go-ontology
|
closed
|
NTR defense response against disruption of plasma membrane integrity
|
New term request multi-species process
|
Hello,
While reviewing GO:0051673 membrane disruption in another organism (see #24008), we found that some host/prey genes could be annotated to 'defense response against disruption of plasma membrane integrity',
Def: The cellular processes and signaling pathways by which a cell responds to disruption of the integrity of its plasma membrane by another organism. Some toxins produced by other organisms can form pores in membranes, or affect the membrane permeability.
PMID:17234446
PMID:21518219
|
1.0
|
NTR defense response against disruption of plasma membrane integrity - Hello,
While reviewing GO:0051673 membrane disruption in another organism (see #24008), we found that some host/prey genes could be annotated to 'defense response against disruption of plasma membrane integrity',
Def: The cellular processes and signaling pathways by which a cell responds to disruption of the integrity of its plasma membrane by another organism. Some toxins produced by other organisms can form pores in membranes, or affect the membrane permeability.
PMID:17234446
PMID:21518219
|
process
|
ntr defense response against disruption of plasma membrane integrity hello while reviewing go membrane disruption in another organism see we found that some host prey genes could be annotated to defense response against disruption of plasma membrane integrity def the cellular processes and signaling pathways by which a cell responds to disruption of the integrity of its plasma membrane by another organism some toxins produced by other organisms can form pores in membranes or affect the membrane permeability pmid pmid
| 1
|
14,372
| 17,395,842,588
|
IssuesEvent
|
2021-08-02 13:23:00
|
MicrosoftDocs/azure-docs
|
https://api.github.com/repos/MicrosoftDocs/azure-docs
|
closed
|
Renew RunAs account certificate permissions
|
Pri1 assigned-to-author automation/svc doc-enhancement process-automation/subsvc product-question triaged
|
One of a common administrative tasks is annual renewal of RunAs account certificate. The "Run As account permissions" section of the document "Automation account authentication overview" mentions that to create or remove an automation certificate a user needs Contributor role at the automation resource group. Having only contributor role triggers an error that Active Directory permissions are not sufficient. Is the AD application administrator role required as well?
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 8721e209-24ce-2170-6caa-ed12a7060080
* Version Independent ID: ac13f91d-460c-cbe9-4778-50d20765b252
* Content: [Azure Automation account authentication overview](https://docs.microsoft.com/en-us/azure/automation/automation-security-overview)
* Content Source: [articles/automation/automation-security-overview.md](https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/automation/automation-security-overview.md)
* Service: **automation**
* Sub-service: **process-automation**
* GitHub Login: @MGoedtel
* Microsoft Alias: **magoedte**
|
1.0
|
Renew RunAs account certificate permissions - One of a common administrative tasks is annual renewal of RunAs account certificate. The "Run As account permissions" section of the document "Automation account authentication overview" mentions that to create or remove an automation certificate a user needs Contributor role at the automation resource group. Having only contributor role triggers an error that Active Directory permissions are not sufficient. Is the AD application administrator role required as well?
---
#### Document Details
⚠ *Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.*
* ID: 8721e209-24ce-2170-6caa-ed12a7060080
* Version Independent ID: ac13f91d-460c-cbe9-4778-50d20765b252
* Content: [Azure Automation account authentication overview](https://docs.microsoft.com/en-us/azure/automation/automation-security-overview)
* Content Source: [articles/automation/automation-security-overview.md](https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/automation/automation-security-overview.md)
* Service: **automation**
* Sub-service: **process-automation**
* GitHub Login: @MGoedtel
* Microsoft Alias: **magoedte**
|
process
|
renew runas account certificate permissions one of a common administrative tasks is annual renewal of runas account certificate the run as account permissions section of the document automation account authentication overview mentions that to create or remove an automation certificate a user needs contributor role at the automation resource group having only contributor role triggers an error that active directory permissions are not sufficient is the ad application administrator role required as well document details ⚠ do not edit this section it is required for docs microsoft com ➟ github issue linking id version independent id content content source service automation sub service process automation github login mgoedtel microsoft alias magoedte
| 1
|
15,752
| 19,911,685,766
|
IssuesEvent
|
2022-01-25 17:47:55
|
input-output-hk/high-assurance-legacy
|
https://api.github.com/repos/input-output-hk/high-assurance-legacy
|
closed
|
Prove the weak analogs of the basic transition rules
|
type: enhancement language: isabelle topic: process calculus
|
Replacing the basic strong transition relation by the basic weak transition relation in the basic transition rules leads to true statements. Our goal is to prove these statements.
|
1.0
|
Prove the weak analogs of the basic transition rules - Replacing the basic strong transition relation by the basic weak transition relation in the basic transition rules leads to true statements. Our goal is to prove these statements.
|
process
|
prove the weak analogs of the basic transition rules replacing the basic strong transition relation by the basic weak transition relation in the basic transition rules leads to true statements our goal is to prove these statements
| 1
|
27,011
| 12,506,574,110
|
IssuesEvent
|
2020-06-02 12:48:59
|
terraform-providers/terraform-provider-azurerm
|
https://api.github.com/repos/terraform-providers/terraform-provider-azurerm
|
closed
|
Install Linkerd in aks cluster
|
question service/kubernetes-cluster
|
Hi Team,
Can I get some samples where I can install LinkerD service mesh using terraform into an aks cluster?
|
1.0
|
Install Linkerd in aks cluster - Hi Team,
Can I get some samples where I can install LinkerD service mesh using terraform into an aks cluster?
|
non_process
|
install linkerd in aks cluster hi team can i get some samples where i can install linkerd service mesh using terraform into an aks cluster
| 0
|
4,133
| 7,089,034,229
|
IssuesEvent
|
2018-01-12 00:11:31
|
triplea-game/triplea
|
https://api.github.com/repos/triplea-game/triplea
|
closed
|
Start working on a breaking release
|
category: dev & admin process discussion type: process
|
With an increasing amount of workarounds to maintain compatibility, I'd like to suggest to go for a new breaking release 1.10.build
### Prerequisites
- [x] We update the lobby and all the bots to the current stable release (or the build after the stable build due to a bug) and keep it running for a month. If we're not encountering any issues, the release can really be considered stable. Some bots are already on 1.9.0.0.7534. Note that the lobby requires it's db scheme to be changed.
- [ ] We create a branch "stable" which will continue to exist in the meantime, in order to be able to fix any P0 and P1 issues
- [ ] _**BIG IF**_: **IF** everything works out as expected, we ***should*** have a code signing certificate at the end of the month, so we **_could_** release a signed binary as latest stable
### Goals (although not final)
- [ ] Fix all checkstyle violations in serializable classes (and those used by the netcode) to get those out of the way, also the engine would no longer need to check if the vars are m_prefixed for game xmls.
- [ ] Switch to @ssoloff's recently reverted savegame format #2612 drop the support for the current one, also drop support for file extensions svg, tsvg and tsvg.gz
- [ ] Completely change the net code, probably the biggest task, this includes removing deprecated methods and passing Instant objects instead of Date objects, also we should switch to dedicated objects instead of passing Maps wrapping a couple lists in order to drastically improve readability. Also we should start working on a better "mute/ban" system, perhaps reputation based.
- [ ] Split up attachment classes. Currently the Attachment classes are like _really messy_. Suggestion would be to turn for example TechAttachment to some sort of interface which is then implemented by the individual TechAttachments. this would simply split up the attachments into different classes to be much more readable.
- [ ] Switch to a 3 number version system, once and for all (although I'd be fine with 4 digits as well although I'm pertty sure we'd never use the 3rd digit then)
#### Optional
- [ ] Perhaps we should convert the gamexml DOM parser to a SAXParser, not really tied to compatibility unless we want to change the required XML Scheme
- [ ] Come up with a good solution for Map downloading, not really tied to compatibility either unless we want to change the map xml scheme, currently the version defined in the triplea_maps.yaml file is pretty much useless. The game engine just downloads a new version from the latest master of the repo, so we could either just use the current hash and insert it into the file whenever a map gets updated, or we go completely crazy and implement some sort of package manager which allows us to map map versions to a hash and a compatible triplea version string, preferably using semver and a backing DB instead of a simple yaml file. (Requires an issue on it's own though)
Your suggestions: What's your opinion on this?
|
2.0
|
Start working on a breaking release - With an increasing amount of workarounds to maintain compatibility, I'd like to suggest to go for a new breaking release 1.10.build
### Prerequisites
- [x] We update the lobby and all the bots to the current stable release (or the build after the stable build due to a bug) and keep it running for a month. If we're not encountering any issues, the release can really be considered stable. Some bots are already on 1.9.0.0.7534. Note that the lobby requires it's db scheme to be changed.
- [ ] We create a branch "stable" which will continue to exist in the meantime, in order to be able to fix any P0 and P1 issues
- [ ] _**BIG IF**_: **IF** everything works out as expected, we ***should*** have a code signing certificate at the end of the month, so we **_could_** release a signed binary as latest stable
### Goals (although not final)
- [ ] Fix all checkstyle violations in serializable classes (and those used by the netcode) to get those out of the way, also the engine would no longer need to check if the vars are m_prefixed for game xmls.
- [ ] Switch to @ssoloff's recently reverted savegame format #2612 drop the support for the current one, also drop support for file extensions svg, tsvg and tsvg.gz
- [ ] Completely change the net code, probably the biggest task, this includes removing deprecated methods and passing Instant objects instead of Date objects, also we should switch to dedicated objects instead of passing Maps wrapping a couple lists in order to drastically improve readability. Also we should start working on a better "mute/ban" system, perhaps reputation based.
- [ ] Split up attachment classes. Currently the Attachment classes are like _really messy_. Suggestion would be to turn for example TechAttachment to some sort of interface which is then implemented by the individual TechAttachments. this would simply split up the attachments into different classes to be much more readable.
- [ ] Switch to a 3 number version system, once and for all (although I'd be fine with 4 digits as well although I'm pertty sure we'd never use the 3rd digit then)
#### Optional
- [ ] Perhaps we should convert the gamexml DOM parser to a SAXParser, not really tied to compatibility unless we want to change the required XML Scheme
- [ ] Come up with a good solution for Map downloading, not really tied to compatibility either unless we want to change the map xml scheme, currently the version defined in the triplea_maps.yaml file is pretty much useless. The game engine just downloads a new version from the latest master of the repo, so we could either just use the current hash and insert it into the file whenever a map gets updated, or we go completely crazy and implement some sort of package manager which allows us to map map versions to a hash and a compatible triplea version string, preferably using semver and a backing DB instead of a simple yaml file. (Requires an issue on it's own though)
Your suggestions: What's your opinion on this?
|
process
|
start working on a breaking release with an increasing amount of workarounds to maintain compatibility i d like to suggest to go for a new breaking release build prerequisites we update the lobby and all the bots to the current stable release or the build after the stable build due to a bug and keep it running for a month if we re not encountering any issues the release can really be considered stable some bots are already on note that the lobby requires it s db scheme to be changed we create a branch stable which will continue to exist in the meantime in order to be able to fix any and issues big if if everything works out as expected we should have a code signing certificate at the end of the month so we could release a signed binary as latest stable goals although not final fix all checkstyle violations in serializable classes and those used by the netcode to get those out of the way also the engine would no longer need to check if the vars are m prefixed for game xmls switch to ssoloff s recently reverted savegame format drop the support for the current one also drop support for file extensions svg tsvg and tsvg gz completely change the net code probably the biggest task this includes removing deprecated methods and passing instant objects instead of date objects also we should switch to dedicated objects instead of passing maps wrapping a couple lists in order to drastically improve readability also we should start working on a better mute ban system perhaps reputation based split up attachment classes currently the attachment classes are like really messy suggestion would be to turn for example techattachment to some sort of interface which is then implemented by the individual techattachments this would simply split up the attachments into different classes to be much more readable switch to a number version system once and for all although i d be fine with digits as well although i m pertty sure we d never use the digit then optional perhaps we should convert the gamexml dom parser to a saxparser not really tied to compatibility unless we want to change the required xml scheme come up with a good solution for map downloading not really tied to compatibility either unless we want to change the map xml scheme currently the version defined in the triplea maps yaml file is pretty much useless the game engine just downloads a new version from the latest master of the repo so we could either just use the current hash and insert it into the file whenever a map gets updated or we go completely crazy and implement some sort of package manager which allows us to map map versions to a hash and a compatible triplea version string preferably using semver and a backing db instead of a simple yaml file requires an issue on it s own though your suggestions what s your opinion on this
| 1
|
12,013
| 14,738,382,973
|
IssuesEvent
|
2021-01-07 04:36:08
|
kdjstudios/SABillingGitlab
|
https://api.github.com/repos/kdjstudios/SABillingGitlab
|
closed
|
SA Hosted - Duplicate account #
|
anc-ops anc-process anp-1 ant-support has attachment
|
In GitLab by @kdjstudios on May 16, 2018, 16:01
**Submitted by:** "Richard Soltoff" <richard.soltoff@answernet.com>
**Helpdesk:** http://www.servicedesk.answernet.com/profiles/ticket/2018-05-16-47359/conversation
**Server:**
**Client/Site:**
**Account:**
**Issue:**
Answer Ally and Colgate Palmolive both have same customer number in SA Hosted.
How is this possible?
How can it be fixed?
[Document3.docx](/uploads/369017b7315e4841b971ac6714a3fc74/Document3.docx)
|
1.0
|
SA Hosted - Duplicate account # - In GitLab by @kdjstudios on May 16, 2018, 16:01
**Submitted by:** "Richard Soltoff" <richard.soltoff@answernet.com>
**Helpdesk:** http://www.servicedesk.answernet.com/profiles/ticket/2018-05-16-47359/conversation
**Server:**
**Client/Site:**
**Account:**
**Issue:**
Answer Ally and Colgate Palmolive both have same customer number in SA Hosted.
How is this possible?
How can it be fixed?
[Document3.docx](/uploads/369017b7315e4841b971ac6714a3fc74/Document3.docx)
|
process
|
sa hosted duplicate account in gitlab by kdjstudios on may submitted by richard soltoff helpdesk server client site account issue answer ally and colgate palmolive both have same customer number in sa hosted how is this possible how can it be fixed uploads docx
| 1
|
18,567
| 24,555,933,555
|
IssuesEvent
|
2022-10-12 15:52:06
|
GoogleCloudPlatform/fda-mystudies
|
https://api.github.com/repos/GoogleCloudPlatform/fda-mystudies
|
closed
|
[Android] [Offline indicator] Enrollment flow > The following offline error message should get displayed when user is offline in the following scenario
|
Bug P1 Android Process: Fixed Process: Tested QA Process: Tested dev
|
**Steps:**
1. Sign up or sign in to the mobile app
2. Click on the study
3. Click on the participate button
4. Turn off the data and observe
**AR:** Offline error message is not getting displayed
**ER:** 'You are offline, You can still use this section but may miss out on the latest content updates' error message should get displayed
[**Note:** Issue should be fixed for Open study closed study and consent update after clicking on review button]
For error message UI please refer to the following attached screen
|
3.0
|
[Android] [Offline indicator] Enrollment flow > The following offline error message should get displayed when user is offline in the following scenario - **Steps:**
1. Sign up or sign in to the mobile app
2. Click on the study
3. Click on the participate button
4. Turn off the data and observe
**AR:** Offline error message is not getting displayed
**ER:** 'You are offline, You can still use this section but may miss out on the latest content updates' error message should get displayed
[**Note:** Issue should be fixed for Open study closed study and consent update after clicking on review button]
For error message UI please refer to the following attached screen
|
process
|
enrollment flow the following offline error message should get displayed when user is offline in the following scenario steps sign up or sign in to the mobile app click on the study click on the participate button turn off the data and observe ar offline error message is not getting displayed er you are offline you can still use this section but may miss out on the latest content updates error message should get displayed for error message ui please refer to the following attached screen
| 1
|
10,624
| 13,439,347,841
|
IssuesEvent
|
2020-09-07 20:48:16
|
timberio/vector
|
https://api.github.com/repos/timberio/vector
|
opened
|
New IP address functions for the remap syntax
|
domain: mapping domain: processing needs: requirements type: feature
|
Working with IP addresses is a common use case when dealing with log data. We should think about functions that help users extract (#1978) parts of an IP address and [process](https://vector.dev/docs/reference/transforms/swimlanes/#field_nameip_cidr_contains). You can see how these needs are already represented in Vector's current features with the aforementioned links.
I'm leaving this issue open so that we can agree on a set of functions that would allow users to:
- [ ] Extract parts of IPv4 and IPv6 addresses.
- [ ] Check if addresses are within larger subsets of addresses ([ex](https://vector.dev/docs/reference/transforms/swimlanes/#field_nameip_cidr_contains)).
|
1.0
|
New IP address functions for the remap syntax - Working with IP addresses is a common use case when dealing with log data. We should think about functions that help users extract (#1978) parts of an IP address and [process](https://vector.dev/docs/reference/transforms/swimlanes/#field_nameip_cidr_contains). You can see how these needs are already represented in Vector's current features with the aforementioned links.
I'm leaving this issue open so that we can agree on a set of functions that would allow users to:
- [ ] Extract parts of IPv4 and IPv6 addresses.
- [ ] Check if addresses are within larger subsets of addresses ([ex](https://vector.dev/docs/reference/transforms/swimlanes/#field_nameip_cidr_contains)).
|
process
|
new ip address functions for the remap syntax working with ip addresses is a common use case when dealing with log data we should think about functions that help users extract parts of an ip address and you can see how these needs are already represented in vector s current features with the aforementioned links i m leaving this issue open so that we can agree on a set of functions that would allow users to extract parts of and addresses check if addresses are within larger subsets of addresses
| 1
|
854
| 3,316,238,438
|
IssuesEvent
|
2015-11-06 16:05:04
|
hammerlab/pileup.js
|
https://api.github.com/repos/hammerlab/pileup.js
|
closed
|
Add types for props and state
|
process
|
It should be possible to specify types for `props` and `state` using Flow:
https://github.com/facebook/flow/blob/28f01d1800618ad9f679d7a394c1feff1edf3a16/lib/react.js#L16
This would greatly improve type safety for visualization tracks.
|
1.0
|
Add types for props and state - It should be possible to specify types for `props` and `state` using Flow:
https://github.com/facebook/flow/blob/28f01d1800618ad9f679d7a394c1feff1edf3a16/lib/react.js#L16
This would greatly improve type safety for visualization tracks.
|
process
|
add types for props and state it should be possible to specify types for props and state using flow this would greatly improve type safety for visualization tracks
| 1
|
156,906
| 12,340,821,012
|
IssuesEvent
|
2020-05-14 20:39:35
|
cockroachdb/cockroach
|
https://api.github.com/repos/cockroachdb/cockroach
|
closed
|
roachtest: schemachange/mixed/tpcc failed
|
C-test-failure O-roachtest O-robot branch-45179 release-blocker
|
[(roachtest).schemachange/mixed/tpcc failed](https://teamcity.cockroachdb.com/viewLog.html?buildId=1760695&tab=buildLog) on [45179@12734324037949d8a864b8150ca6e7861d49369c](https://github.com/cockroachdb/cockroach/commits/12734324037949d8a864b8150ca6e7861d49369c):
```
The test failed on branch=45179, cloud=gce:
test artifacts and logs in: /home/agent/work/.go/src/github.com/cockroachdb/cockroach/artifacts/schemachange/mixed/tpcc/run_1
schemachange.go:476,schemachange.go:439,cluster.go:2344,errgroup.go:57: pq: log-job: concurrent txn use detected. ba: [txn: fd34d738], Get [/Table/3/1/15/2/1,/Min)
cluster.go:2368,tpcc.go:168,schemachange.go:416,test_runner.go:741: error with attached stack trace:
main.(*monitor).WaitE
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2356
main.(*monitor).Wait
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2364
main.runTPCC
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/tpcc.go:168
main.makeMixedSchemaChanges.func1
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/schemachange.go:416
main.(*testRunner).runTest.func2
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/test_runner.go:741
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- monitor failure:
- error with attached stack trace:
main.(*monitor).wait.func2
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2412
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- monitor task failed:
- error with attached stack trace:
main.init
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2309
runtime.doInit
/usr/local/go/src/runtime/proc.go:5222
runtime.main
/usr/local/go/src/runtime/proc.go:190
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- Goexit() was called
```
<details><summary>More</summary><p>
Artifacts: [/schemachange/mixed/tpcc](https://teamcity.cockroachdb.com/viewLog.html?buildId=1760695&tab=artifacts#/schemachange/mixed/tpcc)
Related:
- #45070 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-44941](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-44941) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44955 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202002062136_v19.1.8](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202002062136_v19.1.8) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44862 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-release-19.1](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-release-19.1) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44585 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202001302015_v19.2.3](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202001302015_v19.2.3) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44528 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202001281357_v19.2.3](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202001281357_v19.2.3) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44301 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-release-19.2](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-release-19.2) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #40935 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-master](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-master)
[See this test on roachdash](https://roachdash.crdb.dev/?filter=status%3Aopen+t%3A.%2Aschemachange%2Fmixed%2Ftpcc.%2A&sort=title&restgroup=false&display=lastcommented+project)
<sub>powered by [pkg/cmd/internal/issues](https://github.com/cockroachdb/cockroach/tree/master/pkg/cmd/internal/issues)</sub></p></details>
|
2.0
|
roachtest: schemachange/mixed/tpcc failed - [(roachtest).schemachange/mixed/tpcc failed](https://teamcity.cockroachdb.com/viewLog.html?buildId=1760695&tab=buildLog) on [45179@12734324037949d8a864b8150ca6e7861d49369c](https://github.com/cockroachdb/cockroach/commits/12734324037949d8a864b8150ca6e7861d49369c):
```
The test failed on branch=45179, cloud=gce:
test artifacts and logs in: /home/agent/work/.go/src/github.com/cockroachdb/cockroach/artifacts/schemachange/mixed/tpcc/run_1
schemachange.go:476,schemachange.go:439,cluster.go:2344,errgroup.go:57: pq: log-job: concurrent txn use detected. ba: [txn: fd34d738], Get [/Table/3/1/15/2/1,/Min)
cluster.go:2368,tpcc.go:168,schemachange.go:416,test_runner.go:741: error with attached stack trace:
main.(*monitor).WaitE
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2356
main.(*monitor).Wait
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2364
main.runTPCC
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/tpcc.go:168
main.makeMixedSchemaChanges.func1
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/schemachange.go:416
main.(*testRunner).runTest.func2
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/test_runner.go:741
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- monitor failure:
- error with attached stack trace:
main.(*monitor).wait.func2
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2412
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- monitor task failed:
- error with attached stack trace:
main.init
/home/agent/work/.go/src/github.com/cockroachdb/cockroach/pkg/cmd/roachtest/cluster.go:2309
runtime.doInit
/usr/local/go/src/runtime/proc.go:5222
runtime.main
/usr/local/go/src/runtime/proc.go:190
runtime.goexit
/usr/local/go/src/runtime/asm_amd64.s:1357
- Goexit() was called
```
<details><summary>More</summary><p>
Artifacts: [/schemachange/mixed/tpcc](https://teamcity.cockroachdb.com/viewLog.html?buildId=1760695&tab=artifacts#/schemachange/mixed/tpcc)
Related:
- #45070 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-44941](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-44941) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44955 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202002062136_v19.1.8](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202002062136_v19.1.8) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44862 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-release-19.1](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-release-19.1) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44585 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202001302015_v19.2.3](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202001302015_v19.2.3) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44528 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-provisional_202001281357_v19.2.3](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-provisional_202001281357_v19.2.3) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #44301 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-release-19.2](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-release-19.2) [release-blocker](https://api.github.com/repos/cockroachdb/cockroach/labels/release-blocker)
- #40935 roachtest: schemachange/mixed/tpcc failed [C-test-failure](https://api.github.com/repos/cockroachdb/cockroach/labels/C-test-failure) [O-roachtest](https://api.github.com/repos/cockroachdb/cockroach/labels/O-roachtest) [O-robot](https://api.github.com/repos/cockroachdb/cockroach/labels/O-robot) [branch-master](https://api.github.com/repos/cockroachdb/cockroach/labels/branch-master)
[See this test on roachdash](https://roachdash.crdb.dev/?filter=status%3Aopen+t%3A.%2Aschemachange%2Fmixed%2Ftpcc.%2A&sort=title&restgroup=false&display=lastcommented+project)
<sub>powered by [pkg/cmd/internal/issues](https://github.com/cockroachdb/cockroach/tree/master/pkg/cmd/internal/issues)</sub></p></details>
|
non_process
|
roachtest schemachange mixed tpcc failed on the test failed on branch cloud gce test artifacts and logs in home agent work go src github com cockroachdb cockroach artifacts schemachange mixed tpcc run schemachange go schemachange go cluster go errgroup go pq log job concurrent txn use detected ba get table min cluster go tpcc go schemachange go test runner go error with attached stack trace main monitor waite home agent work go src github com cockroachdb cockroach pkg cmd roachtest cluster go main monitor wait home agent work go src github com cockroachdb cockroach pkg cmd roachtest cluster go main runtpcc home agent work go src github com cockroachdb cockroach pkg cmd roachtest tpcc go main makemixedschemachanges home agent work go src github com cockroachdb cockroach pkg cmd roachtest schemachange go main testrunner runtest home agent work go src github com cockroachdb cockroach pkg cmd roachtest test runner go runtime goexit usr local go src runtime asm s monitor failure error with attached stack trace main monitor wait home agent work go src github com cockroachdb cockroach pkg cmd roachtest cluster go runtime goexit usr local go src runtime asm s monitor task failed error with attached stack trace main init home agent work go src github com cockroachdb cockroach pkg cmd roachtest cluster go runtime doinit usr local go src runtime proc go runtime main usr local go src runtime proc go runtime goexit usr local go src runtime asm s goexit was called more artifacts related roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed roachtest schemachange mixed tpcc failed powered by
| 0
|
167,670
| 20,726,276,322
|
IssuesEvent
|
2022-03-14 02:31:47
|
MythicDrops/mythicdrops-gradle-plugin
|
https://api.github.com/repos/MythicDrops/mythicdrops-gradle-plugin
|
opened
|
CVE-2020-36518 (Medium) detected in jackson-databind-2.13.0.jar, jackson-databind-2.12.4.jar
|
security vulnerability
|
## CVE-2020-36518 - Medium Severity Vulnerability
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>jackson-databind-2.13.0.jar</b>, <b>jackson-databind-2.12.4.jar</b></p></summary>
<p>
<details><summary><b>jackson-databind-2.13.0.jar</b></p></summary>
<p>General data-binding functionality for Jackson: works on core streaming API</p>
<p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p>
<p>Path to dependency file: /build.gradle.kts</p>
<p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.13.0/889672a1721d6d85b2834fcd29d3fda92c8c8891/jackson-databind-2.13.0.jar</p>
<p>
Dependency Hierarchy:
- github-api-1.301.jar (Root Library)
- :x: **jackson-databind-2.13.0.jar** (Vulnerable Library)
</details>
<details><summary><b>jackson-databind-2.12.4.jar</b></p></summary>
<p>General data-binding functionality for Jackson: works on core streaming API</p>
<p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p>
<p>Path to dependency file: /build.gradle.kts</p>
<p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.12.4/69206e02e6a696034f06a59d3ddbfbba5a4cd81/jackson-databind-2.12.4.jar</p>
<p>
Dependency Hierarchy:
- dokka-core-1.6.10.jar (Root Library)
- jackson-dataformat-xml-2.12.4.jar
- :x: **jackson-databind-2.12.4.jar** (Vulnerable Library)
</details>
<p>Found in base branch: <b>main</b></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary>
<p>
jackson-databind before 2.13.0 allows a Java StackOverflow exception and denial of service via a large depth of nested objects.
WhiteSource Note: After conducting further research, WhiteSource has determined that all versions of com.fasterxml.jackson.core:jackson-databind up to version 2.13.2 are vulnerable to CVE-2020-36518.
<p>Publish Date: 2022-03-11
<p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36518>CVE-2020-36518</a></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>5.5</b>)</summary>
<p>
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: Required
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: High
</p>
For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>.
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary>
<p>
<p>Type: Upgrade version</p>
<p>Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2020-36518">https://nvd.nist.gov/vuln/detail/CVE-2020-36518</a></p>
<p>Release Date: 2022-03-11</p>
<p>Fix Resolution: jackson-databind-2.10 - 2.10.1;com.fasterxml.jackson.core.jackson-databind - 2.6.2.v20161117-2150</p>
</p>
</details>
<p></p>
***
Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
|
True
|
CVE-2020-36518 (Medium) detected in jackson-databind-2.13.0.jar, jackson-databind-2.12.4.jar - ## CVE-2020-36518 - Medium Severity Vulnerability
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>jackson-databind-2.13.0.jar</b>, <b>jackson-databind-2.12.4.jar</b></p></summary>
<p>
<details><summary><b>jackson-databind-2.13.0.jar</b></p></summary>
<p>General data-binding functionality for Jackson: works on core streaming API</p>
<p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p>
<p>Path to dependency file: /build.gradle.kts</p>
<p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.13.0/889672a1721d6d85b2834fcd29d3fda92c8c8891/jackson-databind-2.13.0.jar</p>
<p>
Dependency Hierarchy:
- github-api-1.301.jar (Root Library)
- :x: **jackson-databind-2.13.0.jar** (Vulnerable Library)
</details>
<details><summary><b>jackson-databind-2.12.4.jar</b></p></summary>
<p>General data-binding functionality for Jackson: works on core streaming API</p>
<p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p>
<p>Path to dependency file: /build.gradle.kts</p>
<p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.12.4/69206e02e6a696034f06a59d3ddbfbba5a4cd81/jackson-databind-2.12.4.jar</p>
<p>
Dependency Hierarchy:
- dokka-core-1.6.10.jar (Root Library)
- jackson-dataformat-xml-2.12.4.jar
- :x: **jackson-databind-2.12.4.jar** (Vulnerable Library)
</details>
<p>Found in base branch: <b>main</b></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary>
<p>
jackson-databind before 2.13.0 allows a Java StackOverflow exception and denial of service via a large depth of nested objects.
WhiteSource Note: After conducting further research, WhiteSource has determined that all versions of com.fasterxml.jackson.core:jackson-databind up to version 2.13.2 are vulnerable to CVE-2020-36518.
<p>Publish Date: 2022-03-11
<p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36518>CVE-2020-36518</a></p>
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>5.5</b>)</summary>
<p>
Base Score Metrics:
- Exploitability Metrics:
- Attack Vector: Local
- Attack Complexity: Low
- Privileges Required: None
- User Interaction: Required
- Scope: Unchanged
- Impact Metrics:
- Confidentiality Impact: None
- Integrity Impact: None
- Availability Impact: High
</p>
For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>.
</p>
</details>
<p></p>
<details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary>
<p>
<p>Type: Upgrade version</p>
<p>Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2020-36518">https://nvd.nist.gov/vuln/detail/CVE-2020-36518</a></p>
<p>Release Date: 2022-03-11</p>
<p>Fix Resolution: jackson-databind-2.10 - 2.10.1;com.fasterxml.jackson.core.jackson-databind - 2.6.2.v20161117-2150</p>
</p>
</details>
<p></p>
***
Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)
|
non_process
|
cve medium detected in jackson databind jar jackson databind jar cve medium severity vulnerability vulnerable libraries jackson databind jar jackson databind jar jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file build gradle kts path to vulnerable library home wss scanner gradle caches modules files com fasterxml jackson core jackson databind jackson databind jar dependency hierarchy github api jar root library x jackson databind jar vulnerable library jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file build gradle kts path to vulnerable library home wss scanner gradle caches modules files com fasterxml jackson core jackson databind jackson databind jar dependency hierarchy dokka core jar root library jackson dataformat xml jar x jackson databind jar vulnerable library found in base branch main vulnerability details jackson databind before allows a java stackoverflow exception and denial of service via a large depth of nested objects whitesource note after conducting further research whitesource has determined that all versions of com fasterxml jackson core jackson databind up to version are vulnerable to cve publish date url a href cvss score details base score metrics exploitability metrics attack vector local attack complexity low privileges required none user interaction required scope unchanged impact metrics confidentiality impact none integrity impact none availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution jackson databind com fasterxml jackson core jackson databind step up your open source security game with whitesource
| 0
|
89,491
| 10,601,805,513
|
IssuesEvent
|
2019-10-10 13:05:21
|
aarhusstadsarkiv/digital-archive
|
https://api.github.com/repos/aarhusstadsarkiv/digital-archive
|
opened
|
Add autodoc to Sphinx config
|
documentation
|
<!--
Hi! :)
Documentation issues encompass improvements and/or additions to our current documentation. If there is an issue, please provide a direct link.
The markdown syntax for adding links to text is `[text](url)`
-->
See https://medium.com/@eikonomega/getting-started-with-sphinx-autodoc-part-1-2cebbbca5365
|
1.0
|
Add autodoc to Sphinx config - <!--
Hi! :)
Documentation issues encompass improvements and/or additions to our current documentation. If there is an issue, please provide a direct link.
The markdown syntax for adding links to text is `[text](url)`
-->
See https://medium.com/@eikonomega/getting-started-with-sphinx-autodoc-part-1-2cebbbca5365
|
non_process
|
add autodoc to sphinx config hi documentation issues encompass improvements and or additions to our current documentation if there is an issue please provide a direct link the markdown syntax for adding links to text is url see
| 0
|
32,727
| 15,598,373,087
|
IssuesEvent
|
2021-03-18 18:01:12
|
rusefi/rusefi
|
https://api.github.com/repos/rusefi/rusefi
|
closed
|
Measurement of CPU load
|
Performance
|
How busy is the CPU?
Are interrupts regularly waiting on one another?
Is our generous use of global locking causing issues?
|
True
|
Measurement of CPU load - How busy is the CPU?
Are interrupts regularly waiting on one another?
Is our generous use of global locking causing issues?
|
non_process
|
measurement of cpu load how busy is the cpu are interrupts regularly waiting on one another is our generous use of global locking causing issues
| 0
|
49,935
| 7,547,334,088
|
IssuesEvent
|
2018-04-18 07:38:42
|
dvajs/dva
|
https://api.github.com/repos/dvajs/dva
|
closed
|
GettingStarts demo not work at Dva2.x?
|
documentation
|
<!--
Have a question? Ask us on [StackOverflow](https://stackoverflow.com/questions/ask?tags=dvajs).
Found a bug? Please fill out the sections below.
Give enough details and be polite when writing in text. Thanks!
-->
<!--
有问题？请在 [SegmentFault](https://segmentfault.com/t/dva.js) 上提问或者[加微信群互助](https://github.com/dvajs/dva/issues/1150)。
发现 bug？请填写以下内容，尽量详细。
-->
#### Code to reproduce the issue: (请提供可复现的代码或者步骤)
Have tried the same code with gettingstarts.md
#### Expected behavior: (预期的正常效果)
Add dispatched +1 and async minus dispatched to -1 after 1 second
#### Actual behavior: (实际效果)
In redux devTools only see add@start,add@end and minus,but it dosen't add 1 when click the plus button
#### Versions of packages used: (哪个库的哪个版本出现的问题)
dva@2.0.4
|
1.0
|
GettingStarts demo not work at Dva2.x? - <!--
Have a question? Ask us on [StackOverflow](https://stackoverflow.com/questions/ask?tags=dvajs).
Found a bug? Please fill out the sections below.
Give enough details and be polite when writing in text. Thanks!
-->
<!--
有问题？请在 [SegmentFault](https://segmentfault.com/t/dva.js) 上提问或者[加微信群互助](https://github.com/dvajs/dva/issues/1150)。
发现 bug？请填写以下内容，尽量详细。
-->
#### Code to reproduce the issue: (请提供可复现的代码或者步骤)
Have tried the same code with gettingstarts.md
#### Expected behavior: (预期的正常效果)
Add dispatched +1 and async minus dispatched to -1 after 1 second
#### Actual behavior: (实际效果)
In redux devTools only see add@start,add@end and minus,but it dosen't add 1 when click the plus button
#### Versions of packages used: (哪个库的哪个版本出现的问题)
dva@2.0.4
|
non_process
|
gettingstarts demo not work at x have a question ask us on found a bug please fill out the sections below give enough details and be polite when writing in text thanks 有问题？请在 上提问或者 发现 bug？请填写以下内容，尽量详细。 code to reproduce the issue 请提供可复现的代码或者步骤 have tried the same code with gettingstarts md expected behavior 预期的正常效果 add dispatched and async minus dispatched to after second actual behavior 实际效果 in redux devtools only see add start add end and minus but it dosen t add when click the plus button versions of packages used 哪个库的哪个版本出现的问题 dva
| 0
|
76
| 2,543,638,138
|
IssuesEvent
|
2015-01-29 00:22:00
|
tessel/runtime
|
https://api.github.com/repos/tessel/runtime
|
opened
|
net.isIP accepts hostnames that start with IP-like addresses
|
incompatibility-net incompatibility-node
|
```js
console.log("result:", require('net').isIP("192.168.4.114.xip.io"));
```
node.js:
> result: 0
colony:
> result: 4
|
True
|
net.isIP accepts hostnames that start with IP-like addresses - ```js
console.log("result:", require('net').isIP("192.168.4.114.xip.io"));
```
node.js:
> result: 0
colony:
> result: 4
|
non_process
|
net isip accepts hostnames that start with ip like addresses js console log result require net isip xip io node js result colony result
| 0
|
1,244
| 3,783,475,099
|
IssuesEvent
|
2016-03-19 05:15:18
|
niranjv/ucscamsms2015
|
https://api.github.com/repos/niranjv/ucscamsms2015
|
closed
|
Create code snippets in plot.R for plots & table used in Chapters 2 & 3
|
doc: 2-Single Processor doc: 3-Multi Processor
|
* Code used to generate plots and tables in Chapters 2 & 3 will be displayed in the Appendix
* Code must be added to plot.R is labelled chunks
* This code will be executed in the appropriate place in the main text and displayed in the Appendix
|
2.0
|
Create code snippets in plot.R for plots & table used in Chapters 2 & 3 - * Code used to generate plots and tables in Chapters 2 & 3 will be displayed in the Appendix
* Code must be added to plot.R is labelled chunks
* This code will be executed in the appropriate place in the main text and displayed in the Appendix
|
process
|
create code snippets in plot r for plots table used in chapters code used to generate plots and tables in chapters will be displayed in the appendix code must be added to plot r is labelled chunks this code will be executed in the appropriate place in the main text and displayed in the appendix
| 1
|
20,214
| 26,806,330,143
|
IssuesEvent
|
2023-02-01 18:35:39
|
mmattDonk/AI-TTS-Donations
|
https://api.github.com/repos/mmattDonk/AI-TTS-Donations
|
closed
|
Self voice hosting
|
💫 feature_request @solrock/processor processor Low priority
|
At the moment the project spins around being able to use the huge amount of uberduck.ai voices, but it would be really useful to be able to self host our own tacotron2 voices for a faster synthesis
|
2.0
|
Self voice hosting - At the moment the project spins around being able to use the huge amount of uberduck.ai voices, but it would be really useful to be able to self host our own tacotron2 voices for a faster synthesis
|
process
|
self voice hosting at the moment the project spins around being able to use the huge amount of uberduck ai voices but it would be really useful to be able to self host our own voices for a faster synthesis
| 1
|
4,696
| 7,531,315,653
|
IssuesEvent
|
2018-04-15 03:59:26
|
pelias/wof-pip-service
|
https://api.github.com/repos/pelias/wof-pip-service
|
closed
|
Gracefully handle git-lfs files
|
Q1-2017 processed
|
While the Pelias installation docs now suggest using the whosonfirst [downloader script](https://github.com/pelias/whosonfirst/blob/master/download_data.js), some users will continue to clone whosonfirst data using git (either because they have a good reason or because the docs used to tell them to do it that way). One confusing aspect of the git clone approach is that without git-lfs installed, many files in a clone from github are incomplete, and only contain the tiny bit of metadata needed for git-lfs to fetch them. This makes them essentially corrupted in the eyes of our importers..
The importer already does this in some places, but not others. This module handles errors when parsing JSON, but the warning isn't that friendly. It also doesn't handle invalid meta CSV files.
All files read from whosonfirst data first have to be checked to see if they are a git-lfs file, and in that case a friendly, actionable warning should be emitted, and the importer should stop.
Note, this is related to, but _not_ a duplicate of https://github.com/pelias/whosonfirst/issues/170
|
1.0
|
Gracefully handle git-lfs files - While the Pelias installation docs now suggest using the whosonfirst [downloader script](https://github.com/pelias/whosonfirst/blob/master/download_data.js), some users will continue to clone whosonfirst data using git (either because they have a good reason or because the docs used to tell them to do it that way). One confusing aspect of the git clone approach is that without git-lfs installed, many files in a clone from github are incomplete, and only contain the tiny bit of metadata needed for git-lfs to fetch them. This makes them essentially corrupted in the eyes of our importers..
The importer already does this in some places, but not others. This module handles errors when parsing JSON, but the warning isn't that friendly. It also doesn't handle invalid meta CSV files.
All files read from whosonfirst data first have to be checked to see if they are a git-lfs file, and in that case a friendly, actionable warning should be emitted, and the importer should stop.
Note, this is related to, but _not_ a duplicate of https://github.com/pelias/whosonfirst/issues/170
|
process
|
gracefully handle git lfs files while the pelias installation docs now suggest using the whosonfirst some users will continue to clone whosonfirst data using git either because they have a good reason or because the docs used to tell them to do it that way one confusing aspect of the git clone approach is that without git lfs installed many files in a clone from github are incomplete and only contain the tiny bit of metadata needed for git lfs to fetch them this makes them essentially corrupted in the eyes of our importers the importer already does this in some places but not others this module handles errors when parsing json but the warning isn t that friendly it also doesn t handle invalid meta csv files all files read from whosonfirst data first have to be checked to see if they are a git lfs file and in that case a friendly actionable warning should be emitted and the importer should stop note this is related to but not a duplicate of
| 1
|
4,661
| 7,496,826,982
|
IssuesEvent
|
2018-04-08 13:41:47
|
allinurl/goaccess
|
https://api.github.com/repos/allinurl/goaccess
|
closed
|
goaccess 1.2 - Apache 2.4 Logs - Token 'localhost' doesn't match specifier '%h'
|
log-processing log/date/time format question
|
Sample apache 2.4 log (non-virtual host);
```
157.55.39.22 - - [27/Mar/2018:14:55:42 -0400] "GET /robots.txt HTTP/1.1" 302 218 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)"
157.55.39.22 - - [27/Mar/2018:14:55:42 -0400] "GET /robots.txt HTTP/1.1" 200 157
localhost - - [27/Mar/2018:14:56:08 -0400] "GET /server-status?auto HTTP/1.1" 406 - "-" "Python-urllib/2.7"
```
Installed goaccess 1.2 with no issues.
Set goaccess.conf for apache 2.4;
```
-NCSA Combined Log Format %h %^[%d:%t %^] "%r" %s %b "%R" "%u"
-Date Format - %d/%b/%Y
-Time Format - %H:%M:%S
```
Result;
Token 'localhost' doesn't match specifier '%h'
|
1.0
|
goaccess 1.2 - Apache 2.4 Logs - Token 'localhost' doesn't match specifier '%h' - Sample apache 2.4 log (non-virtual host);
```
157.55.39.22 - - [27/Mar/2018:14:55:42 -0400] "GET /robots.txt HTTP/1.1" 302 218 "-" "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)"
157.55.39.22 - - [27/Mar/2018:14:55:42 -0400] "GET /robots.txt HTTP/1.1" 200 157
localhost - - [27/Mar/2018:14:56:08 -0400] "GET /server-status?auto HTTP/1.1" 406 - "-" "Python-urllib/2.7"
```
Installed goaccess 1.2 with no issues.
Set goaccess.conf for apache 2.4;
```
-NCSA Combined Log Format %h %^[%d:%t %^] "%r" %s %b "%R" "%u"
-Date Format - %d/%b/%Y
-Time Format - %H:%M:%S
```
Result;
Token 'localhost' doesn't match specifier '%h'
|
process
|
goaccess apache logs token localhost doesn t match specifier h sample apache log non virtual host get robots txt http mozilla compatible bingbot get robots txt http localhost get server status auto http python urllib installed goaccess with no issues set goaccess conf for apache ncsa combined log format h r s b r u date format d b y time format h m s result token localhost doesn t match specifier h
| 1
|
127,967
| 5,041,364,385
|
IssuesEvent
|
2016-12-19 10:04:52
|
futureworktechnologies2/Ahmed-Restaurant-project
|
https://api.github.com/repos/futureworktechnologies2/Ahmed-Restaurant-project
|
opened
|
When trying to create a THOAG account with email the Keyboard is blocking the view. The Keyboard moves down only when you enter something in the password and repeat password in the fields.
|
High priority
|
When trying to create a THOAG account with email the Keyboard is blocking the view. The Keyboard moves down only when you enter something in the password and repeat password in the fields.
|
1.0
|
When trying to create a THOAG account with email the Keyboard is blocking the view. The Keyboard moves down only when you enter something in the password and repeat password in the fields. - When trying to create a THOAG account with email the Keyboard is blocking the view. The Keyboard moves down only when you enter something in the password and repeat password in the fields.
|
non_process
|
when trying to create a thoag account with email the keyboard is blocking the view the keyboard moves down only when you enter something in the password and repeat password in the fields when trying to create a thoag account with email the keyboard is blocking the view the keyboard moves down only when you enter something in the password and repeat password in the fields
| 0
|
20,944
| 6,122,725,440
|
IssuesEvent
|
2017-06-23 01:03:48
|
ganeti/ganeti
|
https://api.github.com/repos/ganeti/ganeti
|
opened
|
confd seems to have problems loading the config if an instance's NIC has a network
|
imported_from_google_code Status:Released Type-Defect
|
Originally reported of Google Code with ID 365.
```
0. create a network and associate it with the current node group
1. create an instance nic with a network
(eg. --net 0:network=name)
2. check confd log:
2013-02-07 14:09:43,609853000000 UTC: ganeti-confd pid=21813 ERROR Failed to load config: parsing configuration: key 'instances': key 'ultrt-gnt0.hot.corp.google.com': key 'nics': key 'network': Unable to read JSObject
network should be and is a string (currently the network name, in the future perhaps the uuid). What is confd expecting there?
```
Originally added on 2013-02-07 14:17:18 +0000 UTC.
|
1.0
|
confd seems to have problems loading the config if an instance's NIC has a network - Originally reported of Google Code with ID 365.
```
0. create a network and associate it with the current node group
1. create an instance nic with a network
(eg. --net 0:network=name)
2. check confd log:
2013-02-07 14:09:43,609853000000 UTC: ganeti-confd pid=21813 ERROR Failed to load config: parsing configuration: key 'instances': key 'ultrt-gnt0.hot.corp.google.com': key 'nics': key 'network': Unable to read JSObject
network should be and is a string (currently the network name, in the future perhaps the uuid). What is confd expecting there?
```
Originally added on 2013-02-07 14:17:18 +0000 UTC.
|
non_process
|
confd seems to have problems loading the config if an instance s nic has a network originally reported of google code with id create a network and associate it with the current node group create an instance nic with a network eg net network name check confd log utc ganeti confd pid error failed to load config parsing configuration key instances key ultrt hot corp google com key nics key network unable to read jsobject network should be and is a string currently the network name in the future perhaps the uuid what is confd expecting there originally added on utc
| 0
|
13,595
| 16,167,425,394
|
IssuesEvent
|
2021-05-01 19:40:44
|
ooi-data/RS03AXPS-SF03A-4A-NUTNRA301-streamed-nutnr_a_dark_sample
|
https://api.github.com/repos/ooi-data/RS03AXPS-SF03A-4A-NUTNRA301-streamed-nutnr_a_dark_sample
|
opened
|
🛑 Processing failed: ValueError
|
process
|
## Overview
`ValueError` found in `processing_task` task during run ended on 2021-05-01T19:40:43.658360.
## Details
Flow name: `RS03AXPS-SF03A-4A-NUTNRA301-streamed-nutnr_a_dark_sample`
Task name: `processing_task`
Error type: `ValueError`
Error message: Target chunk memory (9903349760) exceeds max_mem (2000000000)
<details>
<summary>Traceback</summary>
```
Traceback (most recent call last):
File "/usr/share/miniconda/envs/harvester/lib/python3.8/site-packages/ooi_harvester/processor/pipeline.py", line 71, in processing_task
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/ooi_harvester/processor/__init__.py", line 296, in finalize_zarr
array_plan = rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 289, in rechunk
copy_spec, intermediate, target = _setup_rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 400, in _setup_rechunk
copy_spec = _setup_array_rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 475, in _setup_array_rechunk
read_chunks, int_chunks, write_chunks = rechunking_plan(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/algorithm.py", line 124, in rechunking_plan
raise ValueError(
ValueError: Target chunk memory (9903349760) exceeds max_mem (2000000000)
```
</details>
|
1.0
|
🛑 Processing failed: ValueError - ## Overview
`ValueError` found in `processing_task` task during run ended on 2021-05-01T19:40:43.658360.
## Details
Flow name: `RS03AXPS-SF03A-4A-NUTNRA301-streamed-nutnr_a_dark_sample`
Task name: `processing_task`
Error type: `ValueError`
Error message: Target chunk memory (9903349760) exceeds max_mem (2000000000)
<details>
<summary>Traceback</summary>
```
Traceback (most recent call last):
File "/usr/share/miniconda/envs/harvester/lib/python3.8/site-packages/ooi_harvester/processor/pipeline.py", line 71, in processing_task
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/ooi_harvester/processor/__init__.py", line 296, in finalize_zarr
array_plan = rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 289, in rechunk
copy_spec, intermediate, target = _setup_rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 400, in _setup_rechunk
copy_spec = _setup_array_rechunk(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/api.py", line 475, in _setup_array_rechunk
read_chunks, int_chunks, write_chunks = rechunking_plan(
File "/srv/conda/envs/notebook/lib/python3.8/site-packages/rechunker/algorithm.py", line 124, in rechunking_plan
raise ValueError(
ValueError: Target chunk memory (9903349760) exceeds max_mem (2000000000)
```
</details>
|
process
|
🛑 processing failed valueerror overview valueerror found in processing task task during run ended on details flow name streamed nutnr a dark sample task name processing task error type valueerror error message target chunk memory exceeds max mem traceback traceback most recent call last file usr share miniconda envs harvester lib site packages ooi harvester processor pipeline py line in processing task file srv conda envs notebook lib site packages ooi harvester processor init py line in finalize zarr array plan rechunk file srv conda envs notebook lib site packages rechunker api py line in rechunk copy spec intermediate target setup rechunk file srv conda envs notebook lib site packages rechunker api py line in setup rechunk copy spec setup array rechunk file srv conda envs notebook lib site packages rechunker api py line in setup array rechunk read chunks int chunks write chunks rechunking plan file srv conda envs notebook lib site packages rechunker algorithm py line in rechunking plan raise valueerror valueerror target chunk memory exceeds max mem
| 1
|
16,056
| 20,198,494,169
|
IssuesEvent
|
2022-02-11 13:00:47
|
didi/mpx
|
https://api.github.com/repos/didi/mpx
|
closed
|
[Bug report] 使用第三方库vant 时，在webpack watch 时出错
|
processing
|
最新mpx init的项目中，使用**vantUI** 作为第三方库
npm run watch 时出现一下问题
```
Watchpack Error (initial scan): Error: ENOTDIR: not a directory,
```
不知道那里配置出问题了？
|
1.0
|
[Bug report] 使用第三方库vant 时，在webpack watch 时出错 - 最新mpx init的项目中，使用**vantUI** 作为第三方库
npm run watch 时出现一下问题
```
Watchpack Error (initial scan): Error: ENOTDIR: not a directory,
```
不知道那里配置出问题了？
|
process
|
使用第三方库vant 时，在webpack watch 时出错 最新mpx init的项目中，使用 vantui 作为第三方库 npm run watch 时出现一下问题 watchpack error initial scan error enotdir not a directory 不知道那里配置出问题了？
| 1
|
118,293
| 17,579,049,026
|
IssuesEvent
|
2021-08-16 03:19:38
|
elastic/elasticsearch
|
https://api.github.com/repos/elastic/elasticsearch
|
closed
|
Add KeyUsage, ExtendedKeyUsage, CipherSuite & Protocol to SSL diagnostics
|
>enhancement good first issue help wanted :Security/TLS Team:Security
|
Per https://discuss.elastic.co/t/ldaps-and-chain-of-certificates/250724 it's possible to get an SSL failure & diagnostic when the cipher requires certain key usage that is not permitted by the certificate.
To assist in such diagnostics, it would be of assistance to print out the ceritficate's KeyUsage and the session's Cipher suite in the message.
While we're doing that, the cert's ExtendedKeyUsage and session Protocol are probably worth including as well.
|
True
|
Add KeyUsage, ExtendedKeyUsage, CipherSuite & Protocol to SSL diagnostics - Per https://discuss.elastic.co/t/ldaps-and-chain-of-certificates/250724 it's possible to get an SSL failure & diagnostic when the cipher requires certain key usage that is not permitted by the certificate.
To assist in such diagnostics, it would be of assistance to print out the ceritficate's KeyUsage and the session's Cipher suite in the message.
While we're doing that, the cert's ExtendedKeyUsage and session Protocol are probably worth including as well.
|
non_process
|
add keyusage extendedkeyusage ciphersuite protocol to ssl diagnostics per it s possible to get an ssl failure diagnostic when the cipher requires certain key usage that is not permitted by the certificate to assist in such diagnostics it would be of assistance to print out the ceritficate s keyusage and the session s cipher suite in the message while we re doing that the cert s extendedkeyusage and session protocol are probably worth including as well
| 0
|
67,216
| 12,888,340,286
|
IssuesEvent
|
2020-07-13 12:52:21
|
Regalis11/Barotrauma
|
https://api.github.com/repos/Regalis11/Barotrauma
|
closed
|
Radiation Sickness Bug and Mission Bug
|
Bug Code
|
- [x] I have searched the issue tracker to check if the issue has already been reported.
**Description**
The radiation sickness bug happened when the radiator blew up but I only had the burn affliction constantly on me and no display of the radiation sickness. Even after fully healing all the burn affected areas, the affliction would return and never displayed the radiation sickness affliction even with a doctor looking at it.
The mission bug happened when my crew was doing a "eliminate a crawler swarm" mission. We killed all but one crawler from the swarm and it fled. When the crew followed to the location of the last crawler, it did not show in the game even though the scanner showed it was suppose to be there.
**Steps To Reproduce**
Possibly do a swarm mission and kill all but one creature from the swarm. No clue on how to replicate the radiation sickness bug
**Version**
Version: v0.9.9.0
Operating System: Windows
**Additional information**
We were using the "Into the Abyss" mod/
|
1.0
|
Radiation Sickness Bug and Mission Bug - - [x] I have searched the issue tracker to check if the issue has already been reported.
**Description**
The radiation sickness bug happened when the radiator blew up but I only had the burn affliction constantly on me and no display of the radiation sickness. Even after fully healing all the burn affected areas, the affliction would return and never displayed the radiation sickness affliction even with a doctor looking at it.
The mission bug happened when my crew was doing a "eliminate a crawler swarm" mission. We killed all but one crawler from the swarm and it fled. When the crew followed to the location of the last crawler, it did not show in the game even though the scanner showed it was suppose to be there.
**Steps To Reproduce**
Possibly do a swarm mission and kill all but one creature from the swarm. No clue on how to replicate the radiation sickness bug
**Version**
Version: v0.9.9.0
Operating System: Windows
**Additional information**
We were using the "Into the Abyss" mod/
|
non_process
|
radiation sickness bug and mission bug i have searched the issue tracker to check if the issue has already been reported description the radiation sickness bug happened when the radiator blew up but i only had the burn affliction constantly on me and no display of the radiation sickness even after fully healing all the burn affected areas the affliction would return and never displayed the radiation sickness affliction even with a doctor looking at it the mission bug happened when my crew was doing a eliminate a crawler swarm mission we killed all but one crawler from the swarm and it fled when the crew followed to the location of the last crawler it did not show in the game even though the scanner showed it was suppose to be there steps to reproduce possibly do a swarm mission and kill all but one creature from the swarm no clue on how to replicate the radiation sickness bug version version operating system windows additional information we were using the into the abyss mod
| 0
|
123,929
| 16,551,466,669
|
IssuesEvent
|
2021-05-28 09:04:11
|
google/web-stories-wp
|
https://api.github.com/repos/google/web-stories-wp
|
closed
|
update dashboard filter styles
|
Group: Dashboard Group: Design System Pod: Pea Type: Enhancement
|
Update dashboard results area (right above the grid or table) to the new specs - some stuff has swapped places. https://www.figma.com/file/bMhG3KyrJF8vIAODgmbeqT/Design-System?node-id=467%3A13745
Can use Text, Button, Icon, Tooltip and Dropdown from Design System
|
1.0
|
update dashboard filter styles - Update dashboard results area (right above the grid or table) to the new specs - some stuff has swapped places. https://www.figma.com/file/bMhG3KyrJF8vIAODgmbeqT/Design-System?node-id=467%3A13745
Can use Text, Button, Icon, Tooltip and Dropdown from Design System
|
non_process
|
update dashboard filter styles update dashboard results area right above the grid or table to the new specs some stuff has swapped places can use text button icon tooltip and dropdown from design system
| 0
|
20,409
| 27,066,940,037
|
IssuesEvent
|
2023-02-14 01:38:28
|
nephio-project/sig-release
|
https://api.github.com/repos/nephio-project/sig-release
|
opened
|
Configure PROW infrastructure with all the different plugin(s) labels and nephio CLA
|
area/process-mgmt
|
We need to configure Prow infrastructure to be aware of and take actions based on labels such as lgtm, donotmerge etc. Similarly we need to make sure CLAs are signed by contributors before contributing code and we need to configure Prow for this as well.
## Todo
- [ ] Configure Prow infrastructure to be sensitive to labels
- [ ] Configure Prow infrastructure to be sensitive to CLAs
|
1.0
|
Configure PROW infrastructure with all the different plugin(s) labels and nephio CLA - We need to configure Prow infrastructure to be aware of and take actions based on labels such as lgtm, donotmerge etc. Similarly we need to make sure CLAs are signed by contributors before contributing code and we need to configure Prow for this as well.
## Todo
- [ ] Configure Prow infrastructure to be sensitive to labels
- [ ] Configure Prow infrastructure to be sensitive to CLAs
|
process
|
configure prow infrastructure with all the different plugin s labels and nephio cla we need to configure prow infrastructure to be aware of and take actions based on labels such as lgtm donotmerge etc similarly we need to make sure clas are signed by contributors before contributing code and we need to configure prow for this as well todo configure prow infrastructure to be sensitive to labels configure prow infrastructure to be sensitive to clas
| 1
|
120,855
| 10,137,170,620
|
IssuesEvent
|
2019-08-02 14:41:14
|
phetsims/equality-explorer
|
https://api.github.com/repos/phetsims/equality-explorer
|
opened
|
CT cannot read property 'count' of undefined
|
type:automated-testing
|
Also in EEB and EETV
```
equality-explorer : fuzz : built : run
Query: fuzz&memoryLimit=1000
Uncaught TypeError: Cannot read property 'count' of undefined
TypeError: Cannot read property 'count' of undefined
at m (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:181345)
at i.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180125)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:628547)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:645884)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:939465)
at t.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943264)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943669)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:1097994
at e.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:165426)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:182818
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : built : run
Query: fuzz&memoryLimit=1000
Uncaught TypeError: Cannot read property 'count' of undefined
TypeError: Cannot read property 'count' of undefined
at m (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:181345)
at i.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180125)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:628547)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:645884)
at t.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943264)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943669)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:1097994
at e.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:165426)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:182818
at n.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180417)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : require.js : run
Query: brand=phet&ea&fuzz&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/SeparateTermsDragListener.js?:70:42)
at SeparateTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ObjectTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ObjectTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : require.js : run
Query: brand=phet&ea&fuzz&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at CombineTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at VariableTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at VariableTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
at VariableTermNode.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : xss-fuzz : run
Query: brand=phet&ea&fuzz&stringTest=xss&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at CombineTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ConstantTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ConstantTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
at ConstantTermNode.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : xss-fuzz : run
Query: brand=phet&ea&fuzz&stringTest=xss&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/SeparateTermsDragListener.js?:70:42)
at SeparateTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ConstantTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ConstantTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
```
|
1.0
|
CT cannot read property 'count' of undefined - Also in EEB and EETV
```
equality-explorer : fuzz : built : run
Query: fuzz&memoryLimit=1000
Uncaught TypeError: Cannot read property 'count' of undefined
TypeError: Cannot read property 'count' of undefined
at m (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:181345)
at i.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180125)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:628547)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:645884)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:939465)
at t.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943264)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943669)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:1097994
at e.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:165426)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:182818
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : built : run
Query: fuzz&memoryLimit=1000
Uncaught TypeError: Cannot read property 'count' of undefined
TypeError: Cannot read property 'count' of undefined
at m (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:181345)
at i.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180125)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:628547)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:645884)
at t.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943264)
at t.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:943669)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:1097994
at e.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:165426)
at https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:182818
at n.value (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/build/phet/equality-explorer_en_phet.html?postMessageOnLoad&postMessageOnError&postMessageOnBeforeUnload&fuzz&memoryLimit=1000:1158:180417)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : require.js : run
Query: brand=phet&ea&fuzz&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/SeparateTermsDragListener.js?:70:42)
at SeparateTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ObjectTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ObjectTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : fuzz : require.js : run
Query: brand=phet&ea&fuzz&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at CombineTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at VariableTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at VariableTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
at VariableTermNode.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : xss-fuzz : run
Query: brand=phet&ea&fuzz&stringTest=xss&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at CombineTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at CombineTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ConstantTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ConstantTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
at ConstantTermNode.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
equality-explorer : xss-fuzz : run
Query: brand=phet&ea&fuzz&stringTest=xss&memoryLimit=1000
Uncaught Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
Error: Assertion failed: type name key is not in cache: ActionIO.Vector2IO
at window.assertions.assertFunction (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/assert/js/assert.js:22:13)
at removeActionIOFromCache (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:303:15)
at Action.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/axon/js/Action.js?:223:7)
at Action.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/scenery/js/input/SimpleDragHandler.js?:414:28)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermDragListener.js?:139:43)
at SeparateTermsDragListener.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/SeparateTermsDragListener.js?:70:42)
at SeparateTermsDragListener.PhetioObject.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/tandem/js/PhetioObject.js?:141:22)
at ConstantTermNode.TermNode.disposeTermNode (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:144:29)
at ConstantTermNode.dispose (https://bayes.colorado.edu/continuous-testing/snapshot-1564707898905/equality-explorer/js/common/view/TermNode.js?:157:12)
id: Bayes Chrome
Approximately 8/1/2019, 7:04:58 PM
```
|
non_process
|
ct cannot read property count of undefined also in eeb and eetv equality explorer fuzz built run query fuzz memorylimit uncaught typeerror cannot read property count of undefined typeerror cannot read property count of undefined at m at i value at t dispose at t dispose at t dispose at t disposetermnode at t dispose at at e value at id bayes chrome approximately pm equality explorer fuzz built run query fuzz memorylimit uncaught typeerror cannot read property count of undefined typeerror cannot read property count of undefined at m at i value at t dispose at t dispose at t disposetermnode at t dispose at at e value at at n value id bayes chrome approximately pm equality explorer fuzz require js run query brand phet ea fuzz memorylimit uncaught error assertion failed type name key is not in cache actionio error assertion failed type name key is not in cache actionio at window assertions assertfunction at removeactioniofromcache at action dispose at action phetioobject dispose at separatetermsdraglistener dispose at separatetermsdraglistener dispose at separatetermsdraglistener dispose at separatetermsdraglistener phetioobject dispose at objecttermnode termnode disposetermnode at objecttermnode dispose id bayes chrome approximately pm equality explorer fuzz require js run query brand phet ea fuzz memorylimit uncaught error assertion failed type name key is not in cache actionio error assertion failed type name key is not in cache actionio at window assertions assertfunction at removeactioniofromcache at action dispose at action phetioobject dispose at combinetermsdraglistener dispose at combinetermsdraglistener dispose at combinetermsdraglistener phetioobject dispose at variabletermnode termnode disposetermnode at variabletermnode dispose at variabletermnode phetioobject dispose id bayes chrome approximately pm equality explorer xss fuzz run query brand phet ea fuzz stringtest xss memorylimit uncaught error assertion failed type name key is not in cache actionio error assertion failed type name key is not in cache actionio at window assertions assertfunction at removeactioniofromcache at action dispose at action phetioobject dispose at combinetermsdraglistener dispose at combinetermsdraglistener dispose at combinetermsdraglistener phetioobject dispose at constanttermnode termnode disposetermnode at constanttermnode dispose at constanttermnode phetioobject dispose id bayes chrome approximately pm equality explorer xss fuzz run query brand phet ea fuzz stringtest xss memorylimit uncaught error assertion failed type name key is not in cache actionio error assertion failed type name key is not in cache actionio at window assertions assertfunction at removeactioniofromcache at action dispose at action phetioobject dispose at separatetermsdraglistener dispose at separatetermsdraglistener dispose at separatetermsdraglistener dispose at separatetermsdraglistener phetioobject dispose at constanttermnode termnode disposetermnode at constanttermnode dispose id bayes chrome approximately pm
| 0
|
19,603
| 25,958,965,887
|
IssuesEvent
|
2022-12-18 16:23:46
|
oyvindln/vhs-decode
|
https://api.github.com/repos/oyvindln/vhs-decode
|
closed
|
Website/wiki
|
in-process
|
We could really use a website or github page giving some basic overview of the project.
|
1.0
|
Website/wiki - We could really use a website or github page giving some basic overview of the project.
|
process
|
website wiki we could really use a website or github page giving some basic overview of the project
| 1
|
6,741
| 9,872,928,554
|
IssuesEvent
|
2019-06-22 09:31:51
|
qgis/QGIS
|
https://api.github.com/repos/qgis/QGIS
|
closed
|
Improvement for Batch Processing
|
Feature Request Processing
|
Author Name: **Nicolas Cadieux** (@NicolasCadieux)
Original Redmine Issue: [18944](https://issues.qgis.org/issues/18944)
Redmine category:processing/gui
---
We should be able to select all or multiple layers and select a field parameter once that will apply to all selected layers. Same thing for adding layers from the project. We should be able to select multiple layer and then click only once for all layers to be added.
It would be nice to be able to add fields or parameters when these are missing in the GUI (like for buildVRT raster where many if not most parameters are missing making the module unusable for anything complicated).
We should be able to drag and drop layers into the Batch processing windows from the Layer Panel.
|
1.0
|
Improvement for Batch Processing - Author Name: **Nicolas Cadieux** (@NicolasCadieux)
Original Redmine Issue: [18944](https://issues.qgis.org/issues/18944)
Redmine category:processing/gui
---
We should be able to select all or multiple layers and select a field parameter once that will apply to all selected layers. Same thing for adding layers from the project. We should be able to select multiple layer and then click only once for all layers to be added.
It would be nice to be able to add fields or parameters when these are missing in the GUI (like for buildVRT raster where many if not most parameters are missing making the module unusable for anything complicated).
We should be able to drag and drop layers into the Batch processing windows from the Layer Panel.
|
process
|
improvement for batch processing author name nicolas cadieux nicolascadieux original redmine issue redmine category processing gui we should be able to select all or multiple layers and select a field parameter once that will apply to all selected layers same thing for adding layers from the project we should be able to select multiple layer and then click only once for all layers to be added it would be nice to be able to add fields or parameters when these are missing in the gui like for buildvrt raster where many if not most parameters are missing making the module unusable for anything complicated we should be able to drag and drop layers into the batch processing windows from the layer panel
| 1
|
410,170
| 11,984,436,653
|
IssuesEvent
|
2020-04-07 15:51:38
|
co-cart/co-cart
|
https://api.github.com/repos/co-cart/co-cart
|
closed
|
Add validation for product types before items are added to cart
|
enhancement priority:moderate scale:tiny
|
There is no validation to check if the product type is allowed to be added to the cart. For example, external products should not be allowed to be added to the cart as they are not local products that the store can sell. This would cause a problem for shop owners when orders are made.
|
1.0
|
Add validation for product types before items are added to cart - There is no validation to check if the product type is allowed to be added to the cart. For example, external products should not be allowed to be added to the cart as they are not local products that the store can sell. This would cause a problem for shop owners when orders are made.
|
non_process
|
add validation for product types before items are added to cart there is no validation to check if the product type is allowed to be added to the cart for example external products should not be allowed to be added to the cart as they are not local products that the store can sell this would cause a problem for shop owners when orders are made
| 0
|
8,609
| 11,765,497,408
|
IssuesEvent
|
2020-03-14 17:40:23
|
MineCake147E/MonoAudio
|
https://api.github.com/repos/MineCake147E/MonoAudio
|
opened
|
`SplineResampler` fix on Android
|
CPU: Qualcomm Snapdragon📱 Feature: Signal Processing 🎛️ Kind: Glitch 🆖 Platform: Android 📱 Priority: High 🚅 Status: Working ▶️ Tag: Linux Distribution Specific 👫
|
# Backgrounds
- Acoustic glitch on Xperia XZ3
- Output without resampling has no glitch
- There's nothing similar on Windows(i7-4790)
# Progress
## Find what's going wrong
- [ ] Dump output sample to file
- [ ] Analyze how glitches appear in Debug mode
## Fix it
- [ ] `Here will be something wrong to fix`
|
1.0
|
`SplineResampler` fix on Android - # Backgrounds
- Acoustic glitch on Xperia XZ3
- Output without resampling has no glitch
- There's nothing similar on Windows(i7-4790)
# Progress
## Find what's going wrong
- [ ] Dump output sample to file
- [ ] Analyze how glitches appear in Debug mode
## Fix it
- [ ] `Here will be something wrong to fix`
|
process
|
splineresampler fix on android backgrounds acoustic glitch on xperia output without resampling has no glitch there s nothing similar on windows progress find what s going wrong dump output sample to file analyze how glitches appear in debug mode fix it here will be something wrong to fix
| 1
|
9,368
| 12,372,678,661
|
IssuesEvent
|
2020-05-18 20:52:17
|
nion-software/nionswift
|
https://api.github.com/repos/nion-software/nionswift
|
opened
|
Add ability to define a custom display transfer function
|
f - displays f - plugins f - processing f - raster-image stage - planning type - enhancement
|
Either packages can supply a predefined transfer function or the user can edit the equation using Python.
ND 2020-05-20:
What I would want is a display calculation, So that I can tweak the formula, that way the data is still there and correct, but something like a log has a tendency to go ugly at the bottom. This would automatically make all possible display transformations available for the advanced user.
|
1.0
|
Add ability to define a custom display transfer function - Either packages can supply a predefined transfer function or the user can edit the equation using Python.
ND 2020-05-20:
What I would want is a display calculation, So that I can tweak the formula, that way the data is still there and correct, but something like a log has a tendency to go ugly at the bottom. This would automatically make all possible display transformations available for the advanced user.
|
process
|
add ability to define a custom display transfer function either packages can supply a predefined transfer function or the user can edit the equation using python nd what i would want is a display calculation so that i can tweak the formula that way the data is still there and correct but something like a log has a tendency to go ugly at the bottom this would automatically make all possible display transformations available for the advanced user
| 1
|
10,661
| 15,690,739,697
|
IssuesEvent
|
2021-03-25 17:03:18
|
ajnorthouse/restaurant-review-frontend
|
https://api.github.com/repos/ajnorthouse/restaurant-review-frontend
|
closed
|
Create skeleton UI for searching for restaurants
|
Front-End core requirement
|
It should have a search bar, and then use a condensed version of restaurant info as list objects that the user can browse up and down.
|
1.0
|
Create skeleton UI for searching for restaurants - It should have a search bar, and then use a condensed version of restaurant info as list objects that the user can browse up and down.
|
non_process
|
create skeleton ui for searching for restaurants it should have a search bar and then use a condensed version of restaurant info as list objects that the user can browse up and down
| 0
|
13,985
| 16,761,019,443
|
IssuesEvent
|
2021-06-13 19:42:20
|
hochschule-darmstadt/openartbrowser
|
https://api.github.com/repos/hochschule-darmstadt/openartbrowser
|
opened
|
Add Architecture to OpenArtBrowser
|
etl process feature high priority refactoring
|
**Reason (Why?)**
Buildings have always been one of the most important ways of expressing art movements. Wikidata contains over 1.9 million buildings, over 700k with images. We should include them into our crawl
**Solution (What?)**
The first step is to simply crawl selected subclasses of the building class. The following QIDs should be added to the crawl:
- palace (Q16560) ×15001
- pyramid (Q12516) ×141
- church building (Q16970) ×297817
- temple (Q44539) ×49405
- monastery (Q44613) ×18281
Buildings should be handled as Artworks (type)
In the second step, the building attributes should be added to the crawl. These are
The first step already includes title, description, abstract, image etc. like all other entites, Inception, country, significant event wie length, width, height bei artwork.
Additional attributes are architect P84, which corresponds to the artist. architectural style P194 corresponds to movement
This is a good opportunity to change the models in crawler to match the models and the inheritance of properties in the frontend.
**Out of scope**
Create a new architecture-dimension or architects or architectual style or something like that
**Acceptance criteria**
Architecture appears in our dataset.
|
1.0
|
Add Architecture to OpenArtBrowser - **Reason (Why?)**
Buildings have always been one of the most important ways of expressing art movements. Wikidata contains over 1.9 million buildings, over 700k with images. We should include them into our crawl
**Solution (What?)**
The first step is to simply crawl selected subclasses of the building class. The following QIDs should be added to the crawl:
- palace (Q16560) ×15001
- pyramid (Q12516) ×141
- church building (Q16970) ×297817
- temple (Q44539) ×49405
- monastery (Q44613) ×18281
Buildings should be handled as Artworks (type)
In the second step, the building attributes should be added to the crawl. These are
The first step already includes title, description, abstract, image etc. like all other entites, Inception, country, significant event wie length, width, height bei artwork.
Additional attributes are architect P84, which corresponds to the artist. architectural style P194 corresponds to movement
This is a good opportunity to change the models in crawler to match the models and the inheritance of properties in the frontend.
**Out of scope**
Create a new architecture-dimension or architects or architectual style or something like that
**Acceptance criteria**
Architecture appears in our dataset.
|
process
|
add architecture to openartbrowser reason why buildings have always been one of the most important ways of expressing art movements wikidata contains over million buildings over with images we should include them into our crawl solution what the first step is to simply crawl selected subclasses of the building class the following qids should be added to the crawl palace × pyramid × church building × temple × monastery × buildings should be handled as artworks type in the second step the building attributes should be added to the crawl these are the first step already includes title description abstract image etc like all other entites inception country significant event wie length width height bei artwork additional attributes are architect which corresponds to the artist architectural style corresponds to movement this is a good opportunity to change the models in crawler to match the models and the inheritance of properties in the frontend out of scope create a new architecture dimension or architects or architectual style or something like that acceptance criteria architecture appears in our dataset
| 1
|
218,833
| 7,332,508,372
|
IssuesEvent
|
2018-03-05 16:31:07
|
NCEAS/metacat
|
https://api.github.com/repos/NCEAS/metacat
|
closed
|
The system metadata table in a Metacat was messed up with the value of archived
|
Component: Bugzilla-Id Priority: Normal Status: Rejected Tracker: Bug
|
---
Author Name: **Jing Tao** (Jing Tao)
Original Redmine Issue: 6035, https://projects.ecoinformatics.org/ecoinfo/issues/6035
Original Date: 2013-07-10
---
In the mn-demo-4.test.dataone.org, I queried the systemmetadata table and got:
metacat=> select count(*) from systemmetadata where obsoleted_by is not null and archived=false;
count
-------
4644
(1 row)
You see, there are 4644 documents which are not archived but have the obsoleted_by value. This is a contradiction.
Also, the xml_documents and xml_revisions table are messed up:
metacat=> select count(*) from xml_documents;
count
-------
4998
(1 row)
metacat=> select count(*) from xml_revisions;
count
-------
0
We need figure out why the metacat has this funny result.
|
1.0
|
The system metadata table in a Metacat was messed up with the value of archived - ---
Author Name: **Jing Tao** (Jing Tao)
Original Redmine Issue: 6035, https://projects.ecoinformatics.org/ecoinfo/issues/6035
Original Date: 2013-07-10
---
In the mn-demo-4.test.dataone.org, I queried the systemmetadata table and got:
metacat=> select count(*) from systemmetadata where obsoleted_by is not null and archived=false;
count
-------
4644
(1 row)
You see, there are 4644 documents which are not archived but have the obsoleted_by value. This is a contradiction.
Also, the xml_documents and xml_revisions table are messed up:
metacat=> select count(*) from xml_documents;
count
-------
4998
(1 row)
metacat=> select count(*) from xml_revisions;
count
-------
0
We need figure out why the metacat has this funny result.
|
non_process
|
the system metadata table in a metacat was messed up with the value of archived author name jing tao jing tao original redmine issue original date in the mn demo test dataone org i queried the systemmetadata table and got metacat select count from systemmetadata where obsoleted by is not null and archived false count row you see there are documents which are not archived but have the obsoleted by value this is a contradiction also the xml documents and xml revisions table are messed up metacat select count from xml documents count row metacat select count from xml revisions count we need figure out why the metacat has this funny result
| 0
|
78,909
| 9,809,139,909
|
IssuesEvent
|
2019-06-12 17:13:10
|
pwa-builder/PWABuilder
|
https://api.github.com/repos/pwa-builder/PWABuilder
|
closed
|
Manifest :: Image upload modal [54]
|
design
|
**Describe the bug**
This is an unnecessary step for image upload.
**Expected behavior**
When user clicks 'upload', prompt default file explorer
**Screenshots**
_in build_
**Additional info (please complete the following information):**
- _OS_ | Windows 10 OS Build 18362.113
- _Browser_ | Google Chrome
- _Browser version:_ 74.0.3729.169 (Official Build) (64-bit) (cohort: Stable)
_Revision_ | 78e4f8db3ce38f6c26cf56eed7ae9b331fc67ada-refs/branch-heads/3729@{#1013}
|
1.0
|
Manifest :: Image upload modal [54] - **Describe the bug**
This is an unnecessary step for image upload.
**Expected behavior**
When user clicks 'upload', prompt default file explorer
**Screenshots**
_in build_
**Additional info (please complete the following information):**
- _OS_ | Windows 10 OS Build 18362.113
- _Browser_ | Google Chrome
- _Browser version:_ 74.0.3729.169 (Official Build) (64-bit) (cohort: Stable)
_Revision_ | 78e4f8db3ce38f6c26cf56eed7ae9b331fc67ada-refs/branch-heads/3729@{#1013}
|
non_process
|
manifest image upload modal describe the bug this is an unnecessary step for image upload expected behavior when user clicks upload prompt default file explorer screenshots in build additional info please complete the following information os windows os build browser google chrome browser version official build bit cohort stable revision refs branch heads
| 0
|
11,756
| 14,590,909,515
|
IssuesEvent
|
2020-12-19 10:18:00
|
emacs-ess/ESS
|
https://api.github.com/repos/emacs-ess/ESS
|
reopened
|
Emacs hangs when opening help (re-open #954)
|
process:eval
|
I'm experiencing what seems to be the same issue as reported in #954. That has been closed as resolved, but I believe should be re-opened. There are two comments on the issue reporting that the problem persists:
https://github.com/emacs-ess/ESS/issues/954#issuecomment-572740250
https://github.com/emacs-ess/ESS/issues/954#issuecomment-655499551
|
1.0
|
Emacs hangs when opening help (re-open #954) - I'm experiencing what seems to be the same issue as reported in #954. That has been closed as resolved, but I believe should be re-opened. There are two comments on the issue reporting that the problem persists:
https://github.com/emacs-ess/ESS/issues/954#issuecomment-572740250
https://github.com/emacs-ess/ESS/issues/954#issuecomment-655499551
|
process
|
emacs hangs when opening help re open i m experiencing what seems to be the same issue as reported in that has been closed as resolved but i believe should be re opened there are two comments on the issue reporting that the problem persists
| 1
|
17,037
| 22,410,093,613
|
IssuesEvent
|
2022-06-18 15:41:45
|
0xffset/rOSt
|
https://api.github.com/repos/0xffset/rOSt
|
closed
|
Better scheduler than round-robin
|
processes important
|
Currently the scheduler just takes the next thread in the list to run. This is very unoptimal and we should replace it with a better alternative that would be fairer.
|
1.0
|
Better scheduler than round-robin - Currently the scheduler just takes the next thread in the list to run. This is very unoptimal and we should replace it with a better alternative that would be fairer.
|
process
|
better scheduler than round robin currently the scheduler just takes the next thread in the list to run this is very unoptimal and we should replace it with a better alternative that would be fairer
| 1
|
766,897
| 26,903,898,955
|
IssuesEvent
|
2023-02-06 17:29:59
|
ankidroid/Anki-Android
|
https://api.github.com/repos/ankidroid/Anki-Android
|
closed
|
[Bug] `deckPath` location should be in a subfolder of `files`
|
Priority-High
|
It is the root of `/files`, should be `/files/AnkiDroid`.
The latter location allows for multiple profiles in an obvious manner
|
1.0
|
[Bug] `deckPath` location should be in a subfolder of `files` - It is the root of `/files`, should be `/files/AnkiDroid`.
The latter location allows for multiple profiles in an obvious manner
|
non_process
|
deckpath location should be in a subfolder of files it is the root of files should be files ankidroid the latter location allows for multiple profiles in an obvious manner
| 0
|
55,678
| 3,074,233,878
|
IssuesEvent
|
2015-08-20 05:17:57
|
pavel-pimenov/flylinkdc-r5xx
|
https://api.github.com/repos/pavel-pimenov/flylinkdc-r5xx
|
closed
|
Не правильно определяется битрейт аудиофайлов
|
bug Component-Logic imported Priority-Medium Usability
|
_From [Tirael...@gmail.com](https://code.google.com/u/108935377450235604965/) on January 08, 2011 17:32:59_
Во вложении скрин и mp3. Флай пишет, что качество 96 Кбит\с, а на самом деле 192. А у песни The Kill пишет 128, хотя тоже 192. Остальные соответствуют правде - 192. Полазил по своему файл листу очень многие песни не соответствуют правде.
Обнаружено Serx.
**Attachment:** [Снимок.JPG A Beautiful Lie.mp3](http://code.google.com/p/flylinkdc/issues/detail?id=280)
_Original issue: http://code.google.com/p/flylinkdc/issues/detail?id=280_
|
1.0
|
Не правильно определяется битрейт аудиофайлов - _From [Tirael...@gmail.com](https://code.google.com/u/108935377450235604965/) on January 08, 2011 17:32:59_
Во вложении скрин и mp3. Флай пишет, что качество 96 Кбит\с, а на самом деле 192. А у песни The Kill пишет 128, хотя тоже 192. Остальные соответствуют правде - 192. Полазил по своему файл листу очень многие песни не соответствуют правде.
Обнаружено Serx.
**Attachment:** [Снимок.JPG A Beautiful Lie.mp3](http://code.google.com/p/flylinkdc/issues/detail?id=280)
_Original issue: http://code.google.com/p/flylinkdc/issues/detail?id=280_
|
non_process
|
не правильно определяется битрейт аудиофайлов from on january во вложении скрин и флай пишет что качество кбит с а на самом деле а у песни the kill пишет хотя тоже остальные соответствуют правде полазил по своему файл листу очень многие песни не соответствуют правде обнаружено serx attachment original issue
| 0
|
447,486
| 12,888,866,596
|
IssuesEvent
|
2020-07-13 13:41:27
|
ansible/galaxy_ng
|
https://api.github.com/repos/ansible/galaxy_ng
|
opened
|
Importer: Missing __init__ file is not caught in current tests
|
area/installer priority/high sprint/3 status/in-progress type/bug
|
`galaxy-importer` can have a missing `__init__` file in a package directory and still run without errors when pip installing from source when running from source dir. Add integration test to run `galaxy-importer` from shell to catch this.
|
1.0
|
Importer: Missing __init__ file is not caught in current tests - `galaxy-importer` can have a missing `__init__` file in a package directory and still run without errors when pip installing from source when running from source dir. Add integration test to run `galaxy-importer` from shell to catch this.
|
non_process
|
importer missing init file is not caught in current tests galaxy importer can have a missing init file in a package directory and still run without errors when pip installing from source when running from source dir add integration test to run galaxy importer from shell to catch this
| 0
|
22,757
| 32,077,969,614
|
IssuesEvent
|
2023-09-25 12:17:36
|
MuttiD/ElectroShop
|
https://api.github.com/repos/MuttiD/ElectroShop
|
opened
|
[USER STORY]: <Order Confirmation>
|
payment processing user story
|
As a **user**, I want to receive an order confirmation email after successfully completing a purchase.
|
1.0
|
[USER STORY]: <Order Confirmation> - As a **user**, I want to receive an order confirmation email after successfully completing a purchase.
|
process
|
as a user i want to receive an order confirmation email after successfully completing a purchase
| 1
|
20,610
| 27,276,569,913
|
IssuesEvent
|
2023-02-23 06:06:26
|
nodejs/node
|
https://api.github.com/repos/nodejs/node
|
closed
|
node 18 cannot spawn executable shell scripts on mac
|
child_process macos
|
### Version
v18.14.1
### Platform
Darwin briansworkmac.local 22.2.0 Darwin Kernel Version 22.2.0: Fri Nov 11 02:08:47 PST 2022; root:xnu-8792.61.2~4/RELEASE_X86_64 x86_64
### Subsystem
child_process
### What steps will reproduce the bug?
save and run this shell script that illustrates the issue
```shell
set -x
SHELL_SCRIPT='print_message.sh'
cat << EOF > "$SHELL_SCRIPT"
echo 'hello world'
EOF
chmod +x $SHELL_SCRIPT
NODE_SCRIPT='spawn_shell_script.js'
cat << EOF2 > "$NODE_SCRIPT"
const { spawn } = require('child_process');
const cp = spawn('./${SHELL_SCRIPT}');
cp.on('error', (e) => {
console.error(e);
});
cp.on('exit', (code) => {
console.log(\`child process exited with code \${code}\`);
});
cp.stdout.on('data', (data) => {
// strip the newline from the child process log message
console.log(\`\${data.slice(0, -1)}\`);
});
EOF2
node $NODE_SCRIPT
```
### How often does it reproduce? Is there a required condition?
always
### What is the expected behavior?
should be able to spawn subprocesses for shell scripts when the executable bit is set
### What do you see instead?
```console
node:internal/child_process:413
throw errnoException(err, 'spawn');
^
Error: spawn Unknown system error -8
at ChildProcess.spawn (node:internal/child_process:413:11)
at spawn (node:child_process:757:9)
at Object.<anonymous> (/Users/brianfreedman/Downloads/spawn_something.js:2:12)
at Module._compile (node:internal/modules/cjs/loader:1254:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1308:10)
at Module.load (node:internal/modules/cjs/loader:1117:32)
at Module._load (node:internal/modules/cjs/loader:958:12)
at Function.executeUserEntryPoint [as runMain] (node:internal/modules/run_main:81:12)
at node:internal/main/run_main_module:23:47 {
errno: -8,
code: 'Unknown system error -8',
syscall: 'spawn'
}
Node.js v18.14.1
```
### Additional information
- not an issue with node 14 and 16
- ~only be an issue with x64 arch~
- worked with node 18.10.0 on arm64
- fails with node 18.14.2 on arm64
- the following is a workaround I've had to add to my actual code where I pass the script as an argument and spawn `sh` instead
```js
let cp;
// NOTE this is a workaround for what seems to be a bug with node 18 on mac not wanting to spawn shell scripts
if (process.platform === 'darwin') {
cp = child_process.spawn('sh', [script], {
stdio: 'ignore'
});
} else {
cp = child_process.spawn(script, {
stdio: 'ignore'
});
}
cp.on('error', (err) => {
console.error(err);
});
cp.on('exit', (code) => {
console.log(`child process exited with code ${code}`);
});
```
|
1.0
|
node 18 cannot spawn executable shell scripts on mac - ### Version
v18.14.1
### Platform
Darwin briansworkmac.local 22.2.0 Darwin Kernel Version 22.2.0: Fri Nov 11 02:08:47 PST 2022; root:xnu-8792.61.2~4/RELEASE_X86_64 x86_64
### Subsystem
child_process
### What steps will reproduce the bug?
save and run this shell script that illustrates the issue
```shell
set -x
SHELL_SCRIPT='print_message.sh'
cat << EOF > "$SHELL_SCRIPT"
echo 'hello world'
EOF
chmod +x $SHELL_SCRIPT
NODE_SCRIPT='spawn_shell_script.js'
cat << EOF2 > "$NODE_SCRIPT"
const { spawn } = require('child_process');
const cp = spawn('./${SHELL_SCRIPT}');
cp.on('error', (e) => {
console.error(e);
});
cp.on('exit', (code) => {
console.log(\`child process exited with code \${code}\`);
});
cp.stdout.on('data', (data) => {
// strip the newline from the child process log message
console.log(\`\${data.slice(0, -1)}\`);
});
EOF2
node $NODE_SCRIPT
```
### How often does it reproduce? Is there a required condition?
always
### What is the expected behavior?
should be able to spawn subprocesses for shell scripts when the executable bit is set
### What do you see instead?
```console
node:internal/child_process:413
throw errnoException(err, 'spawn');
^
Error: spawn Unknown system error -8
at ChildProcess.spawn (node:internal/child_process:413:11)
at spawn (node:child_process:757:9)
at Object.<anonymous> (/Users/brianfreedman/Downloads/spawn_something.js:2:12)
at Module._compile (node:internal/modules/cjs/loader:1254:14)
at Module._extensions..js (node:internal/modules/cjs/loader:1308:10)
at Module.load (node:internal/modules/cjs/loader:1117:32)
at Module._load (node:internal/modules/cjs/loader:958:12)
at Function.executeUserEntryPoint [as runMain] (node:internal/modules/run_main:81:12)
at node:internal/main/run_main_module:23:47 {
errno: -8,
code: 'Unknown system error -8',
syscall: 'spawn'
}
Node.js v18.14.1
```
### Additional information
- not an issue with node 14 and 16
- ~only be an issue with x64 arch~
- worked with node 18.10.0 on arm64
- fails with node 18.14.2 on arm64
- the following is a workaround I've had to add to my actual code where I pass the script as an argument and spawn `sh` instead
```js
let cp;
// NOTE this is a workaround for what seems to be a bug with node 18 on mac not wanting to spawn shell scripts
if (process.platform === 'darwin') {
cp = child_process.spawn('sh', [script], {
stdio: 'ignore'
});
} else {
cp = child_process.spawn(script, {
stdio: 'ignore'
});
}
cp.on('error', (err) => {
console.error(err);
});
cp.on('exit', (code) => {
console.log(`child process exited with code ${code}`);
});
```
|
process
|
node cannot spawn executable shell scripts on mac version platform darwin briansworkmac local darwin kernel version fri nov pst root xnu release subsystem child process what steps will reproduce the bug save and run this shell script that illustrates the issue shell set x shell script print message sh cat shell script echo hello world eof chmod x shell script node script spawn shell script js cat node script const spawn require child process const cp spawn shell script cp on error e console error e cp on exit code console log child process exited with code code cp stdout on data data strip the newline from the child process log message console log data slice node node script how often does it reproduce is there a required condition always what is the expected behavior should be able to spawn subprocesses for shell scripts when the executable bit is set what do you see instead console node internal child process throw errnoexception err spawn error spawn unknown system error at childprocess spawn node internal child process at spawn node child process at object users brianfreedman downloads spawn something js at module compile node internal modules cjs loader at module extensions js node internal modules cjs loader at module load node internal modules cjs loader at module load node internal modules cjs loader at function executeuserentrypoint node internal modules run main at node internal main run main module errno code unknown system error syscall spawn node js additional information not an issue with node and only be an issue with arch worked with node on fails with node on the following is a workaround i ve had to add to my actual code where i pass the script as an argument and spawn sh instead js let cp note this is a workaround for what seems to be a bug with node on mac not wanting to spawn shell scripts if process platform darwin cp child process spawn sh stdio ignore else cp child process spawn script stdio ignore cp on error err console error err cp on exit code console log child process exited with code code
| 1
|
17,108
| 22,630,585,848
|
IssuesEvent
|
2022-06-30 14:21:32
|
GoogleCloudPlatform/fda-mystudies
|
https://api.github.com/repos/GoogleCloudPlatform/fda-mystudies
|
closed
|
[Mobile apps] Getting an error message in the enrollment flow
|
Bug Blocker P0 iOS Android Process: Fixed
|
In enrollment flow,
**AR:** Getting an error message stating as " Sorry, an error has occurred and your request could not be processed. Please try again later. in the mobile apps
**ER:** Participants should be enrolled to the study without getting an error's
|
1.0
|
[Mobile apps] Getting an error message in the enrollment flow - In enrollment flow,
**AR:** Getting an error message stating as " Sorry, an error has occurred and your request could not be processed. Please try again later. in the mobile apps
**ER:** Participants should be enrolled to the study without getting an error's
|
process
|
getting an error message in the enrollment flow in enrollment flow ar getting an error message stating as sorry an error has occurred and your request could not be processed please try again later in the mobile apps er participants should be enrolled to the study without getting an error s
| 1
|
289,997
| 25,031,212,053
|
IssuesEvent
|
2022-11-04 12:33:12
|
TestIntegrations/TestForwarding
|
https://api.github.com/repos/TestIntegrations/TestForwarding
|
opened
|
ANR Input dispatching timed out (Waiting to send non-key event because the touched window has...
|
Yousef test new h n e rtrt
|
**Title:** ANR Input dispatching timed out (Waiting to send non-key event because the touched window has not finished processing certain input events that were delivered to it over 500.0ms ago. Wait queue length: 13. Wait queue head age: 5577.6ms.) (CrashFragment.java:87)
**Number:** 64
**Type:** Crash
**Status:** New
**Reported At:** 2021-08-03 14:12:38 UTC
**Email:**
**Private URL:** https://dashboard.instabug.com/applications/abanoub-android-beta/beta/crashes/64?utm_source=github&utm_medium=integrations
**Categories:**
**App Version:** 1.0-kotlin (1)
**Current View:** com.example.app.crash.CrashFragment
**Device:** HMD Global Nokia 6.1 Plus
**Location:** Cairo, Egypt
**Duration:** 40
**Screen Size:** 1080x2280
**Density:** xhdpi
**User Attributes:**
**key_name 1210058781:** key value bla bla bla la
**User Data:**
**User Steps:**
```
```
**Instabug Log:**
```
```
**Console Log:**
```
```
**Locale:** en
|
1.0
|
ANR Input dispatching timed out (Waiting to send non-key event because the touched window has... - **Title:** ANR Input dispatching timed out (Waiting to send non-key event because the touched window has not finished processing certain input events that were delivered to it over 500.0ms ago. Wait queue length: 13. Wait queue head age: 5577.6ms.) (CrashFragment.java:87)
**Number:** 64
**Type:** Crash
**Status:** New
**Reported At:** 2021-08-03 14:12:38 UTC
**Email:**
**Private URL:** https://dashboard.instabug.com/applications/abanoub-android-beta/beta/crashes/64?utm_source=github&utm_medium=integrations
**Categories:**
**App Version:** 1.0-kotlin (1)
**Current View:** com.example.app.crash.CrashFragment
**Device:** HMD Global Nokia 6.1 Plus
**Location:** Cairo, Egypt
**Duration:** 40
**Screen Size:** 1080x2280
**Density:** xhdpi
**User Attributes:**
**key_name 1210058781:** key value bla bla bla la
**User Data:**
**User Steps:**
```
```
**Instabug Log:**
```
```
**Console Log:**
```
```
**Locale:** en
|
non_process
|
anr input dispatching timed out waiting to send non key event because the touched window has title anr input dispatching timed out waiting to send non key event because the touched window has not finished processing certain input events that were delivered to it over ago wait queue length wait queue head age crashfragment java number type crash status new reported at utc email private url categories app version kotlin current view com example app crash crashfragment device hmd global nokia plus location cairo egypt duration screen size density xhdpi user attributes key name key value bla bla bla la user data user steps instabug log console log locale en
| 0
|
460,130
| 13,205,285,295
|
IssuesEvent
|
2020-08-14 17:37:24
|
open-telemetry/opentelemetry-specification
|
https://api.github.com/repos/open-telemetry/opentelemetry-specification
|
closed
|
Redundancy between glossary and "vocabulary" in overview
|
area:miscellaneous priority:p2 release:required-for-ga spec:miscellaneous
|
Currently we have both
* https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/overview.md and
* https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/glossary.md
both of which contain definitions of various terms. Sometimes there is even overlap within the terms e.g. the overviews "Instrumentation adapter" vs the glossary's "Instrumentation library" (this is discussed in #476).
In any case, I think we need to put links from the actual spec to the glossary/overview in important places, otherwise I fear these documents become write-only.
|
1.0
|
Redundancy between glossary and "vocabulary" in overview - Currently we have both
* https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/overview.md and
* https://github.com/open-telemetry/opentelemetry-specification/blob/master/specification/glossary.md
both of which contain definitions of various terms. Sometimes there is even overlap within the terms e.g. the overviews "Instrumentation adapter" vs the glossary's "Instrumentation library" (this is discussed in #476).
In any case, I think we need to put links from the actual spec to the glossary/overview in important places, otherwise I fear these documents become write-only.
|
non_process
|
redundancy between glossary and vocabulary in overview currently we have both and both of which contain definitions of various terms sometimes there is even overlap within the terms e g the overviews instrumentation adapter vs the glossary s instrumentation library this is discussed in in any case i think we need to put links from the actual spec to the glossary overview in important places otherwise i fear these documents become write only
| 0
|
76,616
| 26,512,890,074
|
IssuesEvent
|
2023-01-18 18:30:10
|
vector-im/element-web
|
https://api.github.com/repos/vector-im/element-web
|
closed
|
Notifications do not appear for any app received from API session
|
T-Defect
|
### Steps to reproduce
I am using a script to send notifications via session ID from a matrix.org account to a room in matrix.org. I have joined this room from my self-hosted user and can see the messages posted from the script. I have notifications turned on for all messages and I do receive popup notifications for messages from matrix.org users who use the element app.
```
const required_input = [
"matrix_url",
"matrix_room",
"matrix_token",
"alert_subject",
"alert_message",
"event_severity",
"event_is_problem",
"event_is_update",
"enable_colors",
"enable_icons",
]
const update_color = "#000000"
const recovery_color = "#098e68"
const severity_colors = [
"#5a5a5a", // Not classified
"#2caed6", // Information
"#d6832c", // Warning
"#d6542c", // Average
"#d62c2c", // High
"#ff0000", // Disaster
]
const update_icon = String.fromCodePoint("0x1f4dd")
const recovery_icon = String.fromCodePoint("0x2705")
const severity_icons = [
String.fromCodePoint("0x2754"), // Not classified
String.fromCodePoint("0x2139"), // Information
String.fromCodePoint("0x26a0"), // Warning
String.fromCodePoint("0x274c"), // Average
String.fromCodePoint("0x1f525"), // High
String.fromCodePoint("0x1f4a5"), // Disaster
]
var Matrix = {
validate: function (params) {
required_input.forEach(function (key) {
if (key in params && params[key] != undefined) {
Matrix[key] = params[key]
} else {
throw "Missing value for key: " + key
}
})
Matrix.alert_subject = Matrix.alert_subject.replace(/\r/g, "")
Matrix.alert_message = Matrix.alert_message.replace(/\r/g, "")
Matrix.event_severity = parseInt(Matrix.event_severity)
Matrix.event_is_problem = parseInt(Matrix.event_is_problem)
Matrix.event_is_update = parseInt(Matrix.event_is_update)
if (typeof params.event_url === "string" && params.event_url.trim() !== "") {
Matrix.event_url = params.event_url
}
Matrix.enable_colors = Matrix.enable_colors.toLowerCase() == "true"
Matrix.enable_icons = Matrix.enable_icons.toLowerCase() == "true"
if (typeof params.http_proxy === "string" && params.http_proxy.trim() !== "") {
Matrix.http_proxy = params.http_proxy
}
if (Matrix.event_is_problem == 1) {
if (Matrix.event_is_update == 0) {
Matrix.kind = "problem"
Matrix.color = severity_colors[Matrix.event_severity]
Matrix.icon = severity_icons[Matrix.event_severity]
} else {
Matrix.kind = "update"
Matrix.color = update_color
Matrix.icon = update_icon
}
} else {
Matrix.kind = "recovery"
Matrix.color = recovery_color
Matrix.icon = recovery_icon
}
},
request: function (path, payload) {
var request = new HttpRequest()
request.addHeader("Content-Type: application/json")
request.addHeader("Authorization: Bearer " + Matrix.matrix_token)
var url = Matrix.matrix_url + path
Zabbix.Log(4, "[Matrix Webhook] new request to: " + url)
if (Matrix.http_proxy != undefined) {
request.setProxy(Matrix.http_proxy)
}
var blob = request.post(url, JSON.stringify(payload))
if (request.getStatus() !== 200) {
var resp = JSON.parse(blob)
if (request.getStatus() == 403 && resp.error.indexOf("not in room") !== -1) {
throw "User is not in room"
}
Zabbix.Log(4, "[Matrix Webhook] Request failed: " + resp.error)
throw "Request failed: " + request.getStatus() + " " + resp.error
}
},
joinRoom: function () {
Matrix.request("/_matrix/client/r0/rooms/" + Matrix.matrix_room + "/join", {})
},
sendMessage: function () {
var body = ""
if (Matrix.enable_icons && Matrix.icon) {
body += "@room\n"
body += Matrix.icon + " "
}
body += Matrix.alert_subject + "\n"
body += Matrix.alert_message
if (Matrix.event_url != undefined) {
body += "\n" + Matrix.event_url
}
var formatted_body = ""
if (Matrix.enable_colors) {
formatted_body += '<span data-mx-color="{color}">'.replace("{color}", Matrix.color)
} else {
formatted_body += "<span>"
}
formatted_body += "<strong>"
if (Matrix.enable_icons && Matrix.icon) {
formatted_body += Matrix.icon + " "
}
if (Matrix.event_url != undefined) {
formatted_body += '<a href="{href}">'.replace("{href}", Matrix.event_url)
}
formatted_body += Matrix.alert_subject
if (Matrix.event_url != undefined) {
formatted_body += "</a>"
}
formatted_body += "</strong><br />"
formatted_body += Matrix.alert_message.replace(/\n/g, "<br />")
formatted_body += "</span>"
const payload = {
body: body,
msgtype: "m.notice",
format: "org.matrix.custom.html",
formatted_body: formatted_body,
}
Matrix.request(
"/_matrix/client/r0/rooms/" + Matrix.matrix_room + "/send/m.room.message",
payload
)
},
}
try {
var params = JSON.parse(value)
Matrix.validate(params)
try {
Matrix.sendMessage()
} catch (error) {
if (error == "User is not in room") {
Matrix.joinRoom()
Matrix.sendMessage()
} else {
throw error
}
}
return "OK"
} catch (error) {
Zabbix.Log(4, "[Matrix Webhook] Error: " + error)
throw "Sending failed: " + error
}
```
### Outcome
#### What did you expect?
I expect to see a notification on my element desktop app and android app via push notification
#### What happened instead?
I can see the messages when I open the room download immediately, even if they are hours old. I do not receive a popup notification on my desktop app, nor via android push notification. This happens in encrypted rooms (even when the script doesn't support E2E, and in non E2E rooms.
### Operating system
Linux FlatPak
### Application version
Element version: 1.11.17 Olm version: 3.2.12
### How did you install the app?
Fedora FlatPak
### Homeserver
script account: matrix.org receiving account: synapse docker
### Will you send logs?
Yes
|
1.0
|
Notifications do not appear for any app received from API session - ### Steps to reproduce
I am using a script to send notifications via session ID from a matrix.org account to a room in matrix.org. I have joined this room from my self-hosted user and can see the messages posted from the script. I have notifications turned on for all messages and I do receive popup notifications for messages from matrix.org users who use the element app.
```
const required_input = [
"matrix_url",
"matrix_room",
"matrix_token",
"alert_subject",
"alert_message",
"event_severity",
"event_is_problem",
"event_is_update",
"enable_colors",
"enable_icons",
]
const update_color = "#000000"
const recovery_color = "#098e68"
const severity_colors = [
"#5a5a5a", // Not classified
"#2caed6", // Information
"#d6832c", // Warning
"#d6542c", // Average
"#d62c2c", // High
"#ff0000", // Disaster
]
const update_icon = String.fromCodePoint("0x1f4dd")
const recovery_icon = String.fromCodePoint("0x2705")
const severity_icons = [
String.fromCodePoint("0x2754"), // Not classified
String.fromCodePoint("0x2139"), // Information
String.fromCodePoint("0x26a0"), // Warning
String.fromCodePoint("0x274c"), // Average
String.fromCodePoint("0x1f525"), // High
String.fromCodePoint("0x1f4a5"), // Disaster
]
var Matrix = {
validate: function (params) {
required_input.forEach(function (key) {
if (key in params && params[key] != undefined) {
Matrix[key] = params[key]
} else {
throw "Missing value for key: " + key
}
})
Matrix.alert_subject = Matrix.alert_subject.replace(/\r/g, "")
Matrix.alert_message = Matrix.alert_message.replace(/\r/g, "")
Matrix.event_severity = parseInt(Matrix.event_severity)
Matrix.event_is_problem = parseInt(Matrix.event_is_problem)
Matrix.event_is_update = parseInt(Matrix.event_is_update)
if (typeof params.event_url === "string" && params.event_url.trim() !== "") {
Matrix.event_url = params.event_url
}
Matrix.enable_colors = Matrix.enable_colors.toLowerCase() == "true"
Matrix.enable_icons = Matrix.enable_icons.toLowerCase() == "true"
if (typeof params.http_proxy === "string" && params.http_proxy.trim() !== "") {
Matrix.http_proxy = params.http_proxy
}
if (Matrix.event_is_problem == 1) {
if (Matrix.event_is_update == 0) {
Matrix.kind = "problem"
Matrix.color = severity_colors[Matrix.event_severity]
Matrix.icon = severity_icons[Matrix.event_severity]
} else {
Matrix.kind = "update"
Matrix.color = update_color
Matrix.icon = update_icon
}
} else {
Matrix.kind = "recovery"
Matrix.color = recovery_color
Matrix.icon = recovery_icon
}
},
request: function (path, payload) {
var request = new HttpRequest()
request.addHeader("Content-Type: application/json")
request.addHeader("Authorization: Bearer " + Matrix.matrix_token)
var url = Matrix.matrix_url + path
Zabbix.Log(4, "[Matrix Webhook] new request to: " + url)
if (Matrix.http_proxy != undefined) {
request.setProxy(Matrix.http_proxy)
}
var blob = request.post(url, JSON.stringify(payload))
if (request.getStatus() !== 200) {
var resp = JSON.parse(blob)
if (request.getStatus() == 403 && resp.error.indexOf("not in room") !== -1) {
throw "User is not in room"
}
Zabbix.Log(4, "[Matrix Webhook] Request failed: " + resp.error)
throw "Request failed: " + request.getStatus() + " " + resp.error
}
},
joinRoom: function () {
Matrix.request("/_matrix/client/r0/rooms/" + Matrix.matrix_room + "/join", {})
},
sendMessage: function () {
var body = ""
if (Matrix.enable_icons && Matrix.icon) {
body += "@room\n"
body += Matrix.icon + " "
}
body += Matrix.alert_subject + "\n"
body += Matrix.alert_message
if (Matrix.event_url != undefined) {
body += "\n" + Matrix.event_url
}
var formatted_body = ""
if (Matrix.enable_colors) {
formatted_body += '<span data-mx-color="{color}">'.replace("{color}", Matrix.color)
} else {
formatted_body += "<span>"
}
formatted_body += "<strong>"
if (Matrix.enable_icons && Matrix.icon) {
formatted_body += Matrix.icon + " "
}
if (Matrix.event_url != undefined) {
formatted_body += '<a href="{href}">'.replace("{href}", Matrix.event_url)
}
formatted_body += Matrix.alert_subject
if (Matrix.event_url != undefined) {
formatted_body += "</a>"
}
formatted_body += "</strong><br />"
formatted_body += Matrix.alert_message.replace(/\n/g, "<br />")
formatted_body += "</span>"
const payload = {
body: body,
msgtype: "m.notice",
format: "org.matrix.custom.html",
formatted_body: formatted_body,
}
Matrix.request(
"/_matrix/client/r0/rooms/" + Matrix.matrix_room + "/send/m.room.message",
payload
)
},
}
try {
var params = JSON.parse(value)
Matrix.validate(params)
try {
Matrix.sendMessage()
} catch (error) {
if (error == "User is not in room") {
Matrix.joinRoom()
Matrix.sendMessage()
} else {
throw error
}
}
return "OK"
} catch (error) {
Zabbix.Log(4, "[Matrix Webhook] Error: " + error)
throw "Sending failed: " + error
}
```
### Outcome
#### What did you expect?
I expect to see a notification on my element desktop app and android app via push notification
#### What happened instead?
I can see the messages when I open the room download immediately, even if they are hours old. I do not receive a popup notification on my desktop app, nor via android push notification. This happens in encrypted rooms (even when the script doesn't support E2E, and in non E2E rooms.
### Operating system
Linux FlatPak
### Application version
Element version: 1.11.17 Olm version: 3.2.12
### How did you install the app?
Fedora FlatPak
### Homeserver
script account: matrix.org receiving account: synapse docker
### Will you send logs?
Yes
|
non_process
|
notifications do not appear for any app received from api session steps to reproduce i am using a script to send notifications via session id from a matrix org account to a room in matrix org i have joined this room from my self hosted user and can see the messages posted from the script i have notifications turned on for all messages and i do receive popup notifications for messages from matrix org users who use the element app const required input matrix url matrix room matrix token alert subject alert message event severity event is problem event is update enable colors enable icons const update color const recovery color const severity colors not classified information warning average high disaster const update icon string fromcodepoint const recovery icon string fromcodepoint const severity icons string fromcodepoint not classified string fromcodepoint information string fromcodepoint warning string fromcodepoint average string fromcodepoint high string fromcodepoint disaster var matrix validate function params required input foreach function key if key in params params undefined matrix params else throw missing value for key key matrix alert subject matrix alert subject replace r g matrix alert message matrix alert message replace r g matrix event severity parseint matrix event severity matrix event is problem parseint matrix event is problem matrix event is update parseint matrix event is update if typeof params event url string params event url trim matrix event url params event url matrix enable colors matrix enable colors tolowercase true matrix enable icons matrix enable icons tolowercase true if typeof params http proxy string params http proxy trim matrix http proxy params http proxy if matrix event is problem if matrix event is update matrix kind problem matrix color severity colors matrix icon severity icons else matrix kind update matrix color update color matrix icon update icon else matrix kind recovery matrix color recovery color matrix icon recovery icon request function path payload var request new httprequest request addheader content type application json request addheader authorization bearer matrix matrix token var url matrix matrix url path zabbix log new request to url if matrix http proxy undefined request setproxy matrix http proxy var blob request post url json stringify payload if request getstatus var resp json parse blob if request getstatus resp error indexof not in room throw user is not in room zabbix log request failed resp error throw request failed request getstatus resp error joinroom function matrix request matrix client rooms matrix matrix room join sendmessage function var body if matrix enable icons matrix icon body room n body matrix icon body matrix alert subject n body matrix alert message if matrix event url undefined body n matrix event url var formatted body if matrix enable colors formatted body replace color matrix color else formatted body formatted body if matrix enable icons matrix icon formatted body matrix icon if matrix event url undefined formatted body replace href matrix event url formatted body matrix alert subject if matrix event url undefined formatted body formatted body formatted body matrix alert message replace n g formatted body const payload body body msgtype m notice format org matrix custom html formatted body formatted body matrix request matrix client rooms matrix matrix room send m room message payload try var params json parse value matrix validate params try matrix sendmessage catch error if error user is not in room matrix joinroom matrix sendmessage else throw error return ok catch error zabbix log error error throw sending failed error outcome what did you expect i expect to see a notification on my element desktop app and android app via push notification what happened instead i can see the messages when i open the room download immediately even if they are hours old i do not receive a popup notification on my desktop app nor via android push notification this happens in encrypted rooms even when the script doesn t support and in non rooms operating system linux flatpak application version element version olm version how did you install the app fedora flatpak homeserver script account matrix org receiving account synapse docker will you send logs yes
| 0
|
563,075
| 16,675,652,007
|
IssuesEvent
|
2021-06-07 15:51:57
|
pa11y/pa11y
|
https://api.github.com/repos/pa11y/pa11y
|
opened
|
Increase default timeout from 30s to 60s
|
priority: medium type: enhancement
|
The default timeout of 30s seems to be too small for many common uses of pa11y:
* CD/CI environments, specially in the free tiers, have usually fairly low CPU power, which cause tests to be slow and things to take much longer.
* Modern pages with loads of third party content may often show load times longer than 30 seconds on a decently powered CPU.
* Pa11y users with lower income or in places with slow internet connections may be experiencing many more timeouts than other users due to the speed and latency of the network between them and the site being tested.
A cursory review of pa11y configs at projects at Springer Nature show that most of them already set the default timeout to 60 seconds.
I propose to increase the default timeout from the current amount, which is 30 seconds if I'm not mistaken, to (at least) 60 seconds.
I like to be conservative with changes and don't want to mess with people's pipelines too much, so it may be safer to make this change breaking and release it for v7, so I've created a milestone and assigned this issue to it, but we can always change that if we don't agree :shrug:.
|
1.0
|
Increase default timeout from 30s to 60s - The default timeout of 30s seems to be too small for many common uses of pa11y:
* CD/CI environments, specially in the free tiers, have usually fairly low CPU power, which cause tests to be slow and things to take much longer.
* Modern pages with loads of third party content may often show load times longer than 30 seconds on a decently powered CPU.
* Pa11y users with lower income or in places with slow internet connections may be experiencing many more timeouts than other users due to the speed and latency of the network between them and the site being tested.
A cursory review of pa11y configs at projects at Springer Nature show that most of them already set the default timeout to 60 seconds.
I propose to increase the default timeout from the current amount, which is 30 seconds if I'm not mistaken, to (at least) 60 seconds.
I like to be conservative with changes and don't want to mess with people's pipelines too much, so it may be safer to make this change breaking and release it for v7, so I've created a milestone and assigned this issue to it, but we can always change that if we don't agree :shrug:.
|
non_process
|
increase default timeout from to the default timeout of seems to be too small for many common uses of cd ci environments specially in the free tiers have usually fairly low cpu power which cause tests to be slow and things to take much longer modern pages with loads of third party content may often show load times longer than seconds on a decently powered cpu users with lower income or in places with slow internet connections may be experiencing many more timeouts than other users due to the speed and latency of the network between them and the site being tested a cursory review of configs at projects at springer nature show that most of them already set the default timeout to seconds i propose to increase the default timeout from the current amount which is seconds if i m not mistaken to at least seconds i like to be conservative with changes and don t want to mess with people s pipelines too much so it may be safer to make this change breaking and release it for so i ve created a milestone and assigned this issue to it but we can always change that if we don t agree shrug
| 0
|
229,794
| 7,594,907,456
|
IssuesEvent
|
2018-04-27 01:58:14
|
adamjamesadair/manage-my-tabs
|
https://api.github.com/repos/adamjamesadair/manage-my-tabs
|
closed
|
Add installation instructions
|
Priority: High Type: Maintenance
|
Add instructions for how to install and use the extension to the README
|
1.0
|
Add installation instructions - Add instructions for how to install and use the extension to the README
|
non_process
|
add installation instructions add instructions for how to install and use the extension to the readme
| 0
|
42,588
| 12,908,737,982
|
IssuesEvent
|
2020-07-15 07:59:07
|
NixOS/nixpkgs
|
https://api.github.com/repos/NixOS/nixpkgs
|
closed
|
Vulnerability roundup 75: imagemagick-6.9.9-34: 2 advisories
|
1.severity: security
|
[search](https://search.nix.gsc.io/?q=imagemagick&i=fosho&repos=nixos-nixpkgs), [files](https://github.com/NixOS/nixpkgs/search?utf8=%E2%9C%93&q=imagemagick+in%3Apath&type=Code)
* [ ] [CVE-2019-14980](https://nvd.nist.gov/vuln/detail/CVE-2019-14980) (nixos-unstable, nixos-19.03)
* [ ] [CVE-2019-14981](https://nvd.nist.gov/vuln/detail/CVE-2019-14981) (nixos-unstable, nixos-19.03)
Scanned versions: nixos-unstable: e19054ab3cd; nixos-19.03: cf018a7c558. May contain false positives.
|
True
|
Vulnerability roundup 75: imagemagick-6.9.9-34: 2 advisories - [search](https://search.nix.gsc.io/?q=imagemagick&i=fosho&repos=nixos-nixpkgs), [files](https://github.com/NixOS/nixpkgs/search?utf8=%E2%9C%93&q=imagemagick+in%3Apath&type=Code)
* [ ] [CVE-2019-14980](https://nvd.nist.gov/vuln/detail/CVE-2019-14980) (nixos-unstable, nixos-19.03)
* [ ] [CVE-2019-14981](https://nvd.nist.gov/vuln/detail/CVE-2019-14981) (nixos-unstable, nixos-19.03)
Scanned versions: nixos-unstable: e19054ab3cd; nixos-19.03: cf018a7c558. May contain false positives.
|
non_process
|
vulnerability roundup imagemagick advisories nixos unstable nixos nixos unstable nixos scanned versions nixos unstable nixos may contain false positives
| 0
|
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