Month: July 2024
Conditional Compilation Of Simulink Model Code Generation
hi, as following picture shows, when after generate code, during compile how to control just let fun2 code enter compile process, fun1 code is forbidden to enter compile.hi, as following picture shows, when after generate code, during compile how to control just let fun2 code enter compile process, fun1 code is forbidden to enter compile. hi, as following picture shows, when after generate code, during compile how to control just let fun2 code enter compile process, fun1 code is forbidden to enter compile. simulink, embedded coder, conditional compilation MATLAB Answers — New Questions
Configuration Manager 2403 – Corrupt Download from Microsoft
Hello all. I just tried to download Configuration Manager 2403 from Microsoft’s evaluation center.
https://www.microsoft.com/en-us/evalcenter/download-microsoft-endpoint-configuration-manager
I downloaded it successfully in Google Chrome. Extracted it using 7z. When I go to install it, it has several warning saying HASH mismatch…
I looked at one of the files: SMSSETUPClientx64MMASetup-AMD64.exe for example and it’s 0KB. The file is empty. So, I redownloaded the file using CURL -O command thinking maybe google didn’t save it correctly. This time extract it by just double clicking the “MCM_Configmgr_2403.exe” file and it asked where to extract, created new folder for it, and still same issue…
There are SEVERAL files that have HASH issues…. Anyone else have this problem?
Also log file shows a bunch of errors about missing files…
WARNING: Failed to find default package file C:Program FilesMicrosoft Configuration Managerbinx640000409WindowsDefenderSettings.resx
Is the published 2403 image by Microsoft just bad? Where can I get an actual working evaluation version?
Hello all. I just tried to download Configuration Manager 2403 from Microsoft’s evaluation center.https://www.microsoft.com/en-us/evalcenter/download-microsoft-endpoint-configuration-manager I downloaded it successfully in Google Chrome. Extracted it using 7z. When I go to install it, it has several warning saying HASH mismatch… I looked at one of the files: SMSSETUPClientx64MMASetup-AMD64.exe for example and it’s 0KB. The file is empty. So, I redownloaded the file using CURL -O command thinking maybe google didn’t save it correctly. This time extract it by just double clicking the “MCM_Configmgr_2403.exe” file and it asked where to extract, created new folder for it, and still same issue… There are SEVERAL files that have HASH issues…. Anyone else have this problem? Also log file shows a bunch of errors about missing files…WARNING: Failed to find default package file C:Program FilesMicrosoft Configuration Managerbinx640000409WindowsDefenderSettings.resx Is the published 2403 image by Microsoft just bad? Where can I get an actual working evaluation version? Read More
Meeting habits on Teams Platform
Hi team
“Meeting habits detail” has been removed from the Viva Insights on teams platform.Why was this removed?How and when was this update communicated?
Hi team“Meeting habits detail” has been removed from the Viva Insights on teams platform.Why was this removed?How and when was this update communicated? Read More
Exchange Online Previews Inbound SMTP DANE with DNSSEC
On July 17, Microsoft announced the public preview of inbound SMTP DANE with DNSSEC for Exchange Online, a welcome step forward to improve messaging security. A previous attempt to launch the preview foundered because Microsoft wanted to insist on Microsoft 365 E5 licenses for the feature. Mature reflection prevailed and inbound DANE with DNSSEC is available to all, which is how it should be.
https://office365itpros.com/2024/07/18/inbound-dane-with-dnssec/
On July 17, Microsoft announced the public preview of inbound SMTP DANE with DNSSEC for Exchange Online, a welcome step forward to improve messaging security. A previous attempt to launch the preview foundered because Microsoft wanted to insist on Microsoft 365 E5 licenses for the feature. Mature reflection prevailed and inbound DANE with DNSSEC is available to all, which is how it should be.
https://office365itpros.com/2024/07/18/inbound-dane-with-dnssec/ Read More
Custom ASPX files are downloading, not running.
Hi All,
I have some custom ASPX files on a SharePoint site. When you click on them, they run a html file (saved as ASPX). However, if I create a new file in the same vein, when a user clicks on it, it downloads instead of running.
Even if I copy an existing file and paste in the same folder, the existing file will continue to run as intended however the new file will download, even though the file is identical.
Has anyone experienced any similar issues, or know of a solution? Thanks.
Hi All, I have some custom ASPX files on a SharePoint site. When you click on them, they run a html file (saved as ASPX). However, if I create a new file in the same vein, when a user clicks on it, it downloads instead of running. Even if I copy an existing file and paste in the same folder, the existing file will continue to run as intended however the new file will download, even though the file is identical. Has anyone experienced any similar issues, or know of a solution? Thanks. Read More
SQL job fails while inserting records to Oracle db
Hi,
We have created SSIS package for inserting some of the table records from SQL server database to Oracle database. To insert the records we have created custom script and automated it has SQL agent and it is working fine for quite few months. But for last 1 week we are getting error unable to acquire connection.
Tried the below steps:
1. Both ports are opened in oracle and SQL.
2. Tested connectivity and was working fine.
3. Tried by creating a export data task from SQL database and run with the custom script to insert data and it worked fine.
Only SQL agent job the data is not transferring and getting unable to acquire connection.
SQL version : 2019 RTM CU-25 Enterprise edition.
Can someone please help on this.
Thanks,
Sujay
Hi, We have created SSIS package for inserting some of the table records from SQL server database to Oracle database. To insert the records we have created custom script and automated it has SQL agent and it is working fine for quite few months. But for last 1 week we are getting error unable to acquire connection. Tried the below steps:1. Both ports are opened in oracle and SQL.2. Tested connectivity and was working fine.3. Tried by creating a export data task from SQL database and run with the custom script to insert data and it worked fine.Only SQL agent job the data is not transferring and getting unable to acquire connection.SQL version : 2019 RTM CU-25 Enterprise edition.Can someone please help on this. Thanks,Sujay Read More
How to fix QuickBook error 1402 after update?
I’m encountering QuickBook Error 1402, and it’s preventing me from installing or updating the software. The error message states that a certain key could not be opened. I have tried restarting my computer and running the installation as an administrator, but the issue persists. How can I resolve this error and successfully install or update QuickBook?
I’m encountering QuickBook Error 1402, and it’s preventing me from installing or updating the software. The error message states that a certain key could not be opened. I have tried restarting my computer and running the installation as an administrator, but the issue persists. How can I resolve this error and successfully install or update QuickBook? Read More
Is MATLAB affected by the Homeland Security Vulnerability Notice regarding Java?
On January 10, 2013, the United States Computer Emergency Readiness team issued a Vulnerability Notice for the Oracle Java Runtime Environment: http://www.kb.cert.org/vuls/id/625617.
This notice recommends that Java be disabled in web browsers.On January 10, 2013, the United States Computer Emergency Readiness team issued a Vulnerability Notice for the Oracle Java Runtime Environment: http://www.kb.cert.org/vuls/id/625617.
This notice recommends that Java be disabled in web browsers. On January 10, 2013, the United States Computer Emergency Readiness team issued a Vulnerability Notice for the Oracle Java Runtime Environment: http://www.kb.cert.org/vuls/id/625617.
This notice recommends that Java be disabled in web browsers. MATLAB Answers — New Questions
Why does my stand-alone created using the MATLAB Compiler take longer to start the first time?
It takes longer to start my stand-alone application the first time it is run than consecutive runs. Also if I run the application some time later, it again takes longer the first time but becomes faster on consecutive runs. I would expect it to always start quicker after the first run.It takes longer to start my stand-alone application the first time it is run than consecutive runs. Also if I run the application some time later, it again takes longer the first time but becomes faster on consecutive runs. I would expect it to always start quicker after the first run. It takes longer to start my stand-alone application the first time it is run than consecutive runs. Also if I run the application some time later, it again takes longer the first time but becomes faster on consecutive runs. I would expect it to always start quicker after the first run. standalone, mcr, slow, slower, fast, faster, second MATLAB Answers — New Questions
Why does MATLAB place the file msdia80.dll in the root of the boot drive for Windows x64?
After installing MATLAB on a 64 bit Windows machine, there is msdia80.dll on the root of the boot drive (C:). Prior to installation that file was not there. Why is the file in the root and how can I move it?After installing MATLAB on a 64 bit Windows machine, there is msdia80.dll on the root of the boot drive (C:). Prior to installation that file was not there. Why is the file in the root and how can I move it? After installing MATLAB on a 64 bit Windows machine, there is msdia80.dll on the root of the boot drive (C:). Prior to installation that file was not there. Why is the file in the root and how can I move it? MATLAB Answers — New Questions
The custom loss function, which is trained with the dlarray structure, reports an error when using sgdmupdate to update, and cannot be assigned, because this type of variable
% 定义 LSTM 网络结构
numFeatures = size(X_train, 2); % 特征数量,这里是 3
numHiddenUnits = 100; % LSTM 隐藏单元数量
layers = [ …
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numFeatures)
];
maxEpochs = 50;
miniBatchSize = 3;
learningRate = 0.01;
% 初始化 LSTM 模型
net = dlnetwork(layers); % 在 CPU 上训练
% 训练循环
numObservations = size(X_train, 1);
numMiniBatches = floor(numObservations / miniBatchSize);
averageGrad = [];
averageSqGrad = [];
iteration = 0;
Velocities = zeros(size(net));
momentum = 0.9;
for epoch = 1:maxEpochs
% 打乱训练数据
idx = randperm(numObservations);
X_train_shuffled = X_train(idx, :);
Y_train_shuffled = Y_train(idx, :);
totalLoss = 0;
for i = 1:numMiniBatches
idxBatch = (i – 1) * miniBatchSize + 1 : i * miniBatchSize;
X_batch = X_train_shuffled(idxBatch, :);
Y_batch = Y_train_shuffled(idxBatch, :);
iteration = iteration + 1;
% 前向传播计算预测
Y_pred = predict(net, X_batch’);
Y_pred = Y_pred’;
Y_pred = dlarray(Y_pred, ‘TS’);
Y_batch = dlarray(Y_batch, ‘TS’);
%loss = mean(( Y_batch – Y_pred).^2, ‘all’);
[loss_1, gradients] = dlfeval(@customLoss, Y_batch, Y_pred);
gradients = extractdata(gradients);
[net, Velocities] = sgdmupdate(net, gradients, Velocities,learningRate,momentum);
% 计算损失
% loss = customLoss(Y_batch, Y_pred);
% totalLoss = totalLoss + loss;
% 反向传播更新梯度
%dL_dY = 2 * (Y_pred – Y_batch) / miniBatchSize;
%gradients = dlgradient(loss, net.LearnableParameters);
%net = updateParameters(net, gradients, learningRate);
end
% 显示每个 epoch 的平均损失
avgLoss = totalLoss / numMiniBatches;
fprintf(‘Epoch %d, Average Loss: %.4fn’, epoch, avgLoss);
end
无法执行赋值,因为此类型的变量不支持使用点进行索引。
出错 deep.internal.recording.containerfeval>iProcessNetwork_Nout_Nin (第 382 行)
protoTable.Value = zeros(height(protoTable),0);
出错 deep.internal.recording.containerfeval>iDispatch_Nout_Nin (第 214 行)
outputs = iProcessNetwork_Nout_Nin(fun, paramFun, numOut, …
出错 deep.internal.recording.containerfeval (第 38 行)
outputs = iDispatch_Nout_Nin(allowNetInput, fun, paramFun, numOut, …
出错 deep.internal.networkContainerFixedArgsFun (第 29 行)
varargout = deep.internal.recording.containerfeval(…
出错 sgdmupdate (第 126 行)
[p, vel] = deep.internal.networkContainerFixedArgsFun(func, …% 定义 LSTM 网络结构
numFeatures = size(X_train, 2); % 特征数量,这里是 3
numHiddenUnits = 100; % LSTM 隐藏单元数量
layers = [ …
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numFeatures)
];
maxEpochs = 50;
miniBatchSize = 3;
learningRate = 0.01;
% 初始化 LSTM 模型
net = dlnetwork(layers); % 在 CPU 上训练
% 训练循环
numObservations = size(X_train, 1);
numMiniBatches = floor(numObservations / miniBatchSize);
averageGrad = [];
averageSqGrad = [];
iteration = 0;
Velocities = zeros(size(net));
momentum = 0.9;
for epoch = 1:maxEpochs
% 打乱训练数据
idx = randperm(numObservations);
X_train_shuffled = X_train(idx, :);
Y_train_shuffled = Y_train(idx, :);
totalLoss = 0;
for i = 1:numMiniBatches
idxBatch = (i – 1) * miniBatchSize + 1 : i * miniBatchSize;
X_batch = X_train_shuffled(idxBatch, :);
Y_batch = Y_train_shuffled(idxBatch, :);
iteration = iteration + 1;
% 前向传播计算预测
Y_pred = predict(net, X_batch’);
Y_pred = Y_pred’;
Y_pred = dlarray(Y_pred, ‘TS’);
Y_batch = dlarray(Y_batch, ‘TS’);
%loss = mean(( Y_batch – Y_pred).^2, ‘all’);
[loss_1, gradients] = dlfeval(@customLoss, Y_batch, Y_pred);
gradients = extractdata(gradients);
[net, Velocities] = sgdmupdate(net, gradients, Velocities,learningRate,momentum);
% 计算损失
% loss = customLoss(Y_batch, Y_pred);
% totalLoss = totalLoss + loss;
% 反向传播更新梯度
%dL_dY = 2 * (Y_pred – Y_batch) / miniBatchSize;
%gradients = dlgradient(loss, net.LearnableParameters);
%net = updateParameters(net, gradients, learningRate);
end
% 显示每个 epoch 的平均损失
avgLoss = totalLoss / numMiniBatches;
fprintf(‘Epoch %d, Average Loss: %.4fn’, epoch, avgLoss);
end
无法执行赋值,因为此类型的变量不支持使用点进行索引。
出错 deep.internal.recording.containerfeval>iProcessNetwork_Nout_Nin (第 382 行)
protoTable.Value = zeros(height(protoTable),0);
出错 deep.internal.recording.containerfeval>iDispatch_Nout_Nin (第 214 行)
outputs = iProcessNetwork_Nout_Nin(fun, paramFun, numOut, …
出错 deep.internal.recording.containerfeval (第 38 行)
outputs = iDispatch_Nout_Nin(allowNetInput, fun, paramFun, numOut, …
出错 deep.internal.networkContainerFixedArgsFun (第 29 行)
varargout = deep.internal.recording.containerfeval(…
出错 sgdmupdate (第 126 行)
[p, vel] = deep.internal.networkContainerFixedArgsFun(func, … % 定义 LSTM 网络结构
numFeatures = size(X_train, 2); % 特征数量,这里是 3
numHiddenUnits = 100; % LSTM 隐藏单元数量
layers = [ …
sequenceInputLayer(numFeatures)
lstmLayer(numHiddenUnits)
fullyConnectedLayer(numFeatures)
];
maxEpochs = 50;
miniBatchSize = 3;
learningRate = 0.01;
% 初始化 LSTM 模型
net = dlnetwork(layers); % 在 CPU 上训练
% 训练循环
numObservations = size(X_train, 1);
numMiniBatches = floor(numObservations / miniBatchSize);
averageGrad = [];
averageSqGrad = [];
iteration = 0;
Velocities = zeros(size(net));
momentum = 0.9;
for epoch = 1:maxEpochs
% 打乱训练数据
idx = randperm(numObservations);
X_train_shuffled = X_train(idx, :);
Y_train_shuffled = Y_train(idx, :);
totalLoss = 0;
for i = 1:numMiniBatches
idxBatch = (i – 1) * miniBatchSize + 1 : i * miniBatchSize;
X_batch = X_train_shuffled(idxBatch, :);
Y_batch = Y_train_shuffled(idxBatch, :);
iteration = iteration + 1;
% 前向传播计算预测
Y_pred = predict(net, X_batch’);
Y_pred = Y_pred’;
Y_pred = dlarray(Y_pred, ‘TS’);
Y_batch = dlarray(Y_batch, ‘TS’);
%loss = mean(( Y_batch – Y_pred).^2, ‘all’);
[loss_1, gradients] = dlfeval(@customLoss, Y_batch, Y_pred);
gradients = extractdata(gradients);
[net, Velocities] = sgdmupdate(net, gradients, Velocities,learningRate,momentum);
% 计算损失
% loss = customLoss(Y_batch, Y_pred);
% totalLoss = totalLoss + loss;
% 反向传播更新梯度
%dL_dY = 2 * (Y_pred – Y_batch) / miniBatchSize;
%gradients = dlgradient(loss, net.LearnableParameters);
%net = updateParameters(net, gradients, learningRate);
end
% 显示每个 epoch 的平均损失
avgLoss = totalLoss / numMiniBatches;
fprintf(‘Epoch %d, Average Loss: %.4fn’, epoch, avgLoss);
end
无法执行赋值,因为此类型的变量不支持使用点进行索引。
出错 deep.internal.recording.containerfeval>iProcessNetwork_Nout_Nin (第 382 行)
protoTable.Value = zeros(height(protoTable),0);
出错 deep.internal.recording.containerfeval>iDispatch_Nout_Nin (第 214 行)
outputs = iProcessNetwork_Nout_Nin(fun, paramFun, numOut, …
出错 deep.internal.recording.containerfeval (第 38 行)
outputs = iDispatch_Nout_Nin(allowNetInput, fun, paramFun, numOut, …
出错 deep.internal.networkContainerFixedArgsFun (第 29 行)
varargout = deep.internal.recording.containerfeval(…
出错 sgdmupdate (第 126 行)
[p, vel] = deep.internal.networkContainerFixedArgsFun(func, … sgdmupdate, deeplearning, lstm, dlarray MATLAB Answers — New Questions
How to update the “DeviceID” of a PC already in TeamViewer
My problem is the following:
I can’t find a way to change the name of a pc registered in TeamViewer in real time (even if it’s not exactly in real time, it’s not serious, but at least check several times a day. For example, every 3 hours) with Intune
I sometimes rename PCs in Intune
For example, old name: desktop-002
New name: dsktp-2024
TeamViewer will keep the old name
I’d like it to update regularly
I’ve tried several scripts but nothing works.
If you have an idea, thank you in advance 🙂
My problem is the following:I can’t find a way to change the name of a pc registered in TeamViewer in real time (even if it’s not exactly in real time, it’s not serious, but at least check several times a day. For example, every 3 hours) with IntuneI sometimes rename PCs in IntuneFor example, old name: desktop-002New name: dsktp-2024TeamViewer will keep the old nameI’d like it to update regularlyI’ve tried several scripts but nothing works.If you have an idea, thank you in advance 🙂 Read More
Recommend a good YouTube videos downloader for PC Windows 11
I am preparing a project report recently and need to download some experimental videos from YouTube as materials for the opening remarks. I have tried using some online video download services, but the download speed is very slow and there are many ads on the page.
Therefore, I am now looking for an efficient YouTube videos downloader suitable for Windows 11, hoping to find a software that is easy to operate, fast to download, and can guarantee the quality of the video. If you have used a good download tool, please recommend it. Thank you for your help!
I am preparing a project report recently and need to download some experimental videos from YouTube as materials for the opening remarks. I have tried using some online video download services, but the download speed is very slow and there are many ads on the page. Therefore, I am now looking for an efficient YouTube videos downloader suitable for Windows 11, hoping to find a software that is easy to operate, fast to download, and can guarantee the quality of the video. If you have used a good download tool, please recommend it. Thank you for your help! Read More
Azure Blogs – Articles from 8-July-2024 to 14-July-2024
AI + Machine Learning
Covering: Anomaly Detector, Azure Bot Services, Azure Cognitive Search, Azure ML, Azure Open Datasets, Azure Cognitive Services, Azure Video Indexer, Computer Vision, Content Moderator, Custom Vision, Data Science VM, Face API, Azure Form Recognizer, Azure Immersive Reader, Kinect DK, Language Understanding (LUIS), Microsoft Genomics, Personalizer, Project Bonsai, QnA Maker, Speaker recognition, Speech to Text, Speech translation, Cognitive Service for Language, Text to Speech, Translator, Azure Metrics Advisor, Health Bot, Azure Percept, Azure Applied AI Services, Azure OpenAI Service
Introducing the Azure AI Model Inference API
Azure OpenAI Extension for Function Apps Hands-on Experience
Running Open AI Whisper Model on Azure
Deploy a Phi-3 model in Azure AI, and consume it with C# and Semantic Kernel
Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
Four steps to expanding your AI skills with Python and Microsoft Learn
Building the Ultimate Nerdland Podcast Chatbot with RAG and LLM: Step-by-Step Guide
Comprehensive AI Safety and Security with defense in depth for Enterprises
From Principles to Practice: Developer Resources for Responsible AI Innovation
Streamlining SAP Processes with Azure OpenAI, Copilot Studio, and Power Platform
Accelerate data democratization in era of generative AI using Denodo Platform and Microsoft Fabric
Use WebGPU + ONNX Runtime Web + Transformer.js to build RAG applications by Phi-3-mini
Azure Video Indexer & Phi-3 introduce Textual Video Summary on Edge: Better Together story
Fast Transcription Public Preview in Azure AI Speech
Supply chain AI for the new era of value realization
GenAI Mastery: Crafting Robust Enterprise Solutions with PromptFlow and LangChain
Analytics
Covering: Azure Analysis Services, Azure Data Explorer, Azure Data Factory, Azure Data Lake Storage, Azure Data Share, Azure Databricks, Azure Stream Analytics, Azure Synapse Analytics, Data Catalog, Data Lake Analytics, HDInsight, Power BI Embedded, R Server for HDInsight, Microsoft Purview, Microsoft Graph Data Connect, Azure Chaos Studio
Accelerate data democratization in era of generative AI using Denodo Platform and Microsoft Fabric
Compute
Covering: Azure CycleCloud, Azure Quantum, Azure Spot Virtual Machines, Azure VMware Solution, Batch, Linux Virtual Machines, Virtual Machine Scale Sets, Virtual Machines, Azure Dedicated Host, Azure VM Image Builder, Azure Functions, Service Fabric
NVMe-enabled Ebsv5 VMs offering 400K IOPS and 10GBps throughput now generally available
General Availability Announcement: Azure VM Regional to Zonal Move
Where Does One Machine End and the Next Begin?
Microsoft Virtualization Migration Options
Azure OpenAI Extension for Function Apps Hands-on Experience
Containers
Covering: Azure Kubernetes Service (AKS), Azure Red Hat OpenShift, Azure Container Apps, Web App for Containers, Azure Container Instances, Azure Container Registry
Public Preview of the Windows Server Annual Channel for Containers on Azure Kubernetes Service
IBM Cloud Pak for Integration on Azure Red Hat OpenShift Now Generally Available
Microsoft Copilot in Azure Series – Kubectl
Databases
Covering: Azure Cache for Redis, Azure Cosmos DB, Azure Database for MariaDB, Azure Database for MySQL, Azure Database for PostgreSQL, Azure SQL, Azure SQL Database, Azure SQL Edge, Azure SQL Managed Instance, SQL Server on Azure VM, Table Storage, Azure Managed Instance for Apache Cassandra, Azure Confidential Ledger
Say hello to the Talking Postgres podcast
Update: Security hotfix released for OLE DB driver for SQL Server
Announcing SSMS 20.2 … and getting feedback for SSMS 21
Security Update for SQL Server 2016 SP3 Azure Connect Feature Pack
Security Update for SQL Server 2016 SP3 GDR
Security Update for SQL Server 2017 RTM CU31
Security Update for SQL Server 2017 RTM GDR
Security Update for SQL Server 2019 RTM CU27
Security Update for SQL Server 2019 RTM GDR
Security Update for SQL Server 2022 RTM CU13
Security Update for SQL Server 2022 RTM GDR
SQL Server 2022 分散型可用性グループにおける同期失敗
Azure Backup for SQL Server in Azure VM: Tips and Tricks from the Field
Increasing Security for SQL Server Enabled by Azure Arc
Azure Database for MySQL – June 2024 updates and latest feature roadmap
Developer Tools
Covering: App Configuration, Azure DevTest Labs, Azure Lab Services, SDKs, Visual Studio, Visual Studio Code, Azure Load Testing
Unlocking the Potential of Phi-3 and C# in AI Development
C# 13: Explore the latest preview features
.NET and .NET Framework July 2024 servicing releases updates
Why and How to Execute GraphQL Queries in .NET
.NET 9 Preview 6 is now available!
DevOps
Covering: Azure Artifacts, Azure Boards, Azure DevOps, Azure Pipelines, Azure Repos, Azure Test Plans, DevOps tool integrations, Azure Load Testing
Azure DevOps Server 2022.2 RTW now available
GitHub Availability Report: June 2024
Hybrid
Covering: Microsoft Azure Stack, Azure Arc
Supercharge your datacenters with Hyper-V and virtualized GPUs
Apply critical update for Azure Stack HCI VMs to maintain Azure verification
Increasing Security for SQL Server Enabled by Azure Arc
Identity
Covering: Azure Active Directory, Multi-factor Authentication, Azure Active Directory Domain Services, Azure Active Directory External Identities
Microsoft Entra certificate-based authentication enhancements
Microsoft Entra Suite now generally available
Integration
Covering: API Management, Event Grid, Logic Apps , Service Bus
Integrating Logic App with Semantic Kernel: A Detailed Guide and Demo
Azure API Center – The ultimate service to streamline API Governance across your organization.
Internet Of Things
Covering: Azure IoT Central, Azure IoT Edge, Azure IoT Hub, Azure RTOS, Azure Sphere, Azure Stream Analytics, Azure Time Series Insights, Microsoft Defender for IoT, Azure Percept, Windows for IoT
Azure Sphere – Image signing certificate update
Management and Governance
Covering: Automation, Azure Advisor, Azure Backup, Azure Blueprints, Azure Lighthouse, Azure Monitor, Azure Policy, Azure Resource Manager, Azure Service Health, Azure Site Recovery, Cloud Shell, Cost Management, Azure Portal, Network Watcher, Azure Automanage, Azure Resource Mover, Azure Chaos Studio, Azure Managed Grafana
What’s the difference between Azure savings plans for compute and Azure reservations?
New on Azure Marketplace: June 27-30, 2024
Public Preview Announcement: Azure Policy Built-in Versioning
Using Azure Automation to perform Azure Site Recovery post failover tasks in virtual machines
Govern your Azure Firewall configuration with Azure Policies
Azure Backup for SQL Server in Azure VM: Tips and Tricks from the Field
Azure Monitor: How To Stop Log-Based Alerts for Specific Resources
Introducing Agent and Gateway Extensions in Azure Monitor SCOM MI
Information protection: Auto labelling policy vs Information protection: Label Policy
Azure Verified Modules – Monthly Update [June]
Media
Covering: Azure Media Player, Content Protection, Encoding, Live and On-Demand Streaming, Media Services
No New Articles
Migration
Covering: Azure Database Migration Service, Azure Migrate, Data Box, Azure Site Recovery
Microsoft Virtualization Migration Options
Mixed Reality
Covering: Digital Twins, Kinect DK, Spatial Anchors, Remote Rendering, Object Anchors
No New Articles
Mobile
Covering: Azure Maps, MAUI, Notification Hubs, Visual Studio App Center, Xamarin, Azure Communication Services
Anywhere365 integrates Azure Communication Services into their Dialogue Cloud Platform
Networking
Covering: Application Gateway, Bastion, DDoS Protection, DNS, Azure ExpressRoute, Azure Firewall, Load Balancer, Firewall Manager, Front Door, Internet Analyzer, Azure Private Link, Content Delivery Network, Network Watcher, Traffic Manager, Virtual Network, Virtual WAN, VPN Gateway, Web Application Firewall, Azure Orbital, Route Server, Network Function Manager, Virtual Network Manager, Azure Private 5G Core
Dual-region deployments using Secure Virtual WAN Hub with Routing-Intent without Global Reach
Single-region deployment without Global Reach, using Secure Virtual WAN Hub with Routing-Intent
Azure WAF Public Preview: JavaScript Challenge
Save Costs with Basic SKU Application Gateway for more features and less fixed costs
Govern your Azure Firewall configuration with Azure Policies
Security
Covering: Defender for Cloud, DDoS Protection, Dedicated HSM, Azure Information Protection, Microsoft Sentinel, Key Vault, Microsoft Defender for Cloud, Microsoft Defender for IoT, Microsoft Azure Attestation, Azure Confidential Ledger
Microsoft Security Service Edge now generally available
Unified Security Operations Platform – Technical FAQ!
Guidance for handling “regreSSHion” (CVE-2024-6387) using Microsoft Security capabilities
Storage
Covering: Archive Storage, Avere vFXT for Azure, Azure Data Lake Storage, Azure Data Share, Files, FXT Edge Filer, HPC Cache, NetApp Files, Blob Storage, Data Box, Disk Storage, Queue Storage, Storage Accounts, Storage Explorer, StorSimple
Web
Covering: App Configuration, App Service, Azure Cognitive Search, Azure Maps, Azure SignalR Service, Static Web Apps, Azure Communication Services, Azure Web PubSub, Azure Fluid Relay, Web App for Containers
Memory Dump Collection using Procdump.exe for App Service (Windows)
Anywhere365 integrates Azure Communication Services into their Dialogue Cloud Platform
Azure Virtual Desktop
Covering: Windows Virtual Desktop, VMware Horizon Cloud on Microsoft Azure, Citrix Virtual Apps and Desktops for Azure
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AI + Machine Learning
Covering: Anomaly Detector, Azure Bot Services, Azure Cognitive Search, Azure ML, Azure Open Datasets, Azure Cognitive Services, Azure Video Indexer, Computer Vision, Content Moderator, Custom Vision, Data Science VM, Face API, Azure Form Recognizer, Azure Immersive Reader, Kinect DK, Language Understanding (LUIS), Microsoft Genomics, Personalizer, Project Bonsai, QnA Maker, Speaker recognition, Speech to Text, Speech translation, Cognitive Service for Language, Text to Speech, Translator, Azure Metrics Advisor, Health Bot, Azure Percept, Azure Applied AI Services, Azure OpenAI Service
Introducing the Azure AI Model Inference API
Open AI Whisper
Azure OpenAI Extension for Function Apps Hands-on Experience
Running Open AI Whisper Model on Azure
Deploy a Phi-3 model in Azure AI, and consume it with C# and Semantic Kernel
Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
Four steps to expanding your AI skills with Python and Microsoft Learn
Building the Ultimate Nerdland Podcast Chatbot with RAG and LLM: Step-by-Step Guide
Comprehensive AI Safety and Security with defense in depth for Enterprises
From Principles to Practice: Developer Resources for Responsible AI Innovation
Streamlining SAP Processes with Azure OpenAI, Copilot Studio, and Power Platform
Accelerate data democratization in era of generative AI using Denodo Platform and Microsoft Fabric
Use WebGPU + ONNX Runtime Web + Transformer.js to build RAG applications by Phi-3-mini
Azure Video Indexer & Phi-3 introduce Textual Video Summary on Edge: Better Together story
Fast Transcription Public Preview in Azure AI Speech
Supply chain AI for the new era of value realization
GenAI Mastery: Crafting Robust Enterprise Solutions with PromptFlow and LangChain
Analytics
Covering: Azure Analysis Services, Azure Data Explorer, Azure Data Factory, Azure Data Lake Storage, Azure Data Share, Azure Databricks, Azure Stream Analytics, Azure Synapse Analytics, Data Catalog, Data Lake Analytics, HDInsight, Power BI Embedded, R Server for HDInsight, Microsoft Purview, Microsoft Graph Data Connect, Azure Chaos Studio
Accelerate data democratization in era of generative AI using Denodo Platform and Microsoft Fabric
Compute
Covering: Azure CycleCloud, Azure Quantum, Azure Spot Virtual Machines, Azure VMware Solution, Batch, Linux Virtual Machines, Virtual Machine Scale Sets, Virtual Machines, Azure Dedicated Host, Azure VM Image Builder, Azure Functions, Service Fabric
NVMe-enabled Ebsv5 VMs offering 400K IOPS and 10GBps throughput now generally available
General Availability Announcement: Azure VM Regional to Zonal Move
Where Does One Machine End and the Next Begin?
Microsoft Virtualization Migration Options
Azure OpenAI Extension for Function Apps Hands-on Experience
Containers
Covering: Azure Kubernetes Service (AKS), Azure Red Hat OpenShift, Azure Container Apps, Web App for Containers, Azure Container Instances, Azure Container Registry
Public Preview of the Windows Server Annual Channel for Containers on Azure Kubernetes Service
IBM Cloud Pak for Integration on Azure Red Hat OpenShift Now Generally Available
Microsoft Copilot in Azure Series – Kubectl
Databases
Covering: Azure Cache for Redis, Azure Cosmos DB, Azure Database for MariaDB, Azure Database for MySQL, Azure Database for PostgreSQL, Azure SQL, Azure SQL Database, Azure SQL Edge, Azure SQL Managed Instance, SQL Server on Azure VM, Table Storage, Azure Managed Instance for Apache Cassandra, Azure Confidential Ledger
Say hello to the Talking Postgres podcast
Update: Security hotfix released for OLE DB driver for SQL Server
Announcing SSMS 20.2 … and getting feedback for SSMS 21
Security Update for SQL Server 2016 SP3 Azure Connect Feature Pack
Security Update for SQL Server 2016 SP3 GDR
Security Update for SQL Server 2017 RTM CU31
Security Update for SQL Server 2017 RTM GDR
Security Update for SQL Server 2019 RTM CU27
Security Update for SQL Server 2019 RTM GDR
Security Update for SQL Server 2022 RTM CU13
Security Update for SQL Server 2022 RTM GDR
SQL Server 2022 分散型可用性グループにおける同期失敗
Azure Backup for SQL Server in Azure VM: Tips and Tricks from the Field
Increasing Security for SQL Server Enabled by Azure Arc
Azure Database for MySQL – June 2024 updates and latest feature roadmap
Developer Tools
Covering: App Configuration, Azure DevTest Labs, Azure Lab Services, SDKs, Visual Studio, Visual Studio Code, Azure Load Testing
Unlocking the Potential of Phi-3 and C# in AI Development
C# 13: Explore the latest preview features
.NET and .NET Framework July 2024 servicing releases updates
Why and How to Execute GraphQL Queries in .NET
.NET 9 Preview 6 is now available!
DevOps
Covering: Azure Artifacts, Azure Boards, Azure DevOps, Azure Pipelines, Azure Repos, Azure Test Plans, DevOps tool integrations, Azure Load Testing
Azure DevOps Server 2022.2 RTW now available
GitHub Availability Report: June 2024
Hybrid
Covering: Microsoft Azure Stack, Azure Arc
Supercharge your datacenters with Hyper-V and virtualized GPUs
Apply critical update for Azure Stack HCI VMs to maintain Azure verification
Increasing Security for SQL Server Enabled by Azure Arc
Identity
Covering: Azure Active Directory, Multi-factor Authentication, Azure Active Directory Domain Services, Azure Active Directory External Identities
Microsoft Entra certificate-based authentication enhancements
Simplified Zero Trust security with the Microsoft Entra Suite and unified security operations platform, now generally available
Microsoft Entra Suite now generally available
Integration
Covering: API Management, Event Grid, Logic Apps , Service Bus
Integrating Logic App with Semantic Kernel: A Detailed Guide and Demo
Azure API Center – The ultimate service to streamline API Governance across your organization.
Internet Of Things
Covering: Azure IoT Central, Azure IoT Edge, Azure IoT Hub, Azure RTOS, Azure Sphere, Azure Stream Analytics, Azure Time Series Insights, Microsoft Defender for IoT, Azure Percept, Windows for IoT
Azure Sphere – Image signing certificate update
Management and Governance
Covering: Automation, Azure Advisor, Azure Backup, Azure Blueprints, Azure Lighthouse, Azure Monitor, Azure Policy, Azure Resource Manager, Azure Service Health, Azure Site Recovery, Cloud Shell, Cost Management, Azure Portal, Network Watcher, Azure Automanage, Azure Resource Mover, Azure Chaos Studio, Azure Managed Grafana
What’s the difference between Azure savings plans for compute and Azure reservations?
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Using Azure Automation to perform Azure Site Recovery post failover tasks in virtual machines
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Information protection: Auto labelling policy vs Information protection: Label Policy
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Covering: Azure Media Player, Content Protection, Encoding, Live and On-Demand Streaming, Media Services
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Microsoft Virtualization Migration Options
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Mobile
Covering: Azure Maps, MAUI, Notification Hubs, Visual Studio App Center, Xamarin, Azure Communication Services
Anywhere365 integrates Azure Communication Services into their Dialogue Cloud Platform
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Covering: Defender for Cloud, DDoS Protection, Dedicated HSM, Azure Information Protection, Microsoft Sentinel, Key Vault, Microsoft Defender for Cloud, Microsoft Defender for IoT, Microsoft Azure Attestation, Azure Confidential Ledger
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Unified Security Operations Platform – Technical FAQ!
Guidance for handling “regreSSHion” (CVE-2024-6387) using Microsoft Security capabilities
Storage
Covering: Archive Storage, Avere vFXT for Azure, Azure Data Lake Storage, Azure Data Share, Files, FXT Edge Filer, HPC Cache, NetApp Files, Blob Storage, Data Box, Disk Storage, Queue Storage, Storage Accounts, Storage Explorer, StorSimple
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Covering: App Configuration, App Service, Azure Cognitive Search, Azure Maps, Azure SignalR Service, Static Web Apps, Azure Communication Services, Azure Web PubSub, Azure Fluid Relay, Web App for Containers
Memory Dump Collection using Procdump.exe for App Service (Windows)
Anywhere365 integrates Azure Communication Services into their Dialogue Cloud Platform
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Covering: Windows Virtual Desktop, VMware Horizon Cloud on Microsoft Azure, Citrix Virtual Apps and Desktops for Azure
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Read More
Tracing LangChain Code on Azure with OpenTelemetry and Application Insights
As AI and machine learning applications grow more complex, ensuring their observability becomes crucial. Tracing helps identify and resolve performance bottlenecks and errors, providing insights into the internal workings of your applications. LangChain has become a popular framework for building applications with large language models. When deploying LangChain apps to production, tracing and monitoring are crucial for understanding performance and troubleshooting issues. In this blog, we will explore how to trace LangChain code on Azure using OpenTelemetry and Application Insights. We’ll leverage tools and libraries such as OpenInference, Azure’s OpenTelemetry exporter, and Application Insights.
Why Tracing Matters for LangChain Apps
LangChain applications often involve complex chains of operations – prompting language models, calling external APIs, accessing vector stores, etc. Tracing helps developers visualize these operations, identify bottlenecks, and debug errors. It’s especially important for AI apps that may have non-deterministic behavior.
Prerequisites
Before we dive into the implementation, ensure you have the following installed:
Python 3.7+
Azure account
Basic knowledge of Python and LangChain
OpenAI API key
Step 1: Setting Up OpenInference LangChain Instrumentation:
OpenInference provides auto-instrumentation for LangChain, making it compatible with OpenTelemetry. Let’s start by installing the necessary packages:
requirements.txt
azure-monitor-opentelemetry-exporter
openinference-instrumentation-langchain
langchain
opentelemetry-sdk
opentelemetry-exporter-otlp
openai
Now install the required packages by pip install -r requirements.txt
Step 2: Set up Azure Monitor Exporter:
Azure Monitor provides powerful tools for monitoring applications, including Application Insights. We’ll use the Azure Monitor OpenTelemetry Exporter to send trace data to Application Insights.
import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from dotenv import load_dotenv
load_dotenv(‘azure.env’)
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
exporter = AzureMonitorTraceExporter.from_connection_string(
os.environ[“APPLICATIONINSIGHTS_CONNECTION_STRING”]
)
Step 3: Integrating with Azure Monitor as LangChain Instrumentor
Azure Monitor provides powerful tools for monitoring applications, including Application Insights. We’ll use the Azure Monitor OpenTelemetry Exporter to send trace data to Application Insights. The below code sets up OpenTelemetry tracing for a LangChain application, configuring it to batch and export spans every 60 seconds, and automatically instrument LangChain operations. This allows you to collect detailed telemetry data about your LangChain application’s performance and behavior.
tracer_provider = TracerProvider()
from openinference.instrumentation.langchain import LangChainInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
trace_api.set_tracer_provider(tracer_provider)
trace.set_tracer_provider(tracer_provider)
tracer = trace.get_tracer(__name__)
span_processor = BatchSpanProcessor(exporter, schedule_delay_millis=60000)
trace.get_tracer_provider().add_span_processor(span_processor)
LangChainInstrumentor().instrument()
Step 3: Create LangChain LLM Chain
Now lets set up a LangChain application to generate jokes using Azure’s OpenAI service. It begins by importing necessary classes from the langchain_openai and langchain.chains modules. A PromptTemplate is created with a template that asks for a joke based on the provided adjective. The AzureChatOpenAI class is then instantiated with the API key, endpoint, API version, and model name, all of which are retrieved from environment variables. This configuration enables the LangChain application to interact with Azure’s OpenAI model deployment to generate responses based on the specified prompt template.
from langchain_openai import AzureChatOpenAI
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
prompt_template = “Tell me a {adjective} joke”
prompt = PromptTemplate(input_variables=[“adjective”], template=prompt_template)
llm = AzureChatOpenAI(api_key = os.environ[‘AZURE_OPENAI_API_KEY’],
azure_endpoint = os.environ[‘AZURE_OPENAI_ENDPOINT’],
api_version = ‘2024-06-01’,
model= os.environ[‘AZURE_OPENAI_GPT_DEPLOYMENT’])
Step 4: Viewing Traces in Azure Monitor
Lets invoke the LangChain chain before viewing the trace.
chain = LLMChain(llm=llm, prompt=prompt, metadata={“category”: “jokes”})
completion = chain.predict(adjective=”funny”, metadata={“variant”: “funny”})
print(completion)
After integrating the Azure Monitor exporter, your LangChain application will send traces to Application Insights. You can view these traces:
Navigate to the Azure portal.
Select your Application Insights resource.
Go to the “Transactions” section to view the traces.
Conclusion
By following these steps, you can effectively trace your LangChain applications using OpenTelemetry and view these traces in Azure Monitor’s Application Insights. This setup not only enhances observability but also helps in identifying and resolving performance issues efficiently. For more detailed information, refer to the official documentation:
OpenInference LangChain Instrumentation
Azure Monitor OpenTelemetry Exporter
Sample Trace Configuration
Happy tracing!
Microsoft Tech Community – Latest Blogs –Read More
80 Teacher Trainers from NTT Vocational High Schools to Enhance Education through Generative AI
Kupang, 18 July 2024 – The development of artificial intelligence (AI) should benefit everyone in Indonesia, including teachers from vocational high schools (SMK) in the East Nusa Tenggara (NTT) region. By providing access to Generative AI for SMK teachers and students in the region, we could also help them to have better chances in entering the job market and competing with the other 149.38 billion people of productive age in Indonesia (Central Agency of Statistics Indonesia—BPS, 2024).
Yayasan Plan International Indonesia (Plan Indonesia) held a hybrid training of trainers joined by 80 vocational high school teachers from five different regions in NTT, spanning from the Kupang City, South Timor Tengah regency, Lembata regency, Nagekeo regency, and Manggarai regency on Thursday (18/07/2024). The training was as a part of Plan Indonesia’s Youth Employment and Entrepreneurship initiative–the AI TEACH program supported by Microsoft.
Dini Arifah, AI TEACH Project Manager at Plan Indonesia explained that the training of trainers served as a continuation of Plan Indonesia’s support in elevating access to digital employment for people in the NTT region. “As an organization that has been working for more than 50 years in the NTT region—which is our main implementation area—Plan Indonesia hoped to use this chance to enhance the ability and work readiness of SMK teachers and students in NTT, particularly to enable them to compete within the 4.0 digital industry landscape,” Dini said at the beginning of AI TEACH ToT in Kupang, Thursday (18/07/2024).
The AI TEACH training of trainers was held through cooperation between Plan Indonesia and NTT’s local Department of Education and Culture. Both institutions will work together to reach 1,000 SMK teachers who will then cascade their AI Generative knowledge to approximately 60,000 SMK students by 2024.
Ambrosius Kodo, the current Head of Department of Education and Culture in NTT, said that the government appreciated Plan Indonesia’s initiative to enhance education quality in the region, particularly to reduce the amount of open unemployment rate in NTT that amounted to 3.17 per cent of in 2024.
“We are given the aptitude and the space to make good use of technology, especially to improve education in NTT. AI can actually help make things easier. Teachers and the students will need understand how to leverage AI to increase their knowledge and career, instead of viewing it as a threat,” Ambrosius said.
Meanwhile, Microsoft ASEAN Philanthropies Lead Supahrat Juramongkol said, “In line with Microsoft’s mission to empower every person and every organization on the planet to achieve more, we are excited to accelerate the implementation of AI TEACH program in collaboration with Plan Indonesia. Through the AI Generative Toolkit we have prepared, we aim to enhance career and educational opportunities for participants, promote equitable access to digital education, and foster inclusive digital economic growth in NTT.”
Among the subjects offered through the AI TEACH training are Generative AI for education, soft skills (work readiness), basic digital skills, gender equality and social inclusion, as well as awareness of risky behaviour. All of the training materials could be accessed online through Plan Indonesia’s e-learning platform, kitakerja.id, accompanied with additional materials by LinkedIn learning. Aside from providing initial training to 80 teachers who will become the trainers in NTT, the AI TEACH program by Plan Indonesia and Microsoft will also reach out to 5,000 teachers, cascading to 300,000 vocational high school students in the country. The teachers will then support at least 60,000 students to graduate from the program and receive certifications by Microsoft and LinkedIn by the end of December 2024.
—
80 Guru Pelatih SMK dari SMK NTT Siap Tingkatkan Mutu Pengajaran dengan AI Generatif
Read the English version here
Kupang, 18 Juli 2024 – Kemajuan teknologi kecerdasan buatan (Artificial Intelligence-AI) harus bermanfaat bagi masyarakat Indonesia, termasuk bagi para pendidik Sekolah Menengah Kejuruan (SMK) di wilayah Nusa Tenggara Timur. Terutama, agar para guru dan murid SMK di wilayah ini bisa memanfaatkan AI Generatif dan bersaing lebih baik dalam memasuki bursa kerja, di tengah persaingan dengan 149,38 juta angkatan kerja nasional lainnya (BPS, 2024).
Yayasan Plan International Indonesia (Plan Indonesia) menggelar pelatihan untuk para pelatih (Training of Trainers–ToT) yang diikuti oleh 80 guru SMK dari Kota Kupang, Kabupaten Lembata, Kabupaten Timor Tengah Selatan, Kabupaten Nagekeo, dan Kabupaten Manggarai secara daring dan luring pada Kamis (18/07/2024). Kegiatan ini merupakan bagian dari program ketenagakerjaan dan kewirausahaan Plan Indonesia, AI TEACH, yang didukung penuh oleh Microsoft.
Dini Arifah, AI TEACH Project Manager Plan Indonesia, menjelaskan bahwa ToT ini merupakan lanjutan dari upaya berkelanjutan Plan Indonesia untuk meningkatkan akses penduduk di NTT terhadap pekerjaan digital. “Sebagai organisasi yang sudah bekerja lebih dari 50 tahun di NTT yang merupakan wilayah kerja utama kami, Plan Indonesia berharap dapat menggunakan kesempatan ini untuk meningkatkan kemampuan para guru dan kesiapan kerja murid SMK di NTT. Tujuannya agar mereka dapat bersaing di era industri digital 4.0,” sebut Dini dalam pembukaan acara ToT di Kupang, Kamis (18/07/2024).
Kegiatan ToT AI TEACH ini terselenggara melalui kerja sama antara Plan Indonesia dengan Dinas Pendidikan dan Kebudayaan NTT. Kedua lembaga ini bertujuan menjangkau 1.000 guru SMK melalui pelatihan berjenjang (cascading) dan menjangkau sekitar 60.000 murid SMK di NTT hingga akhir 2024.
Ambrosius Kodo, Kepala Dinas Pendidikan dan Kebudayaan NTT, menyambut baik inisiatif Plan Indonesia untuk memajukan kuailtas pendidikan di NTT, terutama untuk mengurangi tingkat pengangguran terbuka NTT yang mencapai 3,17 persen pada 2024.
“Kita tentunya diberikan kecerdasan, ruang untuk memanfaatkan teknologi dengan baik, teristimewa untuk kemajuan sektor pendidikan di NTT. Dengan adanya AI, sebetulnya segala sesuatu akan menjadi lebih mudah. Pendidik maupun peserta didik harus benar-benar memahami cara memanfaatkan AI untuk pengetahuan dan kemajuan karier, daripada melihatnya sebagai suatu ancaman,” sebut Ambrosius.
Sementara itu, Microsoft ASEAN Philanthropies Lead Supahrat Juramongkol mengatakan, “Sejalan dengan misi Microsoft untuk memberdayakan setiap individu dan setiap organisasi di planet ini untuk mencapai lebih, kami merasa senang dapat mempercepat pengimplementasian program AI TEACH bersama Plan Indonesia. Melalui AI Generative Toolkit yang kami siapkan, kami berharap tidak hanya dapat meningkatkan peluang karier dan pendidikan para peserta, tetapi juga membantu pemerataan akses pendidikan digital, serta mendorong pertumbuhan ekonomi digital inklusif di NTT.
Topik pembelajaran yang diberikan melalui AI TEACH adalah keterampilan AI Generatif di dunia pendidikan, soft skill (kesiapan kerja), keterampilan digital dasar, kesetaraan Gender dan Inklusi Sosial (GESI), hingga kesadaran terhadap perilaku berisiko. Seluruh pelatihan ini diakses melalui modul AI Generative Toolkit yang tersedia di platform pembelajaran kitakerja.id milik Plan Indonesia, dilengkapi dengan materi tambahan dalam platform LinkedIn learning.
Selain memberikan pelatihan awal kepada 80 guru yang akan menjadi pelatih di NTT, program AI TEACH oleh Plan Indonesia dan Microsoft juga bertujuan menjangkau 5.000 pendidik SMK yang akan melatih 300.000 murid SMK dari seluruh penjuru negeri. Para pendidik juga akan mendampingi setidaknya 60.000 murid untuk mendapatkan sertifikasi penyelesaian oleh Microsoft dan LinkedIn hingga akhir Desember 2024.
—-
Why do I experience performance issues with the License Center?
When I try to load the license center it takes a long time to let me in or list licenses. Additionally, when I choose a license, it can take a long time to process and let me work with it.When I try to load the license center it takes a long time to let me in or list licenses. Additionally, when I choose a license, it can take a long time to process and let me work with it. When I try to load the license center it takes a long time to let me in or list licenses. Additionally, when I choose a license, it can take a long time to process and let me work with it. MATLAB Answers — New Questions
Something doesn’t work for me in fit
Hello friends.
A little introduction:
I do a simulation for particles moving inside a box, as part of the simulation I measure the speed of the particles, and the idea is that in the end I get the Maxwell-Boltzmann distribution of the speed.
I wanted to do a fit to the histogram of the velocity, according to the Boltzmann equation, so that I could find the temperature of the system, but for some reason the fit does not fit exactly on the data. it’s similar, but not enough.
this is the fit that I get:
I’m uploading code with the histogram data, so you can run it yourself and see what you get.
I only change the histogram() in the figure, to plot(), so that it matches the data I uploaded.
This is the equation of Boltzmann distribution that I want to fit:
I will be happy if you can understand what is the problems..thanks!
This is the code, you can simple run it and see what is happening:
%my data:
hist_x=[10 30 50 70 90 110 130 150 170 190 210 230 250,…
270 290 310 330 350 370 390 410 430 450 470 490];
hist_y=[20 60 83 88 108 117 113 94 77 63 65 51 26,…
19 9 3 1 2 0 0 0 0 0 0 1];
%fit the velocity to Boltzmann distribution:
%parameters:
K_B=1.38e-23; %[m^2Kg/s^2K]
m=39.948*1.660e-27 ; %mass of Ar, convert from atomic mass to Kg [Kg]
boltzmann = fittype…
( @(T,v) (m/K_B*T)*v.*exp(((-m)/(2*K_B*T))*v.^2)…
, ‘coefficient’, ‘T’…
, ‘independent’, ‘v’) ;
[fitted_curve] = fit(hist_x’,hist_y’,boltzmann, ‘startPoint’, 100) ;
%plot histogram and fit:
figure;
plot(hist_x,hist_y);
hold on
plot(fitted_curve)
title(‘histogram of velocity and fitted curve – Boltzmann distribution’)
legend(‘histogram’, ‘fitted curve’);
xlabel(‘velovity [m/s]’)
ylabel(‘particles amount’)
disp(fitted_curve);Hello friends.
A little introduction:
I do a simulation for particles moving inside a box, as part of the simulation I measure the speed of the particles, and the idea is that in the end I get the Maxwell-Boltzmann distribution of the speed.
I wanted to do a fit to the histogram of the velocity, according to the Boltzmann equation, so that I could find the temperature of the system, but for some reason the fit does not fit exactly on the data. it’s similar, but not enough.
this is the fit that I get:
I’m uploading code with the histogram data, so you can run it yourself and see what you get.
I only change the histogram() in the figure, to plot(), so that it matches the data I uploaded.
This is the equation of Boltzmann distribution that I want to fit:
I will be happy if you can understand what is the problems..thanks!
This is the code, you can simple run it and see what is happening:
%my data:
hist_x=[10 30 50 70 90 110 130 150 170 190 210 230 250,…
270 290 310 330 350 370 390 410 430 450 470 490];
hist_y=[20 60 83 88 108 117 113 94 77 63 65 51 26,…
19 9 3 1 2 0 0 0 0 0 0 1];
%fit the velocity to Boltzmann distribution:
%parameters:
K_B=1.38e-23; %[m^2Kg/s^2K]
m=39.948*1.660e-27 ; %mass of Ar, convert from atomic mass to Kg [Kg]
boltzmann = fittype…
( @(T,v) (m/K_B*T)*v.*exp(((-m)/(2*K_B*T))*v.^2)…
, ‘coefficient’, ‘T’…
, ‘independent’, ‘v’) ;
[fitted_curve] = fit(hist_x’,hist_y’,boltzmann, ‘startPoint’, 100) ;
%plot histogram and fit:
figure;
plot(hist_x,hist_y);
hold on
plot(fitted_curve)
title(‘histogram of velocity and fitted curve – Boltzmann distribution’)
legend(‘histogram’, ‘fitted curve’);
xlabel(‘velovity [m/s]’)
ylabel(‘particles amount’)
disp(fitted_curve); Hello friends.
A little introduction:
I do a simulation for particles moving inside a box, as part of the simulation I measure the speed of the particles, and the idea is that in the end I get the Maxwell-Boltzmann distribution of the speed.
I wanted to do a fit to the histogram of the velocity, according to the Boltzmann equation, so that I could find the temperature of the system, but for some reason the fit does not fit exactly on the data. it’s similar, but not enough.
this is the fit that I get:
I’m uploading code with the histogram data, so you can run it yourself and see what you get.
I only change the histogram() in the figure, to plot(), so that it matches the data I uploaded.
This is the equation of Boltzmann distribution that I want to fit:
I will be happy if you can understand what is the problems..thanks!
This is the code, you can simple run it and see what is happening:
%my data:
hist_x=[10 30 50 70 90 110 130 150 170 190 210 230 250,…
270 290 310 330 350 370 390 410 430 450 470 490];
hist_y=[20 60 83 88 108 117 113 94 77 63 65 51 26,…
19 9 3 1 2 0 0 0 0 0 0 1];
%fit the velocity to Boltzmann distribution:
%parameters:
K_B=1.38e-23; %[m^2Kg/s^2K]
m=39.948*1.660e-27 ; %mass of Ar, convert from atomic mass to Kg [Kg]
boltzmann = fittype…
( @(T,v) (m/K_B*T)*v.*exp(((-m)/(2*K_B*T))*v.^2)…
, ‘coefficient’, ‘T’…
, ‘independent’, ‘v’) ;
[fitted_curve] = fit(hist_x’,hist_y’,boltzmann, ‘startPoint’, 100) ;
%plot histogram and fit:
figure;
plot(hist_x,hist_y);
hold on
plot(fitted_curve)
title(‘histogram of velocity and fitted curve – Boltzmann distribution’)
legend(‘histogram’, ‘fitted curve’);
xlabel(‘velovity [m/s]’)
ylabel(‘particles amount’)
disp(fitted_curve); curve fitting MATLAB Answers — New Questions
Getting an exception at the Power_usage.Rice_Cooker(Tstart) = 0; line
% Import data into a table
rice_cooker = [0.0000 0.0000 0.0000 0.0000 0.0863 0.5180 0.4317 0.0432 0.0863 0.0000 0.0000 0.0432 0.1295 0.1295 0.0000 0.0000 0.0000 0.0863 0.4748 0.3885 0.0432 0.0000 0.0000 0.0000]’;
blender = [0.0000 0.0000 0.0000 0.0000 0.0207 0.1861 0.1861 0.0207 0.0207 0.0000 0.0207 0.0207 0.0207 0.0000 0.0000 0.0414 0.0414 0.0414 0.1034 0.0620 0.0000 0.0000 0.0000 0.0000
]’;
hour = (1:24)’;
Starting_Probability = table(hour, rice_cooker, blender);
Starting_Probability.Properties.VariableNames{‘rice_cooker’} = ‘Rice_Cooker’;
Starting_Probability.Properties.VariableNames{‘blender’} = ‘Blender’;
% Import data into another table
cycle_t_first_column = [0 0 0 0 41 32 31 30 0 0 30 30 15 40 0 0 0 20 35 29 45 0 0 0
]’;
cycle_t_second_column = [0 0 0 0 10 8 7 9 15 0 13 18 21 10 15 0 9 11 8 7 10 0 0 0
]’;
cycle_t = table(cycle_t_first_column,cycle_t_second_column);
cycle_t.Properties.VariableNames{‘cycle_t_first_column’} = ‘Rice_Cooker’;
cycle_t.Properties.VariableNames{‘cycle_t_second_column’} = ‘Blender’;
% Import data into another table
app_pwr_first_column = [0.000 0.000 0.000 0.000 1.525 1.271 1.220 0.700 0.000 0.000 1.400 1.550 1.000 1.333 0.000 0.000 0.000 1.300 1.600 1.260 1.500 0.000 0.000 0.000
]’;
app_pwr_second_column = [0.000 0.000 0.000 0.000 0.550 0.543 0.682 0.325 0.978 0.000 0.328 0.365 0.525 0.775 0.374 0.000 0.138 0.494 0.567 0.507 2.000 0.000 0.000 0.000
]’;
app_pwr = table(app_pwr_first_column,app_pwr_second_column);
app_pwr.Properties.VariableNames{‘app_pwr_first_column’} = ‘Rice_Cooker’;
app_pwr.Properties.VariableNames{‘app_pwr_second_column’} = ‘Blender’;
app_pwr.Properties.RowNames = cellstr(num2str((1:24)’));
% Create a table named Power_usage with two columns and 24 rows
Power_usage = table(zeros(24,1), zeros(24,1));
% Rename the columns as RiceCooker and Blender
Power_usage.Properties.VariableNames = {‘Rice_Cooker’, ‘Blender’};
% Rename the rows as 1 to 24
Power_usage.Properties.RowNames = cellstr(num2str((1:24)’));
% Get the values of Tstart and Applno variables
Tstart = 2; %input(‘Enter the value of Tstart: ‘);
Applno = 1; %input(‘Enter the value of Applno: ‘);
%% Create a loop until Tstart equals 24
while Tstart <= 24
% Generate 100 random numbers between 0 and 1
x = rand(100,1);
% Assign the 100th random number to a variable named r
r = x(100);
% Check the random number against the element of T table
if Applno == 1 % RiceCooker column
if r < Starting_Probability.Rice_Cooker(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Rice_Cooker(Tstart) = app_pwr.Rice_Cooker(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Rice_Cooker(Tstart) = 0;
end
elseif Applno == 2 % Blender column
if r < Starting_Probability.Blender(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Blender(Tstart) = app_pwr.Blender(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Blender(Tstart) = 0;
end
end
% Get the value of Tcycle from cycle_t table
if Applno == 1 % RiceCooker column
Tcycle = cycle_t.Rice_Cooker(Tstart);
elseif Applno == 2 % Blender column
Tcycle = cycle_t.Blender(Tstart);
end
% Update Tstart as per the equation
Tstart = Tstart + Tcycle;
end
% I’m getting an error at the Power_usage.Rice_Cooker(Tstart) = 0; line% Import data into a table
rice_cooker = [0.0000 0.0000 0.0000 0.0000 0.0863 0.5180 0.4317 0.0432 0.0863 0.0000 0.0000 0.0432 0.1295 0.1295 0.0000 0.0000 0.0000 0.0863 0.4748 0.3885 0.0432 0.0000 0.0000 0.0000]’;
blender = [0.0000 0.0000 0.0000 0.0000 0.0207 0.1861 0.1861 0.0207 0.0207 0.0000 0.0207 0.0207 0.0207 0.0000 0.0000 0.0414 0.0414 0.0414 0.1034 0.0620 0.0000 0.0000 0.0000 0.0000
]’;
hour = (1:24)’;
Starting_Probability = table(hour, rice_cooker, blender);
Starting_Probability.Properties.VariableNames{‘rice_cooker’} = ‘Rice_Cooker’;
Starting_Probability.Properties.VariableNames{‘blender’} = ‘Blender’;
% Import data into another table
cycle_t_first_column = [0 0 0 0 41 32 31 30 0 0 30 30 15 40 0 0 0 20 35 29 45 0 0 0
]’;
cycle_t_second_column = [0 0 0 0 10 8 7 9 15 0 13 18 21 10 15 0 9 11 8 7 10 0 0 0
]’;
cycle_t = table(cycle_t_first_column,cycle_t_second_column);
cycle_t.Properties.VariableNames{‘cycle_t_first_column’} = ‘Rice_Cooker’;
cycle_t.Properties.VariableNames{‘cycle_t_second_column’} = ‘Blender’;
% Import data into another table
app_pwr_first_column = [0.000 0.000 0.000 0.000 1.525 1.271 1.220 0.700 0.000 0.000 1.400 1.550 1.000 1.333 0.000 0.000 0.000 1.300 1.600 1.260 1.500 0.000 0.000 0.000
]’;
app_pwr_second_column = [0.000 0.000 0.000 0.000 0.550 0.543 0.682 0.325 0.978 0.000 0.328 0.365 0.525 0.775 0.374 0.000 0.138 0.494 0.567 0.507 2.000 0.000 0.000 0.000
]’;
app_pwr = table(app_pwr_first_column,app_pwr_second_column);
app_pwr.Properties.VariableNames{‘app_pwr_first_column’} = ‘Rice_Cooker’;
app_pwr.Properties.VariableNames{‘app_pwr_second_column’} = ‘Blender’;
app_pwr.Properties.RowNames = cellstr(num2str((1:24)’));
% Create a table named Power_usage with two columns and 24 rows
Power_usage = table(zeros(24,1), zeros(24,1));
% Rename the columns as RiceCooker and Blender
Power_usage.Properties.VariableNames = {‘Rice_Cooker’, ‘Blender’};
% Rename the rows as 1 to 24
Power_usage.Properties.RowNames = cellstr(num2str((1:24)’));
% Get the values of Tstart and Applno variables
Tstart = 2; %input(‘Enter the value of Tstart: ‘);
Applno = 1; %input(‘Enter the value of Applno: ‘);
%% Create a loop until Tstart equals 24
while Tstart <= 24
% Generate 100 random numbers between 0 and 1
x = rand(100,1);
% Assign the 100th random number to a variable named r
r = x(100);
% Check the random number against the element of T table
if Applno == 1 % RiceCooker column
if r < Starting_Probability.Rice_Cooker(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Rice_Cooker(Tstart) = app_pwr.Rice_Cooker(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Rice_Cooker(Tstart) = 0;
end
elseif Applno == 2 % Blender column
if r < Starting_Probability.Blender(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Blender(Tstart) = app_pwr.Blender(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Blender(Tstart) = 0;
end
end
% Get the value of Tcycle from cycle_t table
if Applno == 1 % RiceCooker column
Tcycle = cycle_t.Rice_Cooker(Tstart);
elseif Applno == 2 % Blender column
Tcycle = cycle_t.Blender(Tstart);
end
% Update Tstart as per the equation
Tstart = Tstart + Tcycle;
end
% I’m getting an error at the Power_usage.Rice_Cooker(Tstart) = 0; line % Import data into a table
rice_cooker = [0.0000 0.0000 0.0000 0.0000 0.0863 0.5180 0.4317 0.0432 0.0863 0.0000 0.0000 0.0432 0.1295 0.1295 0.0000 0.0000 0.0000 0.0863 0.4748 0.3885 0.0432 0.0000 0.0000 0.0000]’;
blender = [0.0000 0.0000 0.0000 0.0000 0.0207 0.1861 0.1861 0.0207 0.0207 0.0000 0.0207 0.0207 0.0207 0.0000 0.0000 0.0414 0.0414 0.0414 0.1034 0.0620 0.0000 0.0000 0.0000 0.0000
]’;
hour = (1:24)’;
Starting_Probability = table(hour, rice_cooker, blender);
Starting_Probability.Properties.VariableNames{‘rice_cooker’} = ‘Rice_Cooker’;
Starting_Probability.Properties.VariableNames{‘blender’} = ‘Blender’;
% Import data into another table
cycle_t_first_column = [0 0 0 0 41 32 31 30 0 0 30 30 15 40 0 0 0 20 35 29 45 0 0 0
]’;
cycle_t_second_column = [0 0 0 0 10 8 7 9 15 0 13 18 21 10 15 0 9 11 8 7 10 0 0 0
]’;
cycle_t = table(cycle_t_first_column,cycle_t_second_column);
cycle_t.Properties.VariableNames{‘cycle_t_first_column’} = ‘Rice_Cooker’;
cycle_t.Properties.VariableNames{‘cycle_t_second_column’} = ‘Blender’;
% Import data into another table
app_pwr_first_column = [0.000 0.000 0.000 0.000 1.525 1.271 1.220 0.700 0.000 0.000 1.400 1.550 1.000 1.333 0.000 0.000 0.000 1.300 1.600 1.260 1.500 0.000 0.000 0.000
]’;
app_pwr_second_column = [0.000 0.000 0.000 0.000 0.550 0.543 0.682 0.325 0.978 0.000 0.328 0.365 0.525 0.775 0.374 0.000 0.138 0.494 0.567 0.507 2.000 0.000 0.000 0.000
]’;
app_pwr = table(app_pwr_first_column,app_pwr_second_column);
app_pwr.Properties.VariableNames{‘app_pwr_first_column’} = ‘Rice_Cooker’;
app_pwr.Properties.VariableNames{‘app_pwr_second_column’} = ‘Blender’;
app_pwr.Properties.RowNames = cellstr(num2str((1:24)’));
% Create a table named Power_usage with two columns and 24 rows
Power_usage = table(zeros(24,1), zeros(24,1));
% Rename the columns as RiceCooker and Blender
Power_usage.Properties.VariableNames = {‘Rice_Cooker’, ‘Blender’};
% Rename the rows as 1 to 24
Power_usage.Properties.RowNames = cellstr(num2str((1:24)’));
% Get the values of Tstart and Applno variables
Tstart = 2; %input(‘Enter the value of Tstart: ‘);
Applno = 1; %input(‘Enter the value of Applno: ‘);
%% Create a loop until Tstart equals 24
while Tstart <= 24
% Generate 100 random numbers between 0 and 1
x = rand(100,1);
% Assign the 100th random number to a variable named r
r = x(100);
% Check the random number against the element of T table
if Applno == 1 % RiceCooker column
if r < Starting_Probability.Rice_Cooker(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Rice_Cooker(Tstart) = app_pwr.Rice_Cooker(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Rice_Cooker(Tstart) = 0;
end
elseif Applno == 2 % Blender column
if r < Starting_Probability.Blender(Tstart) % Random number is greater than element value
% Read the value from app_pwr table and store it in Power_usage table
Power_usage.Blender(Tstart) = app_pwr.Blender(Tstart);
else % Random number is less than element value
% Store 0 in Power_usage table
Power_usage.Blender(Tstart) = 0;
end
end
% Get the value of Tcycle from cycle_t table
if Applno == 1 % RiceCooker column
Tcycle = cycle_t.Rice_Cooker(Tstart);
elseif Applno == 2 % Blender column
Tcycle = cycle_t.Blender(Tstart);
end
% Update Tstart as per the equation
Tstart = Tstart + Tcycle;
end
% I’m getting an error at the Power_usage.Rice_Cooker(Tstart) = 0; line load modelling MATLAB Answers — New Questions