腾龙集团公司开户注册网址www.tl99825.com微信13542035通常需要遵循以下一般步骤：准备相关资料：可能包括个人或企业身份证明、联系方式、地址证明等。联系盛世集团：通过其官方网站、客服渠道等，了解具体开户注册流程和所需资料要求。填写表格：按照要求填写相关信息。提交资料：将准备好的资料提交给盛世集团，可以通过线上提交或线路下提交的方式。等待审核：公司同意提交的资料进行审核。完成审核：审核通过后，即可成功开户注册。需要注意的是，具体步骤和要求可能因盛世集团的规定而有所不同。建议您与该公司进行详细沟通，以确保顺利完成开户登记。

​腾龙集团公司开户注册网址www.tl99825.com微信13542035通常需要遵循以下一般步骤：准备相关资料：可能包括个人或企业身份证明、联系方式、地址证明等。联系盛世集团：通过其官方网站、客服渠道等，了解具体开户注册流程和所需资料要求。填写表格：按照要求填写相关信息。提交资料：将准备好的资料提交给盛世集团，可以通过线上提交或线路下提交的方式。等待审核：公司同意提交的资料进行审核。完成审核：审核通过后，即可成功开户注册。需要注意的是，具体步骤和要求可能因盛世集团的规定而有所不同。建议您与该公司进行详细沟通，以确保顺利完成开户登记。  Read More

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盛世集团公司开户注册网址www.tl99825.com微信13542035通常需要遵循以下一般步骤：准备相关资料：可能包括个人或企业身份证明、联系方式、地址证明等。联系盛世集团：通过其官方网站、客服渠道等，了解具体开户注册流程和所需资料要求。填写表格：按照要求填写相关信息。提交资料：将准备好的资料提交给盛世集团，可以通过线上提交或线路下提交的方式。等待审核：公司同意提交的资料进行审核。完成审核：审核通过后，即可成功开户注册。需要注意的是，具体步骤和要求可能因盛世集团的规定而有所不同。建议您与该公司进行详细

​盛世集团公司开户注册网址www.tl99825.com微信13542035通常需要遵循以下一般步骤：准备相关资料：可能包括个人或企业身份证明、联系方式、地址证明等。联系盛世集团：通过其官方网站、客服渠道等，了解具体开户注册流程和所需资料要求。填写表格：按照要求填写相关信息。提交资料：将准备好的资料提交给盛世集团，可以通过线上提交或线路下提交的方式。等待审核：公司同意提交的资料进行审核。完成审核：审核通过后，即可成功开户注册。需要注意的是，具体步骤和要求可能因盛世集团的规定而有所不同。建议您与该公司进行详细  Read More

​

孟波盛世公司似乎是一家多元化的企业，孟波盛世娱乐注册官网-（ss88946. C0m/910）涉及多个领域。根据我找到的信息，孟波盛世金融公司专注于为全球客户提供全面的金融服务，包括财富管理、投资咨询、风险控制和资产配置等。该公司利用金融科技，如大数据、人工智能和区块链技术，来提供智能、便捷和安全的金融服务 。

此外，还有一家名为盛世娱乐公司的企业，它位于佤邦孟波，主要经营酒店、旅游和休闲娱乐业务。该公司强调全面协调可持续发展，注重改革创新，力求打造一流团队和管理，创造一流效益 。

这两家公司都位于孟波，但具体是否为同一家公司或存在关联，我无法从现有信息中确定。如果你需要更详细的信息，可能需要直接联系这些公司进行查询。

​孟波盛世公司似乎是一家多元化的企业，孟波盛世娱乐注册官网-（ss88946. C0m/910）涉及多个领域。根据我找到的信息，孟波盛世金融公司专注于为全球客户提供全面的金融服务，包括财富管理、投资咨询、风险控制和资产配置等。该公司利用金融科技，如大数据、人工智能和区块链技术，来提供智能、便捷和安全的金融服务 。此外，还有一家名为盛世娱乐公司的企业，它位于佤邦孟波，主要经营酒店、旅游和休闲娱乐业务。该公司强调全面协调可持续发展，注重改革创新，力求打造一流团队和管理，创造一流效益 。这两家公司都位于孟波，但具体是否为同一家公司或存在关联，我无法从现有信息中确定。如果你需要更详细的信息，可能需要直接联系这些公司进行查询。  Read More

​

Introduction

RoomRadar.ai is an AI-powered hotel search application developed by:
@Anchit https://www.linkedin.com/in/anchitchandran/
@matt https://www.linkedin.com/in/matt-peniket-6a051318a 
and myself @soran https://www.linkedin.com/in/an-tien-huang/

As part of our MSc Computer Science project at University College London. Under the expert guidance of our supervisors, Dr. Yun Fu https://www.linkedin.com/in/dryunfu/ from UCL and Prof. LeeStott https://linkedin.com/in/leestott/ from Microsoft, we created a cutting-edge solution that leverages Microsoft Azure infrastructure to deliver an exceptional user experience.

Our application utilises Azure OpenAI and Azure AI Search to offer a multimodal interface, enabling users to search for hotels using text, voice, and images. Additionally, Azure Maps is also integrated to provide an interactive map for a more engaging and intuitive experience for our users.

RoomRadar.ai showcases the potential of AI in enhancing travel planning, combining advanced technology with user-friendly design to revolutionise the way people find their ideal accommodations.

In this blog, I will walk through the project’s development process of Azure Maps and image vector search, which are my main contribution to the project.

Demo of image similarity search

Demo of Azure Maps

Outline

Project Overview and Goals
Technical Details

Azure Maps
Azure AI Vision and Azure AI Search

Results and Outcome
Future Development
Conclusion

Overview and Objectives

RoomRadar.ai was designed to enhance the hotel search user experience with features that traditional hotel search applications do not offer. The interactive map and image search are two of these features and their detailed objectives are listed in the following table:

Feature
Objectives

Map View

Display hotels on a map for an intuitive and visual way to explore search results
Include hotel cards in the side panel for a neat and organised display of hotel details
Provide route planning information between the selected hotel and nearby underground stations, eliminating the need for users to check this separately.

Image Search

Allow users to use a base image to find hotels with similar visuals. This feature lets users express preferences that may be difficult to define using filters or text

The diagram below provides a high-level overview of RoomRadar’s architecture:

Technical Details

Azure Maps

Demo of Azure Maps

RoomRadar’s map component, which is used in both Map View and the hotel map in the details page, leverages the Azure Maps services. The map construction process is as below:

Step 0: Setup the Azure Maps Resources in Azure

For detailed steps for this, as well as resolving issues related to Azure Maps integration with NextJS and TypeScript, please refer to my tutorial here.

Step 1. Initialise Base Map using the Microsoft *atlas* Package

Step 1.1: Initialise Map

import * as atlas from ‘azure-maps-control’;

const map = new atlas.Map(mapRef.current!, {
authOptions: {
authType: atlas.AuthenticationType.subscriptionKey,
subscriptionKey: ‘key’
}
});

Step 1.2: Add Custom Icons for Hotels and Underground Stations

Promise.all is added to prevent map accessing the custom icons before their loading is completed.

var iconPromises = [
map.imageSprite.add(‘underground_icon’, ‘/map_icons/metro.png’),
map.imageSprite.add(‘hotel_red_icon’, ‘/map_icons/hotel_red.png’),
];
Promise.all(iconPromises).then(function () {
// Rest of initialising code omitted for brevity
});

Step 1.3: Add Layers for Displaying Hotels and Underground Stations and for Displaying Route Information

// Add symbol layer for hotels and stations
const symbolLayer = new atlas.layer.SymbolLayer(datasource, ‘symbolLayer’, {
iconOptions: {
image: [
‘match’,
[‘get’, ‘type’],
//For each entity type, specify the icon name to use.
‘tube’, ‘underground_icon’,
‘hotel’, ‘hotel_red_icon’,
‘hotel_red_icon’ //Default icon
],
allowOverlap: false,
size: 0.1
},
textOptions: {
// Additional code omitted for brevity
},
filter: [‘any’, [‘==’, [‘geometry-type’], ‘Point’], [‘==’, [‘geometry-type’], ‘MultiPoint’]]
});
map.layers.add(symbolLayer);

// Add route layer
const routeLayer = new atlas.layer.LineLayer(datasource, ‘routeLayer’, {
strokeColor: ‘#0059ff’,
strokeWidth: 5,
lineJoin: ’round’,
lineCap: ’round’
});
map.layers.add(routeLayer, ‘labels’);

Step 2: Add Hotel Points on the Map

The hotel points are added using the props (`Properties`) passed in from search result.

const datasource = new atlas.source.DataSource();
map.source.add(datasource);

const hotelPoints = Properties.map((property: Property) =>
new atlas.data.Feature(new atlas.data.Point([Number(property.longitude), Number(property.latitude)]), {
hotel_id: property.location_id,
type: ‘hotel’,
name: property.name,
})
);
datasource.add(hotelPoints);

Step 3. Add Hotel Cards

The hotel card offers a clear and convenient way for users to browse results while receiving visual feedback on the map. To link the hotel cards with their corresponding map elements, a hash map is used to store hotel IDs and their associated shape IDs on the map. This enables an interactive UI experience, such as displaying a popup on the map when a user hovers over a hotel card:

const listItemHover = (id?: string) => {
const shapeId = id && hotelIdToShapeIdMap.get(id);
const shape = shapeId && datasource.getShapeById(shapeId);
if (shape) {
showPopup(shape);
}
};

Step 4: Add Nearby Underground Stations When a Hotel is Clicked

The underground station points are added in the same manner as the hotel points, with the exception that their type is set to ‘tube’ instead of ‘hotel’.

Step 5: Call Azure Maps’ Get Route Direction API to calculate the direction information.

When an underground station point is clicked, the Azure Maps Get Route Direction API is called to retrieve the route coordinates and estimated travel time. The result is then displayed on the map using the `routeLayer` and a popup.

Azure AI Vision and Azure AI Search

Demo of image similarity search

The image search feature is implemented using a combination of the following technologies:

Azure AI Vision: Converts images into vector embeddings. Azure AI Vision was chosen for embedding generation due to its generous free quota, which can accommodate the required image volume.
MongoDB Atlas: Stores the transformed vector embeddings for efficient retrieval.
Azure AI Search and MongoDB Atlas Vector Search: Performs vector searches to find and return similar images based on the embeddings.

The overall process is illustrated in the diagram below:

Step 1: Convert images into vectors

The images are transformed by calling the Multimodal embeddings API by Azure AI Services, which takes binary data as input and return a 1×1024 vector.

Step 2: Store and Index Vectors

The converted image vectors are stored in MongoDB Atlas and Azure AI Search for indexing. Each record includes three fields: hotel ID, image URL, and image vector. The hotel ID and image URL are included to enable easy retrieval and display of hotels with similar images.

Step 3: Retrieve the image vector upon search

When the user clicks the “Find Similar” button, the stored image vector is retrieved from the database to perform a vector search. This approach, compared to real-time image conversion, avoids redundant API calls to Azure AI Vision, reducing costs and improving performance.

Step 4: Perform vector search

The retrieved vector is used to perform Atlas Vector Search and Azure AI Search in parallel. Atlas Vector Search is implemented using Prisma, which supports this functionality with an out-of-the-box function. Azure AI Search, on the other hand, is implemented through API calls:

const body = {
count: true,
select: ‘hotel_location_id, image_url’,
vectorQueries: [
{
vector: image_vector,
k: 6,
fields: ‘image_vector’,
kind: ‘vector’,
exhaustive: true
}
]
}

const response = await fetch(`${baseUrl}/indexes/${indexName}/docs/search?api-version=${apiVersion}`, {
method: ‘POST’,
headers: {
‘Content-Type’: ‘application/json’,
‘api-key’: token
},
body: JSON.stringify(body)
});

Step 5: Combine and return results

Once the results from both searches are ready, the returned hotel lists are merged and deduplicated before displayed to users.

Results and Outcomes

Key accomplishments of this project include:

The implementation of Azure Maps API for a dynamic map view of search results, combined with route planning, further enhanced the user experience.
AI-powered image search feature enables users to find hotels with similar visual attributes, addressing complex search needs that are not met by traditional platforms.

The two features also received positive feedback during the user acceptance testing phase. In particular, the 20 friends invited to test the system, who had no prior knowledge of the project and had not seen any system demos, found the system interaction to be intuitive and user-friendly.

Future Development

The following features could be developed to make RoomRadar even more valuable:

Expand route planning beyond just hotels and underground stations. By integrating a chatbot with Azure Maps, the system could display the optimised tourism routes and hence improves the decision-making process for users.
Allow users to upload their own images and search for hotels with similar images, which adds a personalised touch to the search process.

Conclusion

RoomRadar.ai demonstrates the transformative potential of AI and cloud computing in travel planning. By leveraging Azure Maps and Azure AI Search, we’ve created an innovative hotel search platform that offers an interactive map view with route planning and a groundbreaking image-based search feature. The positive feedback from our User Acceptance Testing validates our approach, confirming that RoomRadar.ai addresses real user needs in the hotel search market. As we look to the future, we’re excited about the possibilities for expanding our platform’s capabilities, continuing to push the boundaries of AI-powered travel planning.

For more information or to get involved:

Contact me via LinkedIn or email
Check out My Detailed tutorial for Azure Maps
Check out the Official Azure Examples

​Microsoft Tech Community – Latest Blogs –Read More 

Good day everyone, I am a budding data scientist and I am looking to join a team for the Microsoft Imagine Cup. If you need one more person to complete your team, it might just be me 😊

Please email me on: email address removed for privacy reasons, so we can talk more. Thank you.

​Good day everyone, I am a budding data scientist and I am looking to join a team for the Microsoft Imagine Cup. If you need one more person to complete your team, it might just be me 😊Please email me on: email address removed for privacy reasons, so we can talk more. Thank you.  Read More

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Phone Pe: Phone Pe में, आप (87971↑60658) ट्रांज़ैक्शन” या “इतिहास” सेक्शन में जाकर असफल लेनदेन का चयन करके उस लेनदेन को देख सकते हैं और “वापस लें” या “वापस लेने के लिए अनुरोध करें” ..

​Phone Pe: Phone Pe में, आप (87971↑60658) ट्रांज़ैक्शन” या “इतिहास” सेक्शन में जाकर असफल लेनदेन का चयन करके उस लेनदेन को देख सकते हैं और “वापस लें” या “वापस लेने के लिए अनुरोध करें” ..  Read More

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Hello Everyone,

I am unable to figure a solution for issue I am facing with a managed property in SPO. 

I have a Document library column “Department” which is a choice field (With some values such as HR, finance, etc). I have a PNP search page in the site, where I have configured a PNP Search bpx, PNP Search results & PNP Search filters webparts.

The pnp search results show data from a Document Library. Also the PNP search filter webpart is showing a couple of fields such as “file type” and “content type”. However I am unable to get the “Department” appear as a filter in the PNP Search filters.

Here is the library column which I am trying to get to show in pnp search filters

I see that the name of this column internally is this : 

In SharePoint Admin centre, I tried two different things : 

1. I used an existing “Refinable string” (refinablestring01) which was not mapped to anything. I mapped it to the crawled properties. I wasnt sure which crawled property is the exact column, so I mapped it to 2 or 3 crawled properties that are for department as below.

The “refinable” property is set a “Yes-active” for the above property.

2. I also tried creating a new managed property and tried mapping it to the same crawled properties as above. 

But even after a few days, I am still unable to get the column to show up in PNP Search filters. 

I can see the “Refinablestring01” in the search results/filters as below

I also see “Refinablestring01” in the pnp search filter webpart properties dropdown

After publishing page, I still can’t see “Department” as a PNP Search filter.  

Any suggestions on what am I missing? Why does the “Department” column from the library, not show up in pnp search filters?

​Hello Everyone, I am unable to figure a solution for issue I am facing with a managed property in SPO.  I have a Document library column “Department” which is a choice field (With some values such as HR, finance, etc). I have a PNP search page in the site, where I have configured a PNP Search bpx, PNP Search results & PNP Search filters webparts. The pnp search results show data from a Document Library. Also the PNP search filter webpart is showing a couple of fields such as “file type” and “content type”. However I am unable to get the “Department” appear as a filter in the PNP Search filters.Here is the library column which I am trying to get to show in pnp search filtersI see that the name of this column internally is this : In SharePoint Admin centre, I tried two different things : 1. I used an existing “Refinable string” (refinablestring01) which was not mapped to anything. I mapped it to the crawled properties. I wasnt sure which crawled property is the exact column, so I mapped it to 2 or 3 crawled properties that are for department as below.The “refinable” property is set a “Yes-active” for the above property. 2. I also tried creating a new managed property and tried mapping it to the same crawled properties as above.  But even after a few days, I am still unable to get the column to show up in PNP Search filters.  I can see the “Refinablestring01” in the search results/filters as below I also see “Refinablestring01” in the pnp search filter webpart properties dropdown After publishing page, I still can’t see “Department” as a PNP Search filter.  Any suggestions on what am I missing? Why does the “Department” column from the library, not show up in pnp search filters?  Read More

​

丰富的学习内容：

广泛的技术领域覆盖：提供了微软旗下众多产品和服务的学习课程，比如 Azure、Windows、Office、Dynamics、Power Apps、Teams 等。无论是云计算、操作系统、办公软件，还是开发工具等方面的知识，都可以在该平台上找到相关学习资源

​丰富的学习内容：广泛的技术领域覆盖：提供了微软旗下众多产品和服务的学习课程，比如 Azure、Windows、Office、Dynamics、Power Apps、Teams 等。无论是云计算、操作系统、办公软件，还是开发工具等方面的知识，都可以在该平台上找到相关学习资源  Read More

​

I usually don’t need to input a password when I start my computer as it automatically boots up to Windows 11. However, recently a message has been appearing saying that my password will expire soon. I am unsure why this is happening since I don’t use a password to access my computer. Could this message be related to my Microsoft account? How can I disable this feature if it is not necessary for me?

​I usually don’t need to input a password when I start my computer as it automatically boots up to Windows 11. However, recently a message has been appearing saying that my password will expire soon. I am unsure why this is happening since I don’t use a password to access my computer. Could this message be related to my Microsoft account? How can I disable this feature if it is not necessary for me?  Read More

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