Reporting Summary Information of Spatial Datasets and Non-Compliance Issues U...Safe Software
An overview of two groups of FME workspaces implemented at the Mapping and Charting Establishment (MCE) that include the generation of reports in Excel format is presented here. The first group includes data validation and data compliance assessments. An example showing Self Validation of Spatial Data Input from DND Bases using FME Server is presented. The second group, implemented using FME Desktop, includes the creation of statistical reports for some key datasets distributed by MCE. Two examples of FME workspaces are presented here: the first one showing reports created for NRCan CanVec plus charts, and the second one showing reports created for Open Street Map (OSM) data delivered in FGDB format for custom AOIs.
Reporting Summary Information of Spatial Datasets and Non-Compliance Issues U...Safe Software
An overview of two groups of FME workspaces implemented at the Mapping and Charting Establishment (MCE) that include the generation of reports in Excel format is presented here. The first group includes data validation and data compliance assessments. An example showing Self Validation of Spatial Data Input from DND Bases using FME Server is presented. The second group, implemented using FME Desktop, includes the creation of statistical reports for some key datasets distributed by MCE. Two examples of FME workspaces are presented here: the first one showing reports created for NRCan CanVec plus charts, and the second one showing reports created for Open Street Map (OSM) data delivered in FGDB format for custom AOIs.
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Prepared as part of the IT for Business Intelligence course of MBA @VGSOM, IIT Kharagpur. The tutorial describes how to create an interactive map using the open source software QGIS.
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
The conference presentation of the article
L. Akritidis, P. Bozanis, "Computing Scientometrics in Large-Scale Academic Search Engines with MapReduce", In Proceedings of the 13th International Conference on Web Information System Engineering (WISE), Lecture Notes in Computer Science (LLNCS), vol. 7651, pp. 609-623, 2012.
which was presented in Paphos, Cyprus in November of 2012.
Abstract: With development of the information technology, the scale of data is increasing quickly. The massive data poses a great challenge for data processing and classification. In order to classify the data, there were several algorithm proposed to efficiently cluster the data. One among that is the random forest algorithm, which is used for the feature subset selection. The feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. It is achieved by classifying the given data. The efficiency is calculated based on the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. The existing system deals with fast clustering based feature selection algorithm, which is proven to be powerful, but when the size of the dataset increases rapidly, the current algorithm is found to be less efficient as the clustering of datasets takes quiet more number of time. Hence the new method of implementation is proposed in this project to efficiently cluster the data and persist on the back-end database accordingly to reduce the time. It is achieved by scalable random forest algorithm. The Scalable random forest is implemented using Map Reduce Programming (An implementation of Big Data) to efficiently cluster the data. In works on two phases, the first step deals with the gathering the datasets and persisting on the datastore and the second step deals with the clustering and classification of data. This process is completely implemented using Google App Engine’s hadoop platform, which is a widely used open-source implementation of Google's distributed file system using MapReduce framework for scalable distributed computing or cloud computing. MapReduce programming model provides an efficient framework for processing large datasets in an extremely parallel mining. And it comes to being the most popular parallel model for data processing in cloud computing platform. However, designing the traditional machine learning algorithms with MapReduce programming framework is very necessary in dealing with massive datasets.Keywords: Data mining, Hadoop, Map Reduce, Clustering Tree.
Title: Big Data on Implementation of Many to Many Clustering
Author: Ravi. R, Michael. G
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
MCE GeoProcessing Services for ADM(IE): Self Validation of Spatial Data Input...Safe Software
The Department of National Defence (DND) is developing a central repository for land, building, and infrastructure data. This a joint project between the Assistant Deputy Minister Infrastructure and Environment (ADMIE) and the Mapping and Charting Establishment (MCE). This project involves managing DND real property and other spatial data provided by DND bases and wings across Canada through a unique, integrated and standardized Real Property Spatial Data Warehouse (RPSDW), hosted at MCE, containing a SQL Server database. Data provided by DND bases and wings must meet standards defined and documented by ADMIE, in terms of data formats accepted (GeoMedia MDB, ArcGIS FGDB, MapInfo MIF or AutoCAD SDF), geometry, schema and attribute data types, domains and accepted values for each feature class. An FME workspace and an equivalent tool contained within an ArcGIS Data Interoperability Toolbox were created to provide the geotechs from DND Bases using ArcGIS Data Interoperability or FME Desktop with a toolset, delivered together with a user’s guide, that allows them to perform a self-validation of the DND real property and other spatial data before these data is sent to MCE to be loaded into the RPSDW SQL Server database.
IntraMaps - User Group - November 2010 - Hansen IntegrationDavid Hair
This is the presentation that I gave to the IntraMaps user group meeting at Stonnington Council.
The presentation goes through the steps Maroondah Council have gone through to link IntraMaps and Hansen to view data inside of Intramaps in addition to linking to and from Hansen and IntraMaps.
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Prepared as part of the IT for Business Intelligence course of MBA @VGSOM, IIT Kharagpur. The tutorial describes how to create an interactive map using the open source software QGIS.
Computing Scientometrics in Large-Scale Academic Search Engines with MapReduceLeonidas Akritidis
The conference presentation of the article
L. Akritidis, P. Bozanis, "Computing Scientometrics in Large-Scale Academic Search Engines with MapReduce", In Proceedings of the 13th International Conference on Web Information System Engineering (WISE), Lecture Notes in Computer Science (LLNCS), vol. 7651, pp. 609-623, 2012.
which was presented in Paphos, Cyprus in November of 2012.
Abstract: With development of the information technology, the scale of data is increasing quickly. The massive data poses a great challenge for data processing and classification. In order to classify the data, there were several algorithm proposed to efficiently cluster the data. One among that is the random forest algorithm, which is used for the feature subset selection. The feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. It is achieved by classifying the given data. The efficiency is calculated based on the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. The existing system deals with fast clustering based feature selection algorithm, which is proven to be powerful, but when the size of the dataset increases rapidly, the current algorithm is found to be less efficient as the clustering of datasets takes quiet more number of time. Hence the new method of implementation is proposed in this project to efficiently cluster the data and persist on the back-end database accordingly to reduce the time. It is achieved by scalable random forest algorithm. The Scalable random forest is implemented using Map Reduce Programming (An implementation of Big Data) to efficiently cluster the data. In works on two phases, the first step deals with the gathering the datasets and persisting on the datastore and the second step deals with the clustering and classification of data. This process is completely implemented using Google App Engine’s hadoop platform, which is a widely used open-source implementation of Google's distributed file system using MapReduce framework for scalable distributed computing or cloud computing. MapReduce programming model provides an efficient framework for processing large datasets in an extremely parallel mining. And it comes to being the most popular parallel model for data processing in cloud computing platform. However, designing the traditional machine learning algorithms with MapReduce programming framework is very necessary in dealing with massive datasets.Keywords: Data mining, Hadoop, Map Reduce, Clustering Tree.
Title: Big Data on Implementation of Many to Many Clustering
Author: Ravi. R, Michael. G
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
MCE GeoProcessing Services for ADM(IE): Self Validation of Spatial Data Input...Safe Software
The Department of National Defence (DND) is developing a central repository for land, building, and infrastructure data. This a joint project between the Assistant Deputy Minister Infrastructure and Environment (ADMIE) and the Mapping and Charting Establishment (MCE). This project involves managing DND real property and other spatial data provided by DND bases and wings across Canada through a unique, integrated and standardized Real Property Spatial Data Warehouse (RPSDW), hosted at MCE, containing a SQL Server database. Data provided by DND bases and wings must meet standards defined and documented by ADMIE, in terms of data formats accepted (GeoMedia MDB, ArcGIS FGDB, MapInfo MIF or AutoCAD SDF), geometry, schema and attribute data types, domains and accepted values for each feature class. An FME workspace and an equivalent tool contained within an ArcGIS Data Interoperability Toolbox were created to provide the geotechs from DND Bases using ArcGIS Data Interoperability or FME Desktop with a toolset, delivered together with a user’s guide, that allows them to perform a self-validation of the DND real property and other spatial data before these data is sent to MCE to be loaded into the RPSDW SQL Server database.
IntraMaps - User Group - November 2010 - Hansen IntegrationDavid Hair
This is the presentation that I gave to the IntraMaps user group meeting at Stonnington Council.
The presentation goes through the steps Maroondah Council have gone through to link IntraMaps and Hansen to view data inside of Intramaps in addition to linking to and from Hansen and IntraMaps.
Data mining model for the data retrieval from central server configurationijcsit
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most
relevant and updated for continuous text search queries. This paper focuses on handling continuous text
extraction sustaining high document traffic. The main objective is to retrieve recent updated documents
that are most relevant to the query by applying sliding window technique. Our solution indexes the
streamed documents in the main memory with structure based on the principles of inverted file, and
processes document arrival and expiration events with incremental threshold-based method. It also ensures
elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are
ranked based on user feedback and given higher priority for retrieval.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
School of Computing, Science & EngineeringAssessment Briefin.docxanhlodge
School of Computing, Science & Engineering
Assessment Briefing to Students
Learning Outcomes of this Assessment
A2 - show awareness of a variety of graphics toolkits and select an appropriate one for a given task
A3 - discuss the capabilities of various input and output devices and their relationship to graphics programming
A4 - use appropriate mathematics to perform standard graphical transformations
A5 - application of graphics programming skills in a real-world application
Key Skills to be Assessed
C/C++ programming
Use of OpenGL API
Application of low level graphics principles & data management techniques for developing interactive graphics application
Critical Evaluation of tools used
The Assessment Task
Your task is to demonstrate your newly acquired skills in C and OpenGL programming. This will be achieved by producing a
demonstration application that will offer a simple visualisation comprising a collection of discrete objects located in a navigable
space. Fundamental to the successful completion of this assignment is careful consideration of the management of scene data,
using resizable dynamic memory structures, and the application of appropriate mathematical models for simulation, navigation and
interaction.
The form of this assignment will be a basic solar system simulation in which a dynamic collection of planetary bodies will be
simulated. These bodies will be represented by simple graphical forms and have the ability to show a historical trail of their
movement. The bodies motion should be defined by a simple gravitational system simulation which calculates forces based on the
masses of the bodies and uses this to derive discrete step changes to acceleration and velocity.Inital starting conditions for the
planetary bodies should be random (mass, position, starting velocity and material (colour)). Advanced solutions should consider the
actions taking place when collisions between bodies occur. In these cases the collision should be detected. The mass and
velocities of the bodies should be combined (thereby removing one of the bodies from the data structure) with the major body
taking priority. Ideally the size of the resultant body should be changed to reflect the enhanced mass. You should also provide
mechanisms to add bodies during the runtime of the simulation (based on random data) both at user request and to maintain a set
number of bodies in the system.
Assessment Title : Computer Graphics Assignment 1: OpenGL Programming - Solar System
Module Title : Computer Graphics
You are provided with an example solution to evaluate and a template project, including a maths library, camera model and basic
utilities as a starting point.
The implementation of the assignment problem will be assessed in the following areas
1. Design and implementation of a suitable dynamic data structure that will maintain an ordered list of the render-able objects with
facilities to add and remove entit.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
Spatial Data Integrator - Software Presentation and Use Cases
1. Spatial Data Integrator software presentation and use cases National Geographic Community Meeting Day Ministry of Ecology and Sustainable Development – Ministry of Agroculture mathieu.rajerison
16. It adds a spatial layer to TOS thanks to geospatial access and treatment components
17. Developed on Java: Eclipse environment, UDig elements, GeoTools library, Java Topology Suite, Sextante
18. Place of an ETL in a data infrastructure Dashboards Portal
19. The interface elements the map window This windows enables to visualize geographic data. It is useful when controlling the results of a treatment. This windows is part of UDig Software.
20. The tool The business modeler The business modeler enables to model the job processes Il allows a large public to take part of of the data flow conception and to follow the advancement of development, without requiring any computer skills Modelling in this window has no impact on the job execution
21. The interface elements The repository metadata tab The repository contains, among other things, the metadata part The metadata part is a place where to store the data access parameters. On the image, you can notice-the different types of data sources. Note that the configuration of geographic data is not made inside the metadata part (we'll see that further in the demo)
22. The interface elements The graphical workspace The main window is where you create your jobs You pick your components and put them here There are different types of relations between components that won't be detailed in this keynote.
23. The interface elements The components palette The palette contains the different components. It's a kind of toolbox Spatial Data Integrator adds the geo part to it The palette is extensible thanks to the contributions of developers As it is opensource, you can develop your own components
24. The interface elements The configuration tab the bottom windows is where you configure the behaviour of each component it also enables you to parameter the execution of your job.
26. Configuring the data access and creating the schemas the first step consists in configuring the access to you data source.
27. Connecting the components inside the workspace You put and connect the components inside the workspace
28. Configuring the tMap component Here, the city name links the two tables. Two output flows are generated: one for inner join results, one for the outer join ones.
29. The job execution The job can now be executed There are two modes of execution: - statistics mode displays the number of rows for each flow - traces mode displays its content Each of these modes is executed in streaming.
30. Going further: detecting similarities between rows Here, we use a fuzzy logic component named tFuzzyMatch . It detects the similarities between rows coming from two different flows. It can be useful to see which rows from a reference (lookup) table correspond the most to the outer join results.
32. Scheduling the aggregation of data A web geographic portal demands joining periodically the data from different sources Here, it is an Access database fed by users. We'll associate its entries with the cities objects. WMS Access SHP BDCARTO Map Server Sybase XML ... Client part SCP SHP
34. Merging layers Imagine a data infrastructure where geograhic layers are disseminated in as many files as cities. Consequently, there is one file per city. The jobs aims at merging all these files in one unique table. SHP5 SHP4 SHP3 SHP2 SHP1 SHP
36. Chaining the Quality Control of Digitalized Documents After having digitalized a huge mass of data, we must operate a complete control on it. The geometry of the objects and their attributes must be checked. This task is very time-consuming if we accomplish it with usual mapping softwares. checking the tables structure checking the content checking the geometric compliance comparison to the reference data
37. Chaining the Quality Control of Digitalized Documents With a single click, SDI enables to operate this series of controls Reports will list errors related to the objects geometric compliance or attribute values. checking the tables structure checking the content checking the geometric compliance comparison to the reference data
39. Chaining the Quality Control of Digitalized Documents Job comparing the Urban Planning Project Map to the Cadastral Reference Data.
40. Chaining the Quality Control of Digitalized Documents Tmap joining component Used function Result type row4.the_geom. symDifference (row2.the_geom) géométrique GeometryOperation.GETAREA (row4.the_geom.difference(row2.the_geom)) flottant
41. Migrating data into a PostgreSQL/PostGIS database At a regional scope, we want to mutualize data and integrate it into a PostgreSQL/postGIS database management system Folder tree Relational Database System
![Summary ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)