The document discusses different clustering methods in R including k-means clustering, k-medoids clustering, hierarchical clustering, and density-based clustering. It provides code examples to demonstrate each method using the iris dataset. For k-means and k-medoids clustering, it shows how to interpret the results and check clustering against known classes. For hierarchical clustering, it generates a dendrogram and identifies clusters. For density-based clustering, it identifies clusters of different shapes and sizes and is able to label new prediction data.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Scienc...Edureka!
This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
how,when and why to perform Feature scaling?
Different type of feature scaling Technique.
when to perform feature scaling?
why to perform feature scaling?
MinMax feature scaling techniques.
Unit vector scaling.
KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
This Edureka k-means clustering algorithm tutorial will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/3aseSs
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Scienc...Edureka!
This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples.
Below are the topics covered in this tutorial:
1) Machine Learning Introduction
2) Classification
3) Types of classifiers
4) Decision tree
5) How does Decision tree work?
6) Demo in R
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space.
The goal of this workshop is to introduce fundamental capabilities of R as a tool for performing data analysis. Here, we learn about the most comprehensive statistical analysis language R, to get a basic idea how to analyze real-word data, extract patterns from data and find causality.
Presented: Thursday, February 14, 2013
Presenter: Joseph Rickert, Technical Marketing Manager, Revolution Analytics
We at Revolution Analytics are often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people we have identified three factors that greatly facilitate learning R. For a quick start:
Find a way of orienting yourself in the open source R world
Have a definite application area in mind
Set an initial goal of doing something useful and then build on it
In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:
Provide an orientation to R’s data mining resources
Show how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.
Show the simple R commands to accomplish these same tasks without the GUI
Demonstrate how to build on these fundamental skills to gain further competence in R
Move away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR
Data scientists and analysts using other statistical software as well as students who are new to data mining should come away with a plan for getting started with R.
Pattern Recognition - Designing a minimum distance class mean classifierNayem Nayem
“Minimum Distance to Class Mean Classifier” is used to classify unclassified sample vectors where the vectors clustered in more than one classes are given. We can classify the unclassified sample vectors with Class Mean Classifier.
Here are my slides for my preparation class for possible students in the Master in Electrical Engineering and Computer Science (Specialization in Computer Science)... for the entrance examination here at Cinvestav GDL.
Clustering and Visualisation using R programmingNixon Mendez
Clustering Analysis is a collection of patterns into clusters based on similarity.
Here we will discuss on the following :
Microarray Data of Yeast Cell Cycle
Clustering Analysis :-
Principal Component Analysis (PCA)
Multidimensional Scaling (MDS)
K-Means
Self-Organizing Maps (SOM)
Hierarchical Clustering
Categorized into 2 types visualize the patterns using R Studio with detailed illustration from bivariate to univariate analysis using methods like boxplot, skewness, outliers, hist, par and much more
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Motivation entails the development of a program that automatically performs clustering and outlier detection for a wide variety of numerically represented data.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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.
Epistemic Interaction - tuning interfaces to provide information for AI support
Data Clustering with R
1. Data Clustering with R
Yanchang Zhao
http://www.RDataMining.com
30 September 2014
1 / 30
2. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
2 / 30
3. Data Clustering with R 1
I k-means clustering with kmeans()
I k-medoids clustering with pam() and pamk()
I hierarchical clustering
I density-based clustering with DBSCAN
1Chapter 6: Clustering, in book R and Data Mining: Examples and Case
Studies. http://www.rdatamining.com/docs/RDataMining.pdf
3 / 30
4. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
4 / 30
6. Results of k-Means Clustering
Check clustering result against class labels (Species)
table(iris$Species, kmeans.result$cluster)
##
## 1 2 3
## setosa 0 50 0
## versicolor 2 0 48
## virginica 36 0 14
I Class setosa" can be easily separated from the other clusters
I Classes versicolor" and virginica" are to a small degree
overlapped with each other.
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8. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
8 / 30
9. The k-Medoids Clustering
I Dierence from k-means: a cluster is represented with its
center in the k-means algorithm, but with the object closest
to the center of the cluster in the k-medoids clustering.
I more robust than k-means in presence of outliers
I PAM (Partitioning Around Medoids) is a classic algorithm for
k-medoids clustering.
I The CLARA algorithm is an enhanced technique of PAM by
drawing multiple samples of data, applying PAM on each
sample and then returning the best clustering. It performs
better than PAM on larger data.
I Functions pam() and clara() in package cluster
I Function pamk() in package fpc does not require a user to
choose k.
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10. Clustering with pamk()
library(fpc)
pamk.result - pamk(iris2)
# number of clusters
pamk.result$nc
## [1] 2
# check clustering against actual species
table(pamk.result$pamobject$clustering, iris$Species)
##
## setosa versicolor virginica
## 1 50 1 0
## 2 0 49 50
Two clusters:
I setosa
I a mixture of versicolor and virginica
10 / 30
11. layout(matrix(c(1, 2), 1, 2)) # 2 graphs per page
plot(pamk.result$pamobject)
clusplot(pam(x = sdata, k = k, diss = diss))
−3 −1 0 1 2 3 4
−2 −1 0 1 2 3
Component 1 Component 2
Silhouette plot of pam(x = sdata, k = n = 150 2 clusters Cj
Average silhouette width : 0.69
0.0 0.2 0.4 0.6 0.8 1.0
Silhouette width si
These two components explain 95.81 % of the point variability.
j : nj | aveiÎCj si
1 : 51 | 0.81
2 : 99 | 0.62
layout(matrix(1)) # change back to one graph per page 11 / 30
12. I The left chart is a 2-dimensional clusplot (clustering plot)
of the two clusters and the lines show the distance between
clusters.
I The right chart shows their silhouettes. A large si (almost 1)
suggests that the corresponding observations are very well
clustered, a small si (around 0) means that the observation
lies between two clusters, and observations with a negative si
are probably placed in the wrong cluster.
I Since the average Si are respectively 0.81 and 0.62 in the
above silhouette, the identi
14. Clustering with pam()
# group into 3 clusters
pam.result - pam(iris2, 3)
table(pam.result$clustering, iris$Species)
##
## setosa versicolor virginica
## 1 50 0 0
## 2 0 48 14
## 3 0 2 36
Three clusters:
I Cluster 1 is species setosa and is well separated from the
other two.
I Cluster 2 is mainly composed of versicolor, plus some cases
from virginica.
I The majority of cluster 3 are virginica, with two cases from
versicolor.
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15. layout(matrix(c(1, 2), 1, 2)) # 2 graphs per page
plot(pam.result)
clusplot(pam(x = iris2, k = 3))
−3 −2 −1 0 1 2 3
−3 −2 −1 0 1 2
Component 1 Component 2
Silhouette plot of pam(x = iris2, k = 3)
n = 150 3 clusters Cj
j : nj | aveiÎCj si
1 : 50 | 0.80
2 : 62 | 0.42
3 : 38 | 0.45
0.0 0.2 0.4 0.6 0.8 1.0
Silhouette width si
These two components explain 95.81 % of the point variability.
Average silhouette width : 0.55
layout(matrix(1)) # change back to one graph per page 14 / 30
16. Results of Clustering
I In this example, the result of pam() seems better, because it
identi
17. es three clusters, corresponding to three species.
I Note that we cheated by setting k = 3 when using pam(),
which is already known to us as the number of species.
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18. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
16 / 30
19. Hierarchical Clustering of the iris Data
set.seed(2835)
# draw a sample of 40 records from the iris data, so that the
# clustering plot will not be over crowded
idx - sample(1:dim(iris)[1], 40)
irisSample - iris[idx, ]
# remove class label
irisSample$Species - NULL
# hierarchical clustering
hc - hclust(dist(irisSample), method = ave)
# plot clusters
plot(hc, hang = -1, labels = iris$Species[idx])
# cut tree into 3 clusters
rect.hclust(hc, k = 3)
# get cluster IDs
groups - cutree(hc, k = 3)
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21. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
19 / 30
22. Density-based Clustering
I Group objects into one cluster if they are connected to one
another by densely populated area
I The DBSCAN algorithm from package fpc provides a
density-based clustering for numeric data.
I Two key parameters in DBSCAN:
I eps: reachability distance, which de
23. nes the size of
neighborhood; and
I MinPts: minimum number of points.
I If the number of points in the neighborhood of point is no
less than MinPts, then is a dense point. All the points in its
neighborhood are density-reachable from and are put into
the same cluster as .
I Can discover clusters with various shapes and sizes
I Insensitive to noise
I The k-means algorithm tends to
30. Prediction with Clustering Model
I Label new data, based on their similarity with the clusters
I Draw a sample of 10 objects from iris and add small noises
to them to make a new dataset for labeling
I Random noises are generated with a uniform distribution
using function runif().
# create a new dataset for labeling
set.seed(435)
idx - sample(1:nrow(iris), 10)
# remove class labels
new.data - iris[idx,-5]
# add random noise
new.data - new.data + matrix(runif(10*4, min=0, max=0.2),
nrow=10, ncol=4)
# label new data
pred - predict(ds, iris2, new.data)
25 / 30
34. Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
28 / 30
35. Online Resources
I Chapter 6: Clustering, in book R and Data Mining: Examples
and Case Studies
http://www.rdatamining.com/docs/RDataMining.pdf
I R Reference Card for Data Mining
http://www.rdatamining.com/docs/R-refcard-data-mining.pdf
I Free online courses and documents
http://www.rdatamining.com/resources/
I RDataMining Group on LinkedIn (7,000+ members)
http://group.rdatamining.com
I RDataMining on Twitter (1,700+ followers)
@RDataMining
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