Description of designed and implemented Fast Exact String Pattern-Matching Algorithm for Fixed Length Patterns which is used for fast generating word search games (wordfind).
In computer programming, a data structure may be selected or designed to store data for the purpose of working on it with various algorithms. Each data structure contains information about the data values, relationships between the data and functions that can be applied to the data.
In computer programming, a data structure may be selected or designed to store data for the purpose of working on it with various algorithms. Each data structure contains information about the data values, relationships between the data and functions that can be applied to the data.
Introduction to Data structure & Algorithms - Sethuonline.com | Sathyabama Un...sethuraman R
Introduction to Data structure Algorithms
R.Sethuraman M.E,(PhD).,
Assistant Professor,
Faculty of Computing,
Dept of Computer Science Engineering,
Sathyabama University
http://Sethuonline.com
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
An introduction to the XPath XML query possibilities. In particular, there is a focus on the abbreviations that makes XPath efficient to use. A larger section is allocated to explain and illustrated the use of axes in XPath
This is a presentation of paper we describe fast and easy graph visualization layout we developed. We called it radius degree layout (RDL). RDL can be used for static and also for dynamic (interactive) visualization. RDL is easy to implement in 2D and 3D visualization, it is CPU efficient, also for large graphs, and we can easily orientate in graph using it. In this paper we describe RDL philosophy, implementation, testing with examples, practical using and the enhanced version with modified force directed layout.
Art + science = visualization
Presentation without comments. Visualization of real and random generated data using variety of computer drawing technique.
Introduction to Data structure & Algorithms - Sethuonline.com | Sathyabama Un...sethuraman R
Introduction to Data structure Algorithms
R.Sethuraman M.E,(PhD).,
Assistant Professor,
Faculty of Computing,
Dept of Computer Science Engineering,
Sathyabama University
http://Sethuonline.com
a. Concept and Definition
b. Binary Tree
c. Introduction and application
d. Operation
e. Types of Binary Tree
• Complete
• Strictly
• Almost Complete
f. Huffman algorithm
g. Binary Search Tree
• Insertion
• Deletion
• Searching
h. Tree Traversal
• Pre-order traversal
• In-order traversal
• Post-order traversal
Slides at myblog
http://www.ashimlamichhane.com.np/2016/07/tree-slide-for-data-structure-and-algorithm/
Assignments at github
https://github.com/ashim888/dataStructureAndAlgorithm/tree/dev/Assignments/assignment_7
An introduction to the XPath XML query possibilities. In particular, there is a focus on the abbreviations that makes XPath efficient to use. A larger section is allocated to explain and illustrated the use of axes in XPath
This is a presentation of paper we describe fast and easy graph visualization layout we developed. We called it radius degree layout (RDL). RDL can be used for static and also for dynamic (interactive) visualization. RDL is easy to implement in 2D and 3D visualization, it is CPU efficient, also for large graphs, and we can easily orientate in graph using it. In this paper we describe RDL philosophy, implementation, testing with examples, practical using and the enhanced version with modified force directed layout.
Art + science = visualization
Presentation without comments. Visualization of real and random generated data using variety of computer drawing technique.
Processing data with Python, using standard library modules you (probably) ne...gjcross
Tutorial #2 from PyCon AU 2012
You have data.
You have Python.
You also have a lot of choices about the best way to work with that data...
Ever wondered when you would use a tuple, list, dictionary, set, ordered dictionary, bucket, queue, counter or named tuple? Phew!
Do you know when to use a loop, iterator or generator to work through a data container?
Why are there so many different "containers" to hold data?
What are the best ways to work with these data containers?
This tutorial will give you all the basics to effectively working with data containers and iterators in Python. Along the way we will cover some very useful modules from the standard library that you may not have used before and will end up wondering how you ever did without them.
This tutorial is aimed at Python beginners. Bring along your laptop so you can interactively work through some of the examples in the tutorial. If you can, install ipython (http://ipython.org/) as we will use it for the demonstrations.
Semi-automated Exploration and Extraction of Data in Scientific TablesElsevier
Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university
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Abstract: This PDSG workshop introduces the basics of Python libraries used in machine learning. Libraries covered are Numpy, Pandas and MathlibPlot.
Level: Fundamental
Requirements: One should have some knowledge of programming and some statistics.
Data structure and algorithm using javaNarayan Sau
This presentation created for people who like to go back to basics of data structure and its implementation. This presentation mostly helps B.Tech , Bsc Computer science students as well as all programmer who wants to develop software in core areas.
Python support numpy libary for fast mathematical computations.
Homogeneous memory allocation for elements in array.
Here I have tried to cover all functions of python numpy library
XII Computer science students find it helpful.
Refer my blog for more solutions
https://pythonxiisolutions.blogspot.com/
https://prippython12.blogspot.com/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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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.
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Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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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.
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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.
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SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
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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.
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
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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/
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
Fast Exact String Pattern-Matching Algorithm for Fixed Length Patterns
1. Fast Exact String Pattern-Matching Algorithm for
Fixed Length Patterns
Ing. Ľuboš Takáč
PhD student
Faculty of Management Science and Informatics
University of Žilina
3. Motivation
• Word search game generator
• Searching string patterns with
fixed length
– M . . . E R
– . . . A H
– . . . . .
4. Problem Definition
• Design fast in-memory data structure (class)
• Requirements
– fast searching, if it is possible with O(1) complexity
– each founded word get only once
– each founded word must be randomly chosen
– founded word have to match the pattern
class Model
FastStringPatternSearch
+ FastStringPatternSearch(String[], Random)
+ FastStringPatternSearch(String[])
+ reset() : void
+ searchPattern(String) : String
5. Existing Solutions
• Relational DB table with full-text index - access to hard drive
• Linked List or array in memory – O(N) complexity
• Indexing of array – necessary to index all possible combination of
patterns to have O(1) complexity
Number of
undefined
positions
0 1 2 3 4 5 6 7 8
Example of
pattern
PATTER
NS
PATT-
RNS
PA-TE-
NS
-AT--
RNS
P-T-E--S -A--ER-- --TT---- ----E--- --------
All combinations
count 1 8 28 56 70 56 28 8 1
Total combinations
count 256
6. Our Implemented Algorithm
• Dynamic in-memory tree(s) with linked list of words (id’s) on nodes
• Roots are in 3-dimensional matrix
• Nodes has 2-dimensional matrix of children
7. Root
• 3 dimensional matrix of root nodes with linked shuffled lists
– alphabet dimension
– word length dimension
– character position dimension
• Example
– We put the word “NAUTICAL” into nodes [N][8][1], [A][8][2], [U][8][3], …,
[L][8][8]
– When we search for pattern “. . U . . . . .”, we are looking into root node [U][8][3]
where we find word “NAUTICAL” in linked list
9. Child nodes
• 2 dimensional matrix of child nodes with linked shuffled lists
– alphabet dimension
– word length dimension – can be determine from ancestor
– character position dimension
10. Searching algorithm
• Searching for pattern “. . T . E R . .”
1. Get the first defined character, pattern length and the position of first
defined character (T, 8, 3). Get a node of three-dimensional array data structure
at [character][length][position] ([T][8][3]). Continue to step 2 with this node.
2. If a node is null, string with this pattern does not exists. – END.
If a node is not null and a node has not children (leaf node) or pattern has no
further defined characters, find the first string in a node list which matches the
pattern. Return founded string or null if no string matches the pattern. – END.
If a node is not null and a node has children (not leaf node), take the next
defined character in pattern (E, position 5) and access two-dimensional array of
children nodes of node at element [position][character] ([5][E]), go to step 2
with the given node.
11. Complexity of algorithm
• We can set MaxListSize on leaf nodes, which determine the
complexity to O(L+MaxListSize), where L is the length of the string
• low MaxListSize = fast searching, high memory consumption,
slow initializing
• High MaxListSize = slow searching, low memory consumption,
fast initializing
• Recommendation
– Set it based on purpose, dictionary size
– Create data structure only once and share it
12. Other requirements
• Get every word only once
– Creating array map with boolean value “used” and comparing and updating it
– Function reset, which set all values to “not used” - O(N)
• Get randomly chosen words
– All linked list are shuffled after initialization
– After finding the word, we put the word on the end of linked list – O(1)
• Get words with pattern without character e.g. “. . . . . . .”
– Creating special linked lists with all sizes and put the words from dictionary there
13. Testing Results
• Dictionary with 225 thousands word
• Generating 5 000 word search games of size 25x25
• More than 1300 times faster than naive algorithm
We used for testing HP ProBook 6550b with configuration Win 7 Professional 64bit, Intel® Core ™ i5 CPU M450 2cores 2.40GHz, 4GB RAM, Java 7.
MaxListSize
Initializing
time (s)
Generating
time (s)
Memory
consumption
(MB)
Unlimited
1,508 989,643 86
5000
2,726 839,294 101
1000
4,843 400,539 265
500
7,062 324,728 340
100
16,141 279,410 808
Naive algorithm O(N) 0,095 381 073,600 15
14. Conclusion
• We design and implement fast in-memory data structure for searching
string patterns with fixed length
• Dynamic structure, up to O(1) complexity
• Randomly chosen words matching the pattern, each founded only
once
• Options to reset data structure, to get all words again without
initializing data structure ( complexity O(N) )