Slides cover definition of tree data structure with examples, related terminologies, accessors methods, query methods, generic methods, traversal algorithms (preorder, postorder, inorder) traversal, Binary tree, Binary tree implementation using linked list and array, Binary search
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Presentation On Binary Search Tree using Linked List Concept which includes Traversing the tree in Inorder, Preorder and Postorder Methods and also searching the element in the Tree
Tree and Binary search tree in data structure.
The complete explanation of working of trees and Binary Search Tree is given. It is discussed such a way that everyone can easily understand it. Trees have great role in the data structures.
This Presentation will Clear the idea of non linear Data Structure and implementation of Tree by using array and pointer and also Explain the concept of Binary Search Tree (BST) with example
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Illustration of various types of Online marketing with examples.Slides talk about search engine marketing, display ads, affiliate marketing, lead generation marketing, native marketing, email marketing etc.
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Ordered Maps and corresponding binary search
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Describes basic understanding of priority queues, their applications, methods, implementation with sorted/unsorted list, sorting applications with insertion sort and selection sort with their running times.
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(Download is recommended to make the animations work)
Slides give the basic introduction of linked list, doubly linked list, circular linked list and operations related to it. It has animations, Download is recommended in order to make best out of animations
(Download is recommended to make the animations work)
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/
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
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👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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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.
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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).
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Bob Boule
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Gopinath Rebala
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
8. Trees
Is an abstract data type.
Defined as a set of nodes that stores elements
hierarchically.
Consists of nodes with a
parent- child relation.
10. Tree terminology
Root
If tree T is nonempty, it has a special node, called the
root of T, that has no parent.
Internal Node
Node with at least one child.
External Node (leaf)
Node without children.
11. Tree terminology
Siblings
Nodes that are children of the same parent.
Ancestors of a Node
Parent, grandparent, grand-grandparent.
Descendants of a Node
Child, grand child, grand-grandchild etc.
12. Tree terminology
Edge
An edge of tree T is a pair of nodes (u, v) such
that u is the parent of v, or vice versa.
Path
Path of T is a sequence of nodes such that any
two consecutive nodes in the sequence form an
edge.
13. A
DCB
E F
I J K
G H
ROOT - A
Internal Node- A, B, F,
C
External Node- E, I, J, K, G,
H, D
Sibling of B – C,D
Ancestors of F - A, B,
Descendant of B - E, I, J, K, F
Path – A, B, F, K
14. Tree terminology
Ordered Trees
If there is a linear ordering defined for the children of each
node, visualized by arranging siblings left to right.
Book
Prefac
e
Part A Part B
Referenc
es
Ch. 5 Ch.
6
^ ^ ^ ^
Sec.
1.1 Sec. 1.9
Ch.
1
Sec. 10.1 Sec. 10.9
Ch.
10
15. Tree Abstract Data Type
positions in a tree are its nodes.
position object for a tree supports the method:
position <E> element()
Return the object stored at this position.
16. Tree’s accessors methods
position root():
Return the tree’s root.
position parent(p):
Return the parent of p.
Iterable children(p):
Return an iterable collection containing the children of node
p.
17. Tree’s query methods
boolean isInternal(p):
Test whether node P is internal.
boolean isExternal(p):
Test whether node p is external.
boolean isRoot(p):
Test whether node p is the root
18. Tree’s generic methods
int size()
Return the number of nodes in the tree.
boolean isEmpty()
Indicates whether the tree has any nodes.
iterator iterator()
Return an iterator of all elements stored at nodes of the tree.
iterable position()
Return an iterable collection of all the nodes of the tree.
element replace(v, e)
Replace with e and return the element stored at node v.
19. A
DCB
E F
I J K
G H
Size() - 11
isInternal(A) -
True
isExternal(E) - True
isRoot(C) - False isEmpty - False
21. Tree traversal Algorithms
A traversal of a Tree T is a systematic way of
accessing(or visiting) all the nodes of T.
22. How can we index node?
Position
public Iterable <Position<E>> positions();
How can we index children of node?
Iterable
public Iterator<E> iterator();
23. Tree ADT
Depth of a node
number of ancestors.
Height of a tree
maximum depth of any node.
Subtree
tree consisting of a node and its descendants.
26. A
DCB
E F
I J K
G H
Depth of F – 2
Height of a Tree -
3
Subtree – C, G, H
27. Traversal schemes
preorder traversal
Node is visited before its descendants.
Children of each node of an ordered tree are
ordered from left to right.
postorder traversal
Node is visited after its descendants.
Children of each node of an ordered tree are
ordered from left to right.
28. Preorder traversal
Algorithm preorder(T, v):
visit(v);
for each child w of v do
preorder(T, w);
Application
print a structured document
Running Time
O(n)
30. Postorder traversal
Algorithm postorder(T, v):
– for each child w of v do
– postorder(T, w);
– visit(v);
Application
computer space used by files in a directory and its subdirectories.
Running Time
O(n)
34. Binary Trees
A binary tree is an ordered tree :
Every node has at most two children.
Each child node is labelled as being either a left child or
a right child.
A left child precedes a right child in the
ordering of children of a node.
35. Binary trees Application
A binary tree is proper or full
If each node has either zero or two children.
Application
Representation of arithmetic expressions
Internal nodes : operators
External nodes : operands
37. Binary trees Application
Representation of decision process
Internal nodes : questions with yes/no answer
External nodes : decisions
38. Binary Tree ADT - Accessor methods
position left(p):
Return the left child of p.
position right(p):
Return the right child of p.
boolean hasLeft(p):
Test whether p has a left child.
boolean hasRight(p):
Test whether p has a right child.
40. Binary Tree Implementation
........ .... .
.
A B D G H
Array
If v is the root of T, then p(v) = 1.
If v is the left child of node u, then p(v) = 2p(u).
If v is the right child of node u, then p(v) = 2p(u)
41. Inorder traversal of a Binary Tree
Algorithm inorder(T, v):
if v has a left child u in T then
inorder(T, u) {recursively traverse left subtree}
perform the “visit action for node v
if v has a right child w in T then
inorder(T, w) {recursively traverse right subtree}
42. Inorder traversal of a Binary Tree
(3+1)
(3+1) * 4
((3+1)*4)/
((3+1)*4)/ (9-5)
((3+1)*4)/ ((9-5) + 2)
Answer = 16/6
43. Binary search
x(v) – element stored in any internal node.
For each internal node v of T:
The elements stored in the left subtree of v are less than or
equal to x(v).
The elements stored in the right subtree of v are greater
than or equal to x(v).
The external nodes of T do not store any element.
44. Binary search tree
The blue solid path is traversed when searching (successfully) for 36.
The blue dashed path is traversed when searching (unsuccessfully)
for 70.
Editor's Notes
&lt;number&gt;
Do YOU have a non-linear mind?
frequently turn 5 minute tasks into &apos;all nighters&apos;, can&apos;t put something down until it&apos;s finished, and there are many other symptoms as well,
&lt;number&gt;
according to productivity experts , breakthroughs come by thinking &quot;nonlinearly&quot; ,
&lt;number&gt;
non-linear data structure in computing known as TREES.// Tree structures are a breakthrough in data organization, as they implement a host of algorithms much faster than when using linear data structures, such as list.
&lt;number&gt;
Trees provide a natural organization for data, and consequently have become ubiquitous structures in //file systems, graphical user interfaces, //databases, Web sites, and other computer systems. //Moves in a game are also put in a tree as well.
&lt;number&gt;
When we say that trees are &quot;nonlinear,&quot; we are referring to an organizational relationship that is more than &quot;before&quot; and &quot;after&quot; relationships, relationships in a tree are hierarchical, being &quot;above&quot; and &quot;below&quot;. //Actually, the main terminology for tree data structures comes from family trees, with the terms &quot;parent,&quot; &quot;child,&quot; &quot;ancestor,&quot; and &quot;descendent&quot; being the most common words used to describe relationships.
&lt;number&gt;
&lt;number&gt;
&lt;number&gt;
elaborate the parent child relationship in a tree with this example.// // // // this is how tree is extended from one level to another. A very good example of organisational hierarchy.
&lt;number&gt;
&lt;number&gt;
Ordered trees: A tree is ordered if there is a linear ordering defined for the children of each node; that is, we can identify the children of a node as being the first, second, third, and so on. Such an ordering is usually visualized by arranging siblings left to right, according to their order.
&lt;number&gt;
In a tree abstract data type, positions in a tree are its nodes, and it supports a method called element() that returns the object stored at this position.
&lt;number&gt;
Is A internal , yes it is as it has children, is E external yes, as it has no children, is// //
&lt;number&gt;
Here is the running time for all the nodes, its all O(1) except iterator and position which has O(n) where running time does depend on data size.
&lt;number&gt;
&lt;number&gt;
, in simple words, tree traversal is a systematic way of accessing all the nodes. // Do you think we can access nodes? if yes, how? position method, that returns an iterable collection of the nodes.
if we have to index children of node, then iterator method can be used., iterator() method returns an iterator of the elements stored in the tree
&lt;number&gt;
&lt;number&gt;
The depth of a node v can also be recursively defined as follows: • If v is the root, then the depth of v is 0 , • Otherwise, the depth of v is one plus the depth of the parent of v. this algorithm works recursively. height of a node v in a tree T is also defined recursively: • If v is an external node, then the height of v is 0 • Otherwise, the height of v is one plus the maximum height of a child of v.
&lt;number&gt;
Alorithm postorder: recursively traverse the subtree rooted at w., perform the visit action for node v.
&lt;number&gt;
Algoritm preorder: perform the &quot;visit&quot; action for node v, here it recursively traverse the subtree rooted at w. The preorder traversal algorithm is useful for producing a linear ordering of the nodes of a tree where parents must always come before their children in the ordering. Such orderings have several different applications.
&lt;number&gt;
&lt;number&gt;
Alorithm postorder: recursively traverse the subtree rooted at w., perform the visit action for node v.
&lt;number&gt;
takes O(n) time, assuming that visiting each node takes O(1) time
&lt;number&gt;
If observe closely, each node has two children, and hence this type of tree is called as Binary tree.
&lt;number&gt;
// // natural way to realize a binary tree T is to use a linked structure, // A node is represented by an object storing element, parent node, left child nod, right child node. //And when we make a tree out of these nodes , it looks like something like this//
&lt;number&gt;
&lt;number&gt;
inorder traversal: To traverse a binary tree in Inorder, we traverse the left most subtree starting at the left external node, (ii) Visit the root, and (iii) Traverse the right subtree starting at the left external node.
&lt;number&gt;
// /// // Lets see the example of the binary search tree storing integers. try to find 36 and 70 are in S, by traversing a path down the tree starting at the root.
&lt;number&gt;
Lets do it for 36 first, At each internal node we encountered, we compare our 36 with the element stored at node. If y = x(v), then the search continues in the left subtree of v. If y = x(v), then the search terminates successfully. If y ≥ x(v), then the search continues in the right subtree of v. Finally, if we reach an external node, the search terminates unsuccessfully.
&lt;number&gt;