A grammar is said to be regular, if the production is in the form -
A → αB,
A -> a,
A → ε,
for A, B ∈ N, a ∈ Σ, and ε the empty string
A regular grammar is a 4 tuple -
G = (V, Σ, P, S)
V - It is non-empty, finite set of non-terminal symbols,
Σ - finite set of terminal symbols, (Σ ∈ V),
P - a finite set of productions or rules,
S - start symbol, S ∈ (V - Σ)
Software Engineering - The Making of a Weather Application Shehab Nassef
- Some technical details are not mentioned -
Introduction to making a Weather Forecast mobile applications in which process models, several architecture styles are implemented.
This report is the final project of Software Engineering - CSE334
A grammar is said to be regular, if the production is in the form -
A → αB,
A -> a,
A → ε,
for A, B ∈ N, a ∈ Σ, and ε the empty string
A regular grammar is a 4 tuple -
G = (V, Σ, P, S)
V - It is non-empty, finite set of non-terminal symbols,
Σ - finite set of terminal symbols, (Σ ∈ V),
P - a finite set of productions or rules,
S - start symbol, S ∈ (V - Σ)
Software Engineering - The Making of a Weather Application Shehab Nassef
- Some technical details are not mentioned -
Introduction to making a Weather Forecast mobile applications in which process models, several architecture styles are implemented.
This report is the final project of Software Engineering - CSE334
At the end of this lecture students should be able to;
Define the C standard functions for managing input output.
Apply taught concepts for writing programs.
My slides for Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) for an end-to-end (E2E) ASR speech recognition seminar at Aalto University spring 2020.
Working with Complex Types in DataFrames: Optics to the RescueDatabricks
Working with complex types shouldn’t be a complex job. DataFrames provide a great SQL-oriented API for data transformation, but it doesn’t help much when the time comes to update elements of complex types like structs or arrays. In such cases, your program quickly turns into a humongous code of struct words and parenthesis, while trying to make transformations over inner elements, and reconstructing your column. This is exactly the sample problem that we encounter when working with immutable data structures in functional programming, and to solve that problem, optics were invented. Couldn’t we use something similar to optics in the DataFrame realm?
In this talk, we will show how we can enrich the DataFrame API with design patterns that lenses, one of the most common type of optic, put forward to manipulate immutable data structures. We will show how these patterns are implemented through the spark-optics library, an analogue to the Scala Monocle library, and will illustrate its use with several examples. Last but not least, we will take advantage of the dynamic type system of DataFrames to do more than transforming sub-columns, like pruning elements, and renaming them.
Introduction to control structure in C Programming Language include decision making (if statement, if..else statement, if...else if...else statement, nested if...else statement, switch...case statement), Loop(for loop, while loop, do while loop, nested loop) and using keyword(break, continue and goto)
At the end of this lecture students should be able to;
Define the C standard functions for managing input output.
Apply taught concepts for writing programs.
My slides for Connectionist Temporal Classification (CTC) for automatic speech recognition (ASR) for an end-to-end (E2E) ASR speech recognition seminar at Aalto University spring 2020.
Working with Complex Types in DataFrames: Optics to the RescueDatabricks
Working with complex types shouldn’t be a complex job. DataFrames provide a great SQL-oriented API for data transformation, but it doesn’t help much when the time comes to update elements of complex types like structs or arrays. In such cases, your program quickly turns into a humongous code of struct words and parenthesis, while trying to make transformations over inner elements, and reconstructing your column. This is exactly the sample problem that we encounter when working with immutable data structures in functional programming, and to solve that problem, optics were invented. Couldn’t we use something similar to optics in the DataFrame realm?
In this talk, we will show how we can enrich the DataFrame API with design patterns that lenses, one of the most common type of optic, put forward to manipulate immutable data structures. We will show how these patterns are implemented through the spark-optics library, an analogue to the Scala Monocle library, and will illustrate its use with several examples. Last but not least, we will take advantage of the dynamic type system of DataFrames to do more than transforming sub-columns, like pruning elements, and renaming them.
Introduction to control structure in C Programming Language include decision making (if statement, if..else statement, if...else if...else statement, nested if...else statement, switch...case statement), Loop(for loop, while loop, do while loop, nested loop) and using keyword(break, continue and goto)
Presented on 27th September 2017 to a joint meeting of 'Cork Functional Programmers' and the 'Cork Java Users Group'
Based on the Kotlin Language programming course from Instil. For more details see https://instil.co/courses/kotlin-development/
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
With each of the past 3 Ruby releases, YJIT has delivered higher and higher performance. However, we are seeing diminishing returns, because as JIT-compiled code becomes faster, it makes up less and less of the total execution time, which is now becoming dominated by C function calls. As such, it may appear like there is a fundamental limit to Ruby’s performance.
In the first half of the 20th century, some early airplane designers thought that the speed of sound was a fundamental limit on the speed reachable by airplanes, thus coining the term “sound barrier”. This limit was eventually overcome, as it became understood that airflow behaves differently at supersonic speeds.
In order to break the Ruby performance barrier, it will be necessary to reduce the dependency on C extensions, and start writing more gems in pure Ruby code. In this talk, I want to look at this problem more in depth, and explore how YJIT can help enable writing pure-Ruby software that delivers high performance levels.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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/
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.
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
2. NORMAL FUNCTION
Function definition is above main()
Function is called inside main()
#include <iostream>
#include <algorithm>
#include <vector>
using namespace std;
void assign(int& v){
static int n = 1; v = n++;
}
void print(int v){
cout << v << " ";
}
int main(){
vector<int> vec(10);
for_each(vec.begin(), vec.end(), print);
for_each(vec.begin(), vec.end(), assign);
for_each(vec.begin(), vec.end(), print);
return 0;
}
Assign
Print
3. LAMBDA FUNCTIONS
C++11 provides the ability to create anonymous functions, called lambda
functions.
It allows a function to be defined at the point where it's needed in another
expression.
It is a function that we can write inline in our code in order to pass in to another
function.
4. LAMBDA DECLARATION AND CALLS
Assign
Print
Print
#include <iostream>
#include <algorithm>
#include <vector>
using namespace std;
int main(){
vector<int> vec(10);
for_each(vec.begin(), vec.end(), [ ](int v){cout << v << " “;});
for_each(vec.begin(), vec.end(), [ ](int& v){ static int n = 1; v = n++;});
for_each(vec.begin(), vec.end(), [ ](int v){cout << v << " “;});
return 0;
}
5. MORE ABOUT LAMBDA
It is a function creation inside another function
Output = function (input, input, [ ](output){definition});
This call causes to runtime create variables
It is not available in < g++11 versions so for that we use another procedure called
inline
6. INLINE FUNCTION
Write inline with the function name before the output type
InlineType Name (inputs..)
#include <iostream>
#include <algorithm>
#include <vector>
using namespace std;
Inline void assign(int& v){
static int n = 1; v = n++;
}
Inline void print(int v){
cout << v << " ";
}
int main(){
vector<int> vec(10);
for_each(vec.begin(), vec.end(), print);
for_each(vec.begin(), vec.end(), assign);
for_each(vec.begin(), vec.end(), print);
return 0;
}
Assign
Print
Print
7. MORE ABOUT INLINE
Inline functions are the functions which on call by the compiler are copied to the
set. Where ever the function call is found it is replaced by its code. Inline
functions are declared by adding Inline keyword in front of the name. Rest they
have same defining pattern as normal function.
8. INLINEVS LAMBDA NOTATION FUNCTION
(INLINE IS NOT A REPLACEMENT OF LAMBDA DECLARATION)
Lambda functions, as they are called, are a way of creating a object that
represents a function.
The object is then commonly passed to other functions to control how the other
functions work.
Take a look at sort() or transform() from the standard library to see what I mean.
(There's some templates involved to make this work in C++, but that's outside the
scope of this answer.)
Inline function is always available for call whereas Lambda is gone with
the call.
Thus, these two things are pretty much unrelated to each other.They're not
substitutes of each other in any way at all.