This document discusses computer algorithms and provides examples of algorithms in Python. It begins by defining an algorithm and providing examples of sorting algorithms like insertion sort, selection sort, and merge sort. It then discusses searching algorithms like linear search and binary search, including their time complexities. Other topics covered include advantages of Python, types of problems solved by algorithms, and limitations of binary search.
The document discusses functional programming concepts and provides examples in Python. It defines functional programming, compares it to procedural and object-oriented paradigms, and outlines key concepts like pure functions, recursion, immutable data, and higher-order functions. It also provides examples of map, filter and reduce functions in Python and discusses advantages of the functional style.
This document provides an overview of topics covered in a software developer training program, including:
- UML diagramming (use case, class, sequence diagrams)
- Programming languages and frameworks like C#, .NET, ASP.NET MVC
- Databases and ORM like SQL, NHibernate
- Other technologies like JavaScript, jQuery, Google Maps API, and version control with Subversion
It then goes into more depth on specific programming concepts like classes, methods, generics; database concepts like normalization, transactions; and LINQ for querying data. The training covers both theoretical foundations and practical skills needed for a career as a software developer.
The document provides an overview of PostgreSQL including:
- A brief history of PostgreSQL's development since 1986.
- An explanation of Multiversion Concurrency Control (MVCC) and object-relational features.
- Descriptions of different types of joins that can be performed between tables.
- An overview of data types, indexes, triggers, procedural languages, common table expressions, and window functions supported by PostgreSQL.
- Mentions of extensions like PostGIS and foreign data wrappers.
- A list of recommended books and tools for PostgreSQL development and administration.
This document summarizes a massive open online course on Udemy about fundamental data structures and algorithms using the C language. The 15-hour course covers key topics like stacks, queues, linked lists, trees, recursion, and analyzing algorithm efficiency. It aims to help students strengthen programming skills and prepare for technical interviews at top companies. The course consists of 14 sections and includes weekly quizzes on the Udemy platform.
This document discusses computer algorithms and provides examples of algorithms in Python. It begins by defining an algorithm and providing examples of sorting algorithms like insertion sort, selection sort, and merge sort. It then discusses searching algorithms like linear search and binary search, including their time complexities. Other topics covered include advantages of Python, types of problems solved by algorithms, and limitations of binary search.
The document discusses functional programming concepts and provides examples in Python. It defines functional programming, compares it to procedural and object-oriented paradigms, and outlines key concepts like pure functions, recursion, immutable data, and higher-order functions. It also provides examples of map, filter and reduce functions in Python and discusses advantages of the functional style.
This document provides an overview of topics covered in a software developer training program, including:
- UML diagramming (use case, class, sequence diagrams)
- Programming languages and frameworks like C#, .NET, ASP.NET MVC
- Databases and ORM like SQL, NHibernate
- Other technologies like JavaScript, jQuery, Google Maps API, and version control with Subversion
It then goes into more depth on specific programming concepts like classes, methods, generics; database concepts like normalization, transactions; and LINQ for querying data. The training covers both theoretical foundations and practical skills needed for a career as a software developer.
The document provides an overview of PostgreSQL including:
- A brief history of PostgreSQL's development since 1986.
- An explanation of Multiversion Concurrency Control (MVCC) and object-relational features.
- Descriptions of different types of joins that can be performed between tables.
- An overview of data types, indexes, triggers, procedural languages, common table expressions, and window functions supported by PostgreSQL.
- Mentions of extensions like PostGIS and foreign data wrappers.
- A list of recommended books and tools for PostgreSQL development and administration.
This document summarizes a massive open online course on Udemy about fundamental data structures and algorithms using the C language. The 15-hour course covers key topics like stacks, queues, linked lists, trees, recursion, and analyzing algorithm efficiency. It aims to help students strengthen programming skills and prepare for technical interviews at top companies. The course consists of 14 sections and includes weekly quizzes on the Udemy platform.
The lecture discusses driving autonomous vehicles and covers:
1. The hardware components of the RACECAR including its Intel RealSense camera, depth camera, and LIDAR sensor.
2. How the sensors measure distance and their limitations.
3. Controlling the RACECAR using modules like drive and controller to set speed, angle, and respond to button inputs.
4. Programming paradigms like global variables and start/update functions for the RACECAR code.
Python bootcamp - C4Dlab, University of Nairobikrmboya
This document summarizes a Python bootcamp presentation covering the basics of Python including features, common uses in industry, data types, operators, strings, lists, dictionaries, functions, modules, file I/O, and accessing the web. It provides examples of Python code and concludes with next steps and resources for further learning Python.
This document provides an overview of graph databases and Neo4j. It defines what a graph is mathematically and in the context of databases. It describes the key components of Neo4j including nodes, relationships, properties, labels, paths, traversals, and indexes. It also discusses the Cypher query language, performance advantages of Neo4j over SQL databases, and basic requirements and licensing options.
This document provides an overview of algorithms and data structures. It covers sorting algorithms like insertion sort, bubble sort, merge sort, and quicksort. It discusses complexity analysis using Big O notation and compares the time complexities of different sorting and searching algorithms. Common linear and nonlinear data structures are explained like arrays, linked lists, stacks, queues, trees, and hash tables. Abstract data types like maps, sets, queues and stacks are also introduced. Dijkstra's algorithm for finding shortest paths in graphs is described.
The document provides an introduction to various data structures and algorithms concepts. It discusses different types of data structures like simple, compound, linear and non-linear data structures. It also covers algorithm analysis concepts like time complexity, asymptotic notations and different searching and sorting algorithms like linear search, binary search, bubble sort, selection sort, insertion sort, quick sort and merge sort. It provides pseudocode examples of recursive algorithms like factorial, Fibonacci sequence and towers of Hanoi problem.
jn;lm;lkm';m';;lmppt of data structure.pdfVinayNassa3
This document provides an introduction to data structures, algorithms, and complexity analysis. It begins with an overview of data structures and their classification as simple, compound, linear, or non-linear. Common operations on data structures like adding, deleting, and searching elements are described. The document then covers algorithm analysis, asymptotic notation, and examples of recursive and sorting algorithms like bubble sort, selection sort, and their time complexities. Searching techniques like linear, binary, and Fibonacci search are also summarized.
The presentation of Type4Py at the ICSE'22 conferenceAmir M. Mir
Paper title:
Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python
The paper is published on the technical track of ICSE'22.
Abstract:
Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility and productivity. Lack of static typing can cause run-time exceptions and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing codebases is error-prone and laborious, machine learning (ML)-based approaches have been proposed to enable automatic type inference based on existing, partially annotated codebases. However, previous ML-based approaches are trained and evaluated on human-provided type annotations, which might not always be sound, and hence this may limit the practicality for real-world usage. In this paper, we present Type4Py, a deep similarity learning-based hierarchical neural network model. It learns to discriminate between similar and dissimilar types in a high-dimensional space, which results in clusters of types. Likely types for arguments, variables, and return values can then be inferred through the nearest neighbor search. Unlike previous work, we trained and evaluated our model on a \emph{type-checked} dataset and used mean reciprocal rank (MRR) to reflect the performance perceived by users. The obtained results show that Type4Py achieves an MRR of 77.1%, which is a substantial improvement of 8.1% and 16.7% over the state-of-the-art approaches Typilus and TypeWriter, respectively. Finally, to aid developers with retrofitting types, we released a Visual Studio Code extension, which uses Type4Py to provide ML-based type auto-completion for Python.
JSON is a well-known, lightweight format for data exchange around the web. It’s a structured data format used in modern APIs and goes really well with web applications. But how do you work with these JSON files directly and perform operations on it? This is where JQ comes into play.
JQ is a flexible, lightweight, command-line processor that is like ‘sed’ for JSON data. JQ gets along well with UNIX pipes and offers rich functionality to interrogate, manipulate, and work with JSON files.
In this webinar, you will be introduced to JQ and you will learn how to work with basic filters, operators & functions, conditionals & comparisons. You will also learn how to define functions, work with Regex, and use streaming JSON data in JQ.
Machine Learning Laboratory set of experiments, including ANN, Backpropagation, K-Means, Hierarchical Clustering, Linear Regression, Multivariate Regression, Fuzzy Logic.
Collections and generics allow storing multiple objects of the same type. Generic collections like List<T>, Queue<T>, and Dictionary<TKey, TValue> provide type-safety without boxing/unboxing. Different collection types have different performance characteristics depending on their implementation - arrays and lists have fast indexed access but slow removal while linked lists are slower to access but more memory efficient. Choosing the right collection type depends on the specific access patterns and operations needed.
GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer”GlobalLogic Ukraine
18 травня відбувся GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer” від спікера Романа Івасишина.
У доповіді ми розглянули:
- Список тем, які повинен знати С++ розробник (синтаксис мови, класи, STL, а також дізнались, для чого вчити темплейти та багатопотоковість);
- На що потрібно звернути увагу при вивченні мови;
- Деякі приховані аспекти мови;
- Практичні приклади з С++.
Відео та деталі заходу: https://bit.ly/3Gxmkee
Приєднатись до спільноти: https://www.facebook.com/groups/EmbeddedCommunity
Відкриті C++ позиції у GlobalLogic: https://bit.ly/3GzW03c
Introduction to Python programming LanguageMansiSuthar3
Python is a popular, high-level programming language that is used for a variety of tasks including web development, machine learning, and data science. It has a simple syntax and is readable. Python has built-in data types like integers, floats, booleans, strings, lists, tuples, and dictionaries. It also supports object-oriented programming. Common operations in Python include conditional statements, loops, functions, packages, file handling, classes, and data visualization using libraries like NumPy, Matplotlib, and Seaborn.
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
Deep Learning Module 2A Training MLP.pptxvipul6601
This document provides an overview of deep learning concepts including linear regression, neural networks, and training multilayer perceptrons. It discusses:
1) How linear regression can be used for prediction tasks by learning weights to relate features to targets.
2) How neural networks extend this by using multiple layers of neurons and nonlinear activation functions to learn complex patterns in data.
3) The process of training neural networks, including forward propagation to make predictions, backpropagation to calculate gradients, and updating weights to reduce loss.
4) Key aspects of multilayer perceptrons like their architecture with multiple fully-connected layers, use of activation functions, and training algorithm involving forward/backward passes and parameter updates.
A talk given at PHP Cambridge all about Python
The slides cover Python from any other programmer's prospective - but the talk as given involved comparisons to PHP.
PPT on Python - illustrating Python for BBA, B.Techssuser2678ab
This document provides an overview of the Python programming language. It outlines the topics that will be covered in a course on Python programming, including an introduction to Python, installing Python, data types, variables, strings, lists, tuples, sets, dictionaries, and functions. It also provides details on Python's history and design philosophy, and explains why Python is a popular language for tasks like data analysis, machine learning, and web development.
This document provides an overview of the topics that will be covered in a Django on GAE course, including:
1. Python fundamentals like variables, operators, data structures, and classes.
2. Django topics such as management commands, models, the admin interface, URLs, views, templates, forms, generic views, internationalization, and unit testing.
3. Google App Engine topics such as deploying Django projects using django-nonrel, limitations, features, APIs, and using the BigTable datastore.
Python is a great programming language. It is a complete tutorial of using this programming language.
This slides is split into two parts, and it is the second part. Another part is at: http://www.slideshare.net/moskytw/programming-with-python-basic.
DAFunctor is a symbolic translator that converts NumPy/PyTorch ND-Array operations to equivalent C code. It works by decomposing generative NumPy functions into common properties like value expressions, index expressions, and scatter expressions. This allows DAFunctor to merge operations and generate loop-based C code without dynamic memory allocation or intermediate buffers.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Contenu connexe
Similaire à Rohan Sharma MOOC Course Report (1).pptx
The lecture discusses driving autonomous vehicles and covers:
1. The hardware components of the RACECAR including its Intel RealSense camera, depth camera, and LIDAR sensor.
2. How the sensors measure distance and their limitations.
3. Controlling the RACECAR using modules like drive and controller to set speed, angle, and respond to button inputs.
4. Programming paradigms like global variables and start/update functions for the RACECAR code.
Python bootcamp - C4Dlab, University of Nairobikrmboya
This document summarizes a Python bootcamp presentation covering the basics of Python including features, common uses in industry, data types, operators, strings, lists, dictionaries, functions, modules, file I/O, and accessing the web. It provides examples of Python code and concludes with next steps and resources for further learning Python.
This document provides an overview of graph databases and Neo4j. It defines what a graph is mathematically and in the context of databases. It describes the key components of Neo4j including nodes, relationships, properties, labels, paths, traversals, and indexes. It also discusses the Cypher query language, performance advantages of Neo4j over SQL databases, and basic requirements and licensing options.
This document provides an overview of algorithms and data structures. It covers sorting algorithms like insertion sort, bubble sort, merge sort, and quicksort. It discusses complexity analysis using Big O notation and compares the time complexities of different sorting and searching algorithms. Common linear and nonlinear data structures are explained like arrays, linked lists, stacks, queues, trees, and hash tables. Abstract data types like maps, sets, queues and stacks are also introduced. Dijkstra's algorithm for finding shortest paths in graphs is described.
The document provides an introduction to various data structures and algorithms concepts. It discusses different types of data structures like simple, compound, linear and non-linear data structures. It also covers algorithm analysis concepts like time complexity, asymptotic notations and different searching and sorting algorithms like linear search, binary search, bubble sort, selection sort, insertion sort, quick sort and merge sort. It provides pseudocode examples of recursive algorithms like factorial, Fibonacci sequence and towers of Hanoi problem.
jn;lm;lkm';m';;lmppt of data structure.pdfVinayNassa3
This document provides an introduction to data structures, algorithms, and complexity analysis. It begins with an overview of data structures and their classification as simple, compound, linear, or non-linear. Common operations on data structures like adding, deleting, and searching elements are described. The document then covers algorithm analysis, asymptotic notation, and examples of recursive and sorting algorithms like bubble sort, selection sort, and their time complexities. Searching techniques like linear, binary, and Fibonacci search are also summarized.
The presentation of Type4Py at the ICSE'22 conferenceAmir M. Mir
Paper title:
Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python
The paper is published on the technical track of ICSE'22.
Abstract:
Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility and productivity. Lack of static typing can cause run-time exceptions and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing codebases is error-prone and laborious, machine learning (ML)-based approaches have been proposed to enable automatic type inference based on existing, partially annotated codebases. However, previous ML-based approaches are trained and evaluated on human-provided type annotations, which might not always be sound, and hence this may limit the practicality for real-world usage. In this paper, we present Type4Py, a deep similarity learning-based hierarchical neural network model. It learns to discriminate between similar and dissimilar types in a high-dimensional space, which results in clusters of types. Likely types for arguments, variables, and return values can then be inferred through the nearest neighbor search. Unlike previous work, we trained and evaluated our model on a \emph{type-checked} dataset and used mean reciprocal rank (MRR) to reflect the performance perceived by users. The obtained results show that Type4Py achieves an MRR of 77.1%, which is a substantial improvement of 8.1% and 16.7% over the state-of-the-art approaches Typilus and TypeWriter, respectively. Finally, to aid developers with retrofitting types, we released a Visual Studio Code extension, which uses Type4Py to provide ML-based type auto-completion for Python.
JSON is a well-known, lightweight format for data exchange around the web. It’s a structured data format used in modern APIs and goes really well with web applications. But how do you work with these JSON files directly and perform operations on it? This is where JQ comes into play.
JQ is a flexible, lightweight, command-line processor that is like ‘sed’ for JSON data. JQ gets along well with UNIX pipes and offers rich functionality to interrogate, manipulate, and work with JSON files.
In this webinar, you will be introduced to JQ and you will learn how to work with basic filters, operators & functions, conditionals & comparisons. You will also learn how to define functions, work with Regex, and use streaming JSON data in JQ.
Machine Learning Laboratory set of experiments, including ANN, Backpropagation, K-Means, Hierarchical Clustering, Linear Regression, Multivariate Regression, Fuzzy Logic.
Collections and generics allow storing multiple objects of the same type. Generic collections like List<T>, Queue<T>, and Dictionary<TKey, TValue> provide type-safety without boxing/unboxing. Different collection types have different performance characteristics depending on their implementation - arrays and lists have fast indexed access but slow removal while linked lists are slower to access but more memory efficient. Choosing the right collection type depends on the specific access patterns and operations needed.
GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer”GlobalLogic Ukraine
18 травня відбувся GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer” від спікера Романа Івасишина.
У доповіді ми розглянули:
- Список тем, які повинен знати С++ розробник (синтаксис мови, класи, STL, а також дізнались, для чого вчити темплейти та багатопотоковість);
- На що потрібно звернути увагу при вивченні мови;
- Деякі приховані аспекти мови;
- Практичні приклади з С++.
Відео та деталі заходу: https://bit.ly/3Gxmkee
Приєднатись до спільноти: https://www.facebook.com/groups/EmbeddedCommunity
Відкриті C++ позиції у GlobalLogic: https://bit.ly/3GzW03c
Introduction to Python programming LanguageMansiSuthar3
Python is a popular, high-level programming language that is used for a variety of tasks including web development, machine learning, and data science. It has a simple syntax and is readable. Python has built-in data types like integers, floats, booleans, strings, lists, tuples, and dictionaries. It also supports object-oriented programming. Common operations in Python include conditional statements, loops, functions, packages, file handling, classes, and data visualization using libraries like NumPy, Matplotlib, and Seaborn.
Workshop slides which give an overview of python programming. The slides are accompanied by DIY (do it yourself) programs which can be found as in GitHub (https://github.com/bhalajin/blueprints)
Deep Learning Module 2A Training MLP.pptxvipul6601
This document provides an overview of deep learning concepts including linear regression, neural networks, and training multilayer perceptrons. It discusses:
1) How linear regression can be used for prediction tasks by learning weights to relate features to targets.
2) How neural networks extend this by using multiple layers of neurons and nonlinear activation functions to learn complex patterns in data.
3) The process of training neural networks, including forward propagation to make predictions, backpropagation to calculate gradients, and updating weights to reduce loss.
4) Key aspects of multilayer perceptrons like their architecture with multiple fully-connected layers, use of activation functions, and training algorithm involving forward/backward passes and parameter updates.
A talk given at PHP Cambridge all about Python
The slides cover Python from any other programmer's prospective - but the talk as given involved comparisons to PHP.
PPT on Python - illustrating Python for BBA, B.Techssuser2678ab
This document provides an overview of the Python programming language. It outlines the topics that will be covered in a course on Python programming, including an introduction to Python, installing Python, data types, variables, strings, lists, tuples, sets, dictionaries, and functions. It also provides details on Python's history and design philosophy, and explains why Python is a popular language for tasks like data analysis, machine learning, and web development.
This document provides an overview of the topics that will be covered in a Django on GAE course, including:
1. Python fundamentals like variables, operators, data structures, and classes.
2. Django topics such as management commands, models, the admin interface, URLs, views, templates, forms, generic views, internationalization, and unit testing.
3. Google App Engine topics such as deploying Django projects using django-nonrel, limitations, features, APIs, and using the BigTable datastore.
Python is a great programming language. It is a complete tutorial of using this programming language.
This slides is split into two parts, and it is the second part. Another part is at: http://www.slideshare.net/moskytw/programming-with-python-basic.
DAFunctor is a symbolic translator that converts NumPy/PyTorch ND-Array operations to equivalent C code. It works by decomposing generative NumPy functions into common properties like value expressions, index expressions, and scatter expressions. This allows DAFunctor to merge operations and generate loop-based C code without dynamic memory allocation or intermediate buffers.
Similaire à Rohan Sharma MOOC Course Report (1).pptx (20)
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Applications of artificial Intelligence in Mechanical Engineering.pdf
Rohan Sharma MOOC Course Report (1).pptx
1. A MOOC Course Report
on
PROGRAMMING,
DATA STRUCTURES AND
ALGORITHMS IN PYTHON
Submitted by
Rohan Sharma [RA2011003030052]
under the guidance of
Mr. Lalit Sagar
Under the governing ( NPTEL /COURSEERA/SEMINAR/ INDUSTRIAL TRAINING) body
of
BACHELOR OF TECHNOLOGY
in
COMPUTER SCIENCE & ENGINEERING
of
FACULTY OF ENGINEERING AND TECHNOLOGY
SRM INSTITUTE OF SCIENCE & TECHNOLOGY,NCR CAMPUS
NOV 2022
4. Week1
●Lecture 1: Algorithms and Programming : Simple gcd
●Lecture 2: Improving naive gcd
●Lecture 3:Euclid’s algorithm for gcd
●Lecture 4: Downloading and Installing Python
5. Algorithms, programming
● Algorithm: how to systematically perform a task
● Write down as a sequence of steps
● “Recipe”, or program
● Programming language describes the steps
● What is a step? Degrees of detail
● “Arrange the chairs” vs “Make 8 rows with 10
● chairs in each row”
6. Computing gcd(m,n)
●List out factors of m
●List out factors of n
●Report the largest number that appears on both lists
●Is this a valid algorithm?
●Finite presentation of the “recipe”
●Terminates after a finite number of steps
7. Python program
● def gcd(m,n):
● fm = []
● for i in range(1,m+1):
● if (m%i) == 0:
● fm.append(i)
● fn = []
● for j in range(1,n+1):
● If (n%j) == 0:
● fn.append(j)
● cf = []
● for f in fm:
● if f in fn:
● cf.append(f)
● return(cf[-1])
9. Assignment statement
●Assign a value to a name
●i = 5
●j = 2*i
●j = j + 5
●Left hand side is a name
●Right hand side is an expression
●Operations in expression depend on type of value
10. int vs float
● Why are these different types?
● Internally, a value is stored as a finite sequence of
● 0’s and 1’s (binary digits, or bits)
● For an int, this sequence is read off as a binary
● number
● For a float, this sequence breaks up into a
● mantissa and exponent
● Like “scientific” notation: 0.602 x 1024
11. Strings —type str
●Text values — type str, sequence of characters
●Single character is string of length 1
●Extract individual characters by position
●Slices extract substrings
●+ glues strings together
●Cannot update strings directly — immutable
12. Lists
●Lists are sequences of values
●Values need not be of uniform type
●Lists may be nested
●Can access value at a position, or a slice
●Lists are mutable, can update in place
●Assignment does not copy the value
●Use full slice to make a copy of a list
13. Control flow
●Normally, statements are executed top to bottom,
●in sequence
●Can alter the control flow
●if ... elif ... else — conditional execution
●for i in ... — repeat a fixed number of times
●while ... — repeat based on a condition
14. Week3
●Lecture 1: More about range()
●Lecture 2: Manipulating lists
●Lecture 3: Breaking out of a loop
●Lecture 4: Arrays vs lists, binary search
●Lecture 5: Efficiency
●Lecture 6: Selection Sort
●Lecture 7: Insertion Sort
●Lecture 8: Recursion
15. More about range()
●range(n) has is implicitly from 0 to n-1
●range(i,j,k) produces sequence in steps of k
●Negative k counts down
●Sequence produced by range() is not a list
●Use list(range(..)) to get a list
16. Lists
●To extend lists in place, use l.append(),
●l.extend()
●Can also assign new value, in place, to a slice
●Many built in functions for lists — see
●documentation
●Don’t forget to assign a value to a name before it
●is first used
17. Loops revisited
●Can exit prematurely from loop using break
●Applies to both for and while
●Loop also has an else: clause
●Special action for normal termination
18. ●Are built in lists in Python lists or arrays?
●Documentation suggests they are lists
●Allow efficient expansion, contraction
●However, positional indexing allows us to treat
●them as arrays
●In this course, we will “pretend” they are arrays
●Will later see explicit implementation of lists
19. Efficiency
●Theoretically T(n) = O(nk) is considered efficient
●Polynomial time
●In practice even T(n) = O(n2) has very limited
●effective range
●Inputs larger than size 5000 take very long
20. Sorting
●Finding minimum in unsorted segment of length k
●requires one scan, k steps
●In each iteration, segment to be scanned reduces
●by 1
●T(n) = n + (n-1) + (n-2) + ... + 1 = n(n+1)/2 = O(n2)
21. Insertion Sort
●Inserting a new value in sorted segment of length
●k requires upto k steps in the worst case
●In each iteration, sorted segment in which to insert
●increased by 1
●T(n) = 1 + 2 + ... + n-1 = n(n-1)/2 = O(n2)
22. O(n2) sorting algorithms
●Selection sort and insertion sort are both O(n2)
●O(n2) sorting is infeasible for n over 5000
●Among O(n2) sorts, insertion sort is usually better
●than selection sort
●What happens when we apply insertion sort to
●an already sorted list?
●Next week, some more efficient sorting algorithms
24. Merge Sort
● def mergesort(A,left,right):
● # Sort the slice A[left:right]
● if right - left <= 1: # Base case
● return(A[left:right])
● if right - left > 1: # Recursive call
● mid = (left+right)//2
● L = mergesort(A,left,mid)
● R = mergesort(A,mid,right)
● return(merge(L,R))
25. Quicksort
●Quicksort, as described, is not stable
●Swap operation during partitioning disturbs
●original order
●Merge sort is stable if we merge carefully
●Do not allow elements from right to overtake
●elements from left
●Favour left list when breaking ties
26. Tuples and Dictionaries
● Dictionaries allow a flexible association of values to
● keys
● Keys must be immutable values
● Structure of dictionary is internally optimized for key-
● based lookup
● Use sorted(d.keys()) to retrieve keys in
● predictable order
● Extremely useful for manipulating information from
● text files, tables ... — use column headings as keys
27. Function definitions
●Function definitions behave like other assignments
●of values to names
●Can reassign a new definition, define conditionally
●Can pass function names to other functions
29. Exception handling
●Exception handling allows us to gracefully deal
●with run time errors
●Can check type of error and take appropriate
●action based on type
●Can change coding style to exploit exception
●handling
●When dealing with files and input/output,
●exception handling becomes very important
30. ● Read from keyboard using input()
● Can also display a message
● Print to screen using print()
● Caveat: In Python 2, () is optional for print
● Can control format of print() output
● Optional arguments end="...", sep="..."
● More precise control later
31. ●Use pass for an empty block
●Use del() to remove elements from a list or
●dictionary
●Use the special value None to check if a name has
●been assigned a valid value
32. Week6
●Lecture 1: Backtracking, N queens
●Lecture 2: Global scope , nested function
●Lecture 3: Generating permutations
●Lecture 4: Sets , Stacks , queues
●Lecture 5: Priority queues and heaps
33. ●Python names are looked up inside-out from
●within functions
●Updating a name with immutable value creates a
●local copy of that name
●Can update global names with mutable values
●Use global definition to update immutable values
●Can nest helper function — hidden to the outside
34. Data structures
●Data structures are ways of organising information
●that allow efficient processing in certain contexts
●Python has a built-in implementation of sets
●Stacks are useful to keep track of recursive
●computations
●Queues are useful for breadth-first exploration
35. ● Heaps are a tree implementation of priority queues
● insert( ) and delete_max( ) are both O(log N)
● heapify( ) builds a heap in O(N)
● Tree can be manipulated easily using an array
● Can invert the heap condition
● Each node is smaller than its children
● Min-heap, for insert( ), delete_min( )
36. Week7
●Lecture 1: Abstract datatypes,classes and objects
●Lecture 2: Classes and Objects in Python
●Lecture 3: User defined lists
●Lecture 4: Search trees
37. Abstract datatype
●An abstract data type is a black box description
●Public interface — update/query the data type
●Private implementation — change does not
●affect functionality
●Classes and objects can be used for this
38. Classes and objects
●Class
● Template for a data type
● How data is stored
● How public functions manipulate data
●Object
● Concrete instance of template
39. Complexity
●All operations on search trees walk down a single
●path
●Worst-case: height of the tree
●Balanced trees: height is O(log n) for n nodes
●Tree can be balanced using rotations — look up
●AVL trees
41. Dynamic programming
● Memoization
● Store values of subproblems in a table
● Look up the table before making a recursive call
● Dynamic programming:
● Solve subproblems in order of dependency
● Dependencies must be acyclic
● Iterative evaluation
42. Wrap-Up
●No programming language is “universally” the best
●Otherwise why are there so many?
●Python’s simplicity makes it attractive to learn
●But also results in some limitations
●Use the language that suits your task best
●Learn programming, not programming languages!