with today's advanced technology like photoshop, paint etc. we need to understand some basic concepts like how they are cropping the image , tilt the image etc.
In our presentation you will find basic introduction of 2D transformation.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
This document discusses the Daroko blog, which provides real-world applications of various IT skills. It encourages readers to not just learn computer graphics and other topics but to apply them in business contexts. The blog covers topics like computer graphics, networking, programming, IT jobs, technology news, blogging, website building, and IT companies. It aims to help readers gain practical experience applying their IT knowledge. Readers are instructed to search "Daroko blog" online to access resources on various IT subjects and their business applications.
Turing machines are abstract machines that can simulate any modern computer. They are very powerful and can solve problems by answering yes or no to any input. A Turing machine consists of a finite control, tape, and tape head. The tape is infinite and divided into cells containing symbols. The machine operates by reading/writing symbols on the tape and moving the tape head left or right according to a transition function based on its current state and tape symbol. Variations of Turing machines like those with storage, multiple tracks or tapes are equivalent to basic Turing machines. Turing machines can recognize formal languages and perform computations like arithmetic.
The document discusses Mealy and Moore machine models. Mealy machines have an output function that depends on the present state and input, while Moore machines have an output function that depends only on the present state. The document provides examples of converting between Mealy and Moore machine representations.
Introdution and designing a learning systemswapnac12
The document discusses machine learning and provides definitions and examples. It covers the following key points:
- Machine learning is a subfield of artificial intelligence concerned with developing algorithms that allow computers to learn from data without being explicitly programmed.
- Well-posed learning problems have a defined task, performance measure, and training experience. Examples given include learning to play checkers and recognize handwritten words.
- Designing a machine learning system involves choosing a training experience, target function, representation of the target function, and learning algorithm to approximate the function. A checkers-playing example is used to illustrate these design decisions.
A Turing machine consists of a finite-state control unit, an infinite tape divided into cells to hold symbols, and a read/write head. At each step, the control unit reads the symbol under the head, writes a symbol, and moves the head left or right. It can enter a new state depending on the symbol and its current state. Turing machines can recognize, generate, evaluate, and decide languages by looping through state transitions and tape operations until reaching an accepting or rejecting state. They provide a model for general computation that is theoretically more powerful than finite state machines.
with today's advanced technology like photoshop, paint etc. we need to understand some basic concepts like how they are cropping the image , tilt the image etc.
In our presentation you will find basic introduction of 2D transformation.
This document discusses various mathematical tools used in digital image processing (DIP), including array versus matrix operations, linear versus nonlinear operations, arithmetic operations, set and logical operations, spatial operations, vector and matrix operations, and image transforms. Key points include:
- Array operations are performed on a pixel-by-pixel basis, while matrix operations consider relationships between pixels.
- Linear operators preserve scaling and addition properties, while nonlinear operators like max do not.
- Spatial operations include single-pixel, neighborhood, and geometric transformations of pixel locations and intensities.
- Images can be represented as vectors and transformed using matrix operations.
- Common transforms like Fourier use separable, symmetric kernels to decompose images into frequency domains.
This document discusses the Daroko blog, which provides real-world applications of various IT skills. It encourages readers to not just learn computer graphics and other topics but to apply them in business contexts. The blog covers topics like computer graphics, networking, programming, IT jobs, technology news, blogging, website building, and IT companies. It aims to help readers gain practical experience applying their IT knowledge. Readers are instructed to search "Daroko blog" online to access resources on various IT subjects and their business applications.
Turing machines are abstract machines that can simulate any modern computer. They are very powerful and can solve problems by answering yes or no to any input. A Turing machine consists of a finite control, tape, and tape head. The tape is infinite and divided into cells containing symbols. The machine operates by reading/writing symbols on the tape and moving the tape head left or right according to a transition function based on its current state and tape symbol. Variations of Turing machines like those with storage, multiple tracks or tapes are equivalent to basic Turing machines. Turing machines can recognize formal languages and perform computations like arithmetic.
The document discusses Mealy and Moore machine models. Mealy machines have an output function that depends on the present state and input, while Moore machines have an output function that depends only on the present state. The document provides examples of converting between Mealy and Moore machine representations.
Introdution and designing a learning systemswapnac12
The document discusses machine learning and provides definitions and examples. It covers the following key points:
- Machine learning is a subfield of artificial intelligence concerned with developing algorithms that allow computers to learn from data without being explicitly programmed.
- Well-posed learning problems have a defined task, performance measure, and training experience. Examples given include learning to play checkers and recognize handwritten words.
- Designing a machine learning system involves choosing a training experience, target function, representation of the target function, and learning algorithm to approximate the function. A checkers-playing example is used to illustrate these design decisions.
A Turing machine consists of a finite-state control unit, an infinite tape divided into cells to hold symbols, and a read/write head. At each step, the control unit reads the symbol under the head, writes a symbol, and moves the head left or right. It can enter a new state depending on the symbol and its current state. Turing machines can recognize, generate, evaluate, and decide languages by looping through state transitions and tape operations until reaching an accepting or rejecting state. They provide a model for general computation that is theoretically more powerful than finite state machines.
The document discusses composite transformations, which involve performing two or more transformations in sequence. It provides examples that two successive translations can be represented as a single translation, and two successive rotations can be represented as a single rotation. It also explains that scaling an object with respect to a fixed point can be achieved through a sequence of translations, scaling around the origin, and inverse translations, as represented by a composite matrix.
Open addressiing &rehashing,extendiblevhashingSangeethaSasi1
The document discusses various hash table implementation techniques. It describes open addressing hashing which resolves collisions by probing to the next empty cell. Linear probing is discussed as a collision resolution strategy where the next probe is the current index plus one. The document also covers separate chaining hashing which uses linked lists at each index to handle collisions, and double hashing which uses two hash functions to determine probe sequences.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
The document defines key concepts in automata theory, including alphabets, strings, length of strings, powers and closures of alphabets, languages, and the membership problem. An alphabet is a set of symbols, strings are finite sequences of symbols from an alphabet, and the length of a string is the number of symbols in it. Powers of an alphabet refer to sets of strings of a given length, while closures include strings of all lengths. A language is a subset of the Kleene closure of an alphabet. The membership problem asks whether a given string is part of a given language.
The document discusses how more complex geometric transformations can be performed by combining basic transformations through composition. It provides examples of how scaling and rotation can be done with respect to a fixed point by first translating the object to align the point with the origin, then performing the basic transformation, and finally translating back. Mirror reflection about a line is similarly described as a composite of translations and rotations.
This document discusses variants of Turing machines that increase their computational power and proves that any language accepted by a variant Turing machine is also accepted by a standard Turing machine. It covers variants such as multiple tapes, two-way infinite tapes, and nondeterminism. For each variant, it provides a proof that the standard Turing machine is equally powerful by simulating the variant with the standard model.
In graph theory, a matching is a subset of a graph's edges such hat no two edges meet the same vertex. A matching is maximum if no other matching contains more edges. A trivial solution (exhaustive search) to the problem of finding a maximum matching has exponential complexity. We illustrate polynomial time solutions to the problem that were published between 1965 and 1991.
LISP and PROLOG are early AI programming languages. LISP, created in 1958, uses lists and is functional while PROLOG, created in the 1970s, is logic-based and declarative. Both use recursion and allow programming with lists. They are commonly used for symbolic reasoning, knowledge representation and natural language processing. While different in approach, they both allow developing AI systems through a non-procedural programming style.
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
This document discusses techniques for modeling curves and surfaces in computer graphics. It introduces three common representations of curves and surfaces: explicit, implicit, and parametric forms. It focuses on parametric polynomial forms, specifically discussing cubic polynomial curves, Hermite curves, Bezier curves, B-splines, and NURBS. It also covers rendering curves and surfaces by evaluating polynomials, recursive subdivision of Bezier polynomials, and ray casting for implicit surfaces like quadrics. Finally, it discusses mesh subdivision techniques like Catmull-Clark and Loop subdivision for generating smooth surfaces.
Spatial domain image enhancement techniques operate directly on pixel values. Some common techniques include point processing using gray level transformations, mask processing using filters, and histogram processing. Histogram equalization aims to create a uniform distribution of pixel values by mapping the original histogram to a wider range. This improves contrast by distributing pixels more evenly across gray levels.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
This document discusses graphics hardware components. It describes various graphics input devices like the mouse, joystick, light pen etc. and how they are either analog or digital. It then covers commonly used graphics output devices like CRT displays, plasma displays, LCDs and 3D viewing systems. It provides details on the internal components and working of CRT displays. It also discusses graphics storage formats and the architecture of raster and random graphics systems.
This document provides an overview of Turing machines including:
- Turing machines are mathematical models that can perform any computation like a computer and have an infinite tape with a tape head.
- A Turing machine is defined by 7 tuples including states, alphabets, transition function, and blank symbols.
- Multitape Turing machines have multiple tapes that can be accessed independently by separate heads.
- The instantaneous description specifies the current state, tape symbol under the head, and full tape configuration.
- Transition diagrams visually represent the transition function that specifies how the tape and state change based on the current symbol.
Branch and Bound is a state space search algorithm that involves generating all children of a node before exploring any children. It uses lower bounds to prune parts of the search tree that cannot produce better solutions than what has already been found. The algorithm is demonstrated on problems like the 8-puzzle and Travelling Salesman Problem. For TSP, it works by reducing the cost matrix at each node to calculate lower bounds, and exploring the child with the lowest estimated total cost.
The document describes Turing machines and formal languages. It defines a Turing machine as a mathematical model of computation that consists of an infinite tape divided into cells, a head that reads and writes symbols, internal states, and a transition function. It then discusses Turing machine components like states, tape alphabet, transition function, initial/final states. It also covers time/space complexity, deterministic/non-deterministic TMs, linear bounded automata, recursively enumerable vs recursive languages, Turing machines as enumerators, and universal Turing machines.
From the perspective of Design and Analysis of Algorithm. I made these slide by collecting data from many sites.
I am Danish Javed. Student of BSCS Hons. at ITU Information Technology University Lahore, Punjab, Pakistan.
Turing Machines are a simple mathematical model of a general purpose computer invented by Alan Turing in 1936. A Turing Machine consists of an infinite tape divided into cells, a head that reads and writes symbols on the tape, a finite set of states, and transition rules determining the behavior of the machine. The machine operates by reading a symbol on the tape, updating the symbol according to its transition rules, moving the head left or right, and transitioning to a new state. Turing Machines can simulate any algorithm and are capable of performing any calculation that can be performed by any computing machine.
This document provides an overview of Turing machines, including:
- Turing machines can be conceptualized as either logical or physical devices that operate on an infinite tape.
- A Turing machine has a read/write head, finite state control, and an infinite tape that can be used to store an input string, perform computations, and output a result string.
- Formally, a Turing machine is defined by its set of states, tape alphabet/symbols, transition function, blank symbol, initial state, and accepting/final states.
- Examples are given of Turing machines that can multiply a binary number by 2, perform 2's complement on a binary number, and accept a specific formal language.
The document discusses composite transformations, which involve performing two or more transformations in sequence. It provides examples that two successive translations can be represented as a single translation, and two successive rotations can be represented as a single rotation. It also explains that scaling an object with respect to a fixed point can be achieved through a sequence of translations, scaling around the origin, and inverse translations, as represented by a composite matrix.
Open addressiing &rehashing,extendiblevhashingSangeethaSasi1
The document discusses various hash table implementation techniques. It describes open addressing hashing which resolves collisions by probing to the next empty cell. Linear probing is discussed as a collision resolution strategy where the next probe is the current index plus one. The document also covers separate chaining hashing which uses linked lists at each index to handle collisions, and double hashing which uses two hash functions to determine probe sequences.
The document describes pushdown automata (PDA). A PDA has a tape, stack, finite control, and transition function. It accepts or rejects strings by reading symbols on the tape, pushing/popping symbols on the stack, and changing state according to the transition function. The transition function defines the possible moves of the PDA based on the current state, tape symbol, and stack symbol. If the PDA halts in a final state with an empty stack, the string is accepted. PDAs can recognize any context-free language. Examples are given of PDAs for specific languages.
The document defines key concepts in automata theory, including alphabets, strings, length of strings, powers and closures of alphabets, languages, and the membership problem. An alphabet is a set of symbols, strings are finite sequences of symbols from an alphabet, and the length of a string is the number of symbols in it. Powers of an alphabet refer to sets of strings of a given length, while closures include strings of all lengths. A language is a subset of the Kleene closure of an alphabet. The membership problem asks whether a given string is part of a given language.
The document discusses how more complex geometric transformations can be performed by combining basic transformations through composition. It provides examples of how scaling and rotation can be done with respect to a fixed point by first translating the object to align the point with the origin, then performing the basic transformation, and finally translating back. Mirror reflection about a line is similarly described as a composite of translations and rotations.
This document discusses variants of Turing machines that increase their computational power and proves that any language accepted by a variant Turing machine is also accepted by a standard Turing machine. It covers variants such as multiple tapes, two-way infinite tapes, and nondeterminism. For each variant, it provides a proof that the standard Turing machine is equally powerful by simulating the variant with the standard model.
In graph theory, a matching is a subset of a graph's edges such hat no two edges meet the same vertex. A matching is maximum if no other matching contains more edges. A trivial solution (exhaustive search) to the problem of finding a maximum matching has exponential complexity. We illustrate polynomial time solutions to the problem that were published between 1965 and 1991.
LISP and PROLOG are early AI programming languages. LISP, created in 1958, uses lists and is functional while PROLOG, created in the 1970s, is logic-based and declarative. Both use recursion and allow programming with lists. They are commonly used for symbolic reasoning, knowledge representation and natural language processing. While different in approach, they both allow developing AI systems through a non-procedural programming style.
This file contains the contents about dynamic programming, greedy approach, graph algorithm, spanning tree concepts, backtracking and branch and bound approach.
This document discusses techniques for modeling curves and surfaces in computer graphics. It introduces three common representations of curves and surfaces: explicit, implicit, and parametric forms. It focuses on parametric polynomial forms, specifically discussing cubic polynomial curves, Hermite curves, Bezier curves, B-splines, and NURBS. It also covers rendering curves and surfaces by evaluating polynomials, recursive subdivision of Bezier polynomials, and ray casting for implicit surfaces like quadrics. Finally, it discusses mesh subdivision techniques like Catmull-Clark and Loop subdivision for generating smooth surfaces.
Spatial domain image enhancement techniques operate directly on pixel values. Some common techniques include point processing using gray level transformations, mask processing using filters, and histogram processing. Histogram equalization aims to create a uniform distribution of pixel values by mapping the original histogram to a wider range. This improves contrast by distributing pixels more evenly across gray levels.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
This document discusses graphics hardware components. It describes various graphics input devices like the mouse, joystick, light pen etc. and how they are either analog or digital. It then covers commonly used graphics output devices like CRT displays, plasma displays, LCDs and 3D viewing systems. It provides details on the internal components and working of CRT displays. It also discusses graphics storage formats and the architecture of raster and random graphics systems.
This document provides an overview of Turing machines including:
- Turing machines are mathematical models that can perform any computation like a computer and have an infinite tape with a tape head.
- A Turing machine is defined by 7 tuples including states, alphabets, transition function, and blank symbols.
- Multitape Turing machines have multiple tapes that can be accessed independently by separate heads.
- The instantaneous description specifies the current state, tape symbol under the head, and full tape configuration.
- Transition diagrams visually represent the transition function that specifies how the tape and state change based on the current symbol.
Branch and Bound is a state space search algorithm that involves generating all children of a node before exploring any children. It uses lower bounds to prune parts of the search tree that cannot produce better solutions than what has already been found. The algorithm is demonstrated on problems like the 8-puzzle and Travelling Salesman Problem. For TSP, it works by reducing the cost matrix at each node to calculate lower bounds, and exploring the child with the lowest estimated total cost.
The document describes Turing machines and formal languages. It defines a Turing machine as a mathematical model of computation that consists of an infinite tape divided into cells, a head that reads and writes symbols, internal states, and a transition function. It then discusses Turing machine components like states, tape alphabet, transition function, initial/final states. It also covers time/space complexity, deterministic/non-deterministic TMs, linear bounded automata, recursively enumerable vs recursive languages, Turing machines as enumerators, and universal Turing machines.
From the perspective of Design and Analysis of Algorithm. I made these slide by collecting data from many sites.
I am Danish Javed. Student of BSCS Hons. at ITU Information Technology University Lahore, Punjab, Pakistan.
Turing Machines are a simple mathematical model of a general purpose computer invented by Alan Turing in 1936. A Turing Machine consists of an infinite tape divided into cells, a head that reads and writes symbols on the tape, a finite set of states, and transition rules determining the behavior of the machine. The machine operates by reading a symbol on the tape, updating the symbol according to its transition rules, moving the head left or right, and transitioning to a new state. Turing Machines can simulate any algorithm and are capable of performing any calculation that can be performed by any computing machine.
This document provides an overview of Turing machines, including:
- Turing machines can be conceptualized as either logical or physical devices that operate on an infinite tape.
- A Turing machine has a read/write head, finite state control, and an infinite tape that can be used to store an input string, perform computations, and output a result string.
- Formally, a Turing machine is defined by its set of states, tape alphabet/symbols, transition function, blank symbol, initial state, and accepting/final states.
- Examples are given of Turing machines that can multiply a binary number by 2, perform 2's complement on a binary number, and accept a specific formal language.
This document provides an overview of Turing machines. It introduces Turing machines as a simple mathematical model of a computer proposed by Alan Turing in 1936. A Turing machine consists of a tape divided into cells, a read/write head, finite states, and a transition function. The transition function defines how the machine moves between states and reads/writes symbols on the tape based on its current state and tape symbol. Turing machines can accept languages and compute functions. Variations include multi-tape, non-deterministic, multi-head, offline, multi-dimensional, and stationary-head Turing machines. The properties of Turing machines include their ability to recognize any language generated by a phrase-structure grammar and the Church
1) The document describes a presentation on Turing machines, including their historical background, formal definition, transition functions, and examples.
2) Turing machines were invented in 1936 by Alan Turing as a thought experiment to capture the idea of computation. They helped address the decision and halting problems in theoretical computer science.
3) A Turing machine consists of a tape, head, and finite state control, and formally is defined by an 8-tuple including the state set, tape alphabet, transition function, start/accept/reject states, and direction to move the head.
The document discusses Turing machines and their properties. It introduces the Church-Turing thesis that any problem that can be solved by an algorithm can be modeled by a Turing machine. It then describes different types of Turing machines, such as multi-track, nondeterministic, two-way, multi-tape, and multidimensional Turing machines. The document provides examples of Turing machines that accept specific languages and evaluate mathematical functions through their transition tables and diagrams.
Alan turing's work before, during & after bletchley parkDavid Bew
The slide show combines various accounts in books generally available with new information released more recently. It attempts to portray Turing as a gifted man who found himself in an environment, at Bletchley Park in particular, where his particular skills and abilities, as well as his understanding of what was to be computer programming, were highly valued. The contention is that at Bletchley Park and in certain computer development work afterwards, Turing was able to perform as a specialised worker and at his best
Alan Turing was a homosexual British mathematician who developed an encryption-breaking machine called the "Enigma" during World War 2. This machine helped decrypt thousands of German messages and saved many British lives. The film The Imitation Game tells the story of Turing and his work on the Enigma machine during the war in a thriller style, celebrating his extraordinary achievement. It begins with a close-up of a sad and stressed Turing being interviewed by two police officers at a station, establishing the serious tone and that he is in some kind of trouble.
This document discusses the limitations of the Turing Machine model of computation and proposes alternatives such as simulated annealing and adiabatic quantum computation. It provides examples of computational problems like the traveling salesman problem that become statistical physical problems when viewed through this new lens. The document also discusses using adiabatic quantum computers like D-Wave for problems like map coloring and describes programming the D-Wave for such problems by mapping the problem onto the unit cell and considering neighbors and cloning through the programming code and GUI.
The document summarizes the Turing machine. It describes a Turing machine as having three main elements: an input/output tape, a read/write head that moves bidirectionally along the tape, and a finite state control. It operates by examining the symbol under the head along with its current state to determine the symbol to write, the head's movement, and the next state. The document then provides a formal definition of a Turing machine as a 7-tuple and describes some variations including those with different tape configurations and those that are nondeterministic or have multiple tapes/heads.
Cracking the Enigma Machine - Rejewski, Turing and the Math that saved the worldBradYoung
This presentation demonstrates the historical and mathematical background to the brilliant work done by Polish and British cryptology experts before and during World War II.
The solutions provided by Marian Rejewski, Alan Turing and their co-workers had a major impact on the outcome of the war.
The document discusses the Capability Maturity Model Integration (CMMI). It describes CMMI as a set of best practices for planning and managing product development and services. CMMI covers the product lifecycle from conception to delivery and maintenance. It contains 22 process areas grouped into process, project, engineering, and support categories. The document outlines the goals and components of CMMI, including specific and generic goals, specific and generic practices, and maturity levels. It emphasizes that CMMI is intended to help organizations improve processes through a quantitative and statistical understanding of performance.
This document introduces Generalized Stochastic Petri Nets (GSPNs). [1] GSPNs combine features of Petri Nets and stochastic processes. They contain both timed transitions with exponentially distributed random firing delays and immediate transitions with zero delay. [2] Immediate transitions have priority over timed transitions. GSPNs can model systems with both deterministic and stochastic behavior.
The document discusses different approaches to integrating information from multiple systems, including:
1. Providing a uniform logical view of distributed data through approaches like mediated query systems, portals, federated database systems, and web services.
2. Realizing a common data storage through data warehouses and operational data stores that load and aggregate data from multiple sources.
3. Achieving integration through applications like workflow management systems that coordinate interactions between different systems and users.
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
Simulation Tracking Object Reference Model (STORM)Umar Alharaky
The document outlines a proposed Simulation Tracking Object (STORM) model that allows embedded interactive simulations to be tracked by learning management systems (LMS) at runtime. STORM addresses limitations of SCORM, which does not allow direct communication between simulations and the LMS. STORM includes an objective model defined using XML, an RTI ambassador for routing events between the simulation and LMS, and an assessment module for evaluating objective completion status. It aims to provide a complete design for integrating simulation tracking capabilities into SCORM-compliant e-learning systems.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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.
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).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
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2. … ε ε a0 a1 … an ε ε …
RD Input Tape
Head
Finite
Control
qi
3. Create a Turing Machine that takes as input a string
consists of different parentheses ( ), [ ] or { } and ensure
if that string is balanced – open parentheses are balanced
with closed parentheses – and correct – the smaller one
does not include the bigger –.
Examples:
{[[(()())]()]} Accepted (balanced and correct)
[(())(){()()}] Rejected (balanced but wrong)
{[()}}{[(())]} Rejected (correct but unbalanced)
{()([])}[(()]] Rejected (unbalanced and wrong)
4. Q = { Start , Find1 , Find2 , Find3 , End , Rejected , Accepted }
States Description :
Start : initial state, that begin to find first symbol ), ] or } then check it with X, Y or Z respectively.
Find1 : moving left to find matched symbol (, then check it with X and return to Start state.
Find2 : moving left to find matched symbol [, then check it with Y and return to Start state.
Find3 : moving left to find matched symbol {, then check it with Z and return to Start state.
End : the string is finished, thus moving left to ensure that all symbols are checked.
Rejected : there are unchecked (unbalanced) symbols or the string is wrong, therefore string is rejected.
Accepted : all symbols are checked (balanced) and the string is correct, therefore string is accepted.
7. X/X,L
Find
1
Y/Y,L
[/Y,R
ε/ε,R
Find
Accept End ε/ε,L Start (/(,L {/{,L Z/Z,L ε/ε,L Reject
2
]/Y,L
Y/Y,L
Find
3
X/Y,D
X : Scanned Symbol
Y : Written Symbol [/[,L
D : Move Direction