The document discusses a programming assignment for a CSE340 class on principles of programming languages. It provides an overview of the assignment which involves modifying source code to implement a parser for a grammar. It includes the grammar rules to be implemented, sample code for the parser implementation, and example input programs to test the parser. It also reviews concepts like predictive parsing.
Introduction to Artificial Neural Networks (ANNs) - Step-by-Step Training & T...Ahmed Gad
This is an introduction to artificial neural networks (ANNs) including the idea of classification and how ANNs can classify data into number of distinct classes based on some features.
A basic neural network example is given that uses a single layer perceptron with three inputs and one output to classify data linearly using the Signum activation function.
The presented example is about classifying data about colors into two categories (Red and Blue).
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
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https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
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https://www.researchgate.net/profile/Ahmed_Gad13
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https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
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https://www.facebook.com/ahmed.f.gadd
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Introduction to Artificial Neural Networks (ANNs) - Step-by-Step Training & T...Ahmed Gad
This is an introduction to artificial neural networks (ANNs) including the idea of classification and how ANNs can classify data into number of distinct classes based on some features.
A basic neural network example is given that uses a single layer perceptron with three inputs and one output to classify data linearly using the Signum activation function.
The presented example is about classifying data about colors into two categories (Red and Blue).
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Computer science Investigatory Project Class 12 C++Rushil Aggarwal
This project is based on File Handling . An innovative project on petrol pump management , deals with basic function in a petrol pump .
Do like , share and comment if my work helped you ;)
Introduction to Artificial Neural Networks (ANNs) - Step-by-Step Training & T...Ahmed Gad
This is an introduction to artificial neural networks (ANNs) including the idea of classification and how ANNs can classify data into number of distinct classes based on some features.
A basic neural network example is given that uses a single layer perceptron with three inputs and one output to classify data linearly using the Signum activation function.
The presented example is about classifying data about colors into two categories (Red and Blue).
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Practical Class 12th (c++programs+sql queries and output) Aman Deep
Just download this and do some specific changes in the name section and roll no section . and submit it as it is to your teacher this will surely work and help you out your class 12th board practicals exam . no worries ! ENJOY :) !
c++ program using All data type and operators AMUDHAJAY
1. declared and initialized usinf different data types
2. using do-while & switch case concept
3. all operators are used like arithmetic , logical, increment& decrement, relational & bitwise operator
A73A CQWW 2012 Contest operation from the Desert of QatarTobias Wellnitz
This presentation was given in the Contest forum at the HamRadio convention 2012 in Friedrichshafen, Germany.
A73A was a Multi/Multi contest participation in Fieldday style from the desert of Qatar. Vertical Antennas were used directly over salt water.
201404 Multimodal Detection of Affective States: A Roadmap Through Diverse Te...Javier Gonzalez-Sanchez
This course presents devices and explores methodologies for multimodal detection of affective states, as well as a discussion about presenter’s experiences using them both in learning and gaming scenarios.
Abstract
One important way for systems to adapt to their individual users is related to their ability to show empathy. Being empathetic implies that the computer is able to recognize a user’s affective states and understand the implication of those states. Detection of affective states is a step forward to provide machines with the necessary intelligence to appropriately interact with humans. This course provides a description and demonstration of tools and methodologies for automatically detecting affective states with a multimodal approach.
Objectives
Describe the sensing devices used to detect affective states including brain-computer interfaces, face-based emotion recognition systems, eye-tracking systems, and physiological sensors.
Compare the pros and cons of the sensing devices used to detect affective states.
Describe the data that is gathered from each sensing device and its characteristics.
Examine what it takes to gather, filter, and integrate affective data.
Present approaches and algorithms used to analyze affective data and how it could be used to drive computer functionality or behavior.
This course is open to researchers, practitioners, and educators interested in incorporating detection of affective states as part of their technology toolbox.
Computer science Investigatory Project Class 12 C++Rushil Aggarwal
This project is based on File Handling . An innovative project on petrol pump management , deals with basic function in a petrol pump .
Do like , share and comment if my work helped you ;)
Introduction to Artificial Neural Networks (ANNs) - Step-by-Step Training & T...Ahmed Gad
This is an introduction to artificial neural networks (ANNs) including the idea of classification and how ANNs can classify data into number of distinct classes based on some features.
A basic neural network example is given that uses a single layer perceptron with three inputs and one output to classify data linearly using the Signum activation function.
The presented example is about classifying data about colors into two categories (Red and Blue).
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Artificial neural networks (ANNs) or connectionist systems are a computational model used in machine learning, computer science and other research disciplines, which is based on a large collection of connected simple units called artificial neurons, loosely analogous to axons in a biological brain. Connections between neurons carry an activation signal of varying strength. If the combined incoming signals are strong enough, the neuron becomes activated and the signal travels to other neurons connected to it. Such systems can be trained from examples, rather than explicitly programmed, and excel in areas where the solution or feature detection is difficult to express in a traditional computer program. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are difficult to solve using ordinary rule-based programming.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/
Practical Class 12th (c++programs+sql queries and output) Aman Deep
Just download this and do some specific changes in the name section and roll no section . and submit it as it is to your teacher this will surely work and help you out your class 12th board practicals exam . no worries ! ENJOY :) !
c++ program using All data type and operators AMUDHAJAY
1. declared and initialized usinf different data types
2. using do-while & switch case concept
3. all operators are used like arithmetic , logical, increment& decrement, relational & bitwise operator
A73A CQWW 2012 Contest operation from the Desert of QatarTobias Wellnitz
This presentation was given in the Contest forum at the HamRadio convention 2012 in Friedrichshafen, Germany.
A73A was a Multi/Multi contest participation in Fieldday style from the desert of Qatar. Vertical Antennas were used directly over salt water.
201404 Multimodal Detection of Affective States: A Roadmap Through Diverse Te...Javier Gonzalez-Sanchez
This course presents devices and explores methodologies for multimodal detection of affective states, as well as a discussion about presenter’s experiences using them both in learning and gaming scenarios.
Abstract
One important way for systems to adapt to their individual users is related to their ability to show empathy. Being empathetic implies that the computer is able to recognize a user’s affective states and understand the implication of those states. Detection of affective states is a step forward to provide machines with the necessary intelligence to appropriately interact with humans. This course provides a description and demonstration of tools and methodologies for automatically detecting affective states with a multimodal approach.
Objectives
Describe the sensing devices used to detect affective states including brain-computer interfaces, face-based emotion recognition systems, eye-tracking systems, and physiological sensors.
Compare the pros and cons of the sensing devices used to detect affective states.
Describe the data that is gathered from each sensing device and its characteristics.
Examine what it takes to gather, filter, and integrate affective data.
Present approaches and algorithms used to analyze affective data and how it could be used to drive computer functionality or behavior.
This course is open to researchers, practitioners, and educators interested in incorporating detection of affective states as part of their technology toolbox.
Considering that the human-element as crucial in designing and implementing interactive intelligent systems, this tutorial provides a description and hands-on demonstration on detection of affective states and a description of devices, methodologies and data processing, as well as their impact in instructional design. The information that a computer senses in order to automate the detection of affective states, includes an extensive set of data, it could ranges from brain-waves signals and biofeedback readings from face-based or gesture emotion recognition and posture or pressure sensing. The work presented in this tutorial, is not about the development of the algorithms or hardware that make this works, our concerns are about the encapsulation of preexisting systems (we are actually using all of them) that implements those algorithms and uses these hardware to improve Learning.
A 60-minute webinar on How Hosted Email Archiving Can Save You Money.
View this presentation for an in-depth look at hosted email archiving and discover how much money you can save with software-as-a-service (SaaS) solutions - during a recession or any economic climate.
You'll learn how SaaS email archiving can help you:
- Save more money and time - both upfront as well as on an ongoing basis
- Get more value - in terms of storage, disaster recovery and environmental friendliness
- Worry less - in terms of deployment time, infrastructure and security/compliance
Developer Experience i TypeScript. Najbardziej ikoniczne duoThe Software House
Wiktor Toporek: TypeScript bez wątpienia jest obecnie pewnym standardem wśród obecnych rozwiązań powstałych w JavaScripcie. Ale czy poza byciem dodatkiem który uzupełnia odrobinę dokumentacje i deklaruje kontrakt jakiego typu parametry przyjmują i zwracają np. funkcje jakiejś biblioteki, można wycisnąć z niego coś więcej? Podczas prezentacji wykorzystamy TypeScript do granic możliwości, używając zaawansowanych technik które sprawiają że interfejs naszego API będzie sam kierował używających go developerów na drogę poprawnego użycia, które jest zgodne z naszymi (twórców) założeniami, poprawiając tym samym ich doświadczenia.
An Introduction to Test Driven Development with ReactFITC
Presented at Web Unleashed 2017. More info at www.fitc.ca/webu
Presented by Lawton Spelliscy, Tribalscale
Overview
While there are many best practises and approaches to test driven development or TDD, the basic concept is that a developer writes tests prior to implementing a specific set of functionality. This can feel unintuitive or cumbersome, but test driven development can have numerous benefits. If done correctly, the methodology can help add a stable routine to your development process. It naturally creates short development cycles, well defined functions, and builds a stable suite of unit tests.
React has a strong test utility library, making it easy to test. This presentation will introduce some of the common libraries used for testing in React and the routine Lawton follows when developing.
Objective
To demonstrate the advantages of test driven development
Target Audience
Front-end developers
Assumed Audience Knowledge
Experience with front-end development, an understanding of Javascript with some exposure to the React library.
Five Things Audience Members Will Learn
The benefits and disadvantages of test driven development
How to incorporate test driven development into your development process
Learn about useful React test libraries
How to run tests on JavaScript applications
Ways to isolate your tests to specific functionality
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
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201506 CSE340 Lecture 12
1. CSE340 - Principles of
Programming Languages
Lecture 12:
Parser Implementation II
Javier Gonzalez-Sanchez
javiergs@asu.edu
BYENG M1-38
Office Hours: By appointment
2. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 2
Review
Programming Assignment 2
Level 1
Review and Understand the Source Code
posted in Blackboard.
Particularly the use of DefaultMutableTreeNode)
3. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 3
Review
* Parser.java is the only file that you are allowed to modify
4. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 4
Review
Programming Assignment 2
Level 2
Modify the Source Code
to include the rules PROGRAM and BODY, EXPRESSION, X, Y, R
(from Grammar 2)
9. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 9
Assignment 2 | Input
Are there syntactical errors?
{
float a;
x = 0;
int x;
y = 1 + 1;
x = (0b11) +(05 – 0xFF34);
while (2 == "hi") {
a = 2 > (4 + Y);
if (true) { if( 2 + 2 ) {} else {} }
}
print ("hello" + "world");
}
10. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 10
Assignment 2 | Input
Are there syntactical errors?
{
int x;
x = 5;
x = 05;
x = 0x5ff;
x = 5.55;
x = "five";
x = ’5';
x = false;
}
11. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 11
Assignment 2 | Input
Are there syntactical errors?
{
int x;
float x;
string x;
char x;
void x;
boolean x;
}
12. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 12
Assignment 2 | Input
Are there syntactical errors?
{
x = "hello" + "world" – 'w' * 5 / 3.4;
x = y – hello & 0xffff | 05;
x = -7;
x = !y;
x = (cse340 + cse310) / cse101 ;
}
24. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 24
Concepts
{
int a;
a = 0xFF + 0b111;
while (a != 05) {
if (true) {
a = 2.5e-1 / 7;
} else {
a = 'A’;
while(true) {
}
}
}
print ("hello");
}
PREDICTIVE
DESCENDENT
RECURSIVE
PARSER
25. Javier Gonzalez-Sanchez | CSE340 | Summer 2015 | 25
Homework
Programming Assignment #2
(Complete Levels 1 to 3)
26. CSE340 - Principles of Programming Languages
Javier Gonzalez-Sanchez
javiergs@asu.edu
Summer 2015
Disclaimer. These slides can only be used as study material for the class CSE340 at ASU. They cannot be distributed or used for another purpose.