Ming Rutar has shared 10 slides on Sign Language Recognition with Python. Sign Language Recognition can be used to translate sign language with computer vision to text, then a mathematical model can translate the text into words.
22_11_2019 «Gamifying a software testing course with the Code Defenders Testi...eMadrid network
eMadrid seminar on «Serious games applied to teaching in software engineering», organized by UCM.
Authors: Gordon Fraser and Phil Werli, from Passau University (Germany).
This document provides an overview and introduction to IST 380, a data science course taught by Zach Dodds. The course covers topics like R programming, statistical analysis, machine learning algorithms, and a final project. Students will learn skills in data visualization, predictive modeling, and applying data science techniques to real-world datasets. The course emphasizes hands-on learning through weekly assignments completed in R.
This document summarizes a thesis on automating test routine creation through natural language processing. The author proposes using word embeddings and recommender systems to automatically generate test cases from requirements documents and link them together. The methodology involves representing text as word vectors, calculating similarity between requirements and test blocks, and applying association rule mining on test block sequences. An experiment on a space operations dataset showed the approach improved productivity in test creation and requirements tracing over manual methods. Future work could explore using deep learning models and collecting additional evaluation metrics from users.
Learning Outcome: 1- Gain knowledge and understanding the meaning of computer language? 2- Draw conclusions about concepts: data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
Key Concepts: 1- Concept of computer language. 2- Concept of different data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
Skills: At the completion of the program, students should be able to: 1- understand the structure of the program. 2- Design some programs include different data types, variables, Conditional statements and looping statements. 3- Compile the program (Run).
Essential Questions: 1- What is meant by programming language and give some examples? 2- What are the key features or characteristics of language? Textbook and Resource Materials: https://www.w3schools.com
Evidence of Learning: Create a presentation contains some concepts of computer languages and display the Concepts of different data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
SEC Topic & Code: Using appropriate programming language to produce a project that solves societal or learning problem creatively
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
Sudeep Das presented on recommender systems and advances in deep learning approaches. Matrix factorization is still the foundational method for collaborative filtering, but deep learning models are now augmenting these approaches. Deep neural networks can learn hierarchical representations of users and items from raw data like images, text, and sequences of user actions. Models like wide and deep networks combine the strengths of memorization and generalization. Sequence models like recurrent neural networks have also been applied to sessions for next item recommendation.
Presentation about an eclipse framework that allows to generate ecore model instances as input for tests and benchmarks. Held at the 3rd BigMDE workshop at STAF in L'Aquia, Italy in July 2015.
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdfPo-Chuan Chen
The document proposes an approach to generate natural language summaries for online content using offline reinforcement learning. It involves crawling Twitter data, fine-tuning models like RoBERTa and GPT-2, and using a reinforcement learning algorithm (PPO) to further train the text generation model using a reward function. The methodology, planned experiment, related work and conclusion are discussed over multiple sections and figures.
22_11_2019 «Gamifying a software testing course with the Code Defenders Testi...eMadrid network
eMadrid seminar on «Serious games applied to teaching in software engineering», organized by UCM.
Authors: Gordon Fraser and Phil Werli, from Passau University (Germany).
This document provides an overview and introduction to IST 380, a data science course taught by Zach Dodds. The course covers topics like R programming, statistical analysis, machine learning algorithms, and a final project. Students will learn skills in data visualization, predictive modeling, and applying data science techniques to real-world datasets. The course emphasizes hands-on learning through weekly assignments completed in R.
This document summarizes a thesis on automating test routine creation through natural language processing. The author proposes using word embeddings and recommender systems to automatically generate test cases from requirements documents and link them together. The methodology involves representing text as word vectors, calculating similarity between requirements and test blocks, and applying association rule mining on test block sequences. An experiment on a space operations dataset showed the approach improved productivity in test creation and requirements tracing over manual methods. Future work could explore using deep learning models and collecting additional evaluation metrics from users.
Learning Outcome: 1- Gain knowledge and understanding the meaning of computer language? 2- Draw conclusions about concepts: data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
Key Concepts: 1- Concept of computer language. 2- Concept of different data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
Skills: At the completion of the program, students should be able to: 1- understand the structure of the program. 2- Design some programs include different data types, variables, Conditional statements and looping statements. 3- Compile the program (Run).
Essential Questions: 1- What is meant by programming language and give some examples? 2- What are the key features or characteristics of language? Textbook and Resource Materials: https://www.w3schools.com
Evidence of Learning: Create a presentation contains some concepts of computer languages and display the Concepts of different data types, variables, Conditional statements, looping statements, functions and Object Oriented Programming.
SEC Topic & Code: Using appropriate programming language to produce a project that solves societal or learning problem creatively
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
Sudeep Das presented on recommender systems and advances in deep learning approaches. Matrix factorization is still the foundational method for collaborative filtering, but deep learning models are now augmenting these approaches. Deep neural networks can learn hierarchical representations of users and items from raw data like images, text, and sequences of user actions. Models like wide and deep networks combine the strengths of memorization and generalization. Sequence models like recurrent neural networks have also been applied to sessions for next item recommendation.
Presentation about an eclipse framework that allows to generate ecore model instances as input for tests and benchmarks. Held at the 3rd BigMDE workshop at STAF in L'Aquia, Italy in July 2015.
Offline Reinforcement Learning for Informal Summarization in Online Domains.pdfPo-Chuan Chen
The document proposes an approach to generate natural language summaries for online content using offline reinforcement learning. It involves crawling Twitter data, fine-tuning models like RoBERTa and GPT-2, and using a reinforcement learning algorithm (PPO) to further train the text generation model using a reward function. The methodology, planned experiment, related work and conclusion are discussed over multiple sections and figures.
This document is a project report for the "Learn & Fun" educational software system. It includes acknowledgments, an introduction describing the purpose of the document, and an executive summary providing an overview of the software development lifecycle phases covered. The document contains requirements analysis diagrams, descriptions of the implemented functions and user interface, and a testing plan. The goal of the "Learn & Fun" system is to improve the current educational system for children by allowing them to learn through an interactive digital platform.
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.
This document provides an overview of an introductory data science course (IST 380). It discusses the course content which includes learning the R programming language, descriptive statistics, predictive modeling, and machine learning algorithms. It also covers course logistics like assignments, grading, and academic honesty policies. The goal of the course is to provide students with practical data science skills that can be applied to real-world problems and datasets.
This document provides an overview of an introductory data science course (IST 380). It discusses the course content which includes learning the R programming language, descriptive statistics, predictive modeling, and machine learning algorithms. It also covers the grading scheme, assignments, and final project where students can apply what they learned to a dataset of their choice.
This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.
IRJET - Automatic Lip Reading: Classification of Words and Phrases using Conv...IRJET Journal
This document presents research on developing an automatic lip reading system using convolutional neural networks. The system takes in video frames of a speaker's face without audio and classifies the words or phrases being spoken. The researchers preprocessed the data by detecting faces in video frames and cropping them. They then trained a CNN model on concatenated frames. Their model achieved 80.44% accuracy on the test set in classifying 10 words and 10 phrases from 17 speakers. The researchers concluded the model could be improved by addressing overfitting to unseen speakers with a larger dataset and regularization techniques.
This document surveys and compares several software options for solving dynamic programming problems: LINDO, TORA, and MATLAB. LINDO and TORA are specifically designed for optimization problems like linear programming and can handle a variety of dynamic programming problems. The document provides examples of assigning teachers to courses and solving a transportation problem using LINDO and TORA. Both software packages find the optimal solutions. The document also discusses limitations of the different software. MATLAB is a numerical computing environment that can also solve some dynamic programming problems but was not demonstrated with an example.
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
This document outlines a project to develop a machine learning model to predict house prices in the United States. It describes the company developing the system, provides background on machine learning and the problem domain, and outlines the objectives, requirements, methodology, design, and expected results of the project. The proposed methodology involves collecting house data, preprocessing it, training a random forest model on 80% of the data and testing it on the remaining 20%, and using the trained model to predict house prices. The system is intended to help buyers search for homes within their budget and avoid being misled on prices.
Assistive system for Parkinson's patients - Carnegie Mellon University Spring...KP Kshitij Parashar
The prototype was conceptualized, designed, and developed during Rapid Prototyping of Computer Systems course at Carnegie Mellon University in Spring 2020. I led the Interactions team in the final phase of the course.
The document provides information about a laboratory manual for an Object Oriented Programming course with Java. It includes the vision and mission statements of the institution and computer science departments. It then details the course objectives, outcomes, system requirements and introduces topics that will be covered like installing Java Development Kit and an introduction to object oriented programming concepts. It provides an example program to find the roots of a quadratic equation to demonstrate Java fundamentals.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will get an introduction to Autoencoders and Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
The document describes CMU-Informedia's system for the TRECVID 2013 Multimedia Event Detection task. It introduces their MultiModal Pseudo Relevance Feedback (MMPRF) approach, which constructs a pseudo label set to leverage both high-level semantic concepts and low-level visual features for event search. Experimental results on the MEDTest dataset show MMPRF improves performance over baselines by 158% for pre-specified events and 107% for ad-hoc events. Their full system performed best in the official TRECVID 2013 evaluation.
Jane may be able to help. Let me check with her personal assistant Jane-ML.
NextPrevIndex
Meera checks with Jane-ML
User-Agent Interaction (V)
48
PA_Meera: Mina, do you
have trouble in
debugging?
Mina: Yes, is there
anyone who has done
this?
Personal Agent
[Meera]
Jane-ML: Jane has done a similar debugging problem before. She is available now and willing to help.
compiletheme
Compiling output
- The document summarizes a presentation given by Andy Zaidman at the International Conference on Automation of Software Test (AST 2023) in Melbourne, Australia on May 16th, 2023.
- It discusses findings from studies on how developers engineer test cases and their testing behaviors in IDEs, including strategies like being guided by documentation or code.
- It also presents recommendations to improve developer testing through better tool support, clear adequacy criteria in education, and a focus on improving the user and developer experience of testing tools and processes.
DELAB - sequence generation seminar
Title
Open vocabulary problem
Table of contents
1. Open vocabulary problem
1-1. Open vocabulary problem
1-2. Ignore rare words
1-3. Approximative Softmax
1-4. Back-off Models
1-5. Character-level model
2. Solution1: Byte Pair Encoding(BPE)
3. Solution2: WordPieceModel(WPM)
Tagging based Efficient Web Video Event CategorizationEditor IJCATR
Web video categorization is one of the emerging research fields in the computer vision domain due to its massive volume
growth in the internet which demands to discover events. Due to insufficient, noisy information and large intra class disparity makes it
more daunting task to recognize the events. Most of the recent works focus on constrained (fixed camera, known environment) videos
with supervised labelling to categorize the web videos. In this paper, we propose the subject based Part-Of- Speech (POS) Tagging
technique with the assist of Named Entity Recognition (NER) and WordNet is applied on YouTube video titles to discover the events
based on the subject, not on the objects visualized in the videos. Unsupervised learning method is used on high level features (titles)
because of incoming videos are not known and large intra-class variations. For the experiment, we have chosen topics from Google
Zeitgeist and downloaded the related videos from YouTube. A novel conclusion is derived from the experimental result that use of low
level features will lead to a poor classification in discovering intra class events based on the subject of the videos
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
This document is a project report for the "Learn & Fun" educational software system. It includes acknowledgments, an introduction describing the purpose of the document, and an executive summary providing an overview of the software development lifecycle phases covered. The document contains requirements analysis diagrams, descriptions of the implemented functions and user interface, and a testing plan. The goal of the "Learn & Fun" system is to improve the current educational system for children by allowing them to learn through an interactive digital platform.
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.
This document provides an overview of an introductory data science course (IST 380). It discusses the course content which includes learning the R programming language, descriptive statistics, predictive modeling, and machine learning algorithms. It also covers course logistics like assignments, grading, and academic honesty policies. The goal of the course is to provide students with practical data science skills that can be applied to real-world problems and datasets.
This document provides an overview of an introductory data science course (IST 380). It discusses the course content which includes learning the R programming language, descriptive statistics, predictive modeling, and machine learning algorithms. It also covers the grading scheme, assignments, and final project where students can apply what they learned to a dataset of their choice.
This document provides an overview of an IST 380 data science course. It introduces the instructor, Zach Dodds, and discusses topics that will be covered over the 15 weeks including using R, descriptive statistics, predictive modeling, machine learning algorithms, and a final project. Assignments are due weekly and students can work individually or in pairs. The course aims to provide both specific skills in data analysis and a broad background in data science.
IRJET - Automatic Lip Reading: Classification of Words and Phrases using Conv...IRJET Journal
This document presents research on developing an automatic lip reading system using convolutional neural networks. The system takes in video frames of a speaker's face without audio and classifies the words or phrases being spoken. The researchers preprocessed the data by detecting faces in video frames and cropping them. They then trained a CNN model on concatenated frames. Their model achieved 80.44% accuracy on the test set in classifying 10 words and 10 phrases from 17 speakers. The researchers concluded the model could be improved by addressing overfitting to unseen speakers with a larger dataset and regularization techniques.
This document surveys and compares several software options for solving dynamic programming problems: LINDO, TORA, and MATLAB. LINDO and TORA are specifically designed for optimization problems like linear programming and can handle a variety of dynamic programming problems. The document provides examples of assigning teachers to courses and solving a transportation problem using LINDO and TORA. Both software packages find the optimal solutions. The document also discusses limitations of the different software. MATLAB is a numerical computing environment that can also solve some dynamic programming problems but was not demonstrated with an example.
This document provides an overview of machine learning, including:
1) It defines machine learning as teaching computational machines to solve problems by giving examples to automatically infer rules for associating inputs and outputs.
2) It discusses different machine learning algorithms like linear classifiers, support vector machines, ensemble methods, and deep learning.
3) It emphasizes the need for scalable deployment of machine learning models to handle large and streaming data, covering approaches like distributed and parallel processing using MapReduce and cloud services.
This document outlines a project to develop a machine learning model to predict house prices in the United States. It describes the company developing the system, provides background on machine learning and the problem domain, and outlines the objectives, requirements, methodology, design, and expected results of the project. The proposed methodology involves collecting house data, preprocessing it, training a random forest model on 80% of the data and testing it on the remaining 20%, and using the trained model to predict house prices. The system is intended to help buyers search for homes within their budget and avoid being misled on prices.
Assistive system for Parkinson's patients - Carnegie Mellon University Spring...KP Kshitij Parashar
The prototype was conceptualized, designed, and developed during Rapid Prototyping of Computer Systems course at Carnegie Mellon University in Spring 2020. I led the Interactions team in the final phase of the course.
The document provides information about a laboratory manual for an Object Oriented Programming course with Java. It includes the vision and mission statements of the institution and computer science departments. It then details the course objectives, outcomes, system requirements and introduces topics that will be covered like installing Java Development Kit and an introduction to object oriented programming concepts. It provides an example program to find the roots of a quadratic equation to demonstrate Java fundamentals.
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will get an introduction to Autoencoders and Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
The document describes CMU-Informedia's system for the TRECVID 2013 Multimedia Event Detection task. It introduces their MultiModal Pseudo Relevance Feedback (MMPRF) approach, which constructs a pseudo label set to leverage both high-level semantic concepts and low-level visual features for event search. Experimental results on the MEDTest dataset show MMPRF improves performance over baselines by 158% for pre-specified events and 107% for ad-hoc events. Their full system performed best in the official TRECVID 2013 evaluation.
Jane may be able to help. Let me check with her personal assistant Jane-ML.
NextPrevIndex
Meera checks with Jane-ML
User-Agent Interaction (V)
48
PA_Meera: Mina, do you
have trouble in
debugging?
Mina: Yes, is there
anyone who has done
this?
Personal Agent
[Meera]
Jane-ML: Jane has done a similar debugging problem before. She is available now and willing to help.
compiletheme
Compiling output
- The document summarizes a presentation given by Andy Zaidman at the International Conference on Automation of Software Test (AST 2023) in Melbourne, Australia on May 16th, 2023.
- It discusses findings from studies on how developers engineer test cases and their testing behaviors in IDEs, including strategies like being guided by documentation or code.
- It also presents recommendations to improve developer testing through better tool support, clear adequacy criteria in education, and a focus on improving the user and developer experience of testing tools and processes.
DELAB - sequence generation seminar
Title
Open vocabulary problem
Table of contents
1. Open vocabulary problem
1-1. Open vocabulary problem
1-2. Ignore rare words
1-3. Approximative Softmax
1-4. Back-off Models
1-5. Character-level model
2. Solution1: Byte Pair Encoding(BPE)
3. Solution2: WordPieceModel(WPM)
Tagging based Efficient Web Video Event CategorizationEditor IJCATR
Web video categorization is one of the emerging research fields in the computer vision domain due to its massive volume
growth in the internet which demands to discover events. Due to insufficient, noisy information and large intra class disparity makes it
more daunting task to recognize the events. Most of the recent works focus on constrained (fixed camera, known environment) videos
with supervised labelling to categorize the web videos. In this paper, we propose the subject based Part-Of- Speech (POS) Tagging
technique with the assist of Named Entity Recognition (NER) and WordNet is applied on YouTube video titles to discover the events
based on the subject, not on the objects visualized in the videos. Unsupervised learning method is used on high level features (titles)
because of incoming videos are not known and large intra-class variations. For the experiment, we have chosen topics from Google
Zeitgeist and downloaded the related videos from YouTube. A novel conclusion is derived from the experimental result that use of low
level features will lead to a poor classification in discovering intra class events based on the subject of the videos
Similaire à American sign language recognizer (20)
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
2. ASL Recognizer is a Udacity AI Course Project
Udacity is an online school founded by top AI gurus. http://www.udacity.com
Zillion ideas
floating in
academia
world
Few ideas
made to
Industry
Industry Cutting
Edge
Technologies
Science/Theory
Udacity teaches cutting-edge technologies with
academic depth and hands-on practices on
technologies
Technology/Practice
❖ A course lasts 3 - 6 months with
3-7 projects.
❖ The projects are product-like.
❖ Focus on core technologies and
provide helpers on utilitive tasks,
such as environment setup.
❖ Very active online communities.
Course instructors also
participate.
❖ Student projects are reviewed by
experts of the subject matter.
❖ If one had graduated, he/she can
always access the course
materials, which are adhered
with the technology trend and
updated accordingly.
❖ Affordable price.
3. The task
The overall goal of this project is to build a word recognizer for American Sign Language video
sequences, demonstrating the power of probabalistic models. In particular, this project employs hidden
Markov models (HMM's) to analyze a series of measurements taken from videos of American Sign
Language (ASL) collected for research (see the RWTH-BOSTON-104 Database). In this video, the
right-hand x and y locations are plotted as the speaker signs the sentence.The raw data, train, and test
sets are pre-defined. You will derive a variety of feature sets
4. The Dataset
We recognize the meaning of ASL when watch the hand movement of the speaker. The computer mimic
after us. Nowaday, the technology can tag video, but not in 1990th. The hand gestion data, such as
Cartesian coordinates of left and right hands, and of the nose, which servers as a reference, are
preprocessed (extracted from the video). After load the data, the ‘asl’ dataframe looks like this:
X
Y
nx
ny
lx
rx
ly
ry
5. More about the data
The training input file:
video,speaker,word,startframe,endframe
1,woman-1,JOHN,8,17
1,woman-1,WRITE,22,50
1,woman-1,HOMEWORK,51,77
3,woman-2,IX-1P,4,11
3,woman-2,SEE,12,20
3,woman-2,JOHN,20,31
3,woman-2,YESTERDAY,31,40
3,woman-2,IX,44,52
4,woman-1,JOHN,2,13
4,woman-1,IX-1P,13,18
4,woman-1,SEE,19,27
4,woman-1,IX,28,35
4,woman-1,YESTERDAY,36,47
5,woman-2,LOVE,12,21
The test input file:
video,speaker,word,startframe,endframe
2,woman-1,JOHN,7,20
2,woman-1,WRITE,23,36
2,woman-1,HOMEWORK,38,63
7,man-1,JOHN,22,39
7,man-1,CAN,42,47
7,man-1,GO,48,56
7,man-1,CAN,62,73
12,woman-2,JOHN,9,15
12,woman-2,CAN,19,24
12,woman-2,GO,25,34
12,woman-2,CAN,35,51
21,woman-2,JOHN,6,26
the training data contains 112 unique words; test data contains 66 unique words; in test data, we
have 40 sentences made of 178 words.l
6. Feature Extraction
Features are data we feed into networks. Feature selection is crucial in success of a network. Use common sense to
select features. Examples:
X
Y
g-ly
g-ry
g-rx
g-lx
Feature_ground
features_ground = ['grnd-rx', 'grnd-ry', 'grnd-lx', 'grnd-ly']
asl.df['grnd-ly'] = asl.df['left-y'] - asl.df['nose-y']
asl.df['grnd-lx'] = asl.df['left-x'] - asl.df['nose-x']
...
X
rr
ltheta
lr
rtheta
feature_polar
features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']
asl.df['polar-rr'] = np.sqrt((asl.df['right-x']- asl.df['nose-x'])**2 + (asl.df['right-y']-asl.df['nose-y'])**2)
asl.df['polar-rtheta'] = np.arctan2(asl.df['right-x']- asl.df['nose-x'],asl.df['right-y'] - asl.df['nose-y'])
...
7. HMMLearn
HMMLearn is a library for unsupervised learning. HMM stands for Hidden Markov Model. Just as Neural Network, it can be
represented in Bayesian network:
We use HMMLearn class GausianHMM model. Gausian curve is the famous bell curve. Below is the curves of word
‘Chocolate’ with different number of hidden states
● We initiate the class with number of hidden states,
number of iteration and more, see reference at
http://hmmlearn.readthedocs.io/en/latest/api.html#hm
mlearn.hmm.GaussianHMM
● for training we call method fit() and pass in the training
data, it returns itself.
● for inference, we call method score() with the word, it
emits a float that indicates the likelihood of input.
8. How do we do it
● We train the model one word at time with the training data.
● The words are encoded by associated with a unique integer, the word id
● A word has an associated list of feature set
● We train GaussianHMM model with a word feature set. Try with difference number of hidden states, then
select the best model for the word
● So after training, each word has a model.
● We test the models by building a recognizer that
○ Pick a feature and a model, test them with full sentences:
■ For each word in a sentence, ‘reading’ feature set
■ Pick the model with highest score model
■ From the model we find the word id
○ We decode the sequence of word id to a sentence
○ Company the synthesized sentence with the original sentence and get the Error Rate
● The criteria for passing the project is < 60 % error rate, or recognize 40+% words correctly
9. Model Selection
The raw Gaussian model is a rough cut. In my test, it correctly recognized 58 words out of 178 (about 67% error rate). We
improve the model selection by use 2 popular information criteria:
● Bayesian information criteria (BIC)
○ The purpose is to punish the word with longer seq to prevent overfit.
○ BIC = −2 log L + p log N
■ where p is a parameter, L is Gausian score, N is the hmm length of the word.
■ p is very magical!!!
■ to learn more, check this link http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf
● Discriminative Information Criterion (DIC)
○ DIC scores the discriminant ability of a training set for one word against competing words.
10. Testing and Output
model_selector=SelectorBIC_orig, features=scale_podel
**** WER = 0.43258426966292135
Total correct: 101 out of 178
Video Recognized Correct
=====================================================================================================
2: JOHN WRITE HOMEWORK JOHN WRITE HOMEWORK
7: JOHN *HAVE GO *ARRIVE JOHN CAN GO CAN
12: JOHN *WHAT *GO1 CAN JOHN CAN GO CAN
21: JOHN FISH WONT *WHO BUT *CAR *CHICKEN CHICKEN JOHN FISH WONT EAT BUT CAN EAT CHICKEN
25: JOHN *TELL *LOVE *WHO IX JOHN LIKE IX IX IX
28: JOHN *WHO *WHO *WHO IX JOHN LIKE IX IX IX
30: JOHN *MARY *MARY *MARY *MARY JOHN LIKE IX IX IX
36: MARY VEGETABLE *GIRL *GIVE *MARY *MARY MARY VEGETABLE KNOW IX LIKE CORN1
40: JOHN *VISIT *CORN *JOHN *MARY JOHN IX THINK MARY LOVE
43: JOHN *SHOULD BUY HOUSE JOHN MUST BUY HOUSE
50: *JOHN *SEE BUY CAR SHOULD FUTURE JOHN BUY CAR SHOULD
54: JOHN *JOHN *MARY BUY HOUSE JOHN SHOULD NOT BUY HOUSE
57: JOHN *PREFER VISIT MARY JOHN DECIDE VISIT MARY
67: JOHN *YESTERDAY NOT BUY HOUSE JOHN FUTURE NOT BUY HOUSE
71: JOHN *FUTURE VISIT MARY JOHN WILL VISIT MARY
74: *IX *MARY *MARY MARY JOHN NOT VISIT MARY
77: *JOHN BLAME MARY ANN BLAME MARY
11. The Results
features_customer2 is the winner. features_customer2 is scaled Cartesian coordinates + time delta
by just scale the values of features_podel, scale_podel outperforms features_podel, 101 vs 89 words