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
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
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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