Example Mapping provides a structured approach to help teams tease out the essential business rules and examples that clarify a user story and improve shared understanding of story “doneness”
Requirements modeling involves creating models during requirements analysis to understand current systems or business processes being automated and how users will interact with a new system. It helps with elicitation, validation, and communication of requirements. Common tools for requirements modeling include use cases, state diagrams, UI mockups, storyboards, and prototypes.
Define requirements modeling as representing the customers' needs and the problem domain using models to facilitate understanding between stakeholders.
Explain four roles of requirements modeling as:
1) Guiding elicitation by helping determine questions to ask and uncover hidden requirements
2) Providing progress measures by ensuring model completeness indicates elicitation is complete
3) Hel
Content reuse is one of the key reasons for converting to a single source. But how do you know what the reuse potential really is? Because converting content formats is a major investment, you need to base your business case on real data.
You can find the data with these steps:
• Observation
• Comparison
• Analysis
This session gives you a methodology to learn how to determine the true opportunity for content reuse and quantify it for your business case.
This presentation was given at Information Development World on October 1, 2015.
How to run a great requirements workshop with Use CasesAndreas Hägglund
The slideshare tells how to run a great requirements workshop with use cases as well as defines the basic terms for doing use cases but most important - It tells how to do the teenage use case disco dance!
This document outlines an agenda for a entrepreneurship training program run by Founder Centric. The day includes sessions on iterative teaching, workshops and assignments, the design process and goals, getting feedback, and managing risks. Assignments described include developing personal inventories of skills and resources, conducting customer interviews, optimizing an MVP, and launching constrained startup projects over 1-2 weeks. The document emphasizes adapting curriculum flexibly to student needs, using peer support and optional modules, and avoiding common pitfalls like getting stuck on inconsequential details.
- Greg Roderick is a practice manager at Infusion, a boutique consulting firm with offices globally. Infusion focuses on new technologies and frameworks for enterprise clients.
- Infusion hires many co-ops and new grads from universities like Waterloo. Over 60% of co-ops join full-time after graduating. They provide technical training through a bootcamp.
- The interview process at Infusion has multiple phone screens and in-person technical interviews to assess candidates' technical skills, problem-solving abilities, and fit with the company culture. Interviewers aim to decide if a candidate is a hire within 1 hour.
The document provides tips for developing winning federal proposals. It emphasizes focusing on the customer's needs, customizing the proposal to the specific opportunity, and using a consistent and concise writing style. Key recommendations include putting the customer first, demonstrating a commitment to jointly achieving objectives, tailoring the solution and language to the requester, using compelling and creative elements like graphics and examples, and ensuring technical and political correctness.
An example of typical training material provided to new employees. Even the most experienced designers can use a refresher and it helps to establish common references and understanding.
Requirements modeling involves creating models during requirements analysis to understand current systems or business processes being automated and how users will interact with a new system. It helps with elicitation, validation, and communication of requirements. Common tools for requirements modeling include use cases, state diagrams, UI mockups, storyboards, and prototypes.
Define requirements modeling as representing the customers' needs and the problem domain using models to facilitate understanding between stakeholders.
Explain four roles of requirements modeling as:
1) Guiding elicitation by helping determine questions to ask and uncover hidden requirements
2) Providing progress measures by ensuring model completeness indicates elicitation is complete
3) Hel
Content reuse is one of the key reasons for converting to a single source. But how do you know what the reuse potential really is? Because converting content formats is a major investment, you need to base your business case on real data.
You can find the data with these steps:
• Observation
• Comparison
• Analysis
This session gives you a methodology to learn how to determine the true opportunity for content reuse and quantify it for your business case.
This presentation was given at Information Development World on October 1, 2015.
How to run a great requirements workshop with Use CasesAndreas Hägglund
The slideshare tells how to run a great requirements workshop with use cases as well as defines the basic terms for doing use cases but most important - It tells how to do the teenage use case disco dance!
This document outlines an agenda for a entrepreneurship training program run by Founder Centric. The day includes sessions on iterative teaching, workshops and assignments, the design process and goals, getting feedback, and managing risks. Assignments described include developing personal inventories of skills and resources, conducting customer interviews, optimizing an MVP, and launching constrained startup projects over 1-2 weeks. The document emphasizes adapting curriculum flexibly to student needs, using peer support and optional modules, and avoiding common pitfalls like getting stuck on inconsequential details.
- Greg Roderick is a practice manager at Infusion, a boutique consulting firm with offices globally. Infusion focuses on new technologies and frameworks for enterprise clients.
- Infusion hires many co-ops and new grads from universities like Waterloo. Over 60% of co-ops join full-time after graduating. They provide technical training through a bootcamp.
- The interview process at Infusion has multiple phone screens and in-person technical interviews to assess candidates' technical skills, problem-solving abilities, and fit with the company culture. Interviewers aim to decide if a candidate is a hire within 1 hour.
The document provides tips for developing winning federal proposals. It emphasizes focusing on the customer's needs, customizing the proposal to the specific opportunity, and using a consistent and concise writing style. Key recommendations include putting the customer first, demonstrating a commitment to jointly achieving objectives, tailoring the solution and language to the requester, using compelling and creative elements like graphics and examples, and ensuring technical and political correctness.
An example of typical training material provided to new employees. Even the most experienced designers can use a refresher and it helps to establish common references and understanding.
Xp 2016 superchargeyourproductbacklogwithuserstories-suzannelazLaz Allen
This document summarizes a workshop on supercharging a product backlog with user stories. The workshop covers defining user stories, examples of user stories, splitting large stories into smaller ones, acceptance criteria, and product backlog refinement. Attendees participate in hands-on exercises to practice writing, critiquing, and splitting user stories. The document emphasizes that user stories should describe features from the perspective of users or stakeholders and focus on value and benefits.
What if Scrum had no rules? How would we define it? What if there were no Scrum? How would we create
it? Scrum is based on successful patterns for product development. During this workshop, we will
reflect and share the experiences from our own best projects, and look for patterns in those projects.
Re-uploading my User Story Splitting workshop; it seems to have gone missing.
This is a slide deck I have used for helping people learn various user story splitting techniques.
Building Better Models Faster Using Active LearningCrowdFlower
Active learning is an increasingly popular technique for rapidly iterating the construction of machine learning models, exploiting the fact that the current state of the model can be used to predict which additional examples will be the most informative. Active learning is appealing for two main reasons: it optimizes ongoing human involvement in the model building process, and it helps overcome the negative effects of imbalanced training data. In this talk, Nick explains how active learning helps overcome common obstacles to building successful models, and also offers a peek into how CrowdFlower's new active learning based offering, CrowdFlower AI.
The document discusses automated inductive frame analysis and introduces a new toolkit called INFRA. INFRA uses natural language processing techniques like word frequencies, co-occurrences, visualizations, principal component analysis, and cluster analysis to identify frames in large text corpora. The authors provide an overview of INFRA's design and capabilities, discuss challenges in automated frame analysis, and demonstrate an empirical example analyzing Dutch business news articles.
The document provides guidance on preparing a conference talk proposal and bio. It discusses how to choose a compelling talk title, write an engaging abstract that sells the value of the talk, and draft a bio that establishes relevant expertise. Examples are given of good and bad talk titles. The document also reviews how to identify and communicate one's unique expertise to write a strong proposal and bio.
Slides talk about importance & guidelines of sketching and story boarding. It discusses two approaches about "getting the design right" or getting the right design". Steps and Do's/Dont's of storyboarding
How to Prepare for and Survive a Technical InterviewPerl Careers
This document provides advice on how to prepare for and survive a technical interview. It begins with an introduction of the author and their relevant experience. It then discusses that interviewers have no real idea what they are doing and the goals are to see if candidates can demonstrate their claimed skills and experience, maintain composure, and be likable.
The document provides tips such as doing research on the company and interviewers, preparing for different types of technical challenges by practicing explanations and showing work, focusing on being interesting rather than just providing right answers, and preparing responses to common technical and non-technical questions. It emphasizes practicing answers out loud and prioritizing based on the job requirements and one's own strengths.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have many applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are powered by pattern matching rules and language models stored in AIML. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Using AI chatbots for deep learning and teaching with specific examples to en...Nigel Daly
This talk was given to Senior high School teachers in Taiwan to help them better understand (1) what AI chatbot technology like ChatGPT and Bing Chat is, and (2) how to use it to enhance their own teaching and also their students' learning. Also discussed was how to make prompts and several examples. The examples specifically focused on language learning since the school will soon become a bilingual school. AI technology use was also described in terms of Bloom's taxonomy of learning objectives and connected to ideas of deep learning as advocated by the movement "New Pedagogies for Deep Learning", which the school has become a part of.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. The document outlines different types of machine learning including supervised learning (using labeled data), unsupervised learning (using only unlabeled data), and reinforcement learning (where an agent takes actions and receives rewards or punishments). It provides examples of classification problems and discusses decision tree learning as a supervised learning method, including how decision trees are constructed and potential issues like overfitting.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
We spend so much time focusing on conventional programming. Everyone focuses on standards, code clarity, testing, and what gems to use. Let's chat about what's done before your fingers hit the keys. Let's talk about brainstorming, requirements, stakeholders, mock-ups, and writing solid user stories and acceptance tests with Cucumber. Every project has a story - how will your next one end?
This document provides an overview of chatbots, including:
- How chatbots work using pattern matching and knowledge representation in AIML.
- Examples of atomic, default, and recursive categories in AIML.
- The architecture of a typical chatbot including an AIML interpreter and responder.
- References to learn more about developing chatbots and training them using dialogue corpora.
- Suggestions for potential applications of chatbots such as customer service and an interactive encyclopedia.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. It describes supervised learning where data and labels are provided, unsupervised learning where only data is given, and reinforcement learning where an agent takes actions and receives rewards or punishments. Decision tree learning is discussed as a supervised learning method where trees are constructed by recursively splitting data based on attribute tests that optimize criteria like information gain. Overfitting and techniques like pruning are addressed to improve generalization.
To Deliver, Discover We Must - A value-driven approach to agile planningRaj Indugula
This presentation depicts one organization’s journey from a simplistic Scrum-based planning approach towards a highly disciplined value-driven planning process that follows the precept of progressive elaboration that is repeated systematically at regular intervals, and share ideas, techniques and lessons learned along the way that helped make planning more predictable, and value delivery a priority.
Being Test-Driven: It's not really about testingRaj Indugula
This document discusses test-driven development (TDD) and behavior-driven development (BDD). It describes how BDD uses conversations to discover requirements and build shared understanding. BDD captures conversations as executable specifications expressed in a common language. TDD follows the "Red-Green-Refactor" process of writing a failing test, adding code to pass the test, and refactoring code. TDD drives better design through incremental development guided by tests and prevents overdesign. While TDD can improve design, it also risks overly complex test code and dependencies if not kept simple. The document advocates starting with examples to guide development and testing in a test-driven manner.
Xp 2016 superchargeyourproductbacklogwithuserstories-suzannelazLaz Allen
This document summarizes a workshop on supercharging a product backlog with user stories. The workshop covers defining user stories, examples of user stories, splitting large stories into smaller ones, acceptance criteria, and product backlog refinement. Attendees participate in hands-on exercises to practice writing, critiquing, and splitting user stories. The document emphasizes that user stories should describe features from the perspective of users or stakeholders and focus on value and benefits.
What if Scrum had no rules? How would we define it? What if there were no Scrum? How would we create
it? Scrum is based on successful patterns for product development. During this workshop, we will
reflect and share the experiences from our own best projects, and look for patterns in those projects.
Re-uploading my User Story Splitting workshop; it seems to have gone missing.
This is a slide deck I have used for helping people learn various user story splitting techniques.
Building Better Models Faster Using Active LearningCrowdFlower
Active learning is an increasingly popular technique for rapidly iterating the construction of machine learning models, exploiting the fact that the current state of the model can be used to predict which additional examples will be the most informative. Active learning is appealing for two main reasons: it optimizes ongoing human involvement in the model building process, and it helps overcome the negative effects of imbalanced training data. In this talk, Nick explains how active learning helps overcome common obstacles to building successful models, and also offers a peek into how CrowdFlower's new active learning based offering, CrowdFlower AI.
The document discusses automated inductive frame analysis and introduces a new toolkit called INFRA. INFRA uses natural language processing techniques like word frequencies, co-occurrences, visualizations, principal component analysis, and cluster analysis to identify frames in large text corpora. The authors provide an overview of INFRA's design and capabilities, discuss challenges in automated frame analysis, and demonstrate an empirical example analyzing Dutch business news articles.
The document provides guidance on preparing a conference talk proposal and bio. It discusses how to choose a compelling talk title, write an engaging abstract that sells the value of the talk, and draft a bio that establishes relevant expertise. Examples are given of good and bad talk titles. The document also reviews how to identify and communicate one's unique expertise to write a strong proposal and bio.
Slides talk about importance & guidelines of sketching and story boarding. It discusses two approaches about "getting the design right" or getting the right design". Steps and Do's/Dont's of storyboarding
How to Prepare for and Survive a Technical InterviewPerl Careers
This document provides advice on how to prepare for and survive a technical interview. It begins with an introduction of the author and their relevant experience. It then discusses that interviewers have no real idea what they are doing and the goals are to see if candidates can demonstrate their claimed skills and experience, maintain composure, and be likable.
The document provides tips such as doing research on the company and interviewers, preparing for different types of technical challenges by practicing explanations and showing work, focusing on being interesting rather than just providing right answers, and preparing responses to common technical and non-technical questions. It emphasizes practicing answers out loud and prioritizing based on the job requirements and one's own strengths.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have many applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are powered by pattern matching rules and language models stored in AIML. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Chatbots are conversational agents that interact with users using natural language. They were originally developed to fool humans but now have numerous applications like customer service. Chatbots work using pattern matching and predefined responses rather than true understanding. Early chatbots included ELIZA, which acted as a therapist, and PARRY, which simulated a paranoid schizophrenic. Modern chatbots like ALICE are more sophisticated and use pattern matching and databases of questions and responses. Chatbots have applications in areas like education, customer service, and information retrieval. However, they are limited by their inability to truly understand language and context.
Using AI chatbots for deep learning and teaching with specific examples to en...Nigel Daly
This talk was given to Senior high School teachers in Taiwan to help them better understand (1) what AI chatbot technology like ChatGPT and Bing Chat is, and (2) how to use it to enhance their own teaching and also their students' learning. Also discussed was how to make prompts and several examples. The examples specifically focused on language learning since the school will soon become a bilingual school. AI technology use was also described in terms of Bloom's taxonomy of learning objectives and connected to ideas of deep learning as advocated by the movement "New Pedagogies for Deep Learning", which the school has become a part of.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. The document outlines different types of machine learning including supervised learning (using labeled data), unsupervised learning (using only unlabeled data), and reinforcement learning (where an agent takes actions and receives rewards or punishments). It provides examples of classification problems and discusses decision tree learning as a supervised learning method, including how decision trees are constructed and potential issues like overfitting.
This presentation discusses decision trees as a machine learning technique. This introduces the problem with several examples: cricket player selection, medical C-Section diagnosis and Mobile Phone price predictor. It discusses the ID3 algorithm and discusses how the decision tree is induced. The definition and use of the concepts such as Entropy, Information Gain are discussed.
We spend so much time focusing on conventional programming. Everyone focuses on standards, code clarity, testing, and what gems to use. Let's chat about what's done before your fingers hit the keys. Let's talk about brainstorming, requirements, stakeholders, mock-ups, and writing solid user stories and acceptance tests with Cucumber. Every project has a story - how will your next one end?
This document provides an overview of chatbots, including:
- How chatbots work using pattern matching and knowledge representation in AIML.
- Examples of atomic, default, and recursive categories in AIML.
- The architecture of a typical chatbot including an AIML interpreter and responder.
- References to learn more about developing chatbots and training them using dialogue corpora.
- Suggestions for potential applications of chatbots such as customer service and an interactive encyclopedia.
The document discusses machine learning and various machine learning techniques. It defines machine learning as using data and experience to acquire models and modify decision mechanisms to improve performance. It describes supervised learning where data and labels are provided, unsupervised learning where only data is given, and reinforcement learning where an agent takes actions and receives rewards or punishments. Decision tree learning is discussed as a supervised learning method where trees are constructed by recursively splitting data based on attribute tests that optimize criteria like information gain. Overfitting and techniques like pruning are addressed to improve generalization.
To Deliver, Discover We Must - A value-driven approach to agile planningRaj Indugula
This presentation depicts one organization’s journey from a simplistic Scrum-based planning approach towards a highly disciplined value-driven planning process that follows the precept of progressive elaboration that is repeated systematically at regular intervals, and share ideas, techniques and lessons learned along the way that helped make planning more predictable, and value delivery a priority.
Being Test-Driven: It's not really about testingRaj Indugula
This document discusses test-driven development (TDD) and behavior-driven development (BDD). It describes how BDD uses conversations to discover requirements and build shared understanding. BDD captures conversations as executable specifications expressed in a common language. TDD follows the "Red-Green-Refactor" process of writing a failing test, adding code to pass the test, and refactoring code. TDD drives better design through incremental development guided by tests and prevents overdesign. While TDD can improve design, it also risks overly complex test code and dependencies if not kept simple. The document advocates starting with examples to guide development and testing in a test-driven manner.
Be Ready, Be Done: The Art of Slicing StoriesRaj Indugula
"Can I have my cake and eat it too? Of course, as long as it is one slice at a time!"
Revisit proven strategies and patterns to create small pieces of useful, testable functionality, and explore strategies for getting stories to “ready” and “done”.
What's Measured Improves: Metrics that matterRaj Indugula
“Every line is the perfect length if you don't measure it.”
- Marty Rubin
So your organization has embarked upon a transformation to be more nimble and responsive by employing the latest tools and thinking in the Agile and DevOps arena. In this transformational context, how do you know that your initiatives are effective? Empirical measurements should provide insights on business value flow and delivery efficiency, allowing teams and organizations to see how they are progressing toward achieving their goals, but all too often we find ourselves mired in measurement traps that don't quite provide the right guidance in steering our efforts.
Rooted in contemporary thinking and tested in practice, this talk explores the principles of good measurement, what to measure, what not to measure, and enumerates some key metrics to help guide and inform our Agile and DevOps efforts. If done right, metrics can present a true picture of performance, and any progression, digression of these metrics can drive learning and improvement.
Gain a deeper understanding of what Exploratory Testing (ET) is, the essential elements of the practice with practical tips and techniques, and finally, ideas for integrating ET into the cadence of an agile process
In just a few years, the Lean Startup movement has gained influence by promoting a powerful but simple agile product management toolset—one that complements agile software development approaches such as Scrum and kanban. This presentation explores the tools and techniques product owners at startup companies and others are employing today for project visioning, experimental design, evaluating new feature impact, prototyping, split testing, and gaining early customer feedback.
Despite the belief that a shared context and collaboration drives quality, too often, software testers and quality professionals struggle to find their place within today's integrated agile teams. This session is a practitioner’s view of testing and testing practices within an iterative/incremental development environment. We will begin with a discussion of some of the challenges of testing within an agile environment and delve into the guiding principles of Agile Testing and key enabling practices. Agile Testing necessitates a change in mindset, and it is as much, if not more, about behavior, as it is about skills and tooling, all of which will be explored.
Effective Testing Practices in an Agile EnvironmentRaj Indugula
This is a practitioner’s view of testing and testing practices within an iterative development environment. We will explore the challenges of testing within such an environment and ways to better integrate the QA professional into what is inherently a developer-centric methodology. If quality is paramount, then we ought to move testing to the front of the line and test early and often. Automation lies at the heart of agility and we will look at how test automation techniques and test-first design philosophy might be applied at multiple-levels to drive quality.
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.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptxSunil Jagani
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
3. Have you seen…
• Questions come up in the middle of implementing a story?
• Questions come up while testing an implemented feature?
• Developers and Testers interpret a requirement differently?
3
4. Are we ready to
implement?
• Backlog refinement
• Three Amigos
• Planning session
“The speed of development is the speed of getting
an idea from one brain to another”
– Alistair Cockburn
4
5. What is Example Mapping?
• Structured, collaborative
conversation to discover
essential requirements
and identify uncertainty
• Rules & Examples to
illustrate desired behavior
before pulling story into
development
• Discovered and
popularized by Matt
Wynne
User Story
Rules
Abstract descriptions of how things
should work
Examples
Actual scenarios of things working
as they should
Executable Examples
Ready to automate
If reader buys
three books, the
cheapest of the
three should be
free
Cart $10, $15,
$5
Checkout
Total $26.50
5
6. Example Map - Essentials
User Story under discussion
Rules to satisfy story, or express other
constraints about the story scope
Concrete examples that
illustrate a rule
User
Story
Rule Rule Rule
Example
Example
ExampleExample
Example
Question
QuestionQuestions about scenarios where we
don’t know the right outcome
Acceptance
Criteria
Acceptance
Scenarios
6
7. …password that is
not easy to crack
Minimum of 8
characters
Alphanumeric with at least
one special character
foobar
Invalid – Too short
f00bargobble!
Shouldn’t we require a mix of
uppercase and lowercase?
The case where…
No number
No letter
No special character
7
Let’s build an Example Map!
8. …password that is
not easy to crack
f00bargobble!
Invalid – not mixed Shouldn’t we require a mix of
uppercase and lowercase?
Alphanumeric, mixed case,
with at least one special
character
F00bargobble!
Is there a
maximum length
constraint? 1AaaaaaaA!
What about this scenario? Is
this valid?
8
9. …password that is
not easy to crack
Shouldn’t we require a mix of
uppercase and lowercase?
1AaaaaaaA!
Invalid - repeating
Repeating pattern
(Less than 3
consecutive)
1AaByzdbAxt!
Are dictionary words
allowed?
What about reusing old
passwords?
9
10. Step Back & Reflect
…password that is
not easy to crack
Minimum of 8
characters
foobar
Invalid – Too short
f00bargobble!
Invalid – not mixed
Alphanumeric, mixed case,
with at least one special
character
F00bargobble!
Is there a
maximum length
constraint?
1AaaaaaaA!
Invalid - repeating
Repeating pattern
(Less than 3
consecutive)
1AaByzdbAxt!
What about
dictionary
words?
password history
policy
The case where…
No number
No letter
No special character
10
11. Build Your Example Map
• Pick a story
• Invite a small group with multiple
perspectives
• Time-box the session
• Pick a facilitator
• Bring supplies (4-colored index cards,
sharpies, flipcharts, timer)
Rule Rule Rule
Story
Example
Example
Example
Question
11
12. ATM User Story Backlog
1.
…deposit funds into my
account
Are there limits on amounts?
Number of deposits?
2.
…withdraw funds from my
account
Are there withdrawal limits?
What denominations?
3.
…transfer funds between my
primary and savings account
Any limits? Minimum balance?
Is transfer immediate?
4.
…setup my savings account to
automatically cover overdrafts
from my checking account
What if I don’t opt in?
What if savings is overdrawn?
5.
…make third-party payments
One-time or on-schedule?
12
Rule Rule Rule
Story
Example
Example
Example
Question
Question
New
Story
13. Where
does it
fit?
Scenario: Invalid example
Given I am a new user
When I select "Abc1*$!-**" as a password
Then I can access my account
Source Adaptation: George Dinwiddie & Raj Indugula
Scenario: Valid example
Given I am a new user
When I select "Abc1*$!-**" as a password
Then I can access my account
13
14. Example Mapping helps…
Focus on smallest pieces of behavior
Discover what you don’t know
Fill in the gaps (remove ambiguity)
Drive out implicit requirements
Break stories into manageable chunks
Move conversations quicker
Provide basis for automated tests
14
15. If you want to learn more…
Matt Wynne - Introducing Example Mapping
https://cucumber.io/blog/2015/12/08/example-
mapping-introduction
Aslak Hellesøy - Example Mapping
https://www.youtube.com/watch?v=VwvrGfWm
G_U
15
Raj Indugula
raj.indugula@lithespeed.com
@lithespeed, @raj_indugula