The document discusses the strategy pattern, which allows selecting algorithms or behaviors at runtime. It defines a family of algorithms, encapsulates each one, and makes them interchangeable. The strategy pattern is useful when wanting to choose algorithms dynamically, like for sorting or file compression. It keeps classes focused on a single purpose by extracting conditional logic into strategy objects. The document provides examples of how the strategy pattern can be implemented and used, such as for a robot identifying different objects or relationships.
Software Design Patterns. Part I :: Structural PatternsSergey Aganezov
In a nutshell, software design patterns are generally reusable solutions to a commonly occurring problems. And this says it all! We are going to learn when it is completely unnecessary for you to reinvent the wheel, and what are the best ways to approach each particular problem during software development process.
An introduction to structural design patterns in object orientation. Suitable for intermediate to advanced computing students and those studying software engineering.
Software Design Patterns. Part I :: Structural PatternsSergey Aganezov
In a nutshell, software design patterns are generally reusable solutions to a commonly occurring problems. And this says it all! We are going to learn when it is completely unnecessary for you to reinvent the wheel, and what are the best ways to approach each particular problem during software development process.
An introduction to structural design patterns in object orientation. Suitable for intermediate to advanced computing students and those studying software engineering.
Design patterns are optimized, reusable solutions to the programming problems that we encounter every day. A design pattern is not a class or a library that we can simply plug into our system; it's much more than that. It is a template that has to be implemented in the correct situation. It's not language-specific either. A good design pattern should be implementable in most—if not all—languages, depending on the capabilities of the language. Most importantly, any design pattern can be a double-edged sword— if implemented in the wrong place, it can be disastrous and create many problems for you. However, implemented in the right place, at the right time, it can be your savior.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
In software engineering, a design pattern is a general reusable solution to a commonly occurring problem within a given context in software design. A design pattern is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. Patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system. Object-oriented design patterns typically show relationships and interactions between classes or objects, without specifying the final application classes or objects that are involved. Patterns that imply object-orientation or more generally mutable state, are not as applicable in functional programming languages.
Design patterns reside in the domain of modules and interconnections. At a higher level there are architectural patterns that are larger in scope, usually describing an overall pattern followed by an entire system.[1]
There are many types of design patterns, for instance
Algorithm strategy patterns addressing concerns related to high-level strategies describing how to exploit application characteristics on a computing platform.
Computational design patterns addressing concerns related to key computation identification.
Execution patterns that address concerns related to supporting application execution, including strategies in executing streams of tasks and building blocks to support task synchronization.
Implementation strategy patterns addressing concerns related to implementing source code to support
program organization, and
the common data structures specific to parallel programming.
Structural design patterns addressing concerns related to high-level structures of applications being developed.
Design patterns are optimized, reusable solutions to the programming problems that we encounter every day. A design pattern is not a class or a library that we can simply plug into our system; it's much more than that. It is a template that has to be implemented in the correct situation. It's not language-specific either. A good design pattern should be implementable in most—if not all—languages, depending on the capabilities of the language. Most importantly, any design pattern can be a double-edged sword— if implemented in the wrong place, it can be disastrous and create many problems for you. However, implemented in the right place, at the right time, it can be your savior.
해당 자료는 풀잎스쿨 18기 중 "설명가능한 인공지능 기획!" 진행 중 Counterfactual Explanation 세션에 대해서 정리한 자료입니다.
논문, Youtube 및 하기 자료를 바탕으로 정리되었습니다.
https://christophm.github.io/interpretable-ml-book/
In software engineering, a design pattern is a general reusable solution to a commonly occurring problem within a given context in software design. A design pattern is not a finished design that can be transformed directly into source or machine code. It is a description or template for how to solve a problem that can be used in many different situations. Patterns are formalized best practices that the programmer can use to solve common problems when designing an application or system. Object-oriented design patterns typically show relationships and interactions between classes or objects, without specifying the final application classes or objects that are involved. Patterns that imply object-orientation or more generally mutable state, are not as applicable in functional programming languages.
Design patterns reside in the domain of modules and interconnections. At a higher level there are architectural patterns that are larger in scope, usually describing an overall pattern followed by an entire system.[1]
There are many types of design patterns, for instance
Algorithm strategy patterns addressing concerns related to high-level strategies describing how to exploit application characteristics on a computing platform.
Computational design patterns addressing concerns related to key computation identification.
Execution patterns that address concerns related to supporting application execution, including strategies in executing streams of tasks and building blocks to support task synchronization.
Implementation strategy patterns addressing concerns related to implementing source code to support
program organization, and
the common data structures specific to parallel programming.
Structural design patterns addressing concerns related to high-level structures of applications being developed.
Builder Design Pattern (Generic Construction -Different Representation)Sameer Rathoud
Generic Construction -Different Representation
This presentation provide information to understand builder design pattern, it’s structure, it’s implementation.
What is tackled in the Java EE Security API (Java EE 8)Rudy De Busscher
The Java EE Security API (JSR-375) wants to simplify the implementation of security-related features in your Java EE application. Application server specific configuration changes will be no longer needed and things will be much more app developer friendly. Aligning security with the ease of development we saw in the recent version of Java EE. We will show you the basic goals and concepts behind Java EE Security API. And of course, demos with the current version of the RI, named Soteria, how you can do Authentication and Authorization.
Using Classification and Clustering with Azure Machine Learning Models shows how to use classification and clustering algorithms with Azure Machine Learning.
Novel Ensemble Tree for Fast Prediction on Data StreamsIJERA Editor
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting
the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing
speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge
amount of data stream, means it will manage the data in a large size and also it will be able to manage concept
drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered
much since existing ensemble model predicts in linear time, which is enough for small applications and
accessible models workings on integrating some of the classifier. Although real time application has huge
amount of data stream so we required base classifier to recognize dissimilar model and make a high grade
ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing
structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble
Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time
complexity
Chapter 02 of the lecture Style & Design Principles taught at SAE Institute Hamburg.
Introduction to advanced concepts of object-oriented design, such as delegation, polymorphism, cohesion and coupling, and to behavioral, creational and structural design patterns.
Workshop given to the staff for PhD and Masters Topic Selection in the area of Big Data, Data Science and Machine Learning. It has many interactive online demos to understanding on NLP social media analysis like sentiment analysis , topic modeling , language detection and intent detection. Some of the basic concept about classification and regression and clustering with interactive worksheets. Finally , hands-on machine learning models and comparisons in WEKA tool kit with case study of cars and diabetic patient data.
Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
Initializing and Optimizing Machine Learning Models describes the use of hyperparameters, how to use multiple algorithms and models, and how to score and evaluate models.
Clustering and Classification Algorithms Ankita DubeyAnkita Dubey
Clustering is a process of partitioning a set of data (or objects) into a set of meaningful sub-classes, called clusters. Help users understand the natural grouping or structure in a data set. Used either as a stand-alone tool to get insight into data distribution or as a preprocessing step for other algorithms.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
An basic ideas about needs and concepts of business intelligence.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Maksud Saifullah Pulak
Event Url: https://www.facebook.com/events/512834685530439/
Concept of Big Data in the context of real world data scenario.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Md. Delwar Hiossain
Event Url: https://www.facebook.com/events/512834685530439/
Concept of Clod Computing in the context of real world application development.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Shahriar Hossain
Event Url: https://www.facebook.com/events/512834685530439/
Concept and need of version control and the uses in production implementation.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Ronald Roni Saha
Event Url: https://www.facebook.com/events/512834685530439/
Concept and need of Node.Js and alike frameworks in the context of present development trends.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Md. Sohel Rana
Event Url: https://www.facebook.com/events/512834685530439/
Application development tools needed for development in the context of present development trends.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Shahriar Iqbal Chowdhury
Event Url: https://www.facebook.com/events/512834685530439/
Concept and need of Single Page application in the context of present development trends.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Sk. Tajbir
Event Url: https://www.facebook.com/events/512834685530439/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. What is Strategy
A plan of action or policy designed to achieve a major or overall aim.
3. Why Need Pattern for Strategy
• The Strategy pattern is known as a behavioural pattern - it's used to
manage algorithms, relationships and responsibilities between
objects.
• The Strategy pattern is to be used where you want to choose the
algorithm to use at runtime. A good use of the Strategy pattern would
be saving files in different formats, running various sorting
algorithms, or file compression
4. Strategy Pattern Says
• Define a family of algorithms, encapsulate each one, and make them
interchangeable. Strategy lets the algorithm vary independently from
clients that use it
OR
5. Why, When and How?
1. Allow a class to maintain a single purpose.
2. Switch statement
3. Adding a new implementation will cause a class file to be modified
4. Create a class for each strategy
5. Use a common interface for each strategy
13. Strategy @.Net Framework
• Array and ArrayList provide the capability to sort. Sort method will
use the IComparable implementation for each element to handle the
comparisons. Leaving the choice of comparison algorithm up to the
user of the class like this is an example of the Strategy pattern.
• The use of a Predicate<T> delegate in the FindAll<T> method lets the
caller use any method as a filter for the List<T> so long as it takes the
appropriate object type and returns a Boolean.