This document describes ACT-Stitch, a framework that integrates CPM-GOMS templates for modeling human behavior into the cognitive architecture ACT-R. ACT-Stitch uses a process called macro-compilation to translate CPM-GOMS templates into ACT-R commands and productions. It implements two templates - Slow-Move-Click and Fast-Move-Click - and generates predictions of cognitive, perceptual and motor behavior. The framework allows accurate modeling of lower-level cognitive and perceptual-motor processes involved in human-computer interaction tasks.
This presentation briefs about machine learning technologies, its various learning methodologies, its types. Also it briefs about the Open Computer Vision, Graphics Processing Unit and CUDA Frameworks.
Kostas Traganos from Horse Project: Manufacturing Process Management System (...camunda services GmbH
HORSE project is developing a methodological and technical framework for easy adaptation of robotic solutions towards SMEs to embrace the Industry 4.0 revolution to remain globally competitive.
The skeleton of the framework consists of the Manufacturing Process Management System (MPMS) used to describe and control manufacturing processes, the middleware providing standardized means of communication between components, and the Hybrid Task Supervisor coordinating human operators and robots on the workcell level.
MPMS is built on traditional BPM concepts (process modelling, process execution, agent allocation, task delivery, etc.) and tools. Camunda BPM is used, as a robust, open-source solution to realize MPMS.
Real-Time AI: Designing for Low Latency and High Throughput - Dr. Sergei Izra...Sri Ambati
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/CeOJFynB6BE
Real-Time AI: Designing for Low Latency and High Throughput
Bio: Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development, and scaling of data science-based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery.
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements.
Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “simulation”.
This presentation introduced how to embed Python or MATLAB® codes inside functions to do “simulation” within Capella.
An Integrated Framework for Parameter-based Optimization of Scientific Workflowsvijayskumar
Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.
Simulation Tracking Object Reference Model (STORM)Umar Alharaky
This presentation describes a proposed (may become a reference) model for enabling the embedded interactive simulation in the Sharable Content Object (SCO) to be tracked by the Learning Management System (LMS) at run time.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
This presentation briefs about machine learning technologies, its various learning methodologies, its types. Also it briefs about the Open Computer Vision, Graphics Processing Unit and CUDA Frameworks.
Kostas Traganos from Horse Project: Manufacturing Process Management System (...camunda services GmbH
HORSE project is developing a methodological and technical framework for easy adaptation of robotic solutions towards SMEs to embrace the Industry 4.0 revolution to remain globally competitive.
The skeleton of the framework consists of the Manufacturing Process Management System (MPMS) used to describe and control manufacturing processes, the middleware providing standardized means of communication between components, and the Hybrid Task Supervisor coordinating human operators and robots on the workcell level.
MPMS is built on traditional BPM concepts (process modelling, process execution, agent allocation, task delivery, etc.) and tools. Camunda BPM is used, as a robust, open-source solution to realize MPMS.
Real-Time AI: Designing for Low Latency and High Throughput - Dr. Sergei Izra...Sri Ambati
This talk was recorded in London on October 30th, 2018 and can be viewed here: https://youtu.be/CeOJFynB6BE
Real-Time AI: Designing for Low Latency and High Throughput
Bio: Dr. Sergei Izrailev is Chief Data Scientist at Beeswax, where he is responsible for data strategy and building AI applications powering the next generation of real-time bidding technology. Before Beeswax, Sergei led data science teams at Integral Ad Science and Collective, where he focused on architecture, development, and scaling of data science-based advertising technology products. Prior to advertising, Sergei was a quant/trader and developed trading strategies and portfolio optimization methodologies. Previously, he worked as a senior scientist at Johnson & Johnson, where he developed intelligent tools for structure-based drug discovery.
A common need in system architecture design is to verify that if the architect is correct and can satisfy its requirements.
Execution of system architect model means to interact with state machines to test system’s control logic. It can verify if the logical sequences of functions and interfaces in different scenarios are desired.
However, only sequence itself is not enough to verify its consequence or output. So we need each function to do what it is supposed to do during model execution to verify its output, and that is what we called “simulation”.
This presentation introduced how to embed Python or MATLAB® codes inside functions to do “simulation” within Capella.
An Integrated Framework for Parameter-based Optimization of Scientific Workflowsvijayskumar
Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.
Simulation Tracking Object Reference Model (STORM)Umar Alharaky
This presentation describes a proposed (may become a reference) model for enabling the embedded interactive simulation in the Sharable Content Object (SCO) to be tracked by the Learning Management System (LMS) at run time.
Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Devel...Benjamin Bengfort
This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. ACT-R Modeling
• Cognitive architecture
• Performance modeling
• Psychological theory for reuse at
different levels
• Cannot build all multi-tasking work
3. ACT-Stitch
• CPM-GOMS templates and interleaving
theory into ACT-R
• Perceptual-motor interaction
• Empirically validated
• Reusable template
5. Template (adapted from Gray
and Boehm-Davis, 200)
Moving the cursor the a target and clicking the mouse
6. Macro-Compilation
• ACT-Stitch uses a process of macro-
compilation
• translate CPM-GOMS templates
• Cognitive operator
• ACT-R perceptual-motor commands
• CPM-GOMS perceptual-motor operators
7. ACT-Stitch Details
• Two templates implemented
• Slow-Move-Click
• uncertainty about where the target
appears in each trial
• Fast-Move-Click
• selection of a target at a known
location
12. Discussion
• ACT-Stitch framework
• useful modeling the cognitive, perceptual,
and motor processes involved in HCI
task.
• Simple description of an environment
& task sequence
• Macro-compilation to translate task-level
description of behavior => SIMPLE
• Viewing resource use of the model with
PERT chart tools