This document provides an overview of the MaST (Modeling and Simulation Team) and their approach to addressing challenges of sharing knowledge, information, data, and sets (KIDS) across multi-decadal projects like Constellation. It introduces the team members Mike Conroy and Rebecca Mazzone and discusses the long timelines involved in these projects that outlive people, teams, tools and programs. It outlines MaST's view that knowledge is created through experiences, information is distilled from data, and data comes from various modeling and simulation tools. MaST's goal is to make sharing KIDS possible, easy and desirable by addressing lifecycle needs and replacing obstacles with efficient solutions that involve people and teams.
ExaLearn Overview - ECP Co-Design Center for Machine Learninginside-BigData.com
In this deck from the HPC User Forum, Frank Alexander, from Brookhaven National Laboratory presents: ExaLearn Overview - ECP Co-Design Center for Machine Learning.
"ExaLearn is a co-design center for Exascale Machine Learning (ML) Technologies and is a collaboration initially consisting of experts from eight multipurpose DOE labs. Rapid growth in the amount of data and computational power is driving a revolution in machine learning (ML) and artificial intelligence (AI). Beyond the highly visible successes in machine-based natural language translation, these new ML technologies have profound implications for computational and experimental science and engineering and the exascale computing systems that DOE is deploying to support those disciplines.
To address these challenges, the ExaLearn co-design center will provide exascale ML software for use by ECP Applications projects, other ECP Co-Design Centers and DOE experimental facilities and leadership class computing facilities. The ExaLearn Co-Design Center will also collaborate with ECP PathForward vendors on the development of exascale ML software."
Watch the video: https://wp.me/p3RLHQ-kdJ
Learn more: https://www.exascaleproject.org/ecp-announces-new-co-design-center-to-focus-on-exascale-machine-learning-technologies/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/letter
Improving the Model’s Predictive Power with Ensemble ApproachesSAS Asia Pacific
Bagus Sartono, Lecture at Department of Statistics, Institut Pertanian Bogor (IPB) University,
New Trends in Research Methodoloy & Analytics Technology Update, Nov 28, 2012, Jakarta Indonesia
Building a Design System: A Practitioner's Case Studyuxpin
- How to build a design system from scratch
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Gathering requirements or "User Stories" is always a challenging activity in Agile or in any other approaches.In this session, I propose using mind mapping that focuses to explore "User Wish" - a vague shape of user requirements before it is written into a form of User Stories.
We look at how Kanban can be used to enhance Scrum at the enterprise level.
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As synchonization between teams becomes crucial, we look at how Kanban can enhance the Scrum-of-Scrums to acheive control and sustainability.
Scrum kan vara svårt att använda i stor skala. Vi tittar på hur Kanban kan användas för att förstärka Scrum på företagsnivå genom att förbättra Scrum-of-Scrums, hjälpa produktägaren och stödja god beslutsfattandet på program eller avdelningsnivå.
Talare är Christophe Achouiantz från Avega Group
In recent months, Deep Learning has become the hottest topic in the IT industry. However, its arcane jargon and its intimidating equations often discourage software developers, who wrongly think that they’re “not smart enough”. Through code-level demos based on Apache MXNet, we’ll demonstrate how to build, train and use models based on different types of networks: multi-layer perceptrons, convolutional neural networks and long short-term memory networks. Finally, we’ll share some optimization tips which will help improve the training speed and the performance of your models.
Emotion recognition using image processing in deep learningvishnuv43
User’s emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done.
We propose a compact CNN model for facial expression recognition.
The work has been implemented using Python Open Source Computer Vision Library (OpenCV) and NumPy,pandas,keras packages. The scanned image (testing dataset) is being compared to training dataset and thus emotion is predicted.
КАТЕРИНА АБЗЯТОВА «Ефективне планування тестування ключові аспекти та практ...QADay
Lviv Direction QADay 2024 (Professional Development)
КАТЕРИНА АБЗЯТОВА
«Ефективне планування тестування ключові аспекти та практичні поради»
https://linktr.ee/qadayua
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlPeter Udo Diehl
I'm excited to share my latest predictions on how AI, robotics, and other technological advancements will reshape industries in the coming years. The slides explore the exponential growth of computational power, the future of AI and robotics, and their profound impact on various sectors.
Why this matters:
The success of new products and investments hinges on precise timing and foresight into emerging categories. This deck equips founders, VCs, and industry leaders with insights to align future products with upcoming tech developments. These insights enhance the ability to forecast industry trends, improve market timing, and predict competitor actions.
Highlights:
▪ Exponential Growth in Compute: How $1000 will soon buy the computational power of a human brain
▪ Scaling of AI Models: The journey towards beyond human-scale models and intelligent edge computing
▪ Transformative Technologies: From advanced robotics and brain interfaces to automated healthcare and beyond
▪ Future of Work: How automation will redefine jobs and economic structures by 2040
With so many predictions presented here, some will inevitably be wrong or mistimed, especially with potential external disruptions. For instance, a conflict in Taiwan could severely impact global semiconductor production, affecting compute costs and related advancements. Nonetheless, these slides are intended to guide intuition on future technological trends.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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Mallox decided to go shopping for new malware toys, adding the Remcos RAT, BatCloak, and a sprinkle of Metasploit to their collection. They're now playing a game of "Catch me if you can" with antivirus software, using their FUD obfuscator packers to turn their ransomware into the digital equivalent of a ninja.
-------
This document provides a analysis of the Target Company ransomware group, also known as Smallpox, which has been rapidly evolving since its first identification in June 2021.
The analysis delves into various aspects of the group's operations, including its distinctive practice of appending targeted organizations' names to encrypted files, the evolution of its encryption algorithms, and its tactics for establishing persistence and evading defenses.
The insights gained from this analysis are crucial for informing defense strategies and enhancing preparedness against such evolving cyber threats.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
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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.
НАДІЯ ФЕДЮШКО БАЦ «Професійне зростання QA спеціаліста»
Michael.conroy
1. MaST – CxP Modeling and Simulation Team
Simulation for Multi-Decadal Projects
(Constellation)
Bill Othon
Mike Conroy/Rebecca Mazzone (February 2010)
And Many others before (Monell/George/Adams/Boyce)
Used with Permission
2. Who Are We?
• Mike Conroy
MaST – CxP Modeling and Simulation Team
– Manager, Constellation, SE&I, SAVIO, Software SIG,
Modeling and Simulation Team (MaST)
– Used to:
• Lead CxP Data Presentation and Visualization (until Feb, ‘09)
• Lead Kennedy Operations Simulation
• Office of Chief Engineer Engineering Processes Team (ISE)
– Several other 3 letter words as well
• Rebecca (Bec) Mazzone
– Manager, Constellation, SE&I, SAVIO, Software SIG,
MaST, Data Presentation and Visualization (DPV)
– Used to:
• Lead Distributed Observer Network Project within DPV
3. Our Time Lines…
• Apollo First Lunar Launch
MaST – CxP Modeling and Simulation Team
– Mike was there
– No Bec Yet
• Shuttle STS-1
– Mike was in college, trying to be a NASA Co Op
– Still No Bec, Getting Close
• Constellation
– Mike will be gone before first Moon Launch
– Bec will retire before Constellation does.
– This is by far the most complex System of Systems
• Technology, Organization, Interfaces, Tools, Partners
4. MaST – CxP Modeling and Simulation Team
One Problem
Products Outlive People, Teams,
Tools, Organizations and
Programs
5. Problem Detail…
• CxP is made up of multiple Projects.
MaST – CxP Modeling and Simulation Team
• These Projects are in various Lifecycle Phases.
– Some have hardware being built today, some will not
produce systems for years
• These Projects need to be able to work together
for at least the next 50 years.
– Multiple generations of humans, teams, programs,
partners and tools.
– They need to share to be successful
6. Important Elements to Share/Save
• Share and Save the KIDS
MaST – CxP Modeling and Simulation Team
– Knowledge – Decisions, Experiences, Expertise
– Information – Reports, Recommendations, Rationale
– Data – Numbers, Pictures, Models, Equations
– Sets – From various CxP and partner teams.
• The Knowledge part is really hard
– It is in peoples heads; they are rather attached to them
7. Where do we start
• Understand the KIDS
MaST – CxP Modeling and Simulation Team
– What do they look like
– Where to they live
– How do they play with each other
– How do we make it easier for new KIDS to play too
– How do we protect them from each other (IP Issues)
– How do we best preserve them for the future
• Today we would like to share the MaST approach
8. MaST – CxP Modeling and Simulation Team
MaST Approach
Views on KIDS
Steps Taken
Examples
9. MaST View of Knowledge
• It is created through experiences .
MaST – CxP Modeling and Simulation Team
– What did they look at? How did they use it?
– Who was involved? What did they learn?
– What did they know when they started?
– What tools did they use? When? Which Versions?
What Inputs?
• It lives in the people involved in the experience.
– The test team, the analysis team, the decision makers
• It is by far the hardest component to manage.
– It is very often based on “Being There”
– Everyone cannot “Be There”, some are not born yet
10. MaST View on Information
• It is distilled from the data provided by the tools.
MaST – CxP Modeling and Simulation Team
– Analysis Results
– Recommendations
– Supporting Rationale
– Risk Assessments
• It lives in the documentation provided by the
process and the associated CM systems
– Test Results, Test Reports, Presentations
– These tools have demonstrated their ability to publish
their information for use by others
11. MaST View on Data
• Data comes from tools being used
MaST – CxP Modeling and Simulation Team
– Pro-E for the flight vehicles
– Arena and Extend for the integrated supply chains
– Delmia for the integrated process analysis
– IMSim (Trick) for integrated flight simulations
– ScramNET for Launch Vehicle dynamics
• Data lives in the files and CM systems
– DDMS(s), Common Model Library(s), WIKI(s)
– These tools can publish data for use in other systems.
– Note: This is usually where Intellectual Property (IP)
issues show up.
12. MaST – CxP Modeling and Simulation Team
The Path Taken
13. MaST Goal
• Make it Possible, then Easy, then Desirable to
use our capabilities.
MaST – CxP Modeling and Simulation Team
– Seek out the obstacles and replace them with well
planned solutions that address lifecycle needs for as
many people as possible.
– Make solutions so efficient and effective that they help
from day one.
– Make people, teams and projects part of the solution.
• Recognize that our Leadership has Priorities
– Bask in the glow of successes for something like 45
seconds before starting work on our next success
14. Investigate Prior Work for Tools
• Copy or Adopt is easier than both buy and build.
– National Research Council
MaST – CxP Modeling and Simulation Team
– Center Simulation Tools
– Conferences, Workshops, Research, DoD
– OCE Engineering Process Initiatives (3 letter words)
• Some of this work is in Constellation Today
– HLA for Simulation to Simulation communication
• Foundation for IMSim
– Delmia for Process Development
• In use by Ground Ops and Ares, rumored for Orion
– Shared Visualization Environments
• Foundations for Data Presentation and Visualization
15. Find Where the KIDS Live
• Look at a sample of Constellation Tools
MaST – CxP Modeling and Simulation Team
– Where are KIDS stored, how do they flow in CxP?
– Identify how to get them out, or at least get access?
– Normalize so others can see if their KIDS can play
• We noticed some tool/location groupings
– Some KIDS live in Physics Based Tools
• System State Information, Structural Information
– Some KIDS live in Physical Environment Tools
• Temporal / Spatial Information
– Some KIDS also live in Supply Chain Tools
• What you need when you need it (Next PM Challenge)
16. MaST – CxP Modeling and Simulation Team
Physics Based KIDS
17. Physics - System State Information
• Primarily related to the Flight activities
MaST – CxP Modeling and Simulation Team
– Launch Preparations, Flight and Post Flight
– Start with Guidance, Navigation and Control
– Extend to Flight Dynamics as needed
– Extend wherever else is needed.
• Physics Based Description of Motion
– Physics Based Launch, Ascent, Dock, Entry, Descent,
Landing, Recovery, Retrieval
– Couple with High Resolution Graphics For Human in
the Loop Test and Evaluation
18. For the Physics Example…
• Take Ares, Orion, Ground Ops, LAS and ISS
MaST – CxP Modeling and Simulation Team
– Teach the KIDS to talk to one another
• MAVERIC and ANTARES on Flight Side
• Ground Operations Simulation
• LAS Simulation and ISS Simulation
– Provide infrastructure to let People and Simulations
talk to one another
• High Level Architecture, Trick, DS Net, DON
– Provide the ability to share the new KIDS created with
existing and future generations
• Let us show you how it works…
19. MaST – CxP Modeling and Simulation Team
LAS
Ares 1 Launch (IMSim)
GO
Ares
Orion
20. US / Orion Stage Separation (IMSim)
MaST – CxP Modeling and Simulation Team
Ares Orion
21. MaST – CxP Modeling and Simulation Team
Orion
Orion to ISS Docking (IMSim)
ISS
22. Distributed Review and Storage (DPV)
IMSim
MaST – CxP Modeling and Simulation Team
States
DPV
Users
23. MaST – CxP Modeling and Simulation Team
Physical Environment KIDS
24. Physical – Temporal Spatial Info.
• Primarily related to the Ground Ops activities
MaST – CxP Modeling and Simulation Team
– Operability Assessments and Optimization
– Systems Integration
– Launch Pads, Boosters, Vehicle, Access Platforms
– Work Stands, Crane Envelopes
– Human Factors, Reach, Loads
• Note: there is more than one type of Ground…
– Lunar Architectures
– Surface Systems (Lunar, non-Lunar)
– Surface Concepts
25. For The Physical Example
• Ground Ops Pulls KIDS together
MaST – CxP Modeling and Simulation Team
– CAD, Concepts, Models and Processes from Orion,
Ares, Ground Systems (includes Legacy Data)
• Ground Ops integrates in Delmia (with IT)
– All data pulled into one environment
– Some data sets so big cannot be loaded anywhere
else, teams come to Kennedy to see their data
• MaST supporting export of KIDS from Delmia
collaborative engineering efforts to share, store
and preserve.
26. MaST – CxP Modeling and Simulation Team
OJIVE Panel Study (Delmia)
27. MaST – CxP Modeling and Simulation Team
DELMIA OJIVE Export to DPV
28. MaST – CxP Modeling and Simulation Team
OJIVE HF Detail (Delmia)
29. MaST – CxP Modeling and Simulation Team
OJIVE Delmia HF Detail (DPV)
30. Ares 1X, Sep Analysis Integration (DPV)
MaST – CxP Modeling and Simulation Team
31. MaST – CxP Modeling and Simulation Team
GO Orion Processing Sim (Delmia)
32. MaST – CxP Modeling and Simulation Team
Lunar Architecture Concepts (DPV)
33. MaST – CxP Modeling and Simulation Team
Find How the KIDS Play
Preserve the KIDS for the Future
34. How KIDS Play
I am
SAM
1. Someone provides initial authoritative simulation or source data
MaST – CxP Modeling and Simulation Team
2. MaST Shares SME 3. MaST Publishes Models
across Projects and/or Data Sets
with IMSim, Monte-Carlo,
4. Analysis Teams Use Data,
DPV and/or RF, SME, Com,
DES Apply Expertise, Iterate, DES, IMSim,
DES. Internal Sims,
Create Models and Data Trajectory,
First Process, Abort,
DPV Analysis Second & Off Nom, DPV,
Analysis other Analysis
Third
IM Analysis Fourth
Sim Analysis
Sim Data
NeXIOM
Interop.
5. Simulation Data
Result(s) to CM and DM with IS* (still need to tell IS)
IS for
CM/DM Validate Against Flight
Page 34
* Strong possibility related to MSDB and CML
35. Preserve KIDS for the Future
• Standard IS and ICE Systems
MaST – CxP Modeling and Simulation Team
– Getting more and more services every day
– Well tuned for Data and Information
– Knowledge is a little different
• On the Knowledge Side, you need to be able to
re-experience the learning process
– Since much of what we are doing is simulation, this
means re-experience the simulation that helped
develop the Knowledge.
– However, simulations have a 3 to 5 year lifespan
– But, if we can save the Simulation……
36. Save the Simulation
• This is a Key Mission for the MaST DPV (Data
Presentation and Visualization) Element.
MaST – CxP Modeling and Simulation Team
– Simulators provide a description of the 4-D data that
represents the simulation used to make decisions.
• Can also provide key measurements and images for display
• Will soon be able to provide relevant Meta-Data
• The Simulation can be replayed for team,
analysis or after long term storage
– Without need for the simulation infrastructure
• Goal is to be able to do this forever
– We have already started
37. MaST – CxP Modeling and Simulation Team
IMSim/Delmia Simulation and DPV
38. Conclusions
• Maybe the S is really Simulation
MaST – CxP Modeling and Simulation Team
– Knowledge
– Information
– Data
– Simulations
• MaST is a small part of how Constellation is
dealing with multi-decadal issues.
– There are many others.
• What we presented is alive and at work today
– There is more we did not have time to present.
– We will get that next time.
39. MaST – CxP Modeling and Simulation Team
Questions?
40. MaST – CxP Modeling and Simulation Team
Back Up Deck
41. M&S and the System Lifecycle
MaST – CxP Modeling and Simulation Team
Subjective Assessments Management DPV IMSim
s
• QFD Systems Engineers
ra
de DES SAM
• AHP T
• System Engineering Tools
Systems Engineers
System Analysts
Constructive Assessments Operators DPV IMSim
• Cost – Complete Life-Cycle ign DES SAM
• Risk – Flight, Development, RMS Des
• Conceptual / Prelim Engineering Systems Engineers In Heavy
Performance Capabilities System Analysts Dev. Use
Operators
Designers
Operator in the Loop Assessments Manufacturers
• Ground st DPV IMSim
• Flight Sims Te DES SAM
• Crew
• Data Rich Simulation & Visual
Systems Engineers
Operators
Designers
Hardware- and Software-in-the-Loop Manufacturers DPV IMSim
Assessments
• Test Program Def & Refinement
Test and Verification
DES SAM
Analysis, Modeling and • Hardware & Software Testing
Simulation support • System Integration Modeling
ns
evolves throughout the tio
system’s development ra
life cycle, supporting a Op
e DPV IMSim
DES SAM
In Service Operations Assessments
wide range of • Operations Ramp-up Ramp-down
customers • Upgrades and Improvements
• Anomaly Resolution
41