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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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!
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.
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
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/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
SPICE MODEL of RN1118FV in SPICE PARK
1. Device Modeling Report
COMPONENTS: BRT
PART NUMBER: RN1118FV
MANUFACTURER: TOSHIBA
Bee Technologies Inc.
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
2. PSpice
model Model description
parameter
IS Saturation Current
BF Ideal Maximum Forward Beta
NF Forward Current Emission Coefficient
VAF Forward Early Voltage
IKF Forward Beta Roll-off Knee Current
ISE Non-ideal Base-Emitter Diode Saturation Current
NE Non-ideal Base-Emitter Diode Emission Coefficient
BR Ideal Maximum Reverse Beta
NR Reverse Emission Coefficient
VAR Reverse Early Voltage
IKR Reverse Beta Roll-off Knee Current
ISC Non-ideal Base-Collector Diode Saturation Current
NC Non-ideal Base-Collector Diode Emission Coefficient
NK Forward Beta Roll-off Slope Exponent
RE Emitter Resistance
RB Base Resistance
RC Series Collector Resistance
CJE Zero-bias Emitter-Base Junction Capacitance
VJE Emitter-Base Junction Potential
MJE Emitter-Base Junction Grading Coefficient
CJC Zero-bias Collector-Base Junction Capacitance
VJC Collector-base Junction Potential
MJC Collector-base Junction Grading Coefficient
FC Coefficient for Onset of Forward-bias Depletion
Capacitance
TF Forward Transit Time
XTF Coefficient for TF Dependency on Vce
VTF Voltage for TF Dependency on Vce
ITF Current for TF Dependency on Ic
PTF Excess Phase at f=1/2pi*TF
TR Reverse Transit Time
EG Activation Energy
XTB Forward Beta Temperature Coefficient
XTI Temperature Coefficient for IS
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
3. Input voltage vs. output current (ON characteristics)
Circuit simulation result
30mA
10mA
1.0mA
300uA
1.0V 10V 30V
- I(V1)
V_V2
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
4. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 0.2 V
VI(ON) (V)
Ic(mA) Error (%)
Datasheet Simulation
0.3 3.7 3.7337 0.91081
0.5 3.9 3.8953 -0.12051
1 4 4.1865 4.66250
2 4.4 4.6171 4.93409
5 5.4 5.6281 4.22407
10 7.4 7.1046 -3.99189
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
5. Output current vs. input voltage (OFF characteristics)
Circuit simulation result
5.0mA
1.0mA
100uA
30uA
1.0V 2.0V 3.0V 4.0V 5.0V 6.0V
- I(V1)
V_V2
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
6. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 5 V
VI(OFF) (V)
Ic(uA) Error (%)
Datasheet Simulation
30 3.3 3.2398 -1.82424
50 3.35 3.3315 -0.55224
100 3.45 3.4606 0.30725
200 3.6 3.6127 0.35278
500 3.75 3.8726 3.26933
1000 3.96 4.1494 4.78283
2000 4.35 4.5600 4.82759
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
7. DC current gain vs. output current
Circuit simulation result
300
100
10
1.0mA 10mA 30mA
I(V1)/ I_I1
I(V1)
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
8. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ Vce = 5 V
hFE
Ic(mA) Error (%)
Datasheet Simulation
1 14 13.436 -4.02857
2 24 24.196 0.81667
5 50 48.982 -2.03600
10 78 77.131 -1.11410
20 105 108.991 3.80095
30 120 122.446 2.03833
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
9. Output voltage VS. output current
Circuit simulation result
1.0V
100mV
10mV
5.0mA 10mA 30mA
V(F1:2)
I_I2
Evaluation circuit
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005
10. Comparison Graph
Circuit Simulation Result
Simulation Result
Condition @ IC/IB = 20
VCE (sat) (mV)
Ic(mA) Error (%)
Datasheet Simulation
5 0.07 0.067598 -3.43143
7 0.069 0.067419 -2.29130
10 0.07 0.07166 2.37143
20 0.089 0.086050 -3.31461
30 0.11 0.105577 -4.02091
All Rights Reserved Copyright (c) Bee Technologies Inc. 2005