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.
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.
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
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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!
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.
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.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Methods for Amharic Part-of-Speech Tagging
1. Methods for Amharic Part of Speech Tagging
Björn Gambäck, Fredrik Olsson, Atelach Alemu Argaw, Lars Asker
Amharic
Amharic Experiments and Results
• is used for country-wide
communication in Ethiopia.
Corpus
• is spoken by about 30 million
people as a first or second
Data Set
language.
210k words
• is a Semitic language written
1065 Amharic news articles
from left to right.
• uses a unique script (fidel)
Tag Set
which has has 33 basic forms
30 tags – Full tagset by the Ethiopian Languages Research
and 33 * 7 syllographs Center (ELRC) [Demeke and Getachew, 2006]
• has a rich verbal morphology 11 tags – Basic tagset by ELRC
based on triconsonantal 10 tags – Alternative tagset by Sisay [Fissaha, 2005]
roots. ’
• Subject, gender, number, Tagged Corpus
etc., are indicated as bound • Cleaned
morphemes. • 200,863 words
• nouns (and adjectives) can
be inflected for gender, 10 Fold average statistics
number, definiteness, etc Words Known Unknown
20,086 17,727 2,359
E.g. sbr : verb forms 88.26% 11.74%
form pattern
Root sbr CCC
Results
perfect säbbär CVCCVC
Imperfect säbr CVCC
gerund säbr CVCC ELRC BASIC SISAY
imperative sbär CCVC TnT 85.56 92.55 92.60
causative assäbbär as-CVCCVC STD DEV 0.42 0.31 0.32
passive täsäbbär täs-CVCCVC KNOWN 90.00 93.95 93.99
UNKNOWN 52.13 82.06 82.20
SVMTool 88.30 92.77 92.80
Open Source STD DEV 0.41 0.31 0.37
Taggers KNOWN 89.58 93.37 93.34
UNKNOWN 78.68 88.23 88.74
Own folds 88.69 92.97 92.99
• TnT
STD DEV 0.33 0.17 0.26
• Hidden Markov Model
• Viterbi algorithm
MaxEnt 87.87 92.56 92.60
• Maximize
STD DEV 0.49 0.38 0.43
P(wordn|tagn)*P(tagn|tag1...n−1)
KNOWN 89.44 93.26 93.27
• SVMTool
UNKNOWN 76.05 87.29 87.61
• Support Vector Machines
Own folds 90.83 94.64 94.52
• High dimensional vectors
STD DEV 1.37 1.11 0.69
• Hyperplane separation
BASELINE 35.50 58.26 59.61
algorithm
• MALLET
• Maximum Entropy
Discussion
• Linear classifier
• Log likelihood maximization
• TnT – Best performance for Known words
• SVMTool – Best performance for unknown words and overall
Reported performance of the • MaxEnt – Best performance when it uses own folds
taggers (Wall Street Journal)
Tagger Performance
Future Work
Overall Known Unknown
• Morphological analysis
TnT 96.7% 97.0% 85.5%
• Combining Taggers
• Use external knowledge sources (e.g. machine readable dictionaries)
SVMTool 96.9 % 97.2 % 83.5 %
• Semi supervised / unsupervised learning
Mallet 96.6 % NA NA