Modeling a Child's Learning of What's Hot and Cold. Iowa State University – CPRE585x Spring 2011.
See this link to a YouTube video for a demo of the application developed.
State of the Map US 2018: Analytic Support to Mapping Contributorsrlewis48
Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness.
Gradient Boosting Machines (GBM): from Zero to Hero (with R and Python code)Data Con LA
Data Con LA 2020
Description
This talk will get you started with gradient boosting machines (GBM), a
very popular machine learning technique providing state-of-the-art
accuracy on numerous business prediction problems. After a quick intro
to machine learning and the GBM algorithm, I will show how easy it is to
train and then use GBMs in real-life business applications using some
the most popular open source implementations (xgboost, lightgbm and
h2o). We'll do all this in both R and Python with only a few lines of
code and this talk will be accessible for a wide audience (with limited
prior knowledge of machine learning). Finally, in the last part of the
talk I will provide plenty of references that can get you to the next
level. GBMs are a powerful technique to have in your machine learning
toolbox, because despite all the latest hype about deep learning (neural
nets) and AI, in fact GBMs usually outperform neural networks on
structured/tabular data most often encountered in business applications.
Speaker
Szilard Pafka, Epoch, Chief Scientist
Preparing
Benchmark
How to Load Files on NativeActivity
How to Make Hand Detector
Calculate Histgram of Skin Color
Detect Skin Area from CapImage
Calculate the Largest Skin Area
Matching Histgrams
State of the Map US 2018: Analytic Support to Mapping Contributorsrlewis48
Significant advances in machine learning techniques for image classification, object detection and image segmentation have profound implications for crowdsourced mapping applications. Recent open source initiatives such as SpaceNet have strived to direct more research and development towards specific foundational mapping functions such as building detection and road network and routing identification. As these machine learning techniques mature, mapping contributors need to understand and engage the research community to help structure the application of these new techniques against a diverse of mapping challenges. Yet, currently, it is difficult translate mapping requirements to machine learning evaluation metrics, and vice versa. This presentation will discuss a proposed framework for defining levels of analyst augmentation that will allow mapping contributors and machine learning researchers to better understand each other and help direct the application of these advanced algorithms against mapping problems. Specifically, it will focus on relevant use case of mapping requirements, before, during and after a natural disaster and demonstrate a framework to understand what capabilities are nearing readiness.
Gradient Boosting Machines (GBM): from Zero to Hero (with R and Python code)Data Con LA
Data Con LA 2020
Description
This talk will get you started with gradient boosting machines (GBM), a
very popular machine learning technique providing state-of-the-art
accuracy on numerous business prediction problems. After a quick intro
to machine learning and the GBM algorithm, I will show how easy it is to
train and then use GBMs in real-life business applications using some
the most popular open source implementations (xgboost, lightgbm and
h2o). We'll do all this in both R and Python with only a few lines of
code and this talk will be accessible for a wide audience (with limited
prior knowledge of machine learning). Finally, in the last part of the
talk I will provide plenty of references that can get you to the next
level. GBMs are a powerful technique to have in your machine learning
toolbox, because despite all the latest hype about deep learning (neural
nets) and AI, in fact GBMs usually outperform neural networks on
structured/tabular data most often encountered in business applications.
Speaker
Szilard Pafka, Epoch, Chief Scientist
Preparing
Benchmark
How to Load Files on NativeActivity
How to Make Hand Detector
Calculate Histgram of Skin Color
Detect Skin Area from CapImage
Calculate the Largest Skin Area
Matching Histgrams
Master's degree thesis testing algorithms for image & video understandingEnrico Busto
In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined.
The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.
Model-based Regression Testing of Autonomous RobotsZoltan Micskei
Slides for the paper in 18th Int. Conf. on System Design Languages (SDL 2017). We present a method and a case study on how model-based regression testing can be achieved in the context of autonomous robots. The method uses information from several domain-specific languages for modeling the robot’s context and configuration. Our approach is implemented in a prototype tool, and its scalability is evaluated on models from the case study.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Master's degree thesis testing algorithms for image & video understandingEnrico Busto
In the last few years, many algorithms with remarkable effectiveness for Object Detection have been published but still some comparative metrics haven’t been defined.
The difficulties in making this comparison arise from the fact that different algorithms are based on different Feature Extractors (VGGs, Residual Networks, etc.), different base resolution and different implementation on specific platforms.
Model-based Regression Testing of Autonomous RobotsZoltan Micskei
Slides for the paper in 18th Int. Conf. on System Design Languages (SDL 2017). We present a method and a case study on how model-based regression testing can be achieved in the context of autonomous robots. The method uses information from several domain-specific languages for modeling the robot’s context and configuration. Our approach is implemented in a prototype tool, and its scalability is evaluated on models from the case study.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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/
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Android based Object Detection and Classification
1. Android based Object Detection and Classification: Modeling a Child's Learning of What's Hot and Cold Matthew L Weber Iowa State University – CPRE585x Spring 2011
2.
3. - Introduces a new resource constrained platform for the experiment