Andrei Cotaie and Tiberiu Boros in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The slides and other presentations can be found on https://def.camp/archive
Deep learning is one of the most exciting areas of machine learning and AI. This presentation covers all the very basics of deep neural networks, from the concept down to applications and why this technology is so popular in today's business landscape.
This presentation is provided by the Tesseract Academy, which provides executive education for deep technical subjects such as data science and blockchain. For a video of the presentation please visit https://www.youtube.com/watch?v=RiYGluH_cx0&t=0s&list=PLVce3C5Hi9BBfabvhEzYQTQDYEg2vtuxH&index=2
For an associated blog post about deep learning also visit http://thedatascientist.com/what-deep-learning-is-and-isnt/
This document provides an introduction to deep learning. It begins by discussing modeling human intelligence with machines and the history of neural networks. It then covers concepts like supervised learning, loss functions, and gradient descent. Deep learning frameworks like Theano, Caffe, Keras, and Torch are also introduced. The document provides examples of deep learning applications and discusses challenges for the future of the field like understanding videos and text. Code snippets demonstrate basic network architecture.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
This document provides an introduction to deep learning, including key developments in neural networks from the discovery of the neuron model in 1899 to modern networks with over 100 million parameters. It summarizes influential deep learning models such as AlexNet from 2012, ZF Net and GoogLeNet from 2013-2015, which helped reduce error rates on the ImageNet challenge. Top AI scientists who have contributed significantly to deep learning research are also mentioned. Common activation functions, convolutional neural networks, and deconvolution are briefly explained with examples.
Deep learning refers to artificial neural networks with many layers. This document provides an introduction to deep learning and neural networks, including their strengths and weaknesses. It discusses popular deep learning libraries for R like H2O and MXNet. H2O allows users to perform distributed deep learning on large datasets using R. MXNet provides state-of-the-art deep learning models and efficient GPU computing capabilities for R. The document demonstrates how to customize neural networks and run deep learning models with H2O and MXNet in R.
Deep learning is a class of machine learning algorithms that uses multiple layers of nonlinear processing units for feature extraction and transformation. It can be used for supervised learning tasks like classification and regression or unsupervised learning tasks like clustering. Deep learning models include deep neural networks, deep belief networks, and convolutional neural networks. Deep learning has been applied successfully in domains like computer vision, speech recognition, and natural language processing by companies like Google, Facebook, Microsoft, and others.
Deep learning is one of the most exciting areas of machine learning and AI. This presentation covers all the very basics of deep neural networks, from the concept down to applications and why this technology is so popular in today's business landscape.
This presentation is provided by the Tesseract Academy, which provides executive education for deep technical subjects such as data science and blockchain. For a video of the presentation please visit https://www.youtube.com/watch?v=RiYGluH_cx0&t=0s&list=PLVce3C5Hi9BBfabvhEzYQTQDYEg2vtuxH&index=2
For an associated blog post about deep learning also visit http://thedatascientist.com/what-deep-learning-is-and-isnt/
This document provides an introduction to deep learning. It begins by discussing modeling human intelligence with machines and the history of neural networks. It then covers concepts like supervised learning, loss functions, and gradient descent. Deep learning frameworks like Theano, Caffe, Keras, and Torch are also introduced. The document provides examples of deep learning applications and discusses challenges for the future of the field like understanding videos and text. Code snippets demonstrate basic network architecture.
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...StampedeCon
In this session, we’ll discuss approaches for applying convolutional neural networks to novel computer vision problems, even without having millions of images of your own. Pretrained models and generic image data sets from Google, Kaggle, universities, and other places can be leveraged and adapted to solve industry and business specific problems. We’ll discuss the approaches of transfer learning and fine tuning to help anyone get started on using deep learning to get cutting edge results on their computer vision problems.
Talk given at PYCON Stockholm 2015
Intro to Deep Learning + taking pretrained imagenet network, extracting features, and RBM on top = 97 Accuracy after 1 hour (!) of training (in top 10% of kaggle cat vs dog competition)
This document provides an introduction to deep learning, including key developments in neural networks from the discovery of the neuron model in 1899 to modern networks with over 100 million parameters. It summarizes influential deep learning models such as AlexNet from 2012, ZF Net and GoogLeNet from 2013-2015, which helped reduce error rates on the ImageNet challenge. Top AI scientists who have contributed significantly to deep learning research are also mentioned. Common activation functions, convolutional neural networks, and deconvolution are briefly explained with examples.
Deep learning refers to artificial neural networks with many layers. This document provides an introduction to deep learning and neural networks, including their strengths and weaknesses. It discusses popular deep learning libraries for R like H2O and MXNet. H2O allows users to perform distributed deep learning on large datasets using R. MXNet provides state-of-the-art deep learning models and efficient GPU computing capabilities for R. The document demonstrates how to customize neural networks and run deep learning models with H2O and MXNet in R.
Deep learning is a class of machine learning algorithms that uses multiple layers of nonlinear processing units for feature extraction and transformation. It can be used for supervised learning tasks like classification and regression or unsupervised learning tasks like clustering. Deep learning models include deep neural networks, deep belief networks, and convolutional neural networks. Deep learning has been applied successfully in domains like computer vision, speech recognition, and natural language processing by companies like Google, Facebook, Microsoft, and others.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
This document discusses testing practices for deep learning models. It covers various types of testing including unit testing, integrated testing, black box testing, and smoke testing. It also discusses adversarial examples and how they can be used to test models. The document emphasizes that writing good tests is important for finding bugs early, iterating quickly, debugging easily, and designing better code. It recommends starting testing by focusing on a single functionality, using available tools, and writing tests early.
This document discusses using machine learning and TensorFlow to build a neural network for IT self-service. It provides an overview of machine learning and neural networks, describing how they are inspired by biological neurons. It also discusses the different types of neural networks and machine learning paradigms like supervised, unsupervised and reinforcement learning. Examples are given of problems neural networks could solve for IT like predicting incident assignment groups or diagnosing issues from chatbots. Popular deep learning frameworks like TensorFlow are also summarized.
This document provides an introduction and overview of deep learning, including its history and key figures. Deep learning is a breakthrough in machine learning that uses neural networks with multiple hidden layers to learn representations of data. It has gained traction in recent years due to increases in data, processing power, and algorithmic advances. Popular deep learning algorithms and tools are described.
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
Deep learning is a branch of machine learning that uses neural networks with multiple processing layers to learn representations of data with multiple levels of abstraction. It has been applied to problems like image recognition, natural language processing, and game playing. Deep learning architectures like deep neural networks use techniques like pretraining, dropout, and early stopping to avoid overfitting. Popular deep learning frameworks and libraries include TensorFlow, Keras, and PyTorch.
Explore the world of ethical hacking with CTF (Capture the Flag) in a fun and interactive way. Join us and ensure you bring your laptops to follow along with live CTF challenges. Cybersecurity may seem daunting, but CTF makes it accessible to all.
The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.
This document provides an overview of building a Persian handwritten digit recognition model. It introduces machine learning concepts like supervised and unsupervised learning. It discusses TensorFlow and the MNIST dataset. It demonstrates how to build a basic MNIST model in Python with TensorFlow. It also shows how to create an Android app to detect handwritten digits using a TensorFlow model. Finally, it proposes using Custom Vision AI to create a Persian MNIST dataset and train a model to recognize Persian handwritten digits.
This document discusses computational reproducibility challenges in analyzing non-model organism sequencing data. It describes how shotgun sequencing is used to assemble genomes and transcriptomes and measure gene expression without a reference genome. K-mers are introduced as an implicit alignment method using overlapping fragments. Efficient data structures and algorithms are needed to analyze the large amounts of redundant sequencing data while retaining information. The author's lab approach is to develop novel methods at scale and apply them to real problems, then release everything openly online to enable reproducibility.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
The document discusses developing an exploit from a vulnerability and integrating it into the Metasploit framework. It covers finding a buffer overflow vulnerability in an application called "Free MP3 CD Ripper", using tools like ImmunityDebugger and Mona.py to crash the application and gain control of EIP. It then shows using Mona.py to generate an exploit, testing it works, and submitting it to the Metasploit framework. It also provides an overview of Meterpreter and its capabilities.
The document discusses programming languages and ways they can be improved and customized. It argues that libraries are often overused to extend languages when the compiler itself could be extended instead. This could be done through compiler services that expose compiler information, macros that operate on the syntax tree, and quasi-quotations for building complex AST structures. Extending the compiler allows for more control and avoids issues like dependency cycles that plague library-based extensions.
NanoSec Conference 2019: Malware Classification Using Deep Learning - Mohd Sh...Hafez Kamal
This document discusses using deep learning for malware classification. It describes how a deep neural network was trained on malware behavior data collected from executing samples in a sandbox. The behaviors were converted to fixed-size inputs using n-gram extraction and dimensionality reduction with an autoencoder. The network was trained to classify samples into malware families, achieving 96.3% accuracy on unseen data. However, issues with this approach include inability to detect previously unknown malware and vulnerability to adversarial attacks.
Stephan Gerling in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Time Series Anomaly Detection with Azure and .NETTMarco Parenzan
f you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
This document discusses testing practices for deep learning models. It covers various types of testing including unit testing, integrated testing, black box testing, and smoke testing. It also discusses adversarial examples and how they can be used to test models. The document emphasizes that writing good tests is important for finding bugs early, iterating quickly, debugging easily, and designing better code. It recommends starting testing by focusing on a single functionality, using available tools, and writing tests early.
This document discusses using machine learning and TensorFlow to build a neural network for IT self-service. It provides an overview of machine learning and neural networks, describing how they are inspired by biological neurons. It also discusses the different types of neural networks and machine learning paradigms like supervised, unsupervised and reinforcement learning. Examples are given of problems neural networks could solve for IT like predicting incident assignment groups or diagnosing issues from chatbots. Popular deep learning frameworks like TensorFlow are also summarized.
This document provides an introduction and overview of deep learning, including its history and key figures. Deep learning is a breakthrough in machine learning that uses neural networks with multiple hidden layers to learn representations of data. It has gained traction in recent years due to increases in data, processing power, and algorithmic advances. Popular deep learning algorithms and tools are described.
This is an 1 hour presentation on Neural Networks, Deep Learning, Computer Vision, Recurrent Neural Network and Reinforcement Learning. The talks later have links on how to run Neural Networks on
Deep learning is a branch of machine learning that uses neural networks with multiple processing layers to learn representations of data with multiple levels of abstraction. It has been applied to problems like image recognition, natural language processing, and game playing. Deep learning architectures like deep neural networks use techniques like pretraining, dropout, and early stopping to avoid overfitting. Popular deep learning frameworks and libraries include TensorFlow, Keras, and PyTorch.
Explore the world of ethical hacking with CTF (Capture the Flag) in a fun and interactive way. Join us and ensure you bring your laptops to follow along with live CTF challenges. Cybersecurity may seem daunting, but CTF makes it accessible to all.
The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.
This document provides an overview of building a Persian handwritten digit recognition model. It introduces machine learning concepts like supervised and unsupervised learning. It discusses TensorFlow and the MNIST dataset. It demonstrates how to build a basic MNIST model in Python with TensorFlow. It also shows how to create an Android app to detect handwritten digits using a TensorFlow model. Finally, it proposes using Custom Vision AI to create a Persian MNIST dataset and train a model to recognize Persian handwritten digits.
This document discusses computational reproducibility challenges in analyzing non-model organism sequencing data. It describes how shotgun sequencing is used to assemble genomes and transcriptomes and measure gene expression without a reference genome. K-mers are introduced as an implicit alignment method using overlapping fragments. Efficient data structures and algorithms are needed to analyze the large amounts of redundant sequencing data while retaining information. The author's lab approach is to develop novel methods at scale and apply them to real problems, then release everything openly online to enable reproducibility.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
The document discusses developing an exploit from a vulnerability and integrating it into the Metasploit framework. It covers finding a buffer overflow vulnerability in an application called "Free MP3 CD Ripper", using tools like ImmunityDebugger and Mona.py to crash the application and gain control of EIP. It then shows using Mona.py to generate an exploit, testing it works, and submitting it to the Metasploit framework. It also provides an overview of Meterpreter and its capabilities.
The document discusses programming languages and ways they can be improved and customized. It argues that libraries are often overused to extend languages when the compiler itself could be extended instead. This could be done through compiler services that expose compiler information, macros that operate on the syntax tree, and quasi-quotations for building complex AST structures. Extending the compiler allows for more control and avoids issues like dependency cycles that plague library-based extensions.
NanoSec Conference 2019: Malware Classification Using Deep Learning - Mohd Sh...Hafez Kamal
This document discusses using deep learning for malware classification. It describes how a deep neural network was trained on malware behavior data collected from executing samples in a sandbox. The behaviors were converted to fixed-size inputs using n-gram extraction and dimensionality reduction with an autoencoder. The network was trained to classify samples into malware families, achieving 96.3% accuracy on unseen data. However, issues with this approach include inability to detect previously unknown malware and vulnerability to adversarial attacks.
Similar to Weaponizing Neural Networks. In your browser! (20)
Stephan Gerling in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Stefan Zarinschi in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Bridging the gap between CyberSecurity R&D and UXDefCamp
(1) The document discusses bridging the gap between research and development (R&D) and user experience (UX) in product development.
(2) It emphasizes the importance of asking questions to understand user needs, focusing on user feelings over features, and ensuring users understand how to use products easily.
(3) The key lessons are to thoroughly question requirements, balance R&D and UX priorities, focus on satisfying core users, understand what users truly value, and make products feel intuitive and fast to use.
Drupalgeddon 2 – Yet Another Weapon for the AttackerDefCamp
Radu-Emanuel Chiscariu in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
This document discusses multi-factor authentication (MFA) and methods for bypassing it. It defines MFA as requiring more than one validation procedure to authenticate individuals. It describes the different factors of authentication as something you know, something you have, and something you are. It outlines various deployment modules for each factor type, including passwords, tokens, biometrics. It also covers challenges of MFA implementation and methods attackers could use to bypass MFA security, such as email filtering or legacy protocol exploitation.
Threat Hunting: From Platitudes to Practical ApplicationDefCamp
This document discusses threat hunting and practical approaches to threat hunting. It defines threat hunting as proactively searching through data to detect threats that evaded traditional security measures. It argues that threat hunting is more effective than reacting to incidents. The document provides guidance on log collection, developing situational awareness, hunting hosts and networks, maintaining a flexible mindset, and sharing findings. It suggests starting with small data collection and focusing on important systems and network areas. The goal is to understand normal behavior and detect anomalies.
Building application security with 0 money downDefCamp
Muhammad Mudassar Yamin in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Implementation of information security techniques on modern android based Kio...DefCamp
Muhammad Mudassar Yamin in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
The challenge of building a secure and safe digital environment in healthcareDefCamp
Jelena Milosevic in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Timing attacks against web applications: Are they still practical?DefCamp
This document discusses the practicality of timing attacks against web applications. It begins by explaining what a timing attack is and detailing the author's plan to conduct one against a target application. The plan involved studying the application's code, pinpointing an exploitable function, collecting timing data, filtering noise, and reducing the search space. The author was able to measure response times and identify spikes but encountered challenges averaging server performance. They demonstrate conducting a timing attack to recover hashed credentials over many requests. Ultimately, while timing attacks can be efficient, they are difficult to execute remotely and most applications and servers have protections that render the attacks impractical. Constant-time algorithms and rate limiting are presented as solutions to prevent these types of attacks.
Tor .onions: The Good, The Rotten and The Misconfigured DefCamp
Ionut-Cristian Bucur in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
Needles, Haystacks and Algorithms: Using Machine Learning to detect complex t...DefCamp
Ioan Constantin in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
We will charge you. How to [b]reach vendor’s network using EV charging station.DefCamp
This document summarizes a presentation about vulnerabilities found in electric vehicle charging stations. The presentation covered:
1) Several vulnerabilities were found in the Bluetooth and Wi-Fi stacks that could allow access to the vendor's internal network, including arbitrary file writes, command injection, and buffer overflows.
2) The vulnerabilities were disclosed responsibly to the vendor, who developed a detailed plan and released updated firmware within a few months to address all issues.
3) Electric vehicles and charging stations are an important area for continued security research given the protocols for wireless communication, transactions, and vehicle-to-charger interfaces.
Cristian Pațachia-Sultănoiu in Bucharest, Romania on November 8-9th 2018 at DefCamp #9.
The videos and other presentations can be found on https://def.camp/archive
This document discusses watering hole attacks, a type of cyber attack where hackers compromise frequently visited websites to infect visitors' devices through drive-by exploits. It describes how watering hole attacks work, why they are difficult to detect, and introduces DEKENEAS, an AI-based solution developed by the author to detect watering hole attacks through analyzing obfuscated JavaScript. DEKENEAS trains on over 40,000 malicious redirect samples to recognize behavioral patterns and classify code as malicious or not. When tested on 10,000 new samples and top websites, it achieved 100% detection of unknown implants with no false negatives and a very low false positive rate of 0.00023%.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...DanBrown980551
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
1. Weaponizing Neural Networks
<html>In your browser!</html>
<p>Andrei Cotaie – Senior Security Engineer</p>
<p>Tiberiu Boros - Computer Scientist</p>
Or how to abuse neural networks in learning stupid stuff !
2. The opinions and views expressed in this presentation are based on our
independent research and does not relate on our employer.
The research presented in this presentation should only be used for
educational purposes.
3. Do we trust
machine learning ?
Where are we going?
Where do we come from?
What are we?
5. What we're
going to talk
about
• JavaScript
• Machine Learning
• Neural networks
• Hiding intelligence (overfitting)
• Training of a NN
• Executing NN in HTML pages
• Reverse Engineering the NN JSON/JS
• Natural and Embedded AntiForensic
6. A .js world
Into the Browser:
JavaScript is used by 94.9% of all the websites
Out of the browser:
Wscript.exe, Cscript.exe, node, jsc, rhino etc
JS desktop applications frameworks:
Electron
8. .js obfuscation...
• The GOOD, The BAD and The UGLY
• Obfuscation != Encryption
HOW TO obfuscate your life:
• Dead Code insertion
• Subroutine reordering
• Code transposition
• Instruction substitution
• Code integration
• Register reassignment
32. Antiforensic
• Maybe delete or undefine the
variables/objects ?
(delete OR unset)
• And maybe more legit cover channels might
help
• Make sure transitions between NN calls are
made untraceable. Add some intelligence to
that ?
36. Take away
• Do IT yourself! You can Float too! (using any ML
package)
• Statically reverting input data is almost impossible
using just the latent representations
• Whenever great minds create something
innovative, lazy evil minds will abuse it
• Do we trust neural networks to run in our browsers?