This document discusses applying deep learning techniques like variational autoencoders to cyber security and anomaly detection in network traffic. It notes that while deep learning has made progress in related areas, modeling categorical network flow data poses unique challenges. It proposes using variational inference with a Gumbel softmax relaxation to train a generative model on network flows in an unsupervised manner. The trained model could then be used for tasks like anomaly detection based on the model's predictions or a sample's reconstruction error.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
this is the forth slide for machine learning workshop in Hulu. Machine learning methods are summarized in the beginning of this slide, and boosting tree is introduced then. You are commended to try boosting tree when the feature number is not too much (<1000)
Accelerating Random Forests in Scikit-LearnGilles Louppe
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include:
- An efficient formulation of the decision tree algorithm, tailored for Random Forests;
- Cythonization of the tree induction algorithm;
- CPU cache optimizations, through low-level organization of data into contiguous memory blocks;
- Efficient multi-threading through GIL-free routines;
- A dedicated sorting procedure, taking into account the properties of data;
- Shared pre-computations whenever critical.
Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
this is the forth slide for machine learning workshop in Hulu. Machine learning methods are summarized in the beginning of this slide, and boosting tree is introduced then. You are commended to try boosting tree when the feature number is not too much (<1000)
Unified Analytics in GE’s Predix for the IIoT: Tying Operational Technology t...Altoros
Learn how to achieve holistic operational visibility into IIoT business environments by correlating the data from Operational Technology and IT, and organizing it as a single pane of glass in accordance with business processes.
CernVM-FS for Docker image distribution in Cloud FoundryGeorge Lestaris
Cloud foundry is a PaaS that facilitates deployment and scale of web applications. It aims at providing a good user experience while ensuring that deployed application follow the best practices. Additionally, It provides a full set of features that enable debugging and monitoring of production systems. However, Docker is disrupting more traditional PaaS by making containers a prominent term in the cloud terminology. George, who works for Pivotal implementing Cloud Foundry's container runtime, will introduce Docker and Cloud Foundry, and discuss their differences. He will present the Docker image support in Cloud Foundry and eventually, how CernVM-FS could fit in the area of Docker image distribution.
1) What does it mean to be secure?
2) What are trying to protect?
3) Who are the attackers?
4) Physical access
5) Secure boot techniques
6) Encryption, certificates, code signing, and digital signatures
7) Characteristics of a secure system
8) Stes to secure the data center, border gateway, and the edge devices
Cloud Foundry Diego: The New Cloud Runtime - CloudOpen Europe Talk 2015David Soul
A talk on the extensibility of the new Cloud Foundry platform runtime presented at the CloudOpen Europe conference in Dublin, Oct 2015.
Outlines how the new, flexible cloud primitives in the upcoming Cloud Foundry Diego platform runtime were adapted to support additional workloads and environments, including Docker images and the Lattice project for local development. The talk included a live demo of deploying Docker images to a Lattice runtime running on Amazon EC2. One hour talk given at CloudOpen Europe in 5th October 2015.
Links:
CloudOpen EU Conference - http://events.linuxfoundation.org/events/cloudopen-europe/ and http://sched.co/3xVy
OSS Projects - http://cloudfoundry.org and http://lattice.cf
Talk Photos - http://david-soul.com/?p=555
Abstract: An overview of Diego, the new Cloud Foundry runtime design for orchestrating heterogeneous containerized workloads across multiple cloud infrastructures. Learn how Diego manages tasks and long-running processes using auction-based scheduling and monitoring for Docker and Garden containers.
Credit to the Cloud Foundry dev team and more, including Onsi Fakhouri, Eric Malm, Matt Stine, Amit Gupta, Bridget Kromhout, Renee French and Cornela Davis.
Studies and analysis the aspects of confidentiality, integrity and availability of information(data) with regard to the organisation. Identify exposure to accidental or intentional , destruction, disclosure , modification or interruption of information that may cause serious financial and or information loss Study of cyber security and incident response and has become necessary because attacks frequently cause the compromise of personal and business data. Heightened incidents concerns about national security and exposure of personally identifiable information. Racing awareness of the possible effects of computer barred attacks is the other reason.
How does the Cloud Foundry Diego Project Run at Scale?VMware Tanzu
From Pivotal's Amit Gupta on July 9, 2015, a look at how the Cloud Foundry Diego project runs at scale, and what it took to get there. Offering a look into the Diego project scheduler and the performance testing efforts, all the tools necessary to ensure that Cloud Foundry can scale quickly and effortlessly.
To learn more, visit pivotal.io/platform-as-a-service/pivotal-cloud-foundry
Beyond Matching: Applying Data Science Techniques to IOC-based DetectionAlex Pinto
There is no doubt that indicators of compromise (IOCs) are here to stay. However, even the most mature incident response (IR) teams are currently mainly focused on matching known indicators to their captured traffic or logs. The real “eureka” moments of using threat intelligence mostly come out of analyst intuition. You know, the ones that are almost impossible to hire.
In this session, we show you how you can apply descriptive statistics, graph theory, and non-linear scoring techniques on the relationships of known network IOCs to log data. Learn how to use those techniques to empower IR teams to encode analyst intuition into repeatable data techniques that can be used to simplify the triage stage and get actionable information with minimal human interaction.
With these results, we can make IR teams more productive as soon as the initial triage stages, by providing them data products that provide a “sixth sense” on what events are the ones worth analyst time. They also make painfully evident which IOC feeds an organization consume that are being helpful to their detection process and which ones are not.
The Industrial Internet: Automation and AnalyticsAltoros
he Industrial IoT has move past the thinking phase. Real work is being done, in particular in the form of the testbeds of the Industrial Internet Consortium to validate the technical approach to various IoT challenges in domains such as water management, smart grid, smart cities, manufacturing, predictive maintenance, and more. Several of the testbeds used GE Predix as part of their architecture.
Secure Because Math: A Deep-Dive on Machine Learning-Based Monitoring (#Secur...Alex Pinto
We could all have predicted this with our magical Big Data analytics platforms, but it seems that Machine Learning is the new hotness in Information Security. A great number of startups with ‘cy’ and ‘threat’ in their names that claim that their product will defend or detect more effectively than their neighbour's product "because math". And it should be easy to fool people without a PhD or two that math just works.
Indeed, math is powerful and large scale machine learning is an important cornerstone of much of the systems that we use today. However, not all algorithms and techniques are born equal. Machine Learning is a most powerful tool box, but not every tool can be applied to every problem and that’s where the pitfalls lie.
This presentation will describe the different techniques available for data analysis and machine learning for information security, and discuss their strengths and caveats. The Ghost of Marketing Past will also show how similar the unfulfilled promises of deterministic and exploratory analysis were, and how to avoid making the same mistakes again.
Finally, the presentation will describe the techniques and feature sets that were developed by the presenter on the past year as a part of his ongoing research project on the subject, in particular present some interesting results obtained since the last presentation on DefCon 21, and some ideas that could improve the application of machine learning for use in information security, especially in its use as a helper for security analysts in incident detection and response.
1. Setting up TensorFlow with Ubuntu containers
2. What is transfer learning and how to get an existing model with it?
3. Training old models with new images
4. Testing new models with new images
Slides by Alexander März:
The language of statistics is of probabilistic nature. Any model that falls short of providing quantification of the uncertainty attached to its outcome is likely to provide an incomplete and potentially misleading
picture. While this is an irrevocable consensus in statistics, machine
learning approaches usually lack proper ways of quantifying uncertainty. In fact, a possible distinction between the two modelling cultures can be
attributed to the (non)-existence of uncertainty estimates that allow for,
e.g., hypothesis testing or the construction of estimation/prediction
intervals. However, quantification of uncertainty in general and
probabilistic forecasting in particular doesn’t just provide an average
point forecast, but it rather equips the user with a range of outcomes and the probability of each of those occurring.
In an effort of bringing both disciplines closer together, the audience is
introduced to a new framework of XGBoost that predicts the entire
conditional distribution of a univariate response variable. In particular,
XGBoostLSS models all moments of a parametric distribution (i.e., mean,
location, scale and shape [LSS]) instead of the conditional mean only.
Choosing from a wide range of continuous, discrete and mixed
discrete-continuous distribution, modelling and predicting the entire
conditional distribution greatly enhances the flexibility of XGBoost, as it
allows to gain additional insight into the data generating process, as well
as to create probabilistic forecasts from which prediction intervals and
quantiles of interest can be derived. As such, XGBoostLSS contributes to
the growing literature on statistical machine learning that aims at
weakening the separation between Breiman‘s „Data Modelling Culture“ and „Algorithmic Modelling Culture“, so that models designed mainly for
prediction can also be used to describe and explain the underlying data
generating process of the response of interest.
최근 이수가 되고 있는 Bayesian Deep Learning 관련 이론과 최근 어플리케이션들을 소개합니다. Bayesian Inference 의 이론에 관해서 간단히 설명하고 Yarin Gal 의 Monte Carlo Dropout 의 이론과 어플리케이션들을 소개합니다.
Cuckoo Search Algorithm: An IntroductionXin-She Yang
This presentation explains the fundamental ideas of the standard Cuckoo Search (CS) algorithm, which also contains the links to the free Matlab codes at Mathswork file exchanges and the animations of numerical simulations (video at Youtube). An example of multi-objective cuckoo search (MOCS) is also given with link to the Matlab code.
Abstract : For many years, Machine Learning has focused on a key issue: the design of input features to solve prediction tasks. In this presentation, we show that many learning tasks from structured output prediction to zero-shot learning can benefit from an appropriate design of output features, broadening the scope of regression. As an illustration, I will briefly review different examples and recent results obtained in my team.
Statement of stochastic programming problemsSSA KPI
AACIMP 2010 Summer School lecture by Leonidas Sakalauskas. "Applied Mathematics" stream. "Stochastic Programming and Applications" course. Part 1.
More info at http://summerschool.ssa.org.ua
We develop a new method to optimize portfolios of options in a market where European calls and puts are available with many exercise prices for each of several potentially correlated underlying assets. We identify the combination of asset-specific option payoffs that maximizes the Sharpe ratio of the overall portfolio: such payoffs are the unique solution to a system of integral equations, which reduce to a linear matrix equation under suitable representations of the underlying probabilities. Even when implied volatilities are all higher than historical volatilities, it can be optimal to sell options on some assets while buying options on others, as hedging demand outweighs demand for asset-specific returns.
Asynchronous Stochastic Optimization, New Analysis and AlgorithmsFabian Pedregosa
As datasets continue to increase in size and multi-core computer architectures are developed, asynchronous parallel optimization algorithms become more and more essential to the field of Machine Learning. In this talk I will describe two of our recent contributions to this topic. First, we highlight an important technical issue present in a large fraction of the recent convergence proofs for asynchronous parallel optimization algorithms and propose a new framework that resolves it [1]. Second, we propose a novel asynchronous variant of SAGA, a stochastic method that combines the low cost per iteration of SGD with the fast convergence rates of gradient descent [2]
[1] Leblond, R., Pedregosa, F., & Lacoste-Julien, S. (2018). Improved asynchronous parallel optimization analysis for stochastic incremental methods. arXiv:1801.03749, https://arxiv.org/pdf/1801.03749.pdf
[2] Pedregosa, F., Leblond, R., & Lacoste-Julien, S. (2017). Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. In Advances in Neural Information Processing Systems, http://papers.nips.cc/paper/6611-breaking-the-nonsmooth-barrier-a-scalable-parallel-method-for-composite-optimization.pdf
We consider the problem of model estimation in episodic Block MDPs. In these MDPs, the decision maker has access to rich observations or contexts generated from a small number of latent states. We are interested in estimating the latent state decoding function (the mapping from the observations to latent states) based on data generated under a fixed behavior policy. We derive an information-theoretical lower bound on the error rate for estimating this function and present an algorithm approaching this fundamental limit. In turn, our algorithm also provides estimates of all the components of the MDP.
We apply our results to the problem of learning near-optimal policies in the reward-free setting. Based on our efficient model estimation algorithm, we show that we can infer a policy converging (as the number of collected samples grows large) to the optimal policy at the best possible asymptotic rate. Our analysis provides necessary and sufficient conditions under which exploiting the block structure yields improvements in the sample complexity for identifying near-optimal policies. When these conditions are met, the sample complexity in the minimax reward-free setting is improved by a multiplicative factor $n$, where $n$ is the number of contexts.
In this webinar we will discuss:
- The profile of an organization that is Expert at Kubernetes on Azure and AKS
- How to get to Expert status
- The challenges along the way and how embracing Azure services can help
- A demo of deploying applications with velocity on AKS
Journey Through Four Stages of Kubernetes Deployment MaturityAltoros
In this webinar we will discuss a crawl, walk, run approach to continuous delivery (CD) for applications, point by point:
Where to start, how to advance, and how to reach the level of maximum automation.
How to orchestrate CI/CD processes along with routing and business continuity.
When the automation level is sufficient.
GitOps principles and their benefits.
What tools should be used to automate CI, CD, GitOps, Container Registry, Secrets management, etc
SGX: Improving Privacy, Security, and Trust Across Blockchain NetworksAltoros
These slides explain how to use Intel Software Garden Extensions (SGX) to improve privacy, security, trust, and transparency across blockchain networks that store sensitive data.
Using the Cloud Foundry and Kubernetes Stack as a Part of a Blockchain CI/CD ...Altoros
These slides exemplify how to employ the tools available through Cloud Foundry and Kubernetes to enable a continuous integration and continuous delivery pipeline on blockchain.
The combination of StackPointCloud with NetApp creates NetApp Kubernetes Service, the industry’s first complete Kubernetes platform for multi-cloud deployments and a complete cloud-based stack for Azure, Google Cloud, AWS, and NetApp HCI. Further, Trident is a fully supported open source project maintained by NetApp, designed from the ground up to help meet the sophisticated persistence demands of containerized applications.
With no built-in solutions for managing user accounts, Kubernetes has to rely on external systems for this. Can we use one UAA solution for both Cloud Foundry and Kubernetes authentication while building a hybrid deployment?
Troubleshooting .NET Applications on Cloud FoundryAltoros
These slides overview how logs can be employed to troubleshoot .NET app on Cloud Foundry, as well as how to use metrics to enable preventive maintenance.
Continuous Integration and Deployment with Jenkins for PCFAltoros
Jenkins has been the preferred tool for continuous integration and deployment for many years already due to it's smooth user experience, easy configuration, abundance of available plugins and integrations. During the talk we will tell about best practices on using Jenkins together with Cloud Foundry installations, accelerating cloud-native application delivery and packaging using combination of Docker and Jenkins and thoughtful configuration of CI/CD pipelines and keeping apps up-to-date on all CF environments.
At the Cloud Foundry Summit 2017 in Santa Clara, Altoros and GE Digital talked about a sensor-based solution for tracking luggage from registration to claim belt.
Navigating the Ecosystem of Pivotal Cloud Foundry TilesAltoros
For application developers, PCF tiles are arguably the easiest way to run Redis, Elasticsearch, Cassandra, or any other backing service with applications in the cloud.
Integrating AI into IoT networks is becoming a prerequisite for success in today’s data-driven digital ecosystems. The only way to keep up with IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT. Watch this slides to understand how IoT and AI may work together.
Over-Engineering: Causes, Symptoms, and TreatmentAltoros
If your are using Cloud Foundry, you are most obviously into the microservices architecture and cloud-native app development approach. These are definitely best practices in modern application development, but too much of a good thing is good for nothing. Overuse of these principles may lead to over-engineering, when an application is split into too much microservices and, as such, gets hard to maintain and support. This presentation highlights how far overuse of the microservices concept can go, what issues exist, and how these issues can be avoided.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
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.
1. Deep Learning for Cyber Security
.
Steven Hutt steven.c.hutt@gmail.com
28 February, 2017
2. Cyber Security
www.dropbox.gov
Why now?
• DNC, Sony, Yahoo, ...
• attack vectors constantly changing
• static detection approaches failing
• large amounts of data
• Deep Learning for anomaly detection
• corporate / government motivated
1
3. The Challenge
Can we apply Deep Learning to Cyber Security and better identify malicious
network traffic?
Reasons to be Optimistic:
• Plenty of data
• Good progress in general area of unsupervised feature selection
• Good progress in general area of anomaly detection
• Very topical subject - so that's good!
Reasons to be Skeptical:
• Practical usage requires very low false positive rate
• Essentially no labelled data
• Data is a hybrid of categorical (mostly) and numeric (some)
• Very topical subject - so why are there so few commercial successes?
Reasons to try:
• potentially huge market
• it's fun
2
4. Network Flow: the unit of data
A network flow is a record of the information exchanged via packets
between a source and a destination machine during the course of the
network protocol session.
ipv4Source 192.104.50.16 hasAttach True
ipv4Dest 147.135.57.43 sizeAttach 87
portSource 80 mimeType avi
portDest 1639 numAttach 1
latitude -5.31858 cookie 'name=kpl; expires=...'
longitude 81.52040 subject 'Re: meeting'
duration 13.87 searchString 'free beer'
timeStamp 1486034657716 urlString 'https://test-cloud-p...'
Network flow values are a hybrid of categorical, numerical and text data.
Deep Learning for numerical and text data has been extensively developed.
Here we focus on Deep Learning for categorical data.
3
5. Unsupervised Anomaly Detection
We attempt to fit a probability distribution to the data {xk}N
k=1. Many
approaches are possible. We focus here on generative models:
..z.
x
. pθ(z).
pθ(x)
. prior.
likelihood
.
marginal
.
posterior
.
pθ(x | z)
.
pθ(z | x)
Maximize log-likelihood of data:
pθ(x) =
∑
z
pθ(x | z)pθ(z)
ˆθ = arg min
θ
N∑
k=1
log
∑
z
pθ(xk | z)pθ(z)
Possible uses of generative model:
• A data point x is labelled anomalous if pˆθ(x) is below some threshold.
• The posterior pˆθ(z | x) determines unsupervised feature extraction z of
the data x. Features z can then be used in subsequent anomaly
detection model.
4
6. Categorical Distributions
Deep Learning for categorical data has been less studied than for numerical
or text data, so we focus on modelling multivariate categorical distributions.
Let x = {x1, . . . , xp} be categorical variables with xj ∈ {1, . . . , dj}, j = 1, . . . p.
Let π be a probability distribution on x:
πc1...cp = P(x1 = c1, . . . , xp = cp),
Then there exists:
1. an integer k > 0
2. a mixing variable z ∈ {1, . . . , k} with distribution ν = (ν1, . . . , νk)
3. a set of independent distributions ψ
(j)
hcj
= P(xj = cj | z = h)
such that
πc1...cp = P(x1 = c1, . . . , xp = cp) =
k∑
h=1
νk
p
∏
j=1
ψ
(j)
hcj
.
In other words, every multivariate categorical distribution is a mixture of
multivariate categorical distributions with independent marginals.
5
7. Problems
While the mixture representation result is encouraging there are several
challenges:
1. Direct likelihood maximization is computationally very expensive
2. Stochastic Gradient Descent is not possible as variables are discrete
3. The size k of the mixing variable is geometric in the dimension p
In order to address these challenges, we will utilize the following:
1. variational inference for approximate likelihood maximization
2. Gumbel softmax to relax categorical variables to continuous variables
3. Dirichlet processes to incorporate k as part of the inference
6
8. Variational Autoencoders in a Nutshell
Recall we wish to compute
ˆθ = arg min
θ
N∑
k=1
log
∑
z
pθ(xk | z)pθ(z)
so we need to efficiently compute
∑
z pθ(xk | z)pθ(z).
We can (badly) approximate by sampling:
∑
z
pθ(xk | z)pθ(z) ≃
M∑
i=1
pθ(xk | zi), where zi ∼ pθ(z)
But most of the time pθ(xk | zi) ≃ 0 so sample from zi ∼ pθ(z | xk) instead.
But pθ(z | xk) is the wrong distribution and is unknown...
... so we learn an approximation qϕ(z | xk) to the unknown pθ(z | xk)
... and account for the wrong distribution via a likelihood upper bound:
Lθ,ϕ(xk) = −DKL(qϕ(z | xk)||pθ(z))
regularization term
+ Eqϕ (log pθ(xk | z))
reconstruction term
Think of qϕ(z | x) : x → z as an encoder and pθ(x | z) : z → x as a decoder.
7
9. The Reparametrization Trick
..
input
.hidden . (sample).
output
.
x
.z.
L
.
qϕ(z | x)
.Id .
pθ(x | z)
Stochastic Compute Graph
..
input
.hidden .
output
.
x
.z. ϵ.
L
.
qϕ(z | x)
.Id .
g
.
pθ(x | z)
Deterministic Compute Graph
The feedforward step involves sam-
pling the random variable z ∼ qϕ(z | x).
However, sampling does not admit a
gradient so back-propagation of gradi-
ents fails.
The Reparametrization Trick replaces
z ∼ qϕ(z | x) with
ˆz = g(ϵ) where ϵ ∼ p(ϵ).
Now the back-propagation path is
through deterministic nodes only.
Of course, our variables are categorical so back-propagation of gradients
fails anyway...
8
10. Gumbel Softmax Distribution
τ = 0.0
τ = 0.5
τ = 1.0
How to sample from a categorical random variable?
Let z be of dimension k with probabilities (ν1, . . . , νk).
We may sample from z as follows:
z = one-hot
(
arg max
h
{γ1 + ν1, . . . , γk + νk}
)
where γh ∼ Gumbel(0, 1) are IID, h = 1, . . . , k.
One-hot vectors are vertices of the simplex ∆k−1
.
Define a continuous distribution on the interior of the
simplex
y = (y1, . . . , yk) ∈ ∆k−1
, such that
k∑
h=1
yh = 1,
where yh =
e(ln νh+γh)/τ
∑k
j=1 e(ln νj+γj)/τ
, for τ > 0.
As τ → 0 the continuous distribution y converges to
the categorical distribution z.
9
11. Variational Autoencoder for Categorical Variables
Relax the categorical assumptions of the model in order to obtain a
continuous model on which back-propagation of gradients applies.
..
x
.y. γ.
L
.
Continuous
.
qϕ(y | x)
.Id .
g
.
pθ(x | y)
... τ → 0.
x
. z.
L
.
Categorical
.
qϕ(z | x)
. Id.
pθ(x | z)
Note:
• for τ small: close to categorical but high variance of gradients
• for τ large: far from categorical but low variance of gradients
In practice, start training with large τ and anneal to small τ.
10
12. Application
Train variational autoencoder to obtain parameters ˆθ, ˆϕ.
Possible approaches for anomaly detection:
• Use qˆϕ(z | x) : x → z for input to a machine learning anomaly detector
• Use pˆθ : x → p ∈ [0, 1] to identify rare events
• Use reconstruction error
x
q ˆϕ
−→ z
p ˆθ
−→ x
as anomaly detector
Other approaches are possible...
11