This document discusses machine learning interpretability and explainability. It begins with introducing the problem of making black box machine learning models more interpretable and defining key concepts. Next, it reviews popular interpretability methods like LIME, LRP, DeepLIFT and SHAP. It then describes the authors' proposed model CAMEL, which uses clustering to learn local interpretable models without sampling. The document concludes by discussing evaluation of interpretability models and important considerations like the tradeoff between performance and interpretability.
Image Caption Generation using Convolutional Neural Network and LSTMOmkar Reddy
The document summarizes a student project to generate descriptive captions for images using neural networks. The team used the Flickr8K dataset to train an encoder-decoder model with an InceptionV3 CNN and LSTM. The model was evaluated using BLEU scores, and examples are provided of correct, funny, and incorrect predictions on test images. Potential applications discussed include aiding the visually impaired.
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Cyber power affects war outcomes in modern eraBhadra Thakuri
Cyber space is being emerging as new domain of warfare. In fifth generation of warfare, it is more likely that the cyber power significantly affects the result of wars.
System engineering capabilities of 3 dexperience platform for nuclear market ...Capgemini
Virtual system engineering is a key driver for nuclear market to ensure the compliance and the safety of the nuclear plant design in regards to stakeholder needs and functional requirements. The 3DEXPERIENCE platform is offering a unique framework dedicated to system engineering to enable effective decision making by modelling & simulating complex behaviour , fast assessment of technical solution performance, innovation, cost and end-to-end view with advanced requirements management to secure and verify specification . The new System Traceability tool, fully integrated in 3DExperience suite, offers enhanced system traceability and provides a collaborative environment to exchange on model content through a Web based interactive interface. A specific demo featuring a pump system design for nuclear plant demonstrates the end to end traceability between external word requirement document, a Simulink model and a Control Build model
This document provides a crash course on using OpenAI APIs, focusing on the Chat Completion API. It discusses how to get started by installing the Python library and getting an API key. Examples are given for constructing prompts and messages to generate responses. Function calling is demonstrated to have the model select and call functions. Tips are provided for reducing costs and fine-tuning models. The overall message is that prompt engineering is iterative and specificity is important for reliable responses.
The document discusses adversary emulation and red teaming. It defines red teaming as emulating advanced persistent threat (APT) attacks. It describes APT groups and their goals of espionage and sabotage. Common APT groups and their targeted industries are listed. Methodologies for red teaming like MITRE ATT&CK and the cyber kill chain are explained. The differences between penetration testing and red teaming are outlined. Tools and platforms for adversary emulation like Cobalt Strike and Atomic Red Team are provided. The average costs of data breaches are cited from an IBM report. Defeating APT attacks is discussed as understanding enemies, continuous practice, and implementing security in depth.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
This document discusses machine learning interpretability and explainability. It begins with introducing the problem of making black box machine learning models more interpretable and defining key concepts. Next, it reviews popular interpretability methods like LIME, LRP, DeepLIFT and SHAP. It then describes the authors' proposed model CAMEL, which uses clustering to learn local interpretable models without sampling. The document concludes by discussing evaluation of interpretability models and important considerations like the tradeoff between performance and interpretability.
Image Caption Generation using Convolutional Neural Network and LSTMOmkar Reddy
The document summarizes a student project to generate descriptive captions for images using neural networks. The team used the Flickr8K dataset to train an encoder-decoder model with an InceptionV3 CNN and LSTM. The model was evaluated using BLEU scores, and examples are provided of correct, funny, and incorrect predictions on test images. Potential applications discussed include aiding the visually impaired.
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
Cyber power affects war outcomes in modern eraBhadra Thakuri
Cyber space is being emerging as new domain of warfare. In fifth generation of warfare, it is more likely that the cyber power significantly affects the result of wars.
System engineering capabilities of 3 dexperience platform for nuclear market ...Capgemini
Virtual system engineering is a key driver for nuclear market to ensure the compliance and the safety of the nuclear plant design in regards to stakeholder needs and functional requirements. The 3DEXPERIENCE platform is offering a unique framework dedicated to system engineering to enable effective decision making by modelling & simulating complex behaviour , fast assessment of technical solution performance, innovation, cost and end-to-end view with advanced requirements management to secure and verify specification . The new System Traceability tool, fully integrated in 3DExperience suite, offers enhanced system traceability and provides a collaborative environment to exchange on model content through a Web based interactive interface. A specific demo featuring a pump system design for nuclear plant demonstrates the end to end traceability between external word requirement document, a Simulink model and a Control Build model
This document provides a crash course on using OpenAI APIs, focusing on the Chat Completion API. It discusses how to get started by installing the Python library and getting an API key. Examples are given for constructing prompts and messages to generate responses. Function calling is demonstrated to have the model select and call functions. Tips are provided for reducing costs and fine-tuning models. The overall message is that prompt engineering is iterative and specificity is important for reliable responses.
The document discusses adversary emulation and red teaming. It defines red teaming as emulating advanced persistent threat (APT) attacks. It describes APT groups and their goals of espionage and sabotage. Common APT groups and their targeted industries are listed. Methodologies for red teaming like MITRE ATT&CK and the cyber kill chain are explained. The differences between penetration testing and red teaming are outlined. Tools and platforms for adversary emulation like Cobalt Strike and Atomic Red Team are provided. The average costs of data breaches are cited from an IBM report. Defeating APT attacks is discussed as understanding enemies, continuous practice, and implementing security in depth.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
The document summarizes recent trends in deep learning, including generative models like GANs and VAEs, domain adaptation techniques, meta learning approaches, and methods to model uncertainty in deep learning. It provides an overview of these areas and references key papers, with a focus on generative models and their applications to image-to-image translation tasks. It concludes by suggesting a shift in focus from image classification benchmarks to practical applications that consider real-world problems.
Image captioning with Keras and Tensorflow - Debarko De @ PractoDebarko De
This slideshow talks about how to create a image captioning system just like Google's Show and Tell Model. This will walk you through the training phase and final prediction file.n
Today much of our online world is powered by cloud computing & Amazon Web Services(AWS) offers an amazing depth and breadth of available services. In this event, we will collect our AWS logs by Integrating them with Splunk Observability.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
Gartner magic quadrant for cloud financial planning and analysis solutionsnc27770
This document provides summaries of several vendors that were evaluated in Gartner's 2018 Magic Quadrant for Cloud Financial Planning and Analysis Solutions. It describes each vendor's offerings, strengths, and cautions based on customer references. Key points include:
- Adaptive Insights and Anaplan are Leaders due to their cloud-only focus, customer satisfaction, and scalability. However, Adaptive requires proof of concepts for customization and Anaplan may not be suitable for small organizations.
- BOARD, CCH Tagetik, and Kaufman Hall support complex needs but had longer deployment times. IBM and Jedox struggled in customer satisfaction surveys despite flexible solutions.
- Host Analytics, IBM, and
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
Tutorial on Advances in Bias-aware Recommendation on the Web @ WSDM 2021Mirko Marras
This document provides an overview of an online conference presentation on advances in bias-aware recommendation. The presentation is divided into three sessions:
Session I covers the foundations of recommendation systems and data/algorithmic bias. It includes an introduction to recommendation principles and hands-on with recommender systems.
Session II focuses on techniques for mitigating bias, with slides on common mitigation approaches and another hands-on exercise on popularity bias.
Session III examines unfairness mitigation strategies with slides on unfairness measures and mitigation. It concludes with a hands-on activity related to provider unfairness.
The presentation aims to raise awareness of bias issues in recommendations, showcase bias mitigation techniques, and identify new directions
Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results.
Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. He’s also an Adjunct Professor at George Washington University for the Department of Decision Sciences. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems. https://www.linkedin.com/in/jpatrickhall
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
https://telecombcn-dl.github.io/2017-dlcv/
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 and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Challenges of using Twitter for sentiment analysisAna Canhoto
Presentation discussing the potential of Twitter as a source of insight about customer sentiment towards the brand, but also highlighting the challenges of doing so via automated tools.
For more information, or to join the discussion, check my blog www.anacanhoto.com
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
This document discusses machine learning interpretability. It defines interpretation as giving explanations to humans for machine learning models and decisions. It notes that humans create, are affected by, and demand explanations for decision systems. The document outlines different techniques for model interpretability including intrinsically interpretable models, post-hoc interpretability techniques that provide explanations for black box models, and model-specific and model-agnostic techniques. It provides examples like partial dependence plots, individual conditional expectation, and local surrogate models. It recommends choosing techniques based on the recipient and purpose of explanations.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
CredHub and Secure Credential ManagementVMware Tanzu
SpringOne Platform 2017
Peter Blum, Pivotal; Scott Frederick, Pivotal
From the platform all the way down to the microservices which run upon it, secrets are everywhere and leaking them can be a costly experience. Understanding security best practices, such as encrypting secrets while in transit; encrypting secrets at rest; rotating secrets regularly; preventing secrets from unintentionally leaking when consumed by the final application; and strictly adhering to the principle of least-privilege, where an application only has access to the secrets that it needs—no more, no less.....can be daunting. A new Cloud Foundry Foundation project, CredHub, was designed for these reasons. This session will take a fresh look at how to enhance security within Cloud Foundry and applications through secret management by utilizing CredHub in conjunction with Spring Cloud Services.
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
This document discusses Adoddle, a cloud-based collaboration platform that enables connectivity across supply chains through various features including full service data management, web-native accessibility, and low barriers to entry. It allows project teams, suppliers, and others to collaborate and leverage centralized knowledge and data to improve project execution and profits. The document also outlines Adoddle's security credentials and infrastructure partners. It positions Adoddle as a solution that can help supply chains improve margins through collaborative working practices enabled by its platform.
CIRED1259-No Smart MV-LV station without a smart approach - finalElise Morskieft
1) The document summarizes a project between multiple Dutch Distribution System Operators and suppliers to develop cost-effective instrumentation for medium voltage/low voltage substations.
2) Through several phases, the project established common requirements, had suppliers develop solutions, and conducted pilots of the systems.
3) The results showed a competitive total cost of ownership for instrumentation in "strategic" stations, but not yet for "non-strategic" stations. Further cost reductions are still needed.
The document summarizes recent trends in deep learning, including generative models like GANs and VAEs, domain adaptation techniques, meta learning approaches, and methods to model uncertainty in deep learning. It provides an overview of these areas and references key papers, with a focus on generative models and their applications to image-to-image translation tasks. It concludes by suggesting a shift in focus from image classification benchmarks to practical applications that consider real-world problems.
Image captioning with Keras and Tensorflow - Debarko De @ PractoDebarko De
This slideshow talks about how to create a image captioning system just like Google's Show and Tell Model. This will walk you through the training phase and final prediction file.n
Today much of our online world is powered by cloud computing & Amazon Web Services(AWS) offers an amazing depth and breadth of available services. In this event, we will collect our AWS logs by Integrating them with Splunk Observability.
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, as well as critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we will first motivate the need for model interpretability and explainability in AI from societal, legal, customer/end-user, and model developer perspectives. [Note: Due to time constraints, we will not focus on techniques/tools for providing explainability as part of AI/ML systems.] Then, we will focus on the real-world application of explainability techniques in industry, wherein we present practical challenges / implications for using explainability techniques effectively and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We will present case studies across different companies, spanning application domains such as search and recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we will identify open problems and research directions for the research community.
Gartner magic quadrant for cloud financial planning and analysis solutionsnc27770
This document provides summaries of several vendors that were evaluated in Gartner's 2018 Magic Quadrant for Cloud Financial Planning and Analysis Solutions. It describes each vendor's offerings, strengths, and cautions based on customer references. Key points include:
- Adaptive Insights and Anaplan are Leaders due to their cloud-only focus, customer satisfaction, and scalability. However, Adaptive requires proof of concepts for customization and Anaplan may not be suitable for small organizations.
- BOARD, CCH Tagetik, and Kaufman Hall support complex needs but had longer deployment times. IBM and Jedox struggled in customer satisfaction surveys despite flexible solutions.
- Host Analytics, IBM, and
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
Tutorial on Advances in Bias-aware Recommendation on the Web @ WSDM 2021Mirko Marras
This document provides an overview of an online conference presentation on advances in bias-aware recommendation. The presentation is divided into three sessions:
Session I covers the foundations of recommendation systems and data/algorithmic bias. It includes an introduction to recommendation principles and hands-on with recommender systems.
Session II focuses on techniques for mitigating bias, with slides on common mitigation approaches and another hands-on exercise on popularity bias.
Session III examines unfairness mitigation strategies with slides on unfairness measures and mitigation. It concludes with a hands-on activity related to provider unfairness.
The presentation aims to raise awareness of bias issues in recommendations, showcase bias mitigation techniques, and identify new directions
Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. Scientists, physicians, researchers, and analyst that use these technologies for their important work have the right to trust and understand their models and the answers they generate. This talk is an overview of several techniques for interpreting deep learning and machine learning models and telling stories from their results.
Speaker: Patrick Hall is a Data Scientist and Product Engineer at H2O.ai. He’s also an Adjunct Professor at George Washington University for the Department of Decision Sciences. Prior to joining H2O, Patrick spent many years as a Senior Data Scientist SAS and has worked with many Fortune 500 companies on their data science and machine learning problems. https://www.linkedin.com/in/jpatrickhall
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
https://telecombcn-dl.github.io/2017-dlcv/
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 and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Challenges of using Twitter for sentiment analysisAna Canhoto
Presentation discussing the potential of Twitter as a source of insight about customer sentiment towards the brand, but also highlighting the challenges of doing so via automated tools.
For more information, or to join the discussion, check my blog www.anacanhoto.com
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
This document discusses machine learning interpretability. It defines interpretation as giving explanations to humans for machine learning models and decisions. It notes that humans create, are affected by, and demand explanations for decision systems. The document outlines different techniques for model interpretability including intrinsically interpretable models, post-hoc interpretability techniques that provide explanations for black box models, and model-specific and model-agnostic techniques. It provides examples like partial dependence plots, individual conditional expectation, and local surrogate models. It recommends choosing techniques based on the recipient and purpose of explanations.
This document provides an overview of artificial neural networks (ANNs). It discusses ANN basics such as their structure being inspired by biological neural networks in the brain. The document covers different types of ANNs including feedforward and feedback networks. It also discusses ANN properties like learning strategies, applications, advantages like handling noisy data, and disadvantages like requiring training. The conclusion states that ANNs are flexible and suited for real-time systems due to their parallel architecture.
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-
ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 neurons, consists
of five convolutional layers, some of which are followed by max-pooling layers,
and three fully-connected layers with a final 1000-way softmax. To make train-
ing faster, we used non-saturating neurons and a very efficient GPU implemen-
tation of the convolution operation. To reduce overfitting in the fully-connected
layers we employed a recently-developed regularization method called “dropout”
that proved to be very effective. We also entered a variant of this model in the
ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%,
compared to 26.2% achieved by the second-best entry.
CredHub and Secure Credential ManagementVMware Tanzu
SpringOne Platform 2017
Peter Blum, Pivotal; Scott Frederick, Pivotal
From the platform all the way down to the microservices which run upon it, secrets are everywhere and leaking them can be a costly experience. Understanding security best practices, such as encrypting secrets while in transit; encrypting secrets at rest; rotating secrets regularly; preventing secrets from unintentionally leaking when consumed by the final application; and strictly adhering to the principle of least-privilege, where an application only has access to the secrets that it needs—no more, no less.....can be daunting. A new Cloud Foundry Foundation project, CredHub, was designed for these reasons. This session will take a fresh look at how to enhance security within Cloud Foundry and applications through secret management by utilizing CredHub in conjunction with Spring Cloud Services.
Amsterdam - The Neo4j Graph Data Platform Today & TomorrowNeo4j
This document provides an overview of the Neo4j Graph Data Platform. Some key points:
- Neo4j is a native graph database that is well-suited for connected data use cases that are growing exponentially. Graph databases can handle relationships better than relational databases and support relationship queries better than NoSQL databases.
- The Neo4j Graph Data Platform includes the native graph database, development tools, data science and analytics capabilities, and an ecosystem of integrations. It can be deployed anywhere including as a service on AuraDB.
- Neo4j has pioneered the graph database category since 2010 and continues to drive innovation with features like graph-RBAC security, graph data
This document discusses Adoddle, a cloud-based collaboration platform that enables connectivity across supply chains through various features including full service data management, web-native accessibility, and low barriers to entry. It allows project teams, suppliers, and others to collaborate and leverage centralized knowledge and data to improve project execution and profits. The document also outlines Adoddle's security credentials and infrastructure partners. It positions Adoddle as a solution that can help supply chains improve margins through collaborative working practices enabled by its platform.
CIRED1259-No Smart MV-LV station without a smart approach - finalElise Morskieft
1) The document summarizes a project between multiple Dutch Distribution System Operators and suppliers to develop cost-effective instrumentation for medium voltage/low voltage substations.
2) Through several phases, the project established common requirements, had suppliers develop solutions, and conducted pilots of the systems.
3) The results showed a competitive total cost of ownership for instrumentation in "strategic" stations, but not yet for "non-strategic" stations. Further cost reductions are still needed.
This Operational Telecom Network for the Connected Pipeline System Design Guide documents best
practice design of safe, highly available, and secure infrastructure and applications for Oil and Gas
pipelines. This Design Guide identifies customer use cases, maps those use cases to relevant
architectures, and leverages Cisco and partner technology to deliver unprecedented value for our
customers.
This document provides guidelines for instrumentation project engineering. It outlines the various phases of a project including the initialisation, conceptual engineering, feasibility engineering, detail engineering, construction and commissioning phases. Each phase includes the scope, inputs, processes, outputs and approvals required. The document also includes checklists of user requirements and scope of work factors to consider for instrumentation projects.
This document discusses ADEPP, a tool for configuration management in health, safety, and environmental (HSE) management systems. It describes how ADEPP can be used to engineer requirements, define performance standards, plan activities and assign tasks, and track progress through an online system. The document also discusses how ADEPP can interface with various simulation and modeling software to assess risks and safety measures over the lifecycle of oil and gas projects.
IWSM2014 MEGSUS14 - GQM on energy for SaaS - CETICNesma
This document discusses energy goals and metrics for cloud services. It proposes two goals: 1) assessing the effectiveness of a software's energy consumption behavior under different deployment configurations and workloads, and 2) characterizing a software's energy efficiency by identifying the energy used by different components and features. It also provides templates for questions to evaluate these goals, such as comparing the energy used by virtual machines running different software configurations and processing various workloads. The overall aim is to help cloud service providers optimize their software's energy usage.
Fabian Stretton has over 30 years of experience in systems engineering, product development, and management roles across various industries including defense, aerospace, and telecommunications. He has a background in electrical engineering and business administration and has worked on projects involving military radars, air traffic control systems, electronic warfare systems, and telecommunications networks. The document provides details on his education, qualifications, career history and achievements in various engineering roles for organizations such as Lockheed Martin, Chemring, Uecomm, Thales, and Tenix Defence Systems.
SOA Mainframe Service Architecture and Enablement Practices Best and Worst Pr...Michael Erichsen
This document outlines best and worst practices for mainframe service architecture and enablement. It discusses seven case studies of implementing service-oriented architectures on mainframe systems. The case studies demonstrate different technical approaches to exposing legacy mainframe applications as web services, including using CICS, WebSphere, and middleware to interface with COBOL and other applications. The document also discusses challenges of mapping data between XML and legacy formats like COBOL and ensuring interface definitions are compatible.
This document describes a network rollout solution from Amdocs that aims to reduce the time and cost of network deployment projects. It does this through automating network planning, design, and project management processes. Some key benefits highlighted include reducing deployment costs by up to 25% and design time by over 50% through standardized templates and automatic end-to-end planning. It provides visibility and control over high volume projects to improve management and speed up changes. The solution leverages a catalog-driven approach and integrates with various systems through APIs to orchestrate the entire network rollout lifecycle from demand planning to field deployment.
Creating a Centralized Consumer Profile Management Service with WebSphere Dat...Prolifics
In this presentation will talk about how one of the world's leading Financial Institutions, leveraged WebSphere DataPower to provide a set of centralized consumer profile management services. This central service would be leveraged by internal and external applications, and would align with enterprise marketing capabilities. The solution included a complex security model which included the following products: Tivoli Directory Server, Tivoli Access Manager and Tivoli Federated Identity Manager. We will describe how to build complex orchestrations in WebSphere DataPower, and also go through some of the performance tuning options we implemented to achieve a high degree of efficiency.
Design of Industrial Automation Functional Specifications for PLCs, DCs and S...Living Online
This manual will be useful to both specifiers and implementers providing a theoretical grounding for preparing a control system functional specification for implementation on Industrial control systems consisting of PLC (Programmable Logic Controllers), HMI (Human Machine Interfaces / SCADA devices) or DCS (Distributed Control Systems).
FOR MORE INFORMATION: http://www.idc-online.com/content/design-industrial-automation-functional-specifications-plcs-dcss-and-scada-systems-15
Mrunal Kothari's CV summarizes his career objective and experience in instrumentation and project management over 14+ years. He currently serves as General Manager of Instrumentation & Electrical at Enviro Control Associates, leading automation and electrical work on large wastewater treatment projects across India. Previously, he worked at Larsen & Toubro on oil and gas projects internationally in Qatar and domestically in India. He holds an MBA in Project Management and a B.E. in Instrumentation & Control.
IRJET- Build SDN with Openflow ControllerIRJET Journal
This document summarizes a research paper on building an SDN network using an OpenFlow controller. It discusses how SDN addresses limitations in traditional network technologies by introducing programmability through the OpenFlow protocol. It proposes a firewall system for SDN networks to identify attacks and report intrusion events. The paper also implements a load balancing rule based on SDN specifications using Dijkstra's algorithm to find multiple equal cost paths, helping to scale the network. It describes how SDN can improve common network management tasks through paradigm deployments in the field.
The document discusses key aspects of developing a software requirements specification (SRS) document. It notes that the SRS serves as a contract between developers and customers, detailing functional and non-functional requirements without specifying solutions. An effective SRS is unambiguous, complete, verifiable, consistent, modifiable, traceable and usable for subsequent development and maintenance phases. The document provides examples of both good and bad SRS qualities.
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4. The Holy Trinity
Requirements engineering is
implemented as part of
configuration management.
Design documents must
conform to design requirements
– and physical configuration
must conform to design
documents and requirements.
Picture source IAEA-TECDOC-1335: "Configuration management in nuclear power plants"
4
5. The V model
Requirements are elaborated
to requirements and design.
Elaboration and design
continue until something can
be implemented.
Review and testing is done
against requirements.
Finnish nuclear YVL Guide
requirement B.1 339: “The
requirement specifications
shall be unambiguous,
consistent and traceable. It
shall be possible to verify the
fulfilment of the requirements.”
Picture source https://en.wikipedia.org/wiki/V-Model_(software_development)
5
6. From architecture to systems
Picture source Space and Missile Systems Center Systems Engineering Primer & Handbook
ADLAS is a trademark of Fortum Oyj
Requirements define a
problem for which functional
solutions are found.
The functions are then
allocated to systems in
architectures, also defining
the interfaces between
systems.
ADLAS® implements a
system to define system
architecture based on safety
functions.
6
7. Some nuclear industry specificities
Safety is and must be #1.
In addition of verification/validation there is also the concept of qualification: the
requirement to verify that a system or a component fulfills it’s safety function
and requirements working correctly within all environmental parameters
throughout it’s operational life.
Requirements and design are mostly document based: that means that many
requirements are not identified as such (e.g. with a requirement identifier).
Concepts of requirements as design decisions and design as collection of
requirements are mostly missing in the industry.
Not maybe specific to nuclear industry, but a note that contracts are collection
of requirements.
7
9. Requirements in the Fennovoima NPP program
FH1 Program contains: EPC project, Fuel project, Owner’s Scope projects
Laws, EU directives, standards etc.
– Essentially infinite amount of requirements, but typically not handled as individual requirements but document references
STUK regulations
– ~600 objects, ~300 requirements
YVL Guide requirements
– ~9 000 objects, ~5 600 requirements
NPP Engineering, Procurement and Construction (EPC) contract with RAOS Project Oy
– ~25 000 objects, ~17 000 requirements + the actual contract + appendices + YVL requirements
Nuclear fuel contract with TVEL
– Requirements defined as part of the contract text
ADLAS requirements (elaborated from EPC and YVL requirements)
– Safety architecture and system design, small amount yet, few thousands, will grow during system specification to tens of thousands
Owner’s Scope requirements
– ~ 50 ongoing projects, each with hundreds of requirements
Requirements elaborated by Fennovoima
– Elaboration from e.g. security standards or environmental permits
9
10. Process charter description
Requirement Management
Plan covering the whole
supply chain and
Fennovoima.
Fennovoima internal
– RM Procedure
– RM Procedure for Owner’s
Scope
– DOORS Quick Guide and linking guide
– Requirement attribute instruction
– Requirement writing instruction
– Management of environmental requirements
– …many specific instructions covering requirement management in DOORS
Requirement management process and
instructions
10
11. 11
Three persons in the requirement management team, part of the configuration
management sub-unit
– Administration of Rational DOORS tool
– RM / DOORS training
– Supplier requirement document reviews and audits
– Requirement internal reviews
– Management of requirement changes
– Export and import of requirements data from/to DOORS
– DOORS customizations
Requirement content, change and implementation analysis is the responsibility
of discipline technical specialist whom the requirement management team
support
– Requirement Responsibles named for individual EPC contract and YVL Guide
requirements.
Fennovoima organization for requirements
12. Requirements in the supply chain
Each supplier / sub-supplier
has it’s own requirements
management tool or minimally
a supplier at the end of the
supply chain receives
requirements as appendices to
contracts or in Excel file.
Supplier requirement
management plans and
procedures define transfer
formats for sending
requirement data between
suppliers or to Fennovoima.
AtomProekt Requirement
Management procedure
AtomProekt Requirement
Management procedure
EPC contract (and
Fennovoima Requirement
Management Plan)
RAOS Project Oy
Requirement Management plan
Supplier Requirement
Management procedure
Owner
Supplier
Sub-Suppliers
DOORS
RMS
RMS
Suppliers relevant to safety:
sub-Sub-Suppliers procedures
RMS
RMS
RMS
sub-Sub-Suppliers
RMS = Requirement Management System
EPC contract
Contracts
Contracts
12
13. IBM Rational DOORS is used as requirements
management tool
– Positives: “looks like Excel”, can be customized
as needed, can handle tens of thousands of
requirements and their relationships
– Negatives: missing functionalities for database
wide actions, old client-server solution which is
not easily integrated to modern web solutions, can’t handle well parallel development.
Fennovoima has around 40 user licenses for DOORS.
DOORS database has around 350 user accounts.
Most of the requirements data is in DOORS.
Personnel use DOORS from day one, as most have to study EPC contract and YVL
Guides.
The tool in Fennovoima: Rational DOORS
13
14. Training on requirements engineering and tools
Induction training
– Two week program includes a short session on requirement management.
DOORS Basic Course
– Two hours hands-on training in a computer class
– All personnel get read-only access to Rational DOORS
– After completing this basic course, also modify access is granted.
Writing better requirements
– Half-day workshop on requirements writing aimed at Owner’s Scope personnel.
Reviewing ADLAS documents
– Full-day workshop with exercises going through ADLAS principles, requirement structure and
DOORS tool support.
14
15. Safety requirements andADLAS
Fortum’s ADLAS methodology implements basic principles of system engineering by functional architectural design
based on requirement traceability: requirements allocation and elaboration.
Part of safety related requirements have been selected as input requirements to be elaborated with Fortum’s ADLAS
methodology which will produce licensing documentation along with requirement traceability.
Current coverage of ADLAS for YVL requirements is around 10%, so the fulfilment of the rest YVL requirements has to
be shown by other methods.
15
16. Requirements traceability
As design is mostly document-based,
most of the traceability information is
in the document “List of requirements” table.
Data on requirements traced (selected as input requirements and requirement version used for
this document revision) are also sent to Fennovoima and the data is imported to DOORS and
linked to document data.
16
17. Requirement fulfilment
Requirement fulfilment is followed in two Fennovoima internal projects in which the
discipline specialists fill fulfilment data for EPC requirements or YVL requirements:
– Who are responsible for fulfilment and follow-up of an individual requirement
– What documents show or will show the implementation or fulfilment of this requirement
– What is the justification for the Fennovoima view on fulfilment
– When does Fennovoima expect this requirement to be fulfilled.
Reporting on fulfilment
– Fulfilment metric reports are run with customized script across all EPC/YVL requirements to
report progress.
– Part of the YVL fulfilment data is sent to STUK as an attachment to the licensing documents.
– Part of the EPC fulfilment data is sent to RAOS Project as a Fennovoima view on the fulfilment
of the EPC contract.
17
18. Requirement change management
Requirement change management is part of the “Integrated Change Management”
process which analyses the change requests and assesses their impacts.
If the change request is made against a requirement then after passing this process and
approved the requirement is changed.
Implementation in DOORS
– Change request data for EPC requirements is stored in DOORS and are linked to the affected
requirements creating traceability between change request and requirement.
– Change requests can be viewed by a DOORS traceability view to see what change requests
have been made.
– In addition DOORS history mechanism stores data on all changes made.
– To protect against accidental changes most important attributes in DOORS are read-only for
standard DOORS users.
18
19. Requirements and configuration management
Design requirements are configuration items and thus have connections to configuration
baselines (EPC / YVL / ADLAS / Environmental / OS).
Each requirement is versioned, either by Fennovoima, STUK or by supplier writing the
requirement.
Requirement change requests have a specific configuration baseline where the change is to be
implemented.
Approved requirement change request leads to a new requirement version.
19
20. Customized tools developed
DOORS DXL script development allows to supplement DOORS
functionalities which are missing from out-of-the-box DOORS,
e.g.
– Requirements search across modules
– Traceability views and exports
– Requirements allocation to systems
– Integration of requirements to other design tools
– Reporting on requirement metrics
– Producing formatted exports.
These user scripts are collected as DOORS menu selections.
20
21. Problems found in requirements engineering
Requirements version management in supply chain
– Across hundreds of suppliers it is sometimes hard to be certain that all suppliers have the current
requirements available
– Tools: checking in audits, allocation work by the suppliers
– In the future all the Finnish YVL guide requirements will be updated…
Vocabulary: what does traceability mean
– Not all suppliers are capable of producing data showing the fulfilment of requirements allocated to
them
Vocabulary: what are e.g. “design requirements” or “project requirements”
– What is meant actually meant by the terms? “Design requirements” might be a text document, not a
collection of requirements
Tools and transfers of data, tool support or manual
– Excel is the most used requirements management tool in the world, but that does not mean that Excel is
a good requirements management tool.
21
22. Looking forward: where to go from here
Current focus is on fulfilment of requirements in design and licensing material for construction
license.
After the design V model is fulfilled (and change managed), then the come the V model
fulfilments for construction, installation, commissioning and operation.
Requirement
Fennovoima fulfilment: should be fulfilled by
Supplier traceability: is fulfilled by
Analysis: is the
contract fulfilled?
22