This document discusses software evolution and self-adaptive systems. It describes how systems must evolve over time in response to changes in requirements and their operating environment. A key aspect is the ability of self-adaptive systems to detect changes at runtime, reason about the impacts, and trigger self-adaptations to the system as needed to maintain satisfaction of requirements. The document outlines a lifecycle for self-adaptive systems involving monitoring the environment, reasoning about detected changes, and performing self-adaptations during runtime as opposed to only during development time.
Do Search-Based Approaches Improve the Design of Self-Adaptive Systems ? A Co...Sandro Andrade
Do Search-Based Approaches Improve the Design of Self-Adaptive Systems ? A Controlled Experiment
2o best paper award of the 28th Brazilian Symposium on Software Engineering - SBES
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...Luigi Vanfretti
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
System Engineering large scale Test and Evaluation. Emphasizes determining Fitness for Purpose and/or Readiness for Operation of deployed systems.Focuses on producing actionable knowledge for the warfighter.
SERENE 2014 School: Resilience in Cyber-Physical Systems: Challenges and Oppo...SERENEWorkshop
SERENE 2014 School on Engineering Resilient Cyber Physical Systems
Talk: Resilience in Cyber-Physical Systems: Challenges and Opportunities, by Gabor Karsai
Do Search-Based Approaches Improve the Design of Self-Adaptive Systems ? A Co...Sandro Andrade
Do Search-Based Approaches Improve the Design of Self-Adaptive Systems ? A Controlled Experiment
2o best paper award of the 28th Brazilian Symposium on Software Engineering - SBES
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...Luigi Vanfretti
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
System Engineering large scale Test and Evaluation. Emphasizes determining Fitness for Purpose and/or Readiness for Operation of deployed systems.Focuses on producing actionable knowledge for the warfighter.
SERENE 2014 School: Resilience in Cyber-Physical Systems: Challenges and Oppo...SERENEWorkshop
SERENE 2014 School on Engineering Resilient Cyber Physical Systems
Talk: Resilience in Cyber-Physical Systems: Challenges and Opportunities, by Gabor Karsai
How can we communicate the effectiveness of DevOps to technical and business people?
What metaphors and examples help?
What kind of people should we hire?
This presentation was given as an Ignite talk at DevOps Days Europe 2010 in Hamburg.
Distributed systems in practice, in theory (JAX London)Aysylu Greenberg
Modern systems in production rely on decades of computer science research. Over time, new architectural patterns emerge that enable more resilient and robust systems. In this talk, we’ll discuss some of these patterns from systems I’ve worked on at Google and the related work that provide insights into the motivations behind them.
QCon NYC: Distributed systems in practice, in theoryAysylu Greenberg
Modern systems in production rely on decades of computer science research. Over time, new architectural patterns emerge that enable more resilient and robust systems. In this talk, we'll discuss some of these patterns from systems I've worked on at Google and the related work that provide insights into the motivations behind them.
JDD2015: Sustainability Supporting Data Variability: Keeping Core Components ...PROIDEA
SUSTAINABILITY SUPPORTING DATA VARIABILITY: KEEPING CORE COMPONENTS CLEAN WHILE DEALING WITH DATA VARIABILITY
A big challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. This talk examines strategies, practices, and patterns drawn from real experiences that support new and evolving data-processing requirements while keeping the core architecture clean. As complex systems evolve to meet varying data formats, they can devolve into poorly architected Big Balls of Mud filled with special-case logic and one-off processing. Alternatively, you can isolate core components of your system and protect them from entanglements and unnecessary complexity by designing them to operate on common data formats while providing extension mechanisms that enable processing variations.
JDD2015: Sustainability Supporting Data Variability: Keeping Core Components ...PROIDEA
SUSTAINABILITY SUPPORTING DATA VARIABILITY: KEEPING CORE COMPONENTS CLEAN WHILE DEALING WITH DATA VARIABILITY
A big challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. This talk examines strategies, practices, and patterns drawn from real experiences that support new and evolving data-processing requirements while keeping the core architecture clean. As complex systems evolve to meet varying data formats, they can devolve into poorly architected Big Balls of Mud filled with special-case logic and one-off processing. Alternatively, you can isolate core components of your system and protect them from entanglements and unnecessary complexity by designing them to operate on common data formats while providing extension mechanisms that enable processing variations.
Software Eng. for Critical Systems - Traffic ControllerZiya Ilkem Erogul
The aim of this project is to create a junction with the properly working traffic light system. The key point is having;
- No faults
- No accidents
- No injuries
- No any other unwanted situations
In the project, it is assumed that every junction has its own lights for both pedestrians and drivers, and crossing of cars and pedestrians will be done junction by junction.
Design and Implementation of A Data Stream Management SystemErdi Olmezogullari
This presentation is related to my Master's Thesis at Ozyegin University. We focused on data mining on the real streaming (not binary) data. The most popular data mining algorithm, Association Rule Mining (ARM), was performed during this study from scratch. At the end of the thesis, we published four national/international papers in the different conferences such as Cloud Computing and Big Data.
Building Reactive Systems with Akka (in Java 8 or Scala)Jonas Bonér
Learn how to build Reactive Systems with Akka. Examples in both Java 8 and Scala.
Abstract:
The demands and expectations for applications have changed dramatically in recent years. Applications today are deployed on a wide range of infrastructure; from mobile devices up to thousands of nodes running in the cloud—all powered by multi-core processors. They need to be rich and collaborative, have a real-time feel with millisecond response time and should never stop running. Additionally, modern applications are a mashup of external services that need to be consumed and composed to provide the features at hand. We are seeing a new type of applications emerging to address these new challenges—these are being called Reactive Applications.
In this talk we will introduce you to Akka and discuss how it can help you deliver on the four key traits of Reactive; Responsive, Resilient, Elastic and Message-Driven. We will start with the basics of Akka and work our way towards some of its more advanced modules such as Akka Cluster and Akka Persistence—all driven through code and practical examples.
Performance Evaluation of a Network Using Simulation Tools or Packet TracerIOSRjournaljce
Today, the importance of information and accessing information is increasing rapidly. With the advancement of technology, one of the greatest means of achieving knowledge are, computers have entered in many areas of our lives. But the most important of them are the communication fields. This study will be a practical guide for understanding how to assemble and analyze various parameters in network performance evaluation and when designing a network what is necessary to looking for to remove the consequences of degrading performance. Therefore, what can you do in a network performance evaluation using simulation tools such as Network Simulation or Packet tracer and how various parameters can be brought together successfully? CCNA, CCNP, HCNA and HCNP educational level has been used and important setting has been simulated one by one. At the result this is a good guide for a local or wide area network. Finally, the performance issues precautions described. Considering the necessary parameters, imaginary networks were designed and evaluated both in CISCO Packet Tracer and Huawei's eNSP simulation program. But it should not be left unsaid that the networks have been designed and evaluated in free virtual environments, not in a real laboratory. Therefore, it is impossible to make actual performance appraisal and output as there is no actual data available.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
How can we communicate the effectiveness of DevOps to technical and business people?
What metaphors and examples help?
What kind of people should we hire?
This presentation was given as an Ignite talk at DevOps Days Europe 2010 in Hamburg.
Distributed systems in practice, in theory (JAX London)Aysylu Greenberg
Modern systems in production rely on decades of computer science research. Over time, new architectural patterns emerge that enable more resilient and robust systems. In this talk, we’ll discuss some of these patterns from systems I’ve worked on at Google and the related work that provide insights into the motivations behind them.
QCon NYC: Distributed systems in practice, in theoryAysylu Greenberg
Modern systems in production rely on decades of computer science research. Over time, new architectural patterns emerge that enable more resilient and robust systems. In this talk, we'll discuss some of these patterns from systems I've worked on at Google and the related work that provide insights into the motivations behind them.
JDD2015: Sustainability Supporting Data Variability: Keeping Core Components ...PROIDEA
SUSTAINABILITY SUPPORTING DATA VARIABILITY: KEEPING CORE COMPONENTS CLEAN WHILE DEALING WITH DATA VARIABILITY
A big challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. This talk examines strategies, practices, and patterns drawn from real experiences that support new and evolving data-processing requirements while keeping the core architecture clean. As complex systems evolve to meet varying data formats, they can devolve into poorly architected Big Balls of Mud filled with special-case logic and one-off processing. Alternatively, you can isolate core components of your system and protect them from entanglements and unnecessary complexity by designing them to operate on common data formats while providing extension mechanisms that enable processing variations.
JDD2015: Sustainability Supporting Data Variability: Keeping Core Components ...PROIDEA
SUSTAINABILITY SUPPORTING DATA VARIABILITY: KEEPING CORE COMPONENTS CLEAN WHILE DEALING WITH DATA VARIABILITY
A big challenge in building complex, data-intensive systems is how to sustainably support data variation, schema, and feature evolution. This talk examines strategies, practices, and patterns drawn from real experiences that support new and evolving data-processing requirements while keeping the core architecture clean. As complex systems evolve to meet varying data formats, they can devolve into poorly architected Big Balls of Mud filled with special-case logic and one-off processing. Alternatively, you can isolate core components of your system and protect them from entanglements and unnecessary complexity by designing them to operate on common data formats while providing extension mechanisms that enable processing variations.
Software Eng. for Critical Systems - Traffic ControllerZiya Ilkem Erogul
The aim of this project is to create a junction with the properly working traffic light system. The key point is having;
- No faults
- No accidents
- No injuries
- No any other unwanted situations
In the project, it is assumed that every junction has its own lights for both pedestrians and drivers, and crossing of cars and pedestrians will be done junction by junction.
Design and Implementation of A Data Stream Management SystemErdi Olmezogullari
This presentation is related to my Master's Thesis at Ozyegin University. We focused on data mining on the real streaming (not binary) data. The most popular data mining algorithm, Association Rule Mining (ARM), was performed during this study from scratch. At the end of the thesis, we published four national/international papers in the different conferences such as Cloud Computing and Big Data.
Building Reactive Systems with Akka (in Java 8 or Scala)Jonas Bonér
Learn how to build Reactive Systems with Akka. Examples in both Java 8 and Scala.
Abstract:
The demands and expectations for applications have changed dramatically in recent years. Applications today are deployed on a wide range of infrastructure; from mobile devices up to thousands of nodes running in the cloud—all powered by multi-core processors. They need to be rich and collaborative, have a real-time feel with millisecond response time and should never stop running. Additionally, modern applications are a mashup of external services that need to be consumed and composed to provide the features at hand. We are seeing a new type of applications emerging to address these new challenges—these are being called Reactive Applications.
In this talk we will introduce you to Akka and discuss how it can help you deliver on the four key traits of Reactive; Responsive, Resilient, Elastic and Message-Driven. We will start with the basics of Akka and work our way towards some of its more advanced modules such as Akka Cluster and Akka Persistence—all driven through code and practical examples.
Performance Evaluation of a Network Using Simulation Tools or Packet TracerIOSRjournaljce
Today, the importance of information and accessing information is increasing rapidly. With the advancement of technology, one of the greatest means of achieving knowledge are, computers have entered in many areas of our lives. But the most important of them are the communication fields. This study will be a practical guide for understanding how to assemble and analyze various parameters in network performance evaluation and when designing a network what is necessary to looking for to remove the consequences of degrading performance. Therefore, what can you do in a network performance evaluation using simulation tools such as Network Simulation or Packet tracer and how various parameters can be brought together successfully? CCNA, CCNP, HCNA and HCNP educational level has been used and important setting has been simulated one by one. At the result this is a good guide for a local or wide area network. Finally, the performance issues precautions described. Considering the necessary parameters, imaginary networks were designed and evaluated both in CISCO Packet Tracer and Huawei's eNSP simulation program. But it should not be left unsaid that the networks have been designed and evaluated in free virtual environments, not in a real laboratory. Therefore, it is impossible to make actual performance appraisal and output as there is no actual data available.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
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/
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
Laser 2-change
1. Development of dynamically evolving and
self-adaptive software
2. Understanding and managing change
LASER 2013
Isola d’Elba, September 2013
Carlo Ghezzi
Politecnico di Milano
Deep-SE Group @ DEIB
1
Tuesday, September 10, 13
2. The global picture:
the machine and the world (Jackson/Zave)
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
3. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
4. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
5. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
Goals
Requirements
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
6. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
Shared
phenomena
Goals
Requirements
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
7. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
Shared
phenomena
Goals
Requirements
Specification
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
8. The global picture:
the machine and the world (Jackson/Zave)
World (the environment)
Machine
Domain
properties,
assumptions
Shared
phenomena
Goals
Requirements
Specification
P, Zave, M. Jackson, Four dark corners of requirements engineering, TOSEM 1997
2
Tuesday, September 10, 13
9. Domain properties and assumptions
• Domain property
-
statement about problem world phenomena
often it holds regardless of any software-to-be; e.g.
physics’ laws
avgTrainAcceleration (t1, t2) > 0 implies
trainSpeed (t2) > trainSpeed (t1)
• Assumption
-
statement about problem world phenomena, constraints
may be violated
‣“humans behave as instructed by the machine”
‣“temperature is in the range -40..+40 Celsius”
‣“device generates a measure every 2 ms.”
3
Tuesday, September 10, 13
10. Domain assumptions
May concern
• usage profiles
• users’ responsiveness
• remote servers response time
• network latency
• sensors/actuators behaviors
• ...
“Domain assumptions bridge the
gap between requirements and
specifications”
(M. Jackson & P. Zave)
4
Tuesday, September 10, 13
11. Dependability arguments
• Assume you have a formal representation for
– R = requirements
– S = specification
– D = Dp + Da domain properties and assumptions
if S and D are both satisfied and consistent, it is
necessary to prove
– S, D |= R
5
Tuesday, September 10, 13
12. Change
• Requirements change
• Environment changes
• Change is often a manifestation of uncertainty
• Change asks for evolution (of the machine)
6
Tuesday, September 10, 13
13. Changes may cause evolution
• Changes are exogenous phenomena
that may concern
- R
- D (actually, Da)
• Changes likely break the dependability argument
• Evolution (of the machine) is a consequence of change
‣ we need to change S (and hence the implementation)
to continue to satisfy the dependability argument
S, D |= R
7
Tuesday, September 10, 13
14. Evolution (1970s) = software maintenance
•
•
Traditionally, changes to software (the machine, S) in response
to changes in D, R are performed manually (by software
engineers) and applied off-line, after delivery
Maintenance (Lientz and Swanson) classified as
-
Corrective maintenance
-
Adaptive maintenance
✓Modification to correct discovered problems.
✓Modification to keep it usable in a changed or changing
environment.
-
Perfective maintenance
-
Preventive maintenance.
✓Modification to improve it functionally/non-functionally.
✓Anticipate any of the above.
Lientz B., Swanson E., Software Maintenance Management. Addison Wesley, 1980
8
Tuesday, September 10, 13
15. Early studies on software evolution
• Software evolution recognized as a crucial problem since the 1970’s (work
by M. Lehman and L. Belady)
• Three categories of software
- S-programs
- written according to an exact specification of what the program can do
- P-programs
- written to implement certain procedures that completely determine
what the program can do (e.g., a program to play chess)
- E-programs
- written to perform some real-world activity
- how they should behave is linked to the environment in which they run
- need to adapt to varying requirements and circumstances in that
environment
Lehman, M. , Programs, Life Cycles, and Laws of Software Evolution, Proc. IEEE, 1980
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Tuesday, September 10, 13
16. Lehman’s “laws” of software evolution (1)
•
•
•
•
Continuing Change — E-type systems must be continually
adapted or they become progressively less satisfactory.
Increasing Complexity — As an E-type system evolves its
complexity increases unless work is done to maintain or
reduce it.
Self Regulation — E-type system evolution process is selfregulating with distribution of product and process
measures close to normal.
Conservation of Organizational Stability (invariant work rate)
— The average effective global activity rate in an evolving Etype system is invariant over product lifetime.
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17. Lehman’s “laws” of software evolution (2)
•
•
•
•
Conservation of Familiarity — Over the lifetime of a system,
the incremental change in each release is approximately
constant.
Continuing Growth — The functional content of E-type
systems must be continually increased to maintain user
satisfaction over their lifetime.
Declining Quality — The quality of E-type systems will appear
to be declining unless they are rigorously maintained and
adapted to operational environment changes.
Feedback System — E-type evolution processes constitute
multi-level, multi-loop, multi-agent feedback systems and
must be treated as such to achieve significant improvement
over any reasonable base.
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18. Software evolution (2000): agility
• Common to all variants:
- Incorporate feedback in an iterative
development that supports progressive
calibration of objectives and adjustment
of requirements
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Tuesday, September 10, 13
20. Evolution and adaptation
Adaptation is a special case of evolution due to
changes in domain assumptions, Da
• an increasingly relevant phenomenon, often due to
uncertainty
‣ cyber-physical systems
- interaction with the physical environment
‣ user-intensive systems
- changes in usage profile
‣ cloud/service infrastructure
- platform/software volatility
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Tuesday, September 10, 13
21. On-line evolution and self-adaptive systems
• More and more often systems are required to be
continuously running
• This asks for on-line evolution, i.e. applying changes to
the machine as the system is running and providing
service
• The special case of self-adaptive systems
- on-line adaptation is self-managed
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Tuesday, September 10, 13
22. Self-adaptive system (SaS)
• D decomposed into Df and Dc
– Df is the fixed/stable part
– Dc is the changeable part
S, D |= R
• A SaS should
- detect changes to Dc
- modify itself (the machine --- S, and the
implementation) to keep satisfying the dependability
argument, if necessary
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Tuesday, September 10, 13
23. Paradigm shift
• SaSs ask for a paradigm shift, which involves both
development time (DT) and run time (RT)
• The boundary between DT and RT fades
• Reasoning and reacting capabilities must enrich the RT
environment
- detect change
- reason about the consequences of change
- react to change
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Tuesday, September 10, 13
24. Models+verification@runtime
• To detect change, we need to monitor the
environment
• The changes must be retrofitted to models of the
machine+environment that support reasoning about the
dependability argument (a learning step)
• The updated models must be verified to check for
violations to the dependability argument
• In case of a violation, a self-adaptation must be
triggered
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Tuesday, September 10, 13
28. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Specification
Env
19
Tuesday, September 10, 13
29. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Implementation
Specification
Env
19
Tuesday, September 10, 13
30. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Implementation
Development time
Specification
Env
19
Tuesday, September 10, 13
31. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Implementation
Development time
Specification
Run time
Env
19
Tuesday, September 10, 13
32. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Implementation
Development time
Run time
Specification
Execution
Env
19
Tuesday, September 10, 13
33. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Monitoring
Implementation
Development time
Run time
Specification
Execution
Env
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Tuesday, September 10, 13
34. Lifecycle of self-adaptive systems
Reqs
0
Specification
1
E
Reasoning
Monitoring
Implementation
Development time
Run time
Specification
Execution
Env
19
Tuesday, September 10, 13
35. Lifecycle of self-adaptive systems
Reqs
0
Specification
Implementation
Development time
Run time
1
E
Reasoning
Self-adaptation
Monitoring
Specification
Execution
Env
19
Tuesday, September 10, 13
38. Zooming in
• I. Epifani, C. Ghezzi, R. Mirandola, G. Tamburrelli, "Model Evolution by Run-Time Parameter
Adaptation”, ICSE 2009
• C. Ghezzi, G. Tamburrelli, "Reasoning on Non Functional Requirements for Integrated Services”,
RE 2009
• I. Epifani, C. Ghezzi, G. Tamburrelli, "Change-Point Detection for Black-Box Services”, FSE 2010
• A. Filieri, C. Ghezzi, G. Tamburrelli, " A formal approach to adaptive software: continuous
assurance of non-functional requirements", Formal Aspects of Computing, 24, 2, March 2012.
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Tuesday, September 10, 13
39. Problem setting
• Focus on non-functional requirements
– reliability, performance, energy consumption, cost, …
• Quantitatively stated in probabilistic terms
• Dc decomposed into Du , Ds
– Du = usage profile
– Ds = S1 ∧ .... ∧ Sn Si assumption on i-th service
Integrated Service
?
User
Workflow
W
?<uses>
Service
S1
<uses>
?
Service
S2
?
<uses>
....
Service
Sn
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Tuesday, September 10, 13
49. What happens at run time?
• Actual environment behavior is monitored
• Model updated
• e.g., by using a Bayesian approach to estimate DTMC matrix
(posterior) given run time traces and prior transitions
• Boils down to the following updating rule
26
Tuesday, September 10, 13
50. What happens at run time?
• Actual environment behavior is monitored
• Model updated
• e.g., by using a Bayesian approach to estimate DTMC matrix
(posterior) given run time traces and prior transitions
• Boils down to the following updating rule
26
Tuesday, September 10, 13
51. What happens at run time?
• Actual environment behavior is monitored
• Model updated
• e.g., by using a Bayesian approach to estimate DTMC matrix
(posterior) given run time traces and prior transitions
• Boils down to the following updating rule
A-priori Knowledge
26
Tuesday, September 10, 13
52. What happens at run time?
• Actual environment behavior is monitored
• Model updated
• e.g., by using a Bayesian approach to estimate DTMC matrix
(posterior) given run time traces and prior transitions
• Boils down to the following updating rule
A-priori Knowledge
A-posteriori Knowledge
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Tuesday, September 10, 13
57. Model update and failure prediction
• Model checking applied to after each update
• Model checking may predict requirements violations
• ... and trigger self-adaptations before violations manifest
themselves
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Tuesday, September 10, 13
63. Another example
Discrete Time Markov Reward Model (D-MRM)
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64. Another example
Discrete Time Markov Reward Model (D-MRM)
NrmShipping
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What’s the average cost
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60.625
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65. Another example
Discrete Time Markov Reward Model (D-MRM)
NrmShipping
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Units:
$
= 60.625
.
30