DiCoDE: Digital Content Distribution Ecosystem model v2010Fabian Tilmant
DiCoDE presents 1 model with 5 key concepts, at the crossroad of Digital Content, Advertising and Social Networks throughout Media, Telecom and User Experience universes. It aims to understand, analyse and decode actors, actions and stakes within the Digital Content Distribution Ecosystem. DiCoDE explains how users access, get engaged, consume and interact with any type of digital content. It also defines strategies and tactics accordingly.
DiCoDE: Digital Content Distribution Ecosystem model v2010Fabian Tilmant
DiCoDE presents 1 model with 5 key concepts, at the crossroad of Digital Content, Advertising and Social Networks throughout Media, Telecom and User Experience universes. It aims to understand, analyse and decode actors, actions and stakes within the Digital Content Distribution Ecosystem. DiCoDE explains how users access, get engaged, consume and interact with any type of digital content. It also defines strategies and tactics accordingly.
Software Development Life Cycle Models | What are Software Process Models ?
Here you are going to know What is Software Development Life Cycle Model or What are Software Process Models?
Software Process Models defines a distinct set of activities, actions, tasks, milestones, and work products that are required to engineer high-quality software...
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Software Process Models | Software Development Process Models | SDLC | Traditional Software Process Models | Waterfall Model Incremental Model | Prototyping Model | Evolutionary Process Model
Java programming presentations By Daroko blog
Do not just read java as a programmer, find projects and start making some Money, at DAROKO BLOG,WE Guide you through what you have learned in the classroom to a real business Environment, find java applications to a real business Environment, find also all IT Solutions and How you can apply them, find the best companies where you can get the IT jobs worldwide, Find java contract, Complete and start making some cash, find clients within your Country, refer and get paid when you complete the work.
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CS8461 - Operating System Laboratory Manual prepared for the Engineering graduates admitted under 2017 Regulations, Anna University affiliated institutions of TamilNadu,India
With so many different tools at our disposable, how do you pick which ones to learn? At our latest meetup for Denver Code Club, we explored some best practices on evaluating new technology and how you can choose the right tools for you.
Ontologies for multimedia: the Semantic Culture WebGuus Schreiber
Keynote, International Conference on Semantic and Digital Media Technology (SAMT 2006), Athens, 7 December 2006. Slide design with lots of help of Lora Aroyo.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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
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!
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. Introduction 2
Data, information & knowledge
■ Data
➤ “raw signals”
. . . - - - . . .
■ Information
➤ meaning attached to data
S O S
■ Knowledge
➤ attach purpose and competence to information
➤ potential to generate action
emergency alert → start rescue operation
3. Introduction 3
Knowledge engineering
process of
➤ eliciting,
➤ structuring,
➤ formalizing,
➤ operationalizing
information and knowledge involved in a knowledge-
intensive problem domain,
in order to construct a program that can perform a
difficult task adequately
4. Introduction 4
Problems in knowledge
engineering
■ complex information and knowledge is difficult to
observe
■ experts and other sources differ
■ multiple representations:
➤ textbooks
➤ graphical representations
➤ heuristics
➤ skills
5. Introduction 5
Importance of proper
knowledge engineering
■ Knowledge is valuable and often outlives a particular
implementation
➤ knowledge management
■ Errors in a knowledge-base can cause serious
problems
■ Heavy demands on extendibility and maintenance
➤ changes over time
6. Introduction 6
A Short History of
Knowledge Systems
1965 19851975 1995
general-‐purpos e
s earch
engines
(GPS )
firs t-‐generation
rule-‐bas ed
s ys tems
(MYC IN,
XC ON)
emergence
of
s tructured
methods
(early
K ADS )
mature
methodologies
(C ommonK ADS )
=>
from
art
to
discipline
=>
7. Introduction 7
First generation “Expert”
Systems
■ shallow knowledge base
■ single reasoning principle
■ uniform representation
■ limited explanation
capabilities
reas oning
control
knowledge
bas e
operates
on
8. Introduction 8
Transfer View of KE
■ Extracting knowledge from a human expert
➤ “mining the jewels in the expert’s head”’
■ Transferring this knowledge into KS.
➤ expert is asked what rules are applicable
➤ translation of natural language into rule format
9. Introduction 9
Problems with transfer view
The knowledge providers, the knowledge engineer
and the knowledge-system developer should share
➤ a common view on the problem solving process and
➤ a common vocabulary
in order to make knowledge transfer a viable way of
knowledge engineering
10. Introduction 10
Rapid Prototyping
■ Positive
➤ focuses elicitation and interpretation
➤ motivates the expert
➤ (convinces management)
■ Negative
➤ large gap between verbal data and implementation
➤ architecture constrains the analysis hence: distorted model
➤ difficult to throw away
11. Introduction 11
Methodological pyramid
world
view
theory
methods
tools
use feedback
case
studies
application
projects
C AS E
tools
implementation
environments
life-‐cycle
model,
process
model,
guidelines,
elicitation
techniques
graphical/textual
notations
work sheets,
document
structure
model-‐based
k nowledge
engineering
reuse
of
k nowledge
patterns
12. Introduction 12
World view: Model-Based KE
■ The knowledge-engineering space of choices and
tools can to some extent be controlled by the
introduction of a number of models
■ Each model emphasizes certain aspects of the
system to be built and abstracts from others.
■ Models provide a decomposition of knowledge-
engineering tasks: while building one model, the
knowledge engineer can temporarily neglect certain
other aspects.
13. Introduction 13
CommonKADS principles
■ Knowledge engineering is not some kind of `mining
from the expert's head', but consists of constructing
different aspect models of human knowledge
■ The knowledge-level principle: in knowledge
modeling, first concentrate on the conceptual
structure of knowledge, and leave the programming
details for later
■ Knowledge has a stable internal structure that is
analyzable by distinguishing specific knowledge
types and roles.
14. Introduction 14
CommonKADS theory
■ KBS construction entails the construction of a number
of models that together constitute part of the product
delivered by the project.
■ Supplies the KBS developer with a set of model
templates.
■ This template structure can be configured, refined
and filled during project work.
■ The number and level of elaboration of models
depends on the specific project context.
15. Introduction 15
CommonKADS Model Set
Organization
Model
Task
Model
Agent
Model
Knowledge
Model
Communication
Model
Design
Model
Context
Concept
Artefact
16. Introduction 16
Model Set Overview (1)
■ Organization model
➤ supports analysis of an organization,
➤ Goal: discover problems, opportunities and possible
impacts of KBS development.
■ Task model
➤ describes tasks that are performed or will be performed in
the organizational environment
■ Agent model
➤ describes capabilities, norms, preferences and permissions
of agents (agent = executor of task).
17. Introduction 17
Model Set Overview (2)
■ Knowledge model
➤ gives an implementation-independent description of
knowledge involved in a task.
■ Communication model
➤ models the communicative transactions between agents.
■ Design model
➤ describes the structure of the system that needs to be
constructed.
18. Introduction 18
Principles of the Model Set
■ Divide and conquer.
■ Configuration of an adequate model set for a specific
application.
■ Models evolve through well defined states.
■ The model set supports project management.
■ Model development is driven by project objectives and risk.
■ Models can be developed in parallel.
19. Introduction 19
Models exist in various forms
■ Model template
➤ predefined, fixed structure, can be configured
■ Model instance
➤ objects manipulated during a project.
■ Model versions
➤ versions of a model instance can exist.
■ Multiple model instances
➤ separate instances can be developed
➤ example: ''current'' and ''future'' organization
20. Introduction 20
The Product
■ Instantiated models
➤ represent the important aspects of the environment and the
delivered knowledge based system.
■ Additional documentation
➤ information not represented in the filled model templates
(e.g. project management information)
■ Software
21. Introduction 21
Roles in knowledge-system
development
■ knowledge provider
■ knowledge engineer/analyst
■ knowledge system developer
■ knowledge user
■ project manager
■ knowledge manager
N.B. many-to-many relations between roles and people
22. Introduction 22
Knowledge provider/specialist
■ “traditional” expert
■ person with extensive experience in an application
domain
■ can provide also plan for domain familiarization
➤ “where would you advise a beginner to start?”
■ inter-provider differences are common
■ need to assure cooperatio
23. Introduction 23
Knowledge engineer
■ specific kind of system analyst
■ should avoid becoming an "expert"
■ plays a liaison function between application domain
and system
24. Introduction 24
Knowledge-system developer
■ person that implements a knowledge system on a
particular target platform
■ needs to have general design/implementation
expertise
■ needs to understand knowledge analysis
➤ but only on the “use”-level
■ role is often played by knowledge engineer
25. Introduction 25
Knowledge user
■ Primary users
➤ interact with the prospective system
■ Secondary users
➤ are affected indirectly by the system
■ Level of skill/knowledge is important factor
■ May need extensive interacting facilities
➤ explanation
■ His/her work is often affected by the system
➤ consider attitude / active tole
26. Introduction 26
Project manager
■ responsible for planning, scheduling and monitoring
development work
■ liaises with client
■ typically medium-size projects (4-6 people)
■ profits from structured approach
27. Introduction 27
Knowledge manager
■ background role
■ monitors organizational purpose of
➤ system(s) developed in a project
➤ knowledge assets developed/refined
■ initiates (follow-up) projects
■ should play key role in reuse
■ may help in setting up the right project team
28. Introduction 28
Roles in knowledge-system
development
knowledge
provider/
specialist
project
manager
knowledge
system
developer
knowledge
engineer/
analyst
knowledge
manager
knowledge
user
K S
manages
manages
uses
designs
&
implements
validates
elicits
knowledge
from
elicits
requirements
from
delivers
analysis
models
to
defines
knowledge
strategy
initiates
knowledge
development
projects
facilitates
knowledge
distribution
29. Introduction 29
Terminology
■ Domain
➤ some area of interest
banking, food industry, photocopiers, car manufacturing
■ Task
➤ something that needs to be done by an agent
monitor a process; create a plan; analyze deviant behavior
■ Agent
➤ the executor of a task in a domain
typically either a human or some software system
30. Introduction 30
Terminology
■ Application
➤ The context provided by the combination of a task and a
domain in which this task is carried out by agents
■ Application domain
➤ The particular area of interest involved in an application
■ Application task
➤ The (top-level) task that needs to be performed in a certain
application
31. Introduction 31
Terminology
■ knowledge system (KS)
➤ system that solves a real-life problem using knowledge
about the application domain and the application task
■ expert system
➤ knowledge system that solves a problem which requires a
considerable amount of expertise, when solved by humans.