Agent technologies have emerged from Artificial Intelligence and Social Sciences studies to become an emergent filed within main stream Software Engineering. Their characteristics of scalability, robustness, and easy maintainability made them attractive technique for modelling and implementing variety of systems especially Complex Systems. Whilst their similarity to objects in object-oriented systems make them easy to model and implement using extended versions of existing tools, their association with AI and human notion of agency keep them an ever evolving field.
In this talk, we will be looking at some of our current projects on emotions and intelligent learning environments to a proposal for a new more concentrated effort for emotionally-aware learning environments by utilising intelligent agents concepts and complex systems principles. The presentation will give general background on Cognitive Agents and Emotions Modelling, and on some interesting recent developments in Complex Systems.
Si And Engineering Philosophy Presentation 081110wpe
The document discusses systems intelligence as a lens for understanding engineering philosophy. It proposes that systems intelligence can be viewed as a metaheuristic for engineers to approach complex problems. Specifically, it suggests that systems intelligence involves: 1) considering the systemic context in which engineering challenges are embedded, 2) using both explicit and tacit heuristics adapted to the situation, and 3) incorporating playfulness and reframing to enhance creativity.
This presentation provides an overview of knowledge engineering and knowledge-based systems. It defines knowledge engineering as integrating knowledge into computer systems to solve complex problems requiring human expertise. The presentation discusses different views of knowledge engineering, trends in the field like the paradigm shift from transfer to modeling views, and modeling frameworks like CommonKADS. CommonKADS is highlighted as a leading methodology that supports structured knowledge engineering through detailed task and process analysis and developing knowledge systems to support business processes.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
The goal of the project is to develop a model that can predict additional facts that are implied but not explicitly stated in a knowledge base. Knowledge bases are incomplete and missing relationships between entities. The proposed model uses neural tensor networks to represent entities and their relationships in order to predict new facts by reasoning over known facts. As an example, the model can predict that Francesco Guicciardini's nationality is Italy and gender is male based on known facts about his place of birth and profession.
This document discusses knowledge-based systems (KBS), including:
- KBS deal with unstructured knowledge and can justify decisions and learn.
- Developing KBS is difficult due to high costs, limited expert availability, and risky investments.
- A common KBS development model involves requirements, design, implementation, testing, and knowledge acquisition in multiple rounds.
- Knowledge acquisition involves eliciting, representing, and updating knowledge from domain experts.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Best Practices for Assessing and Developing Leadership Capability - Spectrum ...Kyle Couch
This presentation was delivered to a senior group of Human Resources Professionals as part of a Succession Management Conference. In this presentation, we discuss the key to properly assessing leadership within organizations, and then highlight the best practices approach to developing leadership at all levels. This presentation points to the fact that many organizations are still working to build their succession plan process, and their overall talent strategy as well.
The document provides an overview of the HP ATA – Designing and Deploying Cloud Solutions certification. The certification teaches skills for designing end-to-end IT solutions that meet customer requirements and include on-premises, hosted, and cloud services. It covers topics like cloud technologies, virtualization, designing solutions for small and medium businesses, implementing and configuring solutions, optimizing and troubleshooting solutions, and managing end-to-end solutions. The certification directly applies to IT roles like architects, engineers, administrators, and support engineers.
Si And Engineering Philosophy Presentation 081110wpe
The document discusses systems intelligence as a lens for understanding engineering philosophy. It proposes that systems intelligence can be viewed as a metaheuristic for engineers to approach complex problems. Specifically, it suggests that systems intelligence involves: 1) considering the systemic context in which engineering challenges are embedded, 2) using both explicit and tacit heuristics adapted to the situation, and 3) incorporating playfulness and reframing to enhance creativity.
This presentation provides an overview of knowledge engineering and knowledge-based systems. It defines knowledge engineering as integrating knowledge into computer systems to solve complex problems requiring human expertise. The presentation discusses different views of knowledge engineering, trends in the field like the paradigm shift from transfer to modeling views, and modeling frameworks like CommonKADS. CommonKADS is highlighted as a leading methodology that supports structured knowledge engineering through detailed task and process analysis and developing knowledge systems to support business processes.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
The goal of the project is to develop a model that can predict additional facts that are implied but not explicitly stated in a knowledge base. Knowledge bases are incomplete and missing relationships between entities. The proposed model uses neural tensor networks to represent entities and their relationships in order to predict new facts by reasoning over known facts. As an example, the model can predict that Francesco Guicciardini's nationality is Italy and gender is male based on known facts about his place of birth and profession.
This document discusses knowledge-based systems (KBS), including:
- KBS deal with unstructured knowledge and can justify decisions and learn.
- Developing KBS is difficult due to high costs, limited expert availability, and risky investments.
- A common KBS development model involves requirements, design, implementation, testing, and knowledge acquisition in multiple rounds.
- Knowledge acquisition involves eliciting, representing, and updating knowledge from domain experts.
ON SOFT COMPUTING TECHNIQUES IN VARIOUS AREAScscpconf
Soft Computing refers to the science of reasoning, thinking and deduction that recognizes and uses the real world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and
distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role modelfor soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The main techniques in soft computing are evolutionary computing, artificial neural networks, and fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or
inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in
which mathematical models are not available, possible. Second, it introduced the human knowledge such as cognition,
ecognition, understanding, learning, and others into the fields of
computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various areas of soft computing techniques.
Best Practices for Assessing and Developing Leadership Capability - Spectrum ...Kyle Couch
This presentation was delivered to a senior group of Human Resources Professionals as part of a Succession Management Conference. In this presentation, we discuss the key to properly assessing leadership within organizations, and then highlight the best practices approach to developing leadership at all levels. This presentation points to the fact that many organizations are still working to build their succession plan process, and their overall talent strategy as well.
The document provides an overview of the HP ATA – Designing and Deploying Cloud Solutions certification. The certification teaches skills for designing end-to-end IT solutions that meet customer requirements and include on-premises, hosted, and cloud services. It covers topics like cloud technologies, virtualization, designing solutions for small and medium businesses, implementing and configuring solutions, optimizing and troubleshooting solutions, and managing end-to-end solutions. The certification directly applies to IT roles like architects, engineers, administrators, and support engineers.
Due to technology innovation and affordability in Social, Mobile, Analytics, Cloud and Internet of Things, "People to People", "People to Things" and "Things to Things” are getting connected in real time. We are now living in HYPER CONNECTED world. In this new world, there are two major disruptions taking place, which are significant and cannot be ignored. These are: "People becoming digital natives", and “Convergence of Physical & Digital Worlds”.
In future there will be four types of enterprises (1) Traditional (2) Digital Beginners (3) Digital Visionaries and (4) Digital Leaders.
The journey has to START now to become Future Proof & Future Ready.
SFIA is a framework that defines skills and competencies for IT professionals. It was developed in the UK and is now used globally. SFIA organizes skills into a matrix with categories, subcategories, and 7 levels of competency. It can be used by organizations for tasks like skills assessments, recruitment, and project planning. Individuals can use it for career development and job applications. Ensys Consulting is an accredited partner that provides SFIA services in Australia such as skills assessments and training.
Using SFIA as a basis for defining Enterprise Architecture skillsLouw Labuschagne
The document discusses defining enterprise architecture skills. It provides an overview of the Skills Framework for the Information Age (SFIA), which defines IT skills and levels. SFIA is used to map architecture skills. The document also discusses using an open badge framework with SFIA to standardize skills definition and training for enterprise architects. Finally, it provides an example of mapping SFIA skills to an IT engagement model.
This document discusses competency models and how to create them. It explains that competency models define the key knowledge, skills, and attributes needed for roles at the core, job, and company level. It recommends using a research-based competency library as a foundation and provides examples of competency types. The document stresses that competency models help organizations develop learning plans by identifying skill gaps, aligning learning with business needs, and assessing performance. It concludes by connecting competency models to a learning organization's overall learning and development services.
"This presentation provides an introduction into Competence-based Strategic Management.
Competence-based Strategic Management is a relatively new way of thinking about how organizations gain high performance for a significant period of time. Established as a theory in the early 1990’s, competence-based strategic management theory explains how organizations can develop competitive advantage in a systematic and structural way. In other words, a competent organization has the ability [being capable of] to structurally and systematically coordinate and commit resources for respectively the realization of the organizations goals and objectives and the creation and distribution of customer value, in order to develop competitive advantage. For developing an integrated system of resources, management needs extraordinary analytic and appraisal skills. Furthermore, the idea behind competence-based strategic management is that the difference of the mix available resources between organizations, the speed with which resources are exploited and are develop, plus the costs which are involved, is determinative for the realisation of the organizations competitive advantage. Resources are all elements, tangible or intangible, which an organization can use for the arrangement of products and bring services on the market. The resources an organization can use may be either organization-specific of organization-addressable. An organization from a competence-based perspective is seen as an open social system. Open social system = dynamic and complex collection of elements, interacting as a structured functional entity that continuously interacts with its environment.
Five challenges play a central role in the application of competence-based strategic management in order to realize continuous value creation and distribution [strategic logic]:
- Recognize market opportunities
- Define product offers that create value for customers with targeted preferences
- Attract, retain and improve the best available resources for creating and realizing product offers
- Manage uncertainties in creating and realizing product offers
- Distribute value created to providers of required resources
When managing these challenges management can choose two approaches: statically and dynamically. The focus of the static approach to competence-based management is at entirely exploiting existing resources to develop competitive advantage in the short term. In this case strategy means a maximum exploitation of the current organizational competences of the organization. Answering the three central questions from the static interpretation the central issue is to maintain the existing resources of the organization in order to develop competitive advantage.
The primary aim of the dynamic approach of the competence-based management is realising competitive advantage by constantly improving the existing resources and obtaining new resources. In this case strategy means a fit between exploiting the available resources and obtaining and developing [modify] new resources. At answering the three central questions from the dynamic interpretation the central issue is not only to maintain the existing resources of the organization but also the replacement or modification of these resources, in order to develop competitive advantage."
RHIS Curriculum: Standardizing Core Competencies and Training MaterialsMEASURE Evaluation
This document summarizes the development of a standardized Routine Health Information System (RHIS) curriculum. It describes the need to strengthen RHIS in low and middle-income countries. A consultative meeting in 2015 defined RHIS core competencies and developed a core RHIS course. This was then pilot tested in India in 2016. The finalized curriculum covers 10 modules on topics like data collection, management, analysis and use. Next steps include disseminating the curriculum through training workshops to strengthen RHIS globally.
CGMA Competency Framework for CPAs and Finance / Accounting ProfessionalsTom Hood, CPA,CITP,CGMA
The CGMA (Chartered Global Management Accountants) developed a competency framework that helps provide useful context for the career path of a global finance/accounting professional. It follows a trajectory we call the Bounce, where the initial focus is on building core technical skills and learning to lead yourself. Soon the professional moves from task specific technical work to managing people and projects. This is where momentum and development should shift to include more "success skills" like leadership, change management, collaboration, performance management and more. This turn is where the initial career momentum changes direction and there is more emphasis on the skill of leading others and leading organizations. We use the metaphor of a basketball bounce pass to who momentum and trajectory of today's finance / accounting career.
Our Business Learning Institute has been researching competencies and learning plans for finance and accounting professionals since its founding in 1999. We have mapped courses to this framework and have built curriculums for Fortune 1000 finance / accounting teams to offer a strategic and systematic approach to training. We partner with the AICPA to bring you the state of the art in training from technical to the "success skills required by the Bounce.
The Bounce covers the career trajectory of professionals as they move from technical proficiency to managing people and projects and ultimately organizations. The bounce speaks to the change in direction from technical mastery to acquiring competencies to lead others and leading organizations. It is about velocity and trajectory as you shift direction to the need for more “success skills” as you move up n your career. The Bounce includes the latest research on competencies from the AICPA through the CPA Horizons 2025 Project and the CGMA Competency framework.
More information can be found on our website at http://www.blionline.org and at the CGMA website at http://www.cgma.org
Capabilities Based Planning focuses on assessing the increasing maturity of business capabilities needed to implement enterprise strategy, rather than just delivering features and functions. It emphasizes flexibility and adaptability by taking a modular approach. Program events evaluate the maturity level of capabilities and their effect on the business, separating results from effort. This allows progress to be measured in terms of capabilities rather than just the passage of time.
This document provides a competency model that outlines key leadership outcomes and organizational performance across increasing levels of complexity. It identifies areas such as strategic alignment, commitment and competence, and creating organizational value. The model shows how officers are expected to demonstrate traits like defining markets internationally and leveraging expertise through technology. As roles increase in scope, context and thinking, leaders are expected to adapt strategies, create systems to support change, and manage business results through balanced scorecards.
The document discusses building cloud competencies and transitioning IT skills to a more strategic, commercially-focused "2 speed" model. It emphasizes that new cloud roles require precise definitions of skills using frameworks like SFIA. Organizations need integrated workforce plans to analyze skills gaps, prioritize development, and ensure internal staff adopt new skills through on-the-job learning, learning from others, and a mix of formal and informal training. Change management is also key to motivate staff and gain adoption of new skills and responsibilities.
This document discusses competency mapping. It defines competency mapping as evaluating an individual worker's strengths. It involves measuring an individual's competency in each skill against a performance standard. Competencies include skills, knowledge, behaviors, and motives. Competency mapping is useful for training and development, recruitment and selection, replacement planning, compensation, performance appraisal, and succession planning. It involves job analysis, competency-based job descriptions, performance evaluations, and identifying training needs. Methods for competency mapping include behavioral interviews, competency questionnaires, expert panels, and 360 degree appraisals. Competencies can then be implemented for recruitment, training, career planning, rewards, and performance.
Competency mapping involves determining the skills, knowledge, and behaviors required for a job role. It creates an accurate job profile used for selecting, recruiting, and retaining employees. Competency mapping identifies key attributes for each position through job analysis and behavioral interviews. It allows organizations to focus on core competencies, manage time effectively, and build competitive advantages. Competency mapping aids recruitment, performance appraisal, training, development, and pay systems.
The document discusses competency models, which are clusters of knowledge, skills, abilities, behaviors, and attitudes related to job success. It outlines different approaches for developing competency models, including universal, functional, job-specific, and multiple job models. The document also discusses how competency models can be used for human resource processes like recruitment, selection, performance management, and career development.
Project maturity flow is the incremental delivery of business valueGlen Alleman
Incremental delivery of business value can be defined through the increasing maturity of the outcomes of the project. These Capabilities provide "bookable" value to the business, instead of individual features.
Many in the agile community see MVF as the way to go. But from the business side "full capabilities" need to be in place for the Value to be "booked" using FASB 86.
No incremental partial "features."
An AP system needs the Capability to "pay" and that means receipt of invoice, 3-way match, approved PO, approved vendor and banking interface
Agent-Oriented Systems: From the Primitive to the EmotionalAladdin Ayesh
The document summarizes a talk given by Dr. Aladdin Ayesh on agent-oriented systems from primitive to emotional. It discusses the notion of agency, modeling emotions using different psychological theories, and applying agent models to projects like eLearning and text mining. It also describes collaboration between De Montfort University and Valencia University on modeling emotions in eLearning environments.
What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
Abstract: multi-agent systems and particularly bdi agents are mostly used in a wide range of projects, from agent-based simulations to air-traffic control. They all benefit from the autonomy and proactive behavior that provides agent-based architectures, as well as the characteristics of reasoning that are outlined by the bdi architecture. Thereforethe belief desire intention agent model and agentspeak language have becomea state-of-the-art and one of the challenging research subjects in the agent modeling and programming area.
In particular the bdi architecture is frequently used in the development of agents that try to simulate certainaspects of human behavior, and precisely perception and formulation of beliefs are two of the elements of bdiagents that require special attention in the development of such agents. Thiswork propose a way to extend the reasoning cycle algorithm on bdi agents, in a way that it allows to process inaccurate perceptions in the formulation of beliefs in such agents; it also shows an example implemented in agentspeak as well as the results of its execution within the jason interpreter.Keywords: Agent, Agent Speak, Beliefs, BDI, Fuzzy-BDI, Fuzzy Perceptions, Simulation.
Title :An Extended Reasoning Cycle Algorithm for BDI Agents
Author: Donald Rodriguez-Ubeda, Dora-Luz Flores, Luis Palafox, Manuel Castanon-Puga, Carelia Gaxiola-Pacheco, Ricardo Rosales
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350- 1022
Paper Publications
Abstract: multi-agent systems and particularly bdi agents are mostly used in a wide range of projects, from agent-based simulations to air-traffic control. They all benefit from the autonomy and proactive behavior that provides agent-based architectures, as well as the characteristics of reasoning that are outlined by the bdi architecture. Thereforethe belief desire intention agent model and agentspeak language have becomea state-of-the-art and one of the challenging research subjects in the agent modeling and programming area.
In particular the bdi architecture is frequently used in the development of agents that try to simulate certainaspects of human behavior, and precisely perception and formulation of beliefs are two of the elements of bdiagents that require special attention in the development of such agents. Thiswork propose a way to extend the reasoning cycle algorithm on bdi agents, in a way that it allows to process inaccurate perceptions in the formulation of beliefs in such agents; it also shows an example implemented in agentspeak as well as the results of its execution within the jason interpreter.
This document provides an introduction to software agents, discussing key dimensions of agenthood including autonomy, intelligence, and sociality. It describes how agents can exhibit these dimensions through internal components like beliefs, goals, and plans. Autonomous agents require an internal state and ability to initiate behaviors. Intelligent agents may use reasoning, learning, and decision-making. Social agents can communicate and interact with other agents through models of other agents and capabilities like negotiation. Mobility refers to agents' ability to change locations physically or between execution environments. The document outlines common software constructs used to facilitate these dimensions in agent architectures.
Dr. Hend Ezzeddinne, Cyber Security Practice Director for Expressworks, gave this talk at Austin Bsides conference in March. Folks there were quick to acknowledge that technology is not enough. Hackers are targeting human brains. Hend's talk provides insights into what can be done to help users and companies be more cyber resilient.
Due to technology innovation and affordability in Social, Mobile, Analytics, Cloud and Internet of Things, "People to People", "People to Things" and "Things to Things” are getting connected in real time. We are now living in HYPER CONNECTED world. In this new world, there are two major disruptions taking place, which are significant and cannot be ignored. These are: "People becoming digital natives", and “Convergence of Physical & Digital Worlds”.
In future there will be four types of enterprises (1) Traditional (2) Digital Beginners (3) Digital Visionaries and (4) Digital Leaders.
The journey has to START now to become Future Proof & Future Ready.
SFIA is a framework that defines skills and competencies for IT professionals. It was developed in the UK and is now used globally. SFIA organizes skills into a matrix with categories, subcategories, and 7 levels of competency. It can be used by organizations for tasks like skills assessments, recruitment, and project planning. Individuals can use it for career development and job applications. Ensys Consulting is an accredited partner that provides SFIA services in Australia such as skills assessments and training.
Using SFIA as a basis for defining Enterprise Architecture skillsLouw Labuschagne
The document discusses defining enterprise architecture skills. It provides an overview of the Skills Framework for the Information Age (SFIA), which defines IT skills and levels. SFIA is used to map architecture skills. The document also discusses using an open badge framework with SFIA to standardize skills definition and training for enterprise architects. Finally, it provides an example of mapping SFIA skills to an IT engagement model.
This document discusses competency models and how to create them. It explains that competency models define the key knowledge, skills, and attributes needed for roles at the core, job, and company level. It recommends using a research-based competency library as a foundation and provides examples of competency types. The document stresses that competency models help organizations develop learning plans by identifying skill gaps, aligning learning with business needs, and assessing performance. It concludes by connecting competency models to a learning organization's overall learning and development services.
"This presentation provides an introduction into Competence-based Strategic Management.
Competence-based Strategic Management is a relatively new way of thinking about how organizations gain high performance for a significant period of time. Established as a theory in the early 1990’s, competence-based strategic management theory explains how organizations can develop competitive advantage in a systematic and structural way. In other words, a competent organization has the ability [being capable of] to structurally and systematically coordinate and commit resources for respectively the realization of the organizations goals and objectives and the creation and distribution of customer value, in order to develop competitive advantage. For developing an integrated system of resources, management needs extraordinary analytic and appraisal skills. Furthermore, the idea behind competence-based strategic management is that the difference of the mix available resources between organizations, the speed with which resources are exploited and are develop, plus the costs which are involved, is determinative for the realisation of the organizations competitive advantage. Resources are all elements, tangible or intangible, which an organization can use for the arrangement of products and bring services on the market. The resources an organization can use may be either organization-specific of organization-addressable. An organization from a competence-based perspective is seen as an open social system. Open social system = dynamic and complex collection of elements, interacting as a structured functional entity that continuously interacts with its environment.
Five challenges play a central role in the application of competence-based strategic management in order to realize continuous value creation and distribution [strategic logic]:
- Recognize market opportunities
- Define product offers that create value for customers with targeted preferences
- Attract, retain and improve the best available resources for creating and realizing product offers
- Manage uncertainties in creating and realizing product offers
- Distribute value created to providers of required resources
When managing these challenges management can choose two approaches: statically and dynamically. The focus of the static approach to competence-based management is at entirely exploiting existing resources to develop competitive advantage in the short term. In this case strategy means a maximum exploitation of the current organizational competences of the organization. Answering the three central questions from the static interpretation the central issue is to maintain the existing resources of the organization in order to develop competitive advantage.
The primary aim of the dynamic approach of the competence-based management is realising competitive advantage by constantly improving the existing resources and obtaining new resources. In this case strategy means a fit between exploiting the available resources and obtaining and developing [modify] new resources. At answering the three central questions from the dynamic interpretation the central issue is not only to maintain the existing resources of the organization but also the replacement or modification of these resources, in order to develop competitive advantage."
RHIS Curriculum: Standardizing Core Competencies and Training MaterialsMEASURE Evaluation
This document summarizes the development of a standardized Routine Health Information System (RHIS) curriculum. It describes the need to strengthen RHIS in low and middle-income countries. A consultative meeting in 2015 defined RHIS core competencies and developed a core RHIS course. This was then pilot tested in India in 2016. The finalized curriculum covers 10 modules on topics like data collection, management, analysis and use. Next steps include disseminating the curriculum through training workshops to strengthen RHIS globally.
CGMA Competency Framework for CPAs and Finance / Accounting ProfessionalsTom Hood, CPA,CITP,CGMA
The CGMA (Chartered Global Management Accountants) developed a competency framework that helps provide useful context for the career path of a global finance/accounting professional. It follows a trajectory we call the Bounce, where the initial focus is on building core technical skills and learning to lead yourself. Soon the professional moves from task specific technical work to managing people and projects. This is where momentum and development should shift to include more "success skills" like leadership, change management, collaboration, performance management and more. This turn is where the initial career momentum changes direction and there is more emphasis on the skill of leading others and leading organizations. We use the metaphor of a basketball bounce pass to who momentum and trajectory of today's finance / accounting career.
Our Business Learning Institute has been researching competencies and learning plans for finance and accounting professionals since its founding in 1999. We have mapped courses to this framework and have built curriculums for Fortune 1000 finance / accounting teams to offer a strategic and systematic approach to training. We partner with the AICPA to bring you the state of the art in training from technical to the "success skills required by the Bounce.
The Bounce covers the career trajectory of professionals as they move from technical proficiency to managing people and projects and ultimately organizations. The bounce speaks to the change in direction from technical mastery to acquiring competencies to lead others and leading organizations. It is about velocity and trajectory as you shift direction to the need for more “success skills” as you move up n your career. The Bounce includes the latest research on competencies from the AICPA through the CPA Horizons 2025 Project and the CGMA Competency framework.
More information can be found on our website at http://www.blionline.org and at the CGMA website at http://www.cgma.org
Capabilities Based Planning focuses on assessing the increasing maturity of business capabilities needed to implement enterprise strategy, rather than just delivering features and functions. It emphasizes flexibility and adaptability by taking a modular approach. Program events evaluate the maturity level of capabilities and their effect on the business, separating results from effort. This allows progress to be measured in terms of capabilities rather than just the passage of time.
This document provides a competency model that outlines key leadership outcomes and organizational performance across increasing levels of complexity. It identifies areas such as strategic alignment, commitment and competence, and creating organizational value. The model shows how officers are expected to demonstrate traits like defining markets internationally and leveraging expertise through technology. As roles increase in scope, context and thinking, leaders are expected to adapt strategies, create systems to support change, and manage business results through balanced scorecards.
The document discusses building cloud competencies and transitioning IT skills to a more strategic, commercially-focused "2 speed" model. It emphasizes that new cloud roles require precise definitions of skills using frameworks like SFIA. Organizations need integrated workforce plans to analyze skills gaps, prioritize development, and ensure internal staff adopt new skills through on-the-job learning, learning from others, and a mix of formal and informal training. Change management is also key to motivate staff and gain adoption of new skills and responsibilities.
This document discusses competency mapping. It defines competency mapping as evaluating an individual worker's strengths. It involves measuring an individual's competency in each skill against a performance standard. Competencies include skills, knowledge, behaviors, and motives. Competency mapping is useful for training and development, recruitment and selection, replacement planning, compensation, performance appraisal, and succession planning. It involves job analysis, competency-based job descriptions, performance evaluations, and identifying training needs. Methods for competency mapping include behavioral interviews, competency questionnaires, expert panels, and 360 degree appraisals. Competencies can then be implemented for recruitment, training, career planning, rewards, and performance.
Competency mapping involves determining the skills, knowledge, and behaviors required for a job role. It creates an accurate job profile used for selecting, recruiting, and retaining employees. Competency mapping identifies key attributes for each position through job analysis and behavioral interviews. It allows organizations to focus on core competencies, manage time effectively, and build competitive advantages. Competency mapping aids recruitment, performance appraisal, training, development, and pay systems.
The document discusses competency models, which are clusters of knowledge, skills, abilities, behaviors, and attitudes related to job success. It outlines different approaches for developing competency models, including universal, functional, job-specific, and multiple job models. The document also discusses how competency models can be used for human resource processes like recruitment, selection, performance management, and career development.
Project maturity flow is the incremental delivery of business valueGlen Alleman
Incremental delivery of business value can be defined through the increasing maturity of the outcomes of the project. These Capabilities provide "bookable" value to the business, instead of individual features.
Many in the agile community see MVF as the way to go. But from the business side "full capabilities" need to be in place for the Value to be "booked" using FASB 86.
No incremental partial "features."
An AP system needs the Capability to "pay" and that means receipt of invoice, 3-way match, approved PO, approved vendor and banking interface
Agent-Oriented Systems: From the Primitive to the EmotionalAladdin Ayesh
The document summarizes a talk given by Dr. Aladdin Ayesh on agent-oriented systems from primitive to emotional. It discusses the notion of agency, modeling emotions using different psychological theories, and applying agent models to projects like eLearning and text mining. It also describes collaboration between De Montfort University and Valencia University on modeling emotions in eLearning environments.
What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
Abstract: multi-agent systems and particularly bdi agents are mostly used in a wide range of projects, from agent-based simulations to air-traffic control. They all benefit from the autonomy and proactive behavior that provides agent-based architectures, as well as the characteristics of reasoning that are outlined by the bdi architecture. Thereforethe belief desire intention agent model and agentspeak language have becomea state-of-the-art and one of the challenging research subjects in the agent modeling and programming area.
In particular the bdi architecture is frequently used in the development of agents that try to simulate certainaspects of human behavior, and precisely perception and formulation of beliefs are two of the elements of bdiagents that require special attention in the development of such agents. Thiswork propose a way to extend the reasoning cycle algorithm on bdi agents, in a way that it allows to process inaccurate perceptions in the formulation of beliefs in such agents; it also shows an example implemented in agentspeak as well as the results of its execution within the jason interpreter.Keywords: Agent, Agent Speak, Beliefs, BDI, Fuzzy-BDI, Fuzzy Perceptions, Simulation.
Title :An Extended Reasoning Cycle Algorithm for BDI Agents
Author: Donald Rodriguez-Ubeda, Dora-Luz Flores, Luis Palafox, Manuel Castanon-Puga, Carelia Gaxiola-Pacheco, Ricardo Rosales
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350- 1022
Paper Publications
Abstract: multi-agent systems and particularly bdi agents are mostly used in a wide range of projects, from agent-based simulations to air-traffic control. They all benefit from the autonomy and proactive behavior that provides agent-based architectures, as well as the characteristics of reasoning that are outlined by the bdi architecture. Thereforethe belief desire intention agent model and agentspeak language have becomea state-of-the-art and one of the challenging research subjects in the agent modeling and programming area.
In particular the bdi architecture is frequently used in the development of agents that try to simulate certainaspects of human behavior, and precisely perception and formulation of beliefs are two of the elements of bdiagents that require special attention in the development of such agents. Thiswork propose a way to extend the reasoning cycle algorithm on bdi agents, in a way that it allows to process inaccurate perceptions in the formulation of beliefs in such agents; it also shows an example implemented in agentspeak as well as the results of its execution within the jason interpreter.
This document provides an introduction to software agents, discussing key dimensions of agenthood including autonomy, intelligence, and sociality. It describes how agents can exhibit these dimensions through internal components like beliefs, goals, and plans. Autonomous agents require an internal state and ability to initiate behaviors. Intelligent agents may use reasoning, learning, and decision-making. Social agents can communicate and interact with other agents through models of other agents and capabilities like negotiation. Mobility refers to agents' ability to change locations physically or between execution environments. The document outlines common software constructs used to facilitate these dimensions in agent architectures.
Dr. Hend Ezzeddinne, Cyber Security Practice Director for Expressworks, gave this talk at Austin Bsides conference in March. Folks there were quick to acknowledge that technology is not enough. Hackers are targeting human brains. Hend's talk provides insights into what can be done to help users and companies be more cyber resilient.
This document provides an introduction to agent-based systems, including definitions of key concepts like software agents, intelligent agents, autonomous agents, and multi-agent systems. It discusses the characteristics that distinguish agents from regular software and gives examples of application domains for agent-based systems like workflow, information retrieval, and e-commerce. Finally, it lists some organizations related to agent research and popular agent development kits.
This document provides an introduction to agent-based systems, including definitions of key concepts like agents, intelligent agents, software agents, and multi-agent systems. It discusses the characteristics of agents that distinguish them from regular software and provides examples of application domains for agent-based systems. Finally, it outlines several popular agent development kits and frameworks like JADE, Jason, Cougaar, and ABLE that can be used to build agent-based applications.
The document discusses the Turing Test proposed by Alan Turing in 1950 to provide an operational definition of intelligence. The Turing Test involves an interrogator asking written questions to both a human and a computer without knowing which is which. If the interrogator cannot distinguish the written responses as coming from a human or computer, then the computer is said to have passed the Turing Test. Several capabilities are needed for a computer to pass the test, including natural language processing, knowledge representation, automated reasoning, machine learning, computer vision, and robotics. Drawbacks of the Turing Test are also discussed.
Software architecture by Dr.C.R.Dhivyaa, Assistant Professor,Kongu Engineerin...Dhivyaa C.R
The document discusses software architecture, defining it as "the structure or structures of a system, their elements, the relationships between those elements and the properties of both elements and relations." It notes that every software system has an architecture, whether known or not, and that architecture includes elements, relationships between elements, and elements' behaviors. The document outlines different types of architectural structures, including module, component-and-connector, and allocation structures. It also discusses the importance of views in representing architectures and notes that modern software systems are too complex to understand from a single view.
Complexity Theory and Why Waterfall Development Works (Sometimes)Larry Apke
A huge debate rages on in IT these days. There are two rival camps - traditionalists who subscribe to the "waterfall" methodologies and agilists. Most recent evidence suggests that agile methodologies have an edge in project success rates but the traditional methods are still widely practiced and do result in some project successes. There are reasons for the successes of agile and traditional projects that can be explained by complexity theory. This presentation will examine some interesting information about waterfall and agile methodologies and show why complexity theory can help us to predict the relative success (and failure) of applying these methodologies to software development projects.
The document provides an introduction to agent-based systems, including definitions of key concepts like agents, intelligent agents, and multi-agent systems. It also outlines several applications of agent-based systems, organizations related to promoting agent standards, and various agent development kits for creating agent-based applications.
1. Knowledge based reasoning uses logic and facts stored in a knowledge base to draw conclusions. The knowledge base represents information about the world that an AI system can use to solve problems.
2. A knowledge based agent is an AI system that maintains an internal state of knowledge in its knowledge base, reasons over that knowledge using inference rules, updates its knowledge base after observing new information, and takes actions.
3. A knowledge based agent's architecture includes a knowledge base to store facts, an inference system to apply logical rules to deduce new information and update the knowledge base, and the ability to perceive information, reason over its knowledge, and perform actions.
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.
The document provides an overview of distributed artificial intelligence and multi-agent systems. It discusses topics such as the definition of DAI, types of multi-agent systems, interaction among agents, the Agent Communication Language KQML, basic models of communication, and the definition of an agent. It also covers concepts like reactive agents, cognitive agents, classification of agents, and applications of DAI.
Approach to evaluate a user interface based on "the function it communicates", a personal interpretation derived from reflection of the meaning of GUI components and interaction for the user and the user experience.
Presentation at NordiCHI 2014
8th Nordic Conference on Human-Computer Interaction
Helsinki, Finland
October 27, 2014
ACM Press
http://dx.doi.org/10.1145/2639189.2641209
Abstract:
This paper introduces an approach for evaluating user interfaces built on visual rhetoric and the rhetorical notion of function. A personal informatics mobile application has been selected to exemplify the application of this approach. Through the results of this example evaluation, this paper discusses the consequence of applying a rhetorical evaluation to a user interface. In this discussion, it is observed that inspecting the function performed by interface components takes into account experiences, communication, and meaning. In addition, it fosters reflection and criticism.
Introduction: The Structure of Complex systems, The Inherent Complexity of Software, Attributes of Complex System, Organized and Disorganized Complexity, Bringing Order to Chaos, Designing Complex Systems
Similar to Complex Systems Approach to Emotionally-aware Learning Environments (20)
This document summarizes an invited talk given by Dr. Aladdin Ayesh on artificial intelligence topics. The talk covered definitions of AI, major AI fields like machine learning, planning, natural language processing and computer vision. It also discussed applications of AI such as intelligent interfaces, personalization, smart services and analytics. Throughout the talk, examples and potential future directions were provided for different AI topics.
Creativity Conversations - 2007
Bret Battey Vs. Aladdin Ayesh
Part of Institute Of Creative Technology Creative Conversations.
Video and Photos available from:
http://creem.dmu.ac.uk/CreativityConversations/res171007.htm
Social Robots: From Emotional Consciousness to Buddy DevicesAladdin Ayesh
This document summarizes Dr. Aladdin Ayesh's invited talk at Eton College on social robots. The talk covers several topics: introducing social robots and examples like AIBO and NAO robots; the challenges of creating emotional, personality and social norm abilities in robots; moving from theories of artificial consciousness to practical buddy devices; and ethical issues with increasingly social technologies. The talk explores concepts like intelligent spaces, virtual worlds, medical cyborg applications, and how everyday devices could become more social.
Multi-Agent Modelling With applications to robotics and cognitionAladdin Ayesh
This document summarizes a keynote talk on multi-agent modeling and its applications to robotics and cognition. The talk discusses what constitutes an agent and examines cognition from the perspectives of senses, thinking, emotions, and cognitive architectures. It also explores two types of agent embodiment: robots, which impose challenges related to physical limitations and neurology; and avatars, which raise questions about virtual bodies. The talk aims to bring together different areas of research in developing cognitive systems and modeling human behavior.
Emotions Modelling and Synthetic CharactersAladdin Ayesh
Dr. Aladdin Ayesh presents on emotions modelling and synthetic characters. He discusses some key issues in emotions modelling like selecting emotions to represent, expressing emotions, and recognizing emotions from facial expressions and other cues. He outlines some of his research projects in this area, including computational models of emotion and emotionally expressive communication languages.
Cognitive Reasoning and Inferences through Psychologically based Personalised...Aladdin Ayesh
This presentation discusses developing personalized emotion models using associative classifiers. It begins with background on adaptive user interfaces and computational emotions. A new corpus was created with video recordings of people expressing basic emotions and affects. Initial analysis of Kinect data for happy expressions across subjects is shown. The approach explores generating individualized sets of rules linking facial action units using tree/rule classifiers like M5, with the goal of personalized emotion detection models.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Choosing The Best AWS Service For Your Website + API.pptx
Complex Systems Approach to Emotionally-aware Learning Environments
1. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Complex Systems Approach to Emotionally-aware
Learning Environments
Dr. Aladdin Ayesh
Reader in Artificial Intelligence
De Montfort University
Talk given at Loughborough University
January 8, 2014
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
2. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Agenda
1
2
3
4
5
Agents and Complex Systems
Notion of Agency
Agents as structures and architectures
Context
Oscillating Emotions
Modelling Emotions
Emotional Swarms
Relevant Projects
eLearning
Mobility
Text Mining
Emotions in Learning Environments
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Conclusion
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
3. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Notion of Agency: The Principle
Agent technologies have emerged from Artificial Intelligence and Social
Sciences studies to become an emergent filed within the main stream of
Software Engineering. They are a fine example of complex systems and
many of agents-oriented technologies became the base technologies in
areas of System of Systems, Cloud Computing, and Business Intelligence
amongst others.
Yet these technologies are based on the principle notion of Agency. So
what does this mean?
To simplify the explanation let us take any ”agent” type and examine it:
Estate agent (individual)
Travel agent (individual)
Commercial or distribution agent (company)
Chemical agent (substance)
What all these different agents have in common?
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
4. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Notion of Agency: me, myself and I
The notion of agency is encompassed in the individual (the agent) and
it’s abilities to identify itself, consciously or unconsciously, in relation to
it’s self, surrounding and others. This lead us to some of the principles of
agents and agent-oriented systems:
interaction
autonomy
internal representations
behavioural expressions
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
5. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
6. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
7. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
8. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
9. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
* Easily Maintainable
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
10. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
* Easily Maintainable
* Portable
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
11. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
* Easily Maintainable
* Portable
* Adaptable and can easily be Updated
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
12. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
* Easily Maintainable
* Portable
* Adaptable and can easily be Updated
This makes agent-based systems in compliance with most of
software engineering desirable system features.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
13. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures I
From software engineering viewpoint, agents are interesting structures
that can be used to build systems with:
* Dynamic architectures
* Scalable
* Robust
* Easily Maintainable
* Portable
* Adaptable and can easily be Updated
This makes agent-based systems in compliance with most of
software engineering desirable system features.
But agents give us more ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
14. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures II
The flexibility of the agent structure and plasticity of agent’s definition,
allow us to define an agent as:
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
15. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures II
The flexibility of the agent structure and plasticity of agent’s definition,
allow us to define an agent as:
- A simple data structure such as a mindless particle that is
represented by Position and Velocity < P, V >
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
16. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures II
The flexibility of the agent structure and plasticity of agent’s definition,
allow us to define an agent as:
- A simple data structure such as a mindless particle that is
represented by Position and Velocity < P, V >
- An encompassing structure like classes in OOP with the addition of
a communication interface, such is the case with most of software
agents.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
17. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures II
The flexibility of the agent structure and plasticity of agent’s definition,
allow us to define an agent as:
- A simple data structure such as a mindless particle that is
represented by Position and Velocity < P, V >
- An encompassing structure like classes in OOP with the addition of
a communication interface, such is the case with most of software
agents.
- An all encompassing system with a degree of autonomy and
communication and interaction protocols, such is the case with
cognitive agents. The complexity of the system can vary and so does
the level of intelligence.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
18. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures III
In other words, agents can go from
That exhibits intelligence only in a collective
to
that requires a psychologist!
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
19. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures III
In other words, agents can go from
The Primitive
That exhibits intelligence only in a collective
to
that requires a psychologist!
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
20. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Agents as structures and architectures III
In other words, agents can go from
The Primitive
That exhibits intelligence only in a collective
to
The Emotional
that requires a psychologist!
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
21. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Platforms
There are several platforms for agents-based system development.
- Jade
- Repast-Simphony
- Netlogo
- Agentlets
- Jason
- FIPA-OS
Two good sources of information on platforms and agents standards are
FIPA and EU AgentLink.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
22. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Concepts and Terminology
Some basic concepts and terminologies that distinguish agent-oriented
systems.
- Delegation
- Responsibility
- Social Norms
- Trust
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
23. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Notion of Agency
Agents as structures and architectures
Context
Context and Limitation
Agents-oriented systems field is large and wide in scope. It is by nature a
multi-disciplinary subject criss-crossing multiple areas.
Thus this talk, whilst giving a birds-eye view of agent technologies, will
focus on specific component of an agent, that is
The Representational System
In particular on representing Emotions, especially in the context of
Learning Environments (LE).
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
24. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Modelling Emotions
Emotions work as regulators of human behaviours. They influence the
actions selection process.
In some cases, they act as mere preference, which often view as a
reflection of the personality of the actor.
In other cases, emotions are too complicated to understand in separation
of the full cognitive architecture of the human mind.
Modelling emotions oscillate between these two facts even in psychology
of emotions one finds several theories from simple set of basic emotions
to emotional spaces.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
25. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotions Models I
The simplest of emotion models are models based on Darwain’s basic
emotions: Happiness, Sadness, Anger, Fear, Surprise, Disgust.
The interpretation of these emotions into computational models vary
from basic thresholds to a set of inference rules.
Another set of computational models of emotions are based on single
emotion theories, in particular stress.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
26. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotions Models II
We worked with three psychological theories of emotions:
We examined these models in reinforcement learning, game character
controller and facial expression analysis.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
27. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotions Models II
We worked with three psychological theories of emotions:
1
Darwin’s Basic Emotions using both discreet and fuzzy set
representation.
We examined these models in reinforcement learning, game character
controller and facial expression analysis.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
28. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotions Models II
We worked with three psychological theories of emotions:
1
Darwin’s Basic Emotions using both discreet and fuzzy set
representation.
2
Milenson 3 dimensional space of emotions using fuzzy sets type I
and II, and swarm-based emotional model.
We examined these models in reinforcement learning, game character
controller and facial expression analysis.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
29. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotions Models II
We worked with three psychological theories of emotions:
1
Darwin’s Basic Emotions using both discreet and fuzzy set
representation.
2
Milenson 3 dimensional space of emotions using fuzzy sets type I
and II, and swarm-based emotional model.
3
Geneva wheel inspired model of emotions using fuzzy sets type I and
II
We examined these models in reinforcement learning, game character
controller and facial expression analysis.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
30. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotional Swarms I
One of key applications of agent-based swarm intelligence is crowd
simulation.
The simplest form of crowd is flocks of birds, thus one of the earliest and
most successful swarm intelligence algorithms is flocking algorithm.
In simulating human crowd, however, flocking-like algorithms give us
limited scope for realistic simulation.
Injecting crow elements with aspects of emotional models to influence
their action selection, produces interesting results, e.g. panic as an
emergent behaviour rather than coded.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
31. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotional Swarms II
The greatest use of swarm algorithms, which sometimes exceed their
original use in simulations, is optimization. Several machine learning
algorithms were produced for this purpose:
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
32. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotional Swarms II
The greatest use of swarm algorithms, which sometimes exceed their
original use in simulations, is optimization. Several machine learning
algorithms were produced for this purpose:
1
Particle Swarm Optimization (PSO) is very popular with engineering
applications and in most cases provide an alternative to GAs. The
swarm element here is the mindless particle. Agent technologies
have little or nothing to do with it.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
33. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Emotional Swarms II
The greatest use of swarm algorithms, which sometimes exceed their
original use in simulations, is optimization. Several machine learning
algorithms were produced for this purpose:
1
Particle Swarm Optimization (PSO) is very popular with engineering
applications and in most cases provide an alternative to GAs. The
swarm element here is the mindless particle. Agent technologies
have little or nothing to do with it.
2
Ant Colony Optimization (ACO) is an advanced variation on Ant
System and is very popular with solving optimization problems that
can be presented as graphs, e.g. networks. Agent technologies play
core role in developing these algorithms.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
34. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Social Cognition
There are variations on insects and other biological agents like swarms.
The more complex the agents involved the more complex is the
interaction and the role that sensing-feeling-emotions regulators can play
such is the case in reinforcement learning.
This introduces the subject of social cognition which is at the heart of
many applications of crowd simulation and it is applications in business
intelligence, building design, urban development, etc.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
35. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Modelling Emotions
Emotional Swarms
Social Cognition
There are variations on insects and other biological agents like swarms.
The more complex the agents involved the more complex is the
interaction and the role that sensing-feeling-emotions regulators can play
such is the case in reinforcement learning.
This introduces the subject of social cognition which is at the heart of
many applications of crowd simulation and it is applications in business
intelligence, building design, urban development, etc.
- Social Networking ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
36. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
eLearning I
eLearning systems are by nature distributed and dynamic systems which
are perfect for the use of agents technologies.
We used agents to manage the infrastructure of eLearning systems on
data girds.
We used them to manage the learning process itself to enable student
mobility between institutes and course programs.
But as the use of serious gaming and mobile devices is expanding there
are several venues where agents can be used. Such as ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
37. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
eLearning II
- Agents, coupled with gaming technologies, can be avatars for the
learner in virtual and distributed class rooms.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
38. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
eLearning II
- Agents, coupled with gaming technologies, can be avatars for the
learner in virtual and distributed class rooms.
- Agents, coupled with preference models, can be the engines behind
interactive HCI to enable the learning process.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
39. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
eLearning II
- Agents, coupled with gaming technologies, can be avatars for the
learner in virtual and distributed class rooms.
- Agents, coupled with preference models, can be the engines behind
interactive HCI to enable the learning process.
- Agents, coupled with policy languages, NLP, gaming and emotions,
can be virtual managers of the user interaction with an eLearning
system.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
40. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
Mobility I
Mobile devices are spreading at a rapid speed not seen with any other
technology. To enable these devices a host of hardware and software
infrastructures were developed, continue to be developed and need
continuous maintenance and management.
Agent technologies provide solutions at the various levels of mobility from
managing network infrastructure, to services run on the network, to
services provided to the end user.
As an example for location-based services ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
41. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
Mobility II
With the introduction of easily accessible smartphones, Cloud
Computing, 4G mobile networks, Web 2.0, and Social Media
technologies, data volumes and availability are increasing exponentially.
Dynamic scalable system architectures and new protocols to implement
these systems are ever more needed.
Equally, systems that exploit this rich stream of data are also in
demands. Thus ...
Content management * Sentiment Analysis * Profiling * Security
are some of mobility research concerns that criss-cross agent-oriented
systems research and technologies.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
42. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
eLearning
Mobility
Text Mining
Text Mining
Text mining is an application field of agents as well as a necessary
technology needed by intelligent agents, which may form the basis to
dialogue based systems.
Knowledge intensive agents are often used in text mining. Agent
technologies here include: Blackboard architectures, Description logics,
and Ontology.
Text mining algorithms may be used in agents forming part of business
intelligence system, or agent-based dialogue systems for HCI.
Sentiment analysis is currently a hot topic in computational emotions
research.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
43. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in ITS - I
Going back to our starting point about the Notion of Agency, and the
examples of estate agents, that exact notion makes agent technologies
great for delivering services to the end user, i.e. HCI.
The flexibility of agent-oriented systems also means that we do not have
to develop a full agent or multi-agent system to use agent technology.
So whilst it is not necessary to develop a full cognitive agent in an
eLearning system interface, emotions and emotion models developed
elsewhere can be used.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
44. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in ITS - II
In delivering lectures or labs, a tutor can detect interest and
concentration levels in the students.
According to this highly heuristic human system, the tutor, often to
successful results, adjust the delivery of the lecture to maintain interest in
the subject for the majority of the class.
eLearning systems in general and Intelligent Tutoring Systems (ITS) in
particular lack this much needed ability. Unless ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
45. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in ITS - III
We are trying to detect subtle emotional expressions from Facial
Expressions to discern the learner emotional state, and hence
concentration and interest levels.
This focuses on translating facial expressions into emotional model into
”affect” or ”mental” states into an intelligent response by ITS into an
adaptive user-centred service delivery.
This may be extended later to include Gestures.
Then it may be extended further to incorporate work being done at DMU
on detecting single emotion, stress, from non-visual devices.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
46. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in Game-Based MLE
This is a new project which is at a proposal stage. The idea is to use
game technologies coupled with emotions and preference modelling to
deliver some difficult Engineering topics. Some of the aims are:
The details at this stage is limited but hopefully more will be available
later in the year.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
47. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in Game-Based MLE
This is a new project which is at a proposal stage. The idea is to use
game technologies coupled with emotions and preference modelling to
deliver some difficult Engineering topics. Some of the aims are:
- To relate the topics to real world by which the complexity can be
reduced and concepts can be explained in more accessible forms.
The details at this stage is limited but hopefully more will be available
later in the year.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
48. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Emotions in Game-Based MLE
This is a new project which is at a proposal stage. The idea is to use
game technologies coupled with emotions and preference modelling to
deliver some difficult Engineering topics. Some of the aims are:
- To relate the topics to real world by which the complexity can be
reduced and concepts can be explained in more accessible forms.
- To retain attention of the student and encourage engagement and
active participation with the topics.
The details at this stage is limited but hopefully more will be available
later in the year.
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
49. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Complex Systems Network
As an associate member of Institut des Syst`mes Complexes en
e
Normandie (ISCN) I am engaged in multi-disciplinary Complex Systems
Network in Europe. The most current project is being UniTwin UNESCO
CS-DC Project. One of the main aims of the project is to connect
Universities from developing countries with Universities from developed
countries.
This in part inspired the following project ...
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
50. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Collaboration with Valencia University
Collaboration with DMU Engineering
Complex Systems Network and New Projects
Complex Systems Approach to Emotions
We are already using emotions in learning environments. We have done
work on agents-managed grid systems for eLearning and on policy-based
system supporting student mobility. All of which would be of useful in
international twinning of Universities.
Similarly, we worked on non-standard approaches to emotion modelling
that aligns well with Complex Systems.
Can we develop an integrated platform for Complex Systems Approach
for the use of Emotions in Learning Environments and Emotionally-aware
LEs?
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments
51. Agents and Complex Systems
Oscillating Emotions
Relevant Projects
Emotions in Learning Environments
Conclusion
Conclusion
In this talk, we travelled quickly amongst agent technologies to emotions
modeling to intellgient tutoring systems (ITS) to learning environments
(LE).
Our focus was on emotions and emotion modelling in particular with
related current collaborative projects and future possible projects.
If you are interested in discussing any of the projects or collaboration on
any of the upcoming projects please do contact me.
Dr. Aladdin Ayesh
aayesh@dmu.ac.uk – dr.aladdin.ayesh@ieee.org
www.aladdin-ayesh.info
Dr. Aladdin Ayesh Reader in Artificial Intelligence
Complex Systems Approach to Emotionally-aware Learning Environments