1. The document discusses the domain model of an adaptive learning system for poor comprehenders being developed as part of the TERENCE EU project.
2. It aims to structure the learning material which includes stories and interactive games through developing ontologies for the domain model.
3. The author analyzed relevant literature and conducted expert evaluations to acquire knowledge for building the domain model ontologies, including a story ontology, game ontology, and student model ontology.
Integrating an intelligent tutoring system into a virtual worldParvati Dev
The project goal was to provide effective training to medical professionals on the SALT Triage Protocol, and to improve communication between medical professionals and military during disaster situations.
Intelligent Adaptive Learning - An Essential Element of 21st Century Teaching...DreamBox Learning
Providing truly differentiated, individualized instruction has been a goal of educators for decades, but new technologies available today are empowering schools to implement this form of education in a way never before possible. Intelligent adaptive learning software is able to tailor instruction according to each student’s unique needs, understandings and interests while remaining grounded in sound pedagogy.
Attend this web seminar to hear the latest findings from Cheryl Lemke, of the research firm Metiri Group, about how intelligent adaptive learning works, the role the technology can play in raising student achievement, and the research base required for districts to invest wisely in these new tools.
Intelligent tutoring systems (ITS) for online learningBrandon Muramatsu
Kurt VanLehn's presentation at Conversations on Quality: A Symposium on K-12 Online Learning hosted by MIT and the Bill and Melinda Gates Foundation, January 24-25, 2012, Cambridge, MA.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
Integrating an intelligent tutoring system into a virtual worldParvati Dev
The project goal was to provide effective training to medical professionals on the SALT Triage Protocol, and to improve communication between medical professionals and military during disaster situations.
Intelligent Adaptive Learning - An Essential Element of 21st Century Teaching...DreamBox Learning
Providing truly differentiated, individualized instruction has been a goal of educators for decades, but new technologies available today are empowering schools to implement this form of education in a way never before possible. Intelligent adaptive learning software is able to tailor instruction according to each student’s unique needs, understandings and interests while remaining grounded in sound pedagogy.
Attend this web seminar to hear the latest findings from Cheryl Lemke, of the research firm Metiri Group, about how intelligent adaptive learning works, the role the technology can play in raising student achievement, and the research base required for districts to invest wisely in these new tools.
Intelligent tutoring systems (ITS) for online learningBrandon Muramatsu
Kurt VanLehn's presentation at Conversations on Quality: A Symposium on K-12 Online Learning hosted by MIT and the Bill and Melinda Gates Foundation, January 24-25, 2012, Cambridge, MA.
The project aims at developing an intelligent tutoring system, to be applied in open source learning environments, able to monitor, track, analyze and give formative assessment and feedback loop to students within the learning environment, and give inputs to tutors and teachers involved in distance learning to better their role during the process of learning. The software will be developed in java thus could be easily implemented and re-used in most of the common free platforms for eLearning.
Adaptive Navigation Support and Open Social Learner Modeling for PALPeter Brusilovsky
This presentation is an overview of Open Social Learner Modeling project. It presents Mastery Grids interface, distributed personalized learning architecture Aggregate, and smart content for Java, Python, and SQL
Personalized Online Practice Systems for Learning ProgrammingPeter Brusilovsky
Computer programming is quickly transitioning from being just a key competency in computer and information science majors to being a desired skill for students in a wide range of fields. Yet, it is also one of the most challenging subjects to learn. While learning by doing is a critical component in mastering programming skills, neither the traditional educational process nor standard learning support tools provide sufficient opportunities for programming practice. In this talk, I will present our research on personalized programming practice systems for Java, Python, and SQL, which attempt to bridge this known gap in learning programming. A programming practice system engages students in practicing programming skills beyond a relatively small number of graded assignments and exams. To support learning by doing, an online practice system offers a range of interactive “smart content” such as program animations, worked examples, and various kinds of programming problems with an automatic assessment. The main challenges for online practice systems are to motivate students to practice and to guide them to the most appropriate smart content given their course goals and knowledge levels. In this talk, I will review a range of AI technologies, such as student modeling, navigation support, social comparison, and content recommendation, which support efficient programming practice. I will also discuss how personalized practice system could support COVID-19-influenced switch to online learning while maintaining an extensive level of feedback expected from an efficient learning process.
The Return of Intelligent Textbooks - ITS 2021 keynote talkPeter Brusilovsky
Early research on hypermedia learning and Web-based education featured a strong stream of work on intelligent and adaptive textbooks, which combined the knowledge modeling ideas from the field of intelligent tutoring with rich linking offered by the hypermedia and the Web. However, over the next ten years from 2005 to 2015 this area was relatively quiet as the focus of research in e-learning has shifted to other topics and other creative ideas to leverage the power of Internet. A recent gradual shift of the whole publication industry from printed books to electronic books followed by a rapid growth or the volume of online books re-ignited interests to “more intelligent” textbooks. The research on the new generation of intelligent textbooks engaged a larger set of technologies and engaged scholars from a broader range of areas including machine learning, natural language understanding, social computing, etc. In my talk I will review the past and present of research on intelligent textbooks from its origins to the diverse modern work providing examples of most interesting technologies and research results.
The Value of Social: Comparing Open Student Modeling and Open Social Student ...Peter Brusilovsky
Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55, also available at http://link.springer.com/chapter/10.1007/978-3-319-20267-9_4.
Data-Driven Education: Using Big Educational Data to Improve Teaching and Learning. Keynote slides for 15th International Conference on Web-Based Learning, ICWL 2016, Rome, Italy, October 26–29.
Data-Driven Education 2020: Using Big Educational Data to Improve Teaching an...Peter Brusilovsky
Modern educational settings from regular classrooms to MOOCs produce a a rapidly increasing volume of data that captures individual learning progress of millions of students at different level of granularity. This presence of this data opens a unique opportunity to re-engineer traditional education and build and develop a range of efficient data-driven approaches to support teaching and learning. In my talk, I will present several ways to use big educational data explored in our lab. The focus will be on open social learning modeling and identifying individual differences through sequential pattern mining, but several other approaches will be mentioned. Open social learning modeling and sequential pattern mining provides two considerably different examples on using educational data. One offers an immediate use of class interaction history to develop more engaging content access while another shows how big data could be used to uncover important individual differences that could be used to optimize the process for individual leaners.
From Expert-Driven to Data-Driven Adaptive LearningPeter Brusilovsky
Keynote slides for the Workshop on Advancing Education with Data at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug 14, 2017
Learning to Teach: Improving Instruction with Machine Learning TechniquesBeverly Park Woolf
Machine learning techniques enable instructional systems to learn about their students, topics, and pedagogical strategy. Just like master teachers who learn after years of experience, tutoring systems learn to adapt their teaching strategies to new students, and new domains and to personalize their teaching for individual students. Typical instructional systems persist in the same behavior originally encoded within them. However by using ML, tutoring systems learn from the behavior of earlier students and extend their existing knowledge. A variety of ML techniques are used with intelligent tutors, including HMM, neural networks, expectation maxima, Bayes Networks, statistical learning, regression modeling, causal modeling, and statistical models.
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
To a large extent, current tutorial dialogue systems lack the ability to gauge students’ level of mastery over the curriculum. Human tutors do gauge the level of knowledge and understanding of their tutees to some degree, although they are not very adept at diagnosing the causes of student errors.
We propose integrating a student model that evaluates the student’s understanding of curriculum elements into tutorial dialogue and that doing so can address these differences between human and simulated tutors.
Ontology-based Semantic Approach for Learning Object RecommendationIDES Editor
The main focus of this paper is to apply an ontologybased
approach for semantic learning object recommendation
towards personalized e-learning systems. Ontologies for
learner model, learning objects and semantic mapping rules
are proposed. The recommender can be able to provide
individually learning object by taking the learner preferences
and styles, which used to adjust or fine-tune in learning object
recommending process. In the proposed framework, we
demonstrated how the ontologies can be used to enable
machines to interpret and process learning resources in
recommendation system. The recommendation consists of four
steps: semantic mapping between learner and learning
objects, preference score calculation, learning object ranking
and recommending the learning object. As a result, a
personalized and most suitable learning object is
recommended to the learner.
Development of a ubiquitous learning system with scaffolding and problem base...Panita Wannapiroon Kmutnb
Noppadon Phumeechanya and Panita Wannapiroon, " Development of a Ubiquitous Learning System with Scaffolding and Problem-Based Learning Model to Enhance Problem-Solving Skills and ICT Literacy," International Journal of e-Education, e-Business, e-Management and e-Learning vo. 3, no. 2, pp. 197-201, 2013.
Mastery Grids: An Open Source Social Educational Progress VisualizationPeter Brusilovsky
Presentation for EC-TEL 2015 paper:
Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014, pp. 235-248.
Adaptive Navigation Support and Open Social Learner Modeling for PALPeter Brusilovsky
This presentation is an overview of Open Social Learner Modeling project. It presents Mastery Grids interface, distributed personalized learning architecture Aggregate, and smart content for Java, Python, and SQL
Personalized Online Practice Systems for Learning ProgrammingPeter Brusilovsky
Computer programming is quickly transitioning from being just a key competency in computer and information science majors to being a desired skill for students in a wide range of fields. Yet, it is also one of the most challenging subjects to learn. While learning by doing is a critical component in mastering programming skills, neither the traditional educational process nor standard learning support tools provide sufficient opportunities for programming practice. In this talk, I will present our research on personalized programming practice systems for Java, Python, and SQL, which attempt to bridge this known gap in learning programming. A programming practice system engages students in practicing programming skills beyond a relatively small number of graded assignments and exams. To support learning by doing, an online practice system offers a range of interactive “smart content” such as program animations, worked examples, and various kinds of programming problems with an automatic assessment. The main challenges for online practice systems are to motivate students to practice and to guide them to the most appropriate smart content given their course goals and knowledge levels. In this talk, I will review a range of AI technologies, such as student modeling, navigation support, social comparison, and content recommendation, which support efficient programming practice. I will also discuss how personalized practice system could support COVID-19-influenced switch to online learning while maintaining an extensive level of feedback expected from an efficient learning process.
The Return of Intelligent Textbooks - ITS 2021 keynote talkPeter Brusilovsky
Early research on hypermedia learning and Web-based education featured a strong stream of work on intelligent and adaptive textbooks, which combined the knowledge modeling ideas from the field of intelligent tutoring with rich linking offered by the hypermedia and the Web. However, over the next ten years from 2005 to 2015 this area was relatively quiet as the focus of research in e-learning has shifted to other topics and other creative ideas to leverage the power of Internet. A recent gradual shift of the whole publication industry from printed books to electronic books followed by a rapid growth or the volume of online books re-ignited interests to “more intelligent” textbooks. The research on the new generation of intelligent textbooks engaged a larger set of technologies and engaged scholars from a broader range of areas including machine learning, natural language understanding, social computing, etc. In my talk I will review the past and present of research on intelligent textbooks from its origins to the diverse modern work providing examples of most interesting technologies and research results.
The Value of Social: Comparing Open Student Modeling and Open Social Student ...Peter Brusilovsky
Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., and Zadorozhny, V. (2015) The Value of Social: Comparing Open Student Modeling and Open Social Student Modeling. In: F. Ricci, K. Bontcheva, O. Conlan and S. Lawless (eds.) Proceedings of 23nd Conference on User Modeling, Adaptation and Personalization (UMAP 2015), Dublin, Ireland, , June 29 - July 3, 2015, Springer Verlag, pp. 44-55, also available at http://link.springer.com/chapter/10.1007/978-3-319-20267-9_4.
Data-Driven Education: Using Big Educational Data to Improve Teaching and Learning. Keynote slides for 15th International Conference on Web-Based Learning, ICWL 2016, Rome, Italy, October 26–29.
Data-Driven Education 2020: Using Big Educational Data to Improve Teaching an...Peter Brusilovsky
Modern educational settings from regular classrooms to MOOCs produce a a rapidly increasing volume of data that captures individual learning progress of millions of students at different level of granularity. This presence of this data opens a unique opportunity to re-engineer traditional education and build and develop a range of efficient data-driven approaches to support teaching and learning. In my talk, I will present several ways to use big educational data explored in our lab. The focus will be on open social learning modeling and identifying individual differences through sequential pattern mining, but several other approaches will be mentioned. Open social learning modeling and sequential pattern mining provides two considerably different examples on using educational data. One offers an immediate use of class interaction history to develop more engaging content access while another shows how big data could be used to uncover important individual differences that could be used to optimize the process for individual leaners.
From Expert-Driven to Data-Driven Adaptive LearningPeter Brusilovsky
Keynote slides for the Workshop on Advancing Education with Data at the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, Aug 14, 2017
Learning to Teach: Improving Instruction with Machine Learning TechniquesBeverly Park Woolf
Machine learning techniques enable instructional systems to learn about their students, topics, and pedagogical strategy. Just like master teachers who learn after years of experience, tutoring systems learn to adapt their teaching strategies to new students, and new domains and to personalize their teaching for individual students. Typical instructional systems persist in the same behavior originally encoded within them. However by using ML, tutoring systems learn from the behavior of earlier students and extend their existing knowledge. A variety of ML techniques are used with intelligent tutors, including HMM, neural networks, expectation maxima, Bayes Networks, statistical learning, regression modeling, causal modeling, and statistical models.
Education must capitalize on the trend within technology toward big data. New types of data are becoming available. From evidence approaches to xAPI and the whole Training and Learning Architecture(TLA) big data is the foundation of all.
To a large extent, current tutorial dialogue systems lack the ability to gauge students’ level of mastery over the curriculum. Human tutors do gauge the level of knowledge and understanding of their tutees to some degree, although they are not very adept at diagnosing the causes of student errors.
We propose integrating a student model that evaluates the student’s understanding of curriculum elements into tutorial dialogue and that doing so can address these differences between human and simulated tutors.
Ontology-based Semantic Approach for Learning Object RecommendationIDES Editor
The main focus of this paper is to apply an ontologybased
approach for semantic learning object recommendation
towards personalized e-learning systems. Ontologies for
learner model, learning objects and semantic mapping rules
are proposed. The recommender can be able to provide
individually learning object by taking the learner preferences
and styles, which used to adjust or fine-tune in learning object
recommending process. In the proposed framework, we
demonstrated how the ontologies can be used to enable
machines to interpret and process learning resources in
recommendation system. The recommendation consists of four
steps: semantic mapping between learner and learning
objects, preference score calculation, learning object ranking
and recommending the learning object. As a result, a
personalized and most suitable learning object is
recommended to the learner.
Development of a ubiquitous learning system with scaffolding and problem base...Panita Wannapiroon Kmutnb
Noppadon Phumeechanya and Panita Wannapiroon, " Development of a Ubiquitous Learning System with Scaffolding and Problem-Based Learning Model to Enhance Problem-Solving Skills and ICT Literacy," International Journal of e-Education, e-Business, e-Management and e-Learning vo. 3, no. 2, pp. 197-201, 2013.
Mastery Grids: An Open Source Social Educational Progress VisualizationPeter Brusilovsky
Presentation for EC-TEL 2015 paper:
Loboda, T., Guerra, J., Hosseini, R., and Brusilovsky, P. (2014) Mastery Grids: An Open Source Social Educational Progress Visualization. In: S. de Freitas, C. Rensing, P. J. Muñoz Merino and T. Ley (eds.) Proceedings of 9th European Conference on Technology Enhanced Learning (EC-TEL 2014), Graz, Austria, September 16-19, 2014, pp. 235-248.
folksonomy, social tagging, tag clouds, automatic folksonomy construction, word clouds, wordle,context-preserving word cloud visualisation, CPEWCV, seam carving, inflate and push, star forest, cycle cover, quantitative metrics, realized adjacencies, distortion, area utilization, compactness, aspect ratio, running time, semantics in language technology
Experiences from Two Latin American PhD Students in IrelandThink Latin America
In this presentation, Alfredo Maldonado Guerra and Liliana Mamani Sanchez, two PhD candidates from Trinity College Dublin, a program funded by CNGL, talk about their background and experiences in Ireland as Latin American students, giving an overview of their research as well as discussing career prospects for Latin American graduates.
This PPT describes about Machine learning and its types. The Basic concept of learning also included in this slides. This presentation includes, Perceptron Network, Linear regression and Linear discriminant function.
Critical Thinking – PHI 210
Student Course Guide
Prerequisite: None
Quarter
Meeting Days/Time
Instructor
Instructor Phone
Instructor E-mail
Instructor Office Hours/Location
Academic Office Phone Number
Strayer Technical Support
1-877-642-2999
INSTRUCTIONAL MATERIAL — Required
Kirby, G. R., & Goodpaster, J.R. (2007). Thinking: An interdisciplinary approach to critical and creative thought (4th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.
INSTRUCTIONAL MATERIAL — Supporting
The following resources provide additional background and supporting information for this course. There is no need to purchase these items for the course.
Facione, P. (1998). Critical thinking: What it is and why it counts. Millbrae, CA: California
Academic Press.
Grossman, L. (2005, January 10). Jumping to conclusions. Time, p. 57.
Hurt, F. (1998). Achieving creativity: Four critical steps. Direct Marketing, 60, 40-44.
Useful critical thinking Websites:
· http://www.criticalthinking.org/
· http://www.criticalthinking.org/resources/articles/
· http://mathematics.clc.uc.edu/Vislocky/Critical%20Thinking%20part%20of%20syllabus.htm
· http://austhink.com/critical/
COURSE DESCRIPTION
This course develops the ability to identify, analyze, and evaluate reasoning in everyday discourse. It examines the elements of good reasoning from both a formal and informal perspective and introduces some formal techniques of the basic concepts of deductive and inductive reasoning. It also promotes reasoning skills through examining arguments from literature, politics, business, and the media. This course enables students to identify common fallacies, to reflect on the use of language for the purpose of persuasion, and to think critically about the sources and biases of the vast quantity of information that confronts us in the “Information Age.”
COURSE OUTCOMES
Upon the successful completion of this course, the student will be able to:
1. Define critical thinking.
2. Explain how critical thinking improves the ability to communicate accurately, both orally and in writing.
3. Develop skills for overcoming barriers which limit objective and productive critical thinking.
4. Illustrate the importance of pre-writing, the consideration of audience and tone, organizational strategies, and the recognition of effective language in the various stages of written communication.
5. Apply the principles of argumentation to analyze, evaluate, and compose effective arguments.
6. Analyze the purpose of organizational structure in textbook passages, newspaper articles, moral arguments, and mass media.
7. Identify the informal fallacies, assumptions, and biases involved in manipulative appeals and abuses of language.
8. Devise an action plan for overcoming the hindrances to the decision-making process by applying problem-solving skills to personal, professional, and academic situations and experiences.
9. Create written work utilizing the concepts of critical thinking.
10. Use.
Combining Existential Rules with the Power of CP-Theories
Tommaso Di Noia (Politecnico di Bari); Thomas Lukasiewicz (University of Oxford); Maria Vanina Martinez (Univ. Nacional del Sur and CONICET, Argentina); Gerardo I Simari (Univ. Nacional del Sur and CONICET, Argentina); Oana Tifrea-Marciuska (University of Oxford);
Combining Existential Rules with the Power of CP-Theories
Tommaso Di Noia (Politecnico di Bari); Thomas Lukasiewicz (University of Oxford); Maria Vanina Martinez (Univ. Nacional del Sur and CONICET, Argentina); Gerardo I Simari (Univ. Nacional del Sur and CONICET, Argentina); Oana Tifrea-Marciuska (University of Oxford);
Analyses the best combination of the Semantic Web languages and preference representation languages to answer personalised queries for a group of users.
I organised a talk and than we had a contest. The task is simple: in eighteen minutes, teams of 3,4, 5 people must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one sweat. The sweet needs to be on top.
The event organised by Anita Borg Scholarship Alumni.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
The domain model of adaptive learning system - presentation
1. The Domain
Model of an
Adaptive
Learning
System for
The Domain Model of an
Poor Compre-
henders Adaptive Learning System for
Oana Tifrea
¸
Poor Comprehenders
Outline
Motivations
and Objectives
of My Thesis
Oana Tifrea
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Adaptive
Learning Free University of Bozen-Bolzano
Systems and
Ontologies
The Domain Advisor:
Model Dr. Rosella Gennari
Story Ontology
Game Ontology
Co-advisor:
Work in Dr. Tania di Mascio
Progress: the
Student Model
2. The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders 1 Motivations and Objectives of My Thesis
Oana Tifrea
¸
Outline
2 Adaptive Learning Systems and Ontologies
Motivations
and Objectives
of My Thesis
Adaptive
Learning
3 The Domain Model
Systems and
Ontologies
The Domain
Model 4 Work in Progress: the Student Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
3. Motivation
The Domain
Model of an
Adaptive
Learning
System for
Poor comprehender (PC)
Poor Compre-
henders • Comprehension = identification, understanding and
Oana Tifrea
¸ reasoning
Outline • PC can identify the words, but cannot understand or
Motivations reason about them
and Objectives
of My Thesis • 10% of hearing 8-10 year-old children
Adaptive
Learning
Systems and Problem
Ontologies
The Domain
the requirements of poor comprehenders not clearly specified
Model ⇓
Story Ontology
Game Ontology no learning material easily adaptable to PCs’ requirements
Work in
Progress: the
Student Model
4. The Objective of My Thesis
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre- • The TERENCE EU project aims at building an adaptive
henders
Oana Tifrea
¸
learning system for poor comprehenders.
• In order to build the TERENCE adaptive learning system
Outline
we need to structure its learning material, that is made of
Motivations
and Objectives 1 diverse types of stories,
of My Thesis 2 interactive question-games for reasoning about stories.
Adaptive
Learning • Structuring the learning material is the task of the domain
Systems and
Ontologies model of TERENCE.
The Domain
Model
• The main goal of my thesis is building the domain model
Story Ontology
Game Ontology
for the learning material of TERENCE.
Work in
Progress: the
Student Model
5. Adaptive Learning Systems
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
ALSs adapt the learning material to the user needs.
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
6. The Conceptual Model of an ALS
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders Conceptual Model of an ALS
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
7. Why Ontologies for the Conceptual Model
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
Why ontologies for the TERENCE conceptual model?
henders
Oana Tifrea
¸ 1 OWL has formal semantics and we can to write algorithms.
Outline
2 We can write in OWL both the domain knowledge and the
Motivations
operational knowledge.
and Objectives
of My Thesis 3 To build a common terminology.
Adaptive
Learning
4 To analyze the knowledge to be acquired, and make
Systems and
Ontologies
implicit assumptions explicit.
The Domain 5 In case of the student model, to share adaptation rules
Model
Story Ontology among different ALSs via appropriate web services.
Game Ontology
Work in
Progress: the
Student Model
8. The Ontology Life Cycle
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre- Specification Identify purposes
henders
Determine how to acquire knowledge
Oana Tifrea
¸ Design the ontology architecture
Outline
Motivations
Conceptualization Extract concepts...
and Objectives
of My Thesis
Formalization Choose the level and type of formalism
Adaptive
Learning
Systems and
Ontologies Implementation Choose the implementation language...
The Domain
Model Building stage
Story Ontology
Game Ontology
Manipulation stage
Work in
Progress: the
Student Model Maintainance stage
9. Specification: Ontology Architecture
The Domain
Model of an
Adaptive
Learning
IMPORTED IN
System for story
Poor Compre-
henders
ontology
Oana Tifrea
¸
Outline
Motivations
bridge
and Objectives common ontology
of My Thesis ontology
Adaptive
Learning
Systems and
Ontologies
The Domain
Model game
Story Ontology ontology
Game Ontology
Work in
Progress: the
Student Model DOMAIN ONTOLOGIES
10. Specification: Main Purposes
The Domain Main purpose of the domain model:
Model of an
Adaptive • classifying stories and games for
Learning
System for directing the end user towards the
Poor Compre-
henders most adequate class of stories or
Oana Tifrea
¸ games.
Outline
Specific purposes of the:
Motivations 1 story ontology: analyzing and specifying concepts difficult
and Objectives
of My Thesis for poor comprehenders in stories;
Adaptive
Learning 2 game ontology: analyzing and specifying the related
Systems and
Ontologies
question-games for poor comprehenders;
The Domain 3 common ontology: incorporating the common concepts of
Model
Story Ontology the story and game ontologies, such as the language
Game Ontology
Work in
concept;
Progress: the
Student Model 4 bridge ontology: connecting the story and game
ontologies.
11. Specification: How to Acquire the Domain
Knowledge
The Domain
Model of an
How was the knowledge for building the domain model
Adaptive
Learning
acquired?
System for 1 Via expert-based evaluations with:
Poor Compre-
henders • (psycho-)linguists, e.g., Paul van den Broek;
Oana Tifrea
¸ • psychologists expert of deaf poor comprehenders, e.g.,
Barbara Arf´;
e
Outline
• psychologists expert of hearing poor comprehenders, e.g.,
Motivations
and Objectives Jane Oakhill, Barbara Carretti.
of My Thesis
2 Via a selection of reusable sources from the domain
Adaptive
Learning literature, guided by the domain experts.
Systems and
Ontologies How were the expert evaluations con-
The Domain ducted? Via:
Model
Story Ontology • questionnaires;
Game Ontology
Work in • interviews;
Progress: the
Student Model • two focus-groups: one in l’Aquila in
June; one in Padova in July 2010.
12. Conceptualization: Why the Middle-Out Approach
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
1 We followed the middle-out approach in the
conceptualization, because
Outline • there were no reusable ontologies for poor comprehenders,
Motivations • after analysing the specific purposes of our ontologies, we
and Objectives
of My Thesis could easily identify independent clusters of basic concepts
Adaptive of our domain model, that we then generalized or
Learning
Systems and specialized.
Ontologies
The Domain
2 How?
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
13. Conceptualization: Context of Use for the Domain
Knowledge
The Domain
Model of an
Adaptive
Learning
More general or specific concepts for the domain model were
System for
Poor Compre-
extracted from the context of use that we analyzed, namely:
henders • relevant text/story analysis concepts:
Oana Tifrea
¸
• mainly, concepts of reading difficulty formulae, and the
Outline more refined Coh-metrix concept scheme;
Motivations • general text analysis ontologies;
and Objectives • ontologies/concept schemes for temporal features of texts;
of My Thesis
Adaptive • relevant taxonomies of
Learning
Systems and • reading comprehension;
Ontologies • reading interventions.
The Domain
Model But, how did we decide
Story Ontology
Game Ontology • which concepts were relevant for our domain model,
Work in
Progress: the • and which had to be refined or enriched?
Student Model
14. Conceptualization: User Requirements
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
word, e.g., abstract words,
Outline
hearing = sentence, e.g., word order
Motivations
discourse, e.g., reasoning on events;
and Objectives
of My Thesis
Adaptive
word, e.g., word recognition,
Learning deaf = sentence, e.g., inter-sentence relatives,
Systems and
discourse, e.g., reasoning on events.
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
15. Conceptualization: Hearing Poor Comprehenders
Analysis at Word Level
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
16. Implementation: Main Concepts of the Story
Ontology
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
The story ontology’s main concepts are:
Oana Tifrea
¸
• the syntactic structure of the story (e.g., words, sentences,
Outline paragraphs),
Motivations
and Objectives • the semantic structure of the story (e.g., events),
of My Thesis
Adaptive
• the coherence of the story,
Learning
Systems and • the genre of the story,
Ontologies
The Domain
• the title of the story.
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
17. Implementation: Story Ontology
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
18. Implementation: Local Coherence of the Story
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
19. Implementation: Adjacent Events
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
20. Implementation: A Fragment of the Game
Ontology
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
21. Specification: The Student Ontology and Its
Purpose
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
henders Student Model of an ALS
Oana Tifrea
¸
Outline
Motivations
and Objectives
of My Thesis
Adaptive
Learning
Systems and
Ontologies
The Domain
Model
Story Ontology
Game Ontology
Work in
Progress: the
Student Model
22. Specification: Main Sources for the Student
Ontology
The Domain
Model of an
Adaptive
Learning
System for
Poor Compre-
Main sources for the student ontology:
henders
• KBS-Hyperbook and TRAILS;
Oana Tifrea
¸
• AHA!;
Outline
Motivations
• GUMO/GRAPPLE.
and Objectives
of My Thesis
GUMO-Basic defines generic user characteristics and
Adaptive
Learning personality traits by means of the so-called Characteristics and
Systems and
Ontologies Personality classes.
The Domain
Model We will refine GUMO-Basic with concepts related to the
Story Ontology
Game Ontology
domain ontology and the user requirements.
Work in
Progress: the
Student Model
23. Conclusions
The Domain
Model of an
Adaptive Summing up, my thesis work meant:
Learning
System for
Poor Compre-
1 analyzing the state of the art of ALSs, focusing on their
henders conceptual models,
Oana Tifrea
¸
2 analyzing and specifying the context of use necessary for
Outline building the TERENCE ALS (part of a technical working
Motivations
and Objectives
document of WP1 of TERENCE),
of My Thesis
3 analyzing and specifying the user requirements (part of a
Adaptive
Learning technical working document of WP1 of TERENCE),
Systems and
Ontologies 4 using them for
The Domain • building the ontologies of the domain model,
Model
Story Ontology • specifying the student model.
Game Ontology
Work in Last but not least, all this was done, iteratively, under the
Progress: the
Student Model constant guidance of the domain experts.
24. Acknowledgments
The Domain
Model of an
Adaptive
Learning
System for My thanks to:
Poor Compre-
henders • my supervisor, Rosella Gennari, and co-supervisor, Tania di
Oana Tifrea
¸
Mascio;
Outline • the psychologists and linguists of TERENCE, in particular:
Motivations
and Objectives
B. Arf´, B. Carretti, Padova U.; J. Oakhill, Sussex U.;
e
of My Thesis
• F. Abel, E. Herder, and W. Nejdl from L3S, Hannover U.,
Adaptive
Learning for the GUMO user ontology;
Systems and
Ontologies • ontology engineers, in particular, M. Rodriguez Muro, M.
The Domain
Model
Keet;
Story Ontology
Game Ontology • software engineers from l’Aquila U.
Work in
Progress: the
Student Model
25. level sistency of information in sentences.
A Snapshot have that requireswith accessing thePoor Comprehenders
memory the Hearing
Working
memory
PC
of difficulties the simultaneous [NABCS99]
storage of sentences.
Analysis at Discourse Level
Table 4.2: Poor comprehenders and written sentence comprehen-
sion.
The Domain
Model of an
Adaptive
Learning Poor comprehenders (PC) characteristics at DISCOURSE LEVEL
System for
Poor Compre- Yes No
henders
The cause of difficulties on this level is not memory. [Oak82] [MO09]
Oana Tifrea
¸ [CO07]
A) Inference Making PC have difficulties with [CO07]
Outline inference making. [BCS05]
BK is not a relevant [OCBP01]
Motivations parameter for inference [Oak82]
and Objectives making.
of My Thesis
PC have difficulties with [COE03]
Adaptive inference integration. [LK06]
Learning Inference Integration Inference integration can [OC96]
Systems and be improved with visual-
Ontologies ization.
The Domain PC have problems with [Cai09]
Model consistency checking. [CO06a]
Story Ontology
Logical inferences easier [CO99]
Game Ontology
to improve than the prag-
Work in matic inferences.
Progress: the 1)Logical Inferences PC have difficulties with [Oak82]
Student Model logical inferences. [Chi92]