This document discusses semantically-aware networks and services for training and knowledge management. It describes software developed at CICE/LICEF for building ontologies and semantically referencing resources to enable semantic search and personalized recommendations. The TELOS system uses competency descriptors and comparison methods to power rules-based recommender agents that are integrated into learning scenarios to provide adaptive assistance to users. Future work is aimed at experimental validation, improving group recommendations, automation, and integrating other recommendation methods.
The document discusses Simplify, a framework for enabling fast functional simulation of multiprocessor system-on-chips (MPSoCs). Simplify uses an abstract MPSoC platform model to allow for easy modeling of MPSoC architectures and fast behavioral simulation. It integrates an operating system and supports tasks migration and communication between processors. Experimental results show that Simplify achieves scalable simulation performance and allows for online design, simulation, and debugging of MPSoCs.
This document discusses named entity recognition (NER) tasks and benchmarks for evaluating NER tools. It provides a brief history of NER benchmarks including CoNLL 2003/2005, ACE 2004-2007, TAC 2009, and ETAPE 2012. It also summarizes several standalone and web-based NER tools. The document outlines two human-annotated NER benchmarks, WEKEX 2011 and ISWC 2011, that were used to evaluate various NER tools and measure inter-annotator agreement. Finally, it introduces the NERD framework which aims to standardize and improve NER by developing an ontology, REST API, and linking NER extractions to Linked Open Data.
OpenCloudware Collaborative Project presented at Cloud Expo Europe 2012 in Lo...opencloudware
The OpenCloudware project aims to develop a collaborative cloud platform for building, maintaining, and operating enterprise PaaS applications across multiple infrastructure providers (IaaS). It involves 18 partners over 3 years, funded by the French government. The project will produce open-source middleware and components for modeling, building, deploying, and managing distributed applications on clouds from a self-service portal. It will provide technologies for virtual infrastructure, application templates, PaaS management, multi-cloud support, security, and more. The results will be disseminated within the OW2 Open Source Cloudware initiative.
WebLab, open source media mining platform, OW2con'12, ParisOW2
The Web is large and information is present in many forms. Complex techniques are necessary to discover the hidden structure of content and a single software provider cannot be expert on all them. Thus the integration platform comes as a perfect solution enabling the use of the best tools for each function. In this presentation we will present OSINT challenges and its growing importance. Then we will detail the WebLab approach to build flexible and scalable OSINT applications matching the fast-paced nature of OSINT. From semantic data models to upper architecture passing through selected technologies used, the presentation will do the complete tour of the WebLab project.
Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ec...Alfonso Pierantonio
The document discusses model-driven engineering and metamodeling ecosystems. It notes that in MDE, metamodels are cornerstones that define related artifacts like models, transformations, and editors. When a metamodel changes, it can invalidate these other artifacts in the ecosystem. The document examines challenges in co-evolving all artifacts when a metamodel changes, such as manually adapting models which is tedious and error-prone. It proposes that an ecosystem needs infrastructure to consistently co-evolve artifacts, such as by defining relationships between elements and detecting change impacts to determine necessary adaptations. A megamodel is proposed as a way to formally specify an ecosystem and the dependencies between its elements.
Slides of the second paper on the ULiS project, availiable at http://maxime-lefrancois.info/Publications
We are interested in bridging the world of natural language and the world of the semantic web in particular to support multilingual access to the web of data. In this paper we introduce the ULiS project, that aims at designing a pivot-based NLP technique called Universal Linguistic System, 100% using the semantic web formalisms, and being compliant with the Meaning-Text theory. Through the ULiS, a user could interact with an interlingual knowledge base (IKB) in controlled natural language. Linguistic resources themselves are part of a specific IKB: The Universal Lexical Knowledge base (ULK), so that actors may enhance their controlled natural language, through requests in controlled natural language. We describe a basic interaction scenario at the system level, and provide an overview of the architecture of ULiS. We then introduce the core of the ULiS: the interlingual lexical ontology (ILexicOn), in which each interlingual lexical unit class (ILUc) supports the projection of its semantic decomposition on itself. We validate our model with a standalone ILexicOn, and introduce and explain a concise human-readable notation for it.
The document discusses Simplify, a framework for enabling fast functional simulation of multiprocessor system-on-chips (MPSoCs). Simplify uses an abstract MPSoC platform model to allow for easy modeling of MPSoC architectures and fast behavioral simulation. It integrates an operating system and supports tasks migration and communication between processors. Experimental results show that Simplify achieves scalable simulation performance and allows for online design, simulation, and debugging of MPSoCs.
This document discusses named entity recognition (NER) tasks and benchmarks for evaluating NER tools. It provides a brief history of NER benchmarks including CoNLL 2003/2005, ACE 2004-2007, TAC 2009, and ETAPE 2012. It also summarizes several standalone and web-based NER tools. The document outlines two human-annotated NER benchmarks, WEKEX 2011 and ISWC 2011, that were used to evaluate various NER tools and measure inter-annotator agreement. Finally, it introduces the NERD framework which aims to standardize and improve NER by developing an ontology, REST API, and linking NER extractions to Linked Open Data.
OpenCloudware Collaborative Project presented at Cloud Expo Europe 2012 in Lo...opencloudware
The OpenCloudware project aims to develop a collaborative cloud platform for building, maintaining, and operating enterprise PaaS applications across multiple infrastructure providers (IaaS). It involves 18 partners over 3 years, funded by the French government. The project will produce open-source middleware and components for modeling, building, deploying, and managing distributed applications on clouds from a self-service portal. It will provide technologies for virtual infrastructure, application templates, PaaS management, multi-cloud support, security, and more. The results will be disseminated within the OW2 Open Source Cloudware initiative.
WebLab, open source media mining platform, OW2con'12, ParisOW2
The Web is large and information is present in many forms. Complex techniques are necessary to discover the hidden structure of content and a single software provider cannot be expert on all them. Thus the integration platform comes as a perfect solution enabling the use of the best tools for each function. In this presentation we will present OSINT challenges and its growing importance. Then we will detail the WebLab approach to build flexible and scalable OSINT applications matching the fast-paced nature of OSINT. From semantic data models to upper architecture passing through selected technologies used, the presentation will do the complete tour of the WebLab project.
Evolutionary Togetherness: How to Manage Coupled Evolution in Metamodeling Ec...Alfonso Pierantonio
The document discusses model-driven engineering and metamodeling ecosystems. It notes that in MDE, metamodels are cornerstones that define related artifacts like models, transformations, and editors. When a metamodel changes, it can invalidate these other artifacts in the ecosystem. The document examines challenges in co-evolving all artifacts when a metamodel changes, such as manually adapting models which is tedious and error-prone. It proposes that an ecosystem needs infrastructure to consistently co-evolve artifacts, such as by defining relationships between elements and detecting change impacts to determine necessary adaptations. A megamodel is proposed as a way to formally specify an ecosystem and the dependencies between its elements.
Slides of the second paper on the ULiS project, availiable at http://maxime-lefrancois.info/Publications
We are interested in bridging the world of natural language and the world of the semantic web in particular to support multilingual access to the web of data. In this paper we introduce the ULiS project, that aims at designing a pivot-based NLP technique called Universal Linguistic System, 100% using the semantic web formalisms, and being compliant with the Meaning-Text theory. Through the ULiS, a user could interact with an interlingual knowledge base (IKB) in controlled natural language. Linguistic resources themselves are part of a specific IKB: The Universal Lexical Knowledge base (ULK), so that actors may enhance their controlled natural language, through requests in controlled natural language. We describe a basic interaction scenario at the system level, and provide an overview of the architecture of ULiS. We then introduce the core of the ULiS: the interlingual lexical ontology (ILexicOn), in which each interlingual lexical unit class (ILUc) supports the projection of its semantic decomposition on itself. We validate our model with a standalone ILexicOn, and introduce and explain a concise human-readable notation for it.
Mobile learning- New Tools for a New CurriculumJohn Sloan
This presentation was made at the Pearson Celebrating a 21st Century Education Conference, November 2010.
It gives background research and exemplars of how mobile devices can be used to enhance 21st Century Maths and Science learning
Enterprise search allows organizations to search for information from various sources, such as databases, documents, and intranets. The enterprise search market is growing at a CAGR of 11.2% and is expected to reach $2.6 billion by 2017. Major vendors include Apache Software Foundation, LucidWorks, and Sematext. Emerging trends in enterprise search include cloud-based SaaS solutions, mobile search capabilities, and smart computing technologies that enable self-learning and adaptive search. Buyers are seeking solutions that offer scalability, intuitive interfaces, and relevance through features like categorization, tuning, and analytics.
Angelo Furno and Eugenio Zimeos presentation at the 2nd Awareness Workshop on challenges for achieving self-awareness in autonomic systems at SASO 2012, Lyon.
The aim of this project is to provide a contextualised, social and historical account of urban education, focusing on systems and beliefs that contribute to the construction of the surrounding discourses.
Another aim of this project is to scaffold the trainee teachers’ understanding of what is possible with mobile learning in terms of filed trips.
Extending Recommendation Systems With Semantics And Context AwarenessVictor Codina
This document proposes extending recommendation systems with semantics and context-awareness. It discusses limitations of traditional recommendation models and how semantics and context could help overcome those limitations. The authors propose a model that uses domain concepts with implicit semantics relationships and contextual concepts without semantics. An offline experiment on a pruned MovieLens dataset compares the proposed model to baselines. Results show the proposed contextual-semantic model improves prediction accuracy overall and for cold-start users compared to static and non-semantic models.
This document summarizes Zoran Jeremić's PhD dissertation on using semantic web technologies to support collaborative learning. It outlines the basic concepts of collaborative learning and semantic web technologies. It then describes the DEPTHS system, which uses an ontology and semantic web services to provide context-aware learning resources and tools to support project-based learning of software design patterns. An evaluation with students found the DEPTHS approach was generally effective for learning patterns and the integrated tools were useful.
OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for...Joshwa Philip
(1) The document proposes OmniSuggest, a context-aware recommendation system for mobile social networks. (2) OmniSuggest utilizes a combination of collaborative filtering and social computing techniques to provide personalized venue recommendations to both individual users and groups. (3) It addresses limitations of existing systems like data sparsity and cold start problems through a cloud-based architecture that ranks users and venues and creates similarity graphs.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
invited talk presented for the Distinguished Speaker Series of the Institute for Software Research (ISR) at the University of California, Irvine, 5 April 2013
Contribution to proactivity in mobile context-aware recommender systemsDaniel Gallego Vico
1) The document proposes methods for incorporating proactivity into mobile context-aware recommender systems (CARS) and evaluates their impact on user experience.
2) An architecture is presented for building social mobile CARS that integrates various social data sources while addressing privacy, cross-platform use, and cold start issues.
3) A model is described for generating proactive recommendations in mobile CARS based on assessing the appropriateness of the user's situation and suitability of item recommendations.
Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR).
Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity.
The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas.
In-Time On-Place Learning — Creation, Annotation and Sharing of Location-Base...Teemu Leinonen
Presentation in the 10th International Conference on Mobile Learning 2014, 28 February – 2 March, Madrid, Spain. The aim of the research is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. The paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper presents challenges of supporting learning as part of work practices taking place in the workplace, because learning has different attributes during work than in formal educational contexts: e.g. it is informal, just in time and social. The theoretical framework of the design is the tradition of pragmatism. We start with the concepts of experience, change of practices / habits and reflection, claiming that living through experiences suggest changes for practices and these trigger reflective processing of the situations. We present an Android application ‘Ach So!’ for creating and annotating short videos as potential solution for informal learning for physical work practices. The paper ends in proposing future steps in the development of the application. The co-design process for the application is lean and iterative, where the design receives feedback from the project partners, skilled workers, apprentices and managers of SMEs targeted to be the main users of the application.
Best Practices in Recommender System ChallengesAlan Said
Recommender System Challenges such as the Netflix Prize, KDD Cup, etc. have contributed vastly to the development and adoptability of recommender systems. Each year a number of challenges or contests are organized covering different aspects of recommendation. In this tutorial and panel, we present some of the factors involved in successfully organizing a challenge, whether for reasons purely related to research, industrial challenges, or to widen the scope of recommender systems applications.
Kdd 2014 Tutorial - the recommender problem revisitedXavier Amatriain
This document summarizes a presentation given by Xavier Amatriain at the KDD conference in August 2014 about recommender systems. The presentation discusses the evolution of the recommender problem from search to discovery and recommendation. It then covers traditional recommendation methods like collaborative filtering, both memory-based and model-based using matrix factorization. It discusses challenges like sparsity and explores techniques learned from the Netflix Prize like SVD, restricted Boltzmann machines, and ensemble methods.
This document provides an overview of Apache MXNet and deep learning on AWS. It begins with an introduction to deep learning applications and trends. The rest of the document discusses MXNet features like scalability, language support and frameworks comparisons. It also covers MXNet usage on AWS like integration with services and AI research. The document concludes with developer resources like notebooks, documentation and tools for building models with MXNet.
Pal gov.tutorial3.session3.xpath & xquery (lab1)Mustafa Jarrar
This tutorial document provides an overview of XPath and how it can be used to navigate an XML document by examining the different node types and axes in XPath that represent the hierarchical structure. It describes the XPath data model and how location path expressions use axes, node tests, and predicates to select nodes in an XML document based on their relationship to the context node.
These slides accompanied my presentation about my research process called "demo-driven research". The presentation was first held at Hasso Platner Institute on 2007-11-28.
The workshop discussed the MOOSE software analysis platform and included presentations on:
1) Deciphering an old Cobol system to replace it with a new ERP system
2) Exposing hidden dependencies between DSL scripts and Java classes in a heterogeneous application
3) Translating visualizations from Mondrian to HTML formats
4) Using unit tests to increase accuracy of inter-method dependency analysis in Smalltalk applications
5) The Orion tool for exploring quality improvement paths and AspectMaps for aspect-oriented modeling
The workshop discussed the MOOSE software analysis platform and included presentations on:
1) Deciphering a legacy Cobol system to replace it with a new ERP system
2) Exposing hidden dependencies between DSL scripts and Java classes accessing shared database tables
3) Translating visualizations from Mondrian to HTML for server-side rendering
4) Using unit tests to increase accuracy of inter-method dependency analysis in Smalltalk systems
The workshop was well-attended and generated discussion around various MOOSE-based analysis techniques.
RESTing in the ALPS Mike Amundsen's Presentation from QCon London 2013CA API Management
The document discusses the author's realization that his previous work on H-Factors for describing protocol affordances was missing a consideration of "application affordances". This led to the idea of "vocabularies" for describing shared understanding at the application level. However, vocabularies alone do not describe how to interact with application concepts. The author proposes a new specification called ALPS that combines the description of application concepts ("what") using vocabularies, along with the description of how to interact with those concepts using hypermedia controls and protocols ("how"). ALPS aims to provide shared understanding of both the state and transitions of application domains across different media types and implementations.
Mobile learning- New Tools for a New CurriculumJohn Sloan
This presentation was made at the Pearson Celebrating a 21st Century Education Conference, November 2010.
It gives background research and exemplars of how mobile devices can be used to enhance 21st Century Maths and Science learning
Enterprise search allows organizations to search for information from various sources, such as databases, documents, and intranets. The enterprise search market is growing at a CAGR of 11.2% and is expected to reach $2.6 billion by 2017. Major vendors include Apache Software Foundation, LucidWorks, and Sematext. Emerging trends in enterprise search include cloud-based SaaS solutions, mobile search capabilities, and smart computing technologies that enable self-learning and adaptive search. Buyers are seeking solutions that offer scalability, intuitive interfaces, and relevance through features like categorization, tuning, and analytics.
Angelo Furno and Eugenio Zimeos presentation at the 2nd Awareness Workshop on challenges for achieving self-awareness in autonomic systems at SASO 2012, Lyon.
The aim of this project is to provide a contextualised, social and historical account of urban education, focusing on systems and beliefs that contribute to the construction of the surrounding discourses.
Another aim of this project is to scaffold the trainee teachers’ understanding of what is possible with mobile learning in terms of filed trips.
Extending Recommendation Systems With Semantics And Context AwarenessVictor Codina
This document proposes extending recommendation systems with semantics and context-awareness. It discusses limitations of traditional recommendation models and how semantics and context could help overcome those limitations. The authors propose a model that uses domain concepts with implicit semantics relationships and contextual concepts without semantics. An offline experiment on a pruned MovieLens dataset compares the proposed model to baselines. Results show the proposed contextual-semantic model improves prediction accuracy overall and for cold-start users compared to static and non-semantic models.
This document summarizes Zoran Jeremić's PhD dissertation on using semantic web technologies to support collaborative learning. It outlines the basic concepts of collaborative learning and semantic web technologies. It then describes the DEPTHS system, which uses an ontology and semantic web services to provide context-aware learning resources and tools to support project-based learning of software design patterns. An evaluation with students found the DEPTHS approach was generally effective for learning patterns and the integrated tools were useful.
OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for...Joshwa Philip
(1) The document proposes OmniSuggest, a context-aware recommendation system for mobile social networks. (2) OmniSuggest utilizes a combination of collaborative filtering and social computing techniques to provide personalized venue recommendations to both individual users and groups. (3) It addresses limitations of existing systems like data sparsity and cold start problems through a cloud-based architecture that ranks users and venues and creates similarity graphs.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
invited talk presented for the Distinguished Speaker Series of the Institute for Software Research (ISR) at the University of California, Irvine, 5 April 2013
Contribution to proactivity in mobile context-aware recommender systemsDaniel Gallego Vico
1) The document proposes methods for incorporating proactivity into mobile context-aware recommender systems (CARS) and evaluates their impact on user experience.
2) An architecture is presented for building social mobile CARS that integrates various social data sources while addressing privacy, cross-platform use, and cold start issues.
3) A model is described for generating proactive recommendations in mobile CARS based on assessing the appropriateness of the user's situation and suitability of item recommendations.
Recommenders Systems tutorial slides from the European Summer School of Information Retrieval (ESSIR).
Covers basic ideas on Collaborative Filtering, Content-based methods, Matrix Factorization, Restricted Boltzmann Machines, Ranking, Diversity.
The slides include material from Xavier Amatriain, Saul Vargas and Linas Baltrunas.
In-Time On-Place Learning — Creation, Annotation and Sharing of Location-Base...Teemu Leinonen
Presentation in the 10th International Conference on Mobile Learning 2014, 28 February – 2 March, Madrid, Spain. The aim of the research is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. The paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper presents challenges of supporting learning as part of work practices taking place in the workplace, because learning has different attributes during work than in formal educational contexts: e.g. it is informal, just in time and social. The theoretical framework of the design is the tradition of pragmatism. We start with the concepts of experience, change of practices / habits and reflection, claiming that living through experiences suggest changes for practices and these trigger reflective processing of the situations. We present an Android application ‘Ach So!’ for creating and annotating short videos as potential solution for informal learning for physical work practices. The paper ends in proposing future steps in the development of the application. The co-design process for the application is lean and iterative, where the design receives feedback from the project partners, skilled workers, apprentices and managers of SMEs targeted to be the main users of the application.
Best Practices in Recommender System ChallengesAlan Said
Recommender System Challenges such as the Netflix Prize, KDD Cup, etc. have contributed vastly to the development and adoptability of recommender systems. Each year a number of challenges or contests are organized covering different aspects of recommendation. In this tutorial and panel, we present some of the factors involved in successfully organizing a challenge, whether for reasons purely related to research, industrial challenges, or to widen the scope of recommender systems applications.
Kdd 2014 Tutorial - the recommender problem revisitedXavier Amatriain
This document summarizes a presentation given by Xavier Amatriain at the KDD conference in August 2014 about recommender systems. The presentation discusses the evolution of the recommender problem from search to discovery and recommendation. It then covers traditional recommendation methods like collaborative filtering, both memory-based and model-based using matrix factorization. It discusses challenges like sparsity and explores techniques learned from the Netflix Prize like SVD, restricted Boltzmann machines, and ensemble methods.
This document provides an overview of Apache MXNet and deep learning on AWS. It begins with an introduction to deep learning applications and trends. The rest of the document discusses MXNet features like scalability, language support and frameworks comparisons. It also covers MXNet usage on AWS like integration with services and AI research. The document concludes with developer resources like notebooks, documentation and tools for building models with MXNet.
Pal gov.tutorial3.session3.xpath & xquery (lab1)Mustafa Jarrar
This tutorial document provides an overview of XPath and how it can be used to navigate an XML document by examining the different node types and axes in XPath that represent the hierarchical structure. It describes the XPath data model and how location path expressions use axes, node tests, and predicates to select nodes in an XML document based on their relationship to the context node.
These slides accompanied my presentation about my research process called "demo-driven research". The presentation was first held at Hasso Platner Institute on 2007-11-28.
The workshop discussed the MOOSE software analysis platform and included presentations on:
1) Deciphering an old Cobol system to replace it with a new ERP system
2) Exposing hidden dependencies between DSL scripts and Java classes in a heterogeneous application
3) Translating visualizations from Mondrian to HTML formats
4) Using unit tests to increase accuracy of inter-method dependency analysis in Smalltalk applications
5) The Orion tool for exploring quality improvement paths and AspectMaps for aspect-oriented modeling
The workshop discussed the MOOSE software analysis platform and included presentations on:
1) Deciphering a legacy Cobol system to replace it with a new ERP system
2) Exposing hidden dependencies between DSL scripts and Java classes accessing shared database tables
3) Translating visualizations from Mondrian to HTML for server-side rendering
4) Using unit tests to increase accuracy of inter-method dependency analysis in Smalltalk systems
The workshop was well-attended and generated discussion around various MOOSE-based analysis techniques.
RESTing in the ALPS Mike Amundsen's Presentation from QCon London 2013CA API Management
The document discusses the author's realization that his previous work on H-Factors for describing protocol affordances was missing a consideration of "application affordances". This led to the idea of "vocabularies" for describing shared understanding at the application level. However, vocabularies alone do not describe how to interact with application concepts. The author proposes a new specification called ALPS that combines the description of application concepts ("what") using vocabularies, along with the description of how to interact with those concepts using hypermedia controls and protocols ("how"). ALPS aims to provide shared understanding of both the state and transitions of application domains across different media types and implementations.
The document discusses topic modeling and classification of short texts. It describes using Latent Dirichlet Allocation (LDA) to extract hidden topics from a large universal text corpus consisting of Wikipedia and MEDLINE articles. These topics are then used as features for a maximum entropy classifier to categorize short texts like tweets and web snippets. Parallelized LDA is implemented using the MPI library for improved computational efficiency.
Live to e-Learning, a lecture capture and delivery service based on MediaMosaMediaMosa
L2L (Live to e-Learning) a lecture capture and delivery service based on MediaMosa. Presentation by Matteo Bertazzo from CINECA InterUniversity Consortium at the MediaMosa Community day, November 25, 2010
Orchestrating Machine Learning Training for Netflix Recommendations - MCL317 ...Amazon Web Services
At Netflix, we use machine learning (ML) algorithms extensively to recommend relevant titles to our 100+ million members based on their tastes. Everything on the member home page is an evidence-driven, A/B-tested experience that we roll out backed by ML models. These models are trained using Meson, our workflow orchestration system. Meson distinguishes itself from other workflow engines by handling more sophisticated execution graphs, such as loops and parameterized fan-outs. Meson can schedule Spark jobs, Docker containers, bash scripts, gists of Scala code, and more. Meson also provides a rich visual interface for monitoring active workflows and inspecting execution logs. It has a powerful Scala DSL for authoring workflows as well as the REST API. In this session, we focus on how Meson trains recommendation ML models in production, and how we have re-architected it to scale up for a growing need of broad ETL applications within Netflix. As a driver for this change, we have had to evolve the persistence layer for Meson. We talk about how we migrated from Cassandra to Amazon RDS backed by Amazon Aurora.
Unlocking the Semantics of Multimedia Presentations in the Web with the Multi...Carsten Saathoff
The semantics of rich multimedia presentations in the web such as SMIL, SVG, and Flash cannot or only to a very limited extend be understood by search engines today.
This hampers the retrieval of such presentations and makes their archival and management a difficult task.
Existing metadata models and metadata standards are either conceptually too narrow, focus on a specific media type only, cannot be used and combined together, or are not practically applicable for the semantic description of rich multimedia presentations.
In this paper, we propose the Multimedia Metadata Ontology (M3O) for annotating rich, structured multimedia presentations.
The M3O provides a generic modeling framework for representing sophisticated multimedia metadata.
It allows for integrating the features provided by the existing metadata models and metadata standards.
Our approach bases on Semantic Web technologies and can be easily integrated with multimedia formats such as the W3C standards SMIL and SVG.
With the M3O, we unlock the semantics of rich multimedia presentations in the web by making the semantics machine-readable and machine-understandable.
The M3O is used with our SemanticMM4U framework for the multi-channel generation of semantically-rich multimedia presentations.
Unified Systems Engeneering with GoedelWorksEric Verhulst
1) The document discusses a metamodel for systems engineering called a "systems grammar" developed by Open License Society and used in various EU projects.
2) It is currently commercialized by Altreonic as GoedelWorks and refined by adding structure and properties to avoid overlapping concepts.
3) The metamodel takes a multi-level approach with different views and user levels that correspond to domains like process, engineering, modeling, and software.
The tutorial has been presented at CAISE 2010. The tutorial discusses the state-of-the-art on research addresseing the quality of data at the conceptual level (conceptual schemas) and of Ontologies
Data FAIRport Prototype & Demo - Presentation to Elsevier, Jul 10, 2015Mark Wilkinson
A discussion and demonstration of a functional Data FAIRport, using W3C's Linked Data Platform, Ruben Verborgh's Linked Data Fragments, and Hydra's hypermedia controlled vocabularies. This is the output of the "Skunkworks" working group of the larger Data FAIRport project (http://datafairport.org).
Object-oriented programming has its roots in SIMULA 67. Key aspects of OOP include abstract data types, inheritance, and dynamic binding. Java supports OOP through classes that are subclasses of the root class "Object" and utilize single inheritance. All Java objects are allocated dynamically on the heap using the "new" operator.
Model Simulation, Graphical Animation, and Omniscient Debugging with EcoreToo...Benoit Combemale
You have your shiny new modeling language up and running thanks to the Eclipse Modeling Technologies and you built a powerful graphical editor with Sirius to support it. But how can you see what is going on when a model is executed? Don't you need to debug your design in some way? Wouldn't you want to see your editors being animated directly within your modeling environment based on execution traces or simulator results?
In this talk, we will present Sirius Animator, an add-on to Sirius that provides you a tool- supported approach to complement a modeling language with an execution semantics and a graphical description of an animation layer. The execution semantics is defined thanks to ALE, an Action Language for EMF integrated into Ecore Tools to modularly implement the bodies of your EOperations, and the graphical description of the animation layer is defined thanks to Sirius. From both inputs, Sirius Animator automatically provides an advanced and extensible environment for model simulation, animation and debugging, on top of the graphical editor of Sirius and the debug UI of Eclipse. To illustrate the overall approach, we will demonstrate the ability to seamlessly extend Arduino Designer, in order to provide an advanced debugging environment that includes graphical animation, forward/backward step-by-step, breakpoint definition, etc.
The document describes a library of coupled operators for evolving metamodels and their corresponding models together. It presents classes of coupled operators, including those that preserve the language and those that consider bidirectionality. The library is built from literature, case studies, and aims to be practically complete and useful through its organization into sizes, categories and classifications.
Computing for Human Experience and WellnessAmit Sheth
Talk at Venture Panel in Nov. 2005. Since this very early start, the ideas have substantially matured: a more recent version is at: http://www.slideshare.net/knoesis/computing-for-human-experience-v3
Similar to Semantically-aware Networks and Services for Training and Knowledge Management in Organizations. (20)
Aggrégation des rel (hammamet, nov 2017) [enregistré automatiquement]Gilbert Paquette
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Semantically-aware Networks and Services for Training and Knowledge Management in Organizations.
1. NGNS ’ 12 – Faro, Portugal – DecembrerNetworks and
Services
Semantically-aware Networks and
Services for Training and Knowledge
Management in Organizations
Dr. Gilbert Paquette
www.licef.ca/cice
Canada Research Chair in Instructional and Cognitive
Enginerring (CICE)
LICEF Research Center
Télé-université
3. Why Semantics ?
1. Inform users (students, workers) during the execution of
task or learning activity of the content of the resources
that they use.
2. Assist users and designers in the selection of resources
appropriate to their knowledge and competencies.
3. Create well-balanced learning of work scenarios, locally
and globally.
4. Build user models for the personalization of learning or
work environments.
5. Provide an execution semantic for resources and
scenarios.
4. The Web of Data (Web 3.0)
Web of documents Web of linked data
Relational DB RDF graphs
.
. URIs to identify all kinds of rssources
. Subject/relation/Object triples
Graphs to relate
Normalized syntax ( XML)
7. Semantic Question
Answering
“Give me all the resources of a certain author?”
“Give me all the resources of an organization of a certain
author?”
“Give me all the resources from authors who have
published with a certain list of authors?”
“Give me all the exercises references under “Atomic
Physics” in the Dewey classification and under the
equivalent classifications in my University’s
classifications?”
“Give me all the Geometry tutorials , excluding Euclidian
Geometry ?”
“Give me all the Reports on open source tools that could
replace a certain tool ?””
8. The Adaptive Semantic Web
Add semantic references to scenario
components: actors, tasks and resources within
educational modeling languages such as IMS-LD
(2003)
– Paquette and Marino, 2005
“Include the improved modeling of users and
items, and contextual information into the
recommendation process”
– Adomavicus and Tuzhilin (2005)
The “Adaptive Semantic Web” opens new
approaches for recommenders systems: use of
folksonomies and ontological filtering of resources
– Jannach et al, 2011
9. The PRIOWS Project
Data Documents
Processes
Experts
Integrating data bases
Knowledge Modeling Methods
Ontology
Ontology Modeling
Work Scenario
Query
Assistance
Federated
Search
10. TELOS
LORNET (2003-2008):
A hundred researchers,
assistants, graduate students
17 organizations, NSERC support
Semantic WEB research
TELOS
Specialized TEL op. system
Resource aggregation:
…in multi-actor scenarios
Service-oriented system on NGN
Ontology-driven system
Produces semanticallly aware Web environment
10
11. TELOS Architecture
Server
Technical KB
KB
Ontology
Man
Man KB
KB
TCP/IP ..
Rel.
Rel.
BD
BD
13. Recommendation (assistance)
Principles
Epiphyte – grafted on the scenario process
but external to it; no scenario modification
Multi-agent system: agents are associated to
tasks at different levels in the scenario
Flexible association: one, some or all of the
tasks are assisted.
Delegation between a task agent towards its
super tasks agents; tree topology
15. The implemented recommender
model
Recommender = {rules}
Rule = <targetActor, event, condition, action >
Event =
– Activity transition (started, terminated, revisited,…)
– Time spent (activity, global …)
– Resources opened, reopened,…
Condition = boolean expression comparing:
– Target actor progress in the scenario + knowledge and
competencies acquired + evidence => User persistent model
– Resources: prerequisite and target competencies
– Activities: prerequisite and target competencies
Action = advice, notification, model update
16. Knowledge Descriptors
Classes and instances (From OWL-DL domain ontologies)
General properties:
Domain – Data Properties / Domain – ObjectProperty – Range
Instanciated properties (facts):
Instance – Property / Instance – Property – Value
17. Competency Descriptors
(K, S, P) triples
K: Knowledge descriptor K=Planet
K=Planet
– From a OWL domain
ontology
S: S=Apply
S=Apply
Generic Skill
– From a 10-level taxonomy
(Paquette, 2007)
P=Expert
P=Expert
P: Performance level
– A combination of P-values
(Paquette, 2007)
18. Referencing Process in the
TELOS Implementation
Ontology Resource Semantic
1
1 contruction 2
2 selection 3
3 Referencing
or import Of resources
… and/or
competencies
19. Semantic Search Methods
Type of Search Type of Result
Simple Ressources with an
Using key words from the ontology exact match
Advanced Exact match OR
Using knowledge and competency Semantically
boolean query near match
Exact match OR
Resource Pairing
Semantically
Using semantic comparison between
queried ressource and other resources near match
→ Rests on knowledge and competency comparison
20. Knowledge Comparison
(K1 et K2)
Based on the structure of the ontology where the
knowledge descriptors are stored
Compare the neighbourhoods of K1 and K2
Possible results
– K2 near and more specialized / general than K1
21. Competency Comparison
C1=(K1, S1, P1) et C2=(K2, S2, P2)
Based on knowledge comparison (K)
Base on the distance between skills’ levels (H) and
performance levels distances(P)
Possible results
C2 veryNear / Near C1
C2 stronger / weaker than C1
C2 more specialized / general than C1
23. Competency comparison
within rule conditions
A competency-based condition is a triple:
– ObjectCompetencyList is the list of prerequisite or target
competencies of another actor, a task or a resource to be
compared with user’s actual competency list
– Relation is one of the comparison relations : Identical, Near,
VeryNear, MoreGeneric, MoreSpecific, Stronger, Weaker, or
any combination of these.
– Quantification takes two values: HasOne or HasAll
EX: HasAll /NearMoreSpecific / Target competencies for Essay
EX: HasOne/Weaker/Target competency for Build Table activity
27. Achievements in PRIOWS
Extension of the TELOS Technical Ontology for
semantic referencing of resources, search and
recommendation
Definition of a Typology of semantic descriptors
(ontology descriptors and competenciers)
Search methods for resources ‘identical’ ou ‘near’
sémantically
Recommendation Model: based on competency
comparison between actors, tasks and resources
New integrated suite of tools: Semantic referencer,
Semantic search tools, Competency and Ontology
editors, to Recommander Integration in scenarios,
Recomenders’ rule editor.
28. Future Research
More experimental validation to refine the semantic relations
between OWL-DL references, i.e adding weights to the various
comparison cases
Investigate recommendation methods for groups in
collaborative scenarios (permitted by our model of multi-actor
learning scenarios)
Improve the practical use of the approach, partly automate
tasks, improve the ergonomics
Investigate the integration of other recommendation methods
(e.g. user analytics)
“Free” the suite of tools from TELOS to extend its usability on
the Web of data.
29. NGNS ’ 12 – Faro, Portugal – Decembrer 2, 2012
4th International Conference on Next Generation
Networks and Services
Questions, Comments ?
www.licef.ca/gp
www.licef.ca/cice
www.cogigraph.com
Editor's Notes
Abstract: The presentation will summarize the results of the PRIOWS research program on Ontological Engineering and Semantic Web applications, a three-year program that ended on September 2012. The program achieved an innovation project for technical information management at Hydro-Quebec, plus four related research project on the modeling of target knowledge and competencies, the modeling of work and training multi-actor scenarios, the semantic referencing of actors, tasks and documents, and the assistance to network-distributed user according to their knowledge and competencies. An ontology-driven software framework integrating these various components will be presented, together with an implemented system, TELOS, enabling designers to build and deploy work or training environments on the Web.
Donner des exemples (à l ’oral) pour chaque type de recherche.
Voisinage ‘proche’ au sens qu’on ne descend pas la hiérarchie des classes, propriétés, etc.…