An Overview of Usage Data Formats for Recommendations in TEL
•Download as PPTX, PDF•
0 likes•685 views
Presentation of the paper "An Overview of Usage Data Formats for ecommendations in TEL" held at the RecSysTel WS 2012 in Saarbrücken, Germany - paper available at CEUR-896 (http://ceur-ws.org/Vol-896/)
Report
Share
Report
Share
Recommended
The case for cloud computing in Life Sciences
This document summarizes Ola Spjuth's background and research interests related to cloud computing in life sciences. Spjuth is an associate professor who manages bioinformatics resources at SciLifeLab and UPPMAX. His research focuses on developing e-infrastructure, automation methods, and applied e-science using tools like Docker and Kubernetes. He is working on projects applying these technologies to problems in drug discovery and predictive modeling of image data.
BIOMAJ
The document describes BioMAJ, a workflow engine dedicated to bio-data synchronization and processing. BioMAJ was developed to provide a reliable workflow engine that can download remote data, apply formatting, and make the data available for users and applications. It allows flexible management of sequence databases on a site and rapid implementation of new workflows through bank description files. BioMAJ provides functions for synchronization, pre-processing, post-processing, and supervision of bioinformatics data workflows.
Educational Networking Power Point
Educational networking professional training teaches educators how to use online tools and platforms to vertically align curriculum throughout a school district, collaborate with peers, and communicate openly with the community. Key aspects include setting up class pages to post syllabi, assignments, calendars and more; creating a principal's page for campus-wide events and news; and having a superintendent's page to share important updates from each school.
Educational Networking for Growth in Medical Education
MCI Revised Basic Course on Medical Education Technologies.
Growth Avenues in Medical Education and
Educational Networking for Growth
Read Between The Lines: an Annotation Tool for Multimodal Data
This is the presentation of Read Between The Lines, the paper which we published at the Learning Analytics & Knowledge Conference 2019 in Tempe, Arizona (#LAK19).
Link to the paper available in Open Access ACM library https://dl.acm.org/citation.cfm?id=3303776
Abstract:
This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
The trend analysis of the level of fin metrics and e-stat tools for research ...
This document discusses the trend in use of financial metrics (fin-metrics) and electronic statistical (e-stat) tools for research data analysis among digital immigrant researchers. It analyzes 1720 empirical journals from various fields in Nigeria and beyond. The analysis found that most researchers still rely on manual rather than digital methods, which hinders international acceptance and originality. It was also found to be more common among lecturers without strong IT skills. The document recommends training to improve use of analytical tools and make research more credible, accurate, and internationally competitive.
11.the trend analysis of the level of fin metrics and e-stat tools for resear...
This document discusses the trend in use of financial metrics (fin-metrics) and electronic statistical (e-stat) tools for research data analysis among digital immigrant researchers. It analyzes 1720 empirical journals from various fields in Nigeria and beyond. The study found that most researchers still rely on manual rather than digital analysis, which hinders international acceptance and originality. It recommends training to improve use of analytical tools and make research more credible, accurate and useful to individuals and society.
Meeting the NSF DMP Requirement: March 7, 2012
March 7 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Recommended
The case for cloud computing in Life Sciences
This document summarizes Ola Spjuth's background and research interests related to cloud computing in life sciences. Spjuth is an associate professor who manages bioinformatics resources at SciLifeLab and UPPMAX. His research focuses on developing e-infrastructure, automation methods, and applied e-science using tools like Docker and Kubernetes. He is working on projects applying these technologies to problems in drug discovery and predictive modeling of image data.
BIOMAJ
The document describes BioMAJ, a workflow engine dedicated to bio-data synchronization and processing. BioMAJ was developed to provide a reliable workflow engine that can download remote data, apply formatting, and make the data available for users and applications. It allows flexible management of sequence databases on a site and rapid implementation of new workflows through bank description files. BioMAJ provides functions for synchronization, pre-processing, post-processing, and supervision of bioinformatics data workflows.
Educational Networking Power Point
Educational networking professional training teaches educators how to use online tools and platforms to vertically align curriculum throughout a school district, collaborate with peers, and communicate openly with the community. Key aspects include setting up class pages to post syllabi, assignments, calendars and more; creating a principal's page for campus-wide events and news; and having a superintendent's page to share important updates from each school.
Educational Networking for Growth in Medical Education
MCI Revised Basic Course on Medical Education Technologies.
Growth Avenues in Medical Education and
Educational Networking for Growth
Read Between The Lines: an Annotation Tool for Multimodal Data
This is the presentation of Read Between The Lines, the paper which we published at the Learning Analytics & Knowledge Conference 2019 in Tempe, Arizona (#LAK19).
Link to the paper available in Open Access ACM library https://dl.acm.org/citation.cfm?id=3303776
Abstract:
This paper introduces the Visual Inspection Tool (VIT) which supports researchers in the annotation of multimodal data as well as the processing and exploitation for learning purposes. While most of the existing Multimodal Learning Analytics (MMLA) solutions are tailor-made for specific learning tasks and sensors, the VIT addresses the data annotation for different types of learning tasks that can be captured with a customisable set of sensors in a flexible way. The VIT supports MMLA researchers in 1) triangulating multimodal data with video recordings; 2) segmenting the multimodal data into time-intervals and adding annotations to the time-intervals; 3) downloading the annotated dataset and using it for multimodal data analysis. The VIT is a crucial component that was so far missing in the available tools for MMLA research. By filling this gap we also identified an integrated workflow that characterises current MMLA research. We call this workflow the Multimodal Learning Analytics Pipeline, a toolkit for orchestration, the use and application of various MMLA tools.
The trend analysis of the level of fin metrics and e-stat tools for research ...
This document discusses the trend in use of financial metrics (fin-metrics) and electronic statistical (e-stat) tools for research data analysis among digital immigrant researchers. It analyzes 1720 empirical journals from various fields in Nigeria and beyond. The analysis found that most researchers still rely on manual rather than digital methods, which hinders international acceptance and originality. It was also found to be more common among lecturers without strong IT skills. The document recommends training to improve use of analytical tools and make research more credible, accurate, and internationally competitive.
11.the trend analysis of the level of fin metrics and e-stat tools for resear...
This document discusses the trend in use of financial metrics (fin-metrics) and electronic statistical (e-stat) tools for research data analysis among digital immigrant researchers. It analyzes 1720 empirical journals from various fields in Nigeria and beyond. The study found that most researchers still rely on manual rather than digital analysis, which hinders international acceptance and originality. It recommends training to improve use of analytical tools and make research more credible, accurate and useful to individuals and society.
Meeting the NSF DMP Requirement: March 7, 2012
March 7 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Open Access Statistics: An Examination how to Generate Interoperable Usage In...
The document summarizes the Open Access Statistics (OAS) project, which aimed to develop standards for collecting and exchanging usage statistics across open access repositories and services. The OAS project created a technical infrastructure that allowed different repositories and services to aggregate usage data in a central system and exchange standardized usage information. The project helped pilot the implementation of usage statistics in repositories and demonstrated the ability to generate interoperable usage measures across distributed open access systems. However, further work is still needed to refine metrics and facilitate international collaboration.
Let’s go on a FAIR safari!
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
sourabh_bajaj_resume
Yipei Wang is a Machine Learning Scientist at Particle Media who develops recommendation systems using machine learning techniques. He previously worked as a Research Assistant at Carnegie Mellon University where he conducted research on distributed optimization algorithms and multimedia event detection. Wang has a Master's degree in Artificial Intelligence from Carnegie Mellon University and a Bachelor's degree in Electrical Engineering from Tsinghua University.
Data Resource Management: Good Practices to Make the Most out of a Hidden Tre...
Management of the data resource in the industrial enterprise becomes a strategic capability in the digital age. The talk motivates data resource management, presents proven practices and outlines principles of modern data management approaches.
From allotrope to reference master data management
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
Industrial Natural Language Processing and Information Extraction
Industrial Natural Language Processing and Information ExtractionInstitute for Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing & Information Extraction: a research area of the chair for technologies and management of digital transformation from the university of Wuppertal, Germany.
For more information, see here: https://www.tmdt.uni-wuppertal.de/de SyncMeta: Near Real-time Collaborative Conceptual Modeling on the Web
Framework for near real-time (meta) modeling on the Web. Permits the collaborative editing of meta models and the generation of near real-time collaborative modeling editors. It uses a visual modeling approach.
http://dbis.rwth-aachen.de/cms/research/ACIS/SyncMeta
Mining Social Media Data for Understanding Drugs Usage
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
Text Summarization and Conversion of Speech to Text
This document discusses text summarization and speech to text conversion using deep learning algorithms. It describes how recurrent neural networks can be used for text summarization by identifying key information and semantic meaning from text. Speech recognition uses similar deep learning methods to convert spoken audio to text. The document also provides an overview of the text summarization process, including segmentation, normalization, feature extraction, and modeling steps. It concludes that these models can generate summarized text from extensive documents and meetings.
Linking data, models and tools an overview
This document provides an overview of different frameworks and technologies for linking models, data, and tools for integrated environmental modeling. It begins with definitions of key concepts like architecture, component, interface, and coupling. It then provides a brief alphabetical description of 8 major modeling frameworks: Common Component Architecture (CCA), Earth System Modeling Framework (ESMF), Framework for Risk Analysis of Multi-Media Environmental Systems (FRAMES), High Level Architecture (HLA), Kepler, Model Coupling Toolkit (MCT), and OASIS/PALM. The frameworks differ in their approaches but also complement each other to some degree. The document aims to understand how and why the various approaches address conflicting demands like generality, flexibility, ease of use
Opportunity and risk in social computing environments
Hazel Hall's invited paper presented at SLA Eastern Canada Members' Day, McGill University, Montreal, Canada, 29 April 2009. This presentation draws on the project work discussed in the report at: http://drhazelhall.files.wordpress.com/2013/01/soc_comp_proj_rep_public_2008.pdf
A Generic Scientific Data Model and Ontology for Representation of Chemical Data
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
This document discusses a study that performed sentiment analysis on Twitter posts about elections using Apache Hadoop. The study collected tweets related to an upcoming election through the Twitter API. It then used Hadoop tools like HDFS, MapReduce, Hive, and NiFi to process and analyze the large amount of unstructured Twitter data. Specifically, it extracted sentiment information from the tweets like polarity (positive or negative) and subject to understand public opinions about different political parties and issues discussed in the tweets.
Capturing of Information about Knowledge Document and Learning Resource Usage
The document discusses capturing lifecycle information about knowledge documents and learning resources. It outlines the lifecycles of these materials and different proposed models. It then describes the LIS.KOM framework for capturing metadata about learning objects and knowledge documents. The document introduces its own approach called ReCap.KOM for capturing information as these materials are created, accessed, revised and used in order to support retrieval and reuse. It provides examples of ReCap.KOM add-ins developed for PowerPoint and Word to track usage information and relationships between documents.
M045067275
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
FAIR: standards and services
Overview of standards and FAIR, FAIRsharing and the FAIR Cookbook -> Predictive Epigenetics PEP-NET training network, 1 April 2022
Jtelss - 030619 - Reflection guidance workshop
The document discusses technologies and approaches for data-driven reflective learning. It describes how prompts, diaries, journals, and visualizations can be used to encourage reflection on work activities, knowledge, mood, search behavior, and learning progress. Examples of intelligent mentoring systems and reflective learning apps are provided. Challenges with reflection timing, context, and motivation are also outlined. The goal is to use data and adaptive systems to support lifelong learning from experiences.
turban_dss9e_ch05_unit1.ppt11111111111111111
The document discusses data mining and business intelligence. It defines data mining as the process of identifying valid and useful patterns in large data sets. The key steps in data mining involve data preprocessing, applying algorithms to extract patterns, and assessing the results. Data mining has various applications in domains such as customer relationship management, banking, manufacturing, and healthcare, to gain insights, predict outcomes, and optimize operations. The data mining process typically involves business understanding, data understanding, data preparation, model building, evaluation, and deployment.
Data Governance
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Towards a harmonization of metadata application profiles for agricultural lea...
Metadata interoperability allows the exchange and preservation of crucial learning and teaching information, as well as its future reuse among a large number of different systems and repositories. This paper introduces work around metadata interoperability that has taken place in the context of the Agricultural Learning Repositories Task Force (AgLR-TF), an international community of the stakeholders that are involved in agricultural learning repositories. It particularly focuses on a review and assessment of metadata application profiles that are currently implemented in agricultural learning repositories. The results of this study can be found useful by who are designing, implementing and operating agricultural learning repositories, facilitating thus metadata interoperability in this application field.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Full-RAG: A modern architecture for hyper-personalization
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.
More Related Content
Similar to An Overview of Usage Data Formats for Recommendations in TEL
Open Access Statistics: An Examination how to Generate Interoperable Usage In...
The document summarizes the Open Access Statistics (OAS) project, which aimed to develop standards for collecting and exchanging usage statistics across open access repositories and services. The OAS project created a technical infrastructure that allowed different repositories and services to aggregate usage data in a central system and exchange standardized usage information. The project helped pilot the implementation of usage statistics in repositories and demonstrated the ability to generate interoperable usage measures across distributed open access systems. However, further work is still needed to refine metrics and facilitate international collaboration.
Let’s go on a FAIR safari!
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
sourabh_bajaj_resume
Yipei Wang is a Machine Learning Scientist at Particle Media who develops recommendation systems using machine learning techniques. He previously worked as a Research Assistant at Carnegie Mellon University where he conducted research on distributed optimization algorithms and multimedia event detection. Wang has a Master's degree in Artificial Intelligence from Carnegie Mellon University and a Bachelor's degree in Electrical Engineering from Tsinghua University.
Data Resource Management: Good Practices to Make the Most out of a Hidden Tre...
Management of the data resource in the industrial enterprise becomes a strategic capability in the digital age. The talk motivates data resource management, presents proven practices and outlines principles of modern data management approaches.
From allotrope to reference master data management
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
Industrial Natural Language Processing and Information Extraction
Industrial Natural Language Processing and Information ExtractionInstitute for Technologies and Management of Digital Transformation, University of Wuppertal
Industrial Natural Language Processing & Information Extraction: a research area of the chair for technologies and management of digital transformation from the university of Wuppertal, Germany.
For more information, see here: https://www.tmdt.uni-wuppertal.de/de SyncMeta: Near Real-time Collaborative Conceptual Modeling on the Web
Framework for near real-time (meta) modeling on the Web. Permits the collaborative editing of meta models and the generation of near real-time collaborative modeling editors. It uses a visual modeling approach.
http://dbis.rwth-aachen.de/cms/research/ACIS/SyncMeta
Mining Social Media Data for Understanding Drugs Usage
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
Text Summarization and Conversion of Speech to Text
This document discusses text summarization and speech to text conversion using deep learning algorithms. It describes how recurrent neural networks can be used for text summarization by identifying key information and semantic meaning from text. Speech recognition uses similar deep learning methods to convert spoken audio to text. The document also provides an overview of the text summarization process, including segmentation, normalization, feature extraction, and modeling steps. It concludes that these models can generate summarized text from extensive documents and meetings.
Linking data, models and tools an overview
This document provides an overview of different frameworks and technologies for linking models, data, and tools for integrated environmental modeling. It begins with definitions of key concepts like architecture, component, interface, and coupling. It then provides a brief alphabetical description of 8 major modeling frameworks: Common Component Architecture (CCA), Earth System Modeling Framework (ESMF), Framework for Risk Analysis of Multi-Media Environmental Systems (FRAMES), High Level Architecture (HLA), Kepler, Model Coupling Toolkit (MCT), and OASIS/PALM. The frameworks differ in their approaches but also complement each other to some degree. The document aims to understand how and why the various approaches address conflicting demands like generality, flexibility, ease of use
Opportunity and risk in social computing environments
Hazel Hall's invited paper presented at SLA Eastern Canada Members' Day, McGill University, Montreal, Canada, 29 April 2009. This presentation draws on the project work discussed in the report at: http://drhazelhall.files.wordpress.com/2013/01/soc_comp_proj_rep_public_2008.pdf
A Generic Scientific Data Model and Ontology for Representation of Chemical Data
The current movement toward openness and sharing of data is likely to have a profound effect on the speed of scientific research and the complexity of questions we can answer. However, a fundamental problem with currently available datasets (and their metadata) is heterogeneity in terms of implementation, organization, and representation.
To address this issue we have developed a generic scientific data model (SDM) to organize and annotate raw and processed data, and the associated metadata. This paper will present the current status of the SDM, implementation of the SDM in JSON-LD, and the associated scientific data model ontology (SDMO). Example usage of the SDM to store data from a variety of sources with be discussed along with future plans for the work.
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
This document discusses a study that performed sentiment analysis on Twitter posts about elections using Apache Hadoop. The study collected tweets related to an upcoming election through the Twitter API. It then used Hadoop tools like HDFS, MapReduce, Hive, and NiFi to process and analyze the large amount of unstructured Twitter data. Specifically, it extracted sentiment information from the tweets like polarity (positive or negative) and subject to understand public opinions about different political parties and issues discussed in the tweets.
Capturing of Information about Knowledge Document and Learning Resource Usage
The document discusses capturing lifecycle information about knowledge documents and learning resources. It outlines the lifecycles of these materials and different proposed models. It then describes the LIS.KOM framework for capturing metadata about learning objects and knowledge documents. The document introduces its own approach called ReCap.KOM for capturing information as these materials are created, accessed, revised and used in order to support retrieval and reuse. It provides examples of ReCap.KOM add-ins developed for PowerPoint and Word to track usage information and relationships between documents.
M045067275
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
FAIR: standards and services
Overview of standards and FAIR, FAIRsharing and the FAIR Cookbook -> Predictive Epigenetics PEP-NET training network, 1 April 2022
Jtelss - 030619 - Reflection guidance workshop
The document discusses technologies and approaches for data-driven reflective learning. It describes how prompts, diaries, journals, and visualizations can be used to encourage reflection on work activities, knowledge, mood, search behavior, and learning progress. Examples of intelligent mentoring systems and reflective learning apps are provided. Challenges with reflection timing, context, and motivation are also outlined. The goal is to use data and adaptive systems to support lifelong learning from experiences.
turban_dss9e_ch05_unit1.ppt11111111111111111
The document discusses data mining and business intelligence. It defines data mining as the process of identifying valid and useful patterns in large data sets. The key steps in data mining involve data preprocessing, applying algorithms to extract patterns, and assessing the results. Data mining has various applications in domains such as customer relationship management, banking, manufacturing, and healthcare, to gain insights, predict outcomes, and optimize operations. The data mining process typically involves business understanding, data understanding, data preparation, model building, evaluation, and deployment.
Data Governance
This presentation was part of the IDS Webinar on Data Governance. It gives a brief overview of the history on Data Governance, describes how governing data has to be further developed in the era of business and data ecosystems, and outlines the contribution of the International Data Spaces Association on the topic.
Towards a harmonization of metadata application profiles for agricultural lea...
Metadata interoperability allows the exchange and preservation of crucial learning and teaching information, as well as its future reuse among a large number of different systems and repositories. This paper introduces work around metadata interoperability that has taken place in the context of the Agricultural Learning Repositories Task Force (AgLR-TF), an international community of the stakeholders that are involved in agricultural learning repositories. It particularly focuses on a review and assessment of metadata application profiles that are currently implemented in agricultural learning repositories. The results of this study can be found useful by who are designing, implementing and operating agricultural learning repositories, facilitating thus metadata interoperability in this application field.
Similar to An Overview of Usage Data Formats for Recommendations in TEL (20)
Open Access Statistics: An Examination how to Generate Interoperable Usage In...
Open Access Statistics: An Examination how to Generate Interoperable Usage In...
Data Resource Management: Good Practices to Make the Most out of a Hidden Tre...
Data Resource Management: Good Practices to Make the Most out of a Hidden Tre...
From allotrope to reference master data management
From allotrope to reference master data management
Industrial Natural Language Processing and Information Extraction
Industrial Natural Language Processing and Information Extraction
SyncMeta: Near Real-time Collaborative Conceptual Modeling on the Web
SyncMeta: Near Real-time Collaborative Conceptual Modeling on the Web
Mining Social Media Data for Understanding Drugs Usage
Mining Social Media Data for Understanding Drugs Usage
Text Summarization and Conversion of Speech to Text
Text Summarization and Conversion of Speech to Text
Opportunity and risk in social computing environments
Opportunity and risk in social computing environments
A Generic Scientific Data Model and Ontology for Representation of Chemical Data
A Generic Scientific Data Model and Ontology for Representation of Chemical Data
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
Capturing of Information about Knowledge Document and Learning Resource Usage
Capturing of Information about Knowledge Document and Learning Resource Usage
Towards a harmonization of metadata application profiles for agricultural lea...
Towards a harmonization of metadata application profiles for agricultural lea...
Recently uploaded
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Full-RAG: A modern architecture for hyper-personalization
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.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Artificial Intelligence for XMLDevelopment
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.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Recently uploaded (20)
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
An Overview of Usage Data Formats for Recommendations in TEL
- 1. An Overview of Usage Data Formats for Recommendations in TEL Short paper Katja Niemann Maren Scheffel Martin Wolpers © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 2. Usage Data Focus here on the following domains Learning analytics Educational data mining Examples of successful use Identification of irregularities of learning behaviour of students Reflection and comparison of learning activities among students of a learning group Personalized recommender systems © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 3. Comparison of prominent formats Contextualized Attention Metadata Activity Streams Learning Registry Paradata NSDL Paradata Enhance the interoperability among usage data analysis tools and usage data storage silos © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 4. Contextualized Attention Metadata (CAM) Schmitz, H.-C., Wolpers, M., Kirschenmann, U., Niemann, K. 2012. ‚Contextualized Attention Metadata’. Human Attention in Digital Environments, Eds: Claudia Roda, Cambridge University Press, Cambridge, US, 2012 http://www.cup.es/catalogue/catalogue.asp?isbn=9780521765657 Core features Event representation links entities without fixed roles Linked Data approach to entities and events (open definition) © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 5. Activity Streams Snell, J., Atkins, M., Norris, W., Messina, C., Wilkinson, M., Dolin, R. 2012. JSON Activity Streams 1.0, http://activitystrea.ms/specs/json/1.0/ Core features Activity representation links entities with fixed roles Sequence of activity objects represent activity stream Aims to provide usage info about single objects © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 6. Learning Registry Paradata Paradata Specification V1.0. https://docs.google.com/document/d/1IrOYXd3S0FUwNozaEG5tM7Ki4_AZPrBn-pbyVUz-Bh0/edit?hl=en_US&pli=1# Core features Modified Activity Streams format Used for aggregate representations © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 7. NSDL Paradata NSDL Paradata, 2012. https://wiki.ucar.edu/display/nsdldocs/comm_para+%28paradata+-+usage+data%29 Core features Object-centric representation of usage of resource Collection of all usage info in one representation per object © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 8. Conclusion and next steps Automatic Mapping: no one-size-fits-all mapping among the formats is possible! Extendable schemas Vocabularies not specified completely Open definition of some elements metadata schemas that can include unknown schemas Automatic mapping: rules sets based on specific usage scenarios! Then: Generalize over rules sets to create less scenario specific rule sets. © Fraunhofer-Institut für Angewandte Informationstechnik FIT
- 9. Thanks a lot! Fraunhofer Institut für Angewandte Informationstechnik FIT Katja Niemann Maren Scheffel Martin Wolpers First.last@fit.fraunhofer.de Schloss Birlinghoven 53754 Sankt Augustin © Fraunhofer-Institut für Angewandte Informationstechnik FIT