International Coastal Atlas Network and Web 3.0Adam Leadbetter
The document discusses the history of the World Wide Web and the concept of a semantic web. It then describes online controlled vocabularies, how they ensure consistent metadata and how concepts can be mapped between vocabularies. The document presents the ICAN use case of linking coastline metadata and how it was implemented using a standards-based approach and NETMAR technology to semantically link distributed catalogue services and definitions. The implementation is currently a demonstrator connecting Oregon and Irish metadata nodes.
Manage Your Data: Navigating Data Services at the UW LibrariesJennifer Muilenburg
This document summarizes a presentation about managing research data at the University of Washington Libraries. It discusses considerations for data management like storage, backup, organization, documentation, and sharing. Attendees are encouraged to ask questions and discuss how the topics relate to their own research. The presentation provides an overview of data services available through the libraries and other campus resources. It aims to help researchers navigate requirements and best practices for responsible data management.
This document discusses getting to know data using R. It begins by outlining the typical steps in a data analysis, including defining the question, obtaining and cleaning the data, performing exploratory analysis, modeling, interpreting results, and creating reproducible code. It then describes different types of data science questions from descriptive to mechanistic. The remainder of the document provides more details on descriptive, exploratory, inferential, predictive, causal, and mechanistic analysis. It also discusses R, including its design, packages, data types like vectors, matrices, factors, lists, and data frames.
This document provides an overview of an introductory course on Web Science. It discusses key topics including:
1. What is Web Science and why it matters as an area of scientific study.
2. Key aspects of web architecture like URIs, URLs, HTTP, and file formats.
3. Methods of measuring the web through network analysis and studying structures like the blogosphere and social networks.
4. The Web Science Method which takes an iterative, mixed methods approach of engineering, measuring, and analyzing the web.
5. The social aspects of the web and challenges of incorporating human behavior.
6. Issues of web governance, security, and standards setting.
This document discusses big data and data mining. It defines big data as large volumes of diverse data that require new techniques and technologies to capture, manage and analyze. Factors driving big data growth include increased data collection, sources like the internet and sensors, and data types like text, images and video. Challenges include rapid data growth and developing new algorithms and architectures to handle varied and massive datasets. Data mining extracts useful patterns from large datasets and is an interdisciplinary field drawing from machine learning, statistics, databases and other areas.
In less than a decade the internet has provided us access to enormous quantities of chemistry data. Chemists have embraced the web as a rich source of data and knowledge. However, all that glisters is not gold and while online searches can now provide us access to information associated with many tens of millions of chemicals, can allow us to traverse patents, publications and public domain databases the promise of high quality data on the web needs to be tempered with caution.
In recent years the crowdsourcing approach to developing curated content has been growing. Can such approaches allow us to bring to bear the collective wisdom of the crowd to validate and enhance the availability of trusted chemistry data online or are algorithms likely to be more powerful in terms of validating data? While it is now possible to search the web using a query language form natural to chemists – that of “structure searching the web” - increasingly scientists are likely going to have to accept joint responsibility for the quality of data online for the foreseeable future. Their participation is likely to come through engaging in open science, the provision of data under open licenses and by offering their skills to the community.
This presentation will provide an overview of the present state of chemistry data online, the challenges and risks of managing and accessing data in the wild and how an internet for chemistry continues to expand in scope and possibilities.
Contextual Computing: Laying a Global Data FoundationRichard Wallis
Richard Wallis presented on laying a global data foundation for contextual computing. He discussed how knowledge graphs and structured data on the web are building global context by connecting related entities. This will enable cognitive computing to evolve from local to global contexts, having access to data on flexible models and a de facto vocabulary from millions of websites. Schema.org plays a key role by delivering on the current structured data revolution and laying foundations for cognitive computing through a contextual web of entities.
International Coastal Atlas Network and Web 3.0Adam Leadbetter
The document discusses the history of the World Wide Web and the concept of a semantic web. It then describes online controlled vocabularies, how they ensure consistent metadata and how concepts can be mapped between vocabularies. The document presents the ICAN use case of linking coastline metadata and how it was implemented using a standards-based approach and NETMAR technology to semantically link distributed catalogue services and definitions. The implementation is currently a demonstrator connecting Oregon and Irish metadata nodes.
Manage Your Data: Navigating Data Services at the UW LibrariesJennifer Muilenburg
This document summarizes a presentation about managing research data at the University of Washington Libraries. It discusses considerations for data management like storage, backup, organization, documentation, and sharing. Attendees are encouraged to ask questions and discuss how the topics relate to their own research. The presentation provides an overview of data services available through the libraries and other campus resources. It aims to help researchers navigate requirements and best practices for responsible data management.
This document discusses getting to know data using R. It begins by outlining the typical steps in a data analysis, including defining the question, obtaining and cleaning the data, performing exploratory analysis, modeling, interpreting results, and creating reproducible code. It then describes different types of data science questions from descriptive to mechanistic. The remainder of the document provides more details on descriptive, exploratory, inferential, predictive, causal, and mechanistic analysis. It also discusses R, including its design, packages, data types like vectors, matrices, factors, lists, and data frames.
This document provides an overview of an introductory course on Web Science. It discusses key topics including:
1. What is Web Science and why it matters as an area of scientific study.
2. Key aspects of web architecture like URIs, URLs, HTTP, and file formats.
3. Methods of measuring the web through network analysis and studying structures like the blogosphere and social networks.
4. The Web Science Method which takes an iterative, mixed methods approach of engineering, measuring, and analyzing the web.
5. The social aspects of the web and challenges of incorporating human behavior.
6. Issues of web governance, security, and standards setting.
This document discusses big data and data mining. It defines big data as large volumes of diverse data that require new techniques and technologies to capture, manage and analyze. Factors driving big data growth include increased data collection, sources like the internet and sensors, and data types like text, images and video. Challenges include rapid data growth and developing new algorithms and architectures to handle varied and massive datasets. Data mining extracts useful patterns from large datasets and is an interdisciplinary field drawing from machine learning, statistics, databases and other areas.
In less than a decade the internet has provided us access to enormous quantities of chemistry data. Chemists have embraced the web as a rich source of data and knowledge. However, all that glisters is not gold and while online searches can now provide us access to information associated with many tens of millions of chemicals, can allow us to traverse patents, publications and public domain databases the promise of high quality data on the web needs to be tempered with caution.
In recent years the crowdsourcing approach to developing curated content has been growing. Can such approaches allow us to bring to bear the collective wisdom of the crowd to validate and enhance the availability of trusted chemistry data online or are algorithms likely to be more powerful in terms of validating data? While it is now possible to search the web using a query language form natural to chemists – that of “structure searching the web” - increasingly scientists are likely going to have to accept joint responsibility for the quality of data online for the foreseeable future. Their participation is likely to come through engaging in open science, the provision of data under open licenses and by offering their skills to the community.
This presentation will provide an overview of the present state of chemistry data online, the challenges and risks of managing and accessing data in the wild and how an internet for chemistry continues to expand in scope and possibilities.
Contextual Computing: Laying a Global Data FoundationRichard Wallis
Richard Wallis presented on laying a global data foundation for contextual computing. He discussed how knowledge graphs and structured data on the web are building global context by connecting related entities. This will enable cognitive computing to evolve from local to global contexts, having access to data on flexible models and a de facto vocabulary from millions of websites. Schema.org plays a key role by delivering on the current structured data revolution and laying foundations for cognitive computing through a contextual web of entities.
Richard Wallis, an OCLC Technology Evangelist, discusses how libraries can make their data more visible and connected on the web by publishing it as linked open data using common web vocabularies like Schema.org. Currently, library linked data exists in silos using different local vocabularies, making the data hard to discover and integrate. Adopting Schema.org could help library data reach the billions of web pages and domains that already use this general purpose vocabulary to describe things on the web.
This document summarizes a presentation about collecting and analyzing library usage data and sharing it openly. The presentation discusses the types of data libraries collect, including circulation transactions, search queries, and e-resource usage. It shows examples of visualizing and analyzing the data. It also explains how the University of Huddersfield released two million circulation transactions as open data under Creative Commons, and the potential benefits of further sharing data between libraries. Barriers to sharing data between institutions and benefits of doing so are discussed.
Artificial intelligence breaks into our lives. In the future, everything will probably be clear, but so far, some questions have arisen, and increasingly these issues affect aspects of morality and ethics. Which principles do we need to keep in mind while surfacing machine learning algorithms? How the editorial team affects the day to day development of applications at BBC?
Place: Kharkiv National University of Radio Electronics, Ukraine
When: 17th November 2019.
The history, promise, limits, uses and applications associated with big data. A quick review provides enough knowledge to discuss the topic intelligently.
This document is a presentation on big data given by Susan Saurage-Altenloh at the IABC Southern Region Conference on October 16, 2014. The presentation defines big data, discusses its history and growth over time, examines some of its promises and limitations, and explores various applications and players within the big data field. It provides examples and graphics to illustrate key points about how big data has developed and is being used.
Open data is data that is made publicly available without restrictions on use or distribution. The document discusses what open data is, how it is currently being used in the public, private and non-profit sectors in Ontario, and provides examples of open data projects and resources. It also outlines some of the challenges to open data adoption from data providers and how citizens can get involved with open data initiatives in their communities.
Contextual Computing - Knowledge Graphs & Web of EntitiesRichard Wallis
Richard Wallis gave a presentation on contextual computing and knowledge graphs at the SmartData 2017 conference. He discussed how knowledge graphs powered by structured data on the web are providing global context that enables new applications of cognitive and contextual computing. Schema.org plays a key role by defining a common vocabulary and enabling a web of related entities laid out as a global graph. This graph of entities delivers context on a global scale and lays the foundation for the next revolution in computing.
Deployment of rd_fa_microdata_microformats_on_the_webSTIinnsbruck
This document summarizes the results of a study analyzing the adoption of RDFa, Microdata, and Microformats on over 3 billion web pages:
- RDFa was found on 369 million pages within 2.29 million domains, Microdata on 298 million pages within 1.44 million domains, and Microformats on 35 million pages within 623,000 domains.
- Popular domains using each format included IMDB, BBC and Microsoft for RDFa, eBay, Microsoft and Apple for Microdata, and Wikipedia, Adobe and Taobao for Microformats.
- Most websites only used one format, with people, organizations, blog metadata, navigation and products being popular topics represented across formats, and
The document discusses the evolution of the semantic web and big data. It provides examples of how semantic web technologies can be applied to large datasets from domains such as climate research. It also discusses linked open data and the growth of the linked open data cloud over time. Public open data initiatives are described along with the benefits of a data economy where non-tangible assets like data play a significant role.
Local Weather Information and GNOME Shell ExtensionSammy Fung
This document provides information about an upcoming presentation on local weather information and GNOME Shell extensions. The presentation will discuss obtaining weather data from local meteorological observatories and making it available as open data. It will also cover creating weather widgets for the GNOME Shell desktop environment. The presenter has 15+ years experience in open source communities and organizing conferences in Asia and the US.
Schema.org Structured data the What, Why, & HowRichard Wallis
This document discusses Schema.org structured data, including its origins in the Semantic Web and Linked Open Data movements. Schema.org was created in 2011 to provide a common vocabulary for structured data markup on web pages. It allows search engines and other applications to understand the intended meaning and relationships of information on web pages. The document provides examples of using Schema.org structured data and microdata, and recommends applying it across various page types to help search engines better understand websites.
1. The document discusses various methods for collecting data from websites, including scraping, using APIs, and contacting site owners. It provides examples of projects that used different techniques.
2. Scraping involves programmatically extracting structured data from websites and can be complicated due to legal and ethical issues. APIs provide a safer alternative as long as rate limits are respected.
3. The document provides tips for scraping courteously and effectively, avoiding burdening websites. It also covers common scraping challenges and potential workarounds or alternatives like using APIs or contracting data collection.
This document discusses COVID contact tracing using campus WiFi data. It describes a project by students at Rensselaer Polytechnic Institute to engineer a semantic web application for contact tracing using campus WiFi logs to identify contacts between individuals. The document outlines the objectives of understanding semantic web architecture, building applications, and contributing to the web of data.
The document provides guidelines for publishing data as Linked Data. It discusses identifying appropriate data sources, reusing existing vocabularies and non-ontological resources, generating RDF data from relational databases or geometrical data using tools like R2O, ODEMapster and geometry2rdf, and publishing the data on the web by resolving URIs. The Ontology Engineering Group at Universidad Politécnica de Madrid has published Spanish geospatial and statistical data as part of projects like GeoLinkedData following these guidelines.
Linked Open Data in Libraries Archives & MuseumsJon Voss
The document discusses the growing Linked Open Data (LOD) movement in libraries, archives, and museums (LODLAM). It notes that LODLAM allows these institutions to explore data interoperability both within the cultural sector and more broadly on the web. The document outlines several outcomes of a LODLAM summit, including outreach, education, developing use cases, and examining issues around copyright and licensing of open data. Examples are provided of institutions that have published bibliographic and other cultural data using open licenses.
Linked Open Data in Libraries, Archives & MuseumsJon Voss
This document provides an overview of Linked Open Data for libraries, archives, and museums. It discusses the growing movement of LODLAM and how it allows these cultural institutions to represent their data as graphs using triples that describe entities in a machine-readable format. Key concepts covered include the use of URIs, RDF, vocabularies, and different legal tools for publishing open data.
A summary of DBpedia's History and a detailed analysis of challenges and solutions.
We show how the Linked Data Cloud evolved around DBpedia and also what problems we and other data projects encountered. We included a section on the new solutions that will lead DBpedia into a bright future.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
The document discusses various aspects of web mining such as web structure mining, web usage mining, and web content mining. It provides an overview of how data mining techniques can be applied to extract useful information from web data sources, including the structure of hyperlinks between pages, user behavior patterns on websites, and content within web documents. Challenges of web mining include the noisy, dynamic, and changing nature of web data that requires techniques to handle unstructured information and link analysis.
This presentation was provided by Kevin Hawkins of The University of North Texas Libraries, during the NISO event "Long Form Content: Ebooks, Print Volumes and the Concerns of Those Who Use Both," held on March 20, 2019.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Richard Wallis, an OCLC Technology Evangelist, discusses how libraries can make their data more visible and connected on the web by publishing it as linked open data using common web vocabularies like Schema.org. Currently, library linked data exists in silos using different local vocabularies, making the data hard to discover and integrate. Adopting Schema.org could help library data reach the billions of web pages and domains that already use this general purpose vocabulary to describe things on the web.
This document summarizes a presentation about collecting and analyzing library usage data and sharing it openly. The presentation discusses the types of data libraries collect, including circulation transactions, search queries, and e-resource usage. It shows examples of visualizing and analyzing the data. It also explains how the University of Huddersfield released two million circulation transactions as open data under Creative Commons, and the potential benefits of further sharing data between libraries. Barriers to sharing data between institutions and benefits of doing so are discussed.
Artificial intelligence breaks into our lives. In the future, everything will probably be clear, but so far, some questions have arisen, and increasingly these issues affect aspects of morality and ethics. Which principles do we need to keep in mind while surfacing machine learning algorithms? How the editorial team affects the day to day development of applications at BBC?
Place: Kharkiv National University of Radio Electronics, Ukraine
When: 17th November 2019.
The history, promise, limits, uses and applications associated with big data. A quick review provides enough knowledge to discuss the topic intelligently.
This document is a presentation on big data given by Susan Saurage-Altenloh at the IABC Southern Region Conference on October 16, 2014. The presentation defines big data, discusses its history and growth over time, examines some of its promises and limitations, and explores various applications and players within the big data field. It provides examples and graphics to illustrate key points about how big data has developed and is being used.
Open data is data that is made publicly available without restrictions on use or distribution. The document discusses what open data is, how it is currently being used in the public, private and non-profit sectors in Ontario, and provides examples of open data projects and resources. It also outlines some of the challenges to open data adoption from data providers and how citizens can get involved with open data initiatives in their communities.
Contextual Computing - Knowledge Graphs & Web of EntitiesRichard Wallis
Richard Wallis gave a presentation on contextual computing and knowledge graphs at the SmartData 2017 conference. He discussed how knowledge graphs powered by structured data on the web are providing global context that enables new applications of cognitive and contextual computing. Schema.org plays a key role by defining a common vocabulary and enabling a web of related entities laid out as a global graph. This graph of entities delivers context on a global scale and lays the foundation for the next revolution in computing.
Deployment of rd_fa_microdata_microformats_on_the_webSTIinnsbruck
This document summarizes the results of a study analyzing the adoption of RDFa, Microdata, and Microformats on over 3 billion web pages:
- RDFa was found on 369 million pages within 2.29 million domains, Microdata on 298 million pages within 1.44 million domains, and Microformats on 35 million pages within 623,000 domains.
- Popular domains using each format included IMDB, BBC and Microsoft for RDFa, eBay, Microsoft and Apple for Microdata, and Wikipedia, Adobe and Taobao for Microformats.
- Most websites only used one format, with people, organizations, blog metadata, navigation and products being popular topics represented across formats, and
The document discusses the evolution of the semantic web and big data. It provides examples of how semantic web technologies can be applied to large datasets from domains such as climate research. It also discusses linked open data and the growth of the linked open data cloud over time. Public open data initiatives are described along with the benefits of a data economy where non-tangible assets like data play a significant role.
Local Weather Information and GNOME Shell ExtensionSammy Fung
This document provides information about an upcoming presentation on local weather information and GNOME Shell extensions. The presentation will discuss obtaining weather data from local meteorological observatories and making it available as open data. It will also cover creating weather widgets for the GNOME Shell desktop environment. The presenter has 15+ years experience in open source communities and organizing conferences in Asia and the US.
Schema.org Structured data the What, Why, & HowRichard Wallis
This document discusses Schema.org structured data, including its origins in the Semantic Web and Linked Open Data movements. Schema.org was created in 2011 to provide a common vocabulary for structured data markup on web pages. It allows search engines and other applications to understand the intended meaning and relationships of information on web pages. The document provides examples of using Schema.org structured data and microdata, and recommends applying it across various page types to help search engines better understand websites.
1. The document discusses various methods for collecting data from websites, including scraping, using APIs, and contacting site owners. It provides examples of projects that used different techniques.
2. Scraping involves programmatically extracting structured data from websites and can be complicated due to legal and ethical issues. APIs provide a safer alternative as long as rate limits are respected.
3. The document provides tips for scraping courteously and effectively, avoiding burdening websites. It also covers common scraping challenges and potential workarounds or alternatives like using APIs or contracting data collection.
This document discusses COVID contact tracing using campus WiFi data. It describes a project by students at Rensselaer Polytechnic Institute to engineer a semantic web application for contact tracing using campus WiFi logs to identify contacts between individuals. The document outlines the objectives of understanding semantic web architecture, building applications, and contributing to the web of data.
The document provides guidelines for publishing data as Linked Data. It discusses identifying appropriate data sources, reusing existing vocabularies and non-ontological resources, generating RDF data from relational databases or geometrical data using tools like R2O, ODEMapster and geometry2rdf, and publishing the data on the web by resolving URIs. The Ontology Engineering Group at Universidad Politécnica de Madrid has published Spanish geospatial and statistical data as part of projects like GeoLinkedData following these guidelines.
Linked Open Data in Libraries Archives & MuseumsJon Voss
The document discusses the growing Linked Open Data (LOD) movement in libraries, archives, and museums (LODLAM). It notes that LODLAM allows these institutions to explore data interoperability both within the cultural sector and more broadly on the web. The document outlines several outcomes of a LODLAM summit, including outreach, education, developing use cases, and examining issues around copyright and licensing of open data. Examples are provided of institutions that have published bibliographic and other cultural data using open licenses.
Linked Open Data in Libraries, Archives & MuseumsJon Voss
This document provides an overview of Linked Open Data for libraries, archives, and museums. It discusses the growing movement of LODLAM and how it allows these cultural institutions to represent their data as graphs using triples that describe entities in a machine-readable format. Key concepts covered include the use of URIs, RDF, vocabularies, and different legal tools for publishing open data.
A summary of DBpedia's History and a detailed analysis of challenges and solutions.
We show how the Linked Data Cloud evolved around DBpedia and also what problems we and other data projects encountered. We included a section on the new solutions that will lead DBpedia into a bright future.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
The document discusses various aspects of web mining such as web structure mining, web usage mining, and web content mining. It provides an overview of how data mining techniques can be applied to extract useful information from web data sources, including the structure of hyperlinks between pages, user behavior patterns on websites, and content within web documents. Challenges of web mining include the noisy, dynamic, and changing nature of web data that requires techniques to handle unstructured information and link analysis.
This presentation was provided by Kevin Hawkins of The University of North Texas Libraries, during the NISO event "Long Form Content: Ebooks, Print Volumes and the Concerns of Those Who Use Both," held on March 20, 2019.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
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.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
2. Sea of Information
● Internet data are extremely prevalent
● They can be useful in many applications:
○ Predicting outcomes of political elections
○ Market trend research
○ Sentiment/reputation analysis
○ Stock market prediction
○ Sports science
○ Diffusion of information
○ Natural disasters
○ Diseases, epidemiology, public health
○ … the list goes on and on
Image Source: Unknown
3. Data is the new oil
● We have to “mine” it…
○ Publicly available datasets
■ Raw files made available for download
■ e.g. UCI ML repository, Kaggle competitions, data.gov, NIH Chest X-ray Dataset, …
○ Web crawling/scraping
■ Automated bots/macros to collect data from the web
■ Navigate through websites by tracking down the links
■ e.g. Search engines!
○ API - Application Programming Interface
■ A programing interface to send query & retrieve data
■ e.g. Twitter API
○ Proprietary datasets
Image Source: Wikipedia
7. Web Crawling & Scraping
● Data mining from websites can be incredibly tedious and repetitious
● Web browser macros can automate repetitive web clicks, filling in forms, etc.
https://youtu.be/hytfjJGqlio
8. Web Crawling & Scraping
● Crawler: aka web robot, or web spider
○ A software program that automatically traverses hyperlinks
○ Systematically browses the world wide web
○ Examples:
■ Googlebot: collects documents from the web to build a searchable index.
■ Xenon: is a web crawler used by government tax authorities to detect fraud
● There are many open source crawlers:
○ For example: https://github.com/scrapinghub
○ BeautifulSoup, LXML
9. Web Crawler Policies
● The behavior of a web crawler is the outcome of a combination of policies
○ a selection policy which states the pages to download,
○ a re-visit policy which states when to check for changes to the pages,
○ a politeness policy that states how to avoid overloading Web sites.
○ a parallelization policy that states how to coordinate distributed web crawlers.
10. Web Crawler Policies
● The behavior of a web crawler is the outcome of a combination of policies
○ a selection policy which states the pages to download,
○ a re-visit policy which states when to check for changes to the pages,
○ a politeness policy that states how to avoid overloading Web sites.
○ a parallelization policy that states how to coordinate distributed web crawlers.
● Web crawlers are not always welcome
○ A not so well-behaved crawler can be blacklisted
○ robot.txt: a special file located on a web server that enforces restrictions
■ ‘Allow’ tag: list of pages that can be accessed
■ ‘Disallow’ tag: list of pages that should not be indexed
○ HTML META tags: does the similar thing with robot.txt
■ <META name=”ROBOT” content=”NOFOLLOW”>
■ <META name=”GOOGLEBOT” content=”NOINDEX”>
11. Application Programming Interface (API)
● Set of functions, routines, protocols, and tools for building software
applications
● APIs define the standard way of accessing data
● Examples:
○ Twitter API: https://dev.twitter.com
○ Facebook API: https://developers.facebook.com
○ Yahoo! Finance API
○ Google Map API
○ …
14. ICA - Topic 1
● Debate on the Nobel Prize in Physics 2017: “First Direct Observation of
Gravitational Wave”
○ What is the gravitational wave?
■ https://www.nationalgeographic.com/news/2017/10/gravitational-waves-nobel-prize-
physics-ligo-science-space/
○ The debate:
■ https://arstechnica.com/science/2018/10/danish-physicists-claim-to-cast-doubt-on-
detection-of-gravitational-waves/
● Discuss:
○ What is the gravitational wave in layperson's terms?
○ What’s the root of the debate?
○ What is the correlated noise and what can you do about it?
○ Danish vs American scientists - who do you think is more convincing?
15. ICA - Topic 2
● David Balley. (2018). Why outliers are good for science?
○ https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2018.01105.x
● Discuss:
○ What is the Gaussian distribution (the bell curve) and what is the Cauchy distribution?
○ Is real-world measurement closer to the Gaussian or Cauchy? Why do you think is the
reason?
○ What’s the criteria commonly used to determine outliers? How can they be wrong?
○ What is the author’s point to claim that outliers might actually be good for science?
16. ICA - Topic 3
● Candace Corbeil - Gaps in the Spreadsheet
○ https://www.apa.org/science/about/psa/2016/02/gaps-spreadsheet
● Gerhard Svolba - The origin, detection, treatment and consequences of
missing values in analytics.
○ http://analytics-magazine.org/missing-values/
● Discuss:
○ What are the three types of missing data?
○ What is multiple imputation how can they be useful for data that are missing at random?
○ In case of systematic (non-random) missing data, would you still use multiple imputation? Or
what else can you do?
