Some of my slides from the AES 122 Vienna Convention, workshop on "Music and the Web" (May 6th, 2007). This presentation was dealing with the Music Ontology, and some of the Linked Data concepts.
Mining the social web for music-related data: a hands-on tutorialBen Fields
This is the handout draft of our slidebook for the tutorial Claudio and I will be giving at ISMIR09 in Kobe Japan on 26 October. A series of hands-on examples for mining the web targeted to Music Informatics researchers.
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
Mining the social web for music-related data: a hands-on tutorialBen Fields
This is the handout draft of our slidebook for the tutorial Claudio and I will be giving at ISMIR09 in Kobe Japan on 26 October. A series of hands-on examples for mining the web targeted to Music Informatics researchers.
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
Presented in the panel, "Jewish Music Online: Analog Repositories, Digital Fieldwork, and the Web of Collaborative Tools," at the 42nd Annual Conference of the Association for Jewish Studies, Boston (Mass.), December 20, 2010.
The Streams of Our Lives - Visualizing Listening Histories in ContextDominikus Baur
Slides to the talk I gave at the IEEE InfoVis conference on 29/10/10.
You can download LastHistory here:
http://www.frederikseiffert.de/lasthistory/
Here's the abstract of the paper:
The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a lab- and a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research.
J-P. Fauconnier, J. Roumier. Musonto - A Semantic Search Engine Dedicated to ...MusicNet
Jean-Philippe Fauconnier (Université Catholique de Louvain, Belgium) and Joseph Roumier (CETIC, Belgium).
Music Linked Data Workshop, 12 May 2011, JISC, London.
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Yi-Hsuan Yang is an Associate Research Fellow with Academia Sinica. He received his Ph.D. degree in Communication Engineering from National Taiwan University in 2010, and became an Assistant Research Fellow in Academia Sinica in 2011. He is also an Adjunct Associate Professor with the National Tsing Hua University, Taiwan. His research interests include music information retrieval, machine learning and affective computing. Dr. Yang was a recipient of the 2011 IEEE Signal Processing Society (SPS) Young Author Best Paper Award, the 2012 ACM Multimedia Grand Challenge First Prize, and the 2014 Ta-You Wu Memorial Research Award of the Ministry of Science and Technology, Taiwan. He is an author of the book Music Emotion Recognition (CRC Press 2011) and a tutorial speaker on music affect recognition in the International Society for Music Information Retrieval Conference (ISMIR 2012). In 2014, he served as a Technical Program Co-chair of ISMIR, and a Guest Editor of the IEEE Transactions on Affective Computing and the ACM Transactions on Intelligent Systems and Technology.
ShareTheMusic.com - the first in the world, global, free and legal, music sharing internet platform.
ShareTheMusic.com is an innovative and unique project since it does not distribute music files but it acts as a middleman among those who are willing to share their music - by enabling other Internet users to listen to it.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
Presented in the panel, "Jewish Music Online: Analog Repositories, Digital Fieldwork, and the Web of Collaborative Tools," at the 42nd Annual Conference of the Association for Jewish Studies, Boston (Mass.), December 20, 2010.
The Streams of Our Lives - Visualizing Listening Histories in ContextDominikus Baur
Slides to the talk I gave at the IEEE InfoVis conference on 29/10/10.
You can download LastHistory here:
http://www.frederikseiffert.de/lasthistory/
Here's the abstract of the paper:
The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a lab- and a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research.
J-P. Fauconnier, J. Roumier. Musonto - A Semantic Search Engine Dedicated to ...MusicNet
Jean-Philippe Fauconnier (Université Catholique de Louvain, Belgium) and Joseph Roumier (CETIC, Belgium).
Music Linked Data Workshop, 12 May 2011, JISC, London.
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Music Information Retrieval is about retrieving information from music entities.
The slides will introduce the basic concepts of the music language, passing through different kind of music representations and it will end up describing some low level features that are used when dealing with music entities.
Yi-Hsuan Yang is an Associate Research Fellow with Academia Sinica. He received his Ph.D. degree in Communication Engineering from National Taiwan University in 2010, and became an Assistant Research Fellow in Academia Sinica in 2011. He is also an Adjunct Associate Professor with the National Tsing Hua University, Taiwan. His research interests include music information retrieval, machine learning and affective computing. Dr. Yang was a recipient of the 2011 IEEE Signal Processing Society (SPS) Young Author Best Paper Award, the 2012 ACM Multimedia Grand Challenge First Prize, and the 2014 Ta-You Wu Memorial Research Award of the Ministry of Science and Technology, Taiwan. He is an author of the book Music Emotion Recognition (CRC Press 2011) and a tutorial speaker on music affect recognition in the International Society for Music Information Retrieval Conference (ISMIR 2012). In 2014, he served as a Technical Program Co-chair of ISMIR, and a Guest Editor of the IEEE Transactions on Affective Computing and the ACM Transactions on Intelligent Systems and Technology.
ShareTheMusic.com - the first in the world, global, free and legal, music sharing internet platform.
ShareTheMusic.com is an innovative and unique project since it does not distribute music files but it acts as a middleman among those who are willing to share their music - by enabling other Internet users to listen to it.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
A rework of metade's slides at http://www.slideshare.net/metade/linked-data-on-the-bbc for a SAMT 2009 Industry Day presentation.
Details several linked data projects going on at the BBC, and why/how we do it.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
Towards a musical Semantic Web
1. Towards a musical Semantic Web Yves Raimond Centre for Digital Music, Queen Mary, University of London May 6th, 2007
2.
3. Introduction – Web 1. I ask my favourite search engine for “ Lonah creative commons song” Looking for Creative Commons-licensed song from the French band Lonah
4. Introduction – Web Looking for Creative Commons-licensed song from the French band Lonah 2. I read the context of each of the first results 3. The second one seems ok... 4. I reach this last.fm page: 5. According to the tags, it looks like the band I am looking for... 6. I read “Music available on ...” and decide to visit the linked page 7. I reach the Jamendo website 8. I launch a search for Lonah , and, finally:
5. Introduction – Web Now: Ask your computer to do the same thing! Some requirements emerging from this scenario: - I need an entry point: the search engine - I need to understand the context of the links - I need to find my way into the web maze
6. Introduction – Web of data Turning the Web into a huge, “semantic”, democratic database in order to make machines able to look by themselves for particular informations KB1 KB2 KB3 KB4 Application1 Application 2
7. The Semantic Web Resources on the Web can be far more than just web pages! http://moustaki.org/foaf.rdf#moustaki is a resource representing me http://dbtune.org/jamendo/band/lonah is a resource representing the band Lonah When HTTP-GET ting, Let's leave fancy HTML pages for humans, and let's provide some useful descriptions for the machine! Resource Description Framework http://dbtune.org/jamendo/band/both http://dbtune.org/jamendo/artist/5 Both http://xmlns.com/foaf/0.1/Group
8.
9. Content negotiation http://dbtune.org/jamendo/artist/5 <mo:MusicArtist rdf:about="http://dbtune.org/jamendo/artist/5"> <foaf:based_near rdf:resource="http://dbpedia.org/France"/> <foaf:homepage rdf:resource="http://www.both-world.com"/> <foaf:img rdf:resource="http://img.jamendo.com/artists/b/both.jpg"/> <foaf:name rdf:datatype="&xsd;string">Both</foaf:name> </mo:MusicArtist> HTML for “human consumption” RDF for “machine consumption” And now, let's make both the human and the machine happy!
10.
11. The Timeline ontology First thing to address: representing temporal information “This performance happened the 9 th of March, 1984” “ This beat is occurring around sample 32480” “ The second verse is just before the second chorus” ... Only four concepts: Instant , Interval , TimeLine (and TimeLineMap )
12. The Event ontology We need a way to classify space/time regions : Performances, recordings, beats, verses, composition, ...
13.
14. Music production specific concepts On top of FRBR: MusicalWork , MusicalManifestation ( Album , Track , Playlist, etc.) MusicalItem ( Stream , a particular Vynil , etc.) On top of FOAF: MusicArtist and MusicGroup (defined classes) Arranger , Engineer , Performer , Composer , etc. (same thing) On top of the Event ontology: Composition , Arrangement , Performance , Sound , Recording Others: Signal , Score , Genre , Instrument , etc.
16. Levels of expressiveness Flexibility of the ontolog y - Level 1: purely editorial “ This track is on that particular album and that compilation and was created by that artist” - Level 2: introducing events “ This is a recording of this particular musician playing that jazz-rock arrangement of that particular piece” - Level 3: introducing event decomposition “ In this performance, this key was played at this particular time by this person, who was playing the piano”
17. Extensions Lots of anchor points (instrument, genre, signal, timeline, etc.) Already several extensions available: - Musical feature ontology : uses Event as a way to classify features on a signal' timeline - Instrument taxonomy : thanks to Musicbrainz! - Genre taxonomy : thanks to Wikipedia/DBPedia - The Key ontology Other possible extensions: - Audio recording devices under the Recording concept? - Mixing events dealing with Signal objects? - Sound cognition under the Sound / Listener concepts? - Symbolic music notation under Score ? - Chord ontology?
18. Linking open data on the Semantic Web W3C' Semantic Web Education and Outreach community project Lots of open data available: Wikipedia, Geonames, Musicbrainz, creative commons repositories, etc. Let's interlink them using Semantic Web technologies: DATA MASHUPS So far: - Jamendo - Magnatune - Musicbrainz - DBPedia - GeoNames - RDF book mashup - ...
19. And now?? - Your audio files are just other items of a particular manifestation , which has an URI - Store the corresponding statements in your SW-enabled application - And your collection gets access to the whole web of knowledge (well, in its current state:-) ) Give me all musical works composed in a city with more than 500 000 inhabitants Is there someone nearby really liking this band and the same beer as me, so that we can have a drink tomorrow? Place my collection on a timeline and make me listen something composed in the UK in 1560, followed by a rock song recorded in the 60s Give me all Jimmy Hendrix songs played by Brass Bands with at least 5 members Are there any other performances of this work? Give me one with a small part at 120 bpm