Participation to the Linked Data Mining Challenge at KNOW@LOD, ESWC2016 by Petar Petrovski and myself. Paper available at http://ceur-ws.org/Vol-1586/ldmc4.pdf
Extracting Relations between Non-Standard Entities using Distant Supervision ...Isabelle Augenstein
Poster for our EMNLP paper on extracting non-standard relations from the Web with distant supervision and imitation learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf
Deep learning for audio-based music recommendationRussia.AI
We are delighted to republish slides on Deep learning for audio-based music recommendation by Sander Dieleman.
Sander is a Research Scientist at DeepMind, and was previously involved in scaling up content-based music recommendation at Spotify. Sander is a PhD student at Ghent University.
The presentation covers deep content-based music recommendation approach that is an alternative solution to widely adopted collaborative filtering.
Initially slides were presented at Workshop on Deep Learning for Recommender Systems in Bosoton in September 2016.
Extracting Relations between Non-Standard Entities using Distant Supervision ...Isabelle Augenstein
Poster for our EMNLP paper on extracting non-standard relations from the Web with distant supervision and imitation learning. Read the full paper here: https://aclweb.org/anthology/D/D15/D15-1086.pdf
Deep learning for audio-based music recommendationRussia.AI
We are delighted to republish slides on Deep learning for audio-based music recommendation by Sander Dieleman.
Sander is a Research Scientist at DeepMind, and was previously involved in scaling up content-based music recommendation at Spotify. Sander is a PhD student at Ghent University.
The presentation covers deep content-based music recommendation approach that is an alternative solution to widely adopted collaborative filtering.
Initially slides were presented at Workshop on Deep Learning for Recommender Systems in Bosoton in September 2016.
In large musical catalogs such as in streaming companies, manual curation comes at high cost and the amount of data is considerable with tens of thousands of records delivered every week. While not replacing human resources, automatic systems trained directly from audio data help streaming companies describing audio recordings as well as creating relations between them. We will take a look at what is done by Deezer R&D's team in this domain using machine learning techniques.
MediaEval 2018: AcousticBrainz Genre Task: Content-based Music Genre Recognit...multimediaeval
Paper: http://ceur-ws.org/Vol-2283/MediaEval_18_paper_2.pdf
Youtube: https://youtu.be/eFYYkUpvzxk
Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber, The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. Proc. of MediaEval 2018, 29-31 October 2018, Sophia Antipolis, France.
Abstract: This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.
Presented by Alastair Porter
Music Recommendation and Discovery in the Long TailOscar Celma
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.
Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations.
In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution.
The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
Social Tags and Music Information Retrieval (Part I)Paul Lamere
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)Yi-Hsuan Yang
Slides Hao-Wen Dong and I presented at the ISMIR 2019 tutorial on "Generating Music with GANs—An Overview and Case Studies". More info: https://salu133445.github.io/ismir2019tutorial/
Call for papers, International Conference on "Religions and Political Values,...Encyclopaedia Iranica
The international conference is organized by the Adyan Foundation and the Lebanese American University in order to promote the exchange among scholars, social scientists, theologians, and policy makers.
it include fundamental principles, fundamental values, challenges to democracy, advantages and disadvantages of democracy, pre- requisites of democracy.
In large musical catalogs such as in streaming companies, manual curation comes at high cost and the amount of data is considerable with tens of thousands of records delivered every week. While not replacing human resources, automatic systems trained directly from audio data help streaming companies describing audio recordings as well as creating relations between them. We will take a look at what is done by Deezer R&D's team in this domain using machine learning techniques.
MediaEval 2018: AcousticBrainz Genre Task: Content-based Music Genre Recognit...multimediaeval
Paper: http://ceur-ws.org/Vol-2283/MediaEval_18_paper_2.pdf
Youtube: https://youtu.be/eFYYkUpvzxk
Dmitry Bogdanov, Alastair Porter, Julián Urbano, Hendrik Schreiber, The MediaEval 2018 AcousticBrainz Genre Task: Content-based Music Genre Recognition from Multiple Sources. Proc. of MediaEval 2018, 29-31 October 2018, Sophia Antipolis, France.
Abstract: This paper provides an overview of the AcousticBrainz Genre Task organized as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. The task is focused on content-based music genre recognition using genre annotations from multiple sources and large-scale music features data available in the AcousticBrainz database. The goal of our task is to explore how the same music pieces can be annotated differently by different communities following different genre taxonomies, and how this should be addressed by content-based genre recognition systems. We present the task challenges, the employed ground-truth information and datasets, and the evaluation methodology.
Presented by Alastair Porter
Music Recommendation and Discovery in the Long TailOscar Celma
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.
Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations.
In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution.
The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
Social Tags and Music Information Retrieval (Part I)Paul Lamere
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear
ISMIR 2019 tutorial: Generating music with generative adverairal networks (GANs)Yi-Hsuan Yang
Slides Hao-Wen Dong and I presented at the ISMIR 2019 tutorial on "Generating Music with GANs—An Overview and Case Studies". More info: https://salu133445.github.io/ismir2019tutorial/
Call for papers, International Conference on "Religions and Political Values,...Encyclopaedia Iranica
The international conference is organized by the Adyan Foundation and the Lebanese American University in order to promote the exchange among scholars, social scientists, theologians, and policy makers.
it include fundamental principles, fundamental values, challenges to democracy, advantages and disadvantages of democracy, pre- requisites of democracy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
1. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Can you judge a music
album by its cover?
Petar Petrovski and Anna Lisa Gentile
ESWC2016 – 30th May 2016
2. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Music album classification
TASK
• predicting the rating of music albums
• exploiting any available data from the LOD cloud
DATASET
• a sample of musical albums from Metacritic labeled
as “good” (critics’ score > 79) and “bad” ( <63)
3. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
The idea
Only exploit album covers to make the prediction
4. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Music album classification: pipeline
5. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Similar work
L ̄ıbeks and Turnbull (2011)
• image of an artist can play a role in how we judge a
music album
• predict music genre tags based on image analysis
• assess similarity amongst artists to inform the
music discovery process
6. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
ImageNet - A ConvNet Model
Jia et al. Caffe: Convolutional architecture for fast feature embedding. ACM (2014)
Conv1 Conv2 Conv3 Conv4 Conv5 FC1 FC2
7. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
ConvNet Image Features
Apply image filter to get visual features (example)
8. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
ConvNet Image Features
• Visual features - lower layers
• edges
• orientation
• blotch of color
• Visual features - higher layers
• honeycomb pattern
• wheel-like pattern
9. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Results
Accuracy on the challenge system - 60.3125%
true good true bad class
precision
pred. good 385 282 57.72%
pred. bad 254 356 58.36%
class recall 60.25% 55.80%
10. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Possible future work
Use image features as supplement to traditional
features (artist, genre, # of tracks)
Build additional Neural Network model specifically
built on music album covers
12. KNOW@LOD challenge – P. Petrovski, A. L. Gentile
Can you judge a music
album by its cover?
Petar Petrovski
petar@informatik.uni-mannheim.de
@ppetrovsk