The World Wide Wander 2023 is an online event like no other. It’s a 12-hour walk that takes you across six continents, from Melbourne to São Paulo. It’s a chance to see the world through different eyes, to hear different stories and to share different dreams. It’s an opportunity to imagine better futures together. It’s free and open to all. What are you waiting for? #WWW2023
September 29
https://deriveapp.com/s/v2/wander-the-streets-on-september-29/
📢 Computing and Visualizing Agro-Meteorological Parameters based on an Observational Weather Knowledge Graph
📜 https://dl.acm.org/doi/10.1145/3543873.3587357
at the collocated Web conferences #WebSci23 #TheWebConf2023 @TheWebConf #WWW2023
by Wimmics
📢 Metadatamatic: a Web Application to Create a Dataset Description
📜 https://dl.acm.org/doi/10.1145/3543873.3587328
at the collocated Web conferences #WebSci23 #TheWebConf2023 @TheWebConf #WWW2023
by Wimmics
"On April 30, 1993, something called the World Wide Web launched into the public domain. The web made it simple for anyone to navigate the internet. All users had to do was launch a new program called a 'browser,' type in a URL and hit return. This began the internet's transformation into the vibrant online canvas we use today."
30 years ago, one decision altered the course of our connected world - https://text.npr.org/1172276538
We’re looking forward to a great week ahead at #TheWebConf2023 #WWW2023 in Austin, Texas, with 5 publications from our group.
We start out on Sunday (April 30) with our work on
"Scientific Data Extraction from Oceanographic Papers“ by
Bartal Eyðfinsson Veyhe, Tomer Sagi, @katjahose at
the 3rd International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K, https://sci-k.github.io/2023/)
Details: https://dl.acm.org/doi/10.1145/3543873.3587595
1/4
At The Web Conference track on the History of the Web, we will present "Wikidata: The Making Of" by Markus Krötzsch, @nightrose and me
Austin, TX, on May 2, 2023, at 10am
Paper will be published as Open Access soon
We prepared a trailer for the talk: https://www.youtube.com/watch?v=YxWs_BS31QE
Join us!
#Wikidata #SemanticWeb #TheWebConference #WWW2023 #HistoryOfTheWeb
Another month to wait!!! So nice to see your babies getting life!!
---
RT @scik_workshop
📢📢📢 ANNOUNCEMENT
Sci-K 2023 will take place on 📅30 Apr 2023 as a FULL DAY WORKSHOP. (in about a month, we are sooooo close!)
Please stay tuned for more info such as rooms and more.
As always, check 🌐https://sci-k.github.io/2023/
#scik2023 #www2023 #webconf2023 @TheWebConf
https://twitter.com/scik_workshop/status/1640271138109497350
🧙♂️ "Show me your NFT and I tell you how it will perform" ! 🧙♀️
💥 Check out our postprint of "Multimodal representation learning for NFT selling price prediction", just accepted at #TheWebConf2023 🎉
📄 https://arxiv.org/abs/2302.01676
📝 w/ @starquake and @andreatagarelli
#TheWebConf #WWW2023 #NFT #Web3 #Blockchain #Metaverse #AI #NLP #NetworkScience #MachineLearning #DeepLearning #Transformers #ComputerVision #GNN
@webscience @economics
@networkscience
@complexsystems
@computationalsocialscience
Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.