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‼️ Job Alert! ⚡ We need to cover two developer / research assistant positions urgently!
This time we opened two full-time positions for a short period of time to cover specific tasks to contribute to the development of DATALOG
https://www.upf.edu/documents/d/enginyeria/01_-dtic-plct-d-det-2025-46_udai_2-posicions
New paper out! "A comparative user study of human predictions in algorithm-supported recidivism risk assessment" with Carlos Castillo @chato , Marzieh Karimi-Haghighi, Songül Tolan, Kristian Lum, Antonio Andres Pueyo now published in the Journal of Artificial Intelligence and Law.
We study the effects of using an algorithm-based risk assessment instrument (RAI) to support the prediction of risk of violent recidivism upon release.
https://link.springer.com/article/10.1007/s10506-024-09393-y
In this paper, we study the effects of using an algorithm-based risk assessment instrument (RAI) to support the prediction of risk of violent recidivism upon release. The instrument we used is a machine learning version of RiskCanvi used by the Justice Department of Catalonia, Spain. It was hypothesized that people can improve their performance on defining the risk of recidivism when assisted with a RAI. Also, that professionals can perform better than non-experts on the domain. Participants had to predict whether a person who has been released from prison will commit a new crime leading to re-incarceration, within the next two years. This user study is done with (1) general participants from diverse backgrounds recruited through a crowdsourcing platform, (2) targeted participants who are students and practitioners of data science, criminology, or social work and professionals who work with RisCanvi. We also run focus groups with participants of the targeted study, including people who use RisCanvi in a professional capacity, to interpret the quantitative results. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions from all participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to that of crowdsourced participants. Among other comments, professional participants indicate that they would not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization, and to fine-tune or double-check their predictions on particularly difficult cases. We found that the revised prediction by using a RAI increases the performance of all groups, while professionals show a better performance in general. And, a RAI can be considered for extending professional capacities and skills along their careers.
Hoy a las 12:30 se han generado 9962 MW de solar fotovoltaica y 922 de solar térmica. Nunca se había producido tanto en un mes de enero en España. Algo más de un 28% de la electricidad era solar a esa hora
🐦🔗: https://twitter.com/Strowkyt600/status/1617514338264453121
Yes, this is very historically accurate and useful and should definitely be used in classrooms. This is my convo with Henry Ford where I try to get him to talk about his very well-documented antisemitism. This thing can’t go anywhere NEAR a classroom. https://twitter.com/ivyxvine/status/1614972906345467910
🐦🔗: https://twitter.com/ZaneGTCooper/status/1615577714836275200
El 39% de las motos que se han vendido en el mundo en 2022 son eléctricas. Lo diré de nuevo: 39% DE LAS VENTAS DE MOTOS EN EL MUNDO. 1 MOTO CADA 2,5 QUE SE VENDE ES ELÉCTRICA
🐦🔗: https://twitter.com/Strowkyt600/status/1615659350886076417
Mission accomplished: we've enabled AR navigation in the world's largest store! 😍
🐦🔗: https://twitter.com/AndrewHartAR/status/1615470595172601856
CNET added this gigantic correction to one of its AI-generated articles after we reached out with some questions about its accuracy 🫠
🐦🔗: https://twitter.com/Jon_Christian/status/1615364539083636742
🫣
Accurate Pose Estimation Via WiFi Signals
"our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input"
Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.
