Something being FOSS doesn't necessarily mean it's safe / ethical, but a LOT of FOSS apps are designed with those principles in mind.
However, being FOSS means that if an app claims that it is safe / ethical (ex. In this case, not storing data anywhere but on your device), you or an experienced peer can check the code to verify that fact.
The way she looks over even before he speaks lmao
As for voting under duress, it's also a concern with mail in ballots and voting by phone. It can be worse with online in the same way that scams are easier online.
I know someone who voted by phone this recent election in my home province, and they were eligible to do so because of sudden health issues. The phone call included multiple people who verified their information, took down the vote, and then verified the vote. I imagine something would get flagged if there was any discrepancy.
I'd also like to link to this Tom Scott video
Why Electronic Voting Is Still A Bad Idea: https://youtube.com/watch?v=LkH2r-sNjQs
The strongest argument for me: trust
Even with our paper ballots, hand counting, and many checks along the way, people now have doubts about the accuracy of the results. No matter how good the tech is, it will be impossible to convince the general public that the online votes are accurate.
Also this classic xkcd
I would like to celebrate it as a foundational nutrient for all life on Earth
It actually worked for me, thank you for finding that!
I also saw the error message where the thumbnail should be:
But if you click on the time stamp down below, you get an image:
Pasted the image below:
Good note, I didn't notice that in the original post. I edited the title
Huh
https://en.m.wiktionary.org/wiki/motel
Blend of motor + hotel, from the original Motel Inn of San Luis Obispo in California, USA, established in 1925 by Arthur Heineman (1878–1974).
While looking for a term to describe the original post, and came across this. I'm going to make a separate post about it
Oh this would be amazing, I was using a third party site.
Would it be possible to get a similar one for the dearrowed version? That was a complaint I got once but didn't have an easy a solution for: https://lemmy.ca/post/31890393
I wonder if there is any pattern to those numbers
edit: ah I didn't notice, thanks all :)
I think this is the step between supporting the experienced people around you, and feeling that you're ready to take it on yourself
Fine, I'll do it myself
cross-posted from: https://lemmy.ca/post/23884006
Link to full text study:
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00094-3/fulltext
Background Cooling towers containing Legionella spp are a high-risk source of Legionnaires’ disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.
Methods Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.
Link to full text study:
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00094-3/fulltext
Background Cooling towers containing Legionella spp are a high-risk source of Legionnaires’ disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.
Methods Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model that detects objects in images, and EfficientNet-b5, a model that classifies images. We assessed the primary outcomes of sensitivity and positive predictive value (PPV) of the model against manual labelling on test datasets of 548 images, including from two cities not seen in training (Boston [MA, USA] and Athens [GA, USA]). We compared the search speed of the model with that of manual searching by four epidemiologists.
I'd imagine it's the same as personal finance apps. A spreadsheet can be enough, and it is enough for a lot of people, but a custom app can make things easier:
etc.