Data Attack - It's Personal

Perennial public service announcement that all your data are belong to us:

ParkMobile app update: Deadline for $32.8M data breach settlement is here

Mar 2025, nj.com

https://www.nj.com/news/2025/03/parkmobile-app-update-deadline-for-328m-data-breach-settlement-is-here.html

As many as 21 million people could be eligible for the settlement, which stems from a data breach that happened in 2021.

ParkMobile, popular app used by many Jersey Shore beach towns that collect parking fees, agreed to the $32.8 million settlement to resolve claims “relating to an unknown actor’s unauthorized access to the Personal Information of ParkMobile App users,” the settlement website said.

Mostly unrelated image credit: AI Art - Sandwich Man 2 - 2024

Ex-cop admits hacking into social media accounts of nearly 20 women, distributing naked pics, officials say

Mar 2025, nj.com

https://www.nj.com/burlington/2025/03/ex-cop-admits-hacking-into-social-media-accounts-of-nearly-20-women-distributing-naked-pics-officials-say.html

A former Mount Laurel police officer admitted this week to hacking into multiple women’s social media accounts and distributing their nude photos, authorities said.

The investigation began in September 2022. He was a rookie officer with the Mount Laurel Police Department at the time, was arrested on Oct. 21, 2022 and charged with three counts of computer crime, invasion of privacy and two counts of endangering the welfare of a child, investigators said.

As the investigation continued, 18 more women were found to be victimized by him, police said. Investigators determined that all the victims had a student email account through Rowan College in Burlington County, the office said.

Detectives learned that he illegally accessed approximately 5,000 email accounts associated with the college, authorities said. He hacked the accounts from his own personal devices while on duty as a patrol officer, according to the release.

Look Mom No Data

AKA From Deep Learning to Deep Reasoning

DRNets can solve Sudoku, speed scientific discovery

Sep 2021, phys.org

https://techxplore.com/news/2021-09-drnets-sudoku-scientific-discovery.html

You can teach a machine to recognize a dog by showing it 1,000 pictures of dogs, Gomes said, but scientific discovery is not like that.

"You are not going to have lots and lots of labeled data," she said. "And in general, the examples you have are not exactly what you are looking for, but then you reason about what you know scientifically about the domain, and you can infer new knowledge."

Key to DRNets is the idea of an "interpretable latent space." Basically, it gives DRNets the ability to reason about the constraints of the domain—in this case materials science—from input data.

They started with Sudoku -- de-mixing overlapping handwritten Sudoku puzzles—grids. The computer had to separate the puzzles into two solved Sudokus, without any training data, which it was able to achieve with close to 100% accuracy.

The researchers then put DRNets to work on a real-world problem: automating crystal-structure phase mapping of solar-fuels materials, using X-ray diffraction (XRD) patterns. Crystal-structure phase mapping involves separating the source XRD signals of the desired crystal structures from "noisy" mixtures of XRD patterns, a task for which labeled training data are typically not available. ... DRNets was able to identify and separate a total of 13 crystal phases (single-phase materials) in 19 unique mixtures of the single-phase materials. ... DRNets' findings, verified using manual analysis, enable the discovery of complex mixtures of crystalline materials that convert solar energy into storable solar chemical fuels.

via Cornell University: Di Chen et al, Automating crystal-structure phase mapping by combining deep learning with constraint reasoning, Nature Machine Intelligence (2021). DOI: 10.1038/s42256-021-00384-1

http://dx.doi.org/10.1038/s42256-021-00384-1

Image credit: De-Mixed Sudoko, Cornell University, 2021

Algolords

Mostly unrelated image.

Today in the Can’t Make This Shit Up Department:

Facebook fires human editors, algorithm immediately posts fake news

Ars Technica, Aug 2016

"Earlier this year, Facebook denied criticisms that its Trending feature was surfacing news stories that were biased against conservatives. But in an abrupt reversal, the company fired all the human editors for Trending on Friday afternoon, replacing them with an algorithm that promotes stories based entirely on what Facebook users are talking about. Within 72 hours, according to the Washington Post, the top story on Trending was about how Fox News icon Megyn Kelly was a pro-Clinton "traitor" who had been fired (she wasn't).

…

"There were so many problems with this story, ranging from plagiarism to falsity, that even a fairly simple-minded robot editor should have caught them. The Trending algorithm is clearly not ready for prime time, or maybe Facebook is just trying to redefine what it calls "a breadth of ideas and commentary about a variety of topics."

So, first, we don’t trust humans to give us information because they’re biased. We put it in the hands of the computers instead. Then we realize that since the computers are only doing what we are doing, they can’t be trusted either. … All Hail.