Clips from creators in Minnesota have become primary evidence in attempts from the right-wing to justify ICE's surge on American cities. In the days since a masked federal agent shot and killed Renee Nicole Good, right-wing creators and influencers like Nick Sortor and Cam Higby have descended on Minneapolis, filming protestors and interviewing Immigration and Customs Enforcement (ICE) agents. So far, they've produced a steady stream of content that appears designed to paint Minneapolis as a lawless city, and the actions of ICE agents like Jonathan Ross, who reportedly shot and killed Good, as self-defense. "HELL YES! ICE just SMASHED a leftist activist's car window in and pulled them out after they interfered in ICE's operations in Minneapolis. MORE OF THIS!" Sortor posted to X on Sunday, "Consequences must be STEEP!"

Tiffany Lee Griffith, 36, accused of forcibly dunking a child underwater at Gaylord Palms Resort, was once recognized as Employee of the Quarter by the Punta Gorda Police Department.

With the advent of social media, children are becoming increasingly vulnerable to the risk of grooming in online settings. Detecting grooming instances in an online conversation poses a significant challenge as the interactions are not necessarily sexually explicit, since the predators take time to build trust and a relationship with their victim. Moreover, predators evade detection using indirect and coded language. While previous studies have fine-tuned Transformers to automatically identify grooming in chat conversations, they overlook the impact of coded and indirect language on model predictions, and how these align with human perceptions of grooming. In this paper, we address this gap and evaluate bi-encoders on the task of classifying different degrees of grooming risk in chat contexts, for three different participant groups, i.e. law enforcement officers, real victims, and decoys. Using a fuzzy-theoretic framework, we map human assessments of grooming behaviors to estimate the actual degree of grooming risk. Our analysis reveals that fine-tuned models fail to tag instances where the predator uses indirect speech pathways and coded language to evade detection. Further, we find that such instances are characterized by a higher presence of out-of-vocabulary (OOV) words in samples, causing the model to misclassify. Our findings highlight the need for more robust models to identify coded language from noisy chat inputs in grooming contexts.

An experienced human Observer reading a document -- such as a crime report -- creates a succinct plot-like $\textit{``Working Memory''}$ comprising different actors, their prototypical roles and states at any point, their evolution over time based on their interactions, and even a map of missing Semantic parts anticipating them in the future. $\textit{An equivalent AI Observer currently does not exist}$. We introduce the $\textbf{[G]}$enerative $\textbf{[S]}$emantic $\textbf{[W]}$orkspace (GSW) -- comprising an $\textit{``Operator''}$ and a $\textit{``Reconciler''}$ -- that leverages advancements in LLMs to create a generative-style Semantic framework, as opposed to a traditionally predefined set of lexicon labels. Given a text segment $C_n$ that describes an ongoing situation, the $\textit{Operator}$ instantiates actor-centric Semantic maps (termed ``Workspace instance'' $\mathcal{W}_n$). The $\textit{Reconciler}$ resolves differences between $\mathcal{W}_n$ and a ``Working memory'' $\mathcal{M}_n^*$ to generate the updated $\mathcal{M}_{n+1}^*$. GSW outperforms well-known baselines on several tasks ($\sim 94\%$ vs. FST, GLEN, BertSRL - multi-sentence Semantics extraction, $\sim 15\%$ vs. NLI-BERT, $\sim 35\%$ vs. QA). By mirroring the real Observer, GSW provides the first step towards Spatial Computing assistants capable of understanding individual intentions and predicting future behavior.

As more swatting incidents are reported, two former law enforcement officers warn about artificial intelligence's negative impact on combatting false threats. Artificial intelligence advancements have helped drive an increase in swatting calls, forcing police to dash to scenes prepared for the worst and endangering Americans' emotional health or even their lives, a 22-year FBI veteran told Fox News. "They're doing it to create chaos," said James Turgal, vice president of the information security company Optiv. "They're utilizing this technology, which emboldens them because it's so much harder for law enforcement to track that back." Swatting -- when someone makes a false 911 report to illicit a large and aggressive police response -- has become increasingly common over the last decade as it becomes easier for callers to mask their voices, phone numbers and IP addresses to remain anonymous.

We frequently hear in the 21st century that data is the new oil. Those who controlled oil flows in the 1970s had a near stranglehold on the global economy. Today, those who hold data might well control the new economy. Data, however, is diffuse, hard to track and nearly impossible to regulate, which could have unparalleled implications for human rights and religious freedom. Big data companies have poured billions into research to bring technology and data into direct contact with us every day through artificial intelligence.

You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared. By Kashmir Hill Until recently, Hoan Ton-That's greatest hit was an app that let people put Donald Trump's distinctive yellow hair on their own photos. Then Ton-That did something momentous: He invented a tool that could end your ability to walk down the street anonymously and provided it to hundreds of law enforcement agencies. His tiny company, Clearview AI, devised a groundbreaking facial recognition app. You take a picture of a person, upload it and get to see public photos of that person along with links to where those photos appeared.

Police officer presence at an intersection discourages a potential traffic violator from violating the law. It also alerts the motorists' consciousness to take precaution and follow the rules. However, due to the abundant intersections and shortage of human resources, it is not possible to assign a police officer to every intersection. In this paper, we propose an intelligent and optimal policing strategy for traffic violation prevention. Our model consists of a specific number of targeted intersections and two police officers with no prior knowledge on the number of the traffic violations in the designated intersections. At each time interval, the proposed strategy, assigns the two police officers to different intersections such that at the end of the time horizon, maximum traffic violation prevention is achieved. Our proposed methodology adapts the PROLA (Play and Random Observe Learning Algorithm) algorithm [1] to achieve an optimal traffic violation prevention strategy . Finally, we conduct a case study to evaluate and demonstrate the performance of the proposed method.