Inrecentyears, there has been a significant increase of publicly available unstructured data resources for computer vision and NLP tasks.

A mother of two says she was left penniless after giving her savings to Tinder predator Christopher Harkins in a fake investment scam. The pair matched on the dating app in London in 2020. Caitlyn - not her real name - told how the fraudster and rapist initially tried to talk her into going on holiday with him - a regular ruse of Harkins, now 38. When she said she couldn't afford a holiday, he offered to help by doubling what money she had via his foreign currency exchange business. She's one of four women the BBC is aware of who were targeted by Harkins in the capital - where he fled to after his crimes were exposed in Scotland.

--It is difficult for individuals and organizations to protect personal information without a fundamental understanding of relative privacy risks. By analyzing over 5,000 empirical identity theft and fraud cases, this research identifies which types of personal data are exposed, how frequently exposures occur, and what the consequences of those exposures are. We construct an Identity Ecosystem graph--a foundational, graph-based model in which nodes represent personally identifiable information (PII) attributes and edges represent empirical disclosure relationships between them (e.g., the probability that one PII attribute is exposed due to the exposure of another). Leveraging this graph structure, we develop a privacy risk prediction framework that uses graph theory and graph neural networks to estimate the likelihood of further disclosures when certain PII attributes are compromised. The results show that our approach effectively answers the core question: Can the disclosure of a given identity attribute possibly lead to the disclosure of another attribute? Different individuals and organizations have different sets of personally identifiable information (PII), and therefore have different perspectives on which PII attributes are more vulnerable, more valuable, and in greater need of protection. An individual's PII includes personal data in four different categories--What you Know (e.g., name, address, phone number, mother's maiden name), What you Have (e.g., driver's license, Social Security Card, employee ID, passport), What you Are (e.g., fingerprint, voice, facial image), and What you Do (e.g., patterns of life such as websites visited, GPS locations visited, phone logs) [1]. Protecting PII data can be costly and time-consuming. Research has uncovered various strategies to reduce risks of unintended data disclosure [2], including statistical disclosure limitation (SDL) techniques commonly used by national statistical agencies before releasing public-use data sets.

NetChoice Policy Director Patrick Hedger joins'Fox News Live' to explain why the United States must embrace artificial intelligence to stay ahead in national security. Lately, I've had way too many calls on my shows from people who have lost thousands (sometimes hundreds of thousands) to scams. These are so cleverly evil, it's like Ocean's Eleven but starring a dude with three Instagram followers and a ChatGPT subscription. Last chance to enter to win 500 in giveaway. You see, we're way past scam emails from sketchy Nigerian princes.

William Phelps was at work when he got the call from the FBI that he had to return home at once. It was December 2023 and his wife, Aurora Phelps, was in big trouble, something to do with a fraud scheme. About a dozen agents turned his apartment upside down looking for evidence in their case, and William Phelps wouldn't see his wife again. That is, until this week, when William came to learn the scope of the allegations against his wife. According to federal prosecutors, Aurora was the perpetrator of a deadly romance scam, connecting with older men on the internet, then drugging them and stealing from their bank accounts.

Kurt Knutsson joins "Fox & Friends" to discuss bank scams and a self-driving car that trapped a rider inside. Tech support scams have been around for years, but a new variant called the Phantom Hacker scam is rapidly gaining traction. It has cost victims, primarily older Americans, over 500 million since 2023. This scam is particularly deceptive because it unfolds in three carefully orchestrated phases and uses AI-powered social engineering tactics to avoid detection. Attackers leverage caller ID spoofing and AI-generated voices to make their scheme more persuasive, but there are ways to protect yourself.

Code generation with large language models has shown significant promise, especially when employing retrieval-augmented generation (RAG) with few-shot examples. However, selecting effective examples that enhance generation quality remains a challenging task, particularly when the target programming language (PL) is underrepresented. In this study, we present two key findings: (1) retrieving examples whose presented algorithmic plans can be referenced for generating the desired behavior significantly improves generation accuracy, and (2) converting code into pseudocode effectively captures such algorithmic plans, enhancing retrieval quality even when the source and the target PLs are different. Based on these findings, we propose Plan-as-query Example Retrieval for few-shot prompting in Code generation (PERC), a novel framework that utilizes algorithmic plans to identify and retrieve effective examples. We validate the effectiveness of PERC through extensive experiments on the CodeContests, HumanEval and MultiPL-E benchmarks: PERC consistently outperforms the state-of-the-art RAG methods in code generation, both when the source and target programming languages match or differ, highlighting its adaptability and robustness in diverse coding environments.

Every fingerprint is believed to be unique, making it possible to identify an individual by matching a new fingerprint with an image on file, whether to unlock a mobile phone, access a bank account, or solve a murder. Fingerprint examiners, however, do not always agree on whether two print images match and, asked to recheck their work after several months, they sometimes do not even agree with themselves. That is leading to increased use of neural networks, powerhouses for identifying and matching patterns of all sorts, to automate and improve decisions about whether two fingerprints come from the same person. A group of computer scientists decided to use neural networks to test the assumption that no two fingerprints are the same. Using twin neural networks, researchers from Columbia University, Tufts University, and the State University of New York (SUNY) University at Buffalo looked for similarities between different fingerprints in a database from the National Institute of Standards and Technology (NIST).

This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI). Word order differences are one of the biggest challenges in SI, especially for language pairs with significant structural differences like English and Japanese. We analyzed the characteristics of chunk-wise monotonic translation (CMT) sentences using the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset and identified some grammatical structures that make monotonic translation difficult in English-Japanese SI. We further investigated the features of CMT sentences by evaluating the output from the existing speech translation (ST) and simultaneous speech translation (simulST) models on the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well as on existing test sets. The results indicate the possibility that the existing SI-based test set underestimates the model performance. The results also suggest that using CMT sentences as references gives higher scores to simulST models than ST models, and that using an offline-based test set to evaluate the simulST models underestimates the model performance.