Subject of Research: Data augmentation via code refactoring to improve deep learning-based code smell classification. Enhancing code smell classification with code refactoring-based data augmentation. Refactoring Trick Supercharges AI Detection of Bad Code, Study Finds. “Refactoring Trick Supercharges AI Detection of Bad Code, Study Finds.” “Refactoring Trick Supercharges AI Detection of Bad Code, Study Finds.”
Knowing exactly what kind of jamming is attacking a radar is the first step in defeating it, a task known as radar jamming recognition within electronic counter-countermeasure systems. Radar jamming signals come in many varieties, including deceptive jamming that mimics real target returns, barrage jamming that blankets entire frequency bands, and compound jamming that combines multiple techniques. New AI Model Spots Radar Jamming Even When Signals Are Buried in Noise. “New AI Model Spots Radar Jamming Even When Signals Are Buried in Noise.” “New AI Model Spots Radar Jamming Even When Signals Are Buried in Noise.”
Name Recognition, which notifies deaf and hard-of-hearing users when their name is spoken, and the Hikawa Grip & Stand for iPhone, an adaptive MagSafe accessory, are both already available. Apple’s release describes Name Recognition as working in more than 50 languages globally and describes the Hikawa Grip & Stand as sold in three colours on the Apple Store globally. The Hikawa Grip & Stand for iPhone is described as an adaptive MagSafe accessory for people with grip and mobility challenges, sold in three colours through the Apple Store worldwide. Apple’s own description calls the Hikawa Grip & Stand an accessory that attaches using MagSafe, rather than a built-in part of any iPhone. What Apple has not said Apple has not named a device or operating system requirement for Name Recognition, has not priced the Hikawa Grip & Stand, and has not named its three available colours.
HEBER CITY, Utah, Sept. 12, 2026 (Gephardt Daily) — An IT systems administrator for the Park City School District has been arrested for 10 alleged counts of sexual exploitation of a minor, listed in court documents as a second-degree felony. The arrest was part of an ongoing investigation into Garret Blaine Harmon, 29, who lives in Heber City after moving from a previous residence in Eagle Mountain. The ongoing investigation began in June of 2025 when “law enforcement officers in the United Kingdom arrested and charged a UK citizen with offenses related to child exploitation,” Harmon’s arrest document says. The UK citizen’s phone was examined, and identified the image provider as a Utah resident who had admitted abusing children, Harmon’s arrest document says. investigators used facial recognition technology on a photograph provided by the Utah-based suspect, which led investigators to identify Garrett Blaine Harmon,” says the arrest document, filed by an ICE agent.
Clearview AI’s InquiryIQ was described as an unreleased prototype designed to extend a facial-recognition lead into a much wider online investigation. The prototype Clearview says police have not used Clearview AI CEO Amos Kyler said that no law-enforcement user had used InquiryIQ. InquiryIQ is therefore best understood as a proposal for extending Clearview AI’s existing facial-recognition workflow, not as a tool that police departments can simply sign into today. Ferguson argued that preserving prompts, searches, model choices and investigative paths could make an AI-assisted investigation easier to audit than an undocumented human search. Those are figures about Clearview AI’s broader platform, not measurements of InquiryIQ or proof that the prototype was used by those agencies.
U.S. Marine Corps Sgt. Maj. Donald Reynolds, the command senior enlisted leader of 3rd Marine Littoral Regiment, 3rd Marine Division, reads a Navy and Marine Corps Achievement Medal citation to Marines assigned to 3rd Maintenance Battalion, 3rd Marine Logistics Group, during an awards ceremony at Marine Corps Base Hawaii, Sept. 11, 2026. The ceremony provided an opportunity for 3rd MLR leadership to express appreciation to the maintenance support teams and recognize outstanding performance following their contributions to strengthening 3rd MLR’s force regeneration efforts. Reynolds is from Pennsylvania. (U.S. Marine Corps photo by Lance Cpl.
U.S. Marines assigned to 3rd Maintenance Battalion, 3rd Marine Logistics Group, and 3rd Marine Littoral Regiment, 3rd Marine Division, pose for a photo during an awards ceremony at Marine Corps Base Hawaii, Sept. 11, 2026. The ceremony provided an opportunity for 3rd Marine Littoral Regiment leadership to express appreciation to the maintenance support teams and recognize outstanding performance following their contributions to strengthening 3rd MLR’s force regeneration efforts. (U.S. Marine Corps photo by Lance Cpl. Matthew Benfield)
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A proposed federal class action filed in Chicago in September 2026 alleges that Meta extracted biometric information from people appearing in Facebook and Instagram photographs without adequate notice or consent. They allege that Meta turned facial information in Facebook and Instagram images into biometric data and used it in connection with NameTag and generative-AI systems. Scope, damages and what is not a payout promise The proposed class period reaches back to September 4, 2021. A proposed class action must proceed through later litigation, certification, settlement or judgment before any compensation could be distributed. The proposed class description and the litigation’s later legal steps determine who could ultimately be included.
The extradition was facilitated by the Directorate for Priority Crime Investigation, commonly known as the Hawks, in collaboration with Interpol South Africa. The six men were transported from a correctional facility in Cape Town to Cape Town International Airport (CTIA), where they were handed over to officials from the Federal Bureau of Investigation and the US Secret Service. The Cape Town Magistrate’s Court formally ruled that the alleged members could be extradited to the US. The Institute for Security Studies has reported that South Africa has the highest number of identified Black Axe zones in Africa. The report said members were predominantly Nigerian, with some having lived in South Africa for between five and 30 years.
A soccer match, expressed as a sequence of Gramian Angular Field images and read by a convolutional LSTM, is not merely noise; it is a legible document, and machines are learning to read it. Visualizing victory: employing GAF-transformed images and convolutional LSTM to predict soccer game strategies. Seeing soccer strategy: GAF images and convolutional LSTM predict match tactics. “Seeing soccer strategy: GAF images and convolutional LSTM predict match tactics.” “Seeing soccer strategy: GAF images and convolutional LSTM predict match tactics.”
Meta has said it trained Emu on large quantities of Facebook and Instagram images and text, with chief product officer Chris Cox calling those platforms a “data advantage” for its AI systems. The complaint alleges that the training process illegally harvested biometric information about people who appeared in the images. This is not the first time Meta has faced fines over its handling of biometric data. In 2024, Meta agreed to pay Texas $1.4 billion to resolve separate allegations that it had unlawfully collected biometric data from users. The day after WIRED’s June 4 report about NameTag, Meta removed the code from its app.
ORLANDO, Fla. — 25 years after the September 11 terrorist attacks transformed airport security in the United States, the Transportation Security Administration is looking toward the next generation of technology. That could mean artificial intelligence playing a bigger role in the future of airport security. People must apply in advance and still go through security screening. Experience MCO visitors currently use standard screening and cannot use TSA PreCheck or other expedited screening programs. For now, the Gateside program and MCO's Experience MCO program remain separate.
This talk explores an emerging alternative based on Implicit Neural Representations (INRs), which model biometric traits as continuous functions rather than discrete images. Our speaker will conclude by discussing future opportunities for INRs in biometric recognition, continuous biometric templates, morph attack analysis, privacy-preserving biometrics, and multimodal biometric systems, arguing that continuous neural representations may provide a new foundation for the next generation of biometric technologies. About the speaker Vishal M. Patel is a Professor in the Department of Electrical and Computer Engineering (ECE) at Johns Hopkins University. His research focuses on computer vision, machine learning, image processing, medical image analysis, and biometrics. Patel serves as an associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence journal and IEEE Transactions on Biometrics, Behavior, and Identity Science.
The researchers combined real images of trees from iNaturalist and the Auto Arborist Dataset with AI-generated images, then fed different combinations into a standard image-recognition model to see whether the synthetic images improved its ability to identify trees. Synthetic images improved the model's ability to recognize trees when real images were limited, suggesting that AI-generated images could help supplement real-world data when photographs are scarce. Lake said it's important that the potential of AI-generated images for these kinds of conservation efforts not be overstated. In the study, he and his collaborators found that the synthetic images were less effective than real images overall in training the image-recognition model. Real images contributed by everyday people provide that missing information.
Trending Artsbridge and the City of Belpre are collaborating with students of Belpre High School art teacher Chad Stevens to paint a mural on a wall along Campus Drive leading to the high school. Artsbridge Executive Director Lyndsay Dennis, who is also a member of Belpre City Council, said the mural celebrates the 250th anniversary of the signing of the Declaration of Independence. It came together after a resident suggested doing something with the wall, which has been blank for several years.
Building automated speech recognition has become one of the most resource-intensive arms races in modern computing. For years, the industry standard has centered on scaling foundational transformer models across hundreds of thousands of hours of speech data to drive Word Error Rates closer to zero. Yet, as speech-to-text engines have become ubiquitously integrated into virtual meeting software, streaming platforms, and classroom tools, a fundamental limitation has become increasingly obvious to anyone relying on them for daily communication. While modern speech models are remarkably adept at transcribing vocabulary, they remain largely oblivious to the emotional tone, cadence, and urgency that give spoken language its actual meaning. In high-stakes technical environments—such as engineering sprint retrospectives, architectural design reviews, or advanced university STEM lectures—spoken dialogue is rarely delivered as flat prose. A slight upward inflection can turn an apparent statement of fact into a skeptical question; a sudden drop in vocal pitch can signal a serious warning about a code vulnerability; and an urgent delivery can differentiate a critical design constraint from a casual suggestion. When standard automated speech recognition strips these acoustic cues away, leaving behind an uninflected block of text at the bottom of a display, it creates what Human-Computer Interaction (HCI) researchers call an intention gap. For deaf and hard-of-hearing professionals, the result is continuous cognitive strain, forcing them to guess speaker intent while rapidly darting their visual attention between slides, physical demonstrations, and disconnected caption windows. At the University of California, Irvine and Virginia Tech, HCI researcher Dr. Sunday David Ubur has taken an alternative engineering path to address this challenge. Rather than treating speech recognition solely as a sequence-to-sequence text translation problem, Dr. Ubur approaches the challenge as an asynchronous distributed systems and spatial computing problem. Across several years of published laboratory work, his research has centered on
Here’s a look at how life for air passengers has changed since then. What happened to air travel in the immediate aftermath of September 11, 2001? In some instances, travellers were able to transfer from US domestic flights to international flights without going through any further security checks, either. Armed marshals and reinforced cockpits The Federal Air Marshal Service was expanded, with more armed air marshals placed on flights. Post-9/11 security screening led to more people being subjected to questioning and searches at airports, however.
Social media posts shared hundreds of times have claimed that Kent Police’s facial recognition team said “around 76% of Dover protestors have previous convictions for domestic abuse, paedophilia, GBH or drug dealing”. But this is false—it is based on a joke post from a satire account. Kent Police confirmed the post was inaccurate and that it did not issue or recognise the statistic. Hundreds of masked protestors caused disruption at the Port of Dover on Saturday. The account which appears to have first posted the claim describes itself as an “obvious satire account” in its bio, but many people seem to think the statistic is genuine.
Spectral-spatio-temporal representation In this study, we propose a novel multi-dimensional unified EEG representation designed to capture concurrent spectral, spatial, and temporal dynamics. ShallowConvNet and DeepConvNet30: The reference shallow and deep convolutional pathways for EEG decoding, representing the filter-bank-like and the deep-hierarchy ends of the design space. This evaluation aims to identify systematic variations in model performance that may stem from idiosyncratic neural patterns or inherent disparities in stimulus characteristics. This clustering suggests that the same stimulus triggers highly divergent cognitive responses across different participants, reinforcing the challenge of intersubject variability in EEG decoding. Another approach we hypothesize to be more effective for EEG decoding is self supervised contrastive loss.