photo: orda.kz Kazakhstan is set to roll out new biometric authentication rules that will allow banks to identify clients using facial recognition or palm scans, with just three attempts permitted for verification. The new regulations were developed jointly by the Agency for Regulation and Development of the Financial Market and the National Bank. A draft resolution outlining the changes has already been published, The Caspian Post reports via Kazakh media. Under the updated system, facial recognition will remain in place as the primary method. Banks will compare a customer’s live image with a reference sample stored in the National Bank’s identification data exchange system, which pulls data from the national biometric authentication database. Institutions must also ensure the person is physically present and not using a spoofed image. A new feature now being introduced is palm print authentication - a method not previously available. In this case, biometric data will be collected via specialized scanners with the client’s consent and verified against licensed biometric databases. Notably, palm authentication cannot be conducted using a customer’s smartphone. Clients will be given up to three attempts to pass biometric verification. If unsuccessful, they will be required to switch to facial recognition. The rules also provide alternative verification options for people with disabilities, including video-based identification or specialized verification technologies. Biometric authentication will be mandatory in several cases, including opening a bank account remotely, issuing a digital signature, registering on banking apps or websites, updating client data, and granting loans above a set threshold. The move follows earlier steps to tighten digital security in the financial sector. Since March 19, banks in Kazakhstan have been prohibited from issuing online loans without biometric verification of borrowers. Share on social media
Apr 13, 2026 · via caspianpost.com
访é®é¢ç¹ï¼è¯·ç¨åéè¯
æ¨æè¦è®¿é®ç页é¢ä¸å¨è¿å¿ï¼å®å¯è½æ£å¿çå·ççåï¼ç¨åå°±è½æ¢å¤æ£å¸¸
è¿å主页
Apr 13, 2026 · via news.futunn.com
Launch of VIRAL HALLUCINATIONS series No. III On Networked Protest Cultures and Viral Mobilization Deichtorhallen Hamburg Hamburg 20095 Germany Since 2024, the exhibition and discourse series VIRAL HALLUCINATIONS develops a set of critical tools to archive, analyze, discuss and reflect on the current iterations of photographic and photography-mimicking images, circulating on algorithmic media. Conceived by Nadine Isabelle Henrich (Curator of the House of Photography and Head of the Center of Visual Media, Deichtorhallen Hamburg), the series develops publications and hosts workshops, lectures, exhibitions, performances, research- and world building-sessions to weave the tissue of research and conversations for the new Center of Visual Media at Deichtorhallen Hamburg, set to open its own space in Winter 2027. This third volume of the free publication series is published as part of the discourse program framing the exhibition Philip Montgomery—American Cycles at Deichtorhallen Hamburg, curated by Nadine Isabelle Henrich. The publication is available in print and online. The exhibition is expanded by an audio commentary by Ocean Vuong. "Protest optics looks beyond the implementation of facial recognition technologies into the power dynamics between who needs to be visible at all times and who gets to stay in the shadows." – Sheung Yiu, artist and researcher Edited by Nadine Isabelle Henrich and Mona Behfeld (Researcher, Center for Visual Media), the free publication explores a spectrum of interconnected protest cultures and strategies of digital mobilization—ranging from civic engagement to networked propaganda. Taking images circulating online as its starting point that prepare for, accompany, and perpetuate protests through evolving visual strategies, it traces how photographic practices and political realities mutually shape and transform one another. The publication includes a glossary with visual examples by Behfeld, Sarah Gramotke, and Viktoria Rochambeau (curatorial fellows, Deichtorhallen Hamburg), with commentary by Gwen Schlüter (research assistant, Berlin University of the Arts and
Apr 13, 2026 · via e-flux.com
Six people arrested in Luton following use of Live Facial Recognition Live facial recognition in Luton has led to the arrests of six wanted individuals Bedfordshire Police has been using live facial recognition (LFR) technology in Luton, resulting in the arrest of six wanted people. The operation took place on 8th April, with the LFR team stationed at George Street, aiding in the identification and apprehension of offenders for crimes including criminal damage, causing actual bodily harm, possession of an offensive weapon, and handling stolen goods. Chief Superintendent Ian Taylor, LFR Strategic Lead, said: “This is a fantastic result for the team and demonstrates exactly why we wanted to introduce live facial recognition technology to Bedfordshire. “The technology has enabled us to identify those who are outstanding as wanted for criminal offences, and bring them into the criminal justice system in a timely manner supporting our commitment to be tough on crime, and make Bedfordshire a safer place for all communities.” The technology operates by comparing live camera feeds against a predetermined watchlist to identify persons of interest in real-time. The watchlists contain police images of individuals wanted by law enforcement or subject to bail conditions or court orders. Once an alert has been generated from a potential match, officers then assess the match for accuracy and authenticity. Bedfordshire Police says it “continues to be a leader in the use of new technology to improve efficiency and ensure our communities are kept safe.”
Apr 13, 2026 · via planetradio.co.uk
SEOUL, April 13 (AJP) - The Empathic AI Women's Engineering Talent Training Team at Sookmyung Women's University in South Korea is presenting a series of research papers at major international computer science conferences. As part of the Brain Korea 21 program, the team is developing human-centered artificial intelligence designed to address social isolation and communication barriers. Professor Kim Byung-Gyu leads the multidisciplinary group, which conducts research on emotion recognition, generative AI, and human-computer interaction. His group will present a multimodal emotion recognition model called EmoXFormer at the IEEE International Conference on Pattern Recognition (ICPR) 2026. This technology integrates different signals to identify human emotions more accurately. In the field of virtual reality, a group led by Professor Dong Suh Yeon presented two papers at the IEEE International Conference on Pervasive and Ubiquitous Computing (UbiComp) 2025. Their work involves a method to measure heart rates using the area around the ear when a user's face is obscured by a headset. This allows for stable emotion recognition even when a user is moving. Professor Kim Sangyeon and his team are focusing on digital accessibility for the elderly. At the ACM Conference on Human Factors in Computing Systems (CHI) 2026, they will introduce an AI-based tool that helps older users identify digital buttons and icons more easily through color and spatial cues. Other researchers on the team have focused on cybersecurity and data systems. Professor Jeong Seonghoon developed a model to detect evolving cyber threats for the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2026. Professor Choi Yoonhyuk also presented research on improving recommendation systems at various conferences, including AAAI 2026 and WSDM 2025. "We are expanding the practical applications of empathic AI through these diverse research achievements," said Professor Kim Byung-Gyu. "Based on our global research capabilities, we plan to
Apr 13, 2026 · via m.ajupress.com
About
Press
Copyright
Contact us
Creators
Advertise
Developers
Terms
Privacy
Policy & Safety
How YouTube works
Test new features
NFL Sunday Ticket
© 2026 Google LLC
Apr 12, 2026 · via youtube.com
If you were arrested after an AI facial recognition camera wrongly flagged you as a trespasser, how far would you go to get justice? Jason Killinger is looking to go all the way. The Nevada man recently filed a lawsuit against the city of Reno, after a police officer named Richard Jager placed him under arrest for 12 hours on the guidance of an AI surveillance system. The filing naming the city of Reno is the latest escalation in Killinger’s months-long quest for retribution, coming after federal Judge Miranda Du agreed the city could be named in his suit, the Reno Gazette Journal reported. A lawsuit against Jager is already ongoing, which will now include Reno among its defendants. While placing some bets at an area casino, Killinger was previously flagged as a “100 percent match” for another man who had been banned from the gaming floor at an earlier date. After being detained by casino security, Killinger was placed under arrest by officer Jager, who accused the innocent man of using a fake ID to evade casino staff. The cop made a number of errors, the lawsuit alleged, including refusing to check Killinger for alternative forms of ID (he had at least three in his wallet at the time, he says.) Yet the new lawsuit takes things much further, blaming the city of Reno itself for failing to train police officers properly on the legal use of AI facial recognition tools. This situation, Killinger’s attorneys allege, has led to “thousands of unlawful arrests” using facial ID technology, the Gazette reported. “Jager’s conduct was not a sporadic incident involving the wrongful actions of a rogue employee,” the updated lawsuit declares, “but the result of a widespread custom and practice involving hundreds of municipal employees making thousands of arrests in the
Apr 12, 2026 · via futurism.com
Abstract Stubble burning (SB) is a pervasive practice in Northern India, contributing significantly to winter air pollution, particularly exacerbated by the widespread use of combined harvesters. Despite its illegality, SB continues, releasing harmful pollutants and significantly deteriorating air quality. Recent advancements in computer vision, particularly those utilizing neural networks like YOLO, have significantly improved object detection capabilities. This paper proposes a novel approach to address this pressing issue by developing a cost-effective neural network-based fire and smoke detection model for an automated SB detection system. The proposed model is built upon YOLOv5 to efficiently detect the stubble burning: Architectural modifications of basic YOLOv5 are done by updating the Neck, Backbone and Head enhancing the feature extraction and object localization. We assess the model’s performance on our custom dataset using F-score, Mean Average Precision (mAP), and accuracy. Additionally, we perform an ablation study to examine the effects on inference time and mAP, as well as to evaluate the relationship between modal weights and processing speed. Data availability The datasets curated for the current study can be find here: https://www.kaggle.com/datasets/dgupta18/stubble-burning. References Govardhan, G. et al. Ghude. Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi. Heliyon 9, no. 6 (2023). Chawala, P. & Sandhu, H. A. S. Stubble burn area estimation and its impact on ambient air quality of Patiala & Ludhiana district. Punjab India Heliyon. 6, 1 (2020). Vadrevu, K., Prasad, E., Ellicott, K. V. S. & Badarinath and Eric Vermote. MODIS derived fire characteristics and aerosol optical depth variations during the agricultural residue burning season, north India. Environmental pollution. 159, 6 1560–1569. (2011). Ferreira, F. R. T., Couto, de Domingues, M. B. & L. M., &, G Comparing the efficiency of YOLO-M for face recognition in images and videos degraded by compression artifacts. Evol. Syst. 16
Apr 12, 2026 · via nature.com
The van will visit Market Square on Monday (13/04). The specialist LFR team will be in working with local officers to identify known suspects and deter crime. Police say advance warning means some individuals may choose to avoid the area, but say that preventing crime is just as important as catching those who break the law. Locals are welcome to visit the van to chat to officers and see how the technology works. According to the Thames Valley Police's website (external link): Thames Valley Police uses facial recognition technology to prevent and detect crime and help protect the vulnerable. Facial Recognition is a technology capable of comparing a digital image taken of a human face, against a database of facial images. Live Facial Recognition is used as a precision crime-fighting tactic to locate people who are of interest to the police. It helps us reduce violence and the risk of harm, prevent and detect crime, apprehend and prosecute offenders, protect the public, secure the administration of justice and maintain public confidence. It analyses key facial features and generates a mathematical representation of these features called a biometric template. It then compares this template against the biometric templates of known faces in a database, generating possible matches. Thames Valley Police uses Facial Recognition Technology in two ways: - Live Facial Recognition (LFR) – this compares a live camera feed of faces against a predetermined watch list to find a possible match that generates an alert. - Retrospective Facial Recognition (RFR) – this is a post-event tactic which compares still images of faces of unknown subjects (for example, CCTV from a crime scene) against the Police National Database in order to identify the unknown person.
Apr 12, 2026 · via bucks.radio
The Memorial Centre for Entrepreneurship (MCE) has launched a program aimed at helping Memorial University engineering co-op students identify real-world problems that could lead to the creation of new companies. The initiative, called the Problem Identification Lab Program, is now accepting applications from students who have observed challenges during their co-op placements. The program is delivered by MCE in collaboration with Memorial University’s Engineering Co-op Office. According to MCE, the program is designed to shift the focus of early-stage entrepreneurship from generating ideas to identifying meaningful, industry-rooted problems. Students are encouraged to document issues such as inefficiencies, safety risks, or costly workarounds encountered in the workplace and develop clear, non-confidential problem statements. “In the coming decades, the most successful emerging innovators would be exceptionally skilled at identifying and defining problems,” said Isaac Adejuwon, CEO and Founder of Metricsflow and a MUN alumnus. “They would be entirely problem-driven, rather than solution-driven, because AI would significantly support solutions. Ultimately, the challenges they uncover would help shape the next wave of innovation in our lifetime.” The Problem Identification Lab provides structured support to participating students. This includes access to MCE programs and workspace, one-on-one coaching, and feedback from alumni working in relevant industries. Participants may also receive up to $2,000 in optional in-kind support. As part of the process, students submit a concise problem statement based on their co-op experience. They can then choose to either contribute the problem to a shared “Problem Bank” — a repository intended to support future startup ideas — or continue developing the problem independently with MCE guidance. Submissions are reviewed by a panel of Memorial University alumni with sector-specific experience. The panel evaluates each problem based on clarity, specificity, real-world impact, and relevance to industry needs. Selected submissions may be showcased at an end-of-term event, where top
Apr 12, 2026 · via entrevestor.com
Abstract Burn injuries are a common pediatric health threat with depth assessment relying heavily on subjective visual inspection. While objective techniques like laser Doppler imaging exist, their cost and portability limitations restrict use. We propose SAM-DR to address the challenge of scarce annotated burn data by repurposing pre-trained models with minimal fine-tuning. By replacing SAM’s segmentation head with dense linear regression, our method not only identifies burn locations but also perceives burn depth through continuous depth prediction. Using 294 smartphone images from 94 patients annotated by 9 clinicians, we conducted a pixel-level comparison of human disagreement. SAM-DR achieved a 0.96 Dice score in wound segmentation, establishing state-of-the-art performance, and the use of interactive thresholding enabled segmentation of different burn depths comparable to human experts, suitable for assisted annotation. We developed an interactive tool based on SAM-DR that supports both clinical diagnosis and data annotation, offering a non-contact solution for burn assessment and dataset creation. Similar content being viewed by others Data availability The datasets generated and analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request and with permission from the institutional ethics committee. References Lawrence, J. W., Mason, S. T., Schomer, K. & Klein, M. B. Epidemiology and impact of scarring after burn injury: a systematic review of the literature. J. Burn Care Res. 33, 136–146 (2012). Meng, F. et al. Pediatric burn contractures in low-and lower middle-income countries: A systematic review of causes and factors affecting outcome. Burns 46, 993–1004 (2020). Phelan, H. A. et al. Use of 816 consecutive burn wound biopsies to inform a histologic algorithm for burn depth categorization. J. Burn Care Res. 42, 1162–1167 (2021). Shin, J. Y. & Yi, H. S. Diagnostic accuracy of laser doppler imaging in burn
Apr 12, 2026 · via nature.com
- School Education - 2 min read CBSE mandates computational thinking and AI integration for Class 3 to 8 The notification, issued by CBSE's training unit, introduced a structured curriculum on CT and AI aligned with the National Education Policy (NEP) 2020 and the National Curriculum Framework for School Education 2023. The curriculum aims to develop logical thinking, systematic problem-solving, pattern recognition and an understanding of the ethical use of AI among students. Nagpur: The Central Board of Secondary Education (CBSE) has set computational thinking (CT) and artificial intelligence (AI) as the focus for training in the 2026-27 academic year, instructing all affiliated schools to integrate these topics into the curriculum for Classes 3 to 8 starting this session. The notification, issued by CBSE's training unit, introduced a structured curriculum on CT and AI aligned with the National Education Policy (NEP) 2020 and the National Curriculum Framework for School Education 2023. The curriculum aims to develop logical thinking, systematic problem-solving, pattern recognition and an understanding of the ethical use of AI among students. Schools are to conduct three types of activities during the session. The first is district-level deliberations (DLDs), which are offline one-day workshops where groups of schools or Sahodaya School Complexes collaborate to share and discuss innovative classroom practices on CT and AI for Classes 3 to 8. Each workshop is equivalent to six hours of school-based continuing professional development. The second activity consists of expert-led talks, either online or offline, lasting half a day and counting as three CPD hours. The third involves regional workshops organized by CBSE's Centres of Excellence, with a registration fee of Rs700 per teacher. During the DLD workshops, up to 16 schools can present case studies on best practices, with each presentation lasting 25 minutes. An appreciation committee, including an external CT
Apr 12, 2026 · via education.economictimes.indiatimes.com
The future of beauty: Can AI replace your dermatologist? While the broader narrative surrounding Artificial Intelligence often leans toward dystopian concerns of environmental impact and job security, a more optimistic frontier is emerging at the intersection of beauty and technology. AI-powered skincare tools are poised to revolutionize the DIY beauty industry, promising a shift from generalized routines to high-precision, clinic-grade personal care News.Az reports, citing Vogue. According to industry experts like Tim Roberts of Therabody, the "next frontier" will allow users to leverage computer vision for trusted skin analysis and optimized product application, effectively democratizing access to dermatological expertise. RECOMMENDED STORIES However, current experts urge a degree of caution regarding the technology's present capabilities. Consultant dermatologist Dr. Emma Craythorne notes that we are currently in an "infant stage" where AI often functions more as a user-friendly interface for generalized advice rather than a truly diagnostic medical tool. At present, AI cannot legally or reliably diagnose specific conditions such as acne or deep-seated pigmentation, as such capabilities would categorize these devices as medical hardware subject to rigorous regulation. Most current tools use image recognition to make assumptions based on demographic models, which Dr. Craythorne suggests can still feel "slightly gimmicky" compared to the nuanced evaluation of a human practitioner. The real transformation is expected to unfold over the next five to ten years as large language models begin to mirror the diagnostic reasoning of dermatologists. Future breakthroughs will likely integrate "exposome" data—factors such as sleep, diet, and environment—to understand how lifestyle affects gene expression and skin health. As the industry moves toward this 2030s vision, the ultimate goal is to bridge the gap between over-the-counter beauty and clinical treatment, ensuring that the "patriotic alternative" to traditional skincare is one rooted in data-driven, accessible, and highly effective medical legitimacy. By Leyla Şirinova
Apr 12, 2026 · via news.az
Nationwide 'no set timescale' update for members The building society recently issued an update Nationwide Building Society has shed light on a rule that affects customers accessing services online. The update after a query was raised by a member on social media. The customer explained that they had switched to a new phone several weeks prior and were unable to activate biometrics on their new device. They asked: "How long is the waiting period to get these back?" Biometric authentication is a security feature that uses a unique personal identifier to verify account access. This can include fingerprint scanning or facial recognition to confirm a user's identity. In response to the question, Nationwide stated: "If you get a new phone, you'll need to wait to re-qualify for biometric authentication on that device." Regarding how long this can take, the group said: "There's no set timescale, and we'll usually notify you by text message when it becomes available." The building society also pointed the customer towards an information page on the Nationwide website detailing how to set up biometric authentication. The feature can be set up for everyday banking purposes, allowing customers to log into the app and authorise both small and recurring payments through either the app or internet banking portal. Biometrics can also be used to verify your identity when logging into internet banking. Customers also have the option to set up biometrics within the app, to provide an extra layer of security for more sensitive transactions. This includes larger payments or resetting your passnumber. With account biometrics enabled, there will be no need to use your card reader or debit card to log in or make payments. Nationwide recently issued guidance on how to identify counterfeit currency, following reports of fake notes appearing at one of its branches.
Apr 12, 2026 · via cambridge-news.co.uk
Abstract Brain tumors (BTs) arise due to abnormal cell growth, which has a high mortality rate globally. Millions of lives can be saved through the timely identification of BT. Precise identification and segmentation of BTs are essential to enhance the precision of analysis and the efficiency of therapeutic strategies. Magnetic resonance imaging (MRI) is a broadly utilized analytical tool. Furthermore, deep learning (DL) has recently shown efficiency in addressing several computer vision tasks. Several DL-driven methods are implemented for BT segmentation and attained impressive outcomes. This study presents a Multimodal Deep Feature Fusion Framework for Automated Brain Tumor Detection and Segmentation (MDFF-ABTDS) model. This objective is to develop a multimodal DL that integrates feature fusion and transformer networks for the precise detection and segmentation of BTs from medical images. Initially, image pre-processing is performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and image normalization. Feature extraction is carried out through fusion models such as CapsNet, ResNet-50, and AlexNet. These extracted features are then passed to a bi-directional convolutional long short-term memory combined with transformer (TBConvL-Net) models to classify tumors and non-tumors effectively. Finally, the tumor is classified to identify its location using the nnUNet model for a precise segmentation process. A series of experimental analyses of the MDFF-ABTDS method portrayed a superior accuracy value of 98.91% over existing models under the BT MRI dataset. Similar content being viewed by others References Ahmed, I., Ahmad, M., Chehri, A. & Jeon, G. From data to diagnosis: AI-driven multimodal fusion and generative AI-enhanced GAN-based MRI for brain tumour detection. Information Fusion, 126, p.103527. (2026). Sajid, S., Hussain, S. & Sarwar, A. Brain tumor detection and segmentation in MR images using deep learning. Arab. J. Sci. Eng. 44 (11), 9249–9261 (2019). Hossain, T., Shishir, F. S., Ashraf, M., Nasim, A. & Shah, F. M.
Apr 11, 2026 · via nature.com
Sharp's Poketomo launch in Taiwan illustrates how edge computing, combined with private cloud storage, is reshaping consumer AI devices, promising lower latency, enhanced privacy, and personalized, long-term interactions that could influence device...
The article requires paid subscription. Subscribe Now
Apr 11, 2026 · via digitimes.com
Google's Gemma 4 puts free agentic AI on your phone and no data ever leaves the device Key Points - Google's open-source model Gemma 4 can process text, images, and audio entirely on-device and autonomously use tools like Wikipedia, interactive maps, or QR code generators through built-in agent skills. - The smaller smartphone variants E2B and E4B run on devices with just 6 and 8 GB of RAM respectively, deliver up to four times the speed of the previous generation according to Google, and serve as the foundation for the upcoming Gemini Nano 4 on Android. - All models are released under the commercially friendly Apache 2.0 license, developers can create and share custom skills via GitHub, and the free "Google AI Edge Gallery" app is available for both Android and iOS. Google's new open-source model, Gemma 4, processes text, images, and audio completely on-device. Using agent skills, the AI can independently tap into tools like Wikipedia or interactive maps, no cloud required. The Google AI Edge Gallery app needed to run the model is free on Android and iOS. Since Gemma 4 dropped, the app has shot up to fourth place among the most-downloaded free productivity apps in the iOS App Store, sitting right behind Claude, Gemini, and ChatGPT. Gemma 4 is built on the same research as Google's proprietary Gemini 3 model but ships under the commercially friendly Apache 2.0 license. Google says the Gemma family has racked up over 400 million downloads since the first generation launched. All models handle text, images, and audio across more than 140 languages. Four model sizes cover everything from phones to servers The latest release comes in four variants. E2B and E4B are built specifically for smartphones. The "E" stands for "effective parameters," meaning the number of parameters actually active during
Apr 11, 2026 · via the-decoder.com
Abstract Current graphic design style transfer technology mainly focuses on geometric or texture features, while ignoring overall beauty and artistic expression, resulting in a mismatch between style and content, poor detail processing, and a lack of artistic appeal and true style presentation in the generated design. To this end, this article proposes a graphic design style transfer and aesthetic optimization algorithm based on a generative adversarial network (GAN). First, a graphic design image database with diverse styles is constructed; the GAN architecture is improved through generators, discriminators, and pre-trained VGG (visual geometry group) networks; an efficient channel attention mechanism and optimized inversion residual blocks are applied to enhance the model’s ability to capture aesthetic features; an aesthetic scoring model is designed by combining the loss functions of content, style, and generated images to ensure the visual appeal of generated images; VGG-19 (Visual geometry group-19) networks are used for pre-training, and the neural network parameters are optimized through the Adam algorithm to achieve efficient model training. The results show that the average values of SSIM and MSE (mean square error) for the improved GAN in this article are 0.93 and 0.027, respectively, in terms of content retention; the average value of MSE is 0.020 in terms of style similarity; the average values of PSNR (Peak Signal to Noise Ratio) and SSIM are 34.33 and 0.91 respectively in terms of image clarity. The study shows that the proposed method can not only improve the aesthetic quality and diversity of style transfer but also ensure the stability of image content, providing new theoretical and technical support for the field of graphic design style transfer. Data Availability The data in the manuscript can be obtained by contacting the corresponding author upon reasonable request. References Zhang, H., Sindagi, V. & Patel, V. M. Image de-raining
Apr 11, 2026 · via nature.com
Following IPO, ROC is investing in homegrown security for US market In February, Colorado-based biometrics and vision AI provider ROC closed the first big biometrics IPO of 2026, raising just over $24 million at $6 per share. ROC CEO Scott Swann says it would have been faster to look to private markets for capital that could help it meet some of its objectives, as it expands the scope of its business . But the company “saw the IPO as infrastructure.” “We saw it as a way that we could scale in a really disciplined way. A big part of this was so that we could maintain our culture and our independence.” That same culture is part of what makes ROC appealing to shareholders. “I think that our growth story resonated with people as we described what we were trying to achieve,” Swann says. ROC has always positioned itself front and center as an American-made company. As global political and economic relationships take new shapes, it stands positioned to provide homegrown defense and security capabilities to the U.S. market. “The timing is right,” Swann says. “I think people understand supply chain more now than they ever have. And as we think about artificial intelligence, I think we’ve seen examples where bias can be built into these models. So, you have to have a lot of trust in the entity that builds these models out for you.” “And so, for us understanding the whole geopolitical environment today and being able to really be at the forefront of putting the United States as a leader in this position, that’s something we’re completely committed to doing.” Moving further afield from its origin as a components provider, ROC also recently launched biometric physical access control software, reflecting significant growth forecasted in the biometric physical access
Apr 11, 2026 · via biometricupdate.com
Abstract Concrete structures are a vital component of urban infrastructure, requiring regular maintenance to ensure public safety and structural integrity. A crucial element of this maintenance is the identification of surface cracks, which have traditionally relied on manual inspection methods that were frequently work-intensive, subjective, and sometimes dangerous. This work presents a hybrid methodology that integrates deep feature extraction with machine learning classification for identifying structural deterioration in concrete components. A publicly accessible dataset comprising photos of both cracked and uncracked concrete surfaces was used. Deep features were extracted using VGG16, a convolutional neural network widely recognized for its success in visual pattern recognition. Several machine learning algorithms were used for classification of these features, including Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machines and k-Nearest Neighbors. The experimental results indicate that, the highest accuracy was achieved by SVM (99.883%), followed closely by ANN (99.873%), k-NN (99.598%), and DT (99.580%), while RF performed the lowest (98.050%). Although not limited to seismic applications, the proposed method has the potential to be integrated into post-earthquake structural assessment workflows as part of structural health monitoring systems. Using deep learning and machine learning methodologies to detect damage in concrete infrastructure may enhance efficiency and precision, enhancing urban resilience and risk mitigation. Data availability The dataset can be reached from this link in the public repository “Mendeley data” website https://doi.org/10.17632/5y9wdsg2zt.2, under the title “Concrete Crack Images for Classification”. References Tang, S. W., Yao, Y., Andrade, C. & Li, Z. Recent durability studies on concrete structure. Cem. Concr. Res. 78, 143–154. https://doi.org/10.1016/j.cemconres.2015.05.021 (2015). Zar, A. et al. Towards vibration-based damage detection of civil engineering structures: Overview, challenges, and future prospects. Int. J. Mech. Mater. Des. 20 (3), 591–662. https://doi.org/10.1007/s10999-023-09692-3 (2024). Kabir, S. Imaging-based detection of AAR induced map-crack damage in concrete structure. NDT E
Apr 11, 2026 · via nature.com