Figures Abstract Identifying spatial domains is crucial in spatial transcriptomics, yet effectively integrating gene expression, spatial location, and histology remains challenging. We present STESH, a Spatial Transcriptomics clustering method that combines Expression, Spatial information and Histology. STESH extracts histological features using a convolutional neural network and generates expression, histology, spatial, and collaborative convolution modules for a multi-view graph convolutional network with a decoder and attention mechanism. We evaluated STESH on multiple tissue types and technology platforms. STESH consistently outperformed ten state-of-the-art methods, achieving superior clustering accuracy with the highest scores in adjusted Rand index, normalized mutual information, and Fowlkes-Mallows index. Author summary Identifying spatial domains in spatial transcriptomics is key to understanding tissue structure and gene expression patterns, yet existing approaches struggle to fully and effectively combine gene expression data, spatial location information, and histological images—three critical pieces of spatial transcriptomics data. To address this gap, we developed a new method that integrates all three types of information to accurately detect spatial domains. We used deep learning to extract detailed features from histological images, built separate analytical frameworks for each data type, and fused these frameworks with an attention mechanism to capture the intrinsic links between different data modalities. We tested this method on multiple tissue types and technical platforms, comparing it against ten leading approaches, and found it consistently achieved higher clustering accuracy and could precisely reconstruct complex biological tissue structures, even uncovering fine details of tumor heterogeneity. This method enhances downstream spatial transcriptomics analyses and provides a more reliable tool for researchers, helping to deepen our understanding of the spatial organization of tissues and the relationship between gene expression and histological morphology. Citation: Zhang H, Chang J, Li Z, Sun Y, Hu P, Wang H, et al. (2026) Histology-informed spatial domain identification through multi-view graph convolutional networks. PLoS
Jun 1, 2026 · via journals.plos.org
Robot dogs deployed around World Cup venues in North Texas are being used for security inspections and hazardous-material investigations, not for facial recognition or ticket verification, according to the company behind the technology. The clarification comes after a viral social media video claimed that “face-scanning robots” were being used in Dallas to verify World Cup ticket holders. The video showed a robotic dog bearing the logos of South Korean automaker Hyundai and robotics company Boston Dynamics. The post alleged the machines were scanning faces as part of World Cup security operations. Boston Dynamics disputed that claim. “Boston Dynamics’ Spot robots are being deployed at designated World Cup venues to perform perimeter security inspections and will be used to assist security personnel with investigating things like suspicious packages or other potentially hazardous materials. The robots do not have facial recognition capabilities,” a company spokesperson said, WFAA reported. The robotic dogs, known as Spot robots, are part of security preparations surrounding FIFA World Cup events in North Texas. While some social media users expressed concerns about surveillance, the companies involved said the machines are intended to support security personnel rather than identify spectators. According to Hyundai, the robots are designed to assist with on-site security operations during the tournament. Similar robotic systems have been used in crowded public settings, search-and-rescue missions, hazardous-material assessments, and bomb-detection operations. The robots have also appeared at other sporting events, including cricket matches in England, professional football games in Atlanta, and the Indianapolis 500. Officials in Mexico are also using robotic canines as part of World Cup security efforts, where they help scout potentially dangerous areas and transmit live video to security teams. The viral claims surfaced as officials continue preparing for the tournament across North Texas. In March, the Dallas Police Department announced it had received
Jun 1, 2026 · via dallasexpress.com
BuzzFeed GamesOnly People With Elite Pattern Recognition Can Solve This Color Puzzle Called HuedokuHuedoku #34! Welcome to June — kick off the month with a clean solve. 🌈☀️Posted 7 hours agocommentFacebookPinterestLinkby Crystal RoBuzzFeedBuzzFeed StaffHi, I’m Crystal, a Senior Editor based in Los Angeles and creator of BuzzFeed’s “That Got Dark” newsletter. Huedoku is a daily color puzzle with simple rules and a satisfying solve. It's like sudoku, but with colors instead of numbers. Sign up here to get notified every time we publish a new Huedoku! Come back tomorrow for Huedoku #35 — and share your score to challenge a friend! 🌈 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. 🧩🗂️ Huedoku Archive — Every past puzzle, all in one place! Comments
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Jun 1, 2026 · via buzzfeed.com
FBI seeks industry input on biometric algorithms for NGI modernization The scale of the system is one of the most important details in the notice The Federal Bureau of Investigation (FBI) is surveying the biometrics industry for commercially available algorithms that could support or replace key matching capabilities within the bureau’s Next Generation Identification (NGI) system, one of the largest government repositories of biometric and criminal history information in the world. The Request for Information (RFI), issued by the FBI’s Criminal Justice Information Services (CJIS) division in Clarksburg, West Virginia, provides a detailed view of the bureau’s continuing effort to assess new biometric matching technology for fingerprints, latent prints, facial recognition, iris recognition, and tattoo recognition, as well as the systems needed to transition and maintain these capabilities inside FBI-approved cloud environments. CJIS operates NGI as a central biometric and criminal history platform used by federal, state, local, tribal and territorial law enforcement partners. The FBI says it is looking to scientific advances to improve the “range and quality” of its identification and investigative capabilities. For biometric vendors, the notice is an early signal that National Institute of Standards and Technology (NIST)-tested performance, secure domestic software handling, GovCloud deployment, FedRAMP High compliance, and the ability to manage massive biometric repositories will be central to any future FBI consideration. The RFI is the third in a series of FBI market research notices focused on biometric matching algorithms. This latest notice asks vendors to provide information on ownership structure, secure software build facilities, algorithm capabilities, transition plans, scalability, software maintenance, licensing, response times, and accuracy. A key threshold requirement is participation in National Institute of Standards and Technology (NIST) biometric testing. The FBI says published NIST results will be required for all vendors interested in supplying biometric algorithms to the bureau. A
Jun 1, 2026 · via biometricupdate.com
Massive Attack Is Taking Aim at Palantir ‘I find their declarations, objectives and moral framing pretty terrifying.’ by Gary McQuiggin 1 June 2026 Trip-hop legends Massive Attack are using an artistic rendering of notorious spy-tech firm Palantir’s own product to take aim at the company in their new live show. Custom-made facial recognition software will scan a 75,000-person crowd at Primavera Sound in Barcelona on Thursday, before cameras lock on to individuals, pull their faces up on screen and label them with “11 weeks no time off, burnout”, “unfinished books” and other satirical descriptors. Founded 20 years ago by arch-libertarian billionaire Peter Thiel, with financial backing from the CIA, Palantir has become one of the world’s most infamous surveillance tech companies, counting the US and Israeli militaries, ICE, the FBI and the NHS among its clients. Speaking exclusively to Novara Media in Helsinki, where the new show premiered, Massive Attack frontman Rob Del Naja explained that he wanted to draw the public’s attention to Palantir’s expansion from “kill chain tech” used in Gaza to accessing Britons’ medical records. “We really need a much wider debate on the suitability of a company like this having such capture of our societal infrastructure,” he said. The Bristol band’s intervention couldn’t be more timely. Just last month, London mayor Sadiq Khan blocked Palantir from a lucrative contract with the Metropolitan police, citing concerns over the company’s ethics. Its UK boss, Louis Mosley – a descendant of Britain’s most famous fascist – responded by claiming that Khan’s decision had put Londoners more at risk of rape and theft. Meanwhile, a debate is raging within NHS England. In 2023, Palantir won a £330 million contract to provide technology for a new platform designed to improve data sharing across the service. But senior health workers have described
Jun 1, 2026 · via novaramedia.com
AI watermarking: Why Big Tech is betting on AI provenance, and losing kAm(96C6G6C :?E6C?6E FD6CD EFC?[ E96J 2C6 244@DE65 3J px\86?6C2E65 E6IE[ G:56@D[ 2?5 :>286D[ >2?J @7 H9:49 2C6 F?56E64E23=6] %96 D2?4E:EJ 2?5 ECFDE @7 @?=:?6 DA246D :D 4CF>3=:?8 F?56C E96 H6:89E @7 px\4@?E6?E] k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>^3=@8^2:\D276EJ\6I64FE:G6\@C56CQmq:56?’D px D276EJ 6I64FE:G6 @C56Ck^2m 2?5 E96 k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>^3=@8^2:\24EQmtFC@A62? &?:@?’D px p4Ek^2m 25G@42E65 AC@A6C =236=:?8 @7 px @FEAFE[ 2=D@ <?@H? 2D “H2E6C>2C<:?8[” 3FE E96D6 C68F=2E:@?D H6C6 F?5@?6 :? a_ad] s6DA:E6 ?@ ?2E:@?2= @C :?E6C?2E:@?2= DEC2E68J[ >2?J px 4@>A2?:6D 2C6 DE:== 56G6=@A:?8 2?5 56A=@J:?8 “H2E6C>2C<:?8” D:8?2=D :? E96:C 4@?E6?E] (9:=6 E9:D :D 2 DE6A :? E96 C:89E 5:C64E:@? E@ C682:? AF3=:4 ECFDE 2?5 AC@E64E 4@?DF>6CD[ H2E6C>2C<:?8 42? 62D:=J 36 3JA2DD65]k^Am kAmq6=@H[ k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>Qm!2?8C2> {23Dk^2m 6IA=@C6D 9@H H2E6C>2C<D H@C<[ H9J E96J 72:= 2?5 H9J px 56E64E:@? >6E9@5D E92E C6=J @? 2 DA64:7:4 EJA6 @7 A2EE6C? C64@8?:E:@? 2C6 C6=:23=6]k^Am k9bmpx H2E6C>2C<:?8 :D 56E64E65[ 3FE ?@E D66?k^9bm kAm%@ 9F>2?D[ px H2E6C>2C<:?8 :D :?G:D:3=6] x?DE625 @7 2 EC25:E:@?2= H2E6C>2C< 5:DA=2J:?8 2 4@>A2?J’D ?2>6 @C =@8@[ px H2E6C>2C<D 2C6 6>365565 :?E@ 2? px >@56=VD E6IE[ :>286[ 2?5 G:56@ @FEAFE :? 2? :>A6C46AE:3=6 H2J]k^Am kAmv@@8=6 FD6D k2 9C67lQ9EEADi^^566A>:?5]8@@8=6^>@56=D^DJ?E9:5^Qm$J?E9xsk^2m[ H9:49 6>365D DF3E=6 G2C:2E:@?D :? v6>:?:VD @FEAFE[ 56E64E23=6 3J v@@8=6VD @H? E649?@=@8J] u@C E6IE[ $J?E9xs 2=E6CD E96 AC65:4E23:=:EJ D4@C6D @7 46CE2:? H@C5D 244@C5:?8 E@ 2 AD6F5@C2?5@> 7F?4E:@?[ 8:G:?8 v6>:?: 2 AC676C6?46 7@C FD:?8 46CE2:? H@C5D] %9FD[ H96? E96 E6IE :D 765 324< :?E@ E96 >@56=[ :E 42? C64@8?:K6 :ED @H? 92?5:H@C< 32D65 @? H@C5 7C6BF6?4J 2?2=JD:D]k^Am kAmk2 9C67lQ9EEADi^^HHH]4@AJC:89E]8@G^2:^QmuF==J px\86?6C2E65 4@?E6?E 42??@E 36 4@AJC:89E65 F?56C &]$] =2Hk^2m[ D@ :EVD :? px 4@>A2?:6D’ :?E6C6DE E@ 56G6=@A 2 >6E9@5 7@C 56E6C>:?:?8 E96 AC@G6?2?46 @7 E96:C >@56=VD 4@?E6?E] %92E H2J[ @?46 E96 px\86?6C2E65 E6IE[ :>286 @C G:56@ 86ED 4:C4F=2E65 2C@F?5 E96 H63[ E96C6 :D 2 D:8?2EFC6 E@ :56?E:7J H96C6 :E 42>6 7C@>]k^Am kAmr=2:>:?8 DE2<6 E@ E96:C >@56=’D 4C62E:@?D
Jun 1, 2026 · via newspressnow.com
A contract has been awarded by the UK Home Office to implement AI facial recognition to support officers on the ground with verifying the ages of asylum seekers, where they can’t present documentation. The plans have sparked controversy amid tensions around the government’s approach to border security to tackle immigration. The tool, assisting immigration officers, will target adult migrants posing as unaccompanied children in a bid to gain asylum. They have issued guidance on how to use the AI algorithm, which has been trained on datasets of images of people The FAR is not currently in operational use by the Home Office during a testing phase, whilst there are plans to implement it in 2027. With potential limitations, the guidance states that the FAE system will be used to “provide additional information to help them make initial age decisions. It won’t be relied upon to make a definitive age decision or to replace holistic approaches such as Merton-compliant age assessments. […]” “An initial age assessment will always be made by a properly trained immigration officer who can also call on the advice of colleagues and social workers when needed.” As age algorithms cannot be proven 100% accurate, it raises concerns regarding performance bias according to factors including ethnicity, skin tone, gender, place of birth and image quality. “In its appraisal for using FAE for age assessments, the Home Office has tested industry leading algorithms on images across different ethnicities and genders. These algorithms will be further tested to assess their suitability for use within the immigration system.” The tender was awarded to Akhter Computers, a company based in Essex, to make the facial comparison during standard immigration checks.
Jun 1, 2026 · via identityweek.net
Jiuzi Holdings Announces AI Intelligent Imaging Platform Achieves Milestone Progress and Advances Toward Commercial Deployment as Planned Rhea-AI Summary Jiuzi Holdings (Nasdaq:JZXN) reported milestone progress on its next-generation AI intelligent imaging and data platform and preparation for commercial deployment. The platform aims to help enterprises shift from traditional monitoring to real-time analytics by integrating AI recognition, multimodal data fusion, cloud architecture, and blockchain-based data notarization. According to the company, ongoing pilots and strategic initiatives are expected to validate technical capabilities, support large-scale deployment, and enable potential commercialization across multiple industry scenarios in North America and global markets. AI-generated analysis. Not financial advice. Positive - None. Negative - None. Key Figures Market Reality Check Peers on Argus JZXN was up 1.75% pre-news while only one peer (SDA) appeared in momentum scanners, moving -3.22%. Other peers showed mixed, mostly negative moves, pointing to stock-specific dynamics rather than a sector-wide auto retail shift. Previous AI Reports | Date | Event | Sentiment | Move | Catalyst | |---|---|---|---|---| | May 15 | AI partnership MoU | Positive | +14.4% | AI imaging and Web3 data MoU that previously triggered a strong positive move. | Previous AI-themed news produced a clearly positive price reaction of 14.41%, indicating the market has rewarded JZXN’s AI initiatives historically. Over the last six months, Jiuzi has combined capital markets activity with strategic pivots. A prior AI-focused MoU on May 15, 2026 drove a 14.41% move after outlining intelligent imaging and Web3 infrastructure plans. Earlier, a strategic investor committed $80 million on March 6, 2026, and the company announced electric truck deliveries to Vietnam. Today’s AI platform milestone fits the ongoing shift toward technology and data-centric solutions. Historical Comparison Past AI news for JZXN (1 event) saw an average move of 14.41%. This update extends the same AI imaging
Jun 1, 2026 · via stocktitan.net
Hangzhou, June 01, 2026 (GLOBE NEWSWIRE) -- Jiuzi Holdings, Inc. (Nasdaq: JZXN) (“Jiuzi” or the “Company”) today announced the latest developments regarding its AI intelligent imaging and data platform. The Company is preparing to advance the development of its next-generation AI intelligent imaging platform in accordance with its established strategic roadmap and is making systematic preparations for subsequent commercial deployment. The intelligent imaging platform is designed to enable enterprise customers to transition from traditional image monitoring and standalone data processing models to real-time intelligent analytics and data-driven decision-making. By integrating AI recognition capabilities, data processing architecture, and deployment systems, the Company is preparing to build a unified intelligent imaging and data solution. The Company believes that as digital infrastructure upgrades accelerate, enterprise demand for intelligent imaging systems is undergoing structural transformation. The market is shifting from validation of single algorithm functionalities toward demand for scalable, deployable, and compliant integrated platform architectures. As data volumes expand and scenario complexity increases, operators must continuously address data compliance requirements, algorithm accuracy competition, cross-scenario adaptability, and system stability challenges. Jiuzi believes that platform-level capability development may become a key competitive factor in the industry. In response to these trends, the Company is preparing to develop a unified AI intelligent imaging platform framework covering the full workflow from data acquisition and algorithm processing to scenario applications and business decision support, supporting the evolution of intelligent imaging systems toward infrastructure-level solutions. The Company believes that this integrated architecture differs from market solutions that provide only single recognition modules or isolated algorithm functions. By integrating multi-layer data structures and algorithm capabilities, the platform can transform complex image data into actionable insights, potentially enhancing customer operational efficiency and unlocking additional data asset value. Key features of the platform include: • Real-time facial recognition supporting large-scale multi-scenario identification and
Jun 1, 2026 · via markets.businessinsider.com
Measuring AI Adoption among Firms: How You Ask Matters KEY TAKEAWAYS - Worker-based surveys find higher rates of AI adoption in the U.S. than in Europe. Yet firm-level surveys suggest a higher adoption rate in Europe. - This U.S.-European difference in AI adoption among firms appears to stem from how surveys ask about AI. The main European survey asks broader questions, such as whether AI is used in marketing; the key U.S. survey had focused just on the technology’s use in production of goods and services. - A recent update to the U.S. survey question boosted the reported AI adoption rate among American firms, showing that how AI use is measured is key to understanding the technology’s economic impact. In our first blog post in this series, we documented that U.S. workers are more likely to use artificial intelligence (AI) on the job than workers in Europe. In our second post, we showed that differences in workforce composition and firm management practices go a long way toward explaining that gap. But alongside the worker surveys, we drew on firm-level data, and those data address a puzzle of their own—one that turns out to be less about economics and more about how surveys are designed. For the past two years, a number of researchers have noted a striking disconnect in the U.S. data: Worker surveys suggest that somewhere around 35% to 40% of workers use AI on the job, while the main U.S. firm survey put AI adoption among businesses at just 5% to 7%. That is a very large gap. Are workers using AI without their employers knowing? Are firms in denial about how widespread the technology has become? Or is something else going on? We think the answer is mostly the third option: measurement.This blog post and the previously
Jun 1, 2026 · via stlouisfed.org
Artificial Intelligence Camera Market Trends: Smart Surveillance, Deep Learning Integration & Forecast to 2034 How AI-powered imaging, real-time analytics, and intelligent surveillance systems are transforming security monitoring and automation capabilities in the artificial intelligence camera market According to IMARC Group's latest research publication, The global artificial intelligence (AI) camera market size reached USD 9.0 Billion in 2025. Looking forward, IMARC Group expects the market to reach USD 27.2 Billion by 2034, exhibiting a growth rate (CAGR) of 12.74% during 2026-2034. The growing concerns regarding safety and security, rising adoption of AI-powered CCTV cameras, augmenting sales of premium feature-rich smartphones, and increasing product applications in traffic monitoring and homeland security represent some of the key factors driving the market. How AI is Reshaping the Future of the Artificial Intelligence Camera Market - Predictive Threat Detection and Behavioral Analytics: AI-powered camera systems use deep learning models to identify suspicious behavioral patterns in real time, going far beyond passive video recording. Airports, banking halls, and government buildings are deploying these systems to detect anomalies before incidents escalate. Financial institutions using AI-based surveillance have reported measurable reductions in fraud incidents, as facial recognition and motion-pattern analysis work continuously across high-traffic zones without human fatigue. - Face Recognition and Automated Access Control: The shift from badge-based entry to AI camera-driven biometric access is accelerating across corporate campuses, healthcare facilities, and transit hubs. These systems cross-reference live facial data against registered databases in milliseconds, granting or denying access without physical contact. In large urban transit systems handling millions of daily commuters, this technology significantly cuts bottlenecks at entry points while simultaneously flagging individuals on watchlists. - Computer Vision for Industrial Quality Assurance: Manufacturing lines across automotive, electronics, and food processing sectors are integrating AI cameras to automate visual inspections. These systems detect micro-defects, surface irregularities, and
Jun 1, 2026 · via vocal.media
The standard popular framing of human facial recognition treats it as a problem so complex that large brains and specialised neural regions are required to solve it. The human fusiform face area, a section of the temporal lobe that activates specifically when a person looks at a face, has been studied for decades as the neural basis for this ability. The framing is plausible. It is also, by a 2005 finding that has been replicated and extended in the years since, not quite right. Honeybees, with brains roughly one millimetre across containing about a million neurons, can be trained to recognise individual human faces. The result was published in the Journal of Experimental Biology by Adrian Dyer, then at Johannes Gutenberg University in Mainz and the University of Cambridge, working with Christa Neumeyer of Mainz and Lars Chittka of Queen Mary, University of London. According to the team’s 2005 paper, individual bees trained on photographs from a standard human psychology test could discriminate a target face from a similar distractor face with greater than 80 percent accuracy, and could continue to recognise the trained face two days after training. The bees had never previously been exposed to human faces in any evolutionary or developmental sense. They learned the task because Dyer’s team offered them sugar water for getting it right. How the experiment worked The methodology took advantage of the bees’ famously robust associative learning capabilities. Bees are accomplished pattern learners; they have to be, because the flowers they forage from come in an enormous diversity of shapes and colours, and accurate recognition of rewarding flower types is central to their lives. Dyer reasoned that the same machinery might be applicable to any visual pattern, including one with no evolutionary relevance to the bee at all. The team presented bees
Jun 1, 2026 · via spacedaily.com
AI Decodes Centuries-Old Manuscripts and Ciphers Researchers are using machine learning and neural networks to read damaged, encrypted, and hard-to-decipher historical texts, according to reporting by Digital Trends, the BBC, and Nature. The BBC reports a team led by computational linguist Beáta Megyesi used machine learning to help decode a 408-page Vatican manuscript encoded with 34 obscure symbols and some Arabic, revealing medicinal recipes and remedies; Megyesi is quoted saying, "It is like detective work where every symbol, pattern, and partial solution may bring us closer to someone's secrets and to a lost historical world," (BBC). Nature reports neural-network pipelines and projects such as Fragmentarium are helping to recover text from carbonized Roman scroll fragments from Herculaneum and to digitize tens of thousands of cuneiform tablets. Digital Trends describes broader efforts to train models on historical handwriting and linguistic patterns so systems can restore missing or damaged words. Editorial analysis: These developments expand the volume of readable historical data and create new interdisciplinary workflows for digital-humanities practitioners. What happened Researchers are increasingly applying machine learning and neural networks to recover text from damaged, encrypted, or otherwise unreadable historical documents, as reported by Digital Trends, the BBC, and Nature. The BBC reports a team that includes computational linguist Beáta Megyesi used machine learning to help decode a 408-page Vatican manuscript coded with 34 obscure symbols and some Arabic, revealing recipes and remedies; Megyesi said, "It is like detective work where every symbol, pattern, and partial solution may bring us closer to someone's secrets and to a lost historical world," (BBC). Nature reported that neural-network pipelines and digitization efforts such as Fragmentarium are being used to read carbonized papyrus fragments from Herculaneum and to aggregate tens of thousands of cuneiform records for analysis (Nature). Technical details Editorial analysis: Public reporting highlights two
Jun 1, 2026 · via letsdatascience.com
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Jun 1, 2026 · via moomoo.com
Microsoft ends support for Internet Explorer on June 16, 2022. We recommend using one of the browsers listed below. Please contact your browser provider for download and installation instructions. June 1, 2026 NTT, Inc. News Highlights: TOKYO — June 1, 2026 — NTT, Inc. (Headquarters: Chiyoda-ku, Tokyo; President and CEO: Akira Shimada; hereinafter "NTT") has established Rationale-Enhanced Decoding, a new inference framework designed to improve the reliability of outputs generated by multimodal foundation models that process both images and language. The technology addresses a key issue in CoT reasoning by LVLMs: the tendency to ignore self-generated rationales. Unlike conventional inference methods, the proposed approach separately performs image-based inference and rationale-based inference, then combines them through ensemble decoding. This enables the model to generate responses grounded in information derived from both visual inputs and rationales. This research will be presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026(*1), one of the world's premier international conferences in the field of computer vision, to be held in Denver, Colorado, USA, from June 3 to June 7, 2026. In recent years, the development of Large Vision-Language Models (LVLMs), which integrate Large Language Models (LLMs) with pretrained image encoders, has significantly advanced multimodal reasoning capabilities. Unlike text-only LLMs, LVLMs can directly process visual inputs in addition to text, enabling their use as a foundation for complex multimodal reasoning tasks based on visual content, such as video analysis and document understanding, which are difficult to address using text alone. Similar to LLMs that operate solely on text inputs, Chain-of-Thought (CoT) reasoning has also been regarded as an effective approach for improving inference performance and enabling explainable reasoning in LVLMs. In CoT reasoning, the model first generates intermediate rationales from visual and textual inputs, then appends those rationales to the input sequence to
Jun 1, 2026 · via group.ntt
As South African firms deepen ties with the world’s second-largest economy, local executives flying into Beijing or Shanghai are stepping into a digital system they might never be able to leave. Picture a South African multinational nearing the final stages of a Chinese joint-venture negotiation. Months of closed-door meetings have already shaped pricing, expansion plans and the transfer of proprietary technology. These details are known only to a tightly controlled group of executives on both sides. Then, almost imperceptibly at first, the dynamics shift. A domestic Chinese competitor begins to move with unusual foresight, aligning its products and offers in ways that render the joint venture moot. Nothing is ever publicly leaked and no breach is officially confirmed, yet the coincidence is too difficult to ignore. Inside the company, confidence in the process is hollowed out, replaced by a persistent question that no-one can fully answer: how did this happen? The reality is that across sectors, cases such as this have become common. In fact, disclosures by cybersecurity researchers at NetAskari have exposed a highly automated, internet-based intelligence dashboard used by Chinese state security known as the “dynamic control platform for foreigners”. According to investigative reports, this portal operates as a centralised intelligence system where local police and state agents log in to access extensively documented, searchable visual profiles. Rather than just tracking names the platform aggregates fragmented data points to build an instantaneous, real-time “holographic file” on foreign nationals. In an economy where major industries are still dominated by state-owned enterprises and the boundaries between government oversight and commercial operations are frequently blurred, the existence of such comprehensive data sets creates an inescapable environment of informational asymmetry. The data collection starts at the consulate, pulling information directly from entry visa applications ― including passport numbers, digital ID photos and
Jun 1, 2026 · via businessday.co.za
AI Awareness Training for PM Vishwakarma Beneficiaries Held at DIC Kargil. AI Awareness Training for PM Vishwakarma Beneficiaries Held at DIC Kargil. Kargil, May 30, 2026: The State Project Management Unit (SPMU), PM Vishwakarma, organized an Artificial Intelligence (AI)-based Capacity Building and Awareness Training Programme for PM Vishwakarma beneficiaries in collaboration with the District Industries Centre (DIC), Kargil, at the Conference Hall of DIC Kargil. The programme was conducted in line with the vision of Digital India and IndiaAI to enhance digital awareness and technology adoption among traditional artisans and craftspeople. More than 100 PM Vishwakarma beneficiaries representing various traditional trades and crafts from across Kargil district participated in the training programme. The training session was delivered by Khushbu Joshi, SPMU PM Vishwakarma, and Lobzang Lungtok, IPO, DIC Kargil, who introduced participants to the practical applications of Artificial Intelligence from the perspective of Vishwakarma artisans and craftspeople. The resource persons highlighted various challenges faced by beneficiaries and explained how AI-powered tools can support them in improving productivity, marketing their products, enhancing customer outreach, generating business ideas, and accessing information more efficiently. A hands-on demonstration was also conducted on popular AI applications, including ChatGPT and Gemini, enabling participants to gain practical experience in using these tools for their professional and business needs. During the session, participants were introduced to voice-based AI features, image recognition tools, content generation, and practical business use cases relevant to artisans, tailors, craftspeople, and other traditional occupations covered under the PM Vishwakarma Scheme. The resource persons also emphasized the importance of the responsible and safe use of AI technologies. Participants were advised not to share sensitive personal information, bank account details, passwords, OTPs, or other confidential data while using AI platforms. Beneficiaries were encouraged to use AI tools ethically and responsibly to maximize their benefits. The programme
Jun 1, 2026 · via ladakh.gov.in
Strategic HR Tamil Nadu's HR department to adopt mandatory face-ID attendance from June 1 Tamil Nadu's Human Resource Management Department will begin using a mandatory biometric and facial recognition attendance system from June 1, as the State government seeks to improve punctuality and attendance compliance among employees. The Tamil Nadu government has made biometric and facial recognition-based attendance compulsory for employees of its Human Resource Management Department, introducing a new digital monitoring system aimed at strengthening attendance compliance. The directive will come into force on June 1, 2026, according to an official circular issued by the department. The move follows concerns over employee punctuality and attendance, with Secretariat sources indicating that complaints had been received about some staff members allegedly failing to report to work on time. New attendance protocol combines multiple verification methods According to the circular issued by S. Thankapappa, Deputy Secretary of the Human Resource Management Department, the decision was taken following instructions from the Principal Secretary to the Government. Under the revised system, employees will be required to record their attendance through multiple channels. The circular directs all officers and staff to: - Report to office before 10 am - Mark attendance through the biometric attendance system - Complete face-ID attendance verification - Continue maintaining manual attendance records - Wear official identity cards while on duty The new framework introduces an additional layer of digital verification alongside existing attendance procedures. Department-specific rollout begins on June 1 The order applies to officers and employees working within the Human Resource Management Department. Based on the circular, the measure is currently limited to the department and does not extend to all government departments across Tamil Nadu. The introduction of facial recognition technology marks a notable addition to attendance management practices within the department, combining biometric authentication, face-based verification and
Jun 1, 2026 · via peoplematters.in
Researchers at the ABV-Indian Institute of Information Technology (IIIT), Gwalior, have developed a new artificial intelligence system that combines text analysis, image recognition and fuzzy logic to detect fake news in Indian media with high accuracy. The system, called F2IND-IT! (fuzzy fake Indian news detection using images and text), was described in a recent paper uploaded to arXiv. The researchers say the project addresses a growing challenge in India, where rapid internet penetration and social media use have accelerated the spread of misinformation. According to data from the Press Information Bureau, under the Ministry of Information and Broadcasting, 1,575 fake news cases were reported between 2022 and March 2025. The number rose from 338 in 2022 to 583 in 2024. Data from the National Crime Records Bureau also show a 214 per cent increase in fake news cases during the early pandemic period from 2018 to 2020. A 2024 study by ISB and CyberPeace found that 46 per cent of false information was about politics, and over 77 per cent of it spread through social media platforms. Another survey among Gen Z users in Delhi found that 91 per cent believe fake news can affect election outcomes. To tackle the problem, the researchers designed a multimodal AI model that analyses both the written content of news articles and the accompanying images. The framework uses DistilBERT — a lightweight language-processing model — to understand text semantics, while a convolutional neural network (ResNet-50), which is a deep-learning image recognition system, extracts visual features from photographs. These inputs are then combined using an ‘attention mechanism’ and processed through an adaptive neuro-fuzzy inference system (ANFIS), which produces a probability score indicating whether a news item is fake or genuine. The model was trained and tested on the Indian Fake News Dataset (IFND), which contains
Jun 1, 2026 · via thehindubusinessline.com
Running an optical character recognition (OCR) server might sound like it would need some powerful hardware, like a rack-mounted, water-cooled machine, or at least a nice desktop or laptop. But if you have the time, anything could be used. [Hemant] has a long-running personal project that processes a lot of image data over a long time, and set up the OCR server on an iPhone 8 running entirely with solar power, rather than turn to more typical hardware. Part of what makes this task feasible for low-powered hardware is Apple’s Vision framework, which uses machine learning to aid in things like character recognition (among other tasks). It will run on an iPhone just as easily as a Mac. The phone’s built-in battery already provides the first step of an off-grid setup. This build relies on a separate power bank to integrate the phone with the solar panel more easily. On the software side, [Hemant] reports that the true challenge wasn’t setting up the server as much as it was keeping the iPhone from sleeping or stopping his program from running full-time. A system like this running off-grid, especially considering the costs of the solar panel and power bank, might seem counterproductive. But when comparing electricity costs for running the same software on his server, he estimates he saves about $10 per month with this setup, which has a payback of somewhere around 2-3 years. Not too bad for a phone that would have otherwise ended up in a landfill. Old phones can be surprisingly good choices for servers, too. It helps if they can run Linux, but plenty of phones will support server applications, even when running their native OS. We were doing OCR locally on lame Windows machines 30 years ago, so it’s hardly surprising that it’s still possible.
Jun 1, 2026 · via hackaday.com