We provide the largest compiled publicly available dictionaries of first, middle, and last names for the purpose of imputing race and ethnicity using, for example, Bayesian Improved Surname Geocoding (BISG). The dictionaries are based on the voter files of six Southern states that collect self-reported racial data upon voter registration. Our data cover a much larger scope of names than any comparable dataset, containing roughly one million first names, 1.1 million middle names, and 1.4 million surnames. Individuals are categorized into five mutually exclusive racial and ethnic groups -- White, Black, Hispanic, Asian, and Other -- and racial/ethnic counts by name are provided for every name in each dictionary. Counts can then be normalized row-wise or column-wise to obtain conditional probabilities of race given name or name given race. These conditional probabilities can then be deployed for imputation in a data analytic task for which ground truth racial and ethnic data is not available.

A day after the six-month anniversary of the start of the war in Ukraine, a new report reveals never before seen information about Russia's filtration camp system in eastern Ukraine, in which civilians and prisoners of war are detained, interrogated, and, at times, forcibly deported to Russia. The researchers have also identified what they believe are graves near camps where prisoners of war (POWs) were being held. The camps, all of which are in the eastern region of Donetsk, were identified by the Conflict Observatory, a US-government-funded partnership between Yale University's Humanitarian Research Lab, the Smithsonian Cultural Rescue Initiative, artificial-intelligence company PlanetScape Ai, and the geographic-information-system mapping software Esri. Their report used images from Telegram channels, commercial satellites, and existing documentation to identify the locations of camps used by the Russian military for interrogation, detention, and registration of Ukrainian civilians, some of whom are then forcibly deported to Russia. "This is the first report to conclusively identify to high confidence 21 facilities engaged in the filtration of Ukrainian civilians," says Nathaniel Raymond, a coleader of the Humanitarian Research Lab and lecturer at Yale's Jackson School of Global Affairs.

Federal researchers are looking for updates and feedback on a video analytics initiative regarding ethical technology development, as government agencies plan to incorporate more artificial intelligence research and systems into their operations. The Networking and Information Technology Research and Development Program, a federally-funded research organization that specializes in advanced information technology solutions, issued a request for comment on its updated guidance on federal computer vision and AI technology research. Originally published in March 2020, the NITRD's Federal Video and Image Analytics Research and Development Action Plan is open to public comments and suggestions on how to improve the plan's key pillars to bridge noted gaps in federal research investment, particularly surrounding the incorporation of responsible AI. "As we move into new and novel applications for technology, we must be cognizant of potential harm that AI and other technologies can bring into society, such as identifying and discriminating against certain individuals," a NITRD spokesperson told Nextgov. "The VIA Team seeks input from the public on potential revisions to the VIA R&D action plan to reflect changes in technology and the socio-technical environment--how humans and technology are interrelated in the workplace and in the broader society." The VIA team is particularly interested in revision suggestions that mitigate potential risks related to individual rights and privacy, while establishing "a foundation for human rights" belying AI implementation.

When a disaster strikes Indonesia, residents may well log onto social media before taking shelter. Posts tagging the PetaBencana initiative with a reference to the disaster – be it due to a flood, earthquake, or volcanic eruption – will prompt a chatbot to appear with a link to the PetaBencana platform. Users can then share their location, photos of any visible damage, and details like flood depth. Indonesian government agencies then validate these crowdsourced situation reports, using the data to coordinate emergency response measures. Residents can also consult the resulting collaborative map in real time to make informed decisions about their safety and security.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Taliban says it has not been able to find the body of al Qaeda terrorist leader Ayman al Zawahiri after a U.S. air strike hit the home he was staying at in Afghanistan. The U.S. said it killed al Zawahiri with a drone strike in the Afghan capital of Kabul in July. The al Qaeda leader was standing on the balcony of a home owned by an aide to Sirajuddin Haqqani, top deputy of the Taliban's supreme leader Mullah Haibatallah Akhundzada.

Canadian supercluster based in Montreal, SCALE AI acts as an investment and innovation hub to accelerate the adoption and rapid integration of AI in Canada. This Monday, August 22, it unveiled twelve new projects aimed at optimizing production and transportation through AI. With the goal of addressing critical challenges currently facing supply chains, including the impact of the pandemic, labor shortages, and environmental requirements, it will provide $50 million in unprecedented financial support. Funded by the federal and Quebec governments, SCALE AI brings together the retail, manufacturing, transportation, infrastructure and information and communications technology (ICT) sectors to build smart supply chains. The supercluster has nearly 500 industrial partners, research institutions and other AI players with whom it develops programs to support investment projects by companies implementing concrete AI applications.

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.

Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally, but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks (GANs) to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient ESM CM2Mc-LPJmL. Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the GAN can generalize to future climate scenarios unseen during training. Feature attribution shows that the GAN identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational costs.

Proactive approaches to security, such as adversary emulation, leverage information about threat actors and their techniques (Cyber Threat Intelligence, CTI). However, most CTI still comes in unstructured forms (i.e., natural language), such as incident reports and leaked documents. To support proactive security efforts, we present an experimental study on the automatic classification of unstructured CTI into attack techniques using machine learning (ML). We contribute with two new datasets for CTI analysis, and we evaluate several ML models, including both traditional and deep learning-based ones. We present several lessons learned about how ML can perform at this task, which classifiers perform best and under which conditions, which are the main causes of classification errors, and the challenges ahead for CTI analysis.

Reliability of complex Cyber-Physical Systems is necessary to guarantee availability and/or safety of the provided services. Diverse and complex fault tolerance policies are adopted to enhance reliability, that include a varied mix of redundancy and dynamic reconfiguration to address hardware reliability, as well as specific software reliability techniques like diversity or software rejuvenation. These complex policies call for flexible runtime health checks of system executions that go beyond conventional runtime monitoring of pre-programmed health conditions, also in order to minimize maintenance costs. Defining a suitable monitoring model in the application of this method in complex systems is still a challenge. In this paper we propose a novel approach, Reliability Based Monitoring (RBM), for a flexible runtime monitoring of reliability in complex systems, that exploits a hierarchical reliability model periodically applied to runtime diagnostics data: this allows to dynamically plan maintenance activities aimed at prevent failures. As a proof of concept, we show how to apply RBM to a 2oo3 software system implementing different fault-tolerant policies.