The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs. However, developing IaC programs is challenging, complicated by factors that include the burgeoning complexity of the cloud ecosystem (e.g., diversity of cloud services and workloads), and the relative scarcity of IaC-specific code examples and public repositories. While large language models (LLMs) have shown promise in general code generation and could potentially aid in IaC development, no benchmarks currently exist for evaluating their ability to generate IaC code.

-- In this paper, m ilitary use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre - trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others . First, we interrogate a GPT - based language model (viz. Microsoft Copilot) to make it reveal its own knowledge about their potential military application s and then critically assess the information . Second, we study how commercial cloud services (viz. Microsoft Azure) could be used readily to build such applications and assess which of the m are feasible. We conclude that t he summarization and generative properties of language models directly facilitate many applications at large and other features may find particular uses . This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by ...

For what purpose was the dataset created? Was there a specific task in mind? Who created this dataset (e.g. which team, research group) and on behalf of which Who funded the creation of the dataset? This work is partially funded by Cisco and Amazon. What do the instances that comprise the dataset represent (e.g.

This study proposes an anomaly detection method based on the Transformer architecture with integrated multiscale feature perception, aiming to address the limitations of temporal modeling and scale-aware feature representation in cloud service environments. The method first employs an improved Transformer module to perform temporal modeling on high-dimensional monitoring data, using a self-attention mechanism to capture long-range dependencies and contextual semantics. Then, a multiscale feature construction path is introduced to extract temporal features at different granularities through downsampling and parallel encoding. An attention-weighted fusion module is designed to dynamically adjust the contribution of each scale to the final decision, enhancing the model's robustness in anomaly pattern modeling. In the input modeling stage, standardized multidimensional time series are constructed, covering core signals such as CPU utilization, memory usage, and task scheduling states, while positional encoding is used to strengthen the model's temporal awareness. A systematic experimental setup is designed to evaluate performance, including comparative experiments and hyperparameter sensitivity analysis, focusing on the impact of optimizers, learning rates, anomaly ratios, and noise levels. Experimental results show that the proposed method outperforms mainstream baseline models in key metrics, including precision, recall, AUC, and F1-score, and maintains strong stability and detection performance under various perturbation conditions, demonstrating its superior capability in complex cloud environments.

Infrastructure-as-Code (IaC), an important component of cloud computing, allows the definition of cloud infrastructure in high-level programs. However, developing IaC programs is challenging, complicated by factors that include the burgeoning complexity of the cloud ecosystem (e.g., diversity of cloud services and workloads), and the relative scarcity of IaC-specific code examples and public repositories. While large language models (LLMs) have shown promise in general code generation and could potentially aid in IaC development, no benchmarks currently exist for evaluating their ability to generate IaC code. IaC-Eval's dataset includes 458 human-curated scenarios covering a wide range of popular AWS services, at varying difficulty levels. Each scenario mainly comprises a natural language IaC problem description and an infrastructure intent specification.

Measuring the accuracy of face recognition (FR) systems is essential for improving performance and ensuring responsible use. Accuracy is typically estimated using large annotated datasets, which are costly and difficult to obtain. We propose a novel method for 1:1 face verification that benchmarks FR systems quickly and without manual annotation, starting from approximate labels (e.g., from web search results). Unlike previous methods for training set label cleaning, ours leverages the embedding representation of the models being evaluated, achieving high accuracy in smaller-sized test datasets. Our approach reliably estimates FR accuracy and ranking, significantly reducing the time and cost of manual labeling. We also introduce the first public benchmark of five FR cloud services, revealing demographic biases, particularly lower accuracy for Asian women. Our rapid test method can democratize FR testing, promoting scrutiny and responsible use of the technology.

Adobe said today that it's bringing AI-generated video, aka the Firefly Video Model, to Adobe Premiere Pro plus its Firefly generative art service. Unlike its generative AI image capabilities, however, it won't be free. AI-generated video has been available for months. In December, OpenAI released Sora, its ability to craft AI video clips of several seconds from a text prompt. What Adobe is offering is authenticity.

Backing up your files, ideally in multiple locations, is essential if you don't want to lose any data. Your documents, images, and videos can disappear if anything happens to your computer. It's also a good idea for at least one of your backups to be off-site--if a fire destroys your house it will probably also destroy your backup drive. Most of us know this. The problem is that paying for a dedicated backup service feels silly if you're already paying for cloud storage.