digitado

Network access control and identity legitimacy verification have been implemented by establishing a secure foundation for the trusted establishment of communication entities. However, successful identity authentication alone does not guarantee secure communication. In open-network environments, it remains essential to establish a secure session key via a robust key agreement mechanism—one that prevents explicit disclosure of identity information while ensuring post-quantum security. To address these requirements, we propose a lattice-based key agreement protocol. The protocol integrates identity binding, implicit […]

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The classical binary heap sink operation based on swap has a significant write overhead. We examine two intuitive improvements: swapping siblings (verified via bounded SMT search) and adding a local hint called pref (the hint-assisted variant). In our bounded SMT checks and implementation comparisons, we did not find evidence that these variants provide consistent benefits; PerfView measurements show the hint-assisted variant was slower in most configurations. Our results suggest that reverting to the straightforward hole-based sink is the […]

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This study addresses the problem of zero-shot generalization (ZSG) in deep reinforcement learning by proposing an MNK game strategy learning method based on a Fully Convolutional Deep Q-Network (FCN-DQN). Research in deep reinforcement learning aims to develop algorithms that can generalize well to unseen environments at deployment time, thereby avoiding overfitting to the training environment. Solving this problem is crucial for real-world applications, where environments are diverse, dynamic, and inherently unpredictable. By constructing a fully convolutional reinforcement learning […]

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In the high-stakes bazaar of the dark web, there is a clear hierarchy of value. A stolen credit card is a mere commodity, often trading for the price of a cup of coffee. A Social Security number is a versatile tool. But a comprehensive medical record? That is a masterpiece. It contains a permanent, unchangeable blueprint of a human life — history, genetic markers, and biometric vulnerabilities. Unlike a compromised bank account, which can be frozen and reset, […]

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Modern iOS applications that have evolved over multiple years often accumulate complex UI layers, deeply integrated analytics, and carefully tuned performance optimizations. These applications typically rely on UIKit as the foundational framework, making any transition to SwiftUI a non-trivial architectural decision. SwiftUI introduces a declarative paradigm that improves developer productivity and maintainability, but migrating large production systems requires careful orchestration to avoid regressions in performance, reliability, and user experience. A structured migration approach becomes essential when the goal […]

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Today, data are no longer confined to numerical values arranged in row-by-column matrices or stored neatly within relational databases. One of the defining characteristics of big data is its high variety, encompassing unstructured and multimodal forms such as text, audio, images, and video. These data types dominate contemporary domains including social media, digital humanities, biomedical research, education, and surveillance systems, yet they remain difficult to manage and analyze using traditional data management architectures. To cope with this shift, […]

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Citizen-reporting platforms generate high-volume, multilingual streams of service requests, yet operational triage often relies on coarse category labels and manual inspection. This study develops an explainable, calibration-aware analytics pipeline for FixMyStreet Brussels reports, combining text-based urgency modeling, topic discovery, and spatio-temporal hotspot scoring to support municipal decision-making. From 522,132 raw reports, we build an English-normalized text field for modeling, derive resolution-time outcomes from closed cases, and curate a 1,000-item gold standard with an explicit high-urgency class. A TF–IDF […]

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