technocracy

Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cybersecurity training. While attack traffic can often be layered into training environments using red-teaming or replay methods, generating authentic benign background traffic remains a core challenge — particularly in simulating the complex temporal and communication dynamics of real-world networks. This paper introduces TempoNet, a novel generative model that combines multi-task learning with multi-mark temporal point processes to jointly model […]

Ver mais

Unmanned aerial vehicles (UAVs), commonly known as drones, face fundamental limitations in flight duration due to battery constraints, typically restricting operations to 20-60 minutes. This paper proposes an energy-efficient drone design leveraging a solar-powered hybrid propulsion system to achieve extended endurance, targeting 24 hours continuous flight and beyond under realistic conditions. Drawing inspiration from adaptive edge intelligence frameworks, the design integrates high-efficiency photovoltaic (PV) cells, lithium-based batteries, reinforcement learning (RL) for power management, and swarm coordination for fleet […]

Ver mais

A new milestone for the robot photographer! Here’s a list of new things that the robot is capable of: It has been taught to detect and track humans in Kinect’s depth image! Depth and photographic cameras have been aligned (camera intrinsics and extrinsics have been calibrated). This effectively means that given a point on one image plane (e.g. in the depth image), a robot is able to tell where the corresponding point would be on the other image […]

Ver mais

We often treat current LLMs as massive “reflex” engines (Model-Free policies) – they react to the context based on training habits, but they don’t really “plan” or verify their moves unless we force them with Chain-of-Thought. I’ve been looking at the architecture behind the new Logical Intelligence lab, which is pivoting to Energy-Based Models. From an RL perspective, this feels exactly like the shift from “habit-based” action to “planning-based” control. Instead of just guessing the next token, the […]

Ver mais

Recent advances, such as RegretNet, ALGnet, RegretFormer and CITransNet, use deep learning to approximate optimal multi item auctions by relaxing incentive compatibility (IC) and measuring its violation via ex post regret. However, the true accuracy of these regret estimates remains unclear. Computing exact regret is computationally intractable, and current models rely on gradient based optimizers whose outcomes depend heavily on hyperparameter choices. Through extensive experiments, we reveal that existing methods systematically underestimate actual regret (In some models, the […]

Ver mais

E-Payment has become popular in mobile com-merce, can provide consumers with a convenient way to makepurchases electronically. Currently, however, all too many E-Payment systems are primarily focused on securing a consumer’sfinancial information and do little to prevent privacy leaks andAI-generated scams. This paper defines AEP-M, a novel AI-enhanced anonymous e-payment scheme developed for mobiledevices that uses TrustZone and divisible e-cash. Since mobiledevices have very limited processing power and each transactionmust be performed in real time, the proposed solution […]

Ver mais

arXiv:2601.03262v1 Announce Type: new Abstract: Visually rich documents (VRDs) challenge retrieval-augmented generation (RAG) with layout-dependent semantics, brittle OCR, and evidence spread across complex figures and structured tables. This survey examines how Multimodal Large Language Models (MLLMs) are being used to make VRD retrieval practical for RAG. We organize the literature into three roles: Modality-Unifying Captioners, Multimodal Embedders, and End-to-End Representers. We compare these roles along retrieval granularity, information fidelity, latency and index size, and compatibility with reranking and […]

Ver mais

arXiv:2602.16436v1 Announce Type: cross Abstract: Releasing data once and for all under noninteractive Local Differential Privacy (LDP) enables complete data reusability, but the resulting noise may create bias in subsequent analyses. In this work, we leverage the Weierstrass transform to characterize this bias in binary classification. We prove that inverting this transform leads to a bias-correction method to compute unbiased estimates of nonlinear functions on examples released under LDP. We then build a novel stochastic gradient descent algorithm […]

Ver mais

We study the problem of image denoising where images are assumed to be samples from low dimensional (sub)manifolds. We propose the algorithm of locally linear denoising. The algorithm approximates manifolds with locally linear patches by constructing nearest neighbor graphs. Each image is then locally denoised within its neighborhoods. A global optimal denoising result is then identified by aligning those local estimates. The algorithm has a closed-form solution that is efficient to compute. We evaluated and compared the algorithm […]

Ver mais

Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques have emerged as an efficient alternative to conventional ray-tracing for radio map estimation (RME), most existing DL approaches are confined to 2D near-ground scenarios. They often fail to capture essential 3D signal propagation characteristics and antenna polarization effects, primarily due to the scarcity of 3D data and training challenges. To address these limitations, we present the RadioGen3D framework. […]

Ver mais