arXiv:2604.04952v1 Announce Type: new Abstract: Ransomware and DDoS attacks disproportionately impact hospitals, schools, and small organizations that cannot afford enterprise security solutions. We present ML Defender (aRGus NDR), an open-source network intrusion detection system built in C++20, deployable on commodity hardware at approximately 150-200 USD. ML Defender implements a six-component pipeline over eBPF/XDP packet capture, ZeroMQ transport, and Protocol Buffers serialization, combining a rule-based Fast Detector with an embedded Random Forest classifier. The Maximum Threat Wins policy selects […]

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arXiv:2603.09999v1 Announce Type: new Abstract: This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations and the increasing effort required by applicants and aviation authorities to apply established assessment frameworks, including the Specific Operations Risk Assessment and the Pre-defined Risk Assessment, in a consistent and efficient manner. The proposed approach uses a controlled […]

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The transition to serverless architecture is not a buzzword to developers and software developers involved in developer work in macOS. It is a true revolution in the way apps are built, implemented, and scaled. Gone are the days of staring at terminal windows full of endless server logs and sleepless nights in order to keep the infrastructure from hiccuping. Serverless sets that mess with the work of someone. If you have been developing on macOS, then it is […]

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arXiv:2602.13804v1 Announce Type: cross Abstract: Large language models spend most of their inference cost on attention over long contexts, yet empirical behavior suggests that only a small subset of tokens meaningfully contributes to each query. We formalize this phenomenon by modeling attention as a projection onto the convex hull of key vectors and analyzing its entropic (softmax-like) relaxation. Our main theoretical contribution is a face-stability theorem showing that, under a strict complementarity margin (a support gap (Delta) certified […]

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“You can dream anything” — a lie It is getting scarier by the day to imagine the world that is predominantly dependent on AI. Everyday there is a newer and dystopian version of the future that is being portrayed by the media. How true is it ? I am completely convinced that AI is better at writing code than humans now. I was reflecting on last week’s work and I didn’t reach out to the code in IDE […]

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arXiv:2406.15904v2 Announce Type: replace-cross Abstract: Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shifts are often driven by unobserved confounding, and can in fact alter the concept of which model is best. This paper studies distribution shifts in the domain adaptation problem with unobserved confounding. We postulate a linear structural causal model to account for endogeneity and unobserved confounding, and we leverage exogenous […]

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arXiv:2602.00885v1 Announce Type: new Abstract: Informative data selection is a key requirement for large language models (LLMs) to minimize the amount of data required for fine-tuning, network distillation, and token pruning, enabling fast and efficient deployment, especially under computational and communication constraints. Traditional subset selection methods, including those based on Determinantal Point Processes (DPP), focus on maximizing diversity but assume that selected data batches are always available error-free. This presumption prohibits their use under partial storage outage, imperfect […]

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Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency and complex temporal patterns in network data demand new model approaches. In this paper, based on a systematic multivariate time series model study, we present two deep learning models aiming for learning both temporal patterns and network topological correlations at the same time: […]

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