February 2026

arXiv:2602.21218v1 Announce Type: new Abstract: High-quality data is essential for modern machine learning, yet many valuable corpora are sensitive and cannot be freely shared. Synthetic data offers a practical substitute for downstream development, and large language models (LLMs) have emerged as powerful engines for generating it. However, existing private text generation methods are severely inefficient: they are data-intensive, computationally slow, and often require large private corpora or batch sizes to achieve usable quality. We introduce EPSVec, a differentially-private […]

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arXiv:2602.21217v1 Announce Type: new Abstract: This paper establishes Applied Sociolinguistic AI for Community Development (ASA-CD) as a novel scientific paradigm for addressing community challenges through linguistically grounded, AI-enabled intervention. ASA-CD introduces three key contributions: (1) linguistic biomarkers as computational indicators of discursive fragmentation; (2) development-aligned natural language processing (NLP), an AI optimisation paradigm prioritising collective outcomes; and (3) a standardised five-phase protocol for discursive intervention. A proof-of-concept study, incorporating real-world and synthetic corpora, demonstrates systematic associations between exclusionary […]

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arXiv:2602.21216v1 Announce Type: new Abstract: The EQ-5D (EuroQol 5-Dimensions) is a standardized instrument for the evaluation of health-related quality of life. In health economics, systematic literature reviews (SLRs) depend on the correct identification of publications that use the EQ-5D, but manual screening of large volumes of scientific literature is time-consuming, error-prone, and inconsistent. In this study, we investigate fine-tuning of general-purpose (BERT) and domain-specific (SciBERT, BioBERT) pre-trained language models (PLMs), enriched with biomedical entity information extracted through scispaCy […]

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arXiv:2602.21215v1 Announce Type: new Abstract: Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine grained control over large language models by modulating their output distributions without parameter updates. While effective, existing methods rely on dense intervention at every decoding step. This persistent manipulation not only incurs substantial computational overhead but also risks compromising generation quality by excessively drifting from the model’s intrinsic distribution. In this work, we show that dense intervention is unnecessary and […]

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arXiv:2602.21214v1 Announce Type: new Abstract: Social media platforms have become key channels for spreading and tracking rumors due to their widespread accessibility and ease of information sharing. Rumors can continuously emerge across diverse domains and topics, often with the intent to mislead society for personal or commercial gain. Therefore, developing methods that can accurately detect rumors at early stages is crucial to mitigating their negative impact. While existing approaches often specialize in single-domain detection, their performance degrades when […]

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arXiv:2602.21213v1 Announce Type: new Abstract: We develop a topological lens on relational schema design by encoding functional dependencies (FDs) as simplices of an abstract simplicial complex. This dependency complex exposes multi-attribute interactions and enables homological invariants (Betti numbers) to diagnose cyclic dependency structure. We define Simplicial Normal Form (SNF) as homological acyclicity of the dependency complex in positive dimensions, i.e., vanishing reduced homology for all $n ge 1$. SNF is intentionally weaker than contractibility and does not identify […]

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arXiv:2602.21212v1 Announce Type: new Abstract: Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge and experience necessary to determine appropriate responses and actions. While disaster information is continuously updated, even when utilizing RAG search and large language models for inquiries, obtaining relevant domain knowledge about natural disasters and experiences similar to one’s […]

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Crypto staking has emerged as a popular method for cryptocurrency holders to earn passive income by participating in the network’s operations. By locking up their digital assets, users contribute to the security and efficiency of a blockchain, receiving rewards in return. Below is a comparison of popular staking options, with a detailed look at EMCD Staking. Understanding Crypto Staking Staking involves holding cryptocurrencies in a wallet to support the operations of a Proof-of-Stake (PoS) blockchain network. In exchange […]

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Infrastructure as Code (IaC) has become the control plane of modern cloud environments, and a prime attack surface. Misconfigured Terraform templates, overprivileged IAM roles, exposed state files, and insecure CI/CD pipelines can lead to full cloud compromise in minutes. This guide explains how to secure IaC deployments end-to-end using static code scanning, policy-as-code enforcement, short-lived federated identities, least-privilege IAM, encrypted remote state, and continuous drift detection. The core idea: secure infrastructure before it’s deployed, not after it’s breached.

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This article explores ML-driven dynamic pricing for e-com/e-grocery, tackling the complexity of thousands of SKUs, promotions, and cashback. It proposes a three-engine architecture (Base Price, Promotion, Cashback) managed via an admin UI. The system automates pricing using inputs like demand, inventory, competitor data, and seasonality. Key metrics: Margin & GMV (primary), AOV & conversion (proxy), LTV & coverage (health). Dynamic pricing transforms price into a flexible, A/B-testable product that boosts revenue and scalability while reducing manual errors.

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