February 2026

Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim to find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories but with potentially unsafe steps. Having these objectives, we aim towards learning a policy that maximizes the probability of the most $promising$ trajectories with respect […]

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In the evolving landscape of cybersecurity, the manual and time-consuming process of identifying malware remains a major bottleneck in security analysis. This study presents a novel approach to addressing this challenge by leveraging Natural Language Processing (NLP) techniques. This research focuses on a comparative analysis of two neural networks—a Long Short-Term Memory (LSTM) model and a Transformer model that analyze API call sequences and capture the relationships between API calls. Using a publicly available dataset, the models perform […]

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To minimize cybersecurity risks in weapon systems, the implementation of the Ko-rean Risk Management Framework (K-RMF) has become essential. However, a significant ‘strategic gap’ exists between high-level RMF controls and technical MITRE ATT&CK techniques, rendering manual mapping labor-intensive. This study proposes an automated mitigation-driven pipeline that integrates Sentence-BERT (SBERT) with the struc-tural defense relationships of the ATT&CK knowledge graph. To address the data cover-age limitations of the CTID silver standard, we introduce Recall@restricted as a calibrated performance metric. […]

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Remote sensing imagery is essential for environmental monitoring but often suffers from large gaps due to clouds, sensor failure, or acquisition gaps. Existing interpolation and generative methods struggle to maintain spatial, spectral, and temporal coherence. We present AlignDiff, a diffusion-based framework that formulates reconstruction as a spatiotemporal alignment problem. It employs a three-way strategy: (1) spatial alignment via DEM conditioning, (2) semantic alignment through prompt-based modulation, and (3) distributional alignment with a VGG-Adapter enforcing feature-level consistency. Experiments on […]

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We prove that the set {n∈N: ∃p,q∈N ((n=2^p cdot 3^q) ∧ ∀(x_0,…,x_p)∈N^{p+1} ∃(y_0,…,y_p)∈{0,…,q}^{p+1} ((∀k∈{0,…,p} (1=x_k ⇒ 1=y_k)) ∧ (∀i,j,k∈{0,…,p} (x_i+x_j=x_k ⇒ y_i+y_j=y_k)) ∧ (∀i,j,k∈{0,…,p} (x_i cdot x_j=x_k ⇒ y_i cdot y_j=y_k))))} is not recursively enumerable.

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Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices into data-driven, intelligent production paradigms. Within our proposed Perception-Decision-Execution (PDE) framework, this paper reviews AI technology advances from year 2015 to 2025 for agricultural intelligence. At the Perception level, we highlight progress in environment sensing systems, particularly unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease/pest detection, growth monitoring, and abiotic stress assessment. At the Decision level, integration of heterogeneous data sources, including meteorological recordings, soil […]

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Since computers were invented, the technology of computers has advanced every year. Recently, quantum computing and artificial intelligence have played an important role in computer technology, which has provided an effective platform to address challenges such as climate change and carbon emissions. The main goal of this paper is to demonstrate how the integration of quantum computing and artificial intelligence can transform climate science, with an emphasis on predicting and managing carbon emissions. In this paper, we started […]

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This paper addresses the limitations of traditional protocol anomaly detection methods in handling dynamic state changes and unstructured behaviors. A deep protocol anomaly detection algorithm based on status code sequence modeling is proposed. The method uses the status codes returned during protocol communication as the core input. A state embedding layer is employed to transform discrete status codes into continuous vector representations. A gated recurrent unit (GRU) is then used to capture temporal dependencies and behavior patterns within […]

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Hi! I submitted 6 hours ago, before the deadline, however I still have my paper in state “Intention-to-submit registered”. Just wanted to confirm this is the expected behaviour, it’s the first paper I am submitting to this conference. Thanks! submitted by /u/KingPowa [link] [comments]

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Recent empirical work shows that large language models struggle to self-correct reasoning without external feedback. We propose a possible explanation: correlated error between generator and evaluator. When both components share failure modes, self-evaluation may provide weak evidence of correctness, and repeated self-critique may amplify confidence without adding information. We formalize this with two information-theoretic bounds. We then describe a practical architecture pairing high-entropy proposal generation with low-entropy external selection. This suggests an alternative to extended chain-of-thought in a […]

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