To solve classification problems, we present a novel SAT-based framework for learning NNF networks directly from binary input-output data. In analogy to deep neural networks, negation normal form (NNF) networks exhibit a deep structure with learnable Boolean weights. By encoding this learning problem as a propositional satisfiability instance, our method leverages modern SAT solvers to construct logically correct, compact, and interpretable Boolean models of given datasets. Unlike statistical learners or heuristic rule induction algorithms, our approach guarantees logical fidelity on the training data. Evaluations on synthetic benchmarks and real-world datasets demonstrate that it outperforms state-of-the-art rule-based algorithms as well as a greedy NNF learning algorithm in terms of accuracy.
The location of real eigenvalues provides critical insights into the stability and resonance properties of physical systems. This paper presents a hybrid symbolic numeric approach for certified real eigenvalue localization. Our method combines Gershgorin disk analysis with Hermite matrix certification to compute certified intervals that enclose the real eigenvalues. These intervals can be further refined through bisectionlike procedures to achieve the desired precision. The proposed approach delivers reliable interval certifications while preserving computational efficiency. The effectiveness of the framework is demonstrated through a concise, fully worked computational example.
The large spread of disinformation across digital platforms creates significant challenges to information integrity. This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles, focusing on titles and short text snippets. The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent (which relies on named entity recognition), (iii) a coherence detection agent (using LLM prompt engineering), and (iv) a web-scraped data analyzer that extracts relational triplets for fact checking. The system is orchestrated via the Model Context Protocol (MCP), offering shared context and live learning across components. Results demonstrate that the multi-agent ensemble achieves 95.3
Technical debt prediction involves anticipating problems and challenges in software projects. Because of lousy coding choices, these projects are often far from the optimal design. These issues can accumulate over time and affect long-term maintainability and stability. Numerous studies are available for predicting technical debt. For instance, some studies utilize static analysis tools like Pylint(1), and others use dynamic code analysis tools like SZZ [1]. This study addresses AI predictions using data obtained from static analysis tools. This paper presents technical debt prediction using static code analysis data generated with SonarQube(2) and Radon(3). It covers both short-term and long-term predictions by analyzing code quality metrics such as complexity, duplication, and adherence to standards. The study also integrates git commit classification to analyze commit history, providing valuable insights into the evolution and management of technical debt within the project.
Automated machines are widely used in industrial environments for the production of different items and generate lots of data following these processes. The proper execution of the machines influences the production output, thus the detection of anomalous behavior in machines' activity using the generated data must be considered to avoid unpleasant outcomes. This paper presents a novel ensemble unsupervised anomaly detection method characterized by parameterized prediction. The proposed method consists of two stages - the first stage uses statistical-based methods to assign artificial labels to the input data. In the second stage, the artificially labeled instances and a feature bagging technique are employed to construct the model's estimators - each estimator calculates the percentiles of the distances computed between its centroid and each instance from the training subset. The model's prediction function is parameterized by a percentile rank. Each estimator computes the distance between its centroid and the evaluated instance: if this distance exceeds the value of the specified percentile, the estimator classifies the instance as an anomaly. A majority vote is applied to establish the outcome.
