arXiv ·
This paper introduces a novel fuzzy clustering method for circular time series based on a new dependence measure that considers circular arcs. The algorithm groups series generated from similar stochastic processes and demonstrates computational efficiency. The method is applied to time series of wind direction in Saudi Arabia, showcasing its practical potential.
arXiv ·
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
MBZUAI ·
A new paper coauthored by researchers at The University of Melbourne and MBZUAI explores disagreement in human annotation for AI training. The paper treats disagreement as a signal (human label variation or HLV) rather than noise, and proposes new evaluation metrics based on fuzzy set theory. These metrics adapt accuracy and F-score to cases where multiple labels may plausibly apply, aligning model output with the distribution of human judgments. Why it matters: This research addresses a key challenge in NLP by accounting for the inherent ambiguity in human language, potentially leading to more robust and human-aligned AI systems.
MBZUAI ·
MBZUAI Professor Kun Zhang's research focuses on causality in AI systems, aiming to understand underlying processes beyond data correlation. He emphasizes the importance of causality and graphical representations to model why systems produce observations and account for uncertainty. Zhang served as a program chair at the 38th Conference on Uncertainty in Artificial Intelligence (UAI) in Eindhoven. Why it matters: This highlights the growing importance of causality and uncertainty in AI research, crucial for responsible AI deployment and decision-making in the region.
MBZUAI ·
This article discusses the application of uncertain time series (UTS) approach to manage and analyze big traffic data for high-resolution vehicular transportation services. The study addresses challenges such as data sparseness, decision-making among multiple UTSs, and future forecasting with spatio-temporal correlations. Jilin Hui, previously a Research Associate at the Inception Institute of Artificial Intelligence (UAE), is applying this approach to solve problems related to increased congestion, greenhouse gas emissions, and reduced air quality in urban environments. Why it matters: The application of AI techniques to traffic management could significantly improve urban mobility and environmental sustainability in the GCC region and beyond.
MBZUAI ·
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
arXiv ·
This paper introduces rational counterfactuals, a method for identifying counterfactuals that maximize the attainment of a desired consequent. The approach aims to identify the antecedent that leads to a specific outcome for rational decision-making. The theory is applied to identify variable values that contribute to peace, such as Allies, Contingency, Distance, Major Power, Capability, Democracy, and Economic Interdependency. Why it matters: The research provides a framework for analyzing and promoting conditions conducive to peace using counterfactual reasoning.
MBZUAI ·
Machine learning (ML) algorithms use data to make decisions or predictions, improving over time as more data is provided. ML is a subset of AI, focused on models that learn from data, contrasting with rule-based systems. ML is superior in scenarios where rules are not exhaustive, such as medical scans, but rule-based systems and ML often complement each other. Why it matters: This overview clarifies the role of machine learning within the broader field of AI, highlighting its data-driven approach and its advantages over traditional rule-based systems in complex decision-making scenarios.