Generalized Method-of-Moments for Rank Aggregation

In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives. Our technique is based on breaking the full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. We identify conditions for the output of GMM to be unique, and identify a general class of consistent and inconsistent breakings. We then show by theory and experiments that our algorithms run significantly faster than the classical Minorize-Maximization (MM) algorithm, while achieving competitive statistical efficiency.Engineering and Applied SciencesStatistic

Random Utility Theory for Social Choice

Random utility theory models an agent's preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Plackett-Luce model, for which fast inference methods for maximum likelihood estimators are available. This paper develops conditions on general random utility models that enable fast inference within a Bayesian framework through MC-EM, providing concave loglikelihood functions and bounded sets of global maxima solutions. Results on both real-world and simulated data provide support for the scalability of the approach and capability for model selection among general random utility models including Plackett-Luce.Engineering and Applied Science

Generalized Random Utility Models with Multiple Types

We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types. Our model extends the popular setup in Berry, Levinsohn and Pakes (1995) to allow for the data-driven classification of agents' types using agent-level data. We focus on applications involving data on agents' ranking over alternatives, and present theoretical conditions that establish the identifiability of the model and uni-modality of the likelihood/posterior. Results on both real and simulated data provide support for the scalability of our approach.EconomicsEngineering and Applied SciencesMathematic

Temperature dependent optical properties of CH<inf>3</inf>NH<inf>3</inf>PbI<inf>3</inf> perovskite by spectroscopic ellipsometry

© 2016 AIP Publishing LLC. Mixed organic-inorganic halide perovskites have emerged as a promising new class of semiconductors for photovoltaics with excellent light harvesting properties. Thorough understanding of the optical properties of these materials is important for photovoltaic device optimization and the insight this provides for the knowledge of energy band structures. Here we present an investigation of the sub-room temperature dependent optical properties of polycrystalline thin films of CH3NH3PbI3 perovskites that are of increasing interest for photovoltaics. The complex dielectric function of CH3NH3PbI3 in the energy range of 0.5-4.1 eV is determined between 77 K and 297 K using spectroscopic ellipsometry. An increase in optical permittivity as the temperature decreases is illustrated for CH3NH3PbI3. Optical transitions and critical points were analyzed using the energy dependent second derivative of these dielectric functions as a function of temperature

Enhancing Cybersecurity Threat Detection with Counterfactual Reasoning: A \u27What-If\u27 Ontology Approach Using Large Language Models

This paper proposes a novel approach to enhancing cybersecurity threat detection by integrating counterfactual reasoning with large language models (LLMs) through a structured what-if ontology. Traditional AI-based systems often function as black boxes, identifying threats without offering causal explanations or scenario reasoning. Our framework enables LLMs to simulate hypothetical attack scenarios and assess alternative outcomes, thereby improving detection accuracy and interpretability. Grounded in the TOVE ontology engineering methodology, the system aims to formalize key cybersecurity entities, causal relations, and counterfactual conditions using languages like OWL and SWRL. We evaluate the framework based on metrics such as detection accuracy, narrative quality, and reasoning robustness. By unifying theoretical foundations from causal reasoning, scenario planning, and facets of explainable AI, our ontology serves as a semantic backbone for LLM-guided analysis. This work contributes a proactive, explainable, and extensible model for anticipating cyber threats and guiding defensive strategies, with implications for future research and implementation in intelligent threat detection systems

L’enseignement des langues étrangères a l’université Cadi ayyad – Marrakech : de l’enseignement présentiel a l’apprentissage en ligne

L'émergence de l'enseignement en ligne remonte à 1982 lorsque le Western Behavioural Sciences Institute de Californie a utilisé la technique de conférence par ordinateur pour présenter un programme d'enseignement à distance. Depuis lors, les universités et les instituts d’enseignement supérieur du monde entier utilisent l'apprentissage en ligne comme une méthode complémentaire à l'apprentissage présentiel. Cependant, avec la propagation de la pandémie de Covid-19, l'apprentissage en ligne est devenu une alternative inévitable. A cet égard, notre raisonnement, derrière la conduite de ce travail, est d’aborder comment les langues étrangères sont enseignées à l’université Cadi Ayyad- Marrakech (UCA) en donnant un aperçu sur le passage de l’enseignement présentiel à l’enseignement en ligne (à distance). Pour ce faire, nous avons commencé par l’importance des langues étrangères dans un pays multiculturel et plurilingue comme le Maroc. Ensuite, nous avons découvert les pédagogies, les défis, et les programmes adoptés par les professeurs universitaires à UCA. Finalement, le dernier axe a été consacré à l’enseignement à distance dans la même université, en parlant des difficultés que les professeurs et les étudiants ont éprouvées dans le processus enseignement apprentissage sans oublier les solutions approuvées