1,220 research outputs found

    Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar

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    This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.Comment: To appear in 10th International Conference on. Artificial Intelligence and Symbolic Computation AISC 201

    Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

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    As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

    Network Representation Learning: A Survey

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    With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.Comment: Accepted by IEEE transactions on Big Data; 25 pages, 10 tables, 6 figures and 127 reference

    La tecnología central detrás y más allá de ChatGPT: Una revisión exhaustiva de los modelos de lenguaje en la investigación educativa

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    ChatGPT has garnered significant attention within the education industry. Given the core technology behind ChatGPT is language model, this study aims to critically review related publications and suggest future direction of language model in educational research. We aim to address three questions: i) what is the core technology behind ChatGPT, ii) what is the state of knowledge of related research and iii) the potential research direction. A critical review of related publications was conducted in order to evaluate the current state of knowledge of language model in educational research. In addition, we further suggest a purpose oriented guiding framework for future research of language model in education. Our study promptly responded to the concerns raised by ChatGPT from the education industry and offers the industry with a comprehensive and systematic overview of related technologies. We believe this is the first time that a study has been conducted to systematically review the state of knowledge of language model in educational research. ChatGPT ha atraído una gran atención en el sector educativo. Dado que la tecnología central detrás de ChatGPT es el modelo de lenguaje, este estudio tiene como objetivo revisar críticamente publicaciones relacionadas y sugerir la dirección futura del modelo de lenguaje en la investigación educativa. Nuestro objetivo es abordar tres preguntas: i) cuál es la tecnología central detrás de ChatGPT, ii) cuál es el nivel de conocimiento de la investigación relacionada y iii) la dirección del potencial de investigación. Se llevó a cabo una revisión crítica de publicaciones relacionadas con el fin de evaluar el estado actual del conocimiento del modelo lingüístico en la investigación educativa. Además, sugerimos un marco rector para futuras investigaciones sobre modelos lingüísticos en educación. Nuestro estudio respondió rápidamente a las preocupaciones planteadas por el uso de ChatGPT en la industria educativa y proporciona a la industria una descripción general completa y sistemática de las tecnologías relacionadas. Creemos que esta es la primera vez que se realiza un estudio para revisar sistemáticamente el nivel de conocimiento del modelo lingüístico en la investigación educativa
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