1,721,007 research outputs found
A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information
Get PDFNowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering “why” and “why not” questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning. This approach complements an existing framework very well and demonstrates thus a step towards generating explanations with as little user input as possible. This approach is computationally evaluated in three benchmarks using different Reinforcement Learning methods to highlight that it is independent of the type of model used and the explanations are then rated through a human study. The results obtained are compared to other baseline explanation models to underline the satisfying performance of the framework presented in terms of increasing the understanding, transparency and trust in the action chosen by the agent
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Beyond ESPREssO - Integrative risk assessment 2025 synergies and gaps in climate change adaptation and disaster risk reduction
Get PDFClimate change including the more frequent occurrence and increased intensity of extreme climate events are important drivers of disaster events. This causality is accompanied by the fact that long-term impacts of climate change are connected with a high-level of uncertainty: complex interactions, feedback loops and underlying nonlinear effects that describe the consequences in this dynamic context. Special modelling approaches are required to increase understanding of these connections with climate change and related global issues, like environmental, social, economic and political matters. Resilience is a concept that can be used when tackling climate change impacts and decrease vulnerabilities. The holistic concept goes parallel with the understanding of “managing risks instead of managing disasters”! This contribution elaborates now this line of thought and characterizes a risk-oriented modelling and design-oriented perspective. We present overviews on climate change adaptation (CCA) and disaster risk reduction (DRR), respectively, and the related frameworks and methods. Finally, we consider the links between the ESPREssO project with the PLACARD experience as coordination action. Similarities and differences are characterized in detail. Based on this specific comparison, we propose a solution-oriented approach which might overcome the distinctions regarding the different approaches of the projects towards a transformational resilience management perspective, summarizing synergies and gaps as an example for integrative risk assessment beyond ESPREssO. We conclude with a comprehensive framework based on the 5 priority areas (referred as “mission”, terminology introduced in the Horizon Europe Framework) included in the final document of ESPREssO, which could be seen as an example for an integrative risk management combining quantitative and qualitative approaches
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Electric Load Forecasting Using Support Vector Machines for Robust Regression
No full textLoad forecasting is at the core of nearly all decisions made in energy markets. The electricity load demand is influenced by numerous factors - ranging from weather conditions over seasonal effects to socio-economic influences. In this paper, we present first computational results using a linear approach supported by support vector machines for robust regression
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