Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

http://www.geoplanning.it/test/wp-content/uploads/2012/02/Practical-estimates-of-rock-mass-strength.pdf

Practical estimates of rock mass strength

本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

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Landslide inventory maps: New tools for an old problem

Reply to discussion by Kalkan and Gülkan on “Attenuation relationship based on Turkish strong motion data and iso-acceleration map of Turkey” by Ulusay et al., Eng. Geol., 74: 265–291 (2004)

Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.


/pdf/a-cnn-architecture-for-discontinuity-determination-of-rock-37f85joe.pdf

A cnn architecture for discontinuity determination of rock masses with close range images

Estimating the block size is a major task for the quarry economy. Two approaches such as volumetric joint count and weighted joint density exist in the literature to assess the block size. However, due to the complex nature of discontinuities in the rock masses, this parameter could not be predicted easily everytime. Especially, when working in the rock masses having a wide discontinuity spacing, it is too difficult to perform a scanline survey. In this study, to overcome this difficulty, the photoanalysis method was considered to obtain the data required to construct a predictive model for weighted joint density. Considering the obtained data, a Mamdani fuzzy inference system was construct and its performance was assessed. As a result, a model proposed for predicting the weighted joint density in the present study.

to Predict the Weighted Joint Density

Reply to Discussion by Kayabali on “Attenuation relationship based on Turkish strong motion data and iso-acceleration map of Turkey” by Ulusay et al., Eng. Geol., 37 (2004)

Candan Gokceoglu

Papers

Reply to discussion by Kalkan and Gülkan on “Attenuation relationship based on Turkish strong motion data and iso-acceleration map of Turkey” by Ulusay et al., Eng. Geol., 74: 265–291 (2004)

A cnn architecture for discontinuity determination of rock masses with close range images

to Predict the Weighted Joint Density

Reply to Discussion by Kayabali on “Attenuation relationship based on Turkish strong motion data and iso-acceleration map of Turkey” by Ulusay et al., Eng. Geol., 37 (2004)

Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm