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"Recommendation": models, code, and papers

Common Misconceptions about Population Data

Dec 20, 2021
Peter Christen, Rainer Schnell

Databases covering all individuals of a population are increasingly used for research studies in domains ranging from public health to the social sciences. There is also growing interest by governments and businesses to use population data to support data-driven decision making. The massive size of such databases is often mistaken as a guarantee for valid inferences on the population of interest. However, population data have characteristics that make them challenging to use, including various assumptions being made how such data were collected and what types of processing have been applied to them. Furthermore, the full potential of population data can often only be unlocked when such data are linked to other databases, a process that adds fresh challenges. This article discusses a diverse range of misconceptions about population data that we believe anybody who works with such data needs to be aware of. Many of these misconceptions are not well documented in scientific publications but only discussed anecdotally among researchers and practitioners. We conclude with a set of recommendations for inference when using population data.


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Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification

Nov 02, 2021
Clémence Réda, Andrea Tirinzoni, Rémy Degenne

We study the problem of the identification of m arms with largest means under a fixed error rate $\delta$ (fixed-confidence Top-m identification), for misspecified linear bandit models. This problem is motivated by practical applications, especially in medicine and recommendation systems, where linear models are popular due to their simplicity and the existence of efficient algorithms, but in which data inevitably deviates from linearity. In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem. We show that knowing the scale of the deviation from linearity is necessary to exploit the structure of the problem. We then describe the first algorithm for this setting, which is both practical and adapts to the amount of misspecification. We derive an upper bound to its sample complexity which confirms this adaptivity and that matches the lower bound when $\delta$ $\rightarrow$ 0. Finally, we evaluate our algorithm on both synthetic and real-world data, showing competitive performance with respect to existing baselines.

* Virtual conference 

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Kernel Identification Through Transformers

Jun 15, 2021
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Rasmussen

Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. Drawing inspiration from recent progress in deep learning, we introduce a novel approach named KITT: Kernel Identification Through Transformers. KITT exploits a transformer-based architecture to generate kernel recommendations in under 0.1 seconds, which is several orders of magnitude faster than conventional kernel search algorithms. We train our model using synthetic data generated from priors over a vocabulary of known kernels. By exploiting the nature of the self-attention mechanism, KITT is able to process datasets with inputs of arbitrary dimension. We demonstrate that kernels chosen by KITT yield strong performance over a diverse collection of regression benchmarks.

* 12 pages, 5 figures 

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A Text Extraction-Based Smart Knowledge Graph Composition for Integrating Lessons Learned during the Microchip Design

May 11, 2021
H. Abu-Rasheed, C. Weber, J. Zenkert, P. Czerner, R. Krumm, M. Fathi

The production of microchips is a complex and thus well documented process. Therefore, available textual data about the production can be overwhelming in terms of quantity. This affects the visibility and retrieval of a certain piece of information when it is most needed. In this paper, we propose a dynamic approach to interlink the information extracted from multisource production-relevant documents through the creation of a knowledge graph. This graph is constructed in order to support searchability and enhance user's access to large-scale production information. Text mining methods are firstly utilized to extract data from multiple documentation sources. Document relations are then mined and extracted for the composition of the knowledge graph. Graph search functionality is then supported with a recommendation use-case to enhance users' access to information that is related to the initial documents. The proposed approach is tailored to and tested on microchip design-relevant documents. It enhances the visibility and findability of previous design-failure-cases during the process of a new chip design.

* In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham 

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Decision Support System for an Intelligent Operator of Utility Tunnel Boring Machines

Jan 08, 2021
Gabriel Rodriguez Garcia, Gabriel Michau, Herbert H. Einstein, Olga Fink

In tunnel construction projects, delays induce high costs. Thus, tunnel boring machines (TBM) operators aim for fast advance rates, without safety compromise, a difficult mission in uncertain ground environments. Finding the optimal control parameters based on the TBM sensors' measurements remains an open research question with large practical relevance. In this paper, we propose an intelligent decision support system developed in three steps. First past projects performances are evaluated with an optimality score, taking into account the advance rate and the working pressure safety. Then, a deep learning model learns the mapping between the TBM measurements and this optimality score. Last, in real application, the model provides incremental recommendations to improve the optimality, taking into account the current setting and measurements of the TBM. The proposed approach is evaluated on real micro-tunnelling project and demonstrates great promises for future projects.

* 17 pages, 5 figures, 3 tables 

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Transformer-based approach towards music emotion recognition from lyrics

Jan 06, 2021
Yudhik Agrawal, Ramaguru Guru Ravi Shanker, Vinoo Alluri

The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains under-appreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.

* Appearing in Proceedings of the 43rd European Conference On Information Retrieval (ECIR) 2021 

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Anomaly detection in average fuel consumption with XAI techniques for dynamic generation of explanations

Nov 05, 2020
Alberto Barbado

In this paper we show a complete process for unsupervised anomaly detection for the average fuel consumption of fleet vehicles that is able to explain what variables are affecting the consumption in terms of feature relevance. For doing that, we combine the anomaly detection with a surrogate model that is able to provide that feature relevance. For this part, we evaluate both whitebox models from the literature, as well as novel variations over them, and blackbox models combined with local posthoc feature relevance techniques. The evaluation is done using real IoT data belonging to Telef\'onica, and is measured both in terms of model performance, as well as using Explainable AI metrics that compare the explanations generated in terms representativeness, fidelity, stability and contrastiveness. The explanations generate counterfactual recommendations that show what could have been done to reduce the average fuel consumption of a vehicle and turn it into an inlier. The procedure is combined with domain knowledge expressed in business rules, and is able to adequate the type of explanations depending on the target user profile.

* 22 pages, 14 figures, 10 tables 

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A Comparative Study of Feature Types for Age-Based Text Classification

Sep 24, 2020
Anna Glazkova, Yury Egorov, Maksim Glazkov

The ability to automatically determine the age audience of a novel provides many opportunities for the development of information retrieval tools. Firstly, developers of book recommendation systems and electronic libraries may be interested in filtering texts by the age of the most likely readers. Further, parents may want to select literature for children. Finally, it will be useful for writers and publishers to determine which features influence whether the texts are suitable for children. In this article, we compare the empirical effectiveness of various types of linguistic features for the task of age-based classification of fiction texts. For this purpose, we collected a text corpus of book previews labeled with one of two categories -- children's or adult. We evaluated the following types of features: readability indices, sentiment, lexical, grammatical and general features, and publishing attributes. The results obtained show that the features describing the text at the document level can significantly increase the quality of machine learning models.

* Accepted to AIST-2020 (The 9th International Conference on Analysis of Images, Social Networks and Texts) 

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Experimental Comparison of Global Motion Planning Algorithms for Wheeled Mobile Robots

Mar 07, 2020
Eric Heiden, Luigi Palmieri, Kai O. Arras, Gaurav S. Sukhatme, Sven Koenig

Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, a wide variety of motion planners, steer functions and path-improvement techniques have been proposed for such non-holonomic systems. With the objective of comparing this large assortment of state-of-the-art motion-planning techniques, we introduce a novel open-source motion-planning benchmark for wheeled mobile robots, whose scenarios resemble real-world applications (such as navigating warehouses, moving in cluttered cities or parking), and propose metrics for planning efficiency and path quality. Our benchmark is easy to use and extend, and thus allows practitioners and researchers to evaluate new motion-planning algorithms, scenarios and metrics easily. We use our benchmark to highlight the strengths and weaknesses of several common state-of-the-art motion planners and provide recommendations on when they should be used.

* Extended version of manuscript under review 

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