Reviewerly/exHarmony huggingface dataset:
BREEDS: Benchmarks for Subpopulation Shift

We develop a methodology for assessing the robustness of models to subpopulation shift---specifically, their ability to generalize to novel data subpopulations that were not observed during training. Our approach leverages the class structure underlying existing datasets to control the data subpopulations that comprise the training and test distributions. This enables us to synthesize realistic distribution shifts whose sources can be precisely controlled and characterized, within existing large-scale datasets. Applying this methodology to the ImageNet dataset, we create a suite of subpopulation shift benchmarks of varying granularity. We then validate that the corresponding shifts are tractable by obtaining human baselines for them. Finally, we utilize these benchmarks to measure the sensitivity of standard model architectures as well as the effectiveness of off-the-shelf train-time robustness interventions.
How does machine translation and post-editing affect project management? An interdisciplinary approach

Machine translation (MT) and post-editing (PE) are two services that are increasingly in demand in the translation industry. In a context in which large-scale projects are required within tight deadlines, the deployment of this technology to increase productivity is a reality. However, this increase in productivity can only be achieved with appropriate management of the project: MT must not be understood as a tool, but as a process, and project managers, who are usually responsible for the project from start to finish, have to cope with new MT and PE workflows that pose different challenges. Although much has been written about the use of MT and PE in professional practice (resulting in different lines of research in this field), little attention has been paid to the role of the project manager in MT and PE projects. For this reason, the main objective of this paper is to analyse how MT and PE affect the factors that project managers must keep in mind when managing projects, taking as a starting point the most important reference frameworks in project management. The main objective is to offer an interdisciplinary perspective that explains the new challenges the industry is facing and how these challenges affect the different stakeholders involved in the project.
D2.5 FAIR Semantics Recommendations Second Iteration

This document is the second iteration of recommendations for making semantic artefacts FAIR. These recommendations result from initial discussions during a brainstorming workshop organised by FAIRsFAIR as a co-located event with the 14th RDA Plenary meeting in Helsinki. Based on the feedback received there, 17 preliminary recommendations related to one or more of the FAIR principles, and 10 best practice recommendations on semantic artefacts were documented. These recommendations were first published as Deliverable 2.2 at the beginning of 2020[1]. Following various dissemination and stakeholder engagement activities, including an evaluation workshop held in October 2020, this second iteration of recommendations and best practices are proposed.
Signal Sorting Algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network

In non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm cannot achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Once the neural network training is finished, only one frequency hopping description word of the input signal is required to obtain its own network station label in real time. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with less iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.
Klasifikasi Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing

The world of banking requires a marketer to be able to reduce the risk of borrowing by keeping his customers from occurring non-performing loans. One way to reduce this risk is by using data mining techniques. Data mining provides a powerful technique for finding meaningful and useful information from large amounts of data by way of classification. The classification algorithm that can be used to handle imbalance problems can use the Random Forest (RF) algorithm. However, several references state that an optimization algorithm is needed to improve the classification results of the RF algorithm. Optimization of the RF algorithm can be done using Bagging and Genetic Algorithm (GA). This study aims to classify Bank Marketing data in the form of loan application receipts, which data is taken from the www.data.world site. Classification is carried out using the RF algorithm to obtain a predictive model for loan application acceptance with optimal accuracy. This study will also compare the use of optimization in the RF algorithm with Bagging and Genetic Algorithms. Based on the tests that have been done, the results show that the most optimal performance of the classification of Bank Marketing data is by using the RF algorithm with an accuracy of 88.30%, AUC (+) of 0.500 and AUC (-) of 0.000. The optimization of Bagging and Genetic Algorithm has not been able to improve the performance of the RF algorithm for classification of Bank Marketing data.&#x0D;
Histogram Ordering

Frequency histograms are ubiquitous, being practically used in any field of science.In this paper, we present a partial order for frequency histograms and, to our knowledge, no order of this kind has been yet defined.This order is based on the stochastic order of discrete probability distributions and it has invariance properties that make it unique.First, we model a frequency histogram as a sequence of bins associated with a discrete probability (or relative frequency) distribution.Then, we consider that two histograms are ordered if they are defined on the same sequence of bins and their respective frequency distributions are stochastically ordered.The ordering can be easily spotted because the respective cumulative distribution functions of the frequencies of two ordered histograms do not cross each other.Finally, with each bin we can associate a representative value of the bin, and for two ordered histograms it holds that all quasi-arithmetic means (such as arithmetic, harmonic, and geometric mean) of the representative values weighted by the frequencies are ordered in the same direction than the histograms are.Our theoretical study is supported by three experiments in the fields of image processing, traffic flow, and income distribution.
Problem-based learning and other active methodologies as support for distance teaching during the COVID-19 pandemic

This manuscript presents an innovative experience in the teaching–learning process with three objectives. The first is to incorporate the principles and values of social justice, reciprocity and solidarity in the subject ‘Statistics Applied to Business’ in the bachelor’s degree in Business Administration and Management at the University of the Basque Country. The second objective addresses how to apply active methodologies in the teaching of economics and business from a competency-based approach in order to investigate its impact. The third one, on the other hand, tries to explore how to provide students with active distance learning tools to improve and guarantee the quality of the teaching–learning process. The results of the experience demonstrate the potential of technical subjects for the development of transversal competencies and the capacity of students to design and solve complex problems with creativity and knowledge of social and labour realities. Likewise, greater motivation, better knowledge acquisition and appropriation of the work by the students are also detected. Finally, this article shows the potential of methodologies that involve the combined responsibility of students and teachers in the generation of knowledge that favours a professional development that is permeable and sensitive to the changes occurring in the social and labour world.&#x0D; &#x0D; Keywords: Problem-based learning, distance teaching, pandemic, teaching innovation
Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.
Tackling ordinal regression problem for heterogeneous data: sparse and deep multi-task learning approaches

Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and identically distributed (IID) instances via formulating a single ordinal regression task. However, for heterogeneous non-IID instances with well-defined local geometric structures, e.g., subpopulation groups, multi-task learning (MTL) provides a promising framework to encode task (subgroup) relatedness, bridge data from all tasks, and simultaneously learn multiple related tasks in efforts to improve generalization performance. Even though MTL methods have been extensively studied, there is barely existing work investigating MTL for heterogeneous data with ordinal labels. We tackle this important problem via sparse and deep multi-task approaches. Specifically, we develop a regularized multi-task ordinal regression (MTOR) model for smaller datasets and a deep neural networks based MTOR model for large-scale datasets. We evaluate the performance using three real-world healthcare datasets with applications to multi-stage disease progression diagnosis. Our experiments indicate that the proposed MTOR models markedly improve the prediction performance comparing with single-task ordinal regression models.
The Education Gradient in Maternal Enjoyment of Time in Childcare

Studies document large differences in the amount of time mothers spend in childcare by maternal education, even when controlling for characteristics such as income, employment hours, and work schedules. One possible explanation for this observed difference is that highly educated mothers find time in childcare to be more enjoyable. To inform this hypothesis, we examine education-based differences in mothers’ average feelings during their time in childcare using pooled data from the 2010, 2012, and 2013 Well-being Modules of the American Time Use Survey. Among all mothers, spending time in childcare is associated with higher positive feelings than is spending time in other activities. However, highly educated mothers do not enjoy their time in childcare more than less-educated mothers. Findings are robust to controls for mother fixed effects.
Two Variational Models for Image Denoising Using Jacobian of Normals

Variational models with second order regularizers can efficiently overcome the problems of staircasing effects caused by first order models. However, different types of second order regularizers may lead to different properties of feature preserving in restored images. In this paper, we show two variational models with second order regularizers. The first one is bounded Hessian model with Jacobian of normals, which uses bounded Jacobian of image intensity normals as regularizer, it is an extension of classical bounded Hessian (BH) model. The second one is total generalized variation model with Jacobian of normals, which is an extension to total generalized variation (TGV) model by replacing gradients in TGV with normals. The common objective is to improve feature preserving, such as edge, contrast and smoothness preservation. Additionally, their Alternating Direction Method of Multipliers (ADMM) are designed by introducing some proper auxiliary variables, Lagrange multipliers and penalty parameters to decompose the original models into some simple minimization sub-problems to solve. Extensive comparisons demonstrate that the proposed models are superior to the classical models with Hessian and TGV regularizers, especially in edge and corner preservation, smoothness, contrast enhancement. Moreover, the proposed models can be also extended to image inpainting, deblurring, and image enhancement.
Continuous improvement and adaptation of predictive models in smart manufacturing and model management

Predictive models are increasingly deployed within smart manufacturing for the control of industrial plants. With this arises, the need for long-term monitoring of model performance and adaptation of models if surrounding conditions change and the desired prediction accuracy is no longer met. The heterogeneous landscape of application scenarios, machine learning frameworks, hardware-restricted IIoT platforms, and the diversity of enterprise systems require flexible, yet stable and error resilient solutions that allow the automated adaptation of prediction models. Recommendations are provided for the application and management of predictive models in smart manufacturing. Typical causes for concept drift in real-world smart manufacturing applications are analysed, and essential steps in data and prediction model management are highlighted, to ensure reliability and efficiency in such applications. For this purpose, recommendations and a reference architecture for model management are provided. In addition, experimental results of two model adaptation strategies on an artificial dataset are shown.
Personalized and Automated Feedback in Summative Assessment Using Recommender Systems

In this study we explore the use of recommender systems as a means of providing automated and personalized feedback to students following summative assessment. The intended feedback is a personalized set of test questions ( items ) for each student that they could benefit from practicing with. Recommended items can be beneficial for students as they can support their learning process by targeting specific gaps in their knowledge, especially when there is little time to get feedback from instructors. The items are recommended using several commonly used recommender system algorithms, and are based on the students' scores in a summative assessment. The results show that in the context of the Dutch secondary education final examinations, item recommendations can be made to students with an acceptable level of model performance. Furthermore, it does not take a computationally complex model to do so: a simple baseline model which takes into account global, student-specific, and item-specific averages obtained similar performance to more complex models. Overall, we conclude that recommender systems are a promising tool for helping students in their learning process by combining multiple data sources and new methodologies, without putting additional strain on their instructors.
Application of network protocol improvement and image content search in mathematical calculus 3D modeling video analysis

This article connects mobile terminals through improved network protocols, and connects monitoring devices through improved network protocols in the local area network. At the same time, it can convert mobile terminal requests into requests that comply with improved network protocols and forward them to monitoring devices. Method of three-dimensional feature extraction based on dynamic data. First, normalize the coordinates and scales of the three-dimensional calculus model, and use two kinds of data (voxel representation and pixel representation) to deal with video object recognition. Then construct a convolutional neural network as an input to the network, extract visual information and geometric information, and use deep learning to improve the recognition ability of single-modal features. Based on the convolutional neural network, the geometric descriptor and the view descriptor of the three-dimensional calculus model are extracted separately, and the two feature descriptors are fused in a network to find the association between the patterns. After forming the fused feature descriptor, it is applied to micro Classification and retrieval of integral 3D features. The multi-feature fusion layer can not only learn more distinguishing features of the two descriptors, but also supplement relevant information between the two descriptors. Compared with using these two representations alone, this method produces a much better classifier and improves retrieval efficiency.
Contactless Detection of Natural Bending Frequencies using Embedded Metallic-Glass Ribbons inside Plastic Beams made of 3-D Printing

In the current work, to identify the bending mode harmonics, 30 microns thin magnetoelastic ribbons made of metallic glass are embedded inside 6 mm thick PLA plastic cantilever beams made by 3-D printing. This is possible because the ribbons are of magnetoelastic nature and thus change their mechanical state inducing a corresponding change in their magnetic state. The ribbons are placed at four different depths, starting with zero depth at the beam’s external surface all the way inside to the beam’s mid-plane. This technique is capable of detecting seven harmonics, and remarkably, these frequencies remain the same within a marginal error of 1% for all the depths. The amplitude of the modes drops with the increase in depth but is still strong enough, except at the midplane, to be used as a sensing signal. The harmonics spectrum is the unique signature of the structure’s state; this is a proof of concept that in a contactless fashion, the embedded ribbons provide useful information about the mechanical health of a structure.
Neuro-Evolutionary Direct Policy Search for Multiobjective Optimal Control

Direct policy search (DPS) is emerging as one of the most effective and widely applied reinforcement learning (RL) methods to design optimal control policies for multiobjective Markov decision processes (MOMDPs). Traditionally, DPS defines the control policy within a preselected functional class and searches its optimal parameterization with respect to a given set of objectives. The functional class should be tailored to the problem at hand and its selection is crucial, as it determines the search space within which solutions can be found. In MOMDPs problems, a different objective tradeoff determines a different fitness landscape, requiring a tradeoff-dynamic functional class selection. Yet, in state-of-the-art applications, the policy class is generally selected a priori and kept constant across the multidimensional objective space. In this work, we present a novel policy search routine called neuro-evolutionary multiobjective DPS (NEMODPS), which extends the DPS problem formulation to conjunctively search the policy functional class and its parameterization in a hyperspace containing policy architectures and coefficients. NEMODPS begins with a population of minimally structured approximating networks and progressively builds more sophisticated architectures by topological and parametrical mutation and crossover, and selection of the fittest individuals concerning multiple objectives. We tested NEMODPS for the problem of designing the control policy of a multipurpose water system. Numerical results show that the tradeoff-dynamic structural and parametrical policy search of NEMODPS is consistent across multiple runs, and outperforms the solutions designed via traditional DPS with predefined policy topologies.
Personal Motivation and Learning Self-Management in Students as Result of the Transition to Online Courses during COVID-19 Pandemic

COVID-19 pandemic has changed the normal course of activities around the World. The measures implemented to slow down the spread of the disease and its impact on the health system have also suggested a change in population dynamics. In the educational aspect, the paradigm of the traditional education had to change completely to a remote environment, representing a challenge for students, teachers, and education institutions. In this work we report the students’ response to the actions taken by the faculty of the Chemistry Sciences School of Universidad La Salle México to face online courses ensuring student accompaniment. After the term, a qualitative and quantitative study was performed to the school population using an electronic survey to address the response of students to the applied strategy. Data was analyzed using SPSS® software. 266 responses were obtained, which represents a response rate of 44%. 75.6% of the alumni reported having a good or excellent disposition regarding virtual sessions. Likewise, 48.9% reported adapting appropriately to the remote working modality. Also, 83.5% of students recognize the labor of the teachers by acknowledging that their effort and dedication was excellent or good. Students showed development of soft skills such as self-time and learning management; written, oral and technological communication. These results suggest the advantages perceived by students during the transition to virtual modality, mostly oriented to self-management of their learning and that could conform the basis for the development of new strategies that can be useful not only for a contingency situation, but also in the structuration and innovation of science programs.