Reviewerly/exHarmony huggingface dataset:
Early Detection of Septic Shock Onset Using Interpretable Machine Learners

Background: Developing a decision support system based on advances in machine learning is one area for strategic innovation in healthcare. Predicting a patient’s progression to septic shock is an active field of translational research. The goal of this study was to develop a working model of a clinical decision support system for predicting septic shock in an acute care setting for up to 6 h from the time of admission in an integrated healthcare setting. Method: Clinical data from Electronic Health Record (EHR), at encounter level, were used to build a predictive model for progression from sepsis to septic shock up to 6 h from the time of admission; that is, T = 1, 3, and 6 h from admission. Eight different machine learning algorithms (Random Forest, XGBoost, C5.0, Decision Trees, Boosted Logistic Regression, Support Vector Machine, Logistic Regression, Regularized Logistic, and Bayes Generalized Linear Model) were used for model development. Two adaptive sampling strategies were used to address the class imbalance. Data from two sources (clinical and billing codes) were used to define the case definition (septic shock) using the Centers for Medicare &amp; Medicaid Services (CMS) Sepsis criteria. The model assessment was performed using Area under Receiving Operator Characteristics (AUROC), sensitivity, and specificity. Model predictions for each feature window (1, 3 and 6 h from admission) were consolidated. Results: Retrospective data from April 2005 to September 2018 were extracted from the EHR, Insurance Claims, Billing, and Laboratory Systems to create a dataset for septic shock detection. The clinical criteria and billing information were used to label patients into two classes-septic shock patients and sepsis patients at three different time points from admission, creating two different case-control cohorts. Data from 45,425 unique in-patient visits were used to build 96 prediction models comparing clinical-based definition versus billing-based information as the gold standard. Of the 24 consolidated models (based on eight machine learning algorithms and three feature windows), four models reached an AUROC greater than 0.9. Overall, all the consolidated models reached an AUROC of at least 0.8820 or higher. Based on the AUROC of 0.9483, the best model was based on Random Forest, with a sensitivity of 83.9% and specificity of 88.1%. The sepsis detection window at 6 h outperformed the 1 and 3-h windows. The sepsis definition based on clinical variables had improved performance when compared to the sepsis definition based on only billing information. Conclusion: This study corroborated that machine learning models can be developed to predict septic shock using clinical and administrative data. However, the use of clinical information to define septic shock outperformed models developed based on only administrative data. Intelligent decision support tools can be developed and integrated into the EHR and improve clinical outcomes and facilitate the optimization of resources in real-time.
The Figure of the Teacher-Prosumer for the Development of an Innovative, Sustainable, and Committed Education in Times of COVID-19

Studies on the adaptation from face-to-face to online teaching during lockdown show the before and after in education that faces the double challenge of promoting digital skills and public access to connectivity and electronic devices in the post-COVID-19 era. Therefore, this article contributes to these new emerging lines of educational research by presenting an educational innovation project called “Teachers Versus COVID-19”. This project aimed to verify whether the figure of teacher-prosumer, that is, consumers of media culture and creators of their own educational resources, favors the initial training of teachers during the pandemic. To this end, the following objectives were proposed: firstly, test whether the figure of the teacher-prosumer contributes to improving the adaptation of face-to-face teaching to the virtual modality of the Didactics of Social Sciences in the Degree in Primary Education during lockdown; secondly, analyze the production of content on social networks by the students in the Degree in Primary Education, according to the objectives of sustainable development. To validate our teacher-prosumer proposal, we chose the design-based research (DBR) qualitative methodology. For this, 240 students from the course in Didactics of Social Sciences of the Degree in Primary Education at the University of Malaga created 37 educational videos that teach the social sciences curriculum to children between 6 and 12 years of age from the perspective of relevant social problems and the Sustainable Development Goals. These videos were disseminated through the project’s YouTube channel. The results of this study corroborate the effectiveness of turning students into teachers-prosumers, generating the development of critical, creative, digital, and socio-emotional skills so that they feel committed to playing an active role in social changes for a sustainable world.
Self-supervised Learning from a Multi-view Perspective

As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as object detection and image captioning. Many proposed approaches for self-supervised learning follow naturally a multi-view perspective, where the input (e.g., original images) and the self-supervised signals (e.g., augmented images) can be seen as two redundant views of the data. Building from this multi-view perspective, this paper provides an information-theoretical framework to better understand the properties that encourage successful self-supervised learning. Specifically, we demonstrate that self-supervised learned representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design. In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information. To verify our analysis, we conduct controlled experiments to evaluate the impact of the composite objectives. We also explore our framework's empirical generalization beyond the multi-view perspective, where the cross-view redundancy may not be clearly observed.
ELIXIR‐EXCELERATE: establishing Europe's data infrastructure for the life science research of the future

A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.
Robust SAR Image Registration Using Rank-Based Ratio Self-similarity

Synthetic aperture radar (SAR) images in different polarizations or from different sensors are becoming easily available, but registering these images is challenging because of the presence of significant speckles in SAR images and the existence of radiometric differences between images. To address the problems, we propose a novel feature descriptor named rank-based ratio self-similarity (RRSS) for robust SAR image registration. The descriptor first calculates a ratio surface by replacing the distance surface, as the use of the ratio is more robust to multiplicative noise, and then sorts the ratio values to construct the rank surface. Subsequently, the rank surface is partitioned into an index map, and the index map is then transformed into the descriptor vector based on a restricted adaptive binning grid to discriminatively describe features. Furthermore, a rotation invariance enhancement method is designed for the RRSS descriptor to efficiently calculate descriptor vectors in multiple orientations. We conduct experiments with six SAR image pairs of various bands, polarizations, and resolutions from different sensors, including ALOS-PALSAR, Gaofen-3, Sentinel-1, and TerraSAR-X. The results demonstrate that the proposed descriptor is superior to state-of-the-art descriptors and robust for SAR image registration.
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models

Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem structure for improving optimization efficiency remains under-explored. In this paper, we attempt to peek into the black-box of multilingual optimization through the lens of loss function geometry. We find that gradient similarity measured along the optimization trajectory is an important signal, which correlates well with not only language proximity but also the overall model performance. Such observation helps us to identify a critical limitation of existing gradient-based multi-task learning methods, and thus we derive a simple and scalable optimization procedure, named Gradient Vaccine, which encourages more geometrically aligned parameter updates for close tasks. Empirically, our method obtains significant model performance gains on multilingual machine translation and XTREME benchmark tasks for multilingual language models. Our work reveals the importance of properly measuring and utilizing language proximity in multilingual optimization, and has broader implications for multi-task learning beyond multilingual modeling.
Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning

Given the widespread acceptance of computational thinking (CT) in educational systems around the world, primary and higher education has begun thinking about how to cultivate students' CT competences. The artificial intelligence of things (AIoT) combines artificial intelligence (AI) and the Internet of things (IoT) and involves integrating sensing technologies at the lowest level with relevant algorithms in order to solve real-world problems. Thus, it has now become a popular technological application for CT training. In this study, a novel AIoT learning with Augmented Reality (AR) technology was proposed and explored the effect of CT skills. The students used AR applications to understand AIoT applications in practice, attempted the placement of different AR sensors in actual scenarios, and further generalized and designed algorithms. Based on the results of the experimental course, we explored the influence of prior knowledge and usage intention on students' CT competence training. The results show that proposed AIoT learning can increase students' learning intention and that they had a positive impact on problem solving and comprehension with AR technology, as well as application planning and design.
An Image Encryption Algorithm Based on Bisection Method and One-Dimensional Piecewise Chaotic Map

In this article, an image encryption algorithm via bisection method and one-dimensional piecewise chaotic map is proposed.It depends on the permutation-substitution model.Firstly, the pseudorandom sequence is defined using polynomial values at selected points by repeat interval of the bisection method.The plain image is shuffled using this sequence.Bit-wise XOR operation with a mask generated from the Tent chaotic map is applied on shuffled image to increase the protection of the proposed algorithm.Various types of images are tested using the proposed algorithm.The effectiveness of this algorithm is measured using several statistical tests and compared to a set of other algorithms to verify the security and performance of the algorithm.Furthermore, experimental results and Numerical simulations are performed to verify their ability to resist various attacks.
Beetle antennae search without parameter tuning (BAS-WPT) for multi-objective optimization

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.
Mineralogy, Mineral Chemistry and SWIR Spectral Reflectance of Chlorite and White Mica

Hyperspectral reflectance has the potential to provide rapid and low-cost mineralogical and chemical information that can be used to vector in mineral systems. However, the spectral signature of white mica and chlorite, despite numerous studies, is not fully understood. In this study, we review the mineralogy and chemistry of different white mica and chlorite types and investigate what mineralogical and chemical changes are responsible for the apparent shifts in the shortwave infrared (SWIR) spectroscopic absorption features. We demonstrate that the spectral signature of white mica is more complex than previously documented and is influenced by the Tschermak substitution, as well as the sum of interlayer cations. We show that an increase in the interlayer deficiencies towards illite is associated with a change from steep to shallow slopes between the wavelength position of the 2200 nm feature (2200 W) and Mg, Al(VI) and Si. These changes in slope imply that white micas with different elemental chemistry may be associated with the same 2200 W values and vice versa, contrary to traditional interpretation. We recommend that traditional interpretations should only be used in true white mica with sum interlayer cations (I) &gt; 0.95. The spectral signature of trioctahedral chlorite (clinochlore, sheridanite, chamosite and ripidolite) record similar spectral relationships to those observed in previous studies. However, dioctahedral Al-rich chlorite (sudoite, cookeite and donbassite) has a different spectral response with Mg increasing with 2250 W, which is the opposite of traditional trioctahedral chlorite spectral interpretation. In addition, it was shown that dioctahedral chlorite has a 2200 W absorption feature that may introduce erroneous spectral interpretations of white mica and chlorite mixtures. Therefore, care should be used when interpreting the spectral signature of chlorite. We recommend that spectral studies should be complemented with electron microprobe analyses on a subset of at least 30 samples to identify the type of muscovite and chlorite. This will allow the sum I of white mica to be obtained, as well as estimate the slope of 2200 W absorption trends with Mg, Al(vi), and Si. Preliminary probe data will allow more accurate spectral interpretations and allow the user to understand the limitations in their hyperspectral datasets.
Towards improved breast mass detection using dual-view mammogram matching

Breast cancer screening benefits from the visual analysis of multiple views of routine mammograms. As for clinical practice, computer-aided diagnosis (CAD) systems could be enhanced by integrating multi-view information. In this work, we propose a new multi-tasking framework that combines craniocaudal (CC) and mediolateral-oblique (MLO) mammograms for automatic breast mass detection. Rather than addressing mass recognition only, we exploit multi-tasking properties of deep networks to jointly learn mass matching and classification, towards better detection performance. Specifically, we propose a unified Siamese network that combines patch-level mass/non-mass classification and dual-view mass matching to take full advantage of multi-view information. This model is exploited in a full image detection pipeline based on You-Only-Look-Once (YOLO) region proposals. We carry out exhaustive experiments to highlight the contribution of dual-view matching for both patch-level classification and examination-level detection scenarios. Results demonstrate that mass matching highly improves the full-pipeline detection performance by outperforming conventional single-task schemes with 94.78% as Area Under the Curve (AUC) score and a classification accuracy of 0.8791. Interestingly, mass classification also improves the performance of mass matching, which proves the complementarity of both tasks. Our method further guides clinicians by providing accurate dual-view mass correspondences, which suggests that it could act as a relevant second opinion for mammogram interpretation and breast cancer diagnosis.
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Spatially adaptive normalization layers fuse both inputs and are applied to a full-blown generator architecture consisting of encoder and decoder to take full advantage of the information content in the auxiliary raster data. Our method successfully synthesizes medium (10 m) and high (1 m) resolution images when trained with the corresponding data set. We show the advantage of data fusion of land cover maps and auxiliary information using mean intersection over unions (mIoUs), pixel accuracy, and Fréchet inception distances (FIDs) using pretrained U-Net segmentation models. Handpicked images exemplify how fusing information avoids ambiguities in the synthesized images. By slightly editing the input, our method can be used to synthesize realistic changes, i.e., raising the water levels. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gbaier/rs_img_synth</uri> , and we published the newly created high-resolution data set at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ieee-dataport.org/open-access/geonrw</uri> .
Opportunities and Challenges of M-Learning During the COVID-19 Pandemic

The purpose of this study is to explore the opportunities and challenges of m-learning (mobile learning) during COVID-19 pandemic. The study identified how students accepted the m-learning tools due to the pandemic with noticeable challenges and threats, revealing in particular an increased tendency to use smart technologies and apps to collaborate online learning with mobile devices. The study is an enhanced version which utilized mixed methodology research including a survey questionnaire and a focus group interview from university teachers and students. Results from statistical analysis and focus group interview revealed that university students have numerous opportunities besides some noticeable challenges. The study also highlights the education sustainability during COVID-19 pandemic due to m-learning technology.
A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text

Deep learning has emerged as a new area of machine learning research. It is an approach that can learn features and hierarchical representation purely from data and has been successfully applied to several fields such as images, sounds, text and motion. The techniques developed from deep learning research have already been impacting the research on Natural Language Processing (NLP). Arabic diacritics are vital components of Arabic text that remove ambiguity from words and reinforce the meaning of the text. In this paper, a Deep Belief Network (DBN) is used as a diacritizer for Arabic text. DBN is an algorithm among deep learning that has recently proved to be very effective for a variety of machine learning problems. We evaluate the use of DBNs as classifiers in automatic Arabic text diacritization. The DBN was trained to individually classify each input letter with the corresponding diacritized version. Experiments were conducted using two benchmark datasets, the LDC ATB3 and Tashkeela. Our best settings achieve a DER and WER of 2.21% and 6.73%, receptively, on the ATB3 benchmark with an improvement of 26% over the best published results. On the Tashkeela benchmark, our system continues to achieve high accuracy with a DER of 1.79% and 14% improvement.
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning

Fine-tuned language models have been shown to exhibit biases against protected groups in a host of modeling tasks such as text classification and coreference resolution.Previous works focus on detecting these biases, reducing bias in data representations, and using auxiliary training objectives to mitigate bias during fine-tuning.Although these techniques achieve bias reduction for the task and domain at hand, the effects of bias mitigation may not directly transfer to new tasks, requiring additional data collection and customized annotation of sensitive attributes, and re-evaluation of appropriate fairness metrics.We explore the feasibility and benefits of upstream bias mitigation (UBM) for reducing bias on downstream tasks, by first applying bias mitigation to an upstream model through fine-tuning and subsequently using it for downstream finetuning.We find, in extensive experiments across hate speech detection, toxicity detection, occupation prediction, and coreference resolution tasks over various bias factors, that the effects of UBM are indeed transferable to new downstream tasks or domains via finetuning, creating less biased downstream models than directly fine-tuning on the downstream task or transferring from a vanilla upstream model.Though challenges remain, we show that UBM promises more efficient and accessible bias mitigation in LM fine-tuning. 12