Reviewerly/exHarmony huggingface dataset:
Learning similarity measures from data

Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.
Análisis de la autopercepción sobre el nivel de competencia digital docente en la formación inicial de maestros/as.

La competencia digital en la formación inicial docente es esencial en la actualidad ya que los estudiantes de enseñanza básica están en frecuente contacto con las nuevas tecnologías. Por tanto, sería conveniente que los futuros docentes acabasen el grado con una competencia digital óptima. Los objetivos de este estudio se centran en analizar el grado de competencia digital docente en el alumnado de Grado de Educación Infantil y Primaria mediante el modelo del “Marco Común de Competencia Digital Docente” del INTEF (2017b). La metodología seguida ha sido de carácter cuantitativo observacional. Para ello, se ha utilizado un cuestionario online basado en dicho modelo, en el que han participado un total de 117 estudiantes de 4º curso de los Grados de Maestro en Ed. Infantil y Ed. Primaria de la Facultad de Educación de Albacete. Los resultados adquiridos muestran un nivel básico en las áreas estudiadas en ambas titulaciones tanto al inicio como al final del grado. Finalmente, se concluye que se hace necesaria la formación en TIC en los títulos del Grado para la adaptación de los futuros maestros y maestras a las necesidades del alumnado de Educación Infantil y Primaria. Nowadays, digital competence in the initial training is essential since elementary students are often in touch with ICT. Therefore, it would be advisable that future teachers finish the degree with optimal digital competence. This study aims at analyzing teachers’ digital competence in students of teacher training degrees through the INTEF model (2017) “Common Framework for Teachers’ Digital Competence”. We followed a quantitative and observational methodology. For this purpose, an online questionnaire based on this model was used and 117 students participated. These students were mainly 4th year Early-Childhood and Primary Education students in the Faculty of Education. The results achieved show a basic level in the areas studied both in Early-Childhood and Primary, as well as at the beginning and end of their Education. To conclude, the inclusion of ICT training is necessary in training future Early-Childhood and Primary teachers in order to satisfy the needs of their students.
A Joint Learning Framework With BERT for Spoken Language Understanding

Intent classification and slot filling are two essential tasks for spoken language understanding.Recently, joint learning has been shown to be effective for the two tasks.However, most joint learning methods only consider joint learning using shared parameters on the surface level rather than the semantic level, and these methods suffer from small-scale human-labeled training data, resulting in poor generalization capabilities, especially for rare words.In this paper, we propose a novel encoder-decoder framework based multi-task learning model, which conducts joint training for intent classification and slot filling tasks.For the encoder of our model, we encode the input sequence as context representations using bidirectional encoder representation from transformers (BERT).For the decoder, we implement two-stage decoder process in our model.In the first stage, we use an intent classification decoder to detect the user's intent.In the second stage, we leverage the intent contextual information into the slot filling decoder to predict the semantic concept tags for each word.We conduct experiments on three popular benchmark datasets: ATIS, Snips and Facebook multilingual task-oriented datasets.The experimental results show that our proposed model outperforms the state-of-the-art approaches and achieves new state-of-the-art results on both three datasets.
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks

Recent theoretical work has established connections between over-parametrized neural networks and linearized models governed by he Neural Tangent Kernels (NTKs). NTK theory leads to concrete convergence and generalization results, yet the empirical performance of neural networks are observed to exceed their linearized models, suggesting insufficiency of this theory. Towards closing this gap, we investigate the training of over-parametrized neural networks that are beyond the NTK regime yet still governed by the Taylor expansion of the network. We bring forward the idea of \emph{randomizing} the neural networks, which allows them to escape their NTK and couple with quadratic models. We show that the optimization landscape of randomized two-layer networks are nice and amenable to escaping-saddle algorithms. We prove concrete generalization and expressivity results on these randomized networks, which lead to sample complexity bounds (of learning certain simple functions) that match the NTK and can in addition be better by a dimension factor when mild distributional assumptions are present. We demonstrate that our randomization technique can be generalized systematically beyond the quadratic case, by using it to find networks that are coupled with higher-order terms in their Taylor series.
The delivery drones are coming

When Amazon made public its plans to deliver packages by drone six years ago, many skeptics scoffed-including some at this magazine. It just didn't seem safe or practical to have tiny buzzing robotic aircraft crisscrossing the sky with Amazon orders. Today, views on the prospect of getting stuff swiftly whisked to you this way have shifted, in part because some packages are already being delivered by drone, including examples in Europe, Australia, and Africa, sometimes with life-saving consequences. In 2020, we should see such operations multiply, even in the strictly regulated skies over the United States.
A patient-similarity-based model for diagnostic prediction

To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically and predict diagnoses by the similar/dissimilar patients. We proposed a novel patient-similarity-based framework for diagnostic prediction, which is inspired by the structure-mapping theory about analogy reasoning in psychology. Patient similarity is defined as the similarity between two patients’ diagnoses sets rather than a dichotomous (absence/presence of just one disease). The multilabel classification problem is converted to a single-value regression problem by integrating the pairwise patients’ clinical features into a vector and taking the vector as the input and the patient similarity as the output. In contrast to the common k-NN method which only considering the nearest neighbors, we not only utilize similar patients (positive analogy) to generate diagnostic hypotheses, but also utilize dissimilar patients (negative analogy) are used to reject diagnostic hypotheses. The patient-similarity-based models perform better than the one-vs-all baseline and traditional k-NN methods. The f-1 score of positive-analogy-based prediction is 0.698, significantly higher than the scores of baselines ranging from 0.368 to 0.661. It increases to 0.703 when the negative analogy method is applied to modify the prediction results of positive analogy. The performance of this method is highly promising for larger datasets. The patient-similarity-based model provides diagnostic decision support that is more accurate, generalizable, and interpretable than those of previous methods and is based on heterogeneous and incomplete data. The model also serves as a new application for the use of clinical big data through artificial intelligence technology.
Multimodal hand gesture recognition using single IMU and acoustic measurements at wrist

To facilitate hand gesture recognition, we investigated the use of acoustic signals with an accelerometer and gyroscope at the human wrist. As a proof-of-concept, the prototype consisted of 10 microphone units in contact with the skin placed around the wrist along with an inertial measurement unit (IMU). The gesture recognition performance was evaluated through the identification of 13 gestures used in daily life. The optimal area for acoustic sensor placement at the wrist was examined using the minimum redundancy and maximum relevance feature selection algorithm. We recruited 10 subjects to perform over 10 trials for each set of hand gestures. The accuracy was 75% for a general model with the top 25 features selected, and the intra-subject average classification accuracy was over 80% with the same features using one microphone unit at the mid-anterior wrist and an IMU. These results indicate that acoustic signatures from the human wrist can aid IMU sensing for hand gesture recognition, and the selection of a few common features for all subjects could help with building a general model. The proposed multimodal framework helps address the single IMU sensing bottleneck for hand gestures during arm movement and/or locomotion.
Deep Convolutional Neural Network Based Object Detector for X-Ray Baggage Security Imagery

Detection and classification of X-ray baggage security imagery are of great significance not only in daily life. However, up to now, it is still difficult for the traditional image processing methods to detect and distinguish objects which we want to detect in X-Ray baggage security imagery. Moreover, it is even more challenging to classify different types of objects. Although the current state-of-the-art detectors have achieved impressive performance on various public datasets with visible images, these detectors fail to deal with objects in X-ray images. This paper proposes an object detection algorithm for X-Ray baggage security screening images. Firstly, to outline the detected object from X-Ray baggage security imagery, we propose a fore-ground-background segmentation method which based on color information. Then, to classify and outline different type of object in X-Ray im-age, a deep convolutional neural networks (DCNNs) based object detection framework Faster R-CNN are proposed. In this stage we also use transfer learning method to speed up Faster R-CNN. The proposed method is proved to achieve 77% mAP by in a real-word dataset which contains 32,253 sub-way X-Ray baggage security screening images.
Learning and Fusing Multiple User Interest Representations for Micro-Video and Movie Recommendations

Deep learning is known to be effective at automating the generation of representations, which eliminates the need for handcrafted features. For the task of personalized recommendation, deep learning-based methods have achieved great success by learning efficient representations of multimedia items, especially images and videos. Previous works usually adopt simple, single-modality representations of user interest, such as user embeddings, which cannot fully characterize the diversity and volatility of user interest. To address this problem, in this paper we focus on learning and fusing multiple kinds of user interest representations by leveraging deep networks. Specifically, we consider efficient representations of four aspects of user interest: first, we use latent representation, i.e. user embedding, to profile the overall interest; second, we propose item-level representation, which is learned from and integrates the features of a user's historical items; third, we investigate neighbor-assisted representation, i.e. using neighboring users' information to characterize user interest collaboratively; fourth, we propose category-level representation, which is learned from the categorical attributes of a user's historical items. In order to integrate these multiple user interest representations, we study both early fusion and late fusion; where for early fusion, we study different fusion functions. We validate the proposed method on two real-world video recommendation datasets for micro-video and movie recommendations, respectively. Experimental results demonstrate that our method outperforms existing state-of-the-arts by a significant margin. Our code is publicly available.
A New Model of Automatic and Continuous Online Exam Monitoring

Student cheating during exams is a widespread phenomenon around the world, regardless of the country's development level. With the expansion of e-learning, exam monitoring becomes more difficult to control. Therefore, real-time monitoring is necessary to secure the student's identity continuously throughout the evaluation period. As a result, we present in this article an online exam management system that provides automatic and continuous monitoring. The implemented solution uses face recognition for a strong student authentication. In order to increase the proposed system's performance, we have defined several parameters to detect any fraud behavior during the whole time that the candidate is using the exam management system.
Development of a Human Activity Recognition System for Ballet Tasks

Abstract Background Accurate and detailed measurement of a dancer’s training volume is a key requirement to understanding the relationship between a dancer’s pain and training volume. Currently, no system capable of quantifying a dancer’s training volume, with respect to specific movement activities, exists. The application of machine learning models to wearable sensor data for human activity recognition in sport has previously been applied to cricket, tennis and rugby. Thus, the purpose of this study was to develop a human activity recognition system using wearable sensor data to accurately identify key ballet movements (jumping and lifting the leg). Our primary objective was to determine if machine learning can accurately identify key ballet movements during dance training. The secondary objective was to determine the influence of the location and number of sensors on accuracy. Results Convolutional neural networks were applied to develop two models for every combination of six sensors (6, 5, 4, 3, etc.) with and without the inclusion of transition movements. At the first level of classification, including data from all sensors, without transitions, the model performed with 97.8% accuracy. The degree of accuracy reduced at the second (83.0%) and third (75.1%) levels of classification. The degree of accuracy reduced with inclusion of transitions, reduction in the number of sensors and various sensor combinations. Conclusion The models developed were robust enough to identify jumping and leg lifting tasks in real-world exposures in dancers. The system provides a novel method for measuring dancer training volume through quantification of specific movement tasks. Such a system can be used to further understand the relationship between dancers’ pain and training volume and for athlete monitoring systems. Further, this provides a proof of concept which can be easily translated to other lower limb dominant sporting activities
The Measure-and-Reprogram Technique 2.0: Multi-round Fiat-Shamir and More

We revisit recent works by Don, Fehr, Majenz and Schaffner and by Liu and Zhandry on the security of the Fiat-Shamir (FS) transformation of $$\varSigma $$ -protocols in the quantum random oracle model (QROM). Two natural questions that arise in this context are: (1) whether the results extend to the FS transformation of multi-round interactive proofs, and (2) whether Don et al.’s $$O(q^2)$$ loss in security is optimal. Firstly, we answer question (1) in the affirmative. As a byproduct of solving a technical difficulty in proving this result, we slightly improve the result of Don et al., equipping it with a cleaner bound and an even simpler proof. We apply our result to digital signature schemes showing that it can be used to prove strong security for schemes like MQDSS in the QROM. As another application we prove QROM-security of a non-interactive OR proof by Liu, Wei and Wong. As for question (2), we show via a Grover-search based attack that Don et al.’s quadratic security loss for the FS transformation of $$\varSigma $$ -protocols is optimal up to a small constant factor. This extends to our new multi-round result, proving it tight up to a factor depending on the number of rounds only, i.e. is constant for constant-round interactive proofs.
A Critical Review of Concepts, Benefits, and Pitfalls of Blockchain Technology Using Concept Map

Blockchain is relatively a new area of research. However, a surge of research studies on the blockchain has taken place in recent years. These research studies have mostly focused on designing and developing conceptual frameworks to build more reliable, transparent and efficient digital systems. While blockchain brings a wide variety of benefits, it also imposes certain challenges. Therefore, the objective of this research is to understand the properties of blockchain, its current uses, observed benefits and pitfalls to provide a balanced understanding of blockchain. A systematic literature review approach was adopted in this paper in order to attain the objective. A total of 51 articles were selected and reviewed. As outcomes, this research provides a summary of the state-of-the-art research studies conducted in the area of blockchain. Furthermore, we develop a set of concept maps aiming to provide in-depth knowledge on blockchain technology for its efficient and effective usage in the development of future technological solutions.
The Neural Network Zoo

An overview of neural network architectures is presented. Some of these architectures have been created in recent years, whereas others originate from many decades ago. Apart from providing a practical tool for comparing deep learning models, the Neural Network Zoo also uncovers a taxonomy of network architectures, their chronology, and traces back lineages and inspirations for these neural information processing systems.
A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald-Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist's skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks.
Returning the N to NLP: Towards Contextually Personalized Classification Models

Most NLP models today treat language as universal, even though socio-and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience.This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades of deviation of the NLP tools from sociolingustic findings, allowing to flexibly process the "natural" language of each user rather than enforcing a uniform NLP treatment.It outlines a possible direction to incorporate these aspects into neural NLP models by means of socially contextual personalization, and proposes to shift the focus of our evaluation strategies accordingly.
Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes

A key issue in cluster analysis is the choice of an appropriate clustering method and the determination of the best number of clusters. Different clusterings are optimal on the same data set according to different criteria, and the choice of such criteria depends on the context and aim of clustering. Therefore, researchers need to consider what data analytic characteristics the clusters they are aiming at are supposed to have, among others within-cluster homogeneity, between-clusters separation, and stability. Here, a set of internal clustering validity indexes measuring different aspects of clustering quality is proposed, including some indexes from the literature. Users can choose the indexes that are relevant in the application at hand. In order to measure the overall quality of a clustering (for comparing clusterings from different methods and/or different numbers of clusters), the index values are calibrated for aggregation. Calibration is relative to a set of random clusterings on the same data. Two specific aggregated indexes are proposed and compared with existing indexes on simulated and real data.
When Bitcoin has the flu: on Bitcoin’s performance to hedge equity risk in the early wake of the COVID-19 outbreak

Using the coronavirus COVID-19 outbreak as a set-up for a quasi-experiment, this study derives novel insights on the dynamic correlation between Bitcoin and US stocks. Given the unprecedented scale of infections and the nature of the virus, the potential impact on the dynamic correlation was unpredictable and therefore uncertain. Using a difference-in-differences setting, the dynamic correlation between Bitcoin and stocks is controlled for the dynamic correlation between gold and stocks. This study finds that Bitcoin performed poorly in hedging this tail risk.