Reviewerly/exHarmony huggingface dataset:
The future challenges of scientific and technical higher education

The world is experiencing significant changes, including exponential growth of the global population, global warming and climate change, biodiversity loss, international migration, digitalization, smart agriculture/farming, synthetic biology, and most recently a global human health pandemic. These trends pose a set of relevant challenges for the training of new graduates as well as for the re-skilling of current workers through lifelong learning programs. Our paper seeks to answer two research questions: (1) are current study programs suitable to prepare students for their professional future and (2) are study programs adequate to deliver the needs of current and new generations of students? We analyzed the professional figures and the skills required by the job market, as well as the number of students enrolled in technical-scientific HE study programs in Europe. We discuss the needs of future students considering how the teaching tools and methods enabled by digitalization might contribute to increasing the effectiveness of training these students. Finally, we shed light on the different types of HE study programs that can meet the educational challenges of the future.&#x0D; Received: 23 June 2020Accepted: 04 May 2021
Noise classification for the unified earthquake catalog using ensemble learning: the enhanced image of seismic activity along the Japan Trench by the S-net seafloor network

Abstract Homogeneous and accurate hypocenter distribution along the Japan Trench is important to better understand the process of plate subduction and the occurrence mode of large earthquakes. The seafloor seismic network (S-net) deployed recently along the Japan Trench has revealed new seismic activity including shallow slow earthquakes. However, conventional microseismic observations, such as those reported in the Japan Meteorological Agency (JMA) unified earthquake catalog, have been limited to land-based stations. Thus, steady seismic activity occurring at the shallow plate boundary, which is far from land is not always recorded. In the present study, we construct an automatic earthquake catalog using the nationwide observation network, including S-net. Because false detections caused by noise account for about 5% of automatically determined hypocenters, we attempted to reduce false detections using ensemble learning methods such as random forest and AdaBoost. First, we created a training dataset of earthquakes and noise by visual inspection based on the data recorded in the automatically determined catalog over a 2-month period, and we trained the dataset using the hypocenter and phase data as input. As a result of the training, AdaBoost was able to reduce the noise to about one-fifth of the total false detections, which is equivalent to 1%, while keeping the number of hypocenters above 99%. This method will contribute to significantly improving the efficiency of seismic activity monitoring and cataloging. In addition, the automatic denoised catalog data revealed that from January to August 2020, the completeness magnitude was M 1.6 along the Japan Trench. These microearthquakes were concentrated at depths of 20–50 km around the upper surface of the subducting Pacific Plate and are complementary to the slow earthquakes occurring at 10–20-km depths. Exceptionally, microearthquakes were observed off Iwate and Ibaraki prefectures, which correspond in location to areas of clustered foreshock activity. This spatial heterogeneity in microseismic activity is similar to the spatial complementary between the coseismic slip and afterslip of the 2011 Tohoku earthquake, which may be related to differences in interplate frictional properties and stress changes in the surrounding crust.
POS-Tagging based Neural Machine Translation System for European Languages using Transformers

The interaction between human beings has always faced different kinds of difficulties. One of those difficulties is the language barrier. It would be a tedious task for someone to learn all the syllables in a new language in a short period and converse with a native speaker without grammatical errors. Moreover, having a language translator at all times would be intrusive and expensive. We propose a novel approach to Neural Machine Translation (NMT) system using interlanguage word similaritybased model training and Part-Of-Speech (POS) Tagging based model testing. We compare these approaches using two classical architectures: Luong Attention-based Sequence-to-Sequence architecture and Transformer based model. The sentences for the Luong Attention-based Sequence-to-Sequence were tokenized using SentencePiece tokenizer. The sentences for the Transformer model were tokenized using Subword Text Encoder. Three European languages were selected for modeling, namely, Spanish, French, and German. The datasets were downloaded from multiple sources such as Europarl Corpus, Paracrawl Corpus, and Tatoeba Project Corpus. Sparse Categorical CrossEntropy was the evaluation metric during the training stage, and during the testing stage, the Bilingual Evaluation Understudy (BLEU) Score, Precision Score, and Metric for Evaluation of Translation with Explicit Ordering (METEOR) score were the evaluation metrics.
A Consumer Behavior Prediction Model Based on Multivariate Real-Time Sequence Analysis

With the rapid development of online finance and social networks, a large amount of behavioral data is stored on the Internet, which can fully reflect the shopping tendencies and habits of real users. Using big data to analyze consumer behavior is more scientific and accurate than the traditional sampling survey method. Internet consumption behavior data are time series data. Therefore, this paper proposes a method of analyzing behavioral sequence data, which learns personal consumption interests and habits, and finally predicts payment behavior. The experiments compare the execution effect of different algorithms on multiple databases and verify the feasibility and effectiveness of the proposed algorithm SeqLearn.
CoCo 2019: report on the eighth confluence competition

Abstract We report on the 2019 edition of the Confluence Competition, a competition of software tools that aim to prove or disprove confluence and related (undecidable) properties of rewrite systems automatically.
Fast decentralized non-convex finite-sum optimization with recursive variance reduction

This paper considers decentralized minimization of $N:=nm$ smooth non-convex cost functions equally divided over a directed network of $n$ nodes. Specifically, we describe a stochastic first-order gradient method, called GT-SARAH, that employs a SARAH-type variance reduction technique and gradient tracking (GT) to address the stochastic and decentralized nature of the problem. We show that GT-SARAH, with appropriate algorithmic parameters, finds an $\epsilon$-accurate first-order stationary point with $O\big(\max\big\{N^{\frac{1}{2}},n(1-\lambda)^{-2},n^{\frac{2}{3}}m^{\frac{1}{3}}(1-\lambda)^{-1}\big\}L\epsilon^{-2}\big)$ gradient complexity, where ${(1-\lambda)\in(0,1]}$ is the spectral gap of the network weight matrix and $L$ is the smoothness parameter of the cost functions. This gradient complexity outperforms that of the existing decentralized stochastic gradient methods. In particular, in a big-data regime such that ${n = O(N^{\frac{1}{2}}(1-\lambda)^{3})}$, this gradient complexity furthers reduces to ${O(N^{\frac{1}{2}}L\epsilon^{-2})}$, independent of the network topology, and matches that of the centralized near-optimal variance-reduced methods. Moreover, in this regime GT-SARAH achieves a non-asymptotic linear speedup, in that, the total number of gradient computations at each node is reduced by a factor of $1/n$ compared to the centralized near-optimal algorithms that perform all gradient computations at a single node. To the best of our knowledge, GT-SARAH is the first algorithm that achieves this property. In addition, we show that appropriate choices of local minibatch size balance the trade-offs between the gradient and communication complexity of GT-SARAH. Over infinite time horizon, we establish that all nodes in GT-SARAH asymptotically achieve consensus and converge to a first-order stationary point in the almost sure and mean-squared sense.
A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance

The Wasserstein distance has become increasingly important in machine learning and deep learning. Despite its popularity, the Wasserstein distance is hard to approximate because of the curse of dimensionality. A recently proposed approach to alleviate the curse of dimensionality is to project the sampled data from the high dimensional probability distribution onto a lower-dimensional subspace, and then compute the Wasserstein distance between the projected data. However, this approach requires to solve a max-min problem over the Stiefel manifold, which is very challenging in practice. The only existing work that solves this problem directly is the RGAS (Riemannian Gradient Ascent with Sinkhorn Iteration) algorithm, which requires to solve an entropy-regularized optimal transport problem in each iteration, and thus can be costly for large-scale problems. In this paper, we propose a Riemannian block coordinate descent (RBCD) method to solve this problem, which is based on a novel reformulation of the regularized max-min problem over the Stiefel manifold. We show that the complexity of arithmetic operations for RBCD to obtain an $\epsilon$-stationary point is $O(\epsilon^{-3})$. This significantly improves the corresponding complexity of RGAS, which is $O(\epsilon^{-12})$. Moreover, our RBCD has very low per-iteration complexity, and hence is suitable for large-scale problems. Numerical results on both synthetic and real datasets demonstrate that our method is more efficient than existing methods, especially when the number of sampled data is very large.
Measuring Software Delivery Performance Using the Four Key Metrics of DevOps

Abstract The Four Key Metrics of DevOps have become very popular for measuring IT-performance and DevOps adoption. However, the measurement of the four metrics deployment frequency, lead time for change, time to restore service and change failure rate is often done manually and through surveys - with only few data points. In this work we evaluated how the Four Key Metrics can be measured automatically and developed a prototype for the automatic measurement of the Four Key Metrics. We then evaluated if the measurement is valuable for practitioners in a company. The analysis shows that the chosen measurement approach is both suitable and the results valuable for the team with respect to measuring and improving the software delivery performance.
Natural Disaster Mitigation on Elementary School Teachers: Knowledge, Attitude, and Practices

This study aims to analyze the level of knowledge, attitudes, and practice of teachers in disaster education in elementary schools. This study uses questionnaires distributed to teachers in disaster-prone schools. The data taken are age, gender, subjects taught, recent education, experience in disaster education training, knowledge level, attitude, and practice of disaster education. Respondents who have a good knowledge level are 51.5%, enough knowledge level is 39.4%, and less knowledge level is 9.1%. As many as 98.9% of respondents have a positive attitude and 1.1% have a negative attitude. There are 90.9% of respondents have ever taught and 9.1% have never taught disaster education. This study concludes that teachers of Elementary in disaster-prone schools generally have good knowledge of disaster education, agree that disaster education becomes part of the curriculum, and have taught disaster education to students.
Single Axis Solar Tracker for Maximizing Power Production and Sunlight Overlapping Removal on the Sensors of Tracker

This paper presents the design and execution of a solar tracker system devoted to photovoltaic (PV) conversion panels. The proposed single-axis solar tracker is shifted automatically based on the sunlight detector or tracking sensor. This system also removes incident sunlight overlapping from sensors that are inside the sunlight tracking system. The Light Dependent Resistor (LDR) is used as a sensor to sense the intensity of light accurately. The sensors are placed at a certain distance from each other in the tracker system to avoid sunlight overlapping for maximum power production. The total system is designed by using a microcontroller (PIC16F877A) as a brain to control the whole system. The solar panel converts sunlight into electricity. The PV panel is fixed with a vertical axis of the tracker. This microcontroller will compare the data and rotate a solar panel via a stepper motor in the right direction to collect maximum photon energy from sunlight. From the experimental results, it can be determined that the automatic (PV solar tracker) sun tracking system is 72.45% more efficient than fixed panels, where the output power of the fixed panel and automatically adjusted panel are 8.289 watts and 14.287 watts, respectively.
Parameter estimation and model-based clustering with spherical normal distribution on the unit hypersphere

In directional statistics, the von Mises-Fisher (vMF) distribution is one of the most basic and popular probability distributions for data on the unit hypersphere. Recently, the spherical normal (SN) distribution was proposed as an intrinsic counterpart to the vMF distribution by replacing the standard Euclidean norm with the great-circle distance, which is length of the shortest path joining two points on the unit sphere. Focusing on an isotropic version of SN distribution, it is shown that maximum likelihood estimators uniquely exist under mild support conditions. Since no analytic formula are available for the estimation, efficient numerical routines are proposed for parameter estimation. The estimation is considered in a general setting where non-negative weights are assigned to observations. This leads to a more interesting contribution for model-based clustering on the unit hypersphere by finite mixture model with SN distributions. Efficiency of optimization-based estimation procedures and effectiveness of SN mixture model are validated using simulated and real data examples.
C3HSB: A Transparent Supply Chain for Multi-cloud and Hybrid Cloud Assets Powered by Blockchain

Blockchain networks have made inroads in the supply chain domains due to their transparency, security and immutable logs. For cloud providers, a typical supply chain consists of complex networks of suppliers, manufacturers, distributors, retailers, auditors, and consumers. Even though there is an increased focus on self-serve or cloud provider managed SaaS (Software-as-a-service), a portion of sales for an enterprise customer occurs the old-fashioned way with the sales department drawing up a purchase order to begin the procurement process. In many cases, there could be several disjoint suppliers who fulfill the order behind the scenes unbeknownst to the buyer. In this paper we propose C3HSB, a decentralized distributed application architecture using blockchain and smart contracts to build the supply chain. Motivated by challenges in the industry, this approach can be considered as a building block for future supply chain procurement and deployment of cloud assets. The design goal for our system was to facilitate efficient handling of enterprise-scale purchase orders and alternatives in real-time while maintaining data provenance. C3HSB streamlines complex non-repudiated transaction workflows, promotes trust, and achieves cost savings for the customer and suppliers.
Constructing Taxonomies from Pretrained Language Models

We present a method for constructing taxonomic trees (e.g., WordNet) using pretrained language models. Our approach is composed of two modules, one that predicts parenthood relations and another that reconciles those predictions into trees. The parenthood prediction module produces likelihood scores for each potential parent-child pair, creating a graph of parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and outputs the maximum spanning tree of this graph. We train our model on subtrees sampled from WordNet, and test on non-overlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance. On the task of constructing subtrees of English WordNet, the model achieves 66.7 ancestor F1, a 20.0% relative increase over the previous best published result on this task. In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages.
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces

Modern reinforcement learning (RL) commonly engages practical problems with large state spaces, where function approximation must be deployed to approximate either the value function or the policy. While recent progresses in RL theory address a rich set of RL problems with general function approximation, such successes are mostly restricted to the single-agent setting. It remains elusive how to extend these results to multi-agent RL, especially due to the new challenges arising from its game-theoretical nature. This paper considers two-player zero-sum Markov Games (MGs). We propose a new algorithm that can provably find the Nash equilibrium policy using a polynomial number of samples, for any MG with low multi-agent Bellman-Eluder dimension -- a new complexity measure adapted from its single-agent version (Jin et al., 2021). A key component of our new algorithm is the exploiter, which facilitates the learning of the main player by deliberately exploiting her weakness. Our theoretical framework is generic, which applies to a wide range of models including but not limited to tabular MGs, MGs with linear or kernel function approximation, and MGs with rich observations.
Online Learning with the Eyes of Teachers and Students in Educational Institutions of Ukraine

The article presents an analysis of the attitude of teachers and students, colleges and universities to distance learning during the 2020 in Ukraine. The factors of the success of online learning were analyzed. The main stress factors were identified. Analysis of the results of the dynamics of opinions led to some conclusions and recommendations: for teachers regarding self-development; for school administrations regarding technical, organizational, and moral support for all participants in the educational process; for universities that provide professional training for future teachers.
Understanding Softmax Confidence and Uncertainty

It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution. Yet naively using softmax confidence as a proxy for uncertainty achieves modest success in tasks exclusively testing for this, e.g., out-of-distribution (OOD) detection. This paper investigates this contradiction, identifying two implicit biases that do encourage softmax confidence to correlate with epistemic uncertainty: 1) Approximately optimal decision boundary structure, and 2) Filtering effects of deep networks. It describes why low-dimensional intuitions about softmax confidence are misleading. Diagnostic experiments quantify reasons softmax confidence can fail, finding that extrapolations are less to blame than overlap between training and OOD data in final-layer representations. Pre-trained/fine-tuned networks reduce this overlap.
Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study

Background The secondary use of structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high dimensionality of the data representation. Constructing an effective representation for sEMR data is becoming more and more crucial for subsequent data applications. Objective We aimed to apply the embedding technique used in the natural language processing domain for the sEMR data representation and to explore the feasibility and superiority of the embedding-based feature and patient representations in clinical application. Methods The entire training corpus consisted of records of 104,752 hospitalized patients with 13,757 medical concepts of disease diagnoses, physical examinations and procedures, laboratory tests, medications, etc. Each medical concept was embedded into a 200-dimensional real number vector using the Skip-gram algorithm with some adaptive changes from shuffling the medical concepts in a record 20 times. The average of vectors for all medical concepts in a patient record represented the patient. For embedding-based feature representation evaluation, we used the cosine similarities among the medical concept vectors to capture the latent clinical associations among the medical concepts. We further conducted a clustering analysis on stroke patients to evaluate and compare the embedding-based patient representations. The Hopkins statistic, Silhouette index (SI), and Davies-Bouldin index were used for the unsupervised evaluation, and the precision, recall, and F1 score were used for the supervised evaluation. Results The dimension of patient representation was reduced from 13,757 to 200 using the embedding-based representation. The average cosine similarity of the selected disease (subarachnoid hemorrhage) and its 15 clinically relevant medical concepts was 0.973. Stroke patients were clustered into two clusters with the highest SI (0.852). Clustering analyses conducted on patients with the embedding representations showed higher applicability (Hopkins statistic 0.931), higher aggregation (SI 0.862), and lower dispersion (Davies-Bouldin index 0.551) than those conducted on patients with reference representation methods. The clustering solutions for patients with the embedding-based representation achieved the highest F1 scores of 0.944 and 0.717 for two clusters. Conclusions The feature-level embedding-based representations can reflect the potential clinical associations among medical concepts effectively. The patient-level embedding-based representation is easy to use as continuous input to standard machine learning algorithms and can bring performance improvements. It is expected that the embedding-based representation will be helpful in a wide range of secondary uses of sEMR data.
The Self-Checking Concurrent Error-Detection Systems Synthesis Based on the Boolean Complement to the Bose-Lin Codes with the Modulo Value M=4

The presented paper is devoted to the development of the Boolean complement method for the organization of the self-checking concurrent error-detection (CED) systems for digital devices. The article considers the features of using the modular sum codes (Bose-Lin codes) for these purposes, especially the Bose-Lin code by the modulo M = 4. This code has two check bits and only four different check vectors, this makes it easier to use it in the organization of the self-checking CED system. The article presents the block diagrams of the organization of the CED system by the method of Boolean complement to the considered modular sum code. The examples of the CED system synthesis by the Boolean complement method are given. The article defines the restrictions imposed on the CED systems synthesis procedure, and also forms an algorithm for synthesizing a self-checking CED systems by the method of the Boolean complement to the Bose-Lin code by the modulo M = 4.