Reviewerly/exHarmony huggingface dataset:
Learning Continuity during COVID-19 Pandemic using the Virtual Classroom – A Cross-border experimental Multi Case Approach

Aim. The global pandemic of the COVID-19 outbreak and the subsequent nationwide lockdown have put the endeavours of both academicians and students to the test. The disruptions are even visible in andragogical approaches across higher education institutions both nationally and internationally. Two government-funded universities in India and Bangladesh have decided to explore avenues to switch to an alternative e-platform overcoming the challenges of traditional classroom teaching. The business management faculties of both universities exercise blended learning modules using a design thinking approach.&#x0D; Methods. This exploratory case study involves university students and teachers reflecting the knowledge delivery mechanism in a collaborative online workspace called Virtual Classroom. The authors have adopted the case method to identify evidence-based practices to improve learning gain. This case is motivated by the profound idea of learning continuity where the agenda of teaching-learning should remain status quo as much as possible, even under the constrained situation.&#x0D; Results and Conclusion. This study confirms that the use of curriculum-based blended learning modules based on a design thinking approach is gaining momentum to offer courses in which videoconferencing is a useful tool, as an addition to present methods of teaching inside the classroom, to strengthen learning and to inspire students to practice learning. Acceptance of technology has made learning accessible and pervasive, bridging the digital divide.&#x0D; Originality. It is a pioneering effort to measure the effectiveness of the online learning of two public-funded universities across borders with similar demographics and challenges. This case makes an original contribution through its observation that even the traditional and public-funded universities erstwhile functioning through purely offline mode are gradually switching to online mode. This case also highlights the imminent need for policymakers to augment and integrate blended learning tools with design thinking approaches for tertiary education to reduce the intermittent use of technology, thereby assuring higher learning gain for the students.
Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain.Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains.However, this pipeline makes the source data risky and is inflexible for deploying the target model.This paper tackles a novel setting where only a trained source model is available and different network architectures can be adapted for target domain in terms of deployment environments.We propose a generic framework named Crossdomain Knowledge Distillation (CdKD) without needing any source data.CdKD matches the joint distributions between a trained source model and a set of target data during distilling the knowledge from the source model to the target domain.As a type of important knowledge in the source domain, for the first time, the gradient information is exploited to boost the transfer performance.Experiments on cross-domain text classification demonstrate that CdKD achieves superior performance, which verifies the effectiveness in this novel setting.
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference

Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation. While such a transformation is invertible, its Jacobian is very sparse and thus may lack expressiveness. This work presents a simple remedy by noting that subdivision and (affine) coupling can be repeated recursively within the resulting subsets, leading to an efficiently invertible block with dense, triangular Jacobian. By formulating our recursive coupling scheme via a hierarchical architecture, HINT allows sampling from a joint distribution p(y,x) and the corresponding posterior p(x|y) using a single invertible network. We evaluate our method on some standard data sets and benchmark its full power for density estimation and Bayesian inference on a novel data set of 2D shapes in Fourier parameterization, which enables consistent visualization of samples for different dimensionalities.
AutoOD: Neural Architecture Search for Outlier Detection

Outlier detection is an important data mining task with numerous applications such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific task with complex data, the process of building an effective deep learning based system for outlier detection still highly relies on human expertise and laboring trials. Moreover, while Neural Architecture Search (NAS) has shown its promise in discovering effective deep architectures in various domains, such as image classification, object detection and semantic segmentation, contemporary NAS methods are not suitable for outlier detection due to the lack of intrinsic search space and low sample efficiency. To bridge the gap, in this paper, we propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model within a predefined search space. Specifically, we introduce an experience replay mechanism based on self-imitation learning to improve the sample efficiency. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance, comparing with existing handcrafted models and traditional search methods.
Opening Marine Long-Term Ecological Science: Lesson Learned From the LTER-Italy Site Northern Adriatic Sea

This work presents a practical case study of the Open Science principles applied to the valorization of a long-term marine dataset collected in the Northern Adriatic Sea, one of the Long-Term Ecological Research (LTER) sites of the LTER-Italy network. The dataset covers a temporal range of 50 years (1965–2015), and it is composed of abiotic, and phyto- and zooplankton data, for a total of 21 parameters. The case study involved many actions, which will be described here, distinguishing between the ones affecting the whole research project workflow and those acting more specifically on the dataset. We evaluate strengths, weaknesses, and possible improvements for each action. The present study pointed out that, despite the initial and still some remaining mistrust, opening research projects is more than a best practice. It is (i) important because it improves research transparency (increasing researchers’ credibility, replicability of science, and products reuse), (ii) required by many international initiatives and regulations, and (iii) enriching because it encourages cooperation between scientists across different fields and laboratories.
H-FND: Hierarchical False-Negative Denoising for Distant Supervision Relation Extraction

Although distant supervision automatically generates training data for relation extraction, it also introduces false-positive (FP) and falsenegative (FN) training instances to the generated datasets.While both types of errors degrade the final model performance, previous work on distant supervision denoising focuses more on suppressing FP noise and less on resolving the FN problem.We here propose H-FND, a hierarchical false-negative denoising framework for robust distant supervision relation extraction, as an FN denoising solution.H-FND uses a hierarchical policy which first determines whether non-relation (NA) instances should be kept, discarded, or revised during the training process.For those learning instances which are to be revised, the policy further reassigns them appropriate relations, making them better training inputs.Experiments on SemEval-2010 and TACRED were conducted with controlled FN ratios that randomly turn the relations of training and validation instances into negatives to generate FN instances.In this setting, H-FND can revise FN instances correctly and maintains high F1 scores even when 50% of the instances have been turned into negatives.Experiments on NYT10 is further conducted to show that H-FND is applicable in a realistic setting.
An Energy Efficient Accelerator for Bidirectional Recurrent Neural Networks (BiRNNs) Using Hybrid-Iterative Compression With Error Sensitivity

Recurrent Neural Networks (RNNs) have been widely used in many sequential applications, such as machine translation, speech recognition and sentiment analysis. Long Term Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are widely used variants of RNN due to their effectiveness in overcoming gradient vanishing and exploding problems; however, compared to conventional RNN, their massive storage and computation requirements hinder their application. In addition, the recurrent structure of RNNs makes them prone to accumulate errors, resulting in a severe loss of accuracy. In this work, we propose a hybrid-iterative compression (HIC) algorithm for LSTM/GRU. By exploiting the error sensitivity of RNN, the gating units are divided into error-sensitive and error-insensitive groups, that are compressed using different algorithms. By using this approach, a 37.1×/32.3× compression ratio is achieved with negligible accuracy loss for LSTM/GRU. Further, an energy efficient accelerator for bidirectional RNNs is proposed. In this accelerator, the data flow of the matrix operation unit based on the block structure matrix (MOU-S) is improved through rearranging weights; the utilization of BRAM is improved through a fine-grained parallelism configuration of matrix-vector multiplications (MVMs). Meanwhile, the timing matching strategy alleviates the load-imbalance problem between MOU-S and the matrix operation unit based on top- k pruning (MOU-P). When running at 200MHz on Xilinx ADM-PCIE-7V3 FPGA, the proposed design achieves an improvement in energy efficiency in a range of 5%-237% for LSTM networks, and an improvement of 58% for GRU networks compared with state-of-the-art designs.
Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning process of mobile robot, this paper proposed a Double BP Q-learning algorithm based on the fusion of Double Q-learning algorithm and BP neural network.In order to solve the dimensional disaster problem, two BP neural network fitting value functions with the same network structure were used to replace the two Q value tables in Double Q-Learning algorithm to solve the problem that the Q value table cannot store excessive state information.By adding the mechanism of priority experience replay and using the parameter transfer to initialize the model parameters in different environments, it could accelerate the convergence rate of the algorithm, improve the learning efficiency and the generalization ability of the model.By designing specific action selection strategy in special environment, the deadlock state could be avoided and the mobile robot could reach the target point.Finally, the designed Double BP Q-learning algorithm was simulated and verified, and the probability of mobile robot reaching the target point in the parameter update process was compared with the Double Q-learning algorithm under the same condition of the planned path length.The results showed that the model trained by the improved Double BP Q-learning algorithm had a higher success rate in finding the optimal or sub-optimal path in the dense discrete environment, besides, it had stronger model generalization ability, fewer redundant sections, and could reach the target point without entering the deadlock zone in the special obstacles environment.
Adversarial Examples Detection of Radio Signals Based on Multifeature Fusion

In the field of deep learning, deep neural networks (DNNs) have shown good performance on classification applications. However, a DNN model is vulnerable to adversarial examples, which is formed by adding tiny perturbations on a normal example and can mislead the DNN model to make a wrong estimate during the prediction. In this brief, for adversarial attacks in radio signals field, we propose a novel adversarial example detection strategy based on multifeature fusion and provide a framework which includes generating adversarial examples, extracting the local intrinsic dimensionality (LID) features and the constellation diagram (CD) features, detecting adversarial examples. We obtain the output values of normal examples and adversarial examples in each layer of the model respectively, and then, calculate the LID features values of examples by the maximum likelihood estimate based on a certain neighborhood range. Meanwhile, we calculate the CD features values by the range feature and density feature of the constellation diagram distribution. Finally, a logistic regression classifier is trained based on multifeature fusion values to detect adversarial examples. The experimental results across two benchmark datasets demonstrate that the proposed multifeature fusion method could accurately detect adversarial examples of radio signals. The detection accuracy is up to 98.7% when the perturbation reached 10%.
Model-Driven Deep Learning-Based Signal Detector for CP-Free MIMO-OFDM Systems

The absence of cyclic prefix (CP) can increase the spectral efficiency of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, CP removal will make signal detection challenging. In this paper, we develop a model-driven deep learning (DL)-based detector to resolve this problem. The prototype of the detector is the orthogonal approximate message passing (OAMP) algorithm, which has a strong ability to mitigate interference but involves matrix inversion with high complexity. We first use the conjugate gradient method to reduce the computational complexity of OAMP. Then, by unfolding the revised algorithm into a network and learning the optimal values of its parameters, the detection performance can be significantly improved. Complexity analysis indicates that the proposed scheme can reduce the running time to only a quarter of that required in OAMP while achieving remarkable performance in bit-error rate.
Variations in the Near‐Infrared Spectral Properties of Ferrous Mineral Mixtures With Different Relative Abundances

Abstract In near‐infrared spectral studies, the relationship between the 1‐μm absorption (Band I) center and the band area ratio (BAR, the area ratio of 2–1‐μm absorption features) is useful in compositional and mineralogical analyses of ferrous mineral‐bearing mixtures. Zhang and Cloutis (2020), https://doi.org/10.1029/2020ea001153 , investigated various lunar ferrous iron‐bearing rocks and minerals and found that pyroxene‐bearing materials rich in ilmenite (Ilm), plagioclase (Pl), or glass are offset from the lunar olivine‐clinopyroxene‐orthopyroxene (Ol‐Cpx‐Opx) mixing line in the plot of the BAR versus the Band I center. To analyze the variation trends of the spectral properties of these mixtures with different components, this study presents a systematic evaluation of laboratory spectra of terrestrial and synthetic ferrous iron‐bearing mineral mixtures based on published databases. In general, the mixing trends of the Pl‐pyroxene mixtures, the glass‐pyroxene mixtures, and the Ilm‐basalt mixtures are consistent with the findings of Zhang and Cloutis (2020), https://doi.org/10.1029/2020ea001153 . Moreover, this study also finds that the BAR of the Pl‐pyroxene mixtures varies nonlinearly with different relative abundances and that the BAR is generally not sensitive to Pl abundances below 60%. For glass‐pyroxene mixtures, the corresponding data points are usually appreciably offset from the Ol‐Cpx‐Opx mixing line at glass abundances above 20%. The BAR of the Ilm‐basalt mixtures increases with increasing Ilm content, mainly due to the weakening of 1‐μm absorption generally being greater than that of 2‐μm absorption.
mixSeq: A Simple Data Augmentation Methodfor Neural Machine Translation

Data augmentation, which refers to manipulating the inputs (e.g., adding random noise, masking specific parts) to enlarge the dataset, has been widely adopted in machine learning.Most data augmentation techniques operate on a single input, which limits the diversity of the training corpus.In this paper, we propose a simple yet effective data augmentation technique for neural machine translation, mixSeq, which operates on multiple inputs and their corresponding targets.Specifically, we randomly select two input sequences, concatenate them together as a longer input as well as their corresponding target sequences as an enlarged target, and train models on the augmented dataset.Experiments on nine machine translation tasks demonstrate that such a simple method boosts the baselines by a nontrivial margin.Our method can be further combined with single-input based data augmentation methods to obtain further improvements.
Principales motivaciones en el uso de redes sociales en estudiantes de Comunicación: perspectiva comparada entre Colombia, Ecuador y España

This article presents original results of an investigation carried out in three countries (Colombia, Ecuador and Spain) with 300 Communication students who reflected on the importance, motivations and informative value of social networks. The research presents the results of the last quarter of 2019, and is part of a research project that analyzes the main trends in the use of young Ibero-Americans regarding social networks. The work concludes that users make use of social networks in ubiquitous environments in order to be constantly informed and find out about events in a context of glocality through alternative sources to traditional media companies. In addition, they consider that the networks are emerging spaces for false news that demand a permanent verification of sources (fact-checking) and, therefore, evidence the need to promote media literacy initiatives in university academic environments.
Permutation entropy analysis of the output of a laser diode under stimulated Brillouin scattering optical feedback

The chaotic output emitted by a diode laser with optical feedback has fascinated the community for decades. The external cavity delay time imparts a weak level of periodicity to the laser output (the so-called "time delay signature", TDS) that is a drawback for applications that require random optical signals. A lot of efforts have focused in suppressing the TDS either by post-processing the signal or by using alternative ways to generate random optical signals. Here, we compare the signals generated by two optical feedback setups: in the first one, the stimulated Brillouin backscattered light from a standard optical fibre is re-injected into the laser (stimulated Brillouin scattering optical feedback, SBSOF); in the second one, the light transmitted through the fibre is re-injected into the laser (conventional optical feedback, COF). We analyse the permutation entropy, a well-known measure of complexity that captures order relations between values of a time series. We find that, on average, the signal generated by the SBSOF setup has slightly lower PE than the one generated by the COF setup, except when the sampling time of the intensity signal is an exact multiple of the delay; in that case, due to TDS, the entropy of the COF signal is lower than that of the SBSOF signal. We interpret the lower entropy value of the SBSOF signal as due to oscillations at the Brillouin frequency shift. Taken together, our results show that TDS suppression can have an undesirable side effect: a decrease of the entropy of the signal.
Forecasting the volume of postal services using Savitzky-Golay filter modification

In this paper, an approach for investigating the time series analysis with a goal to forecast the volume of express mail services (EMS) in international traffic in Republic of Serbia is presented. We have developed new algorithm, implemented in MATLAB environment, that is based on the non-weighted symmetric Savitzky-Golay (SG) filtering and compared to simple moving average within the seasonalized autoregressive integrated moving average (SARIMA) model. Based on data from the previous three years, actual and predicted data obtained by these two algorithms were compared and it was found that the prediction errors of Savitzky-Golay filter modification are about 30% smaller then corresponding ones in case of SARIMA model.
Abusive Language on Social Media Through the Legal Looking Glass

Abusive language is a growing phenomenon on social media platforms.Its effects can reach beyond the online context, contributing to mental or emotional stress on users.Automatic tools for detecting abuse can alleviate the issue.In practice, developing automated methods to detect abusive language relies on good quality data.However, there is currently a lack of standards for creating datasets in the field.These standards include definitions of what is considered abusive language, annotation guidelines and reporting on the process.This paper introduces an annotation framework inspired by legal concepts to define abusive language in the context of online harassment.The framework uses a 7point Likert scale for labelling instead of class labels.We also present ALYT -a dataset of Abusive Language on YouTube.ALYT includes YouTube comments in English extracted from videos on different controversial topics and labelled by Law students.The comments were sampled from the actual collected data, without artificial methods for increasing the abusive content.The paper describes the annotation process thoroughly, including all its guidelines and training steps.
scikit-learn Pipelines Meet Knowledge Graphs

Python is currently the most used platform for data science and machine learning. At the same time, public knowledge graphs have been identified as a valuable source of background knowledge in many data science tasks. In this paper, we introduce the kgextension package for Python, which allows for using knowledge graph in data science pipelines built in Python. The demo shows how data from public knowledge graphs such as DBpedia and Wikidata can be used in data mining pipelines based on the popular Python package scikit-learn. We demonstrate the package’s utility by showing that the prediction accuracy on a popular Kaggle task can be significantly increased by using background knowledge from DBpedia.
RaiseWikibase: Fast Inserts into the BERD Instance

We create a knowledge graph of German companies in order to facilitate research with Business, Economic and Related Data (BERD), both modern and historical. For the implementation we chose Wikibase, but the wrappers of the Wikibase API turned out to be slow for filling it with millions of entities. This work presents the open source tool RaiseWikibase for speeding up data filling and knowledge graph construction by inserting data directly into the database. We test its performance for creating the items and wikitexts and share a reusable example for knowledge graph construction.