Reviewerly/exHarmony huggingface dataset:
Integrating strong-motion recordings and twitter data for a rapid shakemap of macroseismic intensity

Rapid estimation of the intensity of seismic ground motions is crucial for an effective rapid response when an earthquake occurs. To this end, maps of updated grond-motion fields (or shakemaps) are produced by using observations or measurements in near real-time to better constrain initial estimates. In this work, two types of observations are integrated to generate shakemaps right after an earthquake: the common type of data recorded by physical sensors (seismic stations) and the data extracted from social sensors (Twitter), or the combination of both. We investigate an approach to extract an approximation of the macroseismic intensity from social sensors 10 min after the earthquake; the approach relies on Twitter feeds to define the “felt area” where the earthquake was felt by the population, and the “unfelt locations” where the earthquake was not reported. Two recent earthquakes in France of moderate magnitude are studied and the results are compared to the official macroseismic intensity maps for validation. For the two studied cases, we note that Peak Ground Acceleration recordings far from the epicenter tend to underestimate the entire macroseismic field, and that the tweets from “felt areas” are complementary for a better estimation of the intensity shakemap. We highlight the importance and the limits of each type of observations when generating the seismic shakemaps.
The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces.&#x0D; In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.
Exploiting Deep Sentential Context for Expressive End-to-End Speech Synthesis

Attention-based seq2seq text-to-speech systems, especially those use self-attention networks (SAN), have achieved state-of-art performance. But an expressive corpus with rich prosody is still challenging to model as 1) prosodic aspects, which span across different sentential granularities and mainly determine acoustic expressiveness, are difficult to quantize and label and 2) the current seq2seq framework extracts prosodic information solely from a text encoder, which is easily collapsed to an averaged expression for expressive contents. In this paper, we propose a context extractor, which is built upon SAN-based text encoder, to sufficiently exploit the sentential context over an expressive corpus for seq2seq-based TTS. Our context extractor first collects prosodic-related sentential context information from different SAN layers and then aggregates them to learn a comprehensive sentence representation to enhance the expressiveness of the final generated speech. Specifically, we investigate two methods of context aggregation: 1) direct aggregation which directly concatenates the outputs of different SAN layers, and 2) weighted aggregation which uses multi-head attention to automatically learn contributions for different SAN layers. Experiments on two expressive corpora show that our approach can produce more natural speech with much richer prosodic variations, and weighted aggregation is more superior in modeling expressivity.
Krise of quality education in the digital economy

Today in the market of educational services, the winner is the one who wins in the fight for ratings.The question of cost and performance must be discussed in a broader context, namely to take into account that education is an essential part of social capital, which ensures success in international competition. Therefore, the savings on education in the long run leads to delays and loss of influence. Management, based on the digital divide is not a panacea. The article sets the task of analyzing the transformation of education in the digital age: 1) The identification of the causes of the crisis in the education system; secondly, the analysis of online courses as tools of modern educational spaces; 2) The impact of electronic educational technologies on the actors of the educational process; fourthly, the updating of teaching practices of meaning, based on understanding the individual learner and teacher; 3) For the development of the reflective experience of new educational programs they must be supplemented with hermeneutic and semiotic teaching techniques that contribute to understanding the meaning and provide live interpersonal communication; 4) Enhancement of the productivity of education through the introduction of digital technologies is accompanied by the strengthening of electronic control and management; 5) It is necessary to create a pedagogical atmosphere in which freedom, responsibility, trust, and friendship is cultivated.
Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit Distance

The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures. This non-euclidean structure includes graphs or manifolds that are called geometric deep learning (GDL). It has opened a new direction for researchers to deal with graphs using deep learning in document processing, outperforming conventional methods. We propose a Deep Graph Neural Network (DGNN) classifier-based on additive angular margin loss for the classification task in document analysis. Another contribution of this work is to investigate the performance of a DGNN as a classifier using different loss functions, which helps to minimize the loss for the document analysis problem. We compare additive angular margin loss, Cosine angular margin loss, and multiplicative angular margin loss. Furthermore, we give a comparison between the mentioned loss functions and the Softmax loss function. We also present the comparisons of results using different graph edit distance (GED) methods. Our quantitative results suggest, that by applying the additive angular marginal loss function makes more compact intra-class ability and increases the inter-class discrepancy which enhances the discriminating power of the DGNN. Enhancing the decision boundaries between the classes increase the intra-class compactness and inter-class discrimination power of the model.
Neural PLDA Modeling for End-to-End Speaker Verification

While deep learning models have made significant advances in supervised classification problems, the application of these models for out-of-set verification tasks like speaker recognition has been limited to deriving feature embeddings. The state-of-the-art x-vector PLDA based speaker verification systems use a generative model based on probabilistic linear discriminant analysis (PLDA) for computing the verification score. Recently, we had proposed a neural network approach for backend modeling in speaker verification called the neural PLDA (NPLDA) where the likelihood ratio score of the generative PLDA model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a verification cost. In this paper, we extend this work to achieve joint optimization of the embedding neural network (x-vector network) with the NPLDA network in an end-to-end (E2E) fashion. This proposed end-to-end model is optimized directly from the acoustic features with a verification cost function and during testing, the model directly outputs the likelihood ratio score. With various experiments using the NIST speaker recognition evaluation (SRE) 2018 and 2019 datasets, we show that the proposed E2E model improves significantly over the x-vector PLDA baseline speaker verification system.
Dynamic Gesture Recognition Based on Deep Learning in Human-to-Computer Interfaces

ABSTRACTCurrently, gesture recognition provides a faster, simpler, convenient, effective and more natural way for human-computer interaction, which has been widely concerned. Gesture recognition plays an important role in real life. The manual feature extraction in traditional gesture recognition methods is time-consuming and strenuous. Moreover, in order to improve the accuracy of recognition, the quantity and quality of features to be extracted are required to be very high, which is a bottleneck for traditional gesture recognition methods. Therefore, we propose a deep learning method for dynamic gesture recognition in Human-to-Computer interfaces. An improved inverted residual network architecture is utilized as the basis of SSD (Single Shot MultiBox Detector) network for feature extraction. And the convolution structure of the auxiliary layer is predicted by using the inverse residual structure combining the cavity convolution. It uses multi-scale information, which can reduce the amount of calculation and parameters number. Transfer learning is used to optimize the trained network model so as to reduce the training time and make the model more convergent. Finally, experimental results show that the proposed method can recognize different gestures quickly and effectively.
Integrating Theories of Working Memory

Multiple theories of working memory are described in the chapters of this book and often these theories are viewed as being mutually incompatible, yet each is associated with a supporting body of empirical evidence. This chapter argues that many of these differences reflect different research questions, different levels of explanation, and differences in how participants perform their assigned tasks in different laboratories, rather than fundamental theoretical adversity. It describes a version of a multiple component working memory in which a range of specialized cognitive functions (or mental tools) act in concert, giving the impression, at a different level of explanation, of a unified cognitive system. The chapter argues that more rapid and more substantial scientific progress on the understanding of the concept of working memory would be achieved through identifying the levels of explanation explored within each theoretical framework, and attempting to integrate theoretical frameworks rather than perpetuating debate with no clear resolution in sight.
How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions

Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to analyze the impact of features on predictions. Feature interactions are the contextual dependence between features that jointly impact predictions. There are a number of methods that extract feature interactions in prediction models; however, the methods that assign attributions to interactions are either uninterpretable, model-specific, or non-axiomatic. We propose an interaction attribution and detection framework called Archipelago which addresses these problems and is also scalable in real-world settings. Our experiments on standard annotation labels indicate our approach provides significantly more interpretable explanations than comparable methods, which is important for analyzing the impact of interactions on predictions. We also provide accompanying visualizations of our approach that give new insights into deep neural networks.
Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud

Partitioning and distributing deep neural networks (DNNs) over physical nodes such as edge, fog, or cloud nodes, could enhance sensor fusion, and reduce bandwidth and inference latency. However, when a DNN is distributed over physical nodes, failure of the physical nodes causes the failure of the DNN units that are placed on these nodes. The performance of the inference task will be unpredictable, and most likely, poor, if the distributed DNN is not specifically designed and properly trained for failures. Motivated by this, we introduce deepFogGuard, a DNN architecture augmentation scheme for making the distributed DNN inference task failure-resilient. To articulate deepFogGuard, we introduce the elements and a model for the resiliency of distributed DNN inference. Inspired by the concept of residual connections in DNNs, we introduce skip hyperconnections in distributed DNNs, which are the basis of deepFogGuard's design to provide resiliency. Next, our extensive experiments using two existing datasets for the sensing and vision applications confirm the ability of deepFogGuard to provide resiliency for distributed DNNs in edge-cloud networks.
Improving Classification Accuracy of Scikit-learn Classifiers with Discrete Fuzzy Interval Values

Understanding machine learning (ML) algorithm from scratch is time consuming. Thus, many software and library packages such as Weka and Scikit-Learn have been introduced to help researchers run simulation on several amounts of well-known classifiers. In ML, different classifiers have different performance and this depends on factor such as type of data used as input for the classification phase. Thus, it is necessary to perform data discretization when dealing with continuous data for classifiers that perform better with discrete data. However, in data mining, depending solely on discretization is not enough as real-world data can be large, imprecise and noisy. In addition, knowledge representation is necessary to help researchers to understand better about the data during the discretization process. Thus, the objective of this study is to observe the effect of fuzzy elements inside the discretization phase on the classification accuracy of Scikit-learn classifiers. In this study, fuzzy logic has been proposed to assist the existing discretization technique through fuzzy membership graph, linguistic variables and discrete interval values. All classifiers in Scikit-learn packages were used during the classification phase through 10-fold cross validation. The simulation results showed that the presence of fuzzy in assisting the discretization process slightly improved the classification accuracy of ensemble type classifiers such as Random Forest and Naive Bayes while slightly degrading the performance of other classifiers.
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers

Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.Entities are represented as per-token labels with a special structure in order to decode them into spans.Current work eschews prior knowledge of how the span encoding scheme works and relies on the CRF learning which transitions are illegal and which are not to facilitate global coherence.We find that by constraining the output to suppress illegal transitions we can train a tagger with a cross-entropy loss twice as fast as a CRF with differences in F1 that are statistically insignificant, effectively eliminating the need for a CRF.We analyze the dynamics of tag co-occurrence to explain when these constraints are most effective and provide open source implementations of our tagger in both PyTorch and TensorFlow.
Corruption, Income Inequality and Human Resource Development in Developing Economies

This study examines the relationship between corruption, income inequality and human resource development by using simultaneous-equation model for a panel of 38 developing countries for the time period 2000-2015. The endogenous variables of human resource development, income inequality and corruption are measured by human resource development index, control of corruption index and Gini coefficient. The three stage least square results indicate that human resource development in negatively influenced by corruption and income inequality. Income inequality is positively affected by corruption and negatively by human resource development. On the other hand corruption is negatively influenced by human resource development and positively by income inequality. Among this troika of indicators corruption and income inequality are helping each other to resist human resource development. In the instrumental variables the urbanization, health expenditures and economic freedom positively contribute in human resource development. For developing economies it is needed to tackle the problem of corruption and income inequality for accelerate the human resource development.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games

We show that Reinforcement Learning (RL) methods for solving Text-Based Games (TBGs) often fail to generalize on unseen games, especially in small data regimes.To address this issue, we propose Context Relevant Episodic State Truncation (CREST) for irrelevant token removal in observation text for improved generalization.Our method first trains a base model using Q-learning, which typically overfits the training games.The base model's action token distribution is used to perform observation pruning that removes irrelevant tokens.A second bootstrapped model is then retrained on the pruned observation text.Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art (SOTA) methods despite requiring fewer number of training episodes. 1
Scaling Systematic Literature Reviews with Machine Learning Pipelines

Systematic reviews, which entail the extraction of data from large numbers of scientific documents, are an ideal avenue for the application of machine learning.They are vital to many fields of science and philanthropy, but are very time-consuming and require experts.Yet the three main stages of a systematic review are easily done automatically: searching for documents can be done via APIs and scrapers, selection of relevant documents can be done via binary classification, and extraction of data can be done via sequence-labelling classification.Despite the promise of automation for this field, little research exists that examines the various ways to automate each of these tasks.We construct a pipeline that automates each of these aspects, and experiment with many human-time vs. system quality trade-offs.We test the ability of classifiers to work well on small amounts of data and to generalise to data from countries not represented in the training data.We test different types of data extraction with varying difficulty in annotation, and five different neural architectures to do the extraction.We find that we can get surprising accuracy and generalisability of the whole pipeline system with only 2 weeks of human-expert annotation, which is only 15% of the time it takes to do the whole review manually and can be repeated and extended to new data with no additional effort.
Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens.During testing, however, the model is instead conditioned on previously generated tokens, resulting in what is termed exposure bias.To reduce this gap between training and testing, we propose using optimal transport (OT) to match the sequences generated in these two modes.An extension is further proposed to improve the OT learning, based on the structural and contextual information of the text sequences.The effectiveness of the proposed method is validated on machine translation, text summarization, and text generation tasks.
Reinforcement Learning-Based Data Association for Multiple Target Tracking in Clutter

Data association is a crucial component of multiple target tracking, in which each measurement obtained by the sensor can be determined whether it belongs to the target. However, many methods reported in the literature may not be able to ensure the accuracy and low computational complexity during the association process, especially in the presence of dense clutters. In this paper, a novel data association method based on reinforcement learning (RL), i.e., the so-called RL-JPDA method, has been proposed for solving the aforementioned problem. In the presented method, the RL is leveraged to acquire available information of measurements. In addition, the motion characteristics of the targets are utilized to ensure the accuracy of the association results. Experiments are performed to compare the proposed method with the global nearest neighbor data association method, the joint probabilistic data association method, the fuzzy optimal membership data association method and the intuitionistic fuzzy joint probabilistic data association method. The results show that the proposed method yields a shorter execution time compared to other methods. Furthermore, it can obtain an effective and feasible estimation in the environment with dense clutters.
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network

Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as representatives of generative models, while the denoising autoencoder (DAE) represented non-generative models. Performance of the models is compared to traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein modification of GAIN, which turns out to be the best imputation model when the degree of missingness is less than or equal to $$30\%$$ . Experiments were performed on real-world and artificial datasets with continuous features where different percentages of features, varying from $$10\%$$ to $$50\%$$ , were missing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.
Can the Blockchain lead to new sustainable business models

New technologies can foster the development of new sustainable business models (SBMs). Our paper wants to investigate how the blockchain can facilitate the development of new SBMs, by analyzing some real-world case studies. Findings highlight how the characteristics of the blockchain can extend existing theories in leading to new SBMs.