Reviewerly/exHarmony huggingface dataset:
The Effects of Educational Games on EFL Vocabulary Learning of Early Childhood Students with Learning Disabilities: A Systematic Review and Meta-analysis

Educational games play an essential role in the life of the new generation. Although there are many criticisms, many studies focus on the importance of educational games in improving EFL vocabulary learning of early childhood students with learning disabilities in the target language. Researchers have begun to research how games are used in class to enhance vocabulary learning. This paper investigates the effects of educational games on EFL vocabulary learning of early childhood students with learning disabilities based on a literature review. Many studies focus on educational games' effect on various aspects of education. This concept paper aims to shed light on some of the benefits of games and the challenges teachers and early childhood children face in using educational games. This paper's results show that games are used not only for the success of early childhood students with learning disabilities in EFL classes but, more importantly, to motivate them and increase cooperation between children. Additionally, the results indicate that there are advantages and disadvantages of using games to learn English vocabulary.
Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers

It is known due to the work of Van den Broeck, Meert and Darwiche that weighted first-order model counting (WFOMC) in the two-variable fragment of first-order logic can be solved in time polynomial in the number of domain elements. In this paper we extend this result to the two-variable fragment with counting quantifiers.
Quantum tomography of entangled qubits by time-resolved single-photon counting with time-continuous measurements

Abstract In this article, we introduce a framework for entanglement characterization by time-resolved single-photon counting with measurement operators defined in the time domain. For a quantum system with unitary dynamics, we generate time-continuous measurements by shifting from the Schrödinger picture to the Heisenberg representation. In particular, we discuss this approach in reference to photonic tomography. To make the measurement scheme realistic, we impose timing uncertainty on photon counts along with the Poisson noise. Then, the framework is tested numerically on quantum tomography of qubits. Next, we investigate the accuracy of the model for polarization-entangled photon pairs. Entanglement detection and precision of state reconstruction are quantified by figures of merit and presented on graphs versus the amount of time uncertainty.
Learning-Based Dissimilarity for Clustering Categorical Data

Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call Learning-Based Dissimilarity, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature.
The Integrated Spatial Assessment of The Flood Hazard Using AHP-GIS: The Case Study of Gorontalo Regency

Mapping of potential flood hazards is an important element in ensuring proper planning of development and implementing flood disaster mitigation efforts for flooded areas. Therefore, this study was conducted to assess the regional-scale flood hazard areas in Gorontalo District using an AHP-GIS integration technique with the focus on factors such as annual rainfall, elevation, slope, soil type, distance to the water bodies, and land-use. The flood hazard was divided into five classes, ranging from very low to very high, and 13.61% of the total area in Gorontalo Regency is classified as either high or very high. Moreover, the distribution of the hazard was verified and 65 events were recorded to have a level of 90.7% and this indicates the model is reliable to assess flood hazard. The results showed GIS-AHP integration is a promising method to accurately predict flood-hazard areas, especially in those with limited data.
Performance Evaluation of Malaria HRP-2 Rapid Diagnostic Test among Febrile Patients with Malaria in Iwo, Osun State, Nigeria

Diagnosis of malaria is a fundamental component of malaria case management.Parasitological confirmation of malaria before treatment was recommended for endemic countries.Microscopy diagnosis of malaria, the gold standard as constraints such as: level of expertise, quality of equipment and reagents may lead to misdiagnosis.The histidine rich protein 2 (HRP-2) based rapid diagnostic test (RDT) detects Plasmodium falciparum only.We therefore evaluated the performance of malaria HRP-2 rapid tests among febrile patients suspected to have malaria in Iwo, Osun state Nigeria.A cross-sectional study was conducted on patients with fever or symptoms suggestive of malaria at the Outpatient's Department of a University Medical Centre in Iwo, Osun State, Nigeria.Malaria diagnosis was performed with the malaria HRP-2-based RDT and microscopy for every participant.Stained Thick and thin films prepared on the same slide, stained with 3% Giemsa stain and evaluated for detection, speciation and parasite density per patient.. Malaria RDT results were read as positive or negative.Of the 289 febrile patients assessed between October 2016 to August 2017, the number positive by Malaria RDT and microscopy were 102 (35.3%) and 98 (33.9%) respectively.Parasite density ranged from 76 p/µL to 165,333p/µL of blood.Overall, the sensitivity, specificity, positive and negative predictive values were: 96.08%, 97.95%, 96.08% and 97.95% respectively.This study showed malaria RDT could be a reliable option to microscopy for effective case management of uncomplicated falciparum malaria in febrile patients in Iwo, Nigeria.
Pioneering easy-to-use forestry data with Forest Explorer

Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it – any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.
Predicting Breast Cancer: A Comparative Analysis of Machine Learning Algorithms

Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.
Development of Speech Recognition Systems in Emergency Call Centers

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.
Performance Comparison for Spam Detection in Social Media Using Deep Learning Algorithms

Social media applications like Twitter, Instagram, Facebook have helped people to connect to each other. This has been eased due to high-speed internet. However, this has invited various spam messages through tweets or Facebook. The sole purpose of such messages is aggregation or exploitation of personal data in terms of finances or medical records, political benefit’s or community violence. This makes spam detection an extreme value-added service. We tend to recommend a 1D CNN algorithmic technique and compare results with variants of CNN and with boosting algorithms. The model is braced with linguistics data in the illustration of the words with the assistance of knowledge-bases such as Word2vec and fast ext. This improves the end to end performance, by providing higher linguistics vector illustration of input testing words. Projected Experimental results show the efficiency of the projected approach from the point of view of accuracy, F1-score and response time.
A Comparison of Machine Learning Techniques for Sentiment Analysis

The availability of the data has increased tremendously due to the excess usage of social media platforms like Twitter and Facebook. Due to the abundant availability of data, scientists, businesses, educationalists and other people working under different roles have started using Sentiment Analysis (SA) to get in-depth knowledge about the sentiments of the people regarding any topic of interest. There are many techniques to implement SA, and one of them is Machine Learning (ML). This study is focused on the comparison of ancient ML methods such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and a modern method, i.e., Deep Learning (DL). The ML techniques are applied to a single dataset to compare their performance in terms of accuracy to understand how they perform against each other. The study found that DL performed the best with 96.41% accuracy followed by NB and SVM with 87.18% and 82.05% respectively. DT performed the poorest with 68.21% accuracy.
Maximum likelihood autoregressive model parameter estimation with noise corrupted independent snapshots

• Development of an EM framework for the solution of multiple-snapshot noisy AR parameter estimation problem. • A computationally efficient implementation for the EM based solution. • Better AR parameter estimation performance in comparison to the state-of-the-art estimators in a wide range of scenarios. Maximum likelihood autoregressive (AR) model parameter estimation problem with independent snapshots observed under white Gaussian measurement noise is studied. In addition to the AR model parameters, the measurement noise variance is also included among the unknowns of the problem to develop a general solution covering several special cases such as the case of known noise variance, noise-free snapshots, the single snapshot operation etc. The presented solution is based on the expectation-maximization method which is formulated by assigning the noise-free snapshots as the missing data. An approximate version of the suggested method, at a significantly reduced computational load with virtually no loss of performance, has also been developed. Numerical results indicate that the suggested solution brings major performance improvements in terms of estimation accuracy and does not suffer from unstable AR filter estimates unlike some other methods in the literature. The suggested method can be especially useful for small-dimensional multiple-snapshot noisy AR modeling applications such as the clutter power spectrum modeling application in radar signal processing.
Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

Contouring of breast gland in planning CT is important to postoperative radiotherapy of patients after breast conserving surgery (BCS). However, the contouring task is difficult because of the poorer contrast of breast gland in planning CT. To improve its efficiency and accuracy, prior information was introduced in a 3D U-Net model to predict the contour of breast gland automatically.The preoperative CT was first aligned to the planning CT via affine registration. The resulting transform was then applied to the contour of breast gland in preoperative CT, and the corresponding contour in planning CT was obtained. This transformed contour was a preliminary estimation of breast gland in planning CT and was used as prior information in a 3D U-Net model to obtain a more accurate contour. For evaluation, the dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess the deep learning (DL) model's prediction accuracy.The average DSC and HD of the prediction model were 0.775±0.065 and 44.979±20.565 for breast gland without the input of prior information, while the average values were 0.830±0.038 and 17.896±5.737 with the input of prior information (0.775 vs. 0.830, P=0.0014<0.05; 44.979 vs. 17.896, P=0.002<0.05).The prediction accuracy was increased significantly with the introduction of prior information, which provided valuable geometrical distribution of target for model training. This method provides an effective way to identify low-contrast targets from surrounding tissues in CT and will be useful in other image modalities.
Quality Estimation without Human-labeled Data

Quality estimation aims to measure the quality of translated content without access to a reference translation.This is crucial for machine translation systems in real-world scenarios where high-quality translation is needed.While many approaches exist for quality estimation, they are based on supervised machine learning requiring costly human labelled data.As an alternative, we propose a technique that does not rely on examples from human-annotators and instead uses synthetic training data.We train off-the-shelf architectures for supervised quality estimation on our synthetic data and show that the resulting models achieve comparable performance to models trained on human-annotated data, both for sentence and word-level prediction.
A symmetric matrix-variate normal local approximation for the Wishart distribution and some applications

The noncentral Wishart distribution has become more mainstream in statistics as the prevalence of applications involving sample covariances with underlying multivariate Gaussian populations as dramatically increased since the advent of computers. Multiple sources in the literature deal with local approximations of the noncentral Wishart distribution with respect to its central counterpart. However, no source has yet developed explicit local approximations for the (central) Wishart distribution in terms of a normal analogue, which is important since Gaussian distributions are at the heart of the asymptotic theory for many statistical methods. In this paper, we prove a precise asymptotic expansion for the ratio of the Wishart density to the symmetric matrix-variate normal density with the same mean and covariances. The result is then used to derive an upper bound on the total variation between the corresponding probability measures and to find the pointwise variance of a new density estimator on the space of positive definite matrices with a Wishart asymmetric kernel. For the sake of completeness, we also find expressions for the pointwise bias of our new estimator, the pointwise variance as we move towards the boundary of its support, the mean squared error, the mean integrated squared error away from the boundary, and we prove its asymptotic normality.
Visual Analytics for Climate Change Detection in Meteorological Time-Series

The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations.
What Sounds “Right” to Me? Experiential Factors in the Perception of Political Ideology

In this paper, we challenge the assumption that political ideology is inherently built into text by presenting an investigation into the impact of experiential factors on annotator perceptions of political ideology.We construct an annotated corpus of U.S. political discussion, where in addition to ideology labels for texts, annotators provide information about their political affiliation, exposure to political news, and familiarity with the source domain of discussion, Reddit.We investigate the variability in ideology judgments across annotators, finding evidence that these experiential factors may influence the consistency of how political ideologies are perceived.Finally, we present evidence that understanding how humans perceive and interpret ideology from texts remains a challenging task for state-ofthe-art language models, pointing towards potential issues when modeling user experiences that may require more contextual knowledge.
Learning from 2D: Pixel-to-Point Knowledge Transfer for 3D Pretraining.

Most of the 3D networks are trained from scratch owning to the lack of large-scale labeled datasets. In this paper, we present a novel 3D pretraining method by leveraging 2D networks learned from rich 2D datasets. We propose the pixel-to-point knowledge transfer to effectively utilize the 2D information by mapping the pixel-level and point-level features into the same embedding space. Due to the heterogeneous nature between 2D and 3D networks, we introduce the back-projection function to align the features between 2D and 3D to make the transfer possible. Additionally, we devise an upsampling feature projection layer to increase the spatial resolution of high-level 2D feature maps, which helps learning fine-grained 3D representations. With a pretrained 2D network, the proposed pretraining process requires no additional 2D or 3D labeled data, further alleviating the expansive 3D data annotation cost. To the best of our knowledge, we are the first to exploit existing 2D trained weights to pretrain 3D deep neural networks. Our intensive experiments show that the 3D models pretrained with 2D knowledge boost the performances across various real-world 3D downstream tasks.