Reviewerly/exHarmony huggingface dataset:
Implementasi Media Audio Visual untuk Meningkatkan Kemampuan Menulis Teks Eksplanasi

This research is a class action research. The research aims to reduce problems in class VI-C SDN 004/V Kuala Tungkal, here is the low ability to write explanatory texts. These problems are reduced by implementing audiovisual media. The results showed an increase in the average score of students in the four aspects measured, namely (1) aspects of the content, the average score of students has increased from 18.77 to 26.30; (2) structural aspects, the average score of students has increased from 14.70 to 17.63; (3) vocabulary aspects, the average student score has increased from 12.33 to 16.30; (4) sentence aspects, the average student score has increased from 13.77 to 16.83; and (4) mechanical aspects, the average student score has increased from 7.23 to 8.33. Keywords: audio-visual media, writing, explanatory text.
Interactive Computer Technologies in the Educational Process

This paper analyzes the relevance of using interactive computer technologies in the process of teaching a foreign language at a technical university.The author has performed a study to assess the impact of computer training on the quality of language acquisition among students of engineering programs at the Moscow Aviation Institute, trying to determine the most promising technologies for use in the classroom.The author has come to the conclusion that that advanced technologies need to be used to create intercultural professionally-oriented communication in teaching a foreign language.The author notes that the "information era" opens up new opportunities for university teachers, but the most effective way of teaching a foreign language is the combination of traditional methods and modern computer technology.An important aspect of teaching is the teacher's ability to navigate in the ever-increasing flow of information and evaluate the degree of its reliability, as well as teach students to find the necessary information and analyze it.
Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)

Abstract Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve ( $$\mathrm{AUC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AUC</mml:mi></mml:math> ) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been subject to a fundamental restriction, in that it applies to dichotomous (yes or no) outcomes only. Here we introduce ROC movies and universal ROC (UROC) curves that apply to just any linearly ordered outcome, along with an associated coefficient of predictive ability ( $${\mathrm{CPA}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CPA</mml:mi></mml:math> ) measure. $${\mathrm{CPA}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CPA</mml:mi></mml:math> equals the area under the UROC curve, and admits appealing interpretations in terms of probabilities and rank based covariances. For binary outcomes $${\mathrm{CPA}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CPA</mml:mi></mml:math> equals $$\mathrm{AUC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>AUC</mml:mi></mml:math> , and for pairwise distinct outcomes $${\mathrm{CPA}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CPA</mml:mi></mml:math> relates linearly to Spearman’s coefficient, in the same way that the C index relates linearly to Kendall’s coefficient. ROC movies, UROC curves, and $${\mathrm{CPA}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>CPA</mml:mi></mml:math> nest and generalize the tools of classical ROC analysis, and are bound to supersede them in a wealth of applications. Their usage is illustrated in data examples from biomedicine and meteorology, where rank based measures yield new insights in the WeatherBench comparison of the predictive performance of convolutional neural networks and physical-numerical models for weather prediction.
Effect of Biopolymer Alginate on some properties of concrete

Alginate from Large brown seaweeds act as natural polymer has been investigated as polymer and has been added to concrete in different percentages ( 0% , 0.5% , 1% and 1.5% ) by the cement weight and the study show the effect of using alginate biopolymer admixtures on some of the fresh properties of the concrete (slump &amp; the density fresh) also in the hardened state ( Compressive strength , Splitting tensile strength and Flexural strength ) at 28 days. The mix proportion was (1:2.26:2.26) (cement: sand: gravel) respectively and at constant w/c equal to 0.47. The results indicate that the use of alginate as a percent of the cement weight possess a positive effect on fresh properties of concrete at 28 days. In other words, increasing the percentages of alginate addition has enhanced the slump and fresh density of concrete at 28 days, so the 1.5% alginate addition as percent of the cement weight showed the higher percentage of increasing where it was 2.5% for fresh density and 41%for slump of concrete at 28days compared with the reference mix without any addition, also the hardened properties (compression ,splitting tensile and flexural strength) at 28 days showed an increasing when using alginate at a percentage from the cement weight, so the highest increase was at 0.5% and 1.5% of alginate addition where it was about 40%.
Topic Detection from Conversational Dialogue Corpus with Parallel Latent Dirichlet Allocation Model and Elbow Method

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period.For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems.In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique.In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters.We evaluate our approach by comparing it with traditional LDA and clustering technique.The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation.
Ensemble Methods to Detect XSS Attacks

Machine learning techniques are gaining popularity and giving better results in detecting Web application attacks.Cross-site scripting is an injection attack widespread in web applications.The existing solutions like filter-based, dynamic analysis, and static analysis are not effective in detecting unknown XSS attacks, and machine learning methods can detect unknown XSS attacks.Existing research to detect XSS attacks by using machine learning methods have issues like single base classifiers, small datasets, and unbalanced datasets.In this paper, supervised ensemble learning techniques trained on a large labeled and balanced dataset to detect XSS attacks.The ensemble methods used in this research are random forest classification, AdaBoost, bagging with SVM, Extra-Trees, gradient boosting, and histogram-based gradient boosting.Analyzed and compared the performance of ensemble learning algorithms by using the confusion matrix.
Assessing the Performance of Factors Affecting the Profitability of the Banking System: Evidence from Kosovo

This review done by empirical surveillance observes the significances of a number of the explicit factors of the bank that are matching to bank governance, financial market configuration on one hand, and macroeconomic aspects of the Kosovo bank's performance on another hand. The Management and assessment of Kosovo's bank effectiveness were accomplished through two indicators return on assets (ROA) and return on equity (ROE). To recognize this assessment, the OLS, and Arellano-Bond (GMM) regression method will be applied with the data used from the financial statements of Kosovo banks on a periodical basis over the period 2006-2019. Based on the challenging environments in which the banking system of Kosovo has passed, we have carefully chosen some specific determinants of the banking industry, as well as some of the macroeconomic determinants. The outcomes propose that upcoming studies may contain diverse dynamic models as well as altered dependent and independent features to elucidate the performance of Kosovo banks. Keywords: Bank’s profitability, time series, OLS regression, GMM.
Analysis of Cross-Referencing Artificial Intelligence Topics Based on Sentence Modeling

Artificial intelligence (AI) is bringing about enormous changes in everyday life and today’s society. Interest in AI is continuously increasing as many countries are creating new AI-related degrees, short-term intensive courses, and secondary school programs. This study was conducted with the aim of identifying the interrelationships among topics based on the understanding of various bodies of knowledge and to provide a foundation for topic compositions to construct an academic body of knowledge of AI. To this end, machine learning-based sentence similarity measurement models used in machine translation, chatbots, and document summarization were applied to the body of knowledge of AI. Consequently, several similar topics related to agent designing in AI, such as algorithm complexity, discrete structures, fundamentals of software development, and parallel and distributed computing were identified. The results of this study provide the knowledge necessary to cultivate talent by identifying relationships with other fields in the edutech field.
An Ensemble DeepBoost Classifier for Software Defect Prediction

The main objective of a software development team is to have maximum customer defects in the software will reduce its quality.Thereby increasing its development cost.Several algorithms have been proposed for predicting software defects.But most of these algorithms are not appropriate when the dataset is imbalanced.In this paper an Ensemble DeepBoost Classifier (EDC) is built to predict the software defects effectively by addressing two major issues -curse of dimensionality and class distribution imbalance problem.Firstly, EDC uses Genetic Algorithm (GA) to find out the features that are relevant for software defect prediction.Thus, achieving dimensionality reduction.Later it uses Safe Line SMOTE (SLS) algorithm to achieve equal class distribution.Finally, it uses DeepBoost algorithm to predict whether the samples are defective or not based on the historical software defect data.The experiment was carried out on 7 PROMISE repository datasets and the results of EDC were compared with similar algorithms.The experimental results indicate that EDC has outperformed various existing algorithms in most evaluation metrics.
Label Distribution for Learning with Noisy Labels

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.
E3SN: Efficient End-to-End Siamese Network for Video Object Segmentation

In the semi-supervised video object segmentation (VOS) field, SiamMask has achieved competitive accuracy and the fastest running speed. However, the two-stage training procedure requires additional manual intervention, and using only single-level features does not maximize the rich hierarchical feature information. This paper proposes an efficient end-to-end Siamese network for VOS. In particular, a supervised sampling strategy is designed to optimize the training procedure. Such an optimization facilitates the training of the entire model in an end-to-end manner. Moreover, a multilevel feature aggregation module is developed to enhance feature representability and improve segmentation accuracy. Experimental results on DAVIS2016 and DAVIS2017 datasets show that the proposed approach outperforms the SiamMask in accuracy with similar FPS. Moreover, this approach also achieves good accuracy-speed trade-off compared with that of other state-of-the-art VOS algorithms.
A Novel Spatio-Temporal Multi-Task Approach for the Prediction of Diabetes-Related Complication: a Cardiopathy Case of Study

The prediction of the risk profile related to the cardiopathy complication is a core research task that could support clinical decision making. However, the design and implementation of a clinical decision support system based on Electronic Health Record (EHR) temporal data comprise of several challenges. Several single task learning approaches consider the prediction of the risk profile related to a specific diabetes complication (i.e., cardiopathy) independent from other complications. Accordingly, the state-of-the-art multi-task learning (MTL) model encapsulates only the temporal relatedness among the EHR data. However, this assumption might be restricted in the clinical scenario where both spatio-temporal constraints should be taken into account. The aim of this study is the proposal of two different MTL procedures, called spatio-temporal lasso (STL-MTL) and spatio-temporal group lasso (STGL-MTL), which encode the spatio-temporal relatedness using a regularization term and a graph-based approach (i.e., encoding the task relatedness using the structure matrix). Experimental results on a real-world EHR dataset demonstrate the robust performance and the interpretability of the proposed approach.
Salient Object Detection with Boundary Information

How to distinguish the low-contrast area near boundaries is a basic challenge in salient object detection. Most of recent state-of-the-art methods can achieve a good performance but still can't work well near boundaries. In this paper, we propose a novel network based on multi-level feature fusion with boundary information to solve this problem. Our model includes two separate decoding sub-networks, one is object sub-network to detect salient objects and another is boundary sub-network which outputs error maps to get boundary information by boundary maps. Moreover, we design a connection and fusion module to exchange and fuse information of objects and boundaries. In addition, to balance the two subnetworks, the optimal weight of loss function is obtained by experiments. The experimental results show that our model can distinguish the low-contrast area near boundaries well by boundary information and achieves the state-of-the-art performance on five common datasets.
Bayesian nonparametric latent class model for longitudinal data

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.
Re-ranking Answer Selection with Similarity Aggregation

Answer selection plays a crucial role in natural language processing. and thus has received much attention. Many recent works treat it as an ad-hoc retrieval problem where ranking optimization accounts for a large proportion. Previous works mainly consider the similarity between answer and question, but rarely utilize similarity and dissimilarity relationship in the answers candidate set. In this paper, we propose a similarity aggregation method to rerank the results produced by different baseline neural networks. The key idea of similarity aggregation is that true matches should not only similar to other true matches, but also dissimilar with false matches, and inspired by multi-view verification, the true answers should have the same ranking to the question in different baseline methods and false answers are the same. The empirical results, from the public benchmark task of answer selection, demonstrate that our method has significant improvement over the baseline methods.
Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization

The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages.In the 2018 shared task there was very little difference in the absolute performance of systems trained with and without additional, synthetically created data, and a new rule prohibiting the use of synthetic data was introduced for the 2019 shared task.Contrary to the findings of the 2018 shared task, we show, in experiments on the English 2018 dataset, that the use of synthetic data can have a substantial positive effect -an improvement of almost 8 BLEU points for a previously state-of-the-art system.We analyse the effects of synthetic data, and we argue that its use should be encouraged rather than prohibited so that future research efforts continue to explore systems that can take advantage of such data.
Quadratic Meta-Reflectors Made of HfO2 Nanopillars with a Large Field of View at Infrared Wavelengths

Metasurfaces, being composed of subwavelength nanostructures, can achieve peculiar optical manipulations of phase, amplitude, etc. A large field of view (FOV) is always one of the most desirable characteristics of optical systems. In this study, metasurface-based quadratic reflectors (i.e., meta-reflectors) made of HfO2 nanopillars are investigated to realize a large FOV at infrared wavelengths. First, the geometrical dependence of HfO2 nanopillars' phase difference is analyzed to show the general principles of designing infrared HfO2 metasurfaces. Then, two meta-reflectors with a quadratic phase profile are investigated to show their large FOV, subwavelength resolution, and long focal depth. Furthermore, the two quadratic reflectors also show a large FOV when deflecting a laser beam with a deflecting-angle range of approximately ±80°. This study presents a flat optical metamaterial with a large FOV for imaging and deflecting, which can greatly simplify the optical-mechanical complexity of infrared systems, particularly with potential applications in high-power optical systems.
CraftAssist Instruction Parsing: Semantic Parsing for a Voxel-World Assistant

We propose a semantic parsing dataset focused on instruction-driven communication with an agent in the game Minecraft 1 .The dataset consists of 7K human utterances and their corresponding parses.Given proper world state, the parses can be interpreted and executed in game.We report the performance of baseline models, and analyze their successes and failures.
Tractable Fragments of Datalog with Metric Temporal Operators

We study the data complexity of reasoning for several fragments of MTL - an extension of Datalog with metric temporal operators over the rational numbers. Reasoning in the full MTL language is PSPACE-complete, which handicaps its application in practice. To achieve tractability we first study the core fragment, which disallows conjunction in rule bodies, and show that reasoning remains PSPACE-hard. Intractability prompts us to also limit the kinds of temporal operators allowed in rules, and we propose a practical core fragment for which reasoning becomes TC0-complete. Finally, we show that this fragment can be extended by allowing linear conjunctions in rule bodies, where at most one atom can be intensional (IDB); we show that the resulting fragment is NL-complete, and hence no harder than plain linear Datalog.
Optimal Margin Distribution Machine for Multi-Instance Learning

Multi-instance learning (MIL) is a celebrated learning framework where each example is represented as a bag of instances. An example is negative if it has no positive instances, and vice versa if at least one positive instance is contained. During the past decades, various MIL algorithms have been proposed, among which the large margin based methods is a very popular class. Recently, the studies on margin theory disclose that the margin distribution is of more importance to generalization ability than the minimal margin. Inspired by this observation, we propose the multi-instance optimal margin distribution machine, which can identify the key instances via explicitly optimizing the margin distribution. We also extend a stochastic accelerated mirror prox method to solve the formulated minimax problem. Extensive experiments show the superiority of the proposed method.
A Complete Shift-Reduce Chinese Discourse Parser with Robust Dynamic Oracle

This work proposes a standalone, complete Chinese discourse parser for practical applications.We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies.We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition.Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance.
A Learning-Based Low Complexity in-Loop Filter for Video Coding

With the continuous development of mobile devices, it becomes possible for people to demand higher definition videos. To alleviate the pressure of deploying the video codec in mobile multimedia, a learning-based low complexity in-loop filter is proposed in this paper. Depthwise separable convolution is combined with batch normalization to construct this model. To enhance its performance, the knowledge from a pre-trained teacher model is transferred to it. However, the over-smoothing problem in the inter frames caused by double enhancing effect remains. To solve this, a Wiener-based filtering algorithm that tries to restore the distortion from the learned residuals is designed and introduces an adequate filtering effect. The experimental results show that our proposed methods achieve considerable BD-rate reduction than HEVC anchor. Compared with the previous state-of-the-art work VR-CNN, our model achieves 1.65% extra BD-rate reduction, 79.1% decrease in FLOPs, 25% decrease in encoding complexity, and 70% decoding complexity decrease.