Reviewerly/exHarmony huggingface dataset:
E-Numeracy: Mobile Application of The Numeracy Understanding Model For Primary School

The purpose of this study is to examine the use of e-Numeracy: Mobile Application of the Numeracy Understanding Model among primary school students. Through e-Numeration, it allows students to solve numerical problems in their respective smart devices and find out the solution for each question. This learning method supports the concept of 21st century learning and facilitates self-learning as well as fun learning. This e-Numeration can be accessed via a tablet or any digital device by using the bar code provided. The app is easy and mobile and can be accessed anywhere. Six Year Four students were selected for this study. They were chosen based on their monthly Mathematics test results. There were two students per level; good, average and weak. For data collection, this study applies qualitative design using interview methods. The result indicated that only four respondents were able to solve all the learning activities at the application level that involved concepts and facts of mathematics, procedures for selection and definition and giving reasons. Two respondents were at the level of knowledge acquisition. This level involved elaborating basic facts, defining and formulating operations that were used in the learning activities. In conclusion, through the e-Numeracy application, student is able to recognize his numeracy level of comprehension and the student can identify their error directly as well as the students enjoyed using this application. This study also indicates that the necessity to increase the level of numeracy understanding for students at elementary level.
THE LANGUAGE DEVELOPMENT IN INDONESIAN PRESIDENTIAL DECREES

President has an authority to establish policies in order to run the government, one of which is through Keputusan Presiden Republik Indonesia (Indonesian Presidential Decree). As written documents, the Decrees have language development which can be seen from the writing patterns. Therefore, this study aims to examine the writing patterns of Decrees from time to time to support forensic linguistic study, such as document forgery analysis. We took sixteen Presidential Decrees from 1945 to 2018 as the data then analyzed them by using qualitative method. The data were obtained from the National Archives of the Republic of Indonesia and the official website of the Cabinet Secretariat of the Republic of Indonesia (www.sipuu.setkab.go.id). This study indicates that language development in Indonesian Presidential Decrees is found in the structure of the Decrees, diction, and sentence structure. On the Decree structure, 1945 and 1946 Decrees contain one long sentence to explain the intent, whereas in the 1947—2018 Decrees, intents are elaborated in several clauses. Additionally, there are various types of diction used in the consideration sections. On the syntax, various types of sentences can be found in the Decrees. 1940s Decrees contain simple sentences and complex sentences while 1950s—2010s Decrees contain simple sentences, complex sentences, compound sentences, and mixed sentences.
Large-scale acoustic characterization of mid-low vowels across American, British, and Singaporean children

This study compares how American, British and Singaporean children differ in their production of mid-low vowels. Read speech was collected from 6–13 years old American children (140 speakers, 43 406 utterances), British children (82 speakers, 32 542 utterances) and Singaporean children (192 speakers, 34 457 utterances), with a balanced gender ratio. All three corpora were designed to be phonetically balanced and formant estimates were extracted from the vowel tokens using the Praat software. Our large-scale acoustic analyses show that for TRAP-BATH split vowels, (1) British and Singaporean children both produce these vowels with higher F1, suggesting a relatively lowered tongue height; (2) These vowels have higher F2 (suggesting a more fronted tongue position) for both American and Singaporean children. One-way ANOVA tests reached statistical significance for all these differences; F(2,411) = 34.59, p &amp;lt; 0.001 for F1; F(2,411) = 544.13, p &amp;lt; 0.001 for F2. When comparing /æ/ and /ɛ/ productions, British speakers demonstrate the clearest distinction between the two vowels; Singaporean and American speakers both exhibit a higher and more fronted tongue position for /æ/, causing /æ/ to be more acoustically similar to /ɛ/. One-way ANOVA tests and post hoc Tukey’s test demonstrated that these differences are all statistically significant too (p &amp;lt; 0.001).
Semantic Retrieval Based on User Intention Recognition in Engineering Domain

In the big data era, the rapid explosion and diversification of knowledge have brought various difficulties to effective knowledge retrieval. The keyword matching-based retrieval can only carry out character matching mechanically, which ignores the semantic content contained in the keyword itself; ontology-based semantic retrieval does not recognize and analyze the structural relationship among keywords user inputs, so it is unable to mine the implicit user intentions; the effectiveness of the intention recognition method based on user’s behaviors is not easy to guarantee because behaviors are usually uncontrollable and variable. In view of the above deficiencies, this paper proposes a semantic retrieval method based on user intention recognition. Firstly, the paper introduces a glossary model to indicate different facets of relations of same type from different dimensions, and mine the inner association among keywords deeply. Secondly, by analyzing the structural relationship of multiple keywords, it acquires the user’s intention which is represented by four parameters using retrieval intention representation method, and gives a specific word expansion strategy directed towards different intentions. Finally, the effectiveness of the method is verified by an example which proves that the method can obtain more knowledge matching with the retrieval intent.
H<sub>∞</sub> State Estimation for Neural Networks Subject to Missing Measurements with Uncertain Missing Probabilities

This paper addresses the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> state estimation problem for a type of neural networks with missing measurements. A series of stochastic variables obeying Bernoulli distributions is adopted to characterize the randomly occurring missing measurements, where the missing probability is considered to be changeable over time and satisfies a norm-bounded condition. This paper aims to construct an effective state estimator such that the mean-square asymptotical stability and the specified H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the augmented estimation error system can be achieved. A criterion is given for the existence of the admissible estimator according to a combination of Lyapunov theory as well as stochastic analysis technique, and the estimator parameters are obtained by dealing with a convex optimization problem. Finally, a simulation example is carried out to illustrate the usefulness of the provided estimation scheme.
How Realistic and How Predictive is My Sequence Stratigraphic Interpretation? Stratal Control Volumes, Area, and Trajectories as a Tool to Test Sequence Stratigraphic Interpretations

It is important to test if a sequence stratigraphic interpretation is realistic. This is particularly true if predictions of stratal geometries are then made on the basis of the interpretation, because clearly the predictive power depends largely on how realistic the interpretation is. Testing is also a basic element of a falsifiable model; a most basic aspect of the scientific method is that in order to be useful, all models and interpretation should be able to be tested and rejected if they fail that test. So how can we test the realism of otherwise of a sequence stratigraphic interpretation? One approach is to determine if rates of change in accommodation and supply controls implied by the interpretation are realistic.
On the Shortest Pivotal Confidence Intervals: An Entropy Measure Technique

For the important role of confidence intervals in statistical inference, we present in this article the shortest pivotal confidence interval using an entropy measure called the Resistor-Average distance, two examples are used to show the application of the proposed technique which include a simulation study.
Describing Images by Semantic Modeling using Attributes and Tags

This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of semantic labels. Motivated by the fact that most attributes describe local properties, we propose exploiting localization cues, through semantic parsing of human face and body to improve person-related attribute prediction. We also demonstrate that image-level attribute labels can be effectively used as weak supervision for the task of semantic segmentation. Next, we analyze the Selfie images by utilizing tags and attributes. We collect the first large-scale Selfie dataset and annotate it with different attributes covering characteristics such as gender, age, race, facial gestures, and hairstyle. We then study the popularity and sentiments of the selfies given an estimated appearance of various semantic concepts. In brief, we automatically infer what makes a good selfie. Despite its extensive usage, the deep learning literature falls short in understanding the characteristics and behavior of the Batch Normalization. We conclude this dissertation by providing a fresh view, in light of information geometry and Fisher kernels to why the batch normalization works. We propose Mixture Normalization that disentangles modes of variation in the underlying distribution of the layer outputs and confirm that it effectively accelerates training of different batch-normalized architectures including Inception-V3, Densely Connected Networks, and Deep Convolutional Generative Adversarial Networks while achieving better generalization error.
Big Data Performance Analysis on a Hadoop Distributed File System Based on Modified Partitional Clustering Algorithm

This paper proposes a Big Data Performance Analysis based on a modified Partitional Clustering Algorithm (PCA) on a Hadoop Distributed File System (HDFS) which is commonly used in various business applications. This paper has utilized an improved K-means clustering algorithm, which selects the initial clustering centers based on the density parameters. After calculating the density parameter, the data with largest density parameter is selected as the first initial clustering center point, all the left data in the field is deleted from the dataset. By repeating the above phases, K initial clustering centers are found. A new method to improve the precision and packing effect of the K-means computation is needed as there is a poor assurance of finding an initial centers. The proposed approach does not select the initial clustering algorithm randomly, so the stable K value can be obtained by calculating Variance based Cluster Validity Index (VCVI). The performance of the proposed method is evaluated with the parameters Precision, Clustering time and Recall. The experimental result shows that the proposed approach reduces the complexity along with various parameters are compared with existing methods.
VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal.

The recent precision increase in image-based depth estimation encourages to use this type of data for mapping. Recent work proposes different approaches to deal with the problem of occlusion generated by different scene perspectives of stereo cameras. However, there is less attention to depth estimation and inpainting for object removal and object occlusion. In this paper, we study recent inpainting approaches for RGB images and apply these methods on depth maps. We propose a Generative Adversarial Network (GAN) for depth feature extraction to estimate the depth inside a masked area, in order to remove objects on disparity images. Our results show that using depth features on the loss function and on the network architecture, increase the result precision and give to the generated image a depth distribution close to the real data. Our main contribution is a GAN, which estimates depth information in a masked area inside a disparity image.
PENGUKURAN PERMITIVITAS KOMPLEKS MATERIAL MAGNETIK MENGGUNAKAN METODE S-PARAMETER DENGAN PENDEKATAN NICOLSON ROSE-WEIR

Dielectric properties of material are important properties because it describes the interaction of material with electromagnetic waves. The complex permittivity value of material is interesting because it can represent on how much energy savings and dissipation when the material gets exposed to electromagnetic waves. The aim of this paper was to determine complex permittivity by Nicolson-Ross-Weir (NWR) method. The NWR method was based on the phenomenon of reflection and transmission of electromagnetic wave that drip on materials. The values of the reflection coefficient () and the transmission coefficient (T) were derived from reflection (S11) and transmission (S12) parameters. Determination of real permittivity in the frequency range of 10 GHz to 15 GHz of BaFe12O19 material was e ’ = 4.36 and e ” = 6.23. The second sample was BaFe9 (MnCo)1,5Ti1,5O19, the values of e ’ = 5.49, while e ” = 9.35. From the two test materials, it was revealed that in the frequency range of 10 GHz to 15 GHz, absorption did not occur in BaFe12O19, while in BaFe9 (MnCo)1.5Ti1,5O19 absorption occured at 13.0 GHz. Keywords : Ferromagnetic, S-parameter, complek permittivity
Nonparametric statistical inference and imputation for incomplete categorical data

Missingness in categorical data is a common problem in various real applications. Traditional approaches either utilize only the complete observations or impute the missing data by some ad hoc methods rather than the true conditional distribution of the missing data, thus losing or distorting the rich information in the partial observations. In this paper, we propose the Dirichlet Process Mixture of Collapsed Product-Multinomials (DPMCPM) to model the full data jointly and compute the model efficiently. By fitting an infinite mixture of product-multinomial distributions, DPMCPM is applicable for any categorical data regardless of the true distribution, which may contain complex association among variables. Under the framework of latent class analysis, we show that DPMCPM can model general missing mechanisms by creating an extra category to denote missingness, which implicitly integrates out the missing part with regard to their true conditional distribution. Through simulation studies and a real application, we demonstrate that DPMCPM outperforms existing approaches on statistical inference and imputation for incomplete categorical data of various missing mechanisms. DPMCPM is implemented as the R package \texttt{MMDai}, which is available from the Comprehensive R Archive Network at this https URL.
Communication between native and non-native speakers

For decades, research in speech communication has ask how an individual perceives and produces speech. However, most prior research has used an idealized, monolingual speaker-listener as its model, which is not the norm in our globalizing world. Real-world situation of communication between native and non-native speakers can result in challenges for successful communication. However, the sources of those miscommunications are not well-understood. While many studies have directly examined how a learner acquires a second language, only in the last decade has research expanded rapidly on how a native listener might improve their ability to understand non-native speech. An understanding of the communication between native and non-native speakers is required to truly understand the processes that underlying speech communication, broadly. In this talk, I address issues of communication between native and non-native speakers in their capacities as speakers and listeners. Specifically, I describe the current state of knowledge about how non-native speakers understand and produce speech in their second (or third) language, how native speakers understand non-native speech, and how both parties can improve their abilities at these tasks. This line of inquiry has implications for both practical goals of improving communication outcomes and theoretical understanding of speech perception and production.
IL4IoT: Incremental Learning for Internet-of-Things Devices

Considering that Internet-of-Things (IoT) devices are often deployed in highly dynamic environments, mainly due to their continuous exposure to end-users’ living environments, it is imperative that the devices can continually learn new concepts from data stream without catastrophic forgetting. Although simply replaying all the previous training samples can alleviate this catastrophic forgetting problem, it not only may pose privacy risks, but also may require huge computing and memory resources, which makes this solution infeasible for resource-constrained IoT devices. In this paper, we propose IL4IoT, a lightweight framework for incremental learning for IoT devices. The framework consists of two cooperative parts: a continually updated knowledge-base and a task-solving model. Through this framework, we can achieve incremental learning while alleviating the catastrophic forgetting issue, without sacrificing privacy-protection and computing-resource efficiency. Our experiments on MNIST dataset and SDA dataset demonstrate the effectiveness and efficiency of our approach.
Sundanese Language Politeness as a Local Wisdom in the Industrial Revolution Era 4.0

The fact shows that one of the cultural forms of the archipelago that belongs to the Sundanese community is politeness in language.Many speakers of Sundanese are deviant in the use of language politeness, whereas in politeness, the value of local wisdom is stored which can be used to deal with the Industrial Revolution Era 4.0.This study aims to explain the problems and the results of repositioning Sundanese language politeness in the Industrial Revolution Era 4.0.In this study a qualitative approach with descriptive methods was used.The research data in the form of cases of use of Sundanese politeness were collected by intuition techniques, documentation techniques, and observation techniques.Data analysis uses immediate constituent analysis.The language elements described in this study are Sundanese-language politeness related to pronunciation, word formation, sentence arrangement, interpretation of word meanings, and socio-pragmatic contexts.From the results of this study, it is expected to obtain a comprehensive explanation of the current situation of Sundanese language politeness and the repositioning of Sundanese language politeness rules that include phonological levels, grammatical, lexico-semantic levels, and socio-pragmatic levels.
“What” perception of sounds and words

Speech perception is a task that, on the surface, is deceptively simple. Every day in most of our interactions, we are able to understand speech from a variety of talkers about a variety of topics without significant challenges. However, the actual task of understanding speech is a complex one, requiring a listener to quickly extract information from a noisy and variable acoustic signal to determine the speaker’s intended meaning. Determining what linguistic features and words a speaker has intended is not trivial, as many factors can influence the acoustic signal. That is, the same intended word or speech segment can be produced with many distinct acoustic realizations depending on the speaker, the surrounding speech context, and other linguistic features, including the speaking rate. Further, speech perception can be even more challenging when a listener is trying to perceive speech in noisy environments, including environmental noise (e.g., construction noise) or competing speech from other talkers. In this talk, I will focus on how listeners are able to extract relatively invariant meaning representations from a highly variable signal. Understanding what is perceived when listeners hear speech is critically important for an understanding of what factors matter for speech privacy.
Method of histopathological diagnosis of mammary nodules through deep learning algorithm

ABSTRACT Introduction: Artificial intelligence systems are promising health care technologies, mainly in medical subareas such as pathology, and can be used as support methods for the histological diagnosis of mammary nodules. Objective: This study describes the method and results of the development of artificial intelligence software for the histopathological analysis of mammary nodules. Methods: The software was developed by using two neural networks - Inception and MobileNet. The database used for learning the conditions analyzed (histologically normal breast, fibroadenoma, fibrocystic changes, in situ ductal carcinoma, invasive carcinoma of no special type and invasive lobular carcinoma) was obtained after authorization of the Path Presenter site with 5,298 images. The 2,740 images used for the validation of the system were obtained from the Pathology Institute of Passo Fundo. Results: The present software had sensitivity of 80.5% [95% confidence interval (CI), 71.9%-89.1%] and specificity of 96.1% (95% CI, 94.3%-97.8%) for MobileNet and sensitivity of 73.8% (95% CI, 52.6%-115%) and specificity of 94.7% (CI 95%, 91.7%-97.7%) for Inception. For the differentiation of malignant conditions, it obtained a maximum sensitivity of 78.7% and specificity of 95.8%; for differentiation of benign conditions, the maximum sensitivity was 82.6% and the specificity was 97.4%. Conclusion: The present software presented promising results in the histopathological analysis of mammary nodules. It reinforced the idea that in the future the presence of diagnostic support systems in breast pathologies may play a crucial role in health care.
The Practice of Reading Approaches in Developing Students’ Reading Competency on Javanese Language among Primary School Teachers

Reading is important to students' academic achievement and contains a problematical process.One important aspect of teaching reading has been ignored, that is the need to teach students the suitable reading approaches.In this study we investigated the use of reading approaches in developing students' reading competency on Javanese language among primary school teachers.For the purpose of the study, twenty school teachers from jepara central java have been randomly selected as the research subjects.A questionnaire survey was administered to define whether the teachers have knowledge of reading approaches and to demonstrate their students.The findings of the study indicated that while teachers were responsive of the use of reading strategies in developing reading capabilities on javanese language, they did not completely utilized them in teaching reading to primary school students.Effective reading approaches such as setting context, recitation the text to students and predicting or interpreting transcript were less frequently used.Other than, teachers also unsecsessful to encourage students' interaction with the script.