Reviewerly/exHarmony huggingface dataset:
PENINGKATAN KEMAMPUAN MENULIS PERMULAAN ANAK MELALUI MEDIA SAND PAPER ALPHABET DI KELOMPOK B TK NURSA BANDAR LAMPUNG

This study is aimed at exploring the writing ability improvement process of group B students in TK Nursa and investigating to what extend the processing result of writing ability can be improved through sand paper alphabet. This study was a classroom action research using Kemmis and Mc. Taggart research model. This study was conducted in 2 cycles with 6 meetings in each cycle where every cycle consisted of the following steps: planning, action and observation, reflection. The sample of this study was 12 Group B students comprissing of 5 male students and 7 female students. The instruments used in this study was observational sheet of students activities using sand paper alphabet. The data in this study were quantitative and qualitative data. Quantitative data were analyzed by using descriptive statistic to compare the result from the first and second cycle. While qualitative data from field notes and interview were analyzed through the following steps: data reduction, data display and data verification. The results of this study show that there is the improvement of writing ability through sand paper alphabet, proven by the writing ability mean score in pre-cycle which was 50,17 % improve to 67,15 % in cycle I and keep increasing becoming 76,04 % in cycle II.
Psychological features of listening comprehension of English-language video materials by technical university students in flipped classroom mode

Listening comprehension, as the most challenging receptive aspect of learning a foreign language at a technical university, is considered. It is noted that in addition to linguistic difficulties, students experience such psychological difficulties as a lack of recipient’s own perceptual experience, uncomfortable perception conditions, anxiety and fear of failure to perform this type of speech activity. It is shown that the perceptual activity of students, aimed at the auditory perception of English discourse, is naturally included in the most relevant students’ communicative competence. The study is aimed at considering psychological and pedagogical aspects in teaching ordinary listening comprehension without a video sequence and with the use of video materials. We reveal the advantages of using video materials that contribute to the creation of psychologically comfortable conditions for students in the educational process. Opinion analysis of first-year students of the St. Petersburg State University of Telecommunications on conducting listening comprehension in regular and flipped classroom modes is presented: it turns out that most students prefer to perform listening practice at home. We describe the audiovisual technology of advanced independent work on listening comprehension to professionally oriented video materials using the electronic resources VideoAnt, Mindmeister, LMS MOODLE. An advertising film of the computer company CISCO (USA) was used as video course basis, with its subject fully corresponding to the “Service” programme of St. Petersburg State University of Telecommunications.
MBRTE: Modified BLEU Algorithm for Recognition Textual Entailment

The BLEU algorithm has become a standard evaluation tool for Machine Translation (e.g Goole Translate).The advantages of this algorithm are simple and fast.This algorithm is quite widely applied in fields other than MT, including in the Recognition Textual Entailment (RTE).The implementation of BLEU algorithm to the RTE requires some modification because of several problems.One of these problems is related to the sensitivity of BLEU scores.This research has modified BLEU to overcome these problems.Experimental data set contains 200 hypotheses with each hypothesis have some text.The experimental results obtained an accuracy of 83,82 % for modified BLEU.While the accuracy results using BLEU without modification is obtained at 77,50%.So that the accuracy increases of 6,32%.
"Digital Image" in the Methodological System of Information Competency Development by University Students

The paper emphasizes the relevance of the process of forming the of a higher education institution student within building the methodological system for developing information competency. The authors of the research describe approaches to defining the phenomenon of an individual's digital image and the influence of transformation on the formation thereof. The modern resources, online projects and platforms for self-training, self-development and forming one's personal are outlined not only in the information system, but also in the methodological system of development of information competency in higher education institution students with a focus on individual achievements. Lifelong education principles are described which support one's profound and unmistakable image. Attention is paid to forming up the methodological system for developing information competency in students – future teachers, – their personal and professional establishment in the era of transformation with an outlook to form a profound and unmistakable of personality. The leading research methods are: the analysis of methodological approaches to studying the phenomenon of an individual's digital image, its prospects and risks; based on summarizing the conceptual provisions about the phenomenon of one's digital image, making the understanding thereof more precise when forming up the methodological system of development of information competency in higher education institution students. The materials of the paper are of practical importance for master-degree students, postgraduate students, and teachers of higher education institutions and other educational institutions conducting research in this domain
The Need to Use a Hash Function to Build a Crypto Algorithm for Blockchain

The study examines the need and prospects of using blockchain technology for the Internet of Things. The possibility of implementing cryptography for open distributed systems of the Internet of Things without limitation of viewing, but with limitation of unauthorized access is considered. Possibilities of data protection against unauthorized access are shown employing the method of constructing hash function and digital signature in the blockchain itself. This is implemented using a cryptographic algorithm based on a pseudorandom number generator.
Annotation-Free Gliomas Segmentation Based on a Few Labeled General Brain Tumor Images

Pixel-level labeling for medical image segmentation is time-consuming and sometimes infeasible. Therefore, using a small amount of labeled data in one domain to help train a reasonable segmentation model for unlabeled data in another domain becomes an important need in medical image segmentation. In this work, we propose a new segmentation framework based on unsupervised domain adaptation and semi-supervised learning, which uses a small amount of labeled general brain tumor images and learns an effective model to segment independent brain gliomas images. Our method contains two major parts. First, we use unsupervised domain adaptation to generate synthetic general brain tumor images from the brain gliomas images. Then, we apply semi-supervised learning method to train a segmentation model with a small number of labeled general brain tumor images and the unlabeled synthetic images. The experimental results show that our proposed method can use approximate 10% of labeled data to achieve a comparable accuracy of the model trained with all labeled data.
Extension of the Time Dependent Travelling Salesman Problem with Interval Valued Intuitionistic Fuzzy Model Applying Memetic Optimization Algorithm

The Time Dependent Traveling Salesman Problem (TD TSP) is an extension of the classic Traveling Salesman Problem towards more realistic conditions. TSP is one of the most extensively studied NP-complete graph search problems. In TD TSP, the edges are assigned different weights, depending on whether they are traveled in the traffic jam regions (such as busy city centers) and during rush hour periods, or not. In such circumstances, edges are assigned higher costs, expressed by a multiplying factor. In this paper, we introduce a novel and even more realistic approach, the Interval Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem (IVIFTD TSP); which is a further extension of the classic TD TSP, with the additional notion of deploying interval valued intuitionistic fuzzy for describing uncertainties. The core concept employs interval valued intuitionistic fuzzy sets for quantifying the traffic jam regions, and the rush hour periods loss (those are additional costs of the travel between nodes), which are always uncertain in real life. Since type-2 (such as inter valued) fuzzy sets have the potential to provide better performance in modeling problems with higher uncertainties than the traditional fuzzy set, the new approach it may be considered as an extended, practically more applicable, extended version of the original abstract problem. The optimization of such a complex model is obviously very difficult; it is a mathematically intractable problem. However, the Discrete Bacterial Memetic Evolutionary Algorithm proposed earlier by the authors' team has shown sufficient efficiency, general applicability for similar type problems and good predictability in terms of problem size, thus it is applied for the optimization of the concrete instances.
Deep Learning-Based Computer Vision Application with Multiple Built-In Data Science-Oriented Capabilities

This paper presents a Data Science-oriented application for image classification tasks that is able to automatically: a) gather images needed for training Deep Learning (DL) models with a built-in search engine crawler; b) remove duplicate images; c) sort images using built-in pre-trained DL models or user’s own trained DL model; d) apply data augmentation; e) train a DL classification model; f) evaluate the performance of a DL model and system by using an accuracy calculator as well as the Accuracy Per Consumption (APC), Accuracy Per Energy Cost (APEC), Time to closest APC (TTCAPC) and Time to closest APEC (TTCAPEC) metrics calculators. Experimental results show that the proposed Computer Vision application has several unique features and advantages, proving to be efficient regarding execution time and much easier to use when compared to similar applications.
Architectural Model of Backpropagation ANN for Prediction of Population-Based on Sub-Districts in Pematangsiantar City

A population is a group of individuals who occupy or live in a place or area that interacts with one another. Because the population has a very important role in an area, it is important to make predictions to find out how much the level of increase or descent of the population in an area, especially in Pematangsiantar. Therefore this research was conducted. This study uses population data in 8 Sub-Districts in Pematangsiantar. Data was taken from the Central Statistics Agency (BPS) of Pematangsiantar city in 2011-2017. The method used is the Artificial Neural Network (ANN) Backpropagation. These data will be processed into 2 parts namely training data and Testing data. This research will use 5 architectural models namely, 3-25-1, 3-30-1, 3-45-1, 3-54-1 and 3-68-1. From these 5 architectural models, after analysis, models 3-45-1 were chosen as the best models with epoch 553 values, MSE training 0,0001108768, MSE testing 0.0012355953 and an accuracy rate of 88%. The results of this paper are expected to be widely useful, especially for academics as further research material, especially those related to population in Pematangsiantar, because this research is still limited to discussing the level of accuracy, not prediction results.
Reading Literacy Movement in Elementary School / Gerakan Literasi Membaca di Sekolah Dasar

The purpose of this study is identifying the problematics the implementation of the Reading Literacy Movement in Primary Schools. The approach in this research is qualitative with descriptive type. Data collection techniques in this study used observation, interviews, and documentation. This research was conducted at SDN X in Malang City. The results showed that the problem of the Reading Literacy Movement at SDN X in Malang was divided into 3 categories, namely the 15 minute reading habit, with the problem of reading and writing skills and the ability to understand student reading, limited reading time, low motivation and interest in reading. At the facility and facility enrichment stage category, with the problem of the availability various books in the library, limited reading areas, reading angles, and literacy posters. The category of supporting resources, with the problem of reading literacy climate, teachers and literacy reading teams, parental and community support.
Harnessing Social Interactions on Twitter for Smart Transportation Using Machine Learning

Twitter is generating a large amount of real-time data in the form of microblogs that has potential knowledge for various applications like traffic incident analysis and urban planning. Social media data represents the unbiased actual insights of citizens' concerns that may be mined in making cities smarter. In this study, a computational framework has been proposed using word embedding and machine learning model to detect traffic incidents using social media data. The study includes the feasibility of using machine learning algorithms with different feature extraction and representation models for the identification of traffic incidents from the Twitter interactions. The comprehensive proposed approach is the combination of following four steps. In the first phase, a dictionary of traffic-related keywords is formed. Secondly, real-time Twitter data has been collected using the dictionary of identified traffic related keywords. In the third step, collected tweets have been pre-processed, and the feature generation model is applied to convert the dataset eligible for a machine learning classifier. Further, a machine learning model is trained and tested to identify the tweets containing traffic incidents. The results of the study show that machine learning models built on top of right feature extraction strategy is very promising to identify the tweets containing traffic incidents from micro-blogs.
iGG-MBS: Iterative Guided-Gaussian Multi-Baseline Stereo Matching

This paper presents an improved dense disparity estimating technique for a collection of multi-baseline stereo (referred to as MBS in the text) images. The flow of the proposed system consists of two main frameworks: a preliminary cost calculation and initial disparity estimating framework, and an iterative cost refinement framework. The first framework implements an accurate multi-baseline stereo cost (referred to as MBSC in the text) calculation method, and a scan line optimization inspired by the Semi Global Matching (SGM) algorithm. Cost volumes of each two-view camera pair are calculated by fusing two pixel dissimilarity measures: i) weighted Census transformation and ii) sum of absolute difference color consistency term (SAD-Census). The initial disparity map between reference and the matching view with the largest baseline displacement is calculated by summing-and-interpolating SAD-Census costs of the current and all neighboring camera pairs in-between, and taking the minimum after aggregating for sixteen directions. The second framework refines the aggregated MBSC volume recursively. In each iteration, individual pair-wise disparity maps are used to warp matching views towards the reference to create binary masks that resemble overlapping differences. White locations in the mask represent incorrect correspondence matches, thus a penalty is added for costs associated with, adapting a Gaussian modulating function. This significantly reduces the selection probability of incorrect disparity minima in proceeding iterations. A Guided filter-based Rolling Guidance filter is applied to further up-vote the probability of pixels with the lowest costs, which are similar or close enough to ground truth readings. Through experimental results evaluated on the Middlebury dataset, we show that our method leads to effective and efficient multi-baseline disparity estimations.
Design and application of gene-pool optimal mixing evolutionary algorithms for genetic programming

Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable outcomes. For example, deep neural networks for self-driving cars have been found to provide wrong steering directions based on the lighting conditions of street lanes (e.g., due to cloudy weather). In other cases, models can capture and reflect unwanted biases that were concealed in the training data. For example, deep neural networks used to predict likely jobs and social status of people based on their pictures, were found to consistently discriminate based on gender and ethnicity–this was later attributed to human bias in the labels of the training data.
Email Threat Detection Using Distinct Neural Network Approaches.

This paper describes different approaches to detect malicious content in email interactions through a combination of machine learning and natural language processing tools. Specifically, several neural network designs are tested on word embedding representations to detect suspicious messages and separate them from non-suspicious, benign email. The proposed approaches are trained and tested on distinct email collections, including datasets constructed from publicly available corpora (such as Enron, APWG, etc.) as well as several smaller, non-public datasets used in recent government evaluations. Experimental results show that back-propagation both with and without recurrent neural layers outperforms current state of the art techniques that include supervised learning algorithms with stylometric elements of texts as features. Our results also demonstrate that word embedding vectors are effective means for capturing certain aspects of text meaning that can be teased out through machine learning in non-linear/complex neural networks, in order to obtain highly accurate detection of malicious emails based on email text alone.
REDUPLICATION OF WORD CLASS OF INDONESIAN PROSEDIC MORPHOLOGY: TOWARDS A SEMANTICO-PRAGMATIC PERSPECTIVE

&lt;p&gt;&lt;em&gt;This research is meant to describe reduplication of word classes in prosedic morphology in the perspective of semantico-pragmatics. This research data is in the form of reduolication of closed class words in the use of language in the mass media. The substantive data source of this research is in the form of text in which there are data in the form of reduplicative forms. The locational data source is the national mass media, i.e. Net TV both print and electronic, which can be reached by the research team around the time of research. After the data is classified and properly verified, the next step is the analysis and interpretation of the data. The analytical method applied is a distributional method with techniques for direct elements. Finally, the results of the analysis and interpretation of the data are presented in an informal method. The results showed that the most dominant reduplication occurred in closed word classes in Indonesian in the mass media was adverb reduplication. In this study, adjective reduplication was also quite significant, even though it was not the case with adverb reduplication and verb reduplication. Reduplication of nouns occupies the least significant portion.&lt;/em&gt; &lt;em&gt;In terms of meanings, research in a semantico-pragmatic perspective rather than semantic linguistics is purely necessary to continue to be pursued so that new perspectives can be born in researching language.&lt;/em&gt;&lt;/p&gt;
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning.

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.
Compression by Substring Enumeration Using Sorted Contingency Tables

This paper proposes two variants of improved Compression by Substring Enumeration (CSE) with a finite alphabet. In previous studies on CSE, an encoder utilizes inequalities which evaluate the number of occurrences of a substring or a minimal forbidden word (MFW) to be encoded. The inequalities are derived from a contingency table including the number of occurrences of a substring or an MFW. Moreover, codeword length of a substring and an MFW grows with the difference between the upper and lower bounds deduced from the inequalities, however the lower bound is not tight. Therefore, we derive a new tight lower bound based on the contingency table and consequently propose a new CSE algorithm using the new inequality. We also propose a new encoding order of substrings and MFWs based on a sorted contingency table such that both its row and column marginal total are sorted in descending order instead of a lexicographical order used in previous studies. We then propose a new CSE algorithm which is the first proposed CSE algorithm using the new encoding order. Experimental results show that compression ratios of all files of the Calgary corpus in the proposed algorithms are better than those of a previous study on CSE with a finite alphabet. Moreover, compression ratios under the second proposed CSE get better than or equal to that under a well-known compressor for 11 files amongst 14 files in the corpus.