Reviewerly/exHarmony huggingface dataset:
IMPROVING STUDENTS’ READING COMPREHENSION OF VIIIGRADE AT SMPN 4 TOLITOLI UTARA THROUGH SUMMARY TECHNIQUE

The objective of this research was to identify whether the use of summary technique effective to improve students’ ability in reading comprehension. An experimental design was used to collect the data, while quantitative approach was used to analyze them. The study was taken by the first grade of SMP Negeri 4 Tolitoli Utara. It was conducted in one class (VIII). The data were obtained through a pre-test and post-test. The pre-test was used to measure the students’ previous ability in reading comprehension, the treatments were applying materials through summary technique, and the post-test was used to find out the students’ reading ability after given treatments. The result of the analysis showed that summary technique contribute greatly to the students in reading comprehensio. It can be seen by the difference of the mean score between pre-test and post-test. The mean score of pre-test was 46,38 while in the post-test improved up to 78,13. Moreover the Minimum Mastery Criterion of English lesson was 75 (seventy-five). Then the data of both test were analyzed by using T-test, and the result showed that the T-tes value 2,229 was greater than T-table value -14,246. It can be inferred that summary technique was very effective to improve students’ ability in reading comprehension. Key words: Summary technique, teaching reading, narative text
A Study of Text Representations for Hate Speech Detection

The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data produced in these platforms has led to automatic tools being a necessary component of the Hate Speech detection task and pipeline. In this study, we examine the performance of several, diverse text representation techniques paired with multiple classification algorithms, on the automatic Hate Speech detection and abusive language discrimination task. We perform an experimental evaluation on binary and multiclass datasets, paired with significance testing. Our results show that simple hate-keyword frequency features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very low-dimensional but rich features for this task. A combination of these representations paired with Logistic Regression or 3-layer neural network classifiers achieved the best detection performance, in terms of micro and macro F-measure.
AI 챗봇 매개 초등영어 과정중심 평가 과업의 개발 및 적용

The purpose of this study aims to develop AI mediated assessment tasks and put them into primary English teaching practice. Taking into account the...
Role of Digital Library in Homeopathic Education in India

The rapid advancement in Information Technologies has revolution the only of libraries. Information Technology has its own niche in every aspect of life within the society. Changes in society & technology made variation within the life sort of Indian people and their views & attitude towards the libraries.Today digital world cares, with creating, sharing & using information in digital form. Information Technology has almost converted the earth into a worldwide village. Digital libraries has brought phenomenal change within the progress of collection, storage, preservation of rare documents, retrieval & dissemination of data Now –a-days the ICT is taken under consideration as an important commodity and is playing an important sole for development altogether fields like education, research etc. this study focuses on the functional aspect of the digital library in homoeopathy education in India. within the present education system, digital libraries are playing a key role in disseminating information by providing differing types of relevant & recent information to the users with a brief span of your time. The digital library can provide open access to the electronic information sources and therefore the users can access the digital content regardless of time & space boundaries. This study also discusses the core elements and functional components of the digital library in homoeopathy education.
The Effect Of Clustering Technique On Students Writing Skill In Recount Text At The Tenth Grade Of SMA Negeri 1 Rantau Utara

The objective of this study is to know the significant effect of Clustering Technique onstudents’ writing skill in recount text at the tenth grade of Senior High School. This studyused quantitative research with quasi-experimental design. It was conducted in tetnth gradeof SMA Negeri 1 Rantau Utara. The researcher took 72 students where 36 students forexperimental class and 36 students for control class. In collecting the data, the researcherused test in essay test. The researcher used two test, pre-test was given before treatmentand post-test was given after treatment in learning process. Then, the researcher analyzedthe data by using statistical formulation and Microsoft Excel 2013 with 0,05 significancelevel. After analyzed the data, the researcher found that the value of tobserved was higher thanttable. It can be seen from tobserved (2,055) > ttable (1,6) at the significance level 0,05 and degreeof freedom (df) = 70. It means that the alternative hypothesis (Ha) was accepted and nullhypothesis (H0) was rejected. It can be conclude that Clustering Technique has significanteffect on students’ writing skills in recount text at tenth grade at SMA Negeri 1 RantauUtara.
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning.

Many real-world applications need path planning algorithms to solve tasks in different areas, such as social applications, autonomous cars, and tracking activities. And most importantly motion planning. Although the use of path planning is sufficient in most motion planning scenarios, they represent potential bottlenecks in large environments with dynamic changes. To tackle this problem, the number of possible routes could be reduced to make it easier for path planning algorithms to find the shortest path with less efforts. An traditional algorithm for path planning is the A*, it uses an heuristic to work faster than other solutions. In this work, we propose a CNN encoder capable of eliminating useless routes for motion planning problems, then we combine the proposed neural network output with A*. To measure the efficiency of our solution, we propose a database with different scenarios of motion planning problems. The evaluated metric is the number of the iterations to find the shortest path. The A* was compared with the CNN Encoder (proposal) with A*. In all evaluated scenarios, our solution reduced the number of iterations by more than 60\%.
Analisis Tingkat Keamanan Informasi : Studi Komparasi Framwork COBIT 5 subdomain manage security services (DSS05) dan NIST SP 800 – 55

Information technology is a very important part of the organization. IT is expected to provide a good profit for the company. However, as technology evolves, it is often exploited by some irresponsible parties that can lead to the emergence of threats and risks from the use of technology. The organization needs to measure the level of information security to identify the system's weaknesses and threats to the organization. Standards for measuring information security are COBIT 5 subdomain manage security services (DSS05) and NIST SP 800-55 revision 1. This study is comparing the two standards. Comparative analysis uses qualitative analysis based on three aspects in information security that are confidentiality, integrity, and availability. Based on the analysis result obtained the advantages and disadvantages of each standard .
Adaptive Single-Pass Stochastic Gradient Descent in Input Sparsity Time

We study sampling algorithms for variance reduction methods for stochastic optimization. Although stochastic gradient descent (SGD) is widely used for large scale machine learning, it sometimes experiences slow convergence rates due to the high variance from uniform sampling. In this paper, we introduce an algorithm that approximately samples a gradient from the optimal distribution for a common finite-sum form with n terms, while just making a single pass over the data, using input sparsity time, and \tOTd space. Our algorithm can be implemented in big data models such as the streaming and distributed models. Moreover, we show that our algorithm can be generalized to approximately sample Hessians and thus provides variance reduction for second-order methods as well. We demonstrate the efficiency of our algorithm on large-scale datasets.
Suggestopedic mobile language learning

The use of suggestopedic teaching methods has been shown to be effective in the domain of language learning. Suggestopaedia is a classroom teaching method that employs certain strategies to enable learners to relax in order to enable more effective learning both consciously and subconsciously. The use of mobile technologies to support language learning has also become very useful and popular. This paper proposes the amalgamation of the two approaches to enable a mobile suggestopedic platform and demonstrates empirical evidence linked to the success of this approach on languge learning. The design of a Suggestopedic mobile language learning app is discussed together with different target groups of learners that can benefit from this type of teaching. Design, development and evaluation of this app forms our future work.
Mobile cross-platform app development tools

This article discusses the existing popular tools for mobile cross-platform application development. It is contrasted with the so – called native application development-when applications are created using the tools provided by the development companies of the respective platforms. Google provides a tool like Android Studio for its Android mobile operating system. The main programming languages used to create applications for this platform are Java and Kotlin. Apple, in turn, offers developers such an integrated development environment as XCode, in which development is carried out using the Swift programming language. The authors reviewed some of the most popular tools for cross-platform development, such as React Native, Xamarin and Flutter. React Native is a product developed by the company Facebook. It inherits the main technological aspects from another framework from Facebook-React. Programming here is conducted in the JavaScript language. React Native is suitable for web developers who have worked with React before and now want to try their hand at developing mobile applications. Xamarin is a framework supported by Microsoft. It provides tools for creating cross-platform applications for the iOS and Android mobile operating systems. This framework is bundled with Microsoft Visual Studio as a downloadable component. What is typical for a Microsoft product, here the C# programming language is used for development. This is a significant advantage of the Xamarin framework: during the creation of a mobile application, developers can use all the important and convenient features of this programming language. Flutter is a tool developed by Google to create apps for Android and iOS using a single code base. Unlike other popular solutions, Flutter is not a framework: it is a set of software development tools that contains everything you need to create cross-platform applications. It includes a visualization engine, ready-made widgets, and tools for working with the command line. The main programming language here is Dart. In the course of studying these tools, the authors made the following conclusions: tools for cross-platform development are an excellent solution if you need to create an application that does not require high performance: displaying information received from the server, simple local information processors, such as applications for social networks, online stores, organizer applications. These tools are not suitable for creating applications that require computing resources.
THE USE OF TASK BASED LANGUAGE TEACHING APPROACH IN TEACHING SPEAKING

Abstract Name: Mauliza Safrina Student No.: 1309200080032 Department/Program: English Education/Graduate Title: The Use of Task Based Language Teaching Approach in Teaching Speaking Keywords: Speaking, Task Based Language Teaching Approach. This study is aimed to investigate the use of Task Based Language Teaching Approach to enhance the students' speaking skills and their responses toward the use of Task Based Language Teaching Approach. It was done experimentally with two classes that were experimental and control groups. The subject of this research was the 2nd grader of SMP Islam Al Mujaddid 2017/2018 with the class VII/B as the experimental group consist with 14 students and class VII/C as the control group consist with 14 students. The Intruments provided were test and questionnaire which were distributed to both experimental and control groups. The data for test was analyzed by using some statistical formulas such as t-test, The Wilcoxon Test, and The Mann-Whitney Test while the questionaire was analyzed with percentage. The results of this study indicated that: (1) TBLT is able to enhance students' ability in speaking skill which is proved by the result of t-test, as the probability / Sig. of t-test is 0.006 therefore the probability / Sig.
Correction to: Applying the concept of implicit HCI to a groupware environment for teaching ethics

A Correction to this paper has been published: https://doi.org/10.1007/s00779-021-01528-1
One Size Doesn't Fit All: Adaptive Label Smoothing

This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs). CNNs have proven to be very good classifiers and generally localize objects well; however, the loss functions typically used to train classification CNNs do not penalize inability to localize an object, nor do they take into account an object's relative size in the given image. During training on ImageNet-1K almost all approaches use random crops on the images and this transformation sometimes provides the CNN with background only samples. This causes the classifiers to depend on context. Context dependence is harmful for safety-critical applications. We present a novel approach to classification that combines the ideas of objectness and label smoothing during training. Unlike previous methods, we compute a smoothing factor that is \emph{adaptive} based on relative object size within an image. This causes our approach to produce confidences that are grounded in the size of the object being classified instead of relying on context to make the correct predictions. We present extensive results using ImageNet to demonstrate that CNNs trained using adaptive label smoothing are much less likely to be overconfident in their predictions. We show qualitative results using class activation maps and quantitative results using classification and transfer learning tasks. Our approach is able to produce an order of magnitude reduction in confidence when predicting on context only images when compared to baselines. Using transfer learning, we gain 0.021AP on MS COCO compared to the hard label approach.
Psychological Component of Remote Learning

Every year the number of information and communication technologies that integrate into the field of education is increasing. The transformation of the learning process into a more flexible and dynamic process is observed. This process has an impact on the psychological component of learning, which requires careful study.
Application Overview and Foresight

With the advent of highly efficient lasers and advance in technical capabilities, it becomes possible to routinely entangle the states of the atoms with photons of different frequencies and with themselves via nonlinear interaction (see Chap. 9 ). The underlying understanding and generation of light with significant quantum features in turn enable the realization of optical processes that are intractable in the regime of classical physics (see Chaps. 8 and 11 ). In light of this, it might be worthy at some point to examine issues related to the inherent quantum traits that can be exploited in processing quantum information. The insight that a two-state quantum system can be in the superposition of the engaged states for instance envisages quantum information processing to be different from the classical counterpart in a way that enables to perform peculiar tasks. The awareness and expectation that the correlation leading to the emergence of quantumness opens multifaceted venues for exploring the ensuing applications of nonclassical properties of light with enhanced performance can therefore be taken as the main motivation for looking into the overview and foresight of continuous variable quantum communication, computation, cryptography and error correction.
Differentiable Graph Optimization for Neural Architecture Search

In this paper, we propose Graph Optimized Neural Architecture Learning (GOAL), a novel gradient-based method for Neural Architecture Search (NAS), to find better architectures with fewer evaluated samples. Popular NAS methods usually employ black-box optimization based approaches like reinforcement learning, evolution algorithm or Bayesian optimization, which may be inefficient when having huge combinatorial NAS search spaces. In contrast, we aim to explicitly model the NAS search space as graphs, and then perform gradient-based optimization to learn graph structure with efficient exploitation. To this end, we learn a differentiable graph neural network as a surrogate model to rank candidate architectures, which enable us to obtain gradient w.r.t the input architectures. To cope with the difficulty in gradient-based optimization on the discrete graph structures, we propose to leverage proximal gradient descent to find potentially better architectures. Our empirical results show that GOAL outperforms mainstream black-box methods on existing NAS benchmarks in terms of search efficiency.
PENGARUH PENERAPAN METODE INTERVENSI SEMANTIK DIVERGEN TERHADAP AFASIA TRANSKORTIKAL MOTORIS

This study aims to determine the application of the divergent semantic intervention method in motor transcortical aphasia clients after stroke, male gender aged 44 years at the Cibabat Regional General Hospital. The research method used is a case study research experiment which aims to determine the condition of the subject before and after intervention through therapy. Data collection was carried out through the WOTS stage, namely (1) interviews with the client's parents, (2) direct observation of the client, (3) conducting tests on the client, and (4) studying the patient's medical record document. The results of the research after doing therapy using the divergent semantic intervention method for 15 meetings obtained good results by assessing the level of words on nouns. The conclusion is that the divergent semantic intervention method has an effect on mentioning the level of objects
A generalized probability kernel on discrete distributions and its application in two-sample test

We propose a generalized probability kernel(GPK) on discrete distributions with finite support. This probability kernel, defined as kernel between distributions instead of samples, generalizes the existing discrepancy statistics such as maximum mean discrepancy(MMD) as well as probability product kernels, and extends to more general cases. For both existing and newly proposed statistics, we estimate them through empirical frequency and illustrate the strategy to analyze the resulting bias and convergence bounds. We further propose power-MMD, a natural extension of MMD in the framework of GPK, illustrating its usage for the task of two-sample test. Our work connects the fields of discrete distribution-property estimation and kernel-based hypothesis test, which might shed light on more new possibilities.
Monotonic Robust Policy Optimization with Model Discrepancy

State-of-the-art deep reinforcement learning (DRL) algorithms tend to overfit in some specific environments due to the lack of data diversity in training. To mitigate the model discrepancy between training and target (testing) environments, domain randomization (DR) can generate plenty of environments with a sufficient diversity by randomly sampling environment parameters in simulator. Though standard DR using a uniform distribution improves the average performance on the whole range of environments, the worst-case environment is usually neglected without any performance guarantee. Since the average and worst-case performance are equally important for the generalization in RL, in this paper, we propose a policy optimization approach for concurrently improving the policy's performance in the average case (i.e., over all possible environments) and the worst-case environment. We theoretically derive a lower bound for the worst-case performance of a given policy over all environments. Guided by this lower bound, we formulate an optimization problem which aims to optimize the policy and sampling distribution together, such that the constrained expected performance of all environments is maximized. We prove that the worst-case performance is monotonically improved by iteratively solving this optimization problem. Based on the proposed lower bound, we develop a practical algorithm, named monotonic robust policy optimization (MRPO), and validate MRPO on several robot control tasks. By modifying the environment parameters in simulation, we obtain environments for the same task but with different transition dynamics for training and testing. We demonstrate that MRPO can improve both the average and worst-case performance in the training environments, and facilitate the learned policy with a better generalization capability in unseen testing environments.
Local Binary Pattern-based Noise Robust Feature for Texture Classification

Abstract: The presence of noise degrades the local binary pattern-based classification efficiency. In the present work, a modified local binary pattern ( —modified noise robust local binary pattern) based classification is proposed. In this, a local binary pattern-based feature is modified, which also captures macrostructure information, whereas the existing features capture microstructure texture information only. The new feature is tested on Outex_TC_00010, Outex_TC_00012 and Brodatz datasets for rotation invariant and noise robust texture classification. The texture images are degraded with multiplicative noise, to evaluate noise robustness of the feature. Nearest neighbour classifier is used for classification which minimises chi-square distance. The proposed feature gives promising results as it is rotation invariant, robust to noise and gives high classification accuracy, especially at high levels of noise. Keywords: Texture classification, local binary pattern, feature extraction, histogram Cite this Article: Simarjot Kaur Randhawa, Ramesh Kumar Sunkaria. Local Binary Pattern-based Noise Robust Feature for Texture Classification. Journal of Image Processing & Pattern Recognition Progress. 2019; 6(3): 31–47p.
Pengujian Aplikasi E-FARMER dalam Perhitungan Keuntungan dengan Metode Blackbox Testing Boundary Value Analysis

A software Testing is one of the stages in building an application where the test device that is implemented imperfectly certainly has a bad effect on the resulting software. E-farmer is an android based application that can provide a farmer experience to calculate the yield of profits for each harvest. The purpose of testing E-farmer is to see the error rate that occurs in the software. So that by testing this application, it is hoped that the application made is in accordance with the function and purpose of the application. Where this test uses the Black-box Testing method with the Boundary Value Analysis approach. The results obtained from this study are testing shows that the application has a success rate of 88, 89%. Three fields need to be improved, namely the id_petani, year, and semester fields.