Reviewerly/exHarmony huggingface dataset:
THE USAGE OF RUNNING DICTATION METHOD TO IMPROVE THE STUDENTS’ WRITING ABILITY

Writing is the difficult skill to master that is why exercise regularly needed. The objective of this study is to know the usage of running dictation method improves the students’ writing descriptive text. This experimental research was carried out in MTs Mesra Pematangsiantar in the academic year of 2020/2021. The research design of this study is classroom action research with quantitative approach. The population of this study is all students grade eight at junior high school, they are 212 students were divided into six classes. And as a sample of this study consist of 30% from the population were VIII-1 as the experimental group and VIII-2 as the control group. Each group consist of 35 students. The experimental group was treated by usage Running Dictation Method, while the control group was treated by using Direct Instruction. The finding of this research are the score pre-test in control group are 1740 where the mean is 49,71 and in experimental group 1542 where the mean is 44,05. The mean score of different in control group is 15,77 and the t-test is 4,86 The standard deviation of the data in control group is 11,01 and the effect size in teaching writing descriptive text in control group is 1,43 It means that the students ability is low. The students’ score post-test in control group are 2292 where mean is 65,48 and in experimental group are 3023 where the mean is 86,37 The mean score of different in experimental group is 42,32 and t-test for experimental group is 5,88. The standard deviation of the data in experimental group is 14,41 and the effect size in experimental group is 2,93. It means that the students ability is high. The data analysis show the following findings: (1) Usage Running Dictation Method is more effective than Direct Instruction in teaching writing, (2) students with high creativity have better writing ability than those having low creativity, (3) there is an interaction between usage running dictation method and teamwork between students in teaching writing
THE USING OF TWO STAY TWO STRAY (TSTS) STRATEGY ON STUDENTS’ READING COMPREHENSION

The objective of this research was to find out the effect of using Two Stay Two Stray (TSTS) strategy on students’ reading comprehension at the tenth grade of SMA Negeri 4 Pematangsiantar. In this research, the researcher used a quantitative research and the method of this research is experimental research by used true-experimental research design. The population of this research was the students of the tenth grade PMIA SMA Negeri 4 Pematangsiantar, there are seven classes and the total of population in this research was 249 students. To determine the sample, the researcher make a lottery, the sample of this research were two classes. The first class is taken from X PMIA-6 with 36 students as control class and the second class is taken from X PMIA-7 with 36 students as experimental class. So, the total of the sample in this research is 72 students. In analyzing the data, the researcher used SPSS version 2.1. The result of this research was the students’ score pre-test in control class are 1876 with the mean score 52.11 and the students’ score post-test in control class are 2152 with the mean score 59.78. Before the treatment of Two Stay Two Stray strategy, the students’ prior knowledge in reading comprehension was less with score pre-test are 1870 with the mean score 51.94. After the treatment using Two Stay Two Stray strategy, the students’ score post-test in reading comprehension had been improved are 2808 with the mean score 78.00. It means that the scores of experimental class are higher than in control class because the researcher conducted the research in experimental class by using a treatment (Two Stay Two Stray Strategy). Then the results of hypothesis testing using T-Test obtained tcount (9.86) &gt;ttable (1.66), it can be concluded that Ho is rejected and Ha is accepted. It means that the using of Two Stay Two Stray strategy affects significantly on students’ reading comprehension at the tenth grade of SMA Negeri 4 Pematangsiantar
Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.
Automating System Test Case Classification and Prioritization for Use Case-Driven Testing in Product Lines

Product Line Engineering (PLE) is a crucial practice in many software development environments where software systems are complex and developed for multiple customers with varying needs. At the same time, many development processes are use case-driven and this strongly influences their requirements engineering and system testing practices. In this paper, we propose, apply, and assess an automated system test case classification and prioritization approach specifically targeting system testing in the context of use case-driven development of product families. Our approach provides: (i) automated support to classify, for a new product in a product family, relevant and valid system test cases associated with previous products, and (ii) automated prioritization of system test cases using multiple risk factors such as fault-proneness of requirements and requirements volatility in a product family. Our evaluation was performed in the context of an industrial product family in the automotive domain. Results provide empirical evidence that we propose a practical and beneficial way to classify and prioritize system test cases for industrial product lines.
Style transfer-based image synthesis as an efficient regularization technique in deep learning

These days deep learning is the fastest-growing area in the field of Machine Learning. Convolutional Neural Networks are currently the main tool used for image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this paper, we have focused on the most frequently mentioned problem in the field of machine learning, that is relatively poor generalization abilities. Partial remedies for this are regularization techniques e.g. dropout, batch normalization, weight decay, transfer learning, early stopping and data augmentation. In this paper, we have focused on data augmentation. We propose to use a method based on a neural style transfer, which allows generating new unlabeled images of a high perceptual quality that combine the content of a base image with the appearance of another one. In a proposed approach, the newly created images are described with pseudo-labels, and then used as a training dataset. Real, labeled images are divided into the validation and test set. We validated the proposed method on a challenging skin lesion classification case study. Four representative neural architectures are examined. Obtained results show the strong potential of the proposed approach.
Automatic Discovery of Privacy-Utility Pareto Fronts

Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can be provided in a particular application while maintaining acceptable utility. Analytical utility guarantees offer a rigorous tool to reason about this trade-off, but are generally only available for relatively simple problems. For more complex tasks, such as training neural networks under differential privacy, the utility achieved by a given algorithm can only be measured empirically. This paper presents a Bayesian optimization methodology for efficiently characterizing the privacy--utility trade-off of any differentially private algorithm using only empirical measurements of its utility. The versatility of our method is illustrated on a number of machine learning tasks involving multiple models, optimizers, and datasets.
Phoenix -- The Arabic Object-Oriented Programming Language

A computer program is a set of electronic instructions executed from within the computer memory by the computer central processing unit. Its purpose is to control the functionalities of the computer allowing it to perform various tasks. Basically, a computer program is written by humans using a programming language. A programming language is the set of grammatical rules and vocabulary that governs the correct writing of a computer program. In practice, the majority of the existing programming languages are written in English-speaking countries and thus they all use the English language to express their syntax and vocabulary. However, many other programming languages were written in non-English languages, for instance, the Chinese BASIC, the Chinese Python, the Russian Rapira, and the Arabic Loughaty. This paper discusses the design and implementation of a new programming language, called Phoenix. It is a General-Purpose, High-Level, Imperative, Object-Oriented, and Compiled Arabic programming language that uses the Arabic language as syntax and vocabulary. The core of Phoenix is a compiler system made up of six components, they are the Preprocessor, the scanner, the parser, the semantic analyzer, the code generator, and the linker. The experiments conducted have illustrated the several powerful features of the Phoenix language including functions, while-loop, and arithmetic operations. As future work, more advanced features are to be developed including inheritance, polymorphism, file processing, graphical user interface, and networking.
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems

Large-scale distributed computing systems often contain thousands of distributed nodes (machines). Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely resource demanding as this requires collecting local measurements of each individual node and constantly sending those measurements to a central controller. Meanwhile, it is often useful to forecast the future system conditions for various purposes such as resource planning/allocation and anomaly detection, but it is usually too resource-consuming to have one forecasting model running for each node, which may also neglect correlations in observed metrics across different nodes. In this paper, we propose a mechanism for collecting and forecasting the resource utilization of machines in a distributed computing system in a scalable manner. We present an algorithm that allows each local node to decide when to transmit its most recent measurement to the central node, so that the transmission frequency is kept below a given constraint value. Based on the measurements received from local nodes, the central node summarizes the received data into a small number of clusters. Since the cluster partitioning can change over time, we also present a method to capture the evolution of clusters and their centroids. As an effective way to reduce the amount of computation, time-series forecasting models are trained on the time-varying centroids of each cluster, to forecast the future resource utilizations of a group of local nodes. The effectiveness of our proposed approach is confirmed by extensive experiments using multiple real-world datasets.
Peningkatan Motivasi Dan Hasil Belajar Materi Luas Bangun Datar Melalui Model Pembelajaran STAD Pada Siswa Kelas V SD 2 Besito

Based on the results of the observation, it was shown that the motivation and learning outcomes of material in the broad wake of students of class V SD 2 Besito were still low. The motivation for average learning is 72.92% and students who complete their learning outcomes are only 50%. For this reason, researchers using the STAD learning model to improve both. Because the STAD learning model has the advantage that higher mental functions will appear in conversations or cooperation between individuals. From the research conducted for two cycles, conclusions were obtained, namely through the application of the STAD learning model to increase learning motivation to 91.66%, and learning completeness to 91%.
Meningkatkan Minat Dan Hasil Belajar Matematika Materi Nilai Tempat Melalui Model Pembelajaran Course Review Horay Pada Siswa Kelas II SD 2 Padurenan

This research is motivated by the initial conditions of the 2nd grade students of SD 2 Padurenan who have constraints, namely low learning interest, which is only 50%, and the completeness of student learning outcomes is only 36%. So from that step is taken to improve both by applying the Course Review Horay (CRH) learning model, because this model can make students more interested and enthusiastic in learning. After two cycles of research using this CRH model, it was concluded that students' learning interest increased to 86%, and the completeness of student learning outcomes increased to 93%.
Penerapan Model Student Teams-Achievment Division (STAD) Berbantu Alat Permainan Pecahan Untuk Meningkatkan Hasil Belajar Siswa Mata Pelajaran Matematika Materi Pecahan Semester II Kelas IV SD 2 Padurenan Kecamatan Gebog Kabupaten Kudus

In general, action research aims to improve mathematics learning outcomes with the subject matter of simplifying fractions Elementary School fourth grade students 2 Padurenan Gebog Kudus in the Academic Year 2017/2018. Learning model used is Student Teams-Achievement Division (STAD) with a subject of research students 2 Padurenan Elementary School fourth grade and time studies conducted over three months consisted of two cycles. Each cycle consists of planning, implementation, observation and reflection. Data collection tool using written tests and non-test. While the techniques of data analysis using qualitative analysis techniques. The results showed learning model STAD can improve student learning outcomes. It can be seen from the increase in average student learning outcomes of the initial condition for 55.56 increased to 67.78 in Cycle 1 with a passing grade of 61.11%, and in cycle 2 to 78.33 with a passing grade of 77, 78%.
Dual Active Sampling on Batch-Incremental Active Learning

Recently, Convolutional Neural Networks (CNNs) have shown unprecedented success in the field of computer vision, especially on challenging image classification tasks by relying on a universal approach, i.e., training a deep model on a massive dataset of supervised examples. While unlabeled data are often an abundant resource, collecting a large set of labeled data, on the other hand, are very expensive, which often require considerable human efforts. One way to ease out this is to effectively select and label highly informative instances from a pool of unlabeled data (i.e., active learning). This paper proposed a new method of batch-mode active learning, Dual Active Sampling(DAS), which is based on a simple assumption, if two deep neural networks (DNNs) of the same structure and trained on the same dataset give significantly different output for a given sample, then that particular sample should be picked for additional training. While other state of the art methods in this field usually require intensive computational power or relying on a complicated structure, DAS is simpler to implement and, managed to get improved results on Cifar-10 with preferable computational time compared to the core-set method.
SIFT Multi-Purpose Forensic Operating System For Digital Forensic Process.

The number of cyber incidents in which computer system or device is currently increasing every day. This requiring the opening up of forensic investigations in to multi-purpose forensic operating system for digital forensic process, a research that can shed light on what has occurred, In order to be able to provide investigators with proper solutions. for performing complete and efficient examinations in this new environment, operating systems platform and devices are being studied from a forensic perspective so that tools and procedures can be designed accordingly Keywords: Forensic Investigations, Cyber Forensic, Multi-Purpose Forensic Operating.
The brain works hard to learn physical skills

Engram neurons responsible for motor memories create both new synaptic inputs and outputs during skill learning.
Privacy Preserving Internet of Things (IoT) Forensics

The Internet of Things (IoT) envisions a network of pervasive, connected, and intelligent nodes that interact autonomously and provide a variety of services. IoT objects were a perfect target for cyber assaults because of their wide dispersion, openness, and relatively high processing power. Furthermore, because many IoT nodes collect and process personal or private data, they are becoming a goldmine of information for cybercriminals. As a result, security, particularly the ability to detect compromised nodes, as well as the collection and preservation of evidence of an attack or malicious activity, emerge as a top priority in the effective deployment of IoT networks. Key words: Information Security, IoT, Privacy Preserving, Cyber Security, Cyber Criminals
survey of privacy-preserving mechanisms for heterogeneous data types

Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users’ privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs.
Pengaruh Penerapan Model Blended Learning Terhadap Hasil Belajar Peserta Didik Materi Sistem Ekskresi

Abstract. This study aims to find outthe learning outcomes of learners who are taught with Blended learning, to find out the influence of blended learning model on the learning outcomes of learners pateri excretion system in MA DDI Kanang and to find out the improvement of learning outcomes of learners of excretion system materials in MA DDI Kanang. This research is a pseudo experimental research (Quasy experimen t) using Pretest Posttest Control Group Design. The sample of the study was selected randomly (random sampling) so thattwo classes were obtained, namely class XI MIPA 2 as an experimental class with blended learning model and XI MIPA 1 as a control class using direct instruction learning model. Data collection techniques used through tests or evaluations. The collected data is analyzed in descriptive statistics and inferential statistics. The results of descriptive statistical analysis showed thatthe learning that was taught by blended learning model was in the category of excellent and good while in the control group that was taught with direct instruction was in the category of excellent, good and sufficient. The data obtained was also analyzed by N-Gain to determine the improvement of learners' learning outcomes and obtained learning outcomes of learners in high categories and classes taught by direct instruction were in the medium category. The results of inference analysis showed that there is an influence of Blended Learning model on the learning outcomes of learners of excretion system material in MA DDI Kanang.Keywords: blended learning, direct instruction, learning outcomes
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment

We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one's motion to an arbitrary animation head. Experiments demonstrate the usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods. Our dataset and code are available at https://github.com/kangyeolk/AnimeCeleb.
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering

Driving simulators play a large role in developing and testing new intelligent vehicle systems. The visual fidelity of the simulation is critical for building vision-based algorithms and conducting human driver experiments. Low visual fidelity breaks immersion for human-in-the-loop driving experiments. Conventional computer graphics pipelines use detailed 3D models, meshes, textures, and rendering engines to generate 2D images from 3D scenes. These processes are labor-intensive, and they do not generate photorealistic imagery. Here we introduce a hybrid generative neural graphics pipeline for improving the visual fidelity of driving simulations. Given a 3D scene, we partially render only important objects of interest, such as vehicles, and use generative adversarial processes to synthesize the background and the rest of the image. To this end, we propose a novel image formation strategy to form 2D semantic images from 3D scenery consisting of simple object models without textures. These semantic images are then converted into photorealistic RGB images with a state-of-the-art Generative Adversarial Network (GAN) trained on real-world driving scenes. This replaces repetitiveness with randomly generated but photorealistic surfaces. Finally, the partially-rendered and GAN synthesized images are blended with a blending GAN. We show that the photorealism of images generated with the proposed method is more similar to real-world driving datasets such as Cityscapes and KITTI than conventional approaches. This comparison is made using semantic retention analysis and Frechet Inception Distance (FID) measurements.