Reviewerly/exHarmony huggingface dataset:
Penerapan Group to Group Exchange (GGE) Blended Learning Berbantuan WhatsApp untuk Meningkatkan Keaktifan dan Hasil Belajar Peserta Didik SMAN 1 Jetis Ponorogo

The purpose of this research is to improve the condition of the online class, especially the activeness of students during the synchronous and asynchronous learning process. This study is also to determine the increase in student learning outcomes on vektor material. Learning uses the Group to Group Exchange (GGE) model in online classes and blended learning assisted by WhatsApp. This study uses a classroom action research design which consists of two cycles. The research subjects were students of Class X MIPA 1 SMAN 1 Jetis Ponorogo. Data on student activity includes discussion activities (asking or answering questions), presentations, and giving responses. This data was collected through observation sheets. Data about student learning outcomes are obtained through written tests or quizzes after the cycle takes place. Data were analyzed using descriptive statistical methods. The results of the analysis showed an increase in activity and learning outcomes after learning using the GGE steps. GGE consists of (1) material distribution; (2) group distribution; (3) group discussion in the WA group; (4) presentation and group responses in the class WA group; and (5) evaluation. Class action taken in the form of reshuffling group members and face-to-face meetings. Student activity increased, in cycle I when online using WhatsApp, all student activities were recorded on WhatsApp chat only 54%. Group discussions in the WA group were less than optimal and presentations using photo sharing and voice notes were also less than optimal. While in the second cycle when blended learning, group discussions using WhatsApp group and face-to-face while face-to-face presentations, student activity became 88%. The learning outcomes of students whose scores are greater than the KKM increased from 24% in the first cycle to 72% in the second cycle, with the class average increasing from 48.48 to 81.76. The results of this study indicate that using a GGE based on WhatsApp-assisted blended learning can significantly increase student activity and learning outcomes.
HUBUNGAN PERAN ORANG TUA PADA PELAKSANAAN BELAJAR DARI RUMAH MATA PELAJARAN MATEMATIKA TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS 5 SD NEGERI 04 SILUNGKANG 3 KECAMATAN SILUNGKANG KOTA SAWAHLUNTO

The purpose of this study was to describe the relationship between the role of parents in the implementation of learning from home mathematics subjects to the mathematics learning outcomes of 5th graders at SD Negeri 04 Silungkang 3, Silungkang District, Sawahlunto City. This type of research is correlative research. The population in this study were 5th-grade students of SD Negeri 04 Silungkang 3 for the 2020/2021 academic year, totaling 25 people. The sampling technique is total sampling. The instrument in this study is a questionnaire on the role of parents in the implementation of learning from home in mathematics. Based on the calculation, the calculated r-value is 0.86 and the r table value is 0.382. Because of the value of r arithmetic &gt; r table, it can be concluded that there is a significant relationship between the role of parents in the implementation of learning from home mathematics subjects to the mathematics learning outcomes of grade 5 students at SD Negeri 04 Silungkang 3 because the value of r count is 0.86 which means that the level of the relationship between the role of parents in the implementation of learning from home mathematics is very strong. So it can be concluded from this study that there is a significant and very strong relationship between the role of parents in the implementation of learning from home mathematics subjects to the mathematics learning outcomes of 5th graders at SD Negeri 04 Silungkang 3, Silungkang District, Sawahlunto City.
The Impact of Youtube on Iraqi EFL Learners’ Academic Achievements in the Islamic University of Diwaiyah: A Critical Essay

The present essay aims at investigating the effects of the educational videos on the college students’ academic achievements. The present paper came after checking the students’ responses to the lecture’s questions submitted by the instructor to the students to see how far the students go with their responses following their tracking YouTube videos. The present essay was based on the quantitative method and data had been collected through a sample survey. The study has revealed that that watching educational videos affected the academic activities and performance of the respondents positively. It also has confirmed that most of the students preferred short animated to summarize the lectures that they were requested to be presented in the upcoming lecture.
Auxiliary BSPedupack functions

Extract Header file bspedupack.h This header file is included in every program file of BSPedupack. It contains necessary file inclusions, useful definitions and macros, and prototypes for the memory allocation and deallocation functions. Utility file bspedupack.c This file contains the functions used by BSPedupack to allocate and deallocate memory dynamically for storing vectors and matrices. The functions are suited for use in a parallel program, because they can abort a parallel computation on all processors if they detect a potential memory overflow on one of the processors. If zero memory space is requested, which could happen if the local part of a distributed vector or matrix is empty, a vector of length 1 or a matrix of size at least 1 in each dimension is still allocated. This creates a unique pointer and prevents problems with registering an array of length 0 for communication by puts or gets. Allocating an m×n...
Preface

Extract Why this book on parallel scientific computation? The first edition of this book appeared in 2004, and the time was ripe for learning how to design and analyse portable parallel algorithms and to write portable parallel programs. Parallel computing became a lot easier and there was no need anymore to suffer from highly esoteric hardware or software systems. The second edition is motivated by the multicore revolution that took place around 2007. Suddenly, CPU clock speeds stopped increasing and, after a while, we found our laptop and desktop computers equipped with several processor cores. Great news for parallelism: parallel computers are everywhere. My yearly poll of the students in my parallel algorithms class reveals: the best equipped student has 12 cores in his personal computer (class of 2018). This is an opportunity for teaching, since students can now run parallel programs on their own computer, and I exploit this in...
A quick reference guide to BSPlib

Extract Table B.1 groups the primitives of BSPlib into three classes: Single Program Multiple Data (SPMD) for creating the overall parallel structure; Direct Remote Memory Access (DRMA) for communication with puts or gets; and Bulk Synchronous Message Passing (BSMP) for communication with sends. The table is meant as a memory aid to the syntax; for a full specification, see the given page. The 22 primitives of modernized BSPlib [153, 318]. Functions bsp_nprocs and bsp_pid return an unsigned int; bsp_time returns a double; bsp_hpmove returns a size_t; and all others return void. A parameter with an asterisk is a pointer; a parameter with two asterisks is a pointer to a pointer. The pointers occurring as parameters in the primitives are of type void *, so they can handle any type of variable, with a few exceptions: the functions bsp_set_tagsize, bsp_qsize, and bsp_get_tag expect...
Naive Bayes using the expectation-maximization algorithm for reject inference

In the last several years, there has been significant research in applying semi-supervised machine learning models to the reject inference problem. When a financial institution wants to build a model to predict the default of credit applicants, the institution only has a known good/bad outcome loan status for the accepted applicants; this causes an inherent bias in the model. Reject inference is used to infer the good or bad loan status of credit applicants that were rejected by a financial institution. This paper presents a reject inference technique in which a semi-supervised framework is developed using a Naive Bayes model. The framework uses the expectation-maximization (EM) algorithm to incorporate rejected applicants into the parameter estimation of the model using a bootstrapping approach. The proposed method has an advantage over traditional reject inference methods because the rejected applicant data will participate in the estimation of the model parameters, thus avoiding the extrapolation problem. The Naive Bayes model using the EM algorithm is compared to logistic regression and several semi-supervised techniques.
A holistic evaluation methodology for configuring production data centers

Summary Performance evaluation is the basis for choosing appropriate system‐level configurations for large‐scale data centers. While the change of a system‐level configuration would impact lots of jobs in the data centers, traditional load‐testing benchmarks are not sufficient to support the decision‐making because they cannot accurately reproduce the complex behaviors of a large number of jobs. Therefore, we expect to further evaluate the system configuration based on the production environment. However, there are technical challenges, namely, the lack of a holistic evaluation method that can unite the evaluation results of various jobs, and the uninterruptable production environment that should not be affected by the evaluation procedure. To address these challenges, we propose a holistic performance evaluation methodology and design its implementation platform. We introduce a simple but powerful performance metric, ERU (effectiveness of resource usage), and combine the ERU of involved jobs into a summarized value to measure the effect of a configuration change. We validate our ERU metric by comparing it with the CPI (Cycle per Instruction) and QPS (query per second) metrics, deploy the platform to production data centers and demonstrate the effectiveness for measuring system‐level configurations of both software (JVM compiler update) and hardware (NUMA on/off) to save 14.44% and 11% resources respectively in advance.
<scp>ERIM</scp>: An ensemble of rare itemset mining and its application in the automotive industry

Abstract Discovering previously unknown anomalies that are rare and dramatically differ from the majority of the data is a critical need for the automotive industry. Rare itemset mining (RIM), one of the pattern‐based methods, has been used for anomaly detection due to providing successful analysis results. However, several aspects still need to be explored, such as improving the mining process by identifying more targeted, valuable and reliable rare itemsets. Motivated by this fact, this study proposes a novel approach, named ensemble of rare itemset mining (ERIM), which investigates weak rare itemsets (WRIs) using different algorithms and aggregates these rules to obtain strong rare itemsets (SRIs). This study also combines four different RIM algorithms (Apriori Rare, Apriori Inverse, CORI and RP‐Growth) as base learners for the first time. The proposed ERIM approach is a general methodology that can be applied to any field, but, in this study, it was used in the automotive industry as a case study. In the experiments, ERIM was applied to a real‐world gear manufacturing dataset to discover anomalies in machine downtimes. The experimental results were evaluated in terms of the number of itemsets and the length of itemsets by giving some samples, as well. The results showed that the proposed ERIM approach gives more reliable common knowledge by jointly considering the relation between WRIs discovered by the base learners. The findings indicated that the proposed ERIM technique was successful in detecting anomalies whose support values are below 7.12. Furthermore, it is clear from the experimental results that the ERIM discovered the highest number of SRIs, 1403, each of which is a 3‐itemset. Finally, the results showed that our method performed 43.37% better on average than state‐of‐the‐art methods on the same dataset.
Methodology improvement of geodetic work on the basis of geoinformation technologies

Abstract In the system of topographic and geodetic works, the introduction of modern technologies based on GPS and GLONASS satellite systems, geoinformation systems, digital and laser-electronic measuring and computing techniques, as well as laser scanning technologies is a modern requirement. Currently, the relevant authorities in the maintenance of state cadastres are working on the creation and formation of a cadastral database. In this research, the results of scientific research on the significant contribution to the development of the Republic by eliminating the inconveniences of geodetic work in mountainous areas of the Republic were given. According to the results, the resulting territorial points were combined according to a scheme (abris) created for use on the ground, and an electronic digital map of the geo-database of the region was formed.
PRACTICAL ASPECTS OF ELABORATION OF INTERNATIONAL INVESTMENT PROJECT IN EDUCATION SPHERE IN JAPAN

In Japan, one of the strongest economies in the world and home to 125 million people, proficiency in English language is associated with the huge advantages and signifies privilege since the Meiji era when English was used as a tool by Japanese elite in a technological catch-up with the West. The seven years of U.S. occupation of the country after the Second World War have also left a big impact on the Japan’s economic, political and social development. The reconstruction of Japan aided by Americans altered the perception of Japanese people, associating English language with such ideas like freedom and democracy. During the seven years of occupation the country faced a lot of political and economic changes, including liberalization, and was slowly transitioning its central industries from agriculture to light manufacturing and later to heavy manufacturing. The economy of Japan has been slowly recovering from an era of economic stagnation, it is on the path of the moderate growth. Today, it is the third strongest economy with the nominal GDP of about $5.06 trillion that is focused on advanced manufacturing and export, agriculture, fishing and tourism. Japan is very special and different from other countries; a lot of great companies and unique business practices were born there. It is also leading in high-tech innovations and manufacturing. While having a lot of opportunities for various businesses, being politically stable and safe country, its bureaucracy and the comparative difficulty of doing business for foreigners makes it a controversial but potentially rewarding choose for an investment project. In the recent years, the necessity of knowing how to communicate in English has increased significantly. The school program focuses mainly on such skills as reading and writing, and a lot of attempts to reform the system has failed. That is why parents and students are approaching private language schools to master the abilities of speaking and listening. In addition, English teaching is one of the most popular jobs for foreigners in Japan. The language school market is quite saturated, but at given circumstances it can be very rewarding. The idea of the investment project is to create a private language school that will focus on giving Japanese people the practical English skills. The project involves the creation of Gōdō Kaisha (a Japanese analogue to the LLC). This type of incorporation fits the company best, considering the size and scale of the activities. As the company is not planning to issue certificates, the need for special licensing from the state is absent. Appointing a representative director is obligatory in Japan, but GK, allows to appoint a representative director who is not a resident in Japan. The company plans to hire the foreigners already living in Japan, that way the company will be
Set-aware Entity Synonym Discovery with Flexible Receptive Fields (Extended Abstract)

Entity synonym discovery (ESD) from text corpus is an essential problem in many entity-leveraging applications. This paper aims to address three limitations that widely exist in the current ESD solutions: 1) the lack of effective utilization for synonym set information; 2) the feature extraction of entities from restricted receptive fields; and 3) the incapacity to capture higher-order contextual information. We propose a novel set-aware ESD model that enables a flexible receptive field for ESD by using entity synonym set information and constructing a two-level network. Extensive experimental results on public datasets show that our model consistently outperforms the state-of-the-art with significant improvement.
Synthetic Face Aging Using CycleGAN

Generative Adversarial Neural Networks have become well liked framework in the Computer Science and Artificial Intelligence fields. Human face and body changes with age. Face aging predicts an individual’s future as well as past appearance. GAN can generate an aged image from a young input face image and vice versa. In this project work, we put forward face aging using CycleGAN which is one of the variants of GANs. It's a method for leveraging the GAN architecture to train unsupervised image translation models with unpaired collections of photos from two separate domains. Unlike the previously proposed frameworks, we have trained our model by using Adam solver optimization algorithm and PyTorch deep learning open-source software. This helps our model to reduce time complexity and requires less memory for the larger dataset. Further, our work preserves the original identity in which facial features are kept intact.
A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as – the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.
Knowledge-Guided Word Embedding Fine-Tuning Model

Most of the existing word embedding models are learned from word co-occurrence pairs, which shows to capture rich semantic information of words. However, only using word co-occurrence information is not enough for training high-quality word embeddings, the huge knowledge graph resources cannot be ignored for improving the performance of the word embeddings. In this paper, we propose KGWE, a novel knowledge-guided word embedding fine-tuning model, which aims to utilize the entity embeddings learned from the knowledge graphs to guide the word embeddings' fine-tuning process. The main idea of KGWE is to exploit an entity embedding as the target to adjust the representation of each word embedding used in the entity description. We conduct experiments with two famous static word embeddings and five knowledge graph embedding models and evaluate the proposed model by conducting extensive experiments on both of intrinsic evaluation and extrinsic evaluation, including the word similarity task with 14 benchmarks, the relation classification task, and the sentence polarity classification task. Our results show that the word embeddings fine-tuned by KGWE can outperform the baselines.
Automatic and Accurate Weapons Detection Model Using an Optimal Neural Network Architecture

The world is becoming more unsafe every day. Crime rates are steadily increasing with shootings in public areas, racially motivated hate crimes, robberies, and homicides\cite{bonn22}. Due to growing unrest everywhere, there is a high demand for safety and security. Systems such as security cameras equipped with real-time weapons detection technologies are being implemented to aid in preserving the safety of citizens. However, current weapons detection technologies possess limitations: they are limited to detecting guns and/or knives. Criminals, on the other hand, are not limited to only guns and knives. Therefore, this research is an exploration of building an optimal neural network architecture to identify 6 commonly used weapons captured from a dataset of images. This research was conducted in order to create a technology that aids in capturing criminals before they commit a crime to preserve security and safety of citizens. The process begins with creating an optimal Convolutional Neural Network (CNN) and an optimal transfer learning model. The validation accuracies of these models are then evaluated and compared to each other. The final neural network model achieved a 99.31\% training accuracy and a 95.55\% validation accuracy, thus correctly classifying images of various weapons.
Multi Image Encryption Algorithm Based on Dynamic Zigzag and Lsmdpcml Spatiotemporal Chaotic Mapping Map

Nowadays, people pay more and more attention to the privacy and information security of images. Our paper proposes a new Logistic-Sine multi-dynamic piecewise coupled mapping lattice ( LSMDPCML ). We have carried out several chaotic performance tests on LSMDPCML. Through the analysis of the performance test results, it is shown that LSMDPCML has higher energy diffusion efficiency between lattices and better chaotic and cryptographic properties. Based on this, we propose a multi-image encryption system based on the new dynamic Zigzag. Divide multiple images into non overlapping sub blocks for image reconstruction, dynamically Zigzag scramble the image sub blocks and image pixels respectively, and finally carry out nonlinear operations on each pixel value to achieve the effect of diffusion. We have carried out several performance tests on the encryption system, and the test results show that our cryptosystem can not only encrypt multiple images at the same time, but also the ciphertext image has good security, which can protect the information security of the image.