Reviewerly/exHarmony huggingface dataset:
Pengaruh Aplikasi Poro Belajar Bahasa Jepang Kanji terhadapKemampuan Kanji pada Mahasiswa Prodi Pendidikan Bahasa Jepang FKIP Universitas Riau

Learning media is one of the supporting components that can help a learning process. One of the learning media that can be applied is the application media in learning kanji. The application is called "Poro Learn Japanese Kanji (Poro)". The purpose of this study was to see the effect of applying application media on kanji learning for class of 2019 students of Japanese Language Education, UniversitasRiau. This study used the experimental method. The data for this study were collected by distributing pre-test and post-test questions via Google Form. The results of this study indicate that of the 34 samples, 24 people experienced the effect of increasing value changes in learning kanji after the application of Poro application media. Then 3 people experienced a decrease in value, while the other 7 people did not have any effect.
Speaker Identification Based on Physical Variation of Speech Signal

Speaker identification for the speech signal processing request, determining the speaker is a challenge due to physical variation. This paper emphasizes a new algorithm based on acoustic feature analysis of text-dependent speech. In this proposed method text-dependent speech changed by ten physical variation methods. Acoustic feature of all types of voice is calculated by its arithmetical correlation coefficients and mean value. The audio characteristic is calculated with Mel-frequency cepstrum coefficient (MFCC), its derivatives and double derivatives. An acoustic characteristic is analysed by using normal voice and changed voice by different speakers, the mixed data used for test and training purpose. Passing all the training and test data through the various classifiers based on identification system. Speaker identification efficiency results are calculated from the different classifier.
Cloud-Based Education As a Service (CEAAS) System Requirements Specification Model of Higher Education Institutions in Industrial Revolution 4.0

In today’s higher education institutions (HEIs), intelligentization and digitization of education process are greatly required. Production industries are presently making changes from large-scale production to specialized or customized production. The term Industry 4.0 (IR 4.0) represents the fourth industrial revolution; the present movement or inclination of automation and data exchange and sharing in manufacturing technologies with the main aim of satisfying the individual customer desire and needs. The quick progressions in production technologies and its uses in the industries enhance production efficiency and change the workplace from tasks-based to the human-focused features. Higher education in the IR 4.0 (HE 4.0) is a complex, intricate and intriguing opportunity which has the potential to change the society for the better. The convergence and integration of man and machine will decrease the subject distance between social science and humanity and also technology and science. This will definitely need in-depth and diverse interdisciplinary teaching, research and innovation. Cloud computing (CC) as an IR 4.0 contributing technology, provides a novel means of educating people that will ultimately disrupt the present HEIs systems. Cloud-based education as a service in the era of fourth industry (CEaaS 4.0) can deliver education services in the shortest, most effective, and best affordability. The aim of this study is to propose a CEaaS system requirements specification (SRS) model that includes functional and non-fictional requirements; aligned with IR 4.0, the next industrial revolution.
Robust Formal Watermarking Model Based on the Hyperbolic Geometry for Image Security

The digital revolution has led to an increase in the production and exchange of valuable digitized documents across institutions, companies and the general public alike. Ensuring the authenticity, integrity and ownership of these official or high-value documents is essential if they are to be considered useful. Digital watermarking is a possible solution to this challenge as it has already been used for copyright protection, source tracking, and video authentication to name just a few applications of its use. It also enables integrity protection, which is of value for numerous documents types (e.g., official documents, medical images). In this paper, we propose a new watermarking solution that is applicable to image watermarking and is based on hyperbolic geometry. Our new solution builds upon existing work in geometrical watermarking.
A Naive based share generation using Random Visual Crypto Technique

With the advancement of technology and desire of Digitalization the sensitive data that are transmitted over the internet has to be secured. Since leakage of Information can lead to security theft, which happens in cybercrime. Hence securing the data which we share over the internet is almost as important in fulfilling the desire of digitalization. With this aim visual cryptography techniques are proposed. In the encryption phase of visual cryptography shares of secret data are created to transmit over the channel. And the shares are combined at the decryption phase to regain the original data. Considering one the existing method called RGB based multiple share creation with aid of Elliptical curve cryptography. The drawback of low PSNR value of retrieved image is boosted by adopting RSA in the second stage of encryption and proposed a Novel Visual cryptography Method that results in ∞ PSNR value along with retaining maximum information with PSNR in the retrieved data after attack.
Solar Tracker

Solar Tracker&#x0D; The generation of power from the reduction of fossil fuels is the biggest challenge for the next half century. The idea of converting solar energy into electrical energy using photovoltaic panels holds its place in the front row compared to other renewable sources. But the continuous change in the relative angle of the sun with reference to the earth reduces the watts delivered by solar panel. Conventional solar panel, fixed with a certain angle, limits their area of exposure from the sun due to rotation of the earth. Output of the solar cells depends on the intensity of the sun and the angle of incidence. To solve this problem, an automatic solar cell is needed, where the Solar Tracker will track the motion of the sun across the sky to ensure that the maximum amount of sunlight strikes the panels throughout the day. By using Light Dependent Resistors, it will navigate the solar panel to get the best angle of exposure of light from the sun.
A novel entropy-based dynamic data placement strategy for data intensive applications in Hadoop clusters

In the last decade, efficient data analysis of data-intensive applications has become an important research issue. Hadoop is the most widely used platform for data intensive application. However, majority of data placement strategies attempt placing related-data close to each other for faster access without considering new datasets, generated or for different MapReduce jobs. This paper deals with improving the map-reduce performance over multi-cluster datasets by means of a novel-entropy-based data placement strategy (EDPS) in three-phases. K-means clustering strategy is employed to extract dependencies among different datasets and group them into data-groups. Then these data-groups are placed in different datacenters while considering heterogeneity. Finally, an entropy-based grouping of the newly generated datasets where these datasets are grouped with most similar existing cluster based on their relative entropy. The experimental results show efficacy of the proposed three-fold dynamic grouping and data placement policy, which significantly reduces the time and improve Hadoop performance.
Constructing contrastive samples via summarization for text classification with limited annotations

Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited.How to construct efficient contrastive samples through data augmentation is key to its success.Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks.In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization.We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations.To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss.Experiments on realworld text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.
Mining

In the context of blockchain networks, mining describes a permissionless process intended to ensure the global consistency of a decentralised ledger.Mining requires the consumption of a costly computational resource to participate in a probabilistic competition that confers specific privileges to a node.These privileges typically relate to the proposal of a new block, including the identity and order of transactions contained within.Mining is incentivised via an algorithmically regulated provision of rewards, usually in the form of newly generated coins and/or transaction fees.
Digital Video Stabilization: Algorithms and Evaluation

Several devices have allowed the acquisition and editing ofvideos in various circumstances, such as digital cameras, smartphones and other mobile devices. However, the use ofcameras under adverse conditions usually results in non-precise motion and occurrence of shaking, which may compromise the stability of the obtained videos. To overcome such problem, digital stabiliza- tion aims to correct camera motion oscillations that occur in the acquisition process, particularly when the cameras are mobile and handled in adverse con- ditions, through software techniques, without the use of specific hardware, to enhance visual quality either with the intention of enhancing human percep- tion or improving final applications, such as detection and tracking of objects. This is important in order to avoid hardware cost and indispensable for videos already recorded. This work proposed three methods to perform digital video stabilization and two other techniques to evaluate video stabilization quality. 1.
OPTIMIZATION OF ATTRIBUTE SELECTION MODEL USING BIO-INSPIRED ALGORITHMS

Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently.Hence, reducing the consistency of the accuracy rate.The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set.This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes.Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set.Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.
Sequence-to-Sequence Lexical Normalization with Multilingual Transformers

Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication.This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data.One way to resolve this issue is through lexical normalization, which is the process of transforming non-standard text, usually from social media, into a more standardized form.In this work, we propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem.As the noisy text is a pervasive problem across languages, not just English, we leverage the multilingual pre-training of mBART to fine-tune it to our data.While current approaches mainly operate at the word or subword level, we argue that this approach is straightforward from a technical standpoint and builds upon existing pre-trained transformer networks.Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.
The Effect of Local Crime on Construction Projects in Padang City

Local crimes often target construction sites for some financial gains. Theft, extortion, and vandalism are common forms of crimes that occurred in a construction project. They can disrupt the process and reduces the contractor’s profits. The security in a project site is necessary to maintain the expected productivity and profits, and often, crime is not taken into account in the project plan. Due to this problem, it is crucial to conduct a proper management plan to control construction site security. This paper presents a research outcome on the effects of local crime towards the success of a construction project in Padang, Indonesia. A purposive sampling technique was adopted for the study. The process of data collection involved obtaining primary data from the respondents by conducting questionnaire and structured interviews. The questionnaires were filled-out by professionals from two categories of construction firms, i.e., contractor and force-account private sector. Five contractor type of project and thirty-four force account projects by the private sector were randomly selected. The results showed that the theft occurred more frequently in general contract projects that are managed by the contractor than in the force-account projects. However, the gain-loss due to theft was four times higher in the force account projects than the contractor one. The vandalism occurred more frequently in general contract projects than in the force-account projects. The occurrence of extortion is similar between general contract projects and force account projects. The extortion is found to be the most frequent security problem occurring on construction projects in Padang City, and can potentially increase the project cost.
Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.
PHP Frameworks Usability in Web Application Development

A framework defined as a structure thatsupports the development of dynamic websites, web applications, and services. Framework code and design are often reusable to assist customization, resource service, and API-related tasks. This study discussed current practice to help a developer understand PHP frameworks adoption for web application development. Three approaches were selected to understand the features suitability of the PHP frameworks: the systematic approach, score criteria evaluation, and PHP framework technical factors. A comparison of 23 different frameworks features also has been studied that involves features such as ORM, Code Generator, Template Engine, and CRUD Generator. Besides PHP framework features, understanding the basic core PHP to build web application would help a lot in learning PHP frameworks. Moreover, new developers should not limit themselves to a particular PHP framework only but also allow themselves to explore various PHP frameworks in the development of web application projects.
Video Deraining for Mutual Motion by Fast Bilateral Filtering on Spatiotemporal Features

In video analytics, out of all the known challenges, (a) adverse unconstrained environmental condition resulting low illumination/ contrast and (b) mutual motion between camera assembly and object of interest are definitely the major ones. The challenges become multi-fold when the scene of interest gets impacted by both the aforementioned simultaneously. The first part of the current work has proposed a novel hierarchical scene categorization methodology based on selected spatiotemporal features. The procedure of determination of feature hierarchy and the detailed description of step by step methodology of the same has been provided along with comprehensive results. The second part of the current work proposes a novel method of processing rainy videos in different mutual motion scenarios by dynamic bilateral filtering and deep auto-encoder on time-sliced video frames. The proposed idea exploited the strength of range-domain filtering of bilateral filter, saliency extraction of deep auto-encoder and spatiotemporal model of time-slicing. Quantitative results of extensive experiments showed the effectiveness of our proposed algorithm in different degree of environmental degradation. The proposal of the scene categorization not only classifies the scenes based on mutual motion between camera assembly and object of interest, but it also triggers and influences the algorithm of deraining dynamically by offering required modulation.
Building a Text Analysis Pipeline for Classical Languages

With large text collections for Ancient Greek and Latin now widely available, classicists are increasingly interested in extracting information systematically from these texts.The fields of information retrieval and natural language processing offer tools and methods to address this, but classicallanguage support can be limited and researchers must often cobble together separate, sometimes incompatible tools to accomplish basic text analysis tasks.In this chapter, I review the tools currently available for digital philological work on Ancient Greek and Latin and introduce the Classical Language Toolkit, an open-source Python framework that addresses the desideratum of a complete text analysis pipeline for historical languages.
A Study on Technological Pedagogical and Content Knowledge among Teacher-Educators in Punjab Region

Effective ICT integration requires teachers to gain proficiency in TPACK (knowledge of technology, content, pedagogy, and the intersection of these) (Mishra &amp; Koehler, 2006; Archambault, &amp; Crippen, 2009). TPACK is perceived as a dynamic framework representing the knowledge that teachers must confide on to design and implement curriculum and instruction while guiding their students’ thinking and learning with digital technologies in various subjects. TPACK competencies are very fruitful in making teaching learning process an ecstatic experience as it would make notable changes in the interaction pattern of educators. Even though TPACK is a boon in teaching and learning, it is a fact that the fruits of these skills are not appropriately utilized by the stakeholders. This study was aimed to evaluate the Technological Pedagogical and Content Knowledge (TPACK) competencies among teacher-educators in teaching training colleges in the state of Punjab, (India). A five-point Likert scale constructed and validated by the researchers was used in the present study. Instrumental survey method was practiced as a tool for the present research. 200 teacher-educators working in different teaching-training colleges in the state of Punjab, (India) were selected through random sampling method. The collected data was analyzed by using SPSS 22.0 software. The findings of the study show that the technological pedagogical and content knowledge (TPACK) competencies have found high in the teacher-educators of Punjab region. The study revealed that there are statistically significant differences in the (TPACK) competencies of teacher-educators with respect to gender, locality of college, stream and type of colleges.
SwipeBuddy

Mobile devices are the core computing platform we use in our everyday life to communicate with friends, watch movies, or read books. For people with severe physical disabilities, such as tetraplegics, who cannot use their hands to operate such devices, these devices are barely usable. Tackling this challenge, we propose SwipeBuddy, a teleoperated robot allowing for touch interaction with a smartphone, tablet, or ebook-reader. The mobile device is mounted on top of the robot and can be teleoperated by a user through head motions and gestures controlling a stylus simulating touch input. Further, the user can control the position and orientation of the mobile device. We demonstrate the SwipeBuddy robot device and its different interaction capabilities.
The empirical structure of psychological characteristics of the students’ formation as subjects of innovative pedagogical activity

А. В. Зобков, И. В
Development of the Descriptive Writing Learning Model using the Audio Visual Media

This development research aims to develop a learning model that has been used by teachers to become a new learning model that is able to create a learning process that is fun, no burden, and not boring. The research procedure follows the 4-D development phase (four D) Thiagarajan namely: (1) defining; (2) design; (3) development; (4) distribution. The product trial was carried out on two high school and city school category categories (high level) established Padangpanjang SMPN 1 and low-level and peripheral group schools (low level) determined by Padangpanjang SMPN 3. The instruments used were interviews, observations, questionnaires, questionnaires, and tests. Development of descriptive writing learning models using audio visual media produces the main syntax of the side, namely: (1) orientation, (2) observing audio-visual media, (3) descriptive writing, (4) presenting descriptive content reports, and (5) reflecting the process learning that has been done. Learning tools developed consisted of 1) Learning Implementation Plans (RPP), 2) descriptive writing learning models, and 3) audio-visual media. The quality of learning devices is tested from three aspects, namely: validity, practicality, and effectiveness. The validity of the learning model is based on the results of the descriptive writing test of the students.