Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN PEMBELAJARAN ONLINE BERBASIS MICROSOFT SWAY UNTUK MENINGKATKAN PRESTASI BELAJAR ( Studi Pengembangan Pada Mata Pelajaran Informatika Siswa Kelas X SMA Negeri 1 Pagar Alam)

The aim of this study was describe development about microsoft sway-based media and test its effectiveness on student achievement in informatics in class X SMA Negeri 1 Pagar Alam. This study used development research, with research subjects 10 students of class X IPS 2 for a limited scale, X IPS 4 for the Experiment Class and X IPS 1 for the control class. Data collection techniques using questionnaires, interviews, validation sheets and test questions. Data analysis using t-test and the average validation value of IT and media experts. After carrying out the development process, the product is validated with the results of material validation 3.46 and 3.81 categories of IT experts are very feasible to be tested and make revisions based on suggestions. After conducting a limited trial in class X IPS 2 with the results of t-count analysis 12.829 and t-table 1.734, there was a significant influence in the use of sway-based media and made revisions based on input from students. The results of the broad-scale Ciba test in the experimental and control class t-count 20.600 while the t-table is 1.669. This means that if the t-count is greater than the t-table, it means that the use of sway-based media in the experimental class has a significant effect on student achievement
Pelatihan Manajemen Seni Pertunjukan kepada Komunitas Seni Kesara

The purpose of this training activity is (1) to provide an understanding of the stages of managing a show or event. (2) So that the event can be walk on the planned. To achieve this, training activities are needed including (1) Providing performing arts management materials, (2) Assisting partners and to be holding Komunitas Kesara that will launch a poetry musical album.
LibGuides: Poetry: Close Reading - Pre-Writing

This is a general guide designed to help students get started reading poetry, aid them in beginning their research, and assist them in conducting a Close Reading.
A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.
Cross-modal verification for 3D object detection

To overcome the deficiency in the single modality of LiDAR point cloud, we propose a cross-modal verification (CMV) model for reducing 3D object detection false positives.The abundant color and texture information in image modality allow the classification of the projection region of 3D bounding box proposal in the image plane.Three 3D object detectors are adopted as backbone and eight evaluation metrics are used to fully investigate the proposed model.The experiment results show that the proposed CMV model removes more than 50% of false positives in 3D object detection proposals and significantly improves the performance of 3D object detection.
Evolutionary Deep Multi-Task Learning

Multi-task learning is an approach to reduce the amount of required training data by learning multiple tasks at the same time.In the context of neural networks, multi-task learning is performed by sharing weights or creating dependencies between weights of task-specific networks.In this work, we propose an algorithm that uses a simple evolutionary algorithm, which is able to match and also surpass learned weight sharing.We evaluate the performance of this method on CIFAR-100, cast as a multi-tasking problem, using an 18-layer residual network, and compare our results to literature.
Introducing k-lingo: a k-depth Bounded Version of ASP System Clingo

Depth-Bounded Boolean Logics (DBBL for short) are well-understood frameworks to model rational agents equipped with limited deductive capabilities. These Logics use a parameter k&gt;=0 to limit the amount of virtual information, i.e., the information that the agent may temporarily assume throughout the deductive process. This restriction brings several advantageous properties over classical Propositional Logic, including polynomial decision procedures for deducibility and refutability. Inspired by DBBL, we propose a limited-depth version of the popular ASP system \clingo, tentatively dubbed k-lingo after the bound k on virtual information. We illustrate the connection between DBBL and ASP through examples involving both proof-theoretical and implementative aspects. The paper concludes with some comments on future work, which include a computational complexity characterization of the system, applications to multi-agent systems and feasible approximations of probability functions.
Fuzzy-Possibilistic Clustering for Categorical Multivariate Data

Fuzzy co-clustering is a technique for extracting co-cluster structures from cooccurrence data among objects and items, where fuzzy partition of objects is associated with possibilistic partition of items. In this paper, with the goal of improving the noise robustness of object fuzzy partition, fuzzy-possibilistic partition is introduced into fuzzy clustering for categorical multivariate data (FCCM). Not only the conventional probabilistic fuzzy memberships but also possibilistic typicality memberships are jointly utilized in estimation of object partition while item partition is still remained to be possibilistic. The characteristics of the proposed algorithm are demonstrated through numerical experiments.
MENGHADIRKAN TEKS-TEKS AUTENTIK DALAM KELAS BIPA DARING

Abstract&#x0D; Indonesian that taught as the foreign language in BIPA (Indonesian for Foreigners) classes and its authenticity are inseparable. Authenticity is related to the originality. In the context of BIPA learning, the authenticity can be presented in class activities related to contexts and real experiences, for examples students are asked to shop at the market, practice to bargain, practice to use public transportations, practice how to introduce themselves, interview native speakers, and many other authentic tasks. However, what makes it challenging today is that BIPA students cannot come directly to Indonesia and the learning process should be conducted online. Nonetheless, the authenticity can be still presented in authentic texts, not only in written but also in spoken. In addition, texts have complete thinking structures and understandings. Before presenting them in the class, teachers should understand the texts, situation context, culture context, authenticity, and types of text. The author also analyzes Graduate Competency Standards (SKL) of BIPA to identify types of text based on student’s level. &#x0D; Keywords: texts, situation contex, culture contex, authenticity, types of texts
The HES-code and the data protection during COVID-19 pandemic in Turkey

Mobile applications are a beneficial tool to fight the coronavirus. With the mobile tracing applications, it became easier to cut the chain of transmission of the virus and reduce the number of daily cases. Many countries developed their applications and made them available to their citizens. While using these applications, it is necessary to protect the fundamental rights and freedoms of the individual. This frequent processing of individuals' health data has created legal problems regarding the protection of personal data. The purpose of this paper is to present a study on the Turkish Covid-19 tracing application “Hayat Eve Sığar-HES” and the legal issues behind the application.
The Correlation between the Teacher's Oral Feedback and the Students' Writing Motivation

This study aims to determine the correlation between the teacher’s oral feedback and the students’ writing motivation. This study is quantitative research with a correlation method. The researcher conducted this study at SMPN 2 Kasihan. The population of this study included all of the 8th graders. The sample of this study consists of 93 students. To gain the data, the researcher distributed the questionnaires to the students. The data analysis technique used in this research used Rank Spearman Correlation, with SPSS version 23 for the windows program. The result of the research showed that there is a correlation between the teacher’s oral feedback and the students’ writing motivation. It is evidenced by the Rank Spearman correlation test results with the significance value of 0.000, where it is &lt;0.05.
Penerapan Media Gambar Ilustrasi Untuk Meningkatkan Prestasi Belajar Siswa Dalam Menanggapi Cerita Yang Didengar Kelas IV SD 29 Saok Laweh

This study aims to describe the use of illustrated visual aids in improving students' skills in responding to stories. This research is a quantitative research using Kurt Lewin’s PTK model which includes planning, action, observation, and reflection. With illustrated visual aids, it is easier for students to accept learning concepts and can attract students' interest to actively participate in the learning process. From the results of the formative test of early learning the average value which was originally only 6.8 increased to 7.2 in the first cycle, while in the second cycle it increased to 79 while the learning completeness from the initial learning which was only 40% increased to 60% in the first cycle, while in the second cycle it increased to 80%. Through increasing the average score of formative test results, and student learning completeness. So the understanding of Indonesian class IV about retelling the contents of folk tales that are read or heard in a coherent and complete way can be improved using the illustration image props.
Comparative performance evaluation of enhancement techniques in breast ultrasound images / Farah Hanani Mohd Ehwan, Nur Afiqah Zulraimi and Maira Nur Iffah Izzati Mohammad Mohid

Image enhancement has been widely used in medical field, particularly for the purpose of pre-processing technique of the images. Ultrasound image is the type of medical image that is usually applied in detecting breast cancer. However, ultrasound image suffers from speckle noise which is generated by the effect of environmental conditions on the image sensor during acquisition of the image. Thus, noise in images must be reduced before the image can be utilized. Therefore, image enhancement techniques namely Bilateral Filter (BF), Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in this research to enhance the breast ultrasound image.In order to measure the performance of these enhancement techniques, Image Quality Assessment Metric (IQM) and segmentation testing were applied on the enhanced images. !QM which consists of Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) of each image was calculated and compared, while Seed Base Region Growing method was chosen to perform segmentation testing. If the contrast of the entire image was taken into consideration, the best enhancement technique is Histogram Equalization.Otherwise, experimental result shows that Bilateral Filter method give high PSNR value which indicates good quality of image. Furthem10re, it shows that the Bilateral Filter method improved the segmentation results. In short, if the quality of the image were taken irito consideration, Bilateral Filter (BF) seems to be the best option.
EDUCATIONAL SCIENTIFIC SEMINAR IN A SOCIAL NETWORK: THE POSSIBILITIES OF INNOVATIVE EDUCATIONAL TECHNOLOGY («THEORETICAL STUDIES»: «HISTORICAL POETICS»)

The article shows how the modern socio-cultural situation dictates new requirements for the educational paradigm: there is a need to use information and communication technologies (ICT), various forms of distance and e-learning in the educational process, which in turn contributes to an increase in the mobility of all participants in the learning process. processing of intensive information flows. The most demanded and productive on this path are innovative educational technologies. The article focuses on the use of the VKontakte social network for conducting a training scientific seminar for students, undergraduates, graduate students. The authors proceed from their own work experience, on the basis of which they propose a methodology for conducting an online event aimed at popularizing scientific knowledge and using the capabilities of a social network. The article reveals how network services help create a unified educational space for the creative and scientific realization of students.
Design and Analysis of a Reliable Communication System in Nuclear Safety Instrument &amp; Control System

Abstract With the development of digital instrument and control technology for nuclear power plants in recent decades, communication networks have become an important part of safety digital control systems, which takes charge in data exchange between the various sub-systems, and extremely impact on the reliability and safety of the entire I&amp;C system. Traditional communication systems where some special features, such as reliability, safety, real-time, certainty, and independence are not strictly required are various illustrated. However, how to implement a communication system in a safety I&amp;C system is rarely stated in current research. In this research, a reliable safety communication system applied in nuclear power plants is designed and analyzed. The five key characteristics of nuclear safety communication networks are explained, followed by explanation of how to achieve these characteristics. The analysis and verification of the designed system are also stated in this paper, which contributes to proving that the designed nuclear safety communication system could applied in the nuclear power plants.
Integrating cloud-based file storage and NoSQL databases with mobile app SQLite

Mobile apps typically make use of user data that needs to be persisted across multiple executions of the app. The primary data source can be stored either locally on the device or remotely in the c...
Website Design Of Sumedang Regency

The purpose of making the Citaleus Village website is one step to introduce the Citaleus Village profile or share information about the village, as well as promote the potential results of human resources in making superior home industry products. The research method used is the Prototype Method because it is more helpful in analyzing needs in the early stages of development, making it easier to make design and design models for the next stage. The result of this practical work is the Citaleus Village website using Open SID and mysql database in XAMPP. The results achieved in this practical work project is a village website which is expected to help and facilitate the community in finding information about Citaleus Village
EXAMINING THE TECHNOLOGY ACCEPTANCE MODEL IN THE ADOPTION OF NARCOTIC PRECURSOR REPORTING SYSTEM (SIPPRE)

The use of Information and Communication Technology (ICT) among pharmacy professionals is increasing. Narcotic Precursor Reporting System (SIPPRE) is one of these technologies and can be used to supervise the distribution of narcotic precursors. The aim of this study is to investigate the predicting factors that influence the intention to use SIPPRE among pharmacy professionals. The Technology Acceptance Model (TAM) was used to shape the theoretical foundation for this study comprising perceived usefulness, perceived ease of use, attitude toward use, and intention to use. A quantitative approach was applied, using a five-point Likert scale questionnaire, adapted from previous studies. The participants for this study comprised 157 pharmacy professionals. The Results obtained using structural equation modeling indicated that pharmacy professionals perceived SIPPRE as an easy to use and useful system, and they favored its use in the future. In addition, perceived ease of use and usefulness significantly influenced attitudes toward the system. Apart from that, attitude toward use had a positive relationship with intention to use SIPPRE among pharmacy professionals. Surprisingly, perceived usefulness had a negative correlation with an intention to use. Overall, the TAM is a valid model to help explain pharmacy professional's intention to use SIPPRE. The results of this study provided useful insights for healthcare agencies to recognize the key elements that could improve the narcotic precursor reporting management.
Best Practices for Information Architecture, Organization, and Retrieval in Digital Language Archives within University Institutional Repositories

This report presents a case study about building a working digital language archive in a hosted university institutional repository. Best practices in language documentation regarding information architecture, organization, and retrieval are considered in relation to university library commitments to resource acquisition/preservation and online cataloging/delivery systems. Despite challenges, findings suggest that constructing digital language archives in university institutional repositories may offer viable collaborative solutions for researchers unable to find suitable, pre-existing archives in which to deposit their language documentary materials. The report concludes that, in such situations, the ability to satisfy best practices may respond to the strengths/weaknesses of particular software implementations as much as it reflects the design team’s vision, as theory and method in language documentation increasingly become matters of library and information science.
Improving Association Rule Mining Using Clustering-Based Data Mining Model for Traffic Accidents

Data mining is a process of knowledge discovery to extract the interesting, previously unknown, potentially useful, and nontrivial patterns from large data sets. Currently, there is an increasing interest in data mining in traffic accidents, which makes it a growing new research community. A large number of traffic accidents in recent years have generated large amounts of traffic accident data. The mining algorithms had a great role in determining the causes of these accidents, especially the association rule algorithms. One challenging problem in data mining is effective association rules mining with the huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use the RapidMiner application to design a process that can generate association rules based on clustering algorithms.
Electronic Resources Validation on the Web Using Analytical Hierarchy Process

Web development goal was information resources sharing between individuals and organizations. While resources is increasing, our processing capacity about a particular subject remains unchanged. Decision making in any field with available information resources on the web has a significant impact on human life. In this paper, an AHP method is proposed which pays attention to the concept of trust dimensions for available information resources credibility evaluation on the web. Our proposed method based on the existing ontology with a semantic search engine extracts various attributes to evaluate the credibility of a resource and assigns a numerical value using experimental evaluation. To confirm the correctness of the proposed method, an experiment has been organized to select a book based on the author's academic degree, author's reputation, publication year, publisher's reputation, organization and structure, price and etc. The feature ranking of mentioned book is performed, and finally, achieved results are expressed.
Modified Approach For Secure Cloud Backup and Recovery Solutions

Cloud computing provides users with a wide range of services. One of the services provided by Cloud Infrastructure is the storage for each service, which stores a large amount of electronic data in a cloud database. This kind of computation gives rise to huge amounts of private data over the main cloud. Thus, the requirement for information recovery services is increasing day-after-day, and efficient and effective information recovery technologies are needed. The main aim of the recovery technique is to assist the user collect data from each backup server when the server deletes its data loss and cannot deliver any information to the client. Many dissimilar techniques have been proposed to get this aim. In this paper, a method that be elected by robust solutions is proposed in the term of “Modified Approach for Cloud Backup and Recovery solutions”. This observes regarding the robust backup recovery methods used in cloud computing.
Entity-Aware Relation Representation Learning for Open Relation Extraction

Open relation extraction aims at extracting novel relations from open-domain corpora. However, most recent works typically treat entities and tokens equally while encoding sentences, without taking full advantage of the guiding role of entities in representation learning. In this work, we propose the Entity-Aware Relation Representation learning framework for open relation extraction and establish the new state-of-the-art on standard benchmarks. It gives more attention to entities when learning representations by leveraging an entity-aware attention mechanism. And we further propose a pair-wise contrastive loss to learn relation representations effectively in terms of alignment and uniformity. Extensive experimental results show that our framework achieves significant improvements compared to state-of-the-art models.
White Blood Cell Classification Using Genetic Algorithm–Enhanced Deep Convolutional Neural Networks

The amount of white blood cells in the blood is of great importance for disease diagnosis. White blood cells include five main classes (eosinophils, lymphocytes, monocytes, neutrophils, basophils), each of which is an important indicator for specific diseases. Deep learning models have been developed to successfully classify the different white blood cell types. The most prominent deep learning models in image classification are deep convolutional neural network (D-CNN) models. A key challenge when solving a problem using deep learning is identifying and setting the hyperparameters for the algorithm. Mostly, these hyperparameters are set manually based on experience. In this study, a new model of deep convolutional neural network is proposed for the classification of four white blood cells types. In this model, the hyperparameters are self-optimized by a genetic algorithm which provides significant improvement in the model. For the verification of the proposed model, four types of white blood cells available from the Kaggle data series were studied. The number of white blood cell images are about 12,000 and are split for training and test sets as 80% and 20%, respectively. When the proposed model was applied to the Kaggle white blood cell data set, the four white blood cell types in the sample data set were classified with high accuracy. The genetic algorithm (GA)–enhanced D-CNN model produced above 93% classification accuracy for the test set demonstrating the success of the proposed enhancement to the D-CNN model with GA. Comparatively, D-CNN models without GA optimization, such as Inception V3 model, produced 84% accuracy, and ResNet-50 model achieved 88% accuracy.