Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN LKPD INTERAKTIF MATERI PANTUN DENGAN BERBANTUAN APLIKASI QUIZIZZ KELAS V SDN PUCANG IV SIDOARJO

Interactive LKPD is a student worksheet that contain subject matter and computer- based questions. The purpose of this research was to know validty and efectivity of interactive LKPD through quizizz application. The type of this research is development study by Borg and Gall. The subject of this research were 48 students of 5th grade Pucang IV Sidoarjo elementary school. Data was collected by interview, questionnaris, and tests. Data analysis techinques were carried out with validty sheet, questionnaries and tests. The result of product validty is 87,8%, question validty is 85,4%, subject matter validty is 88,4%, questionnary get 99% percentage, and test get 85% percentage. Based on this results, we can know that development interactive LKPD through quizizz application is valid and effective to be applied in learning.
Instagram Story as Learning Media in a Form of German Vocabulary Quiz for 10th Grade Students at SMAN 5 Malang

This research aims to describe the use of Instagram Story as German learning media in a form of vocabulary quiz and the responses of students to the use of the media. The research method is descriptive qualitative and there were 19 students as the data source. The data were collected by conducting an observation and questionnaires. As a result of observation, students were enthusiast on taking the vocabulary quiz at Instagram Story because Instagram has become a part of their daily routine. This shows that Instagram Story could be implemented as German learning media since students are familiar to it. Based on the questionnaires result, the students’ responses were positive. The use of Instagram Story as vocabulary quiz has enriched students’ knowledge of German vocabularies especially about Kennenlernen topic. In addition, it motivated them to explore German vocabularies. Moreover, learning German with Instagram Story is attractive for the students.
Synthesizing Novel Spaces for Remote Telepresence Experiences

The emerging field of remote telepresence via spatial computing has opened many exciting opportunities for next-generation computer-mediated-communication platforms. Such techniques enable users to mutually engage in a wide spectrum of applications, previously not possible in 2D screen-based communication methods. Yet, cali-brating and finding a mutual environment compatible with all remote participant's physical environment is considered a challenging task. In this paper, we elaborate on the mutual space finding problem and provide a high-level introduction of our proposed novel Mutual Scene Synthesis (MSS) system. The MSS system takes the partici-pants' surrounding environment as input, and synthesizes a virtual scene that corresponds to the functional features of all participants' physical spaces. By combining a function optimization module with a deep-learning conditional scene augmentation process, the MSS can generate a scene compatible to all participants of a remote telepresence scenario. By performing early comparative user studies via the MatterPort3D dataset, we evaluate the effectiveness of our system and show our proposed MSS approach can be a promising re-search direction for facilitating contextualized telepresence systems for next-generation spatial computing platforms.
Implementasi Algoritma Gronsfeld Cipher dan Steganografi End Of File Untuk Pengamanan Data

Information is a collection of data or facts that are organized or processed in a certain way so that they have meaning for both recipients and users. Data and information are very important things to keep their security or confidentiality so that unauthorized parties cannot find out the data or information. This Data Security Application was developed using the Gronsfeld Cipher algorithm and the End Of File Steganography method. This desktop-based application was created with the aim of increasing data security on confidential information so that unauthorized parties cannot find out the contents of the data. This applicationlis expected to be a solution to prevent the theft of important information by unauthorized parties. From the test results, the developed application can receive input, process input and produce output as expected. Testing the quality of the embedded image gets an average PSNR value of 75,543 dB which can be said to be good because an image can be said to be good if it has a PSNR value above 40 dB, this also shows that the developed application can protect the data embedded in the digital image.
Digital Contact Tracing for COVID 19: A Missed Opportunity or an Expensive Mess

Digital contact tracing is one of the critical components for managing the COVID-19 pandemic adopted by many countries. Individuals with a certain level of exposure are identified using digital technologies like low-energy Bluetooth signals, GPS, and wifi combined in a smartphone application. Despite its rapid adaptation by many countries and heavy investment in developing this technology for rapid control of disease transmission, results have been mixed. Scientists have identified several causes like disparity in smartphone usage, a diverse adaptation of contact tracing protocols, the geographical disparity in the use of technology, and lack of adaption with low adherence responsible for this inevitable failure. Though digital contact tracing missed achieving its anticipated goals, it has provided extensive information to design a better technique in future outbreaks. Key approaches to resolving these problems include eliminating privacy concerns, improving protocol to achieve better signal with higher accuracy, feeding background data in the application for machine learning and adopting advanced technology like blockchain. Overall, the geographical disparity in technology adoption should be considered before investing in any digital intervention.
INKABER AS AN INNOVATION LEARNING MODEL TO IMPROVE READING AND WRITING SKILLS FOR BEGINNERS

This study aims to determine studentsâ€™ reading and writing skills through the inkaber model in MI Islamiyah Reban, how the planning of the Inkaber model in fostering initial reading and writing skills, the implementation of the Inkaber model in fostering initial reading and writing skills and the advantages and disadvantages of using the Inkaber model in learning MI Islamiyah Reban. This research is a type of field research with a qualitative approach to education and data collection by conducting observations, interviews, and documentation to complement the data. The data analysis used is descriptive qualitative by providing an explanation of the data collected. Data checking is done by triangulating or comparing data from various sources and drawing conclusions. From the research results, it can be concluded that the Inkaber model is a learning innovation to foster studentsâ€™ initial reading and writing skills MI Islamiyah Reban which is implemented through presenting familiar pictures by finding vocabulary that matches the picture and writing them down then making these simple sentences can have a good influence on studentsâ€™ reading and writing skills. This is shown in the activeness of students when participating in learning and enthusiasm in learning to read and write. Students are also more interested in the learning process in class because they learn through familiar pictures.
Upaya Meningkatkan Hasil Belajar IPS melalui Penerapan Model Discovery Learning Siswa Kelas IX.2 SMP Negeri 4 Mataram

The purpose of this study was to determine the improvement of social studies learning outcomes through the application of the Discovery Learning model for Class IX.2 students of SMP Negeri 4 Mataram in the 2018/2019 academic year. The subjects of this study were students of class IX.2 SMP Negeri 4 Mataram with a total of 32 students consisting of 16 male students and 16 female students. The selection of class IX.2 aims to improve and enhance students' learning activities in social studies learning by applying the Discovery Learning model. Based on the results of the research analysis obtained, it will be concluded that the application of the Discovery Learning model can improve student learning outcomes, especially social studies subjects. This is evident in the initial conditions with an average value of 70.53% then increased in the first cycle with an average value of 75.41% then increased again in the second cycle with an average value of 79.75. While the completeness of student learning outcomes in the initial conditions was 46.88% while in the first cycle was 68.75% and then increased again in the second cycle, namely 90.63%. In addition to the learning model, Discovery Learning can increase teacher activities in guiding and drawing conclusions from the subject matter. While student activities, students can pay attention to the teacher's explanation during the learning process, students are active in asking questions and answering teacher questions.
Research on Auxiliary Detection Technology for Downlink Telemetry Parameter Anomaly Discovery

In order to solve the problem of efficient and automatic identification for the anomaly detection of carrier rocket downlink telemetry parameters, an automatic identification method based on the statistical characteristics of historical data is proposed for the slowly varying parameters. Under the condition of time slicing, the method first performs numerical filtering on historical measured data, and then Gaussian Process Regression (GPR) algorithm is used to locally model the segmented historical data. Secondly, the prediction output of the GPR sub-model based on each historical sample data is fused by the dynamic Gauss-Markov estimation algorithm to obtain the prediction value and prediction variance of the target data, and the parameter discrimination interval is constructed based on this. The real-time abnormal alarm is given when the statistical distribution of the measured target data of each sub segment in the discrimination interval exceeds the limit. Finally, after accumulating the complete and effective data segments of the measured parameters of the target, the global distribution is counted, and if it exceeds the limit, the parameter abnormality flag is given. Simulation results show that this method can effectively find abnormal parameters, and has strong ability to suppress random noise and more accurate parameter estimation ability.
Computing All-vs-All MEMs in Run-Length-Encoded Collections of HiFi Reads

We describe an algorithm to find maximal exact matches (MEMs) among HiFi reads with homopolymer errors. The main novelty in our work is that we resort to run-length compression to help deal with errors. Our method receives as input a run-length-encoded string collection containing the HiFi reads along with their reverse complements. Subsequently, it splits the encoding into two arrays, one storing the sequence of symbols for equal-symbol runs and another storing the run lengths. The purpose of the split is to get the BWT of the run symbols and reorder their lengths accordingly. We show that this special BWT, as it encodes the HiFi reads and their reverse complements, supports bi-directional queries for the HiFi reads. Then, we propose a variation of the MEM algorithm of Belazzougui et al. (2013) that exploits the run-length encoding and the implicit bi-directional property of our BWT to compute approximate MEMs. Concretely, if the algorithm finds that two substrings, $$a_1 \ldots a_p$$ and $$b_1 \ldots b_p$$ , have a MEM, then it reports the MEM only if their corresponding length sequences, $$\ell ^{a}_1 \ldots \ell ^{a}_p$$ and $$\ell ^{b}_1 \ldots \ell ^{b}_p$$ , do not differ beyond an input threshold. We use a simple metric to calculate the similarity of the length sequences that we call the run-length excess. Our technique facilitates the detection of MEMs with homopolymer errors as it does not require dynamic programming to find approximate matches where the only edits are the lengths of the equal-symbol runs. Finally, we present a method that relies on a geometric data structure to report the text occurrences of the MEMs detected by our algorithm.
Laplacain Pair-Weight Vector Projection with Adaptive Neighbor Graph for Semi-supervised Learning

Recently, Laplacian pair-weight vector projection (LapPVP) algorithm was proposed for semi-supervised classification. Although LapPVP achieves a good classification performance for semi-supervised learning, it may be sensitive to noise and outliers for using the neighbor graph with a fixed similarity matrix. To remedy it, this paper proposes a novel method named Laplacain pair-weight vector projection with adaptive neighbor graph (ANG-LapPVP), in which the graph induced by the Laplacian manifold regularization is adaptively constructed by solving an optimization problem. For binary classification problems, ANG-LapPVP learns a pair of projection vectors by solving the pair-wise optimal formulations in which we maximize the between-class scatter and minimize both the within-class scatter and the adaptive neighbor graph (ANG) regularization. The ANG regularization is to learn the ANG whose similarity matrix varies with iterations, which may solve the issue of LapPVP. Thus, ANG-LapPVP simultaneously learns adaptive similarity matrices and a pair of projection vectors with an iterative process. Experimental results on an artificial and real-world benchmark datasets show the superiority of ANG-LapPVP compared to the related methods. Thus, ANG-LapPVP is promising in semi-supervised learning.
Book Classification Application Using Dewey Decimal Classification Method (DDC) Case Study Library SMAN 4 Kaur

SMAN 4 Kaur library has not used a computerized system in providing services to visitors or students, especially in classifying existing book titles. The purpose of the research is to make a book classification application so that it can facilitate library users or managers. Where so far the book classification at SMAN 4 Kaur is still manually or has not used a system so that in its implementation it is difficult for library managers. With the construction of this application, it can help the SMAN 4 Kaur library in overcoming the process of searching for book data or book classification can run well..
A Survey of Deep Learning Object Detection Algorithm

Object detection is to accurately identify and locate the target in the image. With the development of deep learning, object detection algorithm has achieved very high achievements, for example, object detection algorithm is applied to security, medical, transportation, military, automatic driving and so on. This paper reviews the progress of target detection algorithms by consulting many literatures at home and abroad. Firstly, the research status of object detection is introduced, and the dataset, evaluation index and convolutional neural network used in the process of target detection are provided. Secondly, it compares some mainstream object detection algorithms at this stage. The starting point of this paper is to help others quickly understand the target detection algorithm. Readers can find the appropriate object detection algorithm according to their own project and improve it on this basis. Finally, combined with the current research hotspots, the future development trend is analyzed.
Optimized design of centrifugally driven time-delay microchannels based on Kriging model

Based on the function of local multidimensional parameters, the Kriging model and particle swarm-genetic algorithm are used to carry out the research on the optimal design of microchannels with long delay performance. The simulation model of the time-delay microchannel is established, and the simulation model is verified through experiments, and it is found that the actual time-delay time of the fluid in the microchannel is more consistent with the simulation time curve, which ensures the reliability of the sample data source of the Kriging model. The flow time of fluid in the microchannel is taken as the research object, and the finite element simulation framework is built to obtain the data of the microchannel structure parameters and the delay time under centrifugal response in batch to form the sample data. A Kriging model is established from the sample data to fit a highly nonlinear mapping function between the design variables and the optimization target, and a particle swarm-genetic algorithm is used to optimize the parameters of the linear mapping function fitted by the Kriging model. The results show that the delay time of the fluid in the microchannel driven by centrifugal force is improved by 26.9% after shape optimization.
THE ANALYSIS OF TENSE CHOICE IN NATIONAL COLUMN IN THE JAKARTA POST NEWSPAPER

The purpose of this research is to know what are the tense used in Jakarta Post newspaper in National column and to know what is the most and the less tense that used in the Jakarta Post newspaper in National column. Tense is a unit consisting of verbs that explain and emphasize information, which amounts to 16 tenses, the four main points of these tenses are past tense, present tense, future tense, and past future, then accompanied by four kinds of activities including simple, continuous, perfect and perfect continuous. The source data of this research is Jakarta Post newspaper in the national column and the secondary data are books, journal that supporting the main data in doing the research. This research is qualitative descriptive research that used library research. The result of this research is there are 9 tenses that used in National column in the Jakarta post newspaper they are 47 of simple present, 4 of present continuous, 31 of present perfect, 132 of simple past, 1 of past continuous, 2 of past perfect continuous, 7 of simple future, 12 of past future and 13 of past perfect. The most tense that used in National column in the Jakarta Post newspaper is simple past and the less tense that use in the Jakarta Post newspaper is past continuous.
Randomized multi-goal path planning for Dubins vehicles

In multi-goal path planning, the task is to find a sequence to visit a set of target locations in an environment using the shortest path. Finding the order can be achieved by solving an instance of the Traveling Salesman Problem (TSP). This requires to determine paths and their lengths between the individual targets, which is solved using robotic path planning. In this paper, we propose a randomized planner for multi-goal path planning for non-holonomic mobile robots. Multiple trees are constructed simultaneously from the targets and expanded by collision-free configurations until they touch each other or obstacles. The trees are expanded using Dubins maneuvers. Therefore, the resulting trajectories satisfy the kinematic constraints of the mobile robots and they can also be used by Unmanned Aerial Vehicles flying at a constant altitude. The efficiency of the proposed planning approach is demonstrated in the multi-goal path planning for environments with tens of targets and compared to state-of-the-art approaches.
RPH-UoLi CrossMark Policy

CrossMark is a multi-publisher initiative to provide a standard way for readers to locate the authoritative version of an article or other published content. By applying the CrossMark logo, Research Publishing House - University of Loralai (RPH-UoLi) is committing to maintaining the content it publishes and to alerting readers to changes if and when they occur.
PENERAPAN SISTEM INTELIJENSIA BISNIS DAN K-MEANS CLUSTERING UNTUK MEMANTAU PRODUKSI TANAMAN OBAT

Indonesia has biodiversity including medicinal plants. The medicinal plant business can be a profitable business prospect because it has high export opportunities. Based on the benefits obtained, the production of medicinal plants needs to be considered by monitoring and evaluating production results to increase productivity, especially for areas with low production levels. The Department of Food Crops and Horticulture of West Java Province through the website https://opendata.jabarprov.go.id has provided production datasets for each type of medicinal plant, but it has not yet become a dataset with various types of medicinal plants. The purpose of this study was to design an integrated data storage model in the form of a data warehouse, grouping medicinal plant production areas using data mining and designing data visualization in business intelligence systems. Business intelligence system design was carried out through several stages, namely system requirements analysis, identification of data and information needs, data warehouse design, data warehouse filling, data mining processes, data visualization, and system performance evaluation. The results of the research were the application of a data warehouse using a dimensional model with a star schema; a grouping of production areas using the KMeans algorithm with optimal k=3 and the number of elements produced in each cluster is 24 regions in cluster 0, 1 region in cluster 1, and 2 regions in cluster 2; a business intelligence system is implemented using a dashboard to show information on the amount of production and display the results of grouping potential areas for producing medicinal plants.Keywords: business intelligence, data warehouse, dimensional models, visualization, medicinal plants
Cognitive Clarity of Graph Models: an Approach to Understanding the Idea and a Way to Identify Influencing Factors Based on Visual Analysis

The article describes new authors' approach to understanding the idea of cognitive clarity of graph models.A scheme for structuring concepts related to cognitive clarity is given.In accordance with this scheme cognitive clarity forming factors are distinguished, cognitive clarity itself as a set of internal characteristics of a visual image of a model, as well as the effects of its presence revealed in the visual analysis of the model.Properties of various components of this scheme are discussed, and it is concluded that, due to their constructive nature, cognitive clarity forming factors are of the greatest interest.A detailed diagram of an approach to understanding cognitive clarity is described, based on two levels of a visualization metaphor, and individual components of this diagram are discussed.An approach is proposed to organize experimental studies aimed at studying and evaluating the influence of various factors on cognitive clarity of graph models by measuring various indicators that characterize the degree of manifestation of cognitive clarity presence effects.The idea of this approach is to form a hypothesis about the influence of certain factors, followed by an experiment in which the analyst solves a certain task of visual analysis with the participation of these factors.As a result of registering the given indicators, the achieved effect of changing the level of cognitive clarity is assessed and the nature of the dependence or its absence is revealed, which makes it possible to accept, reject or refine the initial hypothesis.A generalized algorithm for preparing and conducting an experiment within the framework of the described approach is proposed, and implementation features of its individual stages are considered.An example of setting up an experiment is given, the purpose of which is to study the dependence of efficiency indicators of graph model visual analysis on the volume of its visual image.Promising areas of research in this area are discussed in the conclusion.