Reviewerly/exHarmony huggingface dataset:
Aplikasi Pemandu Pencarian Wisata Terdekat Berbasis GIS Android Dengan Algoritma Dijkstra

Sumenep Regency is one of the districts on Madura Island where the tourism sector is experiencing rapid development and improvement. Sumenep Regency has a natural and cultural tourist attraction, making it a destination for tourists. Not only is the attraction of the culture of his cow easy, but Sumenep Regency has a very beautiful natural attraction. The condition of natural tourism consists of natural scenery, high hills, beaches, high asta and diversity of marine potential. One of the popular tourist objects is Pantai Pantai Sembilan, Gili Labak Island and Gilii which have the best oxygen levels in the world. Tourists from outside the region, especially those who first visited Sumenep Regency will have a little difficulty in accessing tourist attractions in this city because they do not know in detail the information about the location of these tourist attractions. The purpose of this research is to build an Android-based Geographical Information System using the Dijkstra Algorithm, which is expected to help tourists from outside the Sumenep City area in displaying routes from one tourist spot to another. The method that is the basis for designing this GIS is the Graph Method, by applying the shortest route search algorithm, Dijkstra's Algorithm. The expected results with this application are that it can help, simplify, and accelerate tourists in finding and obtaining information about the location of tourist attractions in Sumenep Regency. Based on the closest travel search trials using the Android Application it produces an accuracy of 95%.
Android Battle Game Based on Augmented Reality with 2D Object Marker

This research develops the game by applying the Augmented Reality (AR) system in making games. Similar games that have been observed have a weakness where the Augmented Reality characters that appear do not match the marker, there is no calculation of the distance between the marker card as a battle trigger, and no character has raised the urband legend. For this reason, it is necessary to make a similar game by applying the Based Tracking Marker technique by calculating the distance of action between two markers by raising the theme of urband legend Indonesia and foreign legend legend. This research succeeded in making Multimedia battle fighting game products using the Multimedia Development Life Cycle (MDLC) method approach - Luther. Based on the tests that have been carried out, the alpha test results are functionally appropriate and from the beta testing the results of the User Acceptence Test (UAT) functionalities obtained a value of 71% declared feasible to use with the interpretation of "Good" which means this game is feasible and can be developed.
Practical librarians on the libraries’ transformation

The author analyses the results of the survey of the trainees of the Higher Library Courses of the Russian State Library on the key issues of the conceptual paper by Vadim Stepanov “The Libraries and librarians in the nearest 20 years, or waiting for singularity”, published in the “Scientific and technical libraries “ journal, № 2, 2018). Stepanov calls for transforming the li- braries into radically different institutions which he considers the only way to avoid the disaster in the information era. Stepanov considers the libraries to be the outposts of extreme conservatism and archaism, and argues that their acquisitions of printed materials in the era of digital resources makes no sense. The audience of the Higher Library Courses is made mainly of the professionals with the university (non-library) background, who are at the same time the practicing librarians, or those who are learning to become librarians. Generally, they have rejected such attitude and supported the original core nature of libraries though insisted that the libraries had to be continuously enriched with new titles, creatively designed and well equipped for better user services. The trainees of the Higher Library Courses have given their consent to the author to use the survey results.
Workplace Discourse: Constructing Social Identity of Lecturers Through WhatsApp Group

This paper discusses how WhatsApp (WA) group can be used to maintain social and workplace relationship.In the presence of positive mediation of WA, people may have a sense of belonging as WA is a written discourse community.A WhatsApp group of lecturers was used as the source of data.Based on the results of analysis, it was revealed that the lecturers who join this WA group generally attempt to assert their individuality.Yet, they put their efforts to join others by maintaining their sense of status or self-esteem as they are members of the group by doing some imposition.It was also found that rich communication can be realized through WA group.The members' use of greetings and the use of directives are reported.The syntactic and lexical choices made by the lecturers are consciously or subconsciously interpreted by the members of the group.The messages in WA group also revealed that the lecturers in the group show they are able to contribute freely, avoid conflict and create harmonious working relationships via the WA chat forum.The study described here is a case study of a written workplace discourse in an educational institution.Hence no statistical tests have been carried out since it is regarded as exploratory.Further study based on different workplaces and in different contexts needs to be conducted to see how these findings hold for other workplaces.
POS Tagging of Gujarati Text using VITERBI and SVM

Grammatical feature (POS) Labeling is a testing undertaking to distinguish the significance of each word in a sentence.This paper shows the assignment of distinguishing Grammatical form TAG for each transform in a Guajarati sentence utilizing the system of support Vector Machine and Viterbi deciphering method.Guajarati corpus of 1700 words is taken and tried it precisely.Labeling is done utilizing Viterbi and SVM and the outcome is examined in four classifications.In every one of the classifications Viterbi based method gives much better correctness's.
PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE TGT (TEAMS GAMES TOURNAMENT) TERHADAP HASIL BELAJAR MATEMATIKA SISWA

This research is motivated by the fact that teaching in schools is more oriented on the activeness of teachers in the learning process and students who do not like learning that menoton. The main task of education is to improve student learning outcomes in schools. The purpose of this research is to know the influence of learning model of TGT (Teams Games Tournament) to the result of learning of class VII students of SMP Jalan Jawa Surabay. This research is a quasy experiment research using posttest only contril design design. The population used in the study were students of class VII C and VII B junior high school in Java junior year 2017/2018. Samples were obtained randomly, class VII-B as control class and class VII-C as experimental class. The research instrument used is a test of student learning outcomes in the form of posttest. The results showed that in the experimental class the average learning outcome was 85.40 while in the control class the average learning outcome was 79.40. It can be seen that there is a difference of the average of learning outcomes, that in the experimental class the average of learning outcomes is higher than the average of control class learning outcomes. On the test t test tcount &gt; ttable with a value of 2.47 &gt; 1.67. So, it can be concluded that there is influence of learning model TGT Teams Games Tournament) to the results of students studying class VII SMP Jalan Jawa Surabaya.&#x0D; Keywords: teams games tornament, cooperative, learning outcomes
SLA-Based Adaptation Schemes in Distributed Stream Processing Engines

With the upswing in the volume of data, information online, and magnanimous cloud applications, big data analytics becomes mainstream in the research communities in the industry as well as in the scholarly world. This prompted the emergence and development of real-time distributed stream processing frameworks, such as Flink, Storm, Spark, and Samza. These frameworks endorse complex queries on streaming data to be distributed across multiple worker nodes in a cluster. Few of these stream processing frameworks provides fundamental support for controlling the latency and throughput of the system as well as the correctness of the results. However, none has the ability to handle them on the fly at runtime. We present a well-informed and efficient adaptive watermarking and dynamic buffering timeout mechanism for the distributed streaming frameworks. It is designed to increase the overall throughput of the system by making the watermarks adaptive towards the stream of incoming workload, and scale the buffering timeout dynamically for each task tracker on the fly while maintaining the Service Level Agreement (SLA)-based end-to-end latency of the system. This work focuses on tuning the parameters of the system (such as window correctness, buffering timeout, and so on) based on the prediction of incoming workloads and assesses whether a given workload will breach an SLA using output metrics including latency, throughput, and correctness of both intermediate and final results. We used Apache Flink as our testbed distributed processing engine for this work. However, the proposed mechanism can be applied to other streaming frameworks as well. Our results on the testbed model indicate that the proposed system outperforms the status quo of stream processing. With the inclusion of learning models like naïve Bayes, multilayer perceptron (MLP), and sequential minimal optimization (SMO)., the system shows more progress in terms of keeping the SLA intact as well as quality of service (QoS).
A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data

For many tasks, the successful application of deep learning relies on having large amounts of training data, labeled to a high standard. But much of the data in real-world applications suffer from label noise. Data annotation is much more expensive and resource-consuming than data collection, somewhat restricting the successful deployment of deep learning to applications where there are very large and well-labeled datasets. To address this problem, we propose a recursive ensemble learning approach in order to maximize the utilization of data. A disagreement-based annotation method and different voting strategies are the core ideas of the proposed method. Meanwhile, we provide guidelines for how to choose the most suitable among many candidate neural networks, with a pruning strategy that provides convenience. The approach is effective especially when the original dataset contains a significant label noise. We conducted experiments on the datasets of Cats versus Dogs, in which significant amounts of label noise were present, and on the CIFAR-10 dataset, achieving promising results.
Dual Residual Network for Accurate Human Activity Recognition.

Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem, but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of the automatic learning, we propose a novel \textbf{A}symmetric \textbf{R}esidual \textbf{N}etwork, named ARN. ARN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, yet still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of ARN model, we carried out extensive experiments on benchmark datasets (i.e., OPPORTUNITY, UniMiB-SHAR) and compared with some conventional and state-of-the-art learning-based methods. Then, we discuss the influence of networks parameters on performance to provide insights about its optimization. Results from our experiments show that ARN is effective in recognizing human activities via wearable datasets.
A Novel Comparative Linguistic Distance Measure Based on Hesitant Fuzzy Linguistic Term Sets and Its Application in Group Decision-Making

group decision making (FGDM) Distance measure Aggregation approach A B S T R A C T ID:p0065The linguistic approaches are required in order to assess qualitative aspects of many real problems.In most of these problems, decision makers only adopt single and very simple terms which would not reflect exactly what the experts mean for many intricate applications.Frequently, the assessments of decision making problems involve comparative linguistic expressions.Accordingly, we propose a novel distance measure between hesitant fuzzy linguistic term sets (HFLTSs) to solve fuzzy group decision making (FGDM) problems.Firstly we define the characteristic functions to describe the HFLTSs transformed from comparative linguistic expressions.Then we construct a weighted HFLTSs graph containing all notes in the HFLTSs.Distances in the graph of individual assessments are defined by measures considering diversity and specificity of HFLTS's granularity.We put forward a new approach to achieve aggregation results for group decision making to realize the minimal distances with individual assessments.Finally, numerical examples are illustrated.
Dense Convolutional Networks for Efficient Video Analysis

Over the past few years various Convolutional Neural Networks (CNNs) based models exhibited certain human-like performance in a range of image processing problems. Video understanding, action classification, gesture recognition has become a new stage for CNNs. The typical approach for video analyse is based on 2DCNN to extract feature map from a single frame and through 3DCNN or LSTM to merging spatiotemporal information, some approaches will add optical flow on the other branch and then post-hoc fusion. Normally the performance is proportional to the model complexity, as the accuracy keeps improving, the problem is also evolved from accuracy to model size, computing speed, model availability. In this paper, we present a lightweight network architecture framework to learn spatiotemporal feature from video. Our architecture tries to merge long-term content in any network feature map. Keeping the model as small and as fast as possible while maintaining accuracy.
The Positive Impact of Organizational Trust: a Systematic Review

The critical role of trust is to strengthen working relationships to ensure that organizations achieve their goals effectively. This study aims to explore the positive impact of organizational trust, namely trust towards colleagues, employers, and organization. We conducted a systematic review following the PRISMA guideline, highlighting the number of records that were identified, screened, assessed for eligibility, and included for the study. The literature search was conducted online by using the keyword "Organizational Trust " on three platforms, namely ScienceDirect, Sage, and ProQuest. We selected 30 research articles that use organizational trust as either a dependent, mediating or moderating variable. Next, we reviewed and assessed the papers according to their research objectives, role/function of the variable, authors, year of publication, number of subjects/research samples and findings. Findings show: (1) organizational trust can reduce turnover tendency, as well as increase the intention to stay at the organization, effectiveness of communication within the organization, change readiness, continuum improvement, innovative behavior, job satisfaction, organizational citizenship behavior (OCB), organizational commitment, safety motivation, and work performance; (2) As an independent variable, trust in coworkers could impact joint learning activities, willingness to share knowledge and ideas with coworkers, networking commitment, job satisfaction, and organizational commitment; (3) Trust in superiors reinforces organizational identification, work performance (the strongest level of positive correlation), job satisfaction, organizational citizenship behavior, organizational commitment as well as a predictor of burnout and depersonalization.
Improved Image Enhancement Method Based on Retinex Algorithm

In order to improve the visibility of foggy images, this paper uses two models to iteratively refine the image. In the first model, the image is first enhanced by histogram equalization and then enhanced by the Retinex algorithm. In the second model, the image is firstly enhanced with the Retinex algorithm, and then the gamma correction is used to adjust the brightness. From a theoretical analysis and practical experiments, this method improves the sharpness of the image while enhancing the image detail information and restoring the image color.
Measurement model of the omnidirectional structured-light vision and its calibration method

The omnidirectional structured-light vision measurement is significant for inner surface inspections. In existing systems, the camera and the projector are installed inside a glass tube, inevitably causing the refraction distortion. In this paper, we propose a measurement model of the omnidirectional structured-light vision and the corresponding calibration method. The model can correct the refraction distortion and realize the omnidirectional measurement. An aluminum tube with an internal diameter of 288.50 mm is measured by the system. The repeatability and precision reach 0.05 mm and 0.23 mm, respectively. The experimental results prove that the accuracy is improved by 7.9 times compared with the model ignoring distortion.
A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.
Progress and prospectives on engineering application of hyperspectral remote sensing for geology and mineral resources

Remote sensing technology plays an important role in geological survey and plays an irreplaceable role. With the development of remote sensing technology, the appearance of hyperspectral remote sensing makes the application of remote sensing in geological and mineral fields have undergone a qualitative change. After nearly ten years of exploration and practice, the engineering application of hyperspectral remote sensing in geology and mineral resources has been preliminarily realized. With the launch of domestic hyperspectral satellites, it will further promote its application in geology and mineral resources. In this paper, the progress of engineering application of hyperspectral remote sensing in geology and mineral resources is summarized from the aspects of hyperspectral data processing, information extraction, information analysis, prospecting and prediction.
EKSPLORASI NILAI FABEL SEBAGAI SARANA ALTERNATIF EDUKASI SISWA

Children’s character must be built since their early age. Children should be educated and habituated with good characters so that good character will be embedded until they are grown up. A diversity of fables can be used as a medium to build the children's character. The goal in this research is exploration character educational values which are contained in the fable “Kisah Sahabat Rimba” by Yosep Rustandi. The research method uses qualitative descriptive with content analisys to describe the characters in fable “Kisah Sahabat Rimba” by Yosep Rustandi. The result of the research, it was known that there are some universal characters which can be exemplified from the fables “Kisah Sahabat Rimba” by Yosep Rustandi such as thorough, politeness, humble, honest, and respectful.
Research on Large Screen Visualization Based on Docker

Docker is a system-level virtualization container technology, which is commonly used to package application software and provide rapid deployment services based on non-graphical interfaces to resolve dependency conflicts and version iteration problems. However, there are applications that require graphical user interfaces and user interactions, such as large-screen visualization. This work proposes a solution to use Docker and GPU to deploy large-screen visualization applications to display images, videos, WebGL applications, etc. We built a Docker cluster and deployed a large-screen visualization system by launching a microservice, which contains 28 containers, and mapping the host's X11(X11 is widely used to provide a graphical interface to Unix/Linux users) Unix socket. At the same time, we tested the performance and compared it with the circumstance that applications ran on local host. It is concluded that the solution can achieve a good result when applied to large-screen visualizations with acceptable performance degradation.
University students’ self-efficacy: A contributing factor in TOEFL performance

Self-efficacy is one of the often-underestimated factors, which in fact, has an influence in determining human action. This study aims to find out the correlation between self-efficacy and the TOEFL performance of the students in two universities in Banda Aceh, namely Syiah Kuala University and Serambi Mekkah University. This study employed the correlational design to find out the correlation between the two main variables (self-efficacy and TOEFL performance). The sampling technique used in this study was the stratified sampling method with a total sample of 200 university students, both English, and Non-English majors. Furthermore, to collect the data, two instruments were used, namely the self-efficacy questionnaire and the TOEFL test. In analyzing the data, Pearson’s Correlation was used to find out the correlation. The result shows that there is a positive and low correlation between university students’ self-efficacy and their TOEFL performance (r=.249). Despite the correlation is low, self-efficacy is still considered to contribute to the TOEFL score. Therefore, it is suggested that TOEFL instructors consider the variable of self-efficacy in their teaching. Furthermore, it is expected that future research continues this study by enlarging the sample size in different levels of education and in different areas.