Reviewerly/exHarmony huggingface dataset:
Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering

Text mining is the text analysis that automatically discover quality information from a series of texts that is summarized in a document. K-Means Clustering method is often used because of its ability to make a group of large amounts of data with relatively fast and efficient computing time. The purpose of this study is to determine the optimal number of the groups formed from the thesis documents and determine the results of the groups formed. This study is using Nazief and Adriani algorithms for the stemming step, Euclidean Similarity to calculate document distances, and Silhouette Coefficient to test the cluster validity. The sample in this study is 119 thesis documents of Statistics Study Program, Mathematics Department, Faculty of Mathematics and Natural Sciences, graduates of 2016-2018. Based on the results of the analysis, the optimal number of groups formed is two clusters with a silhouette coefficient of 0.12. The results of the grouping formed are two clusters with the total of the first cluster is 85 documents and the second cluster is 34 documents. The first cluster is dominated by studies with data mining especially classification, time series analysis, regression analysis, survival analysis, spatial analysis and operational research, and the second cluster is dominated by studies with multivariate analysis, quality control, and insurance mathematics.
Imagining Platform Socialism

Investigations have demonstrated the ways that the operations of the algorithmic processes that select and sort information for users of YouTube, Facebook, and Twitter not only distribute such malicious content, but also amplify its effects. In order to hold the attention of users, platforms tend to recommend increasingly controversial and sensationalist suggestions, leading users quickly into rabbit holes of conspiracy theories and extremist views that undermine attempts at informed and reasoned debate. Other studies showed that Google’s search engine was capable of exhibiting serious racist bias, and that the platforms’ attempts to limit access to hate speech and misinformation were of limited effectiveness.At the same time, the monopolistic digital platforms are undeniably of great use to activists working for a more democratic world, including socialists. The events that triggered US President Trump’s demands for stricter platform regulation were part of a mobilization of dissent that came to be known by its Twitter hashtag, ‘Black Lives Matter’, and numerous other examples could be found of activists raising awareness in ways that would not be possible with mass media. Social media empowers individuals to participate in the distribution of their own ideas, although this empowerment is limited by the mediation of the flow of information by the owners of the platforms in ways that can have direct effects on how mobilizations are informed.This makes it all the more necessary to explore the contradictions between our expectations of social media and the reality of its use, and determine if there are ways to avoid the harms done to democracy while preserving the benefits. The purpose of the present essay is to inquire whether and how social media could be organized more democratically, so that they allow our expressive capacities to be developed freely and not under conditions determined by capital.
PELAKSANAAN PENDAMPINGAN KEGIATAN TEKNOLOGI INFORMASI DI SD MUHAMMADIYAH PANDES SEBAGAI UPAYA UNTUK PENINGKATAN SUMBER DAYA MANUSIA DALAM MENGOPTIMALKAN TEKNOLOGI INFORMASI

Improving the quality of human resources in an organization becomes an obligation, especially in increasing teacher resources in the world of education. The use of information technology is now a necessity for teachers to help the learning process and in managing school administration. In the implementation of assistance to community service activities, assistance has been carried out in relation to optimizing  information technology in supporting teachers in the learning process and in the administration ofteachers in managing records. The method of activities carried out is to identify the problems that exist in SD Muhammadiyah Pandes, especially those related to information technology and provide assistance to teachers and administration. The teachers are assisted in the development of making information technology-based teaching materials and administrative managers by making electronic  records. The results of dedication carried out is providing training for teachers to be able to develop teaching materials based on information technology using the Sparkol application and quiz creator as well as for administration by using information technology-based archive applications with Arterial applications.
Analisis Cluster Pada Data Kategorik dan Numerik dengan Pendekatan Cluster Ensemble

Cluster analysis used to process categorical and numerical data at once is Cluster Ensemble algorithm Based on Mixed Data Clustering (algCEBMDC), which is a cluster algorithm with an ensemble cluster approach. The method used for numerical data is Agglomerative Nesting (AGNES) algorithm and for categorical data is the RObust Clustering using linK (ROCK) algorithm. The best clustering method and the optimum number of clusters in the AGNES algorithm is selected based on the maximum Pseudo-F value and the minimum icdrate value. The optimum number of clusters in the ROCK algorithm is selected using the minimum value of  ratio . The purpose of this study was to make a group of 179 Puskesmas in East Kalimantan on December 2017. Based on the results of the analysis, obtained 5 optimum cluster for numerical clustering with the AGNES algorithm and 2 optimum cluster for categorical clustering data with the ROCK algorithm. Final cluster for mixed data clustering obtained 2 optimum cluster at a threshold of 0.2 and 0.3 with value of ratio  is . The first cluster consists of 83 Puskesmas and cluster two of 96 Puskesmas.
Use of Persistent Identifiers such as DOI for Digital Archive Contents

Persistent identifiers such as DOIs are being assigned for digitalized books or manuscripts such as those in the National Diet Library's Digital Collection and the National Institute of Japanese Literature's Database of Pre-Modern Japanese Works. However, there has been little progress in assigning DOIs to the contents of general digital archive, such as museum materials. If the DOI is assigned to the digital archive contents, it will make it easier to investigate and collect the use of those contents.
Slouch Detection Using Open Pose Architecture

Slouch detection refers to computer vision techniques that detect human figures in images in order to determine the current posture and check if a person is sitting ergonomically.This is an important subject because following poor posture habits is known to be one of the primary causes of back pain because of its adverse influence on the transverse abdominis muscle.Maintaining good posture could help improve one's health in the long run.This paper implements slouch detection using OpenPose architecture, which first finds the relative position of joints with respect to the body.This information then helps determine whether the human subject in a given image is sitting ergonomically or not.In this paper's implementation, high accuracy was achieved and slouch detection was made more feasible by eliminating the need for several sensors.
CEFLS: 3 Things to Know About...: Home

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The Augmented Commons: How Augmented Reality Aids Agile Self-Organization

Augmented reality (AR) is a real-time, interactive user experience that inserts virtual computer-generated elements into a user’s environment. AR technology utilizes the Internet of Things (IoT) to enable smart devices to capture and identify “scenes” as a user moves through her world, generate contextually relevant holograms, sound, and other sensory inputs, then project those virtual elements through the user’s display. AR technology is coordinative in that it assists in economic calculation, aids in tracking usage, and helps agents overcome some kinds of information asymmetries. We provide examples from existing AR applications and conceptualize how AR strengthens self-organization and enables polycentric loci of private governance to emerge, what we call agile self-organization. Examples include information-enhancing overlays, automatic language translation, individualizing and privatizing the provision of personal and worker safety, reducing emergency response times, and enriching education with overlays and holograms. We conclude that AR technologies could erode traditional policy rationales for intervention and allow private governance to take hold and flourish in situations where it has traditionally had difficulty doing so.
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Target Training: Tricking Adversarial Attacks to Fail

Recent adversarial defense approaches have failed. Untargeted gradient-based attacks cause classifiers to choose any wrong class. Our novel white-box defense tricks untargeted attacks into becoming attacks targeted at designated target classes. From these target classes, we derive the real classes. The Target Training defense tricks the minimization at the core of untargeted, gradient-based adversarial attacks: minimize the sum of (1) perturbation and (2) classifier adversarial loss. Target Training changes the classifier minimally, and trains it with additional duplicated points (at 0 distance) labeled with designated classes. These differently-labeled duplicated samples minimize both terms (1) and (2) of the minimization, steering attack convergence to samples of designated classes, from which correct classification is derived. Importantly, Target Training eliminates the need to know the attack and the overhead of generating adversarial samples of attacks that minimize perturbations. Without using adversarial samples and against an adaptive attack aware of our defense, Target Training exceeds even default, unsecured classifier accuracy of 84.3% for CIFAR10 with 86.6% against DeepFool attack; and achieves 83.2% against CW-L2 (κ=0) attack. Using adversarial samples, we achieve 75.6% against CW-L2 (κ=40). Due to our deliberate choice of low-capacity classifiers, Target Training does not withstand L∞ adaptive attacks in CIFAR10 but withstands CW-L∞ (κ=0) in MNIST. Target Training presents a fundamental change in adversarial defense strategy.
METAPRED: Stata module producing outlier and influence diagnostics for meta-analysis

metapred extends the currently available post-estimation predictions for meta regress to include standardized residuals, studentized residuals, DFITS, Cook's distance, Welsch distance, and covariate ratio. Standardized residuals and studentized residuals produced by metapred correspond to those produced by the R package METAFOR (http://www.metafor-project.org/doku.php). DFITS produced by metapred are identical to those produced by METAFOR when using fixed-effects regression (metapred currently does not implement the adjustment for random-effects models as suggested in Viechtbauer and Cheung [2010]). metapred computes the Cook's distance statistic, covariate ratio and DFBETA according to the formulas used for post estimation following regress (see Methods and formulas). As such, the resulting estimates are consistent with those produced by predict following regress, but are not consistent with estimates computed in METAFOR. metapred offers the Welsch distance metric which is not offered in METAFOR. See the Stata Manual entry under regression post-estimation predictions for how to interpret these statistics following regression. The interpretation following meta regression is the same.
Agile and Resilient Autonomous Flight

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Flying robots, often called drones, are starting to play a major role in several tasks such as search and rescue, interaction with the environment, inspection, patrolling and monitoring. Agile and resilient navigation of Micro Aerial Vehicles (MAVs) through unknown environments poses a number of challenges in terms of perception, state estimation, planning, and control. To achieve this, MAVs have to localize themselves and coordinate between each other in unstructured environments. In this talk, I will present some recent research results agile and resilient navigation of aerial robots for search and rescue, exploration, transportation, physical environment interaction, and human drone collaboration using a minimal on-board sensor suite composed by an IMU and camera sensors information.
Decision Support System for Determination of Promotion Using Simple Additive Weighting

The human resources of a company greatly influence many aspects that determine the success of the work of the company. One of the most important processes in the human resources department of a company or business entity is the promotion process. When determining which employees are eligible for promotion, it is difficult for companies to make a decision. This is because many employees are highly qualified and the number of positions required is limited. In addition, the processing of employee evaluation data in companies is still done manually, so that considering a large number of employees, the possibility of input errors is quite high and takes a relatively long time. For that, we need a decision support system that can assist companies in selecting employees to be promoted to various positions. The method used is the Simple Additive Weighting method, which starts from finding problems, determining goals, determining standards and alternatives, determining weights until the final result is obtained, then carrying out a ranking process, which will select the alternatives given so that employees who deserve to be promoted can be determined.
Fake Detection as a Service: Implication of False Alarm in the Online Marketplace

Almost all consumers use online reviews to decide which product to purchase among a plethora of products on an online marketplace. Though honest reviews are useful, fake or biased reviews pose numerous problems for an online marketplace and her stakeholders as they can misrepresent the quality of a product and mislead consumers. As a remedy, a marketplace can use a free add-on fake review detection service (i.e., fake detection as a service, FDAS), which provides adjusted rating scores, to help consumers separate the wheat from the chaff in a hodgepodge of gushing reviews and identify the true quality of products. One would expect that the consumers, the marketplace and honest sellers on the marketplace would benefit from an accurate FDAS. While we investigate whether and how this free service is effective for the marketplace and her stakeholders, we will show that the marketplace has an incentive to interfere and degrade the quality of such a service. We find that high quality sellers can largely benefit from the FDAS even when the FDAS is not very accurate. Paradoxically, when the FDAS conveys inaccurate information to consumers, the marketplace and sellers can benefit from the FDAS because inaccurate detection helps mitigate price competition between the sellers. This harms the consumers. Furthermore, we identify when and how the marketplace has an incentive to degrade the quality of the FDAS. Our results suggest that, under certain circumstances, a regulator needs to step in to protect the marketplace sellers and consumers.
The Effectiveness of Context Structure Sound Dictionary Strategy to Improve Vocabulary Mastery at the Eighth Grade Students of SMP Negeri 1 Alian Kebumen in the Academic Year of 2015/2016

The aim of this research is to know whether context structure sound dictionary strategy is effective or not to improve vocabulary mastery at the eighth grade students of SMP Negeri 1 AlianKebumen in the academic year of 2015/2016. This experimental research was being carried out at SMP Negeri 1 AlianKebumen from 29 July to 19 August 2015. Here, the researcher took the data from the eighth grade students VIII A and VIII B as the sample of the research. VIII A class is as a control group that consists of 32 students, and VIII B class is as an experimental group consists of 32 students. The researcher has collected the data by giving pre-test and posttest to the sample. The result of the test has been analyzed by using SPSS. The result of this research shows that using context structure sound dictionary strategy is effective for improving vocabulary mastery. It is proven that the result of t-value based on the critical value of t-table by using 5% (0.05) significance level, the value of t-table 2.00. It means that t-value is higher than t-table (2.44 > 2.00). It can be seen from gain score control group and experimental group. The experimental group is higher than control group. Based on the result above, it can be concluded that context structure sound dictionary is effective to improve vocabulary mastery at the eighth grade students of SMP Negeri 1 AlianKebumen in the Academic Year of 2015/2016.
Perangkat Lunak Mobile Trivia Game Untuk Membantu Meningkatkan Hafalan Dan Wawasan Anak Anak Terhadap Surah Pendek Al Quran

The development of mobile trivia game software is motivated by the tendency of children over the age of 7 who prefer digital devices and are better able to utilize the information presented by games. The mobile trivia game software is expected to be a solution that can meet the needs of children aged over 7 years starting from the aspect of education which is very important to be a shared concern for all elements in the society. The development of the software system can be realized by using the Extreme Programming method which modeling can use a CRC card. The work is done in a very short time. The results of the implementation of the development of mobile trivia game software can provide benefits to children aged over 7 years, especially to help improve their memorization and insight into the Qur'an's Juz Amma. As has become the hope for the author, namely creating educational software for children over 7 years
Relative Importance of Blockchain in Cryptocurrency, Banking & Health Care

This paper looks at the opportunities and challenges of implementing blockchain technology across medical, finance and banking sectors, and provides a clear view which can enable Blockchain for more extents. After going through few articles and papers related to applications of Blockchain, we gained a vivid knowledge on pros and cons on Blockchain applications from each article. Few drawbacks that we encountered are using Power of Work (POW) in medical sector which is destroying the computing ability at a stage when system is getting bigger and eventually which is wasting lots of resources. One study over conceptual proposal on blockchain is the idea of using trade credit as the value of transactions, and this trade credit is in the scope of trade secret which improves trade confidentiality and makes it more secure. Moreover, centralization systems have few risks owing to their dependence on a high scale which could tamper the data or can even block access, change the rules and might even completely shut down the system. Few more drawbacks are designing and building a secure blockchain system is much more difficult on large-scale of banking sector with large database. To address these challenges, recent developments in blockchain technology are enabling novel opportunities in every possible sector by using Trust-worthy cloud, IoT and artificial intelligence, which makes it more powerful and secure. After analyzing Blockchain implementations and identifying their limitations, we conclude with several promising directions for future research.
Introduction: Really Cool Stuff in the Future of Archaeological Sciences

This paper primarily describes the content of an introductory presentation given by the author for the Archaeological Sciences session of the CASA 2019 conference. In addition to introducing the session’s speakers and their topics, this presents an abridged perspective of major developments in recent years in the field of archaeological sciences, and discussion of the state of research at present and in the future. Considerations such as the continued exponential growth in data production, interdisciplinary co-operations and conflicts, and the ethical questions around some research approaches form the basis of an examination of possible future trajectories for research in the field.
중급 한국어 학습자의 보조용언 사용 연구

This research aims to analyze patterns of auxiliary predicate element usage by intermediate-level Korean-language learners. The findings revealed that the auxiliary predicate elements frequently used by the learners were limited to some of the items taught in basic-level classes; auxiliary predicates that had been newly taught in intermediate-level classes were not actively used. Errors were analyzed and categorized by their causes. The findings revealed that for accurate production of auxiliary predicate elements, Korean-language learners must be able to (1) understand the meanings and functions of auxiliary predicate elements; (2) identify types and extents of predicates that can be combined with auxiliary predicate elements (3) comprehend change in form according to person; (4) abide by restrictions on clauses that follow auxiliary predicate elements; and (5) discern similar auxiliary predicate elements. This paper concludes with a discussion on classroom interventions to assist Korean-language learners in effectively acquiring the five capabilities and apply them outside of the classroom.(Korea University)
CTA: Stata module for conducting Classification Tree Analysis

Classification tree analysis (CTA) models use one or more attributes to classify a sample of observations into two or more subgroups that are represented as model endpoints (these are called “terminal nodes” in alternative decision-tree methods). Subgroups are known as “sample strata” because the CTA model stratifies the sample into subgroups of observations that -- with respect to model attributes -- are homogeneous within and heterogeneous between strata (Yarnold & Soltysik 2016). The pruned CTA algorithm involves chained optimal discriminant analysis (ODA) models in which the initial (“root”) node represents the attribute achieving the highest effect size for sensitivity (ESS) value for the entire sample, and additional nodes yielding greatest ESS are iteratively added at every step on all model branches while retaining statistical significance (defined by the prune() option). In contrast, the enumerated-optimal CTA algorithm explicitly evaluates all possible combinations of the first three nodes, which dominate the solution. cta is a wrapper program for the Classification Tree Analysis (CTA) software (Yarnold & Soltysik 2016). Therefore, CTA must be installed in order for the cta Stata package to work. CTA software is available at https://odajournal.com/resources/