Reviewerly/exHarmony huggingface dataset:
Penerapan Sistem Informasi Geografis dalam Pemetaan Sebaran Kasus Gizi Buruk

Malnutrition or Malnutrition is a general term that refers to a medical condition caused by an improper or inadequate diet. The main cause of malnutrition is the lack of children in getting enough energy and protein foods. This is with the economic level of the community. In addition, for parents' knowledge about the importance of nutrition for child development. In East Lombok, cases of malnutrition always appear every year, but recently in 2019, cases of malnutrition were rediscovered which ironically was always known too late. This is because the location of malnutrition sufferers is not reachable or is not known by the district government, this is what makes the handling of malnutrition cases in East Lombok district late, which often results in deaths of infants and children. For this reason, in this study the researcher raised the theme of how to map the distribution of the location of well-known cases of malnutrition in East Lombok district. The purpose of this study was to help the East Lombok fogapen government to see the location of people with malnutrition. The research method used is to directly record the coordinates of the location of people with bad nutrition. with this application, it can help the government in alerting the location of cases of malnutrition in East Lombok district
K- Means Clustering on Based Classification Method of Sales Agent

Agent is one of very important assets for distributors. A better knowledge of the agents and their behavior is required, particularly to support decisions related to the company's business strategy and to manage a better relationship with distributors. Such knowledge can be obtained by classifying agents based on their behavior through historical data, such as the sale and purchase transaction data. One approach that can be done is a segmentation approach can be done by dividing the agents into several segments. In this paper, Data Mining techniques i.e. K-means clustering method is exploredto classify sales agents. By implementing k-means, the knowledge about the best agents can be acquired along with the agents that have least contribution to the distributor.
Problems of digital library management

The article considers the organization and management of the processes of digitalization of libraries. The authors give their definition of the concept of a digital library. The paper reviews the advantages of the introduction of information and communication technologies in modern libraries, the demand for innovative digital library platforms from users. The study formulates frequent problems associated with the use of digital innovations, among which are the difficulties of managing electronic library systems. The authors note the lack of compatibility of the functionality of different platforms, which makes it difficult to transfer and adapt them from one library to another, as well as the difficulties of collecting statistics on the attendance of electronic libraries, available to both operators and users. The research paper raises the question about the development of a single standard of statistics for modern digital libraries. The study notes the necessity of creating user-friendly and understandable interfaces in electronic library applications. The problems of financing the services of digital libraries, library personnel, copyright compliance and dissemination of information about the activities of modern libraries as educational and leisure cultural centers are relevant. The authors point to the rethinking of traditional approaches to library management and the work of librarians as one of the most important tasks of organizing library systems.
The Learning Approach To Writing Indonesian In The Middle School: Ethnographic Study of Indonesian Language Learning at SMAN

The purpose of this study in general is to gain a deep understanding of the learning approach to writing at SMAN 1 Cipanas. This study uses a qualitative approach with ethnographic methods based on the consideration that this study intends to examine and examine in more depth the Indonesian language learning approach. The results of the study illustrate that the approaches used at SMAN 1 Cipanas are communicative, scientific, collaborative, text genre, and student-centered approaches. With the approach used by the teacher, students become more active in the learning process, learning is more meaningful and efficient and fosters student interest.
Studi Perencanaan Embung Lapangan Jegreg Kecamatan Lengkong Kabupaten Nganjuk Provinsi Jawa Timur

The problem in dry season is about water availability, while during wet season there are often several puddles in an area in Jegreg Village in Nganjuk Regency. This is due to the increasing need of the population which is not in line with the development of water supply facilities and infrastructure. In this study, the authors make an analysis related to this problem, by planning a reservoir that can be used to accommodate water during the rainy season so that can be used to reduce floods.Based on the analysis, the dead storage volume (Sediment) are 9,49 m3,Â and the effective storage volume are 68,96 m3and then the total storage volume are Â 78,45 m3 . The height of ponds is 3 meters , with the spillway type is side channel spillway with 0,5 meter of Â height. In elevation +40,50 the width of spillway are 17,15 meters. In stability analysis, the analysis used in the slope stability of the reservoir is the Bishop method withÂ Geostudio2012 application and from the results of the analysis, the safety number meets the requirements. For the analysis of spillway safety against shear and rolling forces, in normal, flood condition, and earthquakes condition, the safety figures meet the requirement.
Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification

The continuous growth of the internet has led to the use of social media for various purposes increase. For instance, some irresponsible parties take advantage of the comment feature on social media platforms to harm others by providing spam comments on the shared object. Furthermore, variation of comments creates many features to be processed, thereby negatively impacting the performance of a classification algorithm. Therefore, this study aims to solve the problem associated with spam comments by comparing filter and wrapper based feature selection using text classification techniques. Data collected from training and test data of 4944 and 100 comments showed that the best accuracy, precision, recall, and f-measure of MNB are 96%, 100%, 92%, and 95.8%. The best accuracy is achieved using feature selection by combining Chi-Square and Sequential Forward Selection methods with a subset of 500 features. Furthermore, the accuracy increase in the MNB and SVM classifications are 8% and 4%. This research concludes that the combination of feature selection improves the classification performance of Indonesian language spam comments.
Custom-Design of FDR Encodings: The Case of Red-Black Planning

Classical planning tasks are commonly described in PDDL, while most planning systems operate on a grounded finite-domain representation (FDR). The translation of PDDL into FDR is complex and has a lot of choice points---it involves identifying so called mutex groups---but most systems rely on the translator that comes with Fast Downward. Yet the translation choice points can strongly impact performance. Prior work has considered optimizing FDR encodings in terms of the number of variables produced. Here we go one step further by proposing to custom-design FDR encodings, optimizing the encoding to suit particular planning techniques. We develop such a custom design here for red-black planning, a partial delete relaxation technique. The FDR encoding affects the causal graph and the domain transition graph structures, which govern the tractable fragment of red-black planning and hence affects the respective heuristic function. We develop integer linear programming techniques optimizing the scope of that fragment in the resulting FDR encoding. We empirically show that the performance of red-black planning can be improved through such FDR custom design.
The decompositions of Werner and isotropic states

The decompositions of separable Werner states and isotropic states are well-known tough issues in quantum information theory. In this work, we investigate them in the Bloch vector representation, exploring the symmetric informationally complete positive operator-valued measure (SIC-POVM) in the Hilbert space. In terms of regular simplexes, we successfully get the decomposition for arbitrary Werner state in $${\mathbb {C}}^N\otimes {\mathbb {C}}^N$$ , and the explicit separable decompositions are constructed based on the SIC-POVM. Meanwhile, the decomposition of isotropic states is found related to the decomposition of Werner states via partial transposition. It is interesting to note that when dimension N approaches to infinity, the Werner states are either separable or non-steerably entangled, and most of the isotropic states tend to be steerable.
Computational tracking and forecasting the transient ischemic attack using deep machine intelligence

This kind of abrupt illness, which has the characteristics of being brief in length and occurring often, is known as a transient ischemic attack (TIA). Because the majority of patients may return to their previous lifestyles after the beginning of the illness, it is often overlooked. Medical study has shown that individuals who have transient ischemic episodes are at increased risk of having a stroke within a very short period of time. Consequently, careful monitoring of transient ischemia stroke is very essential, particularly for older individuals who live alone in their homes. Currently, video monitoring and the use of wearable sensors are the most common ways of monitoring transient ischemia episodes, although both of these approaches have their limitations. This article describes the use of a microwave sensor platform operating in the C-Band (4.0 GHz–8.0 GHz) to non-contact monitor transient ischemic attack in the indoor environment in order to enhance risk management of stroke and make it more convenient and accurate. In order to decrease the dimension of the data, the platform first gathers it, then preprocesses it, and then utilizes principal component analysis to reduce its dimension. For the purpose of developing prediction models, the support vector machine (SVM) and the random forest (RF) are two Deep machine intelligence that are employed. SVM and RF methods achieve 97.3 and 98.7 percent accuracy, respectively, according to the experimental findings; this indicates that the system presented in this article is practical and trustworthy.
The Power of Instructional Materials in Teaching Students’ Speaking Skill

Teaching English is very difficult and it is not an easy job. It is different from another subject as it is not our language. In teaching English, the English teacher should have some strategies and they are supposed to build up the students’ motivation, help and mediate the students with some strategies, techniques, and also method of learning therefore they can reach the language development. In SMAN 1 Bontonompo Selatan Gowa the English teacher contributed such many ways in help their students, the researcher is interested in observing and requiring such the information related to the teaching speaking and formulated this research that analyzed (1) the teachers’ strategy in developing students’ speaking English with instructional material (2) the implementation of instructional material. The research design described through descriptive as the result taken from observation, interview, and documentation. The result of this research finds that the teachers develop their strategy with instructional material and the students have good response towards the strategy.
Language Learning Strategies (LLS) used by the Form Four Male Secondary School Students in Speaking Skill

English as an international language serves its importance in many fields such as finance, medicine, technology and education.The four main skills in the acquisition of English are speaking, listening, reading and writing.In order to converse well in this global world, speaking skill is the most important for communication.Language learners deploy many strategies such as cognitive, metacognitive, compensation affective, social and memory strategies to enhance the speaking skills.Therefore, this study is aimed to investigate the language learning strategies employed by Form Four male students.A mixed method design was employed with semi structured interviews and questionnaires as the instruments for data collection.In this intervention, 40 Form 4 students from a secondary school in Negeri Sembilan were selected as the participants.The findings of this paper showed that the most frequent language learning strategies implemented were social, affective and memory strategies in the acquisition of speaking skill.Based on this finding, more studies are expected to be conducted to identify the application of other skills to help the students strive in learning English.
An Analysis of Students’ Error on the Use of Simple Preposition in Narrative Composition Made by the Eleventh Grade of SMK Tunas Karya Pekanbaru

The purpose of this research is to analyze the eleventh grade students’ error in using preposition in writing narrative text at SMK Tunas Karya Pekanbaru. This study was a descriptive research, the subject of this research was the eleventh grade students’ of SMK Tunas Karya Pekanbaru while the object of this research was students’ error in using preposition in writing narrative text. The researcher used purposive sampling technique to take the sample, there were 35 students as sample from 96 students. To collect the data the researcher used test. The test was conducted to know what types of errors were made by the students when they used preposition in writing narrative text. The result of the error analysis process showed that students committee error into three types: omission, insertion and selection. From the calculation of the data, error of insertion is the most frequent errors that done by the eleventh grade students of SMK Tunas Karya Pekanbaru with the percentage 41.9%. Moreover it is followed by error of omission with percentage 30.9%, and error of selection with percentage 27.1%.
Blockchain in Healthcare: Challenges and Opportunities

The significance of an agile and widespread healthcare system was evident from the recent pandemic, Covid-19. Healthcare encompasses different stakeholders and many domains that include pharmaceutical supply chain management (SCM), electronic medical records, patient histories, clinical trial results, imaging and scans, insurance claim records, and doctors' information as well. Currently, there are many domains in healthcare with challenges and issues where improvements are due. Blockchain is the single most imperative technology that could be integrated with healthcare to enhance the capabilities in every aspect of the trivial system. In this paper, the benefits of integrating blockchain with healthcare shall be discussed. A discussion of the challenges and possible solutions in healthcare using blockchain shall also be given.
POTENSI NILAI MANFAAT EKONOMI DAN KESEDIAAN PETANI MEMBAYAR JASA LAYANAN IRIGASI CIPANGARANGAN

This research is motivated by the inequality of availability with the needs of Cipangarangan irrigation water. Farmers using water are proposing to revitalize the irrigation. Based on this background, the objectives of this study are a) to estimate the value of economic benefits from irrigation revitalization; b) analysis of ability (ATP) and willingness to pay WTP) of farmers for irrigation services, and c) knowing ATP and WTP of irrigation services with farmer characteristics. The method used is a mixed method research with primary and secondary data. Respondents were determined using proportional random sampling. The conclusions of the research results: a) Cipangarangan irrigation revitalization is predicted to increase the economic benefit value of Rp. 10,934,091,000 / year, originating from an increase in the net value of rice farming income of Rp. 7,707,231,000 and labor absorption equivalent to Rp. 3,226,860,000 / year. b) The average ATP for irrigation services is relatively smaller (Rp. 38,749) when compared to the average WTP (Rp. 71,195). This condition shows that the range of farmers' ability to pay for irrigation services is smaller than their willingness to pay (ATP &lt;WTP). Conditions like this illustrate the courage of farmers to pay for irrigation services beyond their reach, the utility of irrigation services is relatively higher than the income of service users. This condition is called Captive riders.
A Factor Analysis Perspective on Linear Regression in the ‘More Predictors than Samples’ Case

Linear regression (LR) is a core model in supervised machine learning performing a regression task. One can fit this model using either an analytic/closed-form formula or an iterative algorithm. Fitting it via the analytic formula becomes a problem when the number of predictors is greater than the number of samples because the closed-form solution contains a matrix inverse that is not defined when having more predictors than samples. The standard approach to solve this issue is using the Moore–Penrose inverse or the L2 regularization. We propose another solution starting from a machine learning model that, this time, is used in unsupervised learning performing a dimensionality reduction task or just a density estimation one—factor analysis (FA)—with one-dimensional latent space. The density estimation task represents our focus since, in this case, it can fit a Gaussian distribution even if the dimensionality of the data is greater than the number of samples; hence, we obtain this advantage when creating the supervised counterpart of factor analysis, which is linked to linear regression. We also create its semisupervised counterpart and then extend it to be usable with missing data. We prove an equivalence to linear regression and create experiments for each extension of the factor analysis model. The resulting algorithms are either a closed-form solution or an expectation–maximization (EM) algorithm. The latter is linked to information theory by optimizing a function containing a Kullback–Leibler (KL) divergence or the entropy of a random variable.
Private Stochastic Non-convex Optimization with Improved Utility Rates

We study the differentially private (DP) stochastic nonconvex optimization with a focus on its under-studied utility measures in terms of the expected excess empirical and population risks. While the excess risks are extensively studied for convex optimization, they are rarely studied for nonconvex optimization, especially the expected population risk. For the convex case, recent studies show that it is possible for private optimization to achieve the same order of excess population risk as to the nonprivate optimization under certain conditions. It still remains an open question for the nonconvex case whether such ideal excess population risk is achievable. In this paper, we progress towards an affirmative answer to this open problem: DP nonconvex optimization is indeed capable of achieving the same excess population risk as to the nonprivate algorithm in most common parameter regimes, under certain conditions (i.e., well-conditioned nonconvexity). We achieve such improved utility rates compared to existing results by designing and analyzing the stagewise DP-SGD with early momentum algorithm. We obtain both excess empirical risk and excess population risk to achieve differential privacy. Our algorithm also features the first known results of excess and population risks for DP-SGD with momentum. Experiment results on both shallow and deep neural networks when respectively applied to simple and complex real datasets corroborate the theoretical results.
Public Trolling Detection on Twitter Using Machine Learning

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.
Acceptance Rate of Independent Exercise Mobile Based Learning Application Using Android

Universitas Terbuka (UT) has prepared many learning services, one of which is Independent Exercises that can be used by students in the learning process. The still low utilization of IE by students needs to be examined. This paper will discuss the extent of IE-based mobile learning using Android. This study uses a quantitative descriptive method, with a population of 180 respondents, and with a sample of 71 respondents form the cities of Serang, Bogor, Bandung, Yogyakarta, Manado,and Pangkal Pinang. Te results of the study show that the aspects of the ease of use, the usefulness, and the intention to use the Android Indepedent Exercises application generaly accepted by students, and it is supported by the access rate of them.&#x0D; Universitas Terbuka (UT) telah menyiapkan banyak layanan pembelajaran, salah satunya adalah Latihan Mandiri yang dapat digunakan oleh mahasiswa dalam proses pembelajaran. Masih rendahnya pemanfaatan IE oleh mahasiswa perlu dicermati. Makalah ini akan membahas sejauh mana pembelajaran mobile berbasis IE menggunakan Android. Penelitian ini menggunakan metode deskriptif kuantitatif, dengan jumlah populasi 180 responden, dan dengan sampel 71 responden dari Kota Serang, Bogor, Bandung, Yogyakarta, Manado, dan Pangkal Pinang. Hasil penelitian menunjukkan bahwa aspek kemudahan penggunaan, kemanfaatan, dan niat menggunakan aplikasi Latihan Mandiri Android secara umum diterima oleh siswa, dan didukung oleh tingkat akses mereka.
Self-boosting for Feature Distillation

Knowledge distillation is a simple but effective method for model compression, which obtains a better-performing small network (Student) by learning from a well-trained large network (Teacher). However, when the difference in the model sizes of Student and Teacher is large, the gap in capacity leads to poor performance of Student. Existing methods focus on seeking simplified or more effective knowledge from Teacher to narrow the Teacher-Student gap, while we address this problem by Student's self-boosting. Specifically, we propose a novel distillation method named Self-boosting Feature Distillation (SFD), which eases the Teacher-Student gap by feature integration and self-distillation of Student. Three different modules are designed for feature integration to enhance the discriminability of Student's feature, which leads to improving the order of convergence in theory. Moreover, an easy-to-operate self-distillation strategy is put forward to stabilize the training process and promote the performance of Student, without additional forward propagation or memory consumption. Extensive experiments on multiple benchmarks and networks show that our method is significantly superior to existing methods.