Reviewerly/exHarmony huggingface dataset:
Hubungan Antara Penguasaan Kosakata dengan Keterampilan Menulis Puisi Bahasa Jerman

Abstract. The purpose of this study was to obtain data on the relationship between vocabulary mastery and German writing skills of class XI students of MAN 1 Makassar City. The sample in this study were students of class XI MIA 5 MAN 1 Makassar City which consisted of 1 class totaling 30 students. The sampling technique used was random sampling. The data were collected through a vocabulary mastery test and a poetry writing skill test. The research data were analyzed using the Product Moment correlation analysis technique. The results of this study indicate that there is no significant relationship between vocabulary mastery and writing skills in German, this is indicated by the value of rh (0.108) ≤ rt (0.361), with N = 30 at the 0.05% significance level.
KOHESI DAN KOHERENSI PADA TEKS DESKRIPSI HASIL PENGAMATAN SISWA KELAS VII SEMESTER GENAP SMP NEGERI 18 PESAWARAN TAHUN PELAJARAN 2018/2019

The problem in this study concerns cohesion and coherence in the description text of observations of class VII students in the even semester of the SMP Negeri 18 Pesawaran academic year 2018/2019. Cohesion includes: using interfaith conjunctions, using pronouns, and using reps, while aspects of coherence: clear ideas according to the topic and the development of logical ideas. The purpose of the study was to find out and describe cohesion and coherence of the description text of student observations. The research method used is a qualitative method. The study was conducted in even semester at 18 Public Schools in Pesawaran. The data source of this study is the seventh grade students of the Even Semester of the Middle School 18 Pesawaran 2018/2019 Academic Year. The main technique is to use tests or performance, which is to assign VII grade students to write description text as a result of observation. Data analysis using qualitative analysis. The results showed that cohesion and coherence in the description text of the observation results of class VII students in the even semester of SMP Negeri 18 Pesawaran were still found to be errors. Elements of cohesion: the use of interalimic conjunctions, students use them less while the aspects of pronouns, students also use them incorrectly and the use of reps is inappropriate. Elements of coherence: suitability of ideas with topics, students are also considered to be less capable as well as the development of ideas with topics also made many mistakes.
Implementing a system for Face Fraud Detection in Online Exam

Online examination is an essential component of E-learning, that has grown exponentially day by day. Cheating in online exams is so easy all over the world regardless of the levels of development. This paper presents a robust face liveness detection method using a face that can be used in detecting spoof attacks for differentiation between legitimate and illegitimate users. The system uses the light reflection concept for detecting a photo face or real face while recording a video or taking an image of the examinee during an examination. The face detection is done using Haar cascade and SVM is used for face recognition. The LBP filter technique is used for detecting the light reflection of an image
PatchMatch Filter‐Census: A slanted‐plane stereo matching method for slope modelling application

Image matching is a well-studied problem in computer vision. Conventional image matching is solved using image feature matching algorithms, and later deep learning techniques are also applied to tackle the problem. Here, a slope-modelling framework is proposed by adopting the image matching techniques. First, image pairs of a slope scene are captured and camera calibration as well as image rectification are performed. Then, PatchMatch Filter (PMF-S) and PWC-Net techniques are adapted to solve the matching of image pairs. In the proposed PatchMatch Filter-Census (PMF-Census), slanted-plane modelling, image census transform and gradient difference are employed in matching cost formulation. Later, nine matching points are manually selected from an image pair. Matching point pairs are further used in fitting a transformation matrix to relate the matching between the image pair. Then, the transformation matrix is applied to obtain a ground truth matching image for algorithm evaluation. The challenges in this matching problem are that the slope is of a homogenous region and it has a slanted-surface geometric structure. In this work, it is found out that the error rate of the proposed PMF-Census is significantly lower as compared with the PWC-Net method and is more suitable in this slope-modelling task. In addition, to show the robustness of the proposed PMF-Census against the original PMF-S, further experiments on some image pairs from Middlebury Stereo 2006 dataset are conducted. It is demonstrated that the error percentage by the proposed PMF-Census is reduced significantly especially in the low-texture and photometric distorted region, in comparison to the original PMF-S algorithm. This further verifies the suitability of the PMF-Census in modelling the outdoor low-texture slope scene.
Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks

We present Multi-Scale Label Dependence Relation Networks (MSDN), a novel approach to multi-label classification (MLC) using 1-dimensional convolution kernels to learn label dependencies at multi-scale. Modern multi-label classifiers have been adopting recurrent neural networks (RNNs) as a memory structure to capture and exploit label dependency relations. The RNN-based MLC models however tend to introduce a very large number of parameters that may cause under-/over-fitting problems. The proposed method uses the 1-dimensional convolutional neural network (1D-CNN) to serve the same purpose in a more efficient manner. By training a model with multiple kernel sizes, the method is able to learn the dependency relations among labels at multiple scales, while it uses a drastically smaller number of parameters. With public benchmark datasets, we demonstrate that our model can achieve better accuracies with much smaller number of model parameters compared to RNN-based MLC models.
Fundamental influences related to language-based difficulties in financial mathematics

Motivated in part by a sustained amount of research in South Africa and principally guided by techniques of problem-solving suggested by Polya as well as error analysis by Newman, the current research examines fundamental influencers (underlying factors) relating errors due to language difficulties in financial mathematics concerning the language of instruction. The current research was accomplished using a case study design. The sample size was 105 out of a population of 186, with assumption of confidence and precision levels at 95 per cent and 0.5 respectively. The aim of the study was addressed by using both sets of structured-interview and document analysis for collecting data. Analysis of data was conducted by both content analysis as well as correlation analysis, wherein, the analysis revealed that errors committed by learners in financial mathematics were due to language difficulties. In contrast, misinterpretation of the mathematical semantics was not as a result of not indicating answers as expected, not following instructions, and not understanding instructions.
DEVELOPMENT OF LARGE SCALE STEM PROBLEM DATABASES FOR STUDENT LEARNING AND ASSESSMENT TOOLS

Problem databases in STEM courses are used in tools for the development of student learning andfinal assessment. In addition, large problem databases are used to develop models for automatic assessment and feedback of students’ work. However, the availability of large, open source, problem databases for specificcourses is limited, and in-house development of a wide variety of problems can take years. In this paper, theframework for a problem database in STEM courses was created using semantic analysis of sentence structure and composition. Problem statements were analyzed to determine the key grammatical constructs that are used in commonly posed problems. Based on this analysis, software was developed to create large problemdatabases which allow for simple extension to other courses. Using a first-year mechanics course this softwarewas populated with a few generalized question and sentence structures to create a large problem database.
CHAINGE: A Blockchain Solution to Automate Payment Detail Updates to Subscription Services

The rise of the subscription-based business model has led to a corresponding increase in the number of subscriptions where a customer needs to manage their payments. This management of payments for multiple subscriptions has become a very complicated and insecure task for customers; especially when it comes to renewing payment details when the card is lost, stolen, or expires. In addition, this, mostly manual, process is vulnerable to human error, digital frauds, and data breaches, according to security reports. Thus, in this paper, we propose a novel approach to automate, manage and simplify the Financial Supply Chain involved in the process of updating and managing payments to user' subscriptions. This is done by utilising the Hyperledger Sawtooth blockchain framework, that allows a consumer to enter their payment card details in a central digital wallet and link their subscriptions to their cards. The card being updated triggers an event on the blockchain, which allow for the payment details to be updated on subscription systems automatically. The verification tests performed on the prototype of the proposed system shows that its current implementation has been securely achieved.
A Diffusion Theory For Minima Selection: Stochastic Gradient Descent Exponentially Favors Flat Minima

Stochastic Gradient Descent (SGD) and its variants are mainstream methods fortraining deep networks in practice. SGD is known to find flat minima with alarge neighboring region in parameter space from which each weight vector hassimilar small error. However, the quantitative theory behind stochasticgradients still remains to be further investigated. In this paper, we focus ona fundamental problem in deep learning, How can deep learning select flatminima among so many minima? To answer the question quantitatively, we developa density diffusion theory (DDT) to reveal how minima selection quantitativelydepends on minima sharpness, gradient noise and hyperparameters. We discoverthat stochastic gradient noise can accelerate escaping sharp minimaexponentially in terms of the eigenvalues of minima hessians. Thus, SGDexponentially favors flat minima more than sharp minima, while StochasticGradient Langevin Dynamics (SGLD) only polynomially favors flat minima. We alsoprove that either small-learning-rate or large-batch training requiresexponentially many iterations to escape minima in terms of ratio of batch sizeand learning rate, and thus can't search flat minima well given a realisticcomputational time.
APLIKASI GAME PUZZLE HURUF HIJAHIYAH UNTUK ANAK-ANAK BERBASIS ANDROID

Mobile device technology is growing rapidly, especially with the emergence of smartphones with the Android operating system. Users in this case can download various basic applications available easily on the google play store. But most of them are available in less educational game form. In fact, it often has negative consequences for users, especially for children who are still unable to distinguish positive and negative. Early childhood education stages tend to be interested in games that are easy to play and have an attractive visual appearance with a variety of colors and varied images that attract attention. This stage will also make it easier to remember the lesson. Therefore, researchers made a puzzle game application that can provide entertainment and education to users, especially children. This android based hijaiyah letter puzzle game application was built using Construct 2 converted with Phonegap into an Apk file. The application of this application is by installing a puzzle game application on a smartphone with the specifications of the Android operating system version 5.1+ (lollipop).
Storage Structure and Searching Process of Semantic Network

The present study explored the spreading activation model of semantic networks in Chinese context to know how people research information from long-term memory and what influences the time of research mostly.Based on the three-level semantic networks of Collins and Qullian' s theory and the conclusion, "frequency will affect the reaction time", of Conrad's study, the present study put forward the hypothesis that the category and the frequency of semantic would interactively influence the retrieval time.The participants were 30 undergraduates aged from 19 to 21.The present study found that the level impacted the reaction time very little.On the contrary, the frequency impacted it most.In addition, the interaction between the category and the frequency of semantic was not significant.The present study supported the theories that frequency had great impact on the reaction time and further verified that the elements that would influence the retrieval time were complicated.
Algorithmic speech and freedom of expression

Algorithms have become increasingly common, and with this development, so have algorithms that approximate human speech. This has introduced new issues with which courts and legislators will have to grapple. Courts in the United States have found that search engine results are a form of speech that is protected by the Constitution, and cases in Europe concerning liability for autocomplete suggestions have led to varied results. Beyond these instances, insight into how courts handle algorithmic speech are few and far between.By focusing on three categories of algorithmic speech, defined as curated production, interactive/responsive production, and semiautonomous production, this Article analyzes these various forms of algorithmic speech within the international framework for freedom of expression. After a brief introduction of that framework and a look towards approaches to algorithmic speech in the United States, the Article then examines whether the creators or controllers of different forms of algorithms should be considered content providers or mere intermediaries, the determination of which ultimately has implications for liability, which is also explored. The Article then looks at possible interferences with algorithmic speech, and how such interferences may be examined under the three-part test—particular attention is paid to the balancing of rights and interests at play—in order to answer the question of the extent to which algorithmic speech is worthy of protection under international standards of freedom of expression. Finally, other relevant issues surrounding algorithmic speech are discussed that will have an impact going forward, many of which involve questions of policy and societal values that accompany granting algorithmic speech protection.
Garment Dryer / Mirza Khairina Shaharuddin

Malaysia is a country where the weather is a bit or have a major different from western country. In Malaysia there is almost no seasonal changes in climate. The most cool is the time from November to January when the temperature drops to 26oC. However, the climate of Malaysia is different and depending on which region and states. Rainy season lasts from October to March. The rainy season is celebrated torrential rains continuing for two or three hours, they are usually in the afternoon. In mountainous areas, the rains are often because the high peaks are constantly cloudy. In general, rainfall in Malaysia is about 2000mm per year, in the mountains reaches 5000mm. However, clothes will still be wet even there is no rain, because throughout the year the humidity in Malaysia is 75%. It is a problem to dry clothes around October to March.While time spent at college is a fond memory and a happy experience for most, the student life is not without its rough patches. Everyone’s situation is unique but there are a few problems that almost all college students deal with at least once during their college stays.Most of student have problem managing their time so sometimes when they need to wash their clothes that they desperately need to wear on some occasion and the weather are not really on their side and they have to air dry in the room. Air dry in the room cannot guarantee that their cloth are dry enough. Sometimes the cloth are still be damp. Even if it dries off the cloth might have musty smell. Going to an occasion with musty smell might lower one’s self confidence. Aside from drying cloth this product can also be use in drying shoes. Most shoes are not made to be waterproof, when walking in the rain shoes will get wet and it takes time to dry under the scorching sun. It would be a major problem if it is already evening and student need to wear the same shoes for tomorrow.
Kemampuan Siswa Kelas X SMA Negeri 2 Banda Aceh Menganalisis Isi Debat

This study examines the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the content of the debate. The formulation of the problem is (1) How is the ability of class X students of SMA Negeri 2 Banda Aceh to analyze problems / issues in the debate text? (2) How is the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the perspective of the arguments of several parties in the debate text? (3) How is the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the conclusions in the debate text? The purpose of this study (1) Describe the ability of class X students of SMA Negeri 2 Banda Aceh to analyze problems / issues in the debate text. (2) Describe the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the point of view of the arguments of several parties in the debate text. (3) Describe the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the conclusions in the debate text. This research uses descriptive quantitative research methods. The population of the study was 27th grade students of SMA 2 Banda Aceh, totaling 279 students. Meanwhile, the study sample was 42 students. The research data collection technique is a non-test technique in assignment. Data analysis was carried out from the results of analyzing the problem / issue, the perspective of the arguments of several parties and the conclusions in the debate text. The result of the average value of analyzing the problem / issue is 60. The result of the average value of analyzing the point of view of the arguments of some parties is 82 and the result of the average value analyzing the conclusions is 48. The result of the overall average value is 61. The results of this study indicate that the scores obtained by students in the range of the good (86-100) were 7 (17%) students, in the good category (71-85) as many as 5 (12%) students, in the sufficient category (56-70) as many as 15 (36%) students, and less categories with grades 40-55 as many as 15 (35%) people. Thus, the ability of class X students of SMA Negeri 2 Banda Aceh to analyze the content of the debate can be categorized as sufficient.
Geographical Information System Based Sensitivity Analysis Accurately Predicts Hydrocarbons Contamination Using Drastic Index and Multicriteria Analysis&amp;#160;

&amp;lt;p&amp;gt;Among the numerous groundwater vulnerability assessment methodologies, the geographical information system-based DRASTIC model is the most widely used and have been found to achieve reliable results even in complex areas. However, hydrocarbon contamination cause by Anthropogenic activities has not previously being considered within these groundwater vulnerability assessment model. &amp;amp;#160;This study proposes a new flexible approach for optimizing the identification of input data layers that can help identify vulnerability to hydrocarbon contamination through the principles of sensitivity analysis.The single-parameter (SA ) and map removal analysis(MA)&amp;amp;#160; was employed to obtain effective weights for the&amp;amp;#160; modified model, which were then implemented to improve efficacy Multi-criteria evaluation (MCE) techniques are part of a decision-making process for assigning weights of significance to each input layer to the DRASTIC model. The application is illustrated through a case study focussing on the city of Kano located in Northern Nigeria within west Africa .DRASTIC index model have seven paramters ,Depth of water table,net reharge ,Aquifer media ,soil media, Topography ,impact of vadose zone and hydrlauic conductivty.&amp;amp;#160; The most sensitive parameters are depth of water (22.92%), net recharge, (25.98), impact of Vadose zone (27.07%),&amp;amp;#160; The borehole data includes groundwater samples that were analysed for benzene, ethylbenzene and xylene (Betex) components of crude oil. &amp;amp;#160;Accordingly, the results presented the highest hydrocarbon content (51.66477mg/l) in Dala (western Kano) due to the significantly high number of hydrocarbon sources such as under-storage tanks within the petroleum stations and automobile garages.&amp;lt;/p&amp;gt;
An Improved K-means based Text Document Clustering using Artificial Bee Colony with Support Vector Machine

The usage unsupervised clustering approaches in numerous applications has attracted the researchers as an innovative research area due to separation characteristics of unstructured format of documents in a similar group contains same data. Increasing requirement in numerous research fields and information technologies, led to an increase in the document clustering approaches with maximum efficiency. Therefore, in exiting works, researchers take a lot of time to find similar documents according to the requirement. During the document clustering, several types of issues faced by the researchers like best centroid localization, less uniqueness of clustered data, high clustering time and many more. To minimize these types of problems, most of the previous research focuses on similarity techniques for clustering but the clustering results are not satisfactory. In this research article, we developed an improved K-means based text document clustering using Artificial Bee Colony (ABC) along with the Support Vector Machine (SVM) as a concept of unsupervised mechanism. Here, SVM act as a machine learning technique and helps to classify the best centroid according to optimized centroid by K-means with ABC. Based on this mechanism, proposed work achieved better clustering performance that is validated by comparing with existing works based on the Accuracy, Normalized mutual information (NMI) and Adjusted Rand index (ARI)