Reviewerly/exHarmony huggingface dataset:
Upaya Guru Mengatasi Kesulitan Belajar Administrasi Pajak Siswa Kelas XI Akuntansi Di SMK Batik 1 Surakarta

Learning process must have limitations developed by students. Learning difficulty is a condition of teaching learning process in which learners do not study properly. It is principally a real symptom in behavior manifestation, either direct or indirect. It has impact on the students’ achievement due to the achievement is achieved by learning process at school as well as outside the school and it is based on students’ intention and effort to study. It happens in learning Tax Administration. Thus, teachers’ effort to recover this learning difficulty is important to improve teaching learning in classroom. The study aims to: (1) explain factors causing students’ learning difficulty to understand Tax Administration subject of XI year students in SMK Batik 1 Surakarta. (2) explain how the teachers’ effort to recover students’ learning difficulty to understand Tax Administration of XI year students in SMK Batik 1 Surakarta. It is a descriptive study. The key instrument of the study is the researcher itself. The techniques of collecting data are observation, interview, and document. Data is analyzed by data reduction and drawing conclusion. The result of the study shows that, (1) the students’ knowledge level is very low to understand Tax Administration, they joke to their friend when teacher explains the lesson. The students regard that the lesson is boring and difficult to memorize. Teacher has created various learning steps to have pleasing learning atmosphere. (2) Most students do not assignment given by their teacher, in their opinion the assignment is boring due to it is only essay questions and multiple choice. The students get bored over the assignment. The teacher tries to give various assignments, such as finding information in form of clipping, mind map and so on. (3) Lack of parents support causes students do not study at home. It is shown by most students do not complete the assignment. Parents do not control students in studying. The teacher’ effort to recover this problem is by composing students’ result of study and general students’ behavior. Besides, teacher delivers the problem during parents-teachers meeting at school.
Data Analysis Case Studies

In the last chapter, we looked at the various Python-based visualization libraries and how the functions from these libraries can be used to plot different graphs. Now, we aim to understand the practical applications of the concepts we have discussed so far with the help of case studies.
MFNet: Multi-feature convolutional neural network for high-density crowd counting

The crowd counting task involves the issue of security, so now more and more people are concerned about it. At present, the most difficult problem of population counting consists in: how to make the model distinguish human head features more finely in the densely populated area, such as head overlap and how to find a small-scale local head feature in an image with a wide range of population density. Facing these challenges, we propose a network for multiple feature convolutional neural network, which is called MFNet. It aims to get high-quality density maps in the high-density crowd scene, and at the same time to perform the task of the count and estimation of the crowd. In terms of crowd counting, we use multiple sources of information, that is HOG, LBP and CANNY. With the support vector machine (SVM), each source provides us not merely a separate count estimation, but other statistical measures. In order to effectively solve the problem of extracting scale-related features in crowd counting, we have integrated MFNet, a convolutional neural network architecture. By comparing the experimental results of multiple data sets, MFNet is superior to other population counting methods.
CORRELATION ANALYSIS OF PM10 AIR POLLUTION WITH NDVI (NORMALIZED DIFFERENCE VEGETATION INDEX) BASED ON LANDSAT-8 AND SENTINEL-2A SATELLITE IMAGES (Case Study: Bandung City, West Java)

Dust particulates in the air Suspended Particulate Matter (SPM) is a mixture of various organic and inorganic compounds with small diameters ranging from <1 micron - 500 microns. Particulate (PM10) is an air particle that is less than 10 microns in size. This study correlates the NDVI vegetation index with PM10 in Bandung, West Java in 2001 - 20019 from these results show the relationship between NDVI and air pollution index is negative or inversely proportional, which means the higher (+) NDVI, the air pollution index decreases (-) with a confidence level of 39.35% in 2001, 45.13% in 2006, 60.04% in 2011, 36.83% in 2016, 50.71% in 2019 and 46.96% in 2018 and 48.96% in 2019 with Sentinel. Thus, the NDVI index can be used to obtain air pollution information that indicates if the NDVI index value in the extraction will get a correlation value between Landsat 8 satellite images and Sentinel 2A where the correlation results on field measurements are Landsat at 68.93% and Sentinel 90.15%.
Enhancement of Angry Birds Level Generation from Sketches Using Cycle-Consistent Adversarial Networks

This paper presents our work to enhance a state-of-the-art level generator (Sketch-to-Level Generator) that generates levels for an Angry-Birds-like game from drawn sketches. To achieve this task, Cycle-Consistent Adversarial Networks (Cycle-GAN) are used. CycleGAN is trained using two datasets: sketch drawings and typical level-structures. The former are taken from Google's Quick, Draw! datasets, and the latter from the winning level generator at the 2017 and 2018 AIBIRDS level generation competitions. The output of the trained CycleGAN is used as the input of Sketch-to-Level Generator. Our results show that the proposed preprocessing technique using CycleGAN allows Sketch-to-Level Generator to more successfully generate levels from arbitrary sketch drawings.
i-tStar (3D): Three-Dimensional Interactive Trajectory Star Coordinates

Currently evolved high-dimensional data visualization models and manipulation tools can solve many analysis issues. However, few defections exist: 1) the case where the trajectory set describes a single attribute, the case where the trajectory set describes the attribute is over-drawn, and the case where multiple attributes are described in a multi-linked view. 2) Data projection approach is difficult to obtain data from high-dimensional space and map it to low-dimensional space with minimum error. When data become complex and dynamic, establishing high-dimensional data mining and visualization models can be difficult. In this regard, this paper suggests a trajectory behaviour feature for multi-attribute trajectory analytics.
Sistem Pakar Untuk Mendiagnosa Jenis-Jenis Penyakit Stroke Menggunakan Metode Variable-Centered Intelligent Rule System

Stroke is a disease that occurs due to blockage of the brain blood vessels or rupture of blood vessels in the brain. So that due to blockage or rupture of these blood vessels, certain parts of the brain are reduced and even the oxygen supply is cut off so that they become damaged and even die. Consulting with an expert is expensive. Sometimes sufferers are unable to consult their illness to an expert because of cost issues. In addition, it is difficult for sufferers to meet experts, especially in small areas. The few experts in the area make it even more difficult for the sufferer. Because there are few experts in the area, it requires the sufferer to seek experts in big cities. That of course requires a lot of money. The method used is the Variable-Centered Intelligent Rule System (VCIRS) method. The purpose of this research is to create an expert system that can work more than an expert and is expected to help the public to diagnose the type of stroke so that the treatment of the disease is faster and more accurate. The result of the research is an expert system application program that is able to diagnose the type of stroke. System output in the form of disease diagnosis results obtained by calculations using the Variable-Centered Intelligent Rule System (VCIRS) method and its solution.
Assisting Students Learning Experiences Using an Augmented Reality Model Framework

In learning through experience there are so many techniques that can be used in order to learn and master skills. Strategies used for learning may be in the form of presentation, multimedia, simulation or hands-on. Others might prefer combination of strategies both being able to hear and/or see the actual or real object of machines, tools or equipment. There are advanced visual technologies available in the internet to choose from but most are not designed to the learning process in a school. Augmented reality is an emerging advance technology that shows a lot of use and opportunity as a tool for learning and enhancing experience. AR can simulate real objects into models that be used for education. This study aims to experiment on an AR engine created using the AR model framework to enhance the learning experiences of students in the different learning strategies used in this study. The experiment is focused to find the significant differences of not having and having an AR model into the learning/teaching strategy. To analyze the data frequency, statistical mode is used to find the most frequent response to interpret the nominal and ordinal categories of the variables. The results of using the AR model framework significantly improved the learning experiences of the participants.
Text-to-Speech Duration Models for Resource-Scarce Languages in Neural Architectures

Sequence-to-sequence end-to-end models for text-to-speech have shown significant gains in naturalness of the produced synthetic speech. These models have an encoder-decoder architecture, without an explicit duration model, but rather a learned attention-based alignment mechanism, simplifying the training procedure as well as the reducing the language expertise requirements for building synthetic voices. However there are some drawbacks, attention-based alignment systems such as used in the Tacotron, Tacotron 2, Char2Wav and DC-TTS end-to-end architectures typically suffer from low training efficiency as well as model instability, with several approaches attempted to address these problems. Recent neural acoustic models have moved away from using an attention-based mechanisms to align the linguistic and acoustic encoding and decoding, and have rather reverted to using an explicit duration model for the alignment. In this work we develop an efficient neural network based duration model and compare it to the traditional Gaussian mixture model based architectures as used in hidden Markov model (HMM)-based speech synthesis. We show through objective results that our proposed model is better suited to resource-scarce language settings than the traditional HMM-based models.
PEMBANGUNAN APLIKASI GO-PAJAK BERBASIS MOBILE

Public services are services, both in the form of public goods and public services which are the responsibility and are carried out by the government in an effort to meet the needs of the community and in accordance with statutory provisions. One of the public services provided by the government is the One-Stop Administration System (SAMSAT). According to SAMSAT data in the city of Magelang, data on motorized vehicle ownership has increased and there has been an increase in the number of taxpayers. However, the tax payment service is considered to be less than optimal because there are still long queues when paying taxes.This also causes many people to make payments past the due date or entrust queues to brokerage services. Therefore, to overcome the problem, a mobile-based application (GO-PAJAK) was built which can be used to make tax payments by means of a vehicle document pick-up to pay motor vehicle taxes connected between drivers (SAMSAT employees) and customers (taxpayers). With this application, it is expected to improve service standards at the SAMSAT office and reduce taxpayer queues also reduce the use of brokers in administering public service administration that can increase public trust.&#x0D;
Performance Analysis of Batch Jobs in Cloud Environment

The complexity of running batch jobs in cloud environment is increasing day by day due to huge volume of back end data increasing dynamically in different formats, growing number of online or offline requests from various environments or availability of system resources. This paper explores the various behaviors of batch jobs from production cluster and the following observations are made: (i) The number of user jobs submitted at offline are high thereby increasing the wait time and processing time. (ii) Each job submitted with huge volume of data causing increase in queue size, job failure and errors on completion. (iii) Server utilization is unbalanced during the peak time thereby causing the server crash.
LibGuides: Digital Learning Guide: Online Assignments

This guide was created as part of emergency planning in case we need to close school for weeks at a time
Harassment Beyond Borders: Sexting, Cyber Bullying and Cyber Stalking in Social Media. Can Sri Lanka Protect Victims?

Even though Research and practice have mostly focused on the “bright side” of social media, aiming to understand and help in leveraging the manifold opportunities afforded by this technology, it is increasingly observable that social media present enormous risks for individuals, communities, firms, and even the whole of society as per the above statement. Examples for this “dark side” of social media include cyber bullying, Cyber stalking, addictive use, trolling, online witch hunts, fake news, and privacy abuse. In this article, I aim to illustrate the multidimensionality of the dark side of social media and describe the related various undesirable outcomes. To do this, we adapt the established social media honeycomb framework to explain the dark side implications of each of the seven functional building blocks: conversations, sharing, presence, relationships, reputation, groups, and identity. On the basis of these reflections, we present a number of avenues for future research, so as to facilitate a better understanding and use of social media.
Answering Product-related Questions with Heterogeneous Information.

Providing instant response for product-related questions in E-commerce question answering platforms can greatly improve users’ online shopping experience. However, existing product question answering (PQA) methods only consider a single information source such as user reviews and/or require large amounts of labeled data. In this paper, we propose a novel framework to tackle the PQA task via exploiting heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews. A heterogeneous information encoding component is then designed for obtaining unified representations of information with different formats. The sources of the candidate snippets are also incorporated when measuring the question-snippet relevance. Moreover, the framework is trained with a specifically designed weak supervision paradigm making use of available answers in the training phase. Experiments on a real-world dataset show that our proposed framework achieves superior performance over state-of-the-art models.
Reusing of C source code based on C-XML

There are lots of legacy code in the old system, then C-XML based on Web Service is proposed. The C source code is wrapped into service which can be accessed by service invoker after it is transformed into XML and migrated to the server. Using C-XML, a lot of C code can be reused at lower cost in the new system.
Quantum Solutions Overview

This chapter gives an overview of quantum solutions. You will see how quantum computing can be applied in real life. The problem and solutions are presented with the solution benefits. The solution benefits are automation, cost reduction and profit improvements, efficiency improvement, and defect reduction.
Sentiment Annotated Dataset of Croatian News

We present a collection of sentiment annotations for news articles (article links) in Croatian language. A set of 2025 news articles was gathered from 24sata, one of the leading media companies in Croatia with the highest circulation. 6 annotators annotated the articles on the document level using a five-level Likert scale (1—very negative, 2—negative, 3—neutral, 4—positive, and 5—very positive). The final sentiment of an instance was defined as the average of the sentiment scores given by the different annotators. An instance was labeled as negative, if the average of given scores was less than or equal to 2.4; neutral, if the average of given scores was between 2.4 and 3.6; or positive, if the average of given scores was greater than or equal to 3.6. The annotation guidelines correspond to the Slovenian sentiment-annotated collection of news SentiNews 1.0 (http://hdl.handle.net/11356/1110).If you use the dataset, please cite the following paper (which contains also the details on the dataset creation, and on monolingual and cross-lingual sentiment classification experiments): Pelicon, A.; Pranjic, M.; Miljkovic, D.; Skrlj, B.; Pollak, S. Zero-Shot Learning for Cross-Lingual News Sentiment Classification. Appl. Sci. 2020, 10, 5993. https://doi.org/10.3390/app10175993
Learning to Disentangle Textual Representations and Attributes via Mutual Information

Learning disentangled representations of textual data is essential for many natural language tasks such as fair classification (\textit{e.g.} building classifiers whose decisions cannot disproportionately hurt or benefit specific groups identified by sensitive attributes), style transfer and sentence generation, among others. The existent dominant approaches in the context of text data have been based on training an adversary (discriminator or teacher) that aims at making attribute values difficult to be inferred from the latent code. Although these approaches are remarkably simple and even though the adversary seems to be performing perfectly during the training phase, after training is completed a fair amount of sensitive information to infer the attribute still remains. This paper investigates learning to disentangle representations by minimizing a novel variational (upper) bound of the mutual information between an identified attribute and the latent code of a deep neural network encoder. We demonstrate that our surrogate leads to better disentangled representations on both fair classification and sentence generation tasks while not suffering from the degeneracy of adversarial losses in multi-class scenarios. Furthermore, by optimizing the trade-off between the level of disentanglement and quality of the generated sentences for polarity transfer and sentence generation tasks, we provide some lights to the well-known debate on whether or not \textit{``disentangled representations may be helpful for polarity transfer and sentence generation purposes''}.
API Parameter Recommendations Made Effective

The determination of actual parameters is indispensable in applying API methods that require parameters. While researches on the problem of API parameter recommendation are emerging, they all rely on simple parameter usage patterns. In this paper, we propose a program analysis and machine learning combined method for recommending API parameters. This approach first uses the slicing technique to capture complex usage patterns of the parameters and then employs deep neural networks to recommend API parameters based on the slices.
Communication Behaviour: A Struggle Against Illiteracy in Eastern Indonesia

The illiteracy phenomenon is a trial that requires effort in order to defeat it. The children of Kadi Wone village, East Wewewa District, Southwest Sumba, East Nusa Tenggara, Indonesia attempt to defeat this phenomenon by engaging in their education and studying. However as this is deemed insufficient, children may choose to join a reading centre (Taman Bacaan Masyarakat) called Dyatame. The social circle of Dyatame reading centre provides an opportunity to enhance interest in reading and learning. The purpose of this study was to reveal the meaning of reading in terms of it being a resource to fight illiteracy and further, to study the communication behaviour created through interactions in the social circle of Dyatame reading centre. The results of this study reveal that children can be empowered with learning spirit in the fight against illiteracy. This motivation can also build their capacity through an understanding of the meaning of reading, as a source of insight and an information tool. This study also envisages that this learning process can shape communication behaviour, verbally and non-verbally in both positive and negative ways.
Algorithms for the Generalized NTRU Equations and their Storage Analysis

In LATTE, a lattice based hierarchical identity-based encryption (HIBE) scheme, each hierarchical level user delegates a trapdoor basis to the next level by solving a generalized NTRU equation of level ℓ ≥ 3. For ℓ = 2, Howgrave-Graham, Pipher, Silverman, and Whyte presented an algorithm using resultant and Pornin and Prest presented an algorithm using a field norm with complexity analysis. Even though their ideas of solving NTRU equations can be conceptually extended for ℓ ≥ 3, no explicit algorithmic extensions with the storage analysis are known so far. In this paper, we interpret the generalized NTRU equation as the determinant of a matrix. By using the mathematical properties of the determinant, we show that how to construct algorithms for solving the generalized NTRU equation either using resultant or a field norm for any ℓ ≥ 3. We also obtain an upper bound of the size of solutions by using the properties of the determinant. From our analysis, the storage requirement of the algorithm using resultant is O(ℓ2n2 logB) and that of the algorithm using a field norm is O(ℓ2n logB), where B is an upper bound of the coefficients of the input polynomials of the generalized NTRU equations. We present examples of our algorithms for ℓ = 3 and the average storage requirements for ℓ = 3; 4.
OPPORTUNITIES OF INNOVATION TECHNOLOGIES IN HIGHER EDUCATION

This article highlights the benefits of continuing education, including the use of innovative technologies in higher education. The role of the “Economic Theory” science in developing the knowledge and understanding of economic laws in society, the understanding of the processes of market economy reform, the formation of creative ideas in the younger generation, the enhancement of economic culture. the importance of this discipline through the implementation of important tasks. Knowledge is a multi-tiered, complex process of perception of object properties, highlighting important aspects of it and expressing it in conclusions, comparing it with other objects and making judgments, and generalizing them.
A Distributed Algorithm for Force Directed Edge Bundling

Existing edge bundling algorithms typically require the global information structure of a graph. Therefore, with a simple division of the edges of a graph, it is challenging to conduct edge bundling in a distributed environment and achieve scalable performance. We select a representative edge bundling algorithm, Force-Directed Edge Bundling (FDEB), and parallelize it in a distributed environment. Particularly, to address the difficulties of partitioning and distributions of a large graph among processors, we first create a high dimensional space to represent the data distribution of a graph in FDEB. Second, we map each edge as a data point in this high dimensional space, and then partition and distribute the point cloud among processors. In this way, we can significantly reduce the data communication across processors, and ensure each processor assigned with a similar workload.