Reviewerly/exHarmony huggingface dataset:
Analisis Efektivitas Pengelolaan Dana BOS Hubungannya dengan Perencanaan dan Penggunaan Dana SD Negeri 238/IX Nyogan Sugandi Kecamatan Mestong Kabupaten Muaro Jambi

The conclusions in this study are: Provisions in the management of BOS funds at 238 Nyogan Sugandi Public Elementary Schools, Mestong District, Muaro Jambi Regency based on a student ratio of 127, which is obtained as much as Rp. 114.400.000,- which is detailed in the first quarter RKAS Rp. 23,080,000,- second quarter Rp. 45.160.000,- third quarter Rp. 23,080,000,- and the fourth quarter Rp. 23,080,000,- The planning process in determining the amount of School Operational Assistance funds at State Elementary School 238 Nyogan Sugandi, Mestong District, Muaro Jambi Regency must be based on technical instructions for use. The allocation for the first quarter is 40% of the total budget, the second quarter is 20% of the total budget, the third quarter is 20% of the total budget and the fourth quarter is 20% of the total budget. Effectiveness of planning in managing School Operational Assistance funds at State Elementary School 238 Nyogan Sugandi, Mestong District, Muaro Jambi Regency, nominally the effectiveness of BOS fund management is included in effective
Metacognitive skills in the development of self-regulated learning in higher education students

Metacognition is a multifaceted conception, generated during educational investigations, mainly carried out during class experiences. This analysis has been handled under certain characteristics considered traditional until the situation generated by Covid-19. This work analyzes the relationship between metacognition and self-regulated learning in higher education students, through the analysis of the role that metacognitive skills have in the development of self-regulated learning in higher education students, through the study of the role of metacognitive skills in higher education. the development of self-regulated learning processes, during the 2020-2021 school period in which classes were taught in a virtual environment. The work was carried out with 50 volunteer students of the second and fourth cycle of an important higher education center, through systematic observation, and the application and analysis of a survey, it was concluded that metacognitive skills play an important role at the higher level. education students, through planning, monitoring and evaluation.
Despeckling Techniques for Ultrasound Images

Biomedical imaging shows a substantial role in the era for diagnosis of cancer. Ultrasound (US) imaging modality is widely used in comparison to other modalities for disease diagnosis because it not being or involve in invasive medical procedure. US is a real-time imaging modality, economically, reliable and not uses harmful radiation during the diagnosis. The noise present in US, reduces the visuality and quality of US images is called speckle noise. It degrades the fine details of US image which causes difficulty in effective feature calculation, analysis and edge detection. Speckle noise effects image interpretation and causes misdiagnosis due to bed quality of the images. Many speckle noise reduction techniques have been investigated by various researchers for noise free images. In this paper, a detailed overview about different despeckling filters is presented with different evaluation parameters. This study will help the researchers to select efficient despeckling technique for preprocessing of US images.
IMPROVING STUDENTS’ COMPETENCE IN READING RECOUNT TEXT BY USING DIRECTED READING ACTIVITY METHOD AT THE EIGHTH GRADE OF SMP NEGERI 4 TARUTUNG

This study concerns on Improving Students’ Competence In Reading Recount Text By Using Directed Reading Activity Method. The objective was to find out the improvinf students’ competence in reading recount text By Using Directed Reading Activity Method. This research was conducted by using action reseacrh method. The subject of the study was the eighth grade students of SMP NEGERI 4 TARUTUNG as many as one hundred fifty one. Thenumber of samples were thirty students, consisted of thirty boys and elevent girls. They were taught recount text reading by using directed reading activity method. The instrument used multiple choice tes, interview, and observation sheet. The techniques for data analaysis were qualiatative and quantitative. In analyzing the data, the mean of the students’ score for the pre test was 42.6 for the post test was 51.6, for the post test ii was 83.6 and the total precentage of the improvement from the pre test to post test was 96%. The conclusion is that directed reading activity can improve the students’ achievement in reading recount text. It is suggested that the teacher should apply directed reading activity method in reading recount text to improve students’ achievement in reading recount text.
Simulation of Water Losses Control in Dam From the Evaporation by Aquatic Plants Application (Case Study Wonogiri Dam)

Indonesia’s Economic and population growth increase the demand of the water supply especially for drinking water, industry, irrigation, and energy. The water conflict regard to the supply will affect the dam operation in order to fill the demand. Based on that problem, study about efficiency of the water losses in the dam need consider about evaporation beside the others. This study aim is to evaluate the aquatic plants such as (Hyacinth, torpedo grass, and duckweed) ability to reduce water losses through evaporation. Crop Coefficients (K c ) were obtained and water loss from evaporation was calculated by evapotranspiration (ET c ). Results presented the aquatic plants able to reduce the water losses from the evaporation in the dam. The duckweed can reduced the water losses by around 40% meanwhile Hyacinth and torpedo grass can reduced the water losses by around 27% and 29% respectively. Total water losses that duckweed can reduced is 1.319.354 m3, Hyacinth is 883.388 m 3 and torpedo grass is 938.661 m 3
Legal Records at Risk: A strategy for safeguarding our legal heritage

Why do so few institutions in the legal sector have professional records managers or archivists on their staff?This book is the culmination of a three year project by experienced archivist and records managers on private sector legal records at risk in England at Wales. It summarises the work of the Legal Records at Risk (LRAR) project and its predecessors, diagnoses the problems of preservation of archives in the legal sector in England and Wales and outlines a national strategy for such records.
CROWDBC A Blockchain Base Decentralized Framework for Crowdsourcing

Due to lack of server reliability and user data privacy encryption of data is required before the cloud is outsourced. We have found a compromised method within the blockchain in order to perform a keyword search which is secured on data that is encrypted against malicious service providers and users in cloud. SSE: Privately offers the cloud storage used in symmetric search encryption (SSE) systems, which cannot be regarded as a true cloud. The cloud service is also known to be credible. Let us start by emphasizing the importance of data storage within a public chain. The client is allowed by system to upload them in form which is encrypted, data content is distributed to the nodes of cloud and make sure that the data is available through encryption techniques. Presentation of a blockchain based system for providing the keyword search service with secure storage of distributed data. TKSE performs verifiability on server side so that true cloud servers are protected from being posed by owners of malicious data in the data storage process. Furthermore, technology of blockchain and hash functions are used to allow payment which is fair without third parties involvement for research fees, although if cloud or user is harmful. Our review of security and evaluation of performance show that TKSE is efficient and safe and be suited for cloud computing.
WATERFALL METHODS FOR APPLICATION OF ACCOUNTING INFORMATION SYSTEMS IN HOTEL INCOME MANAGEMENT CASE STUDY: CITRA GRAND HOTEL KARAWANG

The need for information systems in the business more increase as the same as technological developments in the digital era which give the impacts rapidly changes in all sectors. The accounting cycle in the company is inseparable from the existence of financial recording activities in a certain period. Thus it requires an application to provides the processing financial data or it’s called accounting information system. The Grand Hotel Karawang is the focus of research on building income management accounting information system. Based on the data collection method used: observation, interviews and literature studies (library research), it can be concluded that in managing hotel rental income is still manual or not computerized so that why the research to provide solutions based on the need by designing income accounting information system using waterfall software development method, with phases including needs analysis, design, implementation of program code based on open source in the implementation of the Java programming language to make easier to implement into desktop-based applications. Testing is carried out using blackbox testing as a tool for testing each process step by step in the application that all processes are running well (valid) and can be implemented as needed.
Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model

Moderation of reader comments is a significant problem for online news platforms.Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper.Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content varies across the different sections of the newspaper.We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification decision.Our results show that topic information improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model's outputs.
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects

We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image. While previous approaches address the deblurring problem only in the 2D image domain, our proposed rigorous modeling of all object properties in the 3D domain enables the correct description of arbitrary object motion. This leads to significantly better image decomposition and sharper deblurring results. We model the observed appearance of a motion-blurred object as a combination of the background and a 3D object with constant translation and rotation. Our method minimizes a loss on reconstructing the input image via differentiable rendering with suitable regularizers. This enables estimating the textured 3D mesh of the blurred object with high fidelity. Our method substantially outperforms competing approaches on several benchmarks for fast moving objects deblurring. Qualitative results show that the reconstructed 3D mesh generates high-quality temporal super-resolution and novel views of the deblurred object.
Intrinsic evaluation of language models for code-switching

Language models used in speech recognition are often either evaluated intrinsically using perplexity on test data, or extrinsically with an automatic speech recognition (ASR) system.The former evaluation does not always correlate well with ASR performance, while the latter could be specific to particular ASR systems.Recent work proposed to evaluate language models by using them to classify ground truth sentences among alternative phonetically similar sentences generated by a fine state transducer.Underlying such an evaluation is the assumption that the generated sentences are linguistically incorrect.In this paper, we first put this assumption into question, and observe that alternatively generated sentences could often be linguistically correct when they differ from the ground truth by only one edit.Secondly, we showed that by using multi-lingual BERT, we can achieve better performance than previous work on two codeswitching data sets.Our implementation is publicly available on Github. 1
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Semantic segmentation requires per-pixel prediction for a given image. Typically, the output resolution of a segmentation network is severely reduced due to the downsampling operations in the CNN backbone. Most previous methods employ upsampling decoders to recover the spatial resolution. Various decoders were designed in the literature. Here, we propose a novel decoder, termed dynamic neural representational decoder (NRD), which is simple yet significantly more efficient. As each location on the encoder's output corresponds to a local patch of the semantic labels, in this work, we represent these local patches of labels with compact neural networks. This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient. Furthermore, these neural representations are dynamically generated and conditioned on the outputs of the encoder networks. The desired semantic labels can be efficiently decoded from the neural representations, resulting in high-resolution semantic segmentation predictions. We empirically show that our proposed decoder can outperform the decoder in DeeplabV3+ with only 30% computational complexity, and achieve competitive performance with the methods using dilated encoders with only 15% computation. Experiments on the Cityscapes, ADE20K, and PASCAL Context datasets demonstrate the effectiveness and efficiency of our proposed method.
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

Statistical language modeling and translation with transformers have found many successful applications in program understanding and generation tasks, setting high benchmarks for tools in modern software development environments. The finite context window of these neural models means, however, that they will be unable to leverage the entire relevant context of large files and packages for any given task. While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window. Using concrete syntax trees of each source file we extract syntactic hierarchies and integrate them into context window by selectively removing from view more specific, less relevant scopes for a given task. We evaluate this approach on code generation tasks and joint translation of natural language and source code in Python programming language, achieving a new state-of-the-art in code completion and summarization for Python in the CodeXGLUE benchmark. We also introduce new CodeXGLUE benchmarks for user-experience-motivated tasks: code completion with normalized literals, method body completion/code summarization conditioned on file-level context.
Unbiassed Algorithm to Efficiently Evaluate the Quality and Significance of New Research

We live in an information age, characterized by the explosion and proliferation of information and research of many forms. However, this quickly leads to the problems, given limited human eyeball/ attention time, of how to identify and also not miss good relevant research quickly, and how to avoid bad, secondary or irrelevant research, without wasting too much time on pre-reading?For old and established information and research, there are a number of mechanisms to help selection, including: peer-review, publication in high-ranking specialist subject journals e.g. Nature, Science, Cell, Journal of Finance, Econometrica, etc, specialist book series e.g. Springer Mathematics, publication impact factors, cross-citation and citation indices, download and read statistics (the latter all vital for SSRN) and online web referrals.However for new and potentially the most cutting-edge information and research, and some subject areas such as law, these instant quality proxies may not be available, so a new evaluation method may be needed. The new method recommended below is adapted, mutatis mutandis, from Barrow’s method for producing an unbiassed estimator of the number of typos in a doctoral thesis.Here is the algorithm: Specifically 2 (or more or less, considered later) subject expert reviewers independently review the same representative sample of the new work and each reviewer makes one list of all the significant good points they observe in the sample and another list of all the significant problem points they observe in the sample. The reviewers then compare all the lists and identify which points they saw in common, and which points only one reviewer saw. On the assumption that the reviewers were independent and each only saw an unknown proportion of the total good and bad points in the sample, then these results are enough to allow an unbiassed estimate of the total number of good and bad points in the sample to be estimated, and by extension in the whole work under review.This basic algorithm can be generalized in various ways for example by having more than 2 expert reviewers, and by allocating a numerical quality grading scale e.g. +10 to -10 for each good and bad point identified. This would then allow an estimated distribution and variability of quality of the new points across the research to be estimated, as well as estimates of the likely ratio of good and bad and more to less significant points. (Also if only 1 expert were available, whose relative success rate for identifying good and bad points in a sample had or could be calibrated, e.g. from a previous track record, or test analysis, then only 1 expert sample review alone might suffice.)Moreover, looking slightly further ahead, it is quite possible that for specific subject areas, the expert reviewers could even be expert AI systems and this quality and significance review service, with associated metrics, could be provided online as a discretionary service, or automatically bundled in by a platform provider such as Google.
PENGARUH KECANGGIHAN TEKNOLOGI INFORMASI DAN KEMAMPUAN TEKNIK PEMAKAI SISTEM INFORMASI AKUNTANSI TERHADAP KINERJA INDIVIDU PADA PT INDOMARCO PRISMATAMA CABANG BANJARMASIN

The  research  objective  is  to  empirically  prove  the  effect  of  system  quality and information quality on user satisfaction of accounting information systems at PT Indomarco Prismatama Banjarmasin Branch.The method used in this study is a quantitative method by distributing questionnaires to 70 employees of PT Indomarco Prismatama Banjarmasin Branch who were used as respondents using SPSS Version 22.00 test equipment.Conclusion partially and simultaneously variables of information technology sophistication and technical ability of users of accounting information systems affect individual performance at PT Indomarco Prismatama Banjarmasin Branch
Pengaruh Motivasi Belajar dan Pendapatan Orang Tua Terhadap Minat Mahasiswa Mengikuti Pendidikan Profesi Guru (PPG)

This study aims to investigate the effects of the learning motivation and parents’ incomes on the college student interest in joining the PPG program study at the Department of Economic education FE UNP. This research is associative descriptive. The research variables were the college student interest in joining the PPG program, learning motivation, and parents’ incomes. The research sample comprised college students 2015 and 2016 admission year using the Proportional Random Sampling technique with a total of 130 college students. The data was collected through a questionnaire. Methods of data analysis using multiple regression. The results of the study partially show: (1) there is a significant negative influence between learning motivation and college student interest in joining the PPG program, (2) there is a significant positive effect of parents' income on college student interest. in joining the PPG program, and (3) there is a significant positive effect between learning motivation and parents' income on college students' interest in joining the PPG program. The coefficient of determination (R²) is 0.086. this means that the contribution of independent variables of learning motivation and parent's income to the dependent variable of student interest in joining the PPG program was 8,6%. Keywords: ppg program, learning motivation, parents’ incomes
Analisis fluktuasi jumlah produksi gula tebu perbandingan bertahap triangular fuzzy inference system

Sugar production owned by PT X (Persero) for the last 10 years still shows fluctuation. One of the factors is climate, including rainfall. Judging from the development, sugarcane is still vulnerable to the climate. Even so, there are still strategies to reduce the resulting risks, including by means of an appropriate cropping system. However, the safety stock of raw materials cannot be maintained because the quality of the sugarcane deteriorates very quickly. Therefore, sugarcane is continuously sourced in varying quantities and qualities from hundreds of geographically dispersed varieties and supplied to the milling process and due to changing weather conditions so that throughout the year, the time window must be considered for harvesting. Fuzzy logic is a science of uncertainty that has superior ability to process reasoning in language. In fuzzy logic theory, it is known that the concept of fuzzy systems is used in the prediction process and generally contains four stages: fuzzification, formation of fuzzy rules, fuzzy inference system reasoning, and defuzzification. Variable rainfall (mm/year), average yield (%/year), total sugarcane production (million tonnes/year) based on a triangular model of incremental uncertainty as an information attribute in the Fuzzy Inference System (FIS). The selection obtained by using the fuzzy inference system is approximately 5 points from the uncertainty factor that arises from the effect of the input on the total output of the resulting sugar cane production.
Study of Blacklisted Malicious Domains from a Microsoft Windows End-user Perspective: Is It Safe Behind the Wall?

The Internet is a dangerous place, filled with dierent cyber threats, including malware. To withstand this, blacklists have been utilized for a long time to block known infection and delivery sources. However, through blacklisting the domain names we are leaving a landscape of threats to be unknown and forgotten. In this paper, first, we investigate the current state-of-the-art in cyber threats available on such blacklists. Then, we study the corresponding malicious actors and reveal that those persistently appear since 2006. By shedding light on this part of the cyber threat landscape we target increased information security perception of the landscape from the perspective of the average end-user. Moreover, it is clear that the blacklisting the domains should not be one-way function and need to be regularly re-evaluated. Moreover, blacklisting might not be enforced by client applications in addition to outdated system software leaving real danger. For practical evaluation, we created a multi-focused experimental setup employing different MS Windows OS and browser versions. This allowed us to perform a thorough analysis of blacklisted domains from the perspective of the published information, content retrieved and possible malware distribution campaigns. We believe that this paper serves as a stepping stone in a re-evaluation of the once found and then blacklisted domains from the perspective of minimal security protection of a general user, who might not be equipped with a blacklisting mechanism.