Reviewerly/exHarmony huggingface dataset:
Masjid, sistem laporan keuangan, Rancang Bangun Sistem Informasi laporan Keuangan Masjid Paripurna Baitul Muktamar Dengan Metode End User Development (EUD)

Baitul muktamar is the paripurna mosque at umban sari road, rumbai, the city of new pekan. Do not currently have a system to facilitate transactions and reporting, and conventional data processing makes the mosque's administrator a challenge for processing data. Therefore, there is a need for a website based financial information system for facilitating the process of financial transactions and reporting. In this research the system developed by end user development methods that could adjust the flow of mosque finance. It can be customized by organizers ranging from systemic users, so that organizers can add to the user and provide access to system users. Managers can manage transactions according to the needs through the services provided by the system. With this system it can be customized to some desired views both the whole system view and the transaction table view. It fit the dynamic financial flow of a mosque. From the development of those systems and the testing being done, that the system has been running as expected. With this system the process of financial transactions made easy, systems were not too difficult to understand, and stewards could use them flexible and thus follow the development of the existing financial flow.
An Analysis of Speaking Learning Strategies of EFL Learners in University of Sultan Ageng Tirtayasa

The study aimed to find out the speaking learning strategies mostly used by the students of the 2 nd semester at the Department of English Education in Sultan Ageng Tirtayasa University and the distinctions of speaking learning strategies used by the high achievers, the middle achievers, and the low achievers. This study was conducted at Sultan Ageng Tirtayasa University involving 37 students of 2019/2020. Quantitative and qualitative data were employed in this study. The closed-response questionnaire was used as an instrument to attain data which was calculated by descriptive statistics based on Heigham & Crocker (2009). A semi-structured interview was used as an instrument to attain data that was analyzed based on Miles & Huberman (1994). This study revealed that metacognitive and social strategies included direct strategies were the most frequently used by students with the highest score of 76.6%. The distinctions in speaking learning strategies used by the high achievers, the middle achievers, and the low achievers were found based on frequency of strategy, strategy category, and variety of strategy. It also showed that language learning strategies can be a contributing factor for effective learners in learning speaking skills.
Muons point to new physics

The way that some subatomic particles spin offers hints of unknown physical forces
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures.

Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text is available. This was successfully applied in many publications with AED systems, but only very limited in the context of other ASR architectures. We investigate the effect of varying pre-processing, the speaker embedding and input encoding of the TTS system w.r.t. the effectiveness of the synthesized data for AED-ASR training. Additionally, we also consider internal language model subtraction for the first time, resulting in up to 38% relative improvement. We compare the AED results to a state-of-the-art hybrid ASR system, a monophone based system using connectionist-temporal-classification (CTC) and a monotonic transducer based system. We show that for the later systems the addition of synthetic data has no relevant effect, but they still outperform the AED systems on LibriSpeech-100h. We achieve a final word-error-rate of 3.3%/10.0% with a hybrid system on the clean/noisy test-sets, surpassing any previous state-of-the-art systems on Librispeech-100h that do not include unlabeled audio data.
StylePeople: A Generative Model of Fullbody Human Avatars

We propose a new type of full-body human avatars, which combines parametric mesh-based body model with a neural texture. We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches. We also show how these avatars can be created from multiple frames of a video using backpropagation. We then propose a generative model for such avatars that can be trained from datasets of images and videos of people. The generative model allows us to sample random avatars as well as to create dressed avatars of people from one or few images. The code for the project is available at this http URL.
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text.

Classical approaches to question calibration are either subjective or require newly created questions to be deployed before being calibrated. Recent works explored the possibility of estimating question difficulty from text, but did not experiment with the most recent NLP models, in particular Transformers. In this paper, we compare the performance of previous literature with Transformer models experimenting on a public and a private dataset. Our experimental results show that Transformers are capable of outperforming previously proposed models. Moreover, if an additional corpus of related documents is available, Transformers can leverage that information to further improve calibration accuracy. We characterize the dependence of the model performance on some properties of the questions, showing that it performs best on questions ending with a question mark and Multiple-Choice Questions (MCQs) with one correct choice.
Learning to Communicate with Strangers via Channel Randomisation Methods

We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task. The methods are: (1) `message mutation' during the generation of the communication protocol; and (2) random permutations of the communication channel. These proposals are tested using a simple two-player game involving a `teacher' who generates a communication protocol and sends a message, and a `student' who interprets the message. After training multiple agents via self-play we analyse the performance of these agents when they are matched with a stranger, i.e. their zero-shot communication performance. We find that both message mutation and channel permutation positively influence performance, and we discuss their effects.
Creating a semantic hierarchy of SUN database object labels using WordNet

Using a semantic hierarchy as a labeling scheme can provide object detection systems with a more robust expert knowledge of the relationships between object classes. This knowledge can be used to improve object class prediction in cases where an object detector encounters an object of a class upon which it was not trained, known as zero-shot object detection or open-set recognition. Datasets which are useful for a particular application, domain, or task may not have their object labels organized into an appropriate semantic hierarchy. For example, the Scene UNderstanding (SUN<sup>1</sup>) Database has image scenes organized into a hierarchy, but no such organization exists with the object labels. Objects in the images of this dataset were annotated in a crowd-sourced manner which allowed annotators to define the polygons which bound the objects as well as assign the labels. The challenge taken up by the method presented in this paper was to take the original object labels of the SUN Database and create a semantic hierarchy such that each child-parent pair of object classes demonstrates an “IS-A” relationship. By associating common labels within the dataset with the most relevant and fine-grained WordNet<sup>2 </sup>synonym, this approach resulted in a multi-layered semantic hierarchy for SUN Database object labels. The product is a tree-structured graph where each node is a WordNet synonym of the original label and a node’s parent is determined by its WordNet hypernym. Other ontological frameworks, such as Basic Formal Ontology<sup>3</sup> and the Operational Environment Ontology Suite,<sup>4</sup> are also discussed.
Basic Goal Management

Goal Management provides SAP SuccessFactors customers with an online means of setting and tracking business objectives across their organization. Furthermore, it provides the starting point for many integrated features in SAP SuccessFactors that allow companies to run smoothly and provide employees positive direction and reward. Customers can configure their goal plan template by identifying the fields used to develop and track each goal. Additionally, customers can decide if employees’ goals are individual goals, team goals, and/or group goals. Whichever goal type the customer selects to include on the goal plan, the results can be updated and tracked throughout the year and even incorporated on performance forms.
Klasifikasi Pneumonia pada Chest X-Ray Paru-paru dengan Ekstraksi Fitur Local Binary Pattern Menggunakan Support Vector Machine

Pneumonia is a type of lung disease caused by bacteria, viruses, fungi, or parasites. One way to find out pneumonia is by X-ray or x-ray. X-ray results will be analyzed by experts in the field to find out whether there is pneumonia or not. This study aims to classify the x-ray results whether there is pneumonia or normal. The method used for the classification of Support Vector Machine (SVM) and Local Binary Pattern (LBP) feature extraction. Stages carried out in the image before the classification are Cropping and Resize, then feature extraction using Local Binary Pattern and in classification using Support Vector Machine produces the best accuracy of 65.63%.&#x0D; &#x0D; Keywords : Pneumonia, Chest X-Ray, LBP, SVM
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in Transformers

In a world filled with serious challenges like climate change, religious and political conflicts, global pandemics, terrorism, and racial discrimination, an internet full of hate speech, abusive and offensive content is the last thing we desire for. In this paper, we work to identify and promote positive and supportive content on these platforms. We work with several transformer-based models to classify social media comments as hope speech or not-hope speech in English, Malayalam and Tamil languages. This paper portrays our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021- EACL 2021.
PELATIHAN PEMBUATAN MEDIA PEMBELAJARAN MATEMATIKA MENGGUNAKAN WHITEBOARD ANIMATION PADA GURU-GURU MGMP MATEMATIKA SMP KOTA PEKALONGAN

Mathematics is one of the compulsory subjects in the junior high school education unit. The presentation of Mathematics learning is not supported by creative learning media that utilize the latest technology. The challenge of teachers facing the 4.0 revolution, namely junior high school teachers must be able to utilize technology in learning in addition to varying interactive learning models. The objectives of this training are (1) to introduce mathematics teachers to the use of Animated Chalkboard that can be used as learning media, (2) to improve the ability of Mathematics teachers to create Mathematics learning media using Animation Chalkboard, (3) to produce Mathematics learning media products Middle school uploaded via social media youtube. The training for Pekalongan City Middle School Mathematics teachers was held for three months. The methods used in this activity are (1) Socialization of the use of Animated Whiteboards as a medium of Mathematics learning, (2) assistance in making Mathematics learning media using Animated Whiteboards, (3) evaluation of instructional media products and training activities. The results obtained are an increase in the ability of teachers to create Mathematics learning media using Animation Chalkboards and increase learning media products uploaded to YouTube that can be utilized by Junior High School Mathematics teachers in learning. The product produced is Mathematics learning media that has been uploaded on YouTube social media.
IMPERATIVE SOCIOPRAGMATICS IN THE STUDENTS’ FINAL DEBATE COMPETITION

This research aims to analyze the form and meaning of imperative that is used by participants in debate competition at Tidar University when arguing the debate motion. The benefit of the research is to increase the insight regarding the form and meaning of imperative students’ debate competition at Tidar University. The methodology used in this study is a descriptive methodology. It can be categorized in qualitative form. The technique of collecting data in this study is done by doing observation and collecting documentation were used in collecting data. The technique of presenting and analyzing data is by using a flowchart model. To get the trustworthiness, triangulation is used. This research concludes that there are five imperative forms. Those are the imperative meaning of the invitation in 8 sentences, the imperative meaning for ordering in 8 sentences, the imperative for granting permission in 6 sentences, the imperative for requesting in 7 sentences, and the imperative for prohibition 4 sentences.
AN ADAPTIVE RESOURCE SCHEDULING FRAMEWORK WITH SCHEDULE INTERVAL FILLING FOR CLOUD SERVICES

Application level resource scheduling in distributed cloud computing is a significant research objective that grabbed the attention of many researchers in recent literature. Minimal resource scheduling failures, robust task completion and fair resource usage are the critical factors of the resource scheduling strategies. Hence, this manuscript proposed a scalable resource-scheduling model for distributed cloud computing environments that aimed to achieve the scheduling metrics. The proposed model called " Modified Resource Scheduling with Schedule Interval Filling " schedules the resource to respective task such that the optimal utilization of resource idle time achieved. The proposed model performs the scheduling in hierarchical order and they are optimal idle resource allocation, if no individual resource is found to allocate then it allocates optimal multiple idle resources with considerable schedule intervals filling. The experimental results evincing that the proposed model is scalable and robust under the adapted metrics.
Breast Cancer Detection and Classification using Deeper Convolutional Neural Networks based on Wavelet Packet Decomposition Techniques

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, both Deep learning Network and traditional Convolution Network has been employed for Digital Database for Screening Mammography (DDSM) dataset. In this work, breast cancer images are subjected to removal of background images followed by Weiner filtering and Contrast Limited Histogram Equalization (CLAHE) filter for image restoration. Wavelet Packet Decomposition (WPD) using Daubechies wavelet level 3 (db3) is employed to improve the smoothness of the images. In the first part of breast cancer recognition, these preprocessed images are fed to a deep convolution neural network, namely GoogleNet and AlexNetfor ADAM. RMSPROP and SGDM optimizers for different learning rates such as 0.01, 0.001, and 0.0001. As medical image necessitates discriminative features for classification, the pre-trained GoogleNet architectures extract the complicated features from the image and increase the recognition rate. In the latter part of this paper, Particle Swarm Optimization based Multi-Layer Perceptron (PSO-MLP), and Ant Colony Optimization based Multi-Layer Perceptron (ACO-MLP) are employed for breast cancer recognition using statistical features like skewness, kurtosis, variance, entropy, contrast, correlation, energy, homogeneity, mean which are extracted from the preprocessed image. The performance of this GoogleNet has been compared with AlexNet, PSO-MLP, and ACO-MLP in terms of accuracy, loss rate, and runtime and achieves an accuracy of 99%, with less loss rate of 0.1547 and the lowest run time of 4.14 minutes.
Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July, 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.
LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textit{layers of explanation} (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textit{sensitivity} (emotional responsiveness) of features and the level of \textit{stakes} (decision's consequence) in a domain to determine whether different types of explanations are \textit{appropriate} in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.
GRAPH DATABASE MANAGEMENT SYSTEMS AND GRAPH THEORY

In recent years, graph databases have become far more important. They have been proven to be an excellent choice for storing and managing large amounts of interconnected data. Since graph databases (GDB) rely on a graph data model based on graph theory, this study examines whether currently available graph database management systems support the principles of graph theory, and, if so, to what extent. We also show how these systems differ in terms of implementation and languages, and we also discuss which graph database management systems are used today and why.
The Entry of Bitcoin Mining and its Welfare Implications

Mining is one of the key features to maintain the value and the security of the Bitcoin system. Like mining of other commodities, Bitcoin mining requires a non-trivial fixed cost to enter the business. We extend a framework that captures some key features of Bitcoin to incorporate entry costs. We use the model to investigate the welfare effect of barriers to entry in Bitcoin mining. We find that barriers to entry can help to improve welfare. Our quantitative results show that this welfare gain is moderate when comparing to an economy without entry barriers. The source of welfare gains is coming from the saving in mining costs due to the reduction in competition rather than the benefit from the drop in a delayed settlement.
이탈리아 ‘Bonus Cultura’와 프랑스 ‘Pass Culture’ 비교연구를 통한 공공문화서비스 활성화 방안

The purpose of the present study is to suggest strategies for improving public cultural services through the comparative study between ‘Bouns cultura’ in Italy and ‘Pass Culture’ in France. Bonus Cultura and Pass culture represent a welcome for those who reach the age of 18 and a reminder of how crucial culture is, both for personal enrichment and for strengthening the social fabric of the country.In 2016 the Italian government and in 2018 the french government introduced a 500€cultural bonus for all 18-year-old people living in their nation. Young people can spend it on cultural items and activities such as going to theatres, concerts and museums, buying books, etc. They have to register online and then spend the money through the dedicated website, accessible from any device.This study reviews several controversies on the cultural voucher policy in terms of the following debates: 1) democratization vs. cultural democracy, 2) economically or culturally underprivileged, 3) point system vs. card system, 4) pre-selected vs. free chosen, f) social welfare system vs. cultural welfare system.As a conclusion, the study proposes the following suggestions: 1) the cultural voucher policy should make a partnership with sharing projects, social arts education programs, and public subsidy programs, 2) educating cultural welfare professionals should be accomplished for the policy’s more effective long term development.
Deploying an Application to Cloud Platform Using Continous Integration and Continous Delivery

Cloud Computing is the best way for bussiness owner deploy an application to reduce cost issue because it is implement pay as you go concept. Generally, an application on production level or deployed into cloud instance should not have any error or bug. It should be tested and maintain properly. The problem when an application have intensive development that takes more effort to test the application and deploy. So, need a strategy to deploy an application into cloud instance to make the proccess more efficient. Nowadays, Version Control System (VCS) platform provide Continous Integration and Continous Delivery (CI/CD) feature. Users can utilize that platform to perform automated test and deployment easily. This reasearch purposed to examine how to use CI/CD feature and evaluate it in case of deploying web application to Cloud Platform. Researcher use Gitlab wich is provide CI/CD feature for free and deploy the app to Amazon Web Service. The researcher also utilize docker container to accommodate all processes. The result are Continous Integration can improove application quality because most lines of codes are tested using unit or feature test scenario. Using CI/CD feature improove security issue of deployment. Deployment proccess run automatically without human intervention so it will reduce human error factors. This feature also ensure high availability of an application. Deployment proccess will take zero downtime. The application can quickly update without any downtime and configuration. Last, docker container take an important role for deployment of application into cloud instance.
Crossover Learning for Fast Online Video Instance Segmentation

Modeling temporal visual context across frames is critical for video instance segmentation (VIS) and other video understanding tasks. In this paper, we propose a fast online VIS model named CrossVIS. For temporal information modeling in VIS, we present a novel crossover learning scheme that uses the instance feature in the current frame to pixel-wisely localize the same instance in other frames. Different from previous schemes, crossover learning does not require any additional network parameters for feature enhancement. By integrating with the instance segmentation loss, crossover learning enables efficient cross-frame instance-to-pixel relation learning and brings cost-free improvement during inference. Besides, a global balanced instance embedding branch is proposed for more accurate and more stable online instance association. We conduct extensive experiments on three challenging VIS benchmarks, \ie, YouTube-VIS-2019, OVIS, and YouTube-VIS-2021 to evaluate our methods. To our knowledge, CrossVIS achieves state-of-the-art performance among all online VIS methods and shows a decent trade-off between latency and accuracy. Code will be available to facilitate future research.