Reviewerly/exHarmony huggingface dataset:
Pengembangan Buku Interaktif Layanan Lembaga Keuangan Syariah Berbasis Scientific Approach Di SMK

The developmet technology and information in education present various form of text books. Research as aimed to explain the development interactive book and effectiviness of the used of the book. The final results of this research are produces interative book based on scientific approach sharia financial institution services that can used by student. The research prosedure in the Four D model by Thiagarajan Semmel and Semmel. Which are performed in a complete maner with out excluding a single stage. The instrumens of data included open questionnaries and closes qustionnaries. The results of the due diligence carried out by expert, explained with verry decent that the product can be used apllied as a source of learning in this subject. The trials was carried out in a limited manner with a sample of 20 students who cme from SMK Negeri 1 Lamongan XII class majoring in Islamic Banking giving very positive kriteri to the product being developed.
An Analysis of Abbreviations in Technology as Found in Kompas.com

The use of abbreviation is often used in news writing in order to conserve the news space to the readers. So that abbreviation can make the news become clearer, simple and easy to understand by the readers. Abbreviation can be found easily whether it in newspaper or on website such as KOMPAS.com. This research is aimed to figure out the types of abbreviation on KOMPAS.com website especially at technology section. The source of the data of this research is news-text in Bahasa Indonesia which from KOMPAS.com website, at technology section especially at gadget and hardware sections, which is issued on 1st January 2017 until 30th December 2018. This research uses descriptive method. The data analysis is done based on abbreviation theory which is stated by three experts Schendl (2001), Weakly (2006), Andrew (2007). According to them, there are 6 abbreviation types that are acronym (an abbreviation which comes from the combination of the initial letters of every word), contraction (an abbreviation which comes from the combination of the initial and suffix letters from one word), shortening word (an abbreviation which comes by eliminating suffix of one word), clipping word (an abbreviation which comes by eliminating some parts of initial or suffix of one word), initialism (an abbreviation which comes by taking the initial letter of one word), blend (an abbreviation which comes by the result of 2 abbreviations combined). The result of this research shows that there are 6 types of abbreviation on KOMPAS.com especially at technology section: acronym (23%), contraction (2%), shortening word (2%), clipping word (2%), initialism (70%), blend (1%). So, it can be concluded that intialism is the most dominated abbreviation type on KOMPAS.com especially at the technology section, while another dominated abbreviation type found even it is less from 25% is acronym. While the most rarely found abbreviation types on this website are contraction, shortening, clipping word and blend.
Confronting Violence in the Novel Between the World and Me (2015) by Ta-Nehisi Coates

This thesis is the analysis of the novel Between the World and Me (2015) by Ta-Nehisi Coates. The topic explain how the character tries to deal with violence perpetrated by White. The problem of this analysis is to what extent the novel reveals the character’s attempts to deal with the subtle violence against black people, and how much fictional devices such as setting, character and conflict have an effect of showing the character’s efforts to deal with violence. The purpose of this analysis is to reveal the character’s efforts against violence done by white, and determine the role of character, setting and conflict in helping to know the character’s efforts to deal with violence. The theory in this analysis is the concept of self-awareness by Duval and Wicklund. The method is text-based interpretation and context-based interpretation. The results of this analysis shows that the character’s efforts in dealing violence can save him from death. In other words, black can survive from violence by trying to face and fight the violence itself.Key words: Violence , Black, Self-awareness
Media content analysis of stem cells boundaries in Spain: Speech act theory, performativity and literacy in action

This paper will give an account of how the term performativity has been a much disputed subject within the field of linguistics, gender studies and actor network theory. And how it provides a theoretical framework of researching biomaterials as eggs in these technologies, which will facilitate new cultural patterns of analysis in IFV – Stem Cells – Cloning boundaries and policies. In an attempt to explore the crossing character of these biomaterials in the biotechnologies, it was analysed 2006 year since a new Assisted Reproduction Act was passed and the first Draft Bill of Biomedicine was launched in Spain. Selecting the texts from the two main national newspapers in Spain, were coded by a qualitative analysis program to set their dynamics through IVF, Stem Cells and Cloning news. Data management and analysis was done by a quantitative statistical program. Once texts were coded, linked and marked, the process of model specification was carried out. Through speech act theory and performativity, it was possible to study the discursive practices shown in these biotechnologies where identities are positional, unstable and contingent but supported by the requirement for negotiation. Therefore, this paper offers a new approach to include gender studies in media content analysis of embryo economies.
APPLICATION OF OPINION CLASSIFICATION FOR FASTFOOD RESTAURANT POPULARITY USING INSTAGRAM

The purpose of this study is to identify several opinions regarding Instagram account of fast food restaurant in Indonesia. By using a system that can provide the information about the popularity ranking of fast food restaurants, researcher conduct an analysis using Naïve Bayes Classifier. To identify the popularity ranking, researcher collected restaurants data from Instagram, preprocessing text, using Naïve Bayes classification, and implementing the programing. The design of this system be able to determine the popularity ranking of fast food restaurants using collected data (number of likes and comments on Instagram’s post). All of the predictions obtained exceeds of 90%, indicating a fairly accurate prediction of the popularity ranking, where all results almost touch the maximum score. Data collected from the restaurants’ Instagram account, indicating positive results and ratings, based on number of likes and comments per post. The result of this research shows that, Burger King restaurants are the most popular, second is Kentucky Fried Chicken, third is McDonalds, fourth is Hokben, and fifth are Wingstop..
PEMBUATAN GAME PARTY MULTIPLAYER 2D “ARCATE MANIA” PADA PLATFORM PC

“Arcate Mania” is a two-dimenional arcade party game which contains 10 minigames that can be played by two to four players. The game was created using Unity 2D game engine for PC platform. Rewired are used as plugins to configure multiplayer feature and control for minigames. Photoshop were used to design characters and other 2D images for the game. Each minigames have different gameplay and theme. Players have to compete to collect points by winning minigames. Testing was done by blackbox testing method, alpha testing method by lecturers, and beta testing method by survey form given to 44 respondents. The results show that “Arcate Mania” is a multiplayer game with interesting graphic that is easy to play for people with range from teenage to adult and support different kinds of controllers.
PEMBUATAN GAME RHYTHM VR “SABEAT” PADA ANDROID

The game is developed by Unity Game engine and Monodevelop as a script maker in C# language. The game consist of 10 Electro musics. A blue or red flashlight is used to control the game. The player use the flashlight as a saber to cut the cube. To win the game the player has to cut the cube more than 70% of cube. Testing is done by blackbox testing, alpha testing by the supervisor, and beta testing by using survey of 31 respondents. The test results show that the game "Sabeat" is an interesting game, entertaining and has unique controls for a Rhythm game, but because of these unique controls sometimes players find it difficult to get or use a controller, so more preparations such as flashlights and VR tools are needed to play the game.
PEMBUATAN GAME ARCADE “SAVE OUR PLANET” BERBASIS ANDROID

“Save Our Planet” is an arcade game that has a campaign theme. This game is Android based and has a minimum API Level of Android 4.4 ‘Kit Kat’ (API Level 19). This game is developed Unity 2019.3.15f1 with C# as the programming language. This game has six mini games and each of them provides information to remind the importance of preserving the environment. When a player gets a score that is higher than fifteen, the player could unlock the next mini game and received a tree that will be displayed on the main menu. They could also see the advantages of different trees on the information menu. Based on the results of the beta testing,“Save Our Planet” has already succeeded in providing information about the importance of preserving the environment.
PEMBUATAN ARCADE GAME “GUESS THE ALPHABET!” BERBASIS ANDROID

Game “Guess The Alphabets!” is designed using Unity and C# Sharp for the game’s script. This game has 2 Modes, namely Monster Mode and Learn Monster Mode. Monster Mode is a Mode where players are asked to guess the letters that are on the body of a Monster. Learn Monster Mode is a Mode where players are asked to type the English words. The player must complete the first stage in Monster Mode then proceed to the easy stage in Learn Monster Mode. And so on until the last stage in Monster Mode and Learn Monster Mode. Monster Mode consists of 4 stages. Each stage in Monster Mode consists of 5 levels. Learn Monster Mode consists of 4 stages, namely easy, normal, hard, and extreme stages. Testing is done by this method blackbox testing, alpha testing, and beta testing through a survey of 31 respondents. Test results show that the game “Guess The Alphabet!” is an interesting and challenging game because the players keep trying until it's finish the game.
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation

Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle's surroundings are particularly crucial in path planning and localization tasks. This paper proposes a novel real-time architecture for motion segmentation of Light Detection and Ranging (LiDAR) data. We use three successive scans of LiDAR data in 2D Bird's Eye View (BEV) representation to perform pixel-wise classification as static or moving. Furthermore, we propose a novel data augmentation technique to reduce the significant class imbalance between static and moving objects. We achieve this by artificially synthesizing moving objects by cutting and pasting static vehicles. We demonstrate a low latency of 8 ms on a commonly used automotive embedded platform, namely Nvidia Jetson Xavier. To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space. We provide quantitative results on the challenging SemanticKITTI dataset, and qualitative results are provided in https://youtu.be/2aJ-cL8b0LI.
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction

Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations. Neural Networks (NNs) are widely applied, yet their weight space is still not fully understood. Therefore, we propose to use SSL to learn hyper-representations of the weights of populations of NNs. To that end, we introduce domain specific data augmentations and an adapted attention architecture. Our empirical evaluation demonstrates that self-supervised representation learning in this domain is able to recover diverse NN model characteristics. Further, we show that the proposed learned representations outperform prior work for predicting hyper-parameters, test accuracy, and generalization gap as well as transfer to out-of-distribution settings.
The Grammar of Persuasion: An Analysis of The Rumiyah of ISIS

ISIS is known for its influential propaganda activities, which includes their online publication (e.g. the Rumiyah magazine). Their language of propaganda has proven to be very effective in recruiting foreign fighters from many countries. It therefore makes it worth analyzing it. This research examines the grammar of ISIS’s language of persuasion, more specifically, the mood types of the clauses, their speech-function realization, and the SFL patterns of persuasion. A discourse analysis on the clauses of an article (text) in the Rumiyah, entitled, ‘Just Terror Tactics’, was applied. The Hallidayan SFL theory was used as the basis of analysis. The result shows that the writer mainly applied declarative moods functioning as indirect directives and presented them as suggestions to soften their instructions to do knife terror. In terms of clause patterns, the writer applied four typical patterns to make suggestions, four typical patterns to make trivialization, and four typical patterns to make amplification.
STUDY REGARDING THE ANTHROPOMETRIC PROFILE OF THE 7TH GRADE STUDENT

This study represents the anthropometric evaluation of the seventh grade students, from the Technological High School "Iorgu Vârnav Liteanu" Liteni, Suceava County.To carry out this study we researched the theoretical ideas of specialists in the field of somatic assessment, we appreciated the degree of development of functional capacity in students and we initiated a procedure structure specific to anthropometric measurements in children in the seventh grade.Research proves the effectiveness of the methodical practice of physical exercise on normal growth and harmonious development, on increasing functional capacity and improving sports results in juniors and children of both sexes.The purpose of this experimental study on the anthropometric profile of the seventh grade student is to identify the results of the two measurements, initial and final, comparing them and recommending means of somato-functional development in physical education to discover an ideal body shape human.
Library's personnel resources development as an integral part of its successful activity

The article is devoted to the problem of development of human resources of libraries as an integral part of their successful activity, as well as the peculiarities of advanced training system in Ukrainian libraries. It was found that the basis of providing libraries with professional staff is a system of continuing information and library education as a purposeful process of training and retraining of library staff, that involves updating, expanding knowledge, learning modern information technology, retraining and obtaining additional specialties. The main characteristics of a new type of library staff are considered, including the ability to organize and conduct project activities; possession of communication skills; possession of new social and intellectual technologies; comprehensive erudition, knowledge of modern literature, innovative methods of promoting reading; computer literacy, awareness in the field of information technology and their active use in professional activities. Particular attention is paid to organization of advanced training system for employees in Odessa National Scientific Library. In addition to traditional forms of learning, which are widely used in ONNB, distance education is becoming increasingly popular. A wide range of home online platforms is provided, which gives library staff effective tools for professional development, acquisition of new knowledge and useful skills. There are six information resources that offer training courses: Prometheus, EdEra, Diia.Digital Education, Culture and Creativity, European Space, VUM. Eighteen online courses (series) have been singled out in author's opinion to be useful for librarians. Emphasis is placed on increasing the importance of self-studying and shifting the priority in the system of professional development towards the responsibility of each employee for independent acquisition of professional knowledge and skills.
Integrating Deep Reinforcement Learning Networks with Health System Simulations

Background and motivation: Combining Deep Reinforcement Learning (Deep RL) and Health Systems Simulations has significant potential, for both research into improving Deep RL performance and safety, and in operational practice. While individual toolkits exist for Deep RL and Health Systems Simulations, no framework to integrate the two has been established. Aim: Provide a framework for integrating Deep RL Networks with Health System Simulations, and to ensure this framework is compatible with Deep RL agents that have been developed and tested using OpenAI Gym. Methods: We developed our framework based on the OpenAI Gym framework, and demonstrate its use on a simple hospital bed capacity model. We built the Deep RL agents using PyTorch, and the Hospital Simulatation using SimPy. Results: We demonstrate example models using a Double Deep Q Network or a Duelling Double Deep Q Network as the Deep RL agent. Conclusion: SimPy may be used to create Health System Simulations that are compatible with agents developed and tested on OpenAI Gym environments. GitHub repository of code: https://github.com/MichaelAllen1966/learninghospital
STUDENTS� TARDINESS HABIT TO ATTEND THE CLASS AT THE STUDENTS OF STIE MUHAMMADIYAH MAMUJU

This article aims at providing a description of students tardiness habit to attend the class at the very students of STIE Muhammadiyah Mamuju. This research employed qualitative research. The participants constituted nine students, three lecturers, and three administrators. The instruments were involvement observation, in-depth interview sheet, focus group discussion sheet, and additional questionnaire. The data were transcribed, reduced, and analyzed descriptively. It discovered that there were lots of students internal tardiness inhibitions and external tardiness inhibitions which impeded the students to be puctual to attend the lectures. Students tardiness shared profoundly negative impacts on themselves, lecturers, and other punctual students. It is eligible not to allow the tardy students to attend the class if they are late for 1 minute. However, tardiness policy for 15 minutes is given only for unavoidable lateness as long as there is a confirmation to the lecturers or even administrators via phone before the class.Corresponding author:Hamzar Acha RHDOI: 10.24071/ijiet.2020.040213
Laguerre-Gauss Preprocessing: Line Profiles as Image Features for Aerial Images Classification

An image preprocessing methodology based on Fourier analysis together with the Laguerre-Gauss Spatial Filter is proposed. This is an alternative to obtain features from aerial images that reduces the feature space significantly, preserving enough information for classification tasks. Experiments on a challenging data set of aerial images show that it is possible to learn a robust classifier from this transformed and smaller feature space using simple models, with similar performance to the complete feature space and more complex models.
Vein Pattern Uncovering and RPPVSM based Personnel Recognition System using Skin Images

Face Recognition is not possible in certain scenarios to identify persons, due to unavailability of sufficient face data. The same challenges are encountered in the cases of child sexual abuse where pedophiles’ faces and tattoos are rarely visible in the evidence images or purposely blurred to avoid recognition. In order to identify criminals, the RPPVSM automated identification system consists of skin segmentation, the detection of RPPVSM and matching algorithms of RPPVSM in these skin pictures. The identification system RPPVSM has been evaluated on background images of certain topics and it is shown that our detection algorithm RPPVSM produces stable patterns of skin marks to detect skin marks compared with other methods. The system achieved high precision in identification.
Explaining predictive models with mixed features using Shapley values and conditional inference trees

It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a sound method to explain predictions from any type of machine learning model. The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. This methodology was then extended to explain dependent features with an underlying continuous distribution. In this paper, we propose a method to explain mixed (i.e. continuous, discrete, ordinal, and categorical) dependent features by modeling the dependence structure of the features using conditional inference trees. We demonstrate our proposed method against the current industry standards in various simulation studies and find that our method often outperforms the other approaches. Finally, we apply our method to a real financial data set used in the 2018 FICO Explainable Machine Learning Challenge and show how our explanations compare to the FICO challenge Recognition Award winning team.
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning

We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartite matching algorithm and utilizes the Fisher Information matrix. We provide theoretical analyses demonstrating that the proposed score is mathematically well-defined, and subsequently use the affinity score to propose a novel algorithm for the few-shot learning problem. In particular, using this score, we find relevant training data labels to the test data and leverage the discovered relevant data for episodically fine-tuning a few-shot model. Results on various few-shot benchmark datasets demonstrate the efficacy of the proposed approach by improving the classification accuracy over the state-of-the-art methods even when using smaller models.
Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing

We review two features of mixture of experts (MoE) models which we call averaging and clustering effects in the context of graph-based dependency parsers learned in a supervised probabilistic framework.Averaging corresponds to the ensemble combination of parsers and is responsible for variance reduction which helps stabilizing and improving parsing accuracy.Clustering describes the capacity of MoE models to give more credit to experts believed to be more accurate given an input.Although promising, this is difficult to achieve, especially without additional data.We design an experimental set-up to study the impact of these effects.Whereas averaging is always beneficial, clustering requires good initialization and stabilization techniques, but its advantages over mere averaging seem to eventually vanish when enough experts are present.As a by product, we show how this leads to state-of-the-art results on the PTB and the CoNLL09 Chinese treebank, with low variance across experiments.