Reviewerly/exHarmony huggingface dataset:
Aplikasi self-organizing mapping sebagai alat deteksi anemia pada citra sel darah merah

Application of self-organizing mapping as anemia detection using an image of red blood cellsBackground: Anemia is a nutritional problem characterized by changes in blood cell size, especially in microcytic or macrocytic anemia. Iron deficiency anemia is included in hypochromic microcytic anemia because it has a smaller than normal size red blood cell and has a lower than normal hemoglobin (Hb) arising from reduced supply of iron for erythropoiesis (cell maturation process red blood). Analysis based on red blood cell image is a tool to detect anemia using technology applications. Self-organizing mapping (SOM) is one of the artificial neural networks by dividing the input pattern into several groups, so the network output is in the form of groups that are most similar to the input.Objective: To measure the accuracy of SOM for detecting the size of red blood cells in anemia condition.Methods: The type of research was an observational laboratory. The study was conducted at the Electrobiomedical Laboratory of Universitas Respati Yogyakarta from January to August 2018. The sample consisted of anemia and non-anemia red blood cells which had been tested in a laboratory of 92 blood preparations. Stage of measuring red blood cells consisted of pre-processing (cropping, gray scaling, contrast enhancement, and screening), segmentation, feature extraction, and image identification with SOM. The image identification results were concluded by calculating the accuracy of the anemia detection system based on laboratory examination results.Results: The characteristic that distinguishes anemia and non-anemia was in the size of red blood cells. Anemic red blood cells had different pixel intensities than non-anemic red blood cells. The image of non-anemia red blood cells had a full round or oval image. From as many as 92 detections of blood images, five blood images were not by the target results of laboratory tests. The accuracy achieved by the system was 94.57%.Conclusions: The accuracy value of anemia detection using SOM can be used to identify the type of anemia based on red blood cell size.
Identifying Code Mixing in the Social Media Conversation (Instagram and Whatsapp)

This researcher aims to determine the types and dominant types of code mixing used by sixth semester students of English Language Education Study Program at Cokroaminoto Palopo University. This research used a qualitative method. The instrument of this research was documentation. The result of this research indicates that (1) there are 64 codes mixing which is used by sixth semester students. The students used 4 types of code mixing. Those are 30 codes mixing in the form of word (46.9%); 28 codes mixing in the form of phrase insertion (43.8%); 5 codes mixing in hybrid insertion form (7.8%); and there is only 1 code mixing in the form of a reduplication/repetition insertion (1.5%). (2) The dominant type of code mixing used by the students is in the form of word insertion.
Cost Implications for a Discrete Mathematics Intelligent Tutoring System

An Intelligent Tutoring System (ITS) is software that dynamically modifies the presentation of educational material to students based on Artificial Intelligence. ITSs enable tutoring to be done by computers; teaching is customized for the student using the system, enabling faster learning; and there is a potential to enable scaling up, teaching of millions of students, using online platforms or platform independent programs which is not possible in the case of human tutoring. In this paper we focus on the cost implications of an ITS designed for teaching Discrete Mathematics at the undergraduate level at the University Of Botswana. The ITS discussed, DS-ITS, makes use of probabilistic graphical networks to relate core concepts of Discrete Mathematics concepts with verifiable outcomes (demonstrable knowledge). A student's understanding of a concept is inferred; it can be viewed as the cause of failing to answer a question correctly. Our contribution is in estimating the cost required to build a prototype, which can be used for certain Discrete Mathematics topics as taught at the undergraduate level. We compare the cost implications with the alternative of not having such a tool and instead relying purely on lecturers and teaching assistants. Our findings show the benefit of developing such a tool.
TEACHING SPEAKING BY USING SNAKE AND LADDER BOARD GAME

This research focuses on the use of the Snake and Ladder Board Game in teaching speaking to the tenth grade students at SMA Kristen Mercusuar Kupang. This research aims at finding out the appropriate way of teaching speaking by using snake and ladder board game for students in the tenth grade students at SMA Kristen Mercusuar Kupang..This research used Classroom Action Research (CAR). The subject of the research is the tenth grade students at SMA Kristen Mercusuar Kupang.. The procedure of the research applied two cycles namely cycle I and cycle II. Every single cycle had fourth steps namely planning, implementing, observing and reflecting. The result of this research shown that there were significant developments on students speaking skill at the tenth grade students at SMA Kristen Mercusuar Kupang after conducting the treatments through snake and ladder board game. In which the score of a cycle 1 am 43,06 and the score of cycle II is 71,88. It means that the snake and ladder board game is an appropriate way to improve the students` speaking skills. In addition, the researcher found the result of the observation that almost all of the students gave positive responses, such as they gave attention seriously in learning process, they had been more active than before, make them happy and enjoy the class, and of course they could improve their speaking toward snake and ladder board game in improving speaking skill
Implementation artificial neural network nguyen widrow algorithm for lupus prediction

Abstract Lupus is a type of skin disease known as 1000 face disease. This term arises due to this chronic disease causing symptoms and signs that are almost similar to other diseases. Artificial neural network is one method that can be used to help the medical world in managing clinical data. Nguyen Widrow is one of the algorithms and artifical neural network that can be used to make predictions and improve the iteration process. this study applying Nguyen Widrow algorithm in predicting lupus by Matlab. Based on the results of the training process, the Nguyen Widrow initialization algorithm is able to recognize patterns of 80% or range 16 of the 20 datasets. As for the results of testing Nguyen Widrow initialization algorithm with the initials activation function hidden = logsig, activating output logsig, target error 0.1 and learning rate 0.1, Epoch 10000 able to recognize 100% of patterns from 10 existing datasets. So from that it can be concluded that the Nguyen Widrow initialization algorithm can be used to predict disease patterns.
On codes over \(\mathbb{R}=\mathbb{Z}_{2}+u\mathbb{Z}_{2}+u^2\mathbb{Z}_{2}\) where \(u^3=0\) and its related parameters

In ring R = Z 2 + uZ 2 + u 2 Z 2 where u 3 = 0, using Lee weight and generalized Lee weight, some lower bound and upper bound on the covering radius of codes is given and also to find the covering radius for various repetition codes with respect to same and different length in R.
Modeling language constructs with compatibility intervals

We describe a representation for modeling meaning of natural language constructs that is closely related to fuzzy sets.Same as fuzzy sets, it allows to express quantitative relationships between different concepts, and is designed to support vagueness and imprecision common to natural language.We compare the representations using several examples, and argue that in some cases the proposed representation may be a good alternative to the fuzzy set based representation, and that it may also be easier to learn from data.
Three-dimensional shape measurement of specular objects based on infrared phase-measuring deflectometry

Phase-measuring deflectometry (PMD)-based methods have been widely used in the measurement of the threedimensional (3D) shape of specular objects, and the existing PMD methods utilize visible light. However, specular surfaces are sensitive to ambient light. As a result, the reconstructed 3D shape results are mostly affected by the external environment in actual measurements. To overcome this problem, a novel infrared-PMD (IR-PMD) method is proposed to measure specular objects by directly establishing the relationship between the absolute phase and depth data. In addition, a new calibration method for the measurement system has been proposed by combining fringe projection and fringe reflection. The proposed IR-PMD method uses an IR projector to project sinusoidal fringe patterns onto a ground glass, which can be regarded as an IR digital screen. The IR fringe patterns are reflected by the measured specular surfaces and the deformed fringe patterns captured by an IR camera. The multiple-step phase-shifting algorithm and the optimum three-fringe number selection method are applied to the deformed fringe patterns to obtain the wrapped and unwrapped phase data, respectively. Then 3D shape data can be directly calculated by the unwrapped phase data on the screen located at two positions. The results have validated the effectiveness and accuracy of the proposed method. It can be used to measure specular components in the application fields of advanced manufacturing, automobile industry, and aerospace industry.
Q1 Hamlet

The case for Q1 Hamlet as a pre-Q2 text is coming into focus as the findings of several scholars are reconciled. Additionally, a finding related to the Brudermord text has helped mark that text as a predecessor to both Q1 and Q2/F, with further implications for Q1 as a pre-Q2 text. As this view of Q1 becomes accepted, patterns of Q1 vs. Q2 variance advance the case for Q1 as being the author’s draft of the text that became the censor-approved ‘allowed book’. There is no way to know how most of the Q1 vs. Q2 textual variants progressed from Q1 as printed, to the non-extant allowed book from which the players’ parts were copied, and finally to Q2. However, the 577 Q1 lines that are identifiably concordant but variant to lines in Q2 represent a category of Q1 lines that will be of interest to those planning to edit or stage Q1 Hamlet.
A Content Analysis of "Joyful" English Textbook for Primary Grade II

It is widely known that textbook holds an important role as one of learning sources in education. Textbooks used in schools must be compatible with 2013 curriculum, the newest curriculum applied in Indonesia. But it is found that some teachers complained how the content of textbooks doesn’t fit the objectives specified in 2013 curriculum. Therefore, research regarding analysis of textbook content feasibility is crucial to be conducted to decide whether a particular textbook feasible to be used for academic purposes. In this research, the researchers chose “Joyful” English textbook for Primary Grade II as the research object. The analysis was based on BSNP textbook content feasibility rubric which includes the compatibility of materials with KI and KD, the accuracy of materials and supporting materials. The aim of this research is to know how “Joyful” English textbook for Primary Grade II fulfill the textbook content feasibility specified in 2013 curriculum. Qualitative approach was applied in this research. The method used was content analysis method. In collecting the data, the technique used by the researchers was documentation. The data was analyzed using document checklist and percentage formula. The results showed that “Joyful” English textbook for Primary Grade II fulfills the textbook content feasibility by 79,17% so it was concluded that “Joyful” English textbook for Primary Grade II is feasible to be used at schools. However, because it isn’t 100% feasible, the researchers encouraged English teachers who want to use “Joyful” English textbook for Primary Grade II to cover the weaknesses by finding another learning source relevant to the learning objectives specified in 2013 curriculum.
Difference-Seeking Generative Adversarial Network--Unseen Sample Generation

Unseen data, which are not samples from the distribution of training data and are difficult to collect, have exhibited importance in numerous applications, ({\em e.g.,} novelty detection, semi-supervised learning, and adversarial training). In this paper, we introduce a general framework called \textbf{d}ifference-\textbf{s}eeking \textbf{g}enerative \textbf{a}dversarial \textbf{n}etwork (DSGAN), to generate various types of unseen data. Its novelty is the consideration of the probability density of the unseen data distribution as the difference between two distributions $p_{\bar{d}}$ and $p_{d}$ whose samples are relatively easy to collect. The DSGAN can learn the target distribution, $p_{t}$, (or the unseen data distribution) from only the samples from the two distributions, $p_{d}$ and $p_{\bar{d}}$. In our scenario, $p_d$ is the distribution of the seen data, and $p_{\bar{d}}$ can be obtained from $p_{d}$ via simple operations, so that we only need the samples of $p_{d}$ during the training. Two key applications, semi-supervised learning and novelty detection, are taken as case studies to illustrate that the DSGAN enables the production of various unseen data. We also provide theoretical analyses about the convergence of the DSGAN.
Fifth International Scientific Conference «Creativity as a National Element: a General and Special in the Modern Socio-Cultural Space»

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Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent. The agent needs to quickly adapt to the task over few episodes during adaptation phase to maximize the return in the test phase. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference (MSGI), which infers the latent parameter of the task by interacting with the environment and maximizes the return given the latent parameter. To facilitate learning, we adopt an intrinsic reward inspired by upper confidence bound (UCB) that encourages efficient exploration. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter, and to adapt more efficiently than existing meta RL and hierarchical RL methods.
Community Participation in Flood Disaster Mitigation in Solok Selatan Regency-Indonesia

The purpose of the study was to analyze community participation Solok Selatan in flood disaster mitigation. Type of the study was a descriptive qualitative research with purposive sampling data retrieval method that was, a society that found when the community affected by flash flooding in Solok Selatan. Techniques of data collection were observation, interviews and documentation. Information from the community were reduced, then drawn conclusions of public participation Solok Selatan in the pre mitigation disaster such as: (1) cleaning up their own house, burning garbage, do not throw garbage into the river and wild, (2)  planting trees along the river flow, (3) evacuating to higher ground. In short, public participation after the disaster werr working together to improve homes, public facilities, make emergency tents.
Implementation And Evaluation Of The mCityPASS Protocol For Secure And Private Access To Associated Touristic Services

Abstract Touristic City Cards are managed by some city councils in order to make a step toward the SmartCity concept. Touristic City Cards enable the integration of different touristic services so as to facilitate the access to transport, cultural sites and touristic attractions to visitors. In this paper, we introduce a secure mobile system called mCityPASS that allows the smart management and secure access to this kind of service. We have implemented the system in a smartphone platform in such a way that the mobile device contains the purchased access element that holds a set of e-tickets that enables the visitors of a city to make use of the specified touristic services. The paper presents the details of the whole implementation of the system and an evaluation of the performance and the effectiveness of it. Also, an analysis of the privacy and the security properties of the presented scheme has been conducted. As a result, we can assure that the system preserves the privacy of the tourists and fulfills the more challenging security properties: fairness, non-overspending, exculpability, unsplittability and selective reusability.
Towards Natural Language Story Understanding with Rich Logical Schemas

Generating "commonsense" knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input.Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text.The idea of our approach to this problem is to provide a learning system with a "head start" consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one-or two-year-old could reasonably be presumed to possess.We match these initial schemas to simple children's stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas.Both the initial and generated schemas are specified using a rich, expressive logical form.Unlike the slot-and-filler structures often used in knowledge harvesting, this logical form allows us to specify complex relations and constraints over the slots.Though formal, the representations are language-like, and as such readily relatable to NL text.The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations.To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories.We include a walkthrough of matching a children's story to these schemas and generating inferences from these matches.
Robot Based Interactive Game for Teaching Arabic Spelling

Game based learning is becoming a widespread technique used to enhance motivation, involvement and educational experience of learners.Games have the potential to support educational curricula when designed effectively.In this work, an educational game to teach Arabic spelling to children is proposed.The game consists of two main parts; a robot and a desktop application.The robot is connected to the desktop application to form the complete game.Our mere focus is to develop an interactive, adaptive game to motivate students and let them interact joyfully in their environment while learning simple Arabic spelling rules.The interaction was implemented through designing an interaction model between the user and the robot, where the robot responds to user input with appropriate facial expressions and vocal statements.On the other hand, adaption and intelligence of the game is done through utilizing the nutshell of expert systems' framework with some alterations.Our proposed game is based on the curriculum of Saudi Arabia in elementary schools.It is anticipated that the deployment of robot-based games in the classroom will advance students' engagement and enthusiasm about learning Arabic spelling.
INTERNET ADDICTION AMONG GRAMMAR SCHOOL STUDENTS IN THE CZECH REPUBLIC

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 4664-4672ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1150Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Content Analysis of Student Book When English Rings A Bell (Revised Edition) for Grade VIII of Junior High School

The aim of this study was to analyze the relevance of the materials with the cognitive and psychomotor domains in the 2013 English curriculum competence in English Rings a Bell textbook for grade eight of junior high school. This study used qualitative methodology. The researcher conducted the analysis of textbook based on theory of Anderson and Krathwohl’s (2001) taxonomy of cognitive domain and Simpson’s (1972) taxonomy of psychomotor domain in order to more focus on it relevancy with materials in the textbook. The instrument used to collect the data were observation in the form of checklists and document analysis. The result showed that in term of cognitive domain, there were 23 materials in the book which were relevant or 74.19%, 7 materials which were partly relevant or 22.58%, and only 1 material which irrelevant or 3.2%. Meanwhile, the in term of psychomotor domain, there were 10 materials in the book which were relevant or 32.25%, 10 materials also which were partly relevant, and 11 materials which were irrelevant or 35.48 %. Thus, it can be concluded that the materials in this textbook materials are relevant with 2013 curriculum, in term of cognitive domain. Even though, it lacks of relevant materials of psychomotor domain.&#x0D; Keywords: textbook analysis; When English Rings a Bell; 2013 Curriculum; cognitive domain; psychomotor domain
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature

We consider the problem of Named Entity Recognition (NER) on biomedical scientific literature, and more specifically the genomic variants recognition in this work. Significant success has been achieved for NER on canonical tasks in recent years where large data sets are generally available. However, it remains a challenging problem on many domain-specific areas, especially the domains where only small gold annotations can be obtained. In addition, genomic variant entities exhibit diverse linguistic heterogeneity, differing much from those that have been characterized in existing canonical NER tasks. The state-of-the-art machine learning approaches heavily rely on arduous feature engineering to characterize those unique patterns. In this work, we present the first successful end-to-end deep learning approach to bridge the gap between generic NER algorithms and low-resource applications through genomic variants recognition. Our proposed model can result in promising performance without any hand-crafted features or post-processing rules. Our extensive experiments and results may shed light on other similar low-resource NER applications.