Reviewerly/exHarmony huggingface dataset:
Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages

Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource language (LRL) pairs, i.e., language pairs for which few or no parallel corpora exist. Our work adapts variants of seq2seq models to perform transduction of such words from Hindi to Bhojpuri (an LRL instance), learning from a set of cognate pairs built from a bilingual dictionary of Hindi - Bhojpuri words. We demonstrate that our models can be effectively used for language pairs that have limited parallel corpora; our models work at the character level to grasp phonetic and orthographic similarities across multiple types of word adaptations, whether synchronic or diachronic, loan words or cognates. We describe the training aspects of several character level NMT systems that we adapted to this task and characterize their typical errors. Our method improves BLEU score by 6.3 on the Hindi-to-Bhojpuri translation task. Further, we show that such transductions can generalize well to other languages by applying it successfully to Hindi - Bangla cognate pairs. Our work can be seen as an important step in the process of: (i) resolving the OOV words problem arising in MT tasks; (ii) creating effective parallel corpora for resource constrained languages; and (iii) leveraging the enhanced semantic knowledge captured by word-level embeddings to perform character-level tasks.
Graphical tools of discrete longitudinal data presentation in R

Good graphical presentation of data is useful during the whole analysis process from the first glimpse into the data to the model fitting and presentation of results.The most popular way of longitudinal data presentation are separate (for each wave, in cross-sectional dimension) comparisons of figures.However, plotting the data over time is useful in suggesting appropriate modeling techniques to deal with the heterogeneity observed in the trajectories.The main aim of this paper is to present the changing perceptions of the financial situation in Poland using different graphical tools for the heterogonous discrete longitudinal data sets and present demographics features for those changes.We will focus on the most important features of the categorical longitudinal data -category sequences and their graphical presentation.We aim to characterize the analyzed sequences on the basis of unidimensional indicators and composite complexity measures, as well as using mainly TraMineR [Gabadinho et al. 2017] package of R.
HUBUNGAN FUNGSI AFEKTIF KELUARGA DENGAN KECERDASAN EMOSIONAL REMAJA DI SMA NEGERI 1 KECAMATAN PANAI HULU KABUPATEN LABUHAN BATU TAHUN 2019

One of the family vital function is affective function. Fulfilling the affective function of the family is defined as the ability of the family to meet the need for comfort and happiness. Adolescents are now very worried because they experience difficulties in emotional intelligence due to the lack of family support they receive and is at risk of the teenager doing negative things or deviating. This type of research is descriptive with an approach Cross Sectional to know relationship between family affective and adolescent emotional intelligence. Location of this research was conducted at SMA Negeri 1 Sub District Panai Hulu District Labuhan Batu and this research was conducted in November 2018. The sample of this study was students on SMA Negeri 1 Sub District Panai Hulu District Labuhan Batu which numbered 86 people whose sampling uses techniques Starified Random Sampling. Data analysis using test Chi-Square. Result of statistical test say, there is a relationship between family affective and adolescent emotional intelligence in SMA Negeri 1 Sub District Panai Hulu Dixtrict Labuhan Batu. The results of the study show the importance of good family affectuive functions so that adolescent emotional intelligence can be applied in a positive form.
Cooperation: A new force for boosting generative adversarial nets with dual‐networkstructure

The principle of generative adversarial net is to fit the given data distribution by combining a generative model and discriminative model. There are two major challenges to conventional systems – they are difficult to train and they easily fall into ‘mode collapse’. To improve it, this study describes a novel network structure with dual generators. A ‘cooperation’ mechanism is introduced to help the generators work together. During training, generators not only learn from discriminative feedback but also from each other (like a study group). Compared with a single-generator network, a dual-generator network could capture many more ‘modes’ and eventually reduce the impact of ‘mode collapse.’ Dual networks also require extra computational resources. However, our experiment shows that even with network parameters of similar size, dual networks still achieved better results. Additionally, a dual-generator structure could be extended to multiple generators. The proposed network structure is also very robust and flexible. It can be adapted to various application scenarios, such as high-resolution image generation, domain adaptation and 3D model generation. The experimental results showed that with the same computing resources, multiple generators can generate better quality synthetic data, including 2D images, 3D objects, style transferring etc.
An Unsupervised Query Rewriting Approach Using N-gram Co-occurrence Statistics to Find Similar Phrases in Large Text Corpora

We present our work towards developing a system that should find, in a large text corpus, contiguous phrases expressing similar meaning as a query phrase of arbitrary length. Depending on the use case, this task can be seen as a form of (phrase-level) query rewriting. The suggested approach works in a generative manner, is unsupervised and uses a combination of a semantic word n-gram model, a statistical language model and a document search engine. A central component is a distributional semantic model containing word n-grams vectors (or embeddings) which models semantic similarities between n-grams of different order. As data we use a large corpus of PubMed abstracts. The presented experiment is based on manual evaluation of extracted phrases for arbitrary queries provided by a group of evaluators. The results indicate that the proposed approach is promising and that the use of distributional semantic models trained with uni-, bi- and trigrams seems to work better than a more traditional unigram model.
BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion.

In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases. However, the recently introduced notion of Pre-mappability (PreM) has shown that, in key applications of interest, aggregates can be used in recursion to optimize the perfect-model semantics of aggregate-stratified programs. Therefore we can preserve the declarative formal semantics of such programs while achieving a highly efficient operational semantics that is conducive to scalable implementations on parallel and distributed platforms. In this paper, we show that with PreM, a wide spectrum of classical algorithms of practical interest, ranging from graph analytics and dynamic programming based optimization problems to data mining and machine learning applications can be concisely expressed in declarative languages by using aggregates in recursion. Our examples are also used to show that PreM can be checked using simple techniques and templatized verification strategies. A wide range of advanced BigData applications can now be expressed declaratively in logic-based languages, including Datalog, Prolog, and even SQL, while enabling their execution with superior performance and scalability.
Visualization Design Based on Personal Health Data and Persona Analysis

Today, thanks to the development of wearable devices, people can easily accumulate and get access to their own health data. Through these health data, individuals can better quantify and manage their health status. However, there is a problem that health data is not fully used due to its complexity that non-expert individuals experiencing difficult in understanding. Visualization is widely used in helping people to understand data. In this study, we design a visualization system to solve the problem. Based on the intuitive design and intelligible interaction concept, we propose four functions: overview, detailed annotation, temporal comparison and horizontal comparison in the visualization system. We utilize persona analysis to help interaction design in the visualization system and describe application scenarios.
PENGPENGEMBANGAN MEDIA KARTU BERGAMBAR UNTUK MENINGKATKAN PENGETAHUAN PROFESI BAGI ANAK-ANAK SD KELAS ATAS

This research uses â€œResearch and Developmentâ€ method, which is developed by Borg and Gall. The purpose of this research is to develop a picture product that can be used for students in particularly as introduction of occupations in Indonesia. Looking at the feasibility, the product can be used at small scale as well as helping the upper level of elementary students to achieve competence skill to the stage of accommodation in accordance with SKKPD of Elementary School. This research uses the method of â€œResearch and Developmentâ€ by Borg and Gall. This research is categorized into small scale â€œR&amp;Dâ€ since it only uses multiple stages, those are: research and information collecting, planning, developing of initial product format, initial product validating, and product revising. The result shows from each validator that media experts assess 90,28% (very decent), material experts assess 78,09% (decent), and students as users assess 78,52% (very decent). The conclusion of this research is picture cards media can be categorized as worthy with average percentage of 78,52%. These picture cards media are expected to be tested further in order to be used effectively, furthermore this research is expected to be implemented in other schools.&#x0D; &#x0D; Keywords: picture cards, occupations, career information, students
Effect of the application of the pediatric triage “JumpSTART” method on the readiness of initial handling of emergency cases in children

Emergency cases can happen unexpectedly, occur anywhere, and may put the school in danger. The schools should prepare themselves for emergencies including how to handle the situation. The aim of this study is to find the influence of the application of paediatric triage JumpSTART method on the readiness of the initial handling emergency cases on child. This study used an experimental research with pretest and posttest of one group design. The population covers the whole teachers and employees with the total of 21 respondents taken by using purposive sampling. Data was analyzed using the Marginal Homogenity test. The results of the research showed an effect of the Paediatric Triage JumpSTART method on the readiness of the initial handling of emergencies cases with p value 0.001 (p&lt;0.05).
Suggesting new words to extract keywords from title and abstract

When talking about the fundamentals of writing research papers, we find that keywords are still present in most research papers, but that does not mean that they exist in all of them, we can find papers that do not contain keywords. Keywords are those words or phrases that accurately reflect the content of the research paper. Keywords are an exact abbreviation of what the research carries in its content. The right keywords may increase the chance of finding the article or research paper and chances of reaching more people who should reach them. The importance of keywords and the essence of the research and address is mainly to attract these highly specialized and highly influential writers in their fields and who specialize in reading what holds the appropriate characteristics but they do not read and cannot read everything. In this paper, we extract new keywords by suggesting a set of words, these words were suggested according to the many mentioned in the researches with multiple disciplines in the field of computer. In our system, we take a number of words (as many as specified in the program) that come before the proposed words and consider it as new keywords. This system proved to be effective in finding keywords that correspond to some extent with the keywords developed by the author in his research.
Pedagogical Model of Preservice Teachers Social Adaptation to the Future Professional Activity

The article is dedicated to the students' social adjustment to acknowledged informative exercises problems. The study uses a modeling method to build preservice teachers social adaptation pedagogical model. The model comprises a list of operations contributory to successful students social evolution to instructional activities. Students' successful conversion to professional activity is permissible under the status of the university, specially organized work, which is focused on the lecturer teaching student composition of natures. The article details the university organizational and pedagogical conditions, which will help the cerebral and social restructuring of students' experience and will form an adequate position in the teaching staff. The following means are proposed which increase the social evolution level: the development of curricula which contribute to the professional social adaptation; the educational practice programs improvement; development of a system of integrated psychological and pedagogical support in the learning process; the organization of the psychological and pedagogical training.
Tek Gizli Katmanlı İleri Beslemeli Sinir Ağlarında Gizli Nöronların Etkisi

Especially in the last decade, Artificial Intelligence is gaining popularity increasingly since deep learning and neural networks have fast and powerful machine learning-based techniques that can solve many real-time problems efficiently. In this study, Extreme Learning Machine (ELM), capable of high and fast learning is used for optimization parameters of Single hidden Layer Feedforward Neural networks (SLFN)s. The learning capability of such systems is directly related to the effectiveness of the parameters and the calculation methods. Hidden neurons number, one of the parameters in the calculations is discussed and its role is examined. The importance of the appropriate selection of this value will not only be emphasized but also a new method will be proposed for proper selection. The proposed method, Normalized Average Value (NAV) is a simple and effective formulation that originates from statistical methods in this field. Experimental results for determining a correct number of hidden neurons (L) show that random selection of this number causes either overfitting or under fitting problems. NAV can improve any algorithm in order to reach better learning rates. The results show that it provides a 10-15% performance increase due to random selection if the number of hidden neurons, L is determined according to the result of the study.
Development of an electrocardiograph prototype for educational purposes

This paper presents the design and the development of an electrocardiograph prototype with an interface based in a heart animation for educational purposes. The research main features is to prove the potential for opening possibilities to design and to create laboratory practices in subjects such as biophysics, animal physiology and human physiology. The system is composed of an analog circuit that retrieves and amplifies the signals received from the body to enable further processing using three electrodes of silver chloride (AgCI). We present a bioexperiment acquiring the electrocardiogram (ECG) signal using a computer, Arduino and Matlab by three central derivations. The presented result is from a student of a group of fifteen people, this shows that the basic records allow the analysis of the QRS complex of a cardiac cycle as well as the frequency during different conditions, such as rigorous exercise and resting state.
Compositional Stochastic Model Checking Probabilistic Automata via Symmetric Assume-Guarantee Rule

Stochastic model checking is the extension and generalization of the classical model checking. Compared with classical model checking, stochastic model checking faces more severe state explosion problem, because it combines classical model checking algorithms and numerical methods for calculating probabilities. For dealing with this, we first apply the symmetric assume-guarantee rule (SYM) into stochastic model checking in this paper, and propose a compositional stochastic model checking framework of probabilistic automata based on the NL* algorithm. It optimizes the existed compositional stochastic model checking process to draw a conclusion quickly, in cases the system model does not satisfy the quantitative properties. We implement the framework based on the PRISM tool, and several large cases are used to demonstrate the performance of it.
A Linguistics-based Stacking Approach to Disposable Domains Detection

More Internet services tend to collect the one-time information from clients via DNS queries. Notably, the uncertainty of such transient information makes these domain names be queried only once in their lifetime. This type of domain is called disposable domain. Although they are not malicious, the efficiency of DNS infrastructures will still be affected by their ever-increasing number. In this paper, we propose Vogers, a linguistics-based stacking model, to detect the disposable domains. Our evaluation demonstrates that Vogers decreases the false positive rate by more than 19%, compared with the prior art, while maintaining the true positive rate above 98.9%
Studi kapasitas masyarakat sebagai mekanisme bertahan menghadapi bencana banjir di Desa Sitiarjo, Kecamatan Sumbermanjing Wetan, Kabupaten Malang

The aim of this study was to determine the capacity of communities as a mechanism to withstand floods Sitiarjo Village, District Sumbermanjing Wetan.This research is descriptive qualitative.The research phase was divided into three, i.e: (1) pre-field observation; (2) field observation;(3) data processing.The method used in data collection is FGD (Focused Group Disscusion) and in-depth interviews.The results of the study indicate that the community has a strong capacity in social, cultural, economic and government aspects in dealing with floods.In the socio-cultural aspect, the community develops a local early warning system through "titir" and a communication chain involving the community along the Panguluran watershed armed with traditional technology, as well as the mutual development of a public evacuation site in the form of multi-storey buildings, in addition to every family capable of building terraced houses .In addition, the community has the capacity to survive in saving economic assets by creating "plenggrongan" and planting calendar systems for crisis management.Diversification of plants such as planting bananas and sengon for daily use and annual savings when agricultural land was destroyed by flood.
AXIOMATIC-DEDUCTIVE STRATEGY FOR IT DISCIPLINE CONTENT FORMATION

The paper presents the axiomatic-deductive strategy of organizing the content of an academic discipline with the help of ontological approach in the e-learning systems in the field of information technologies. The authors have taken into account that the necessary property of the system of axiomatic statements is their consistency. On the basis of axiomatic-deductive strategy, new approaches to the formation of the discipline content are proposed. It is proved that the system of true statements of an academic discipline is based on its terminology-conceptual apparatus, in particular, axiomatic statements. The developed mathematical structures that describe the axiomatic-deductive substrategy of the organization of the academic discipline general statements and the taxonomically oriented substrategy of the deployment of the academic discipline content are presented in the article. This ensures the transition from the content form of representation of the set of statements of the academic discipline to its presentation by means of artificial languages of mathematical logic. The use of descriptive logic ensures the formalization of the procedure for displaying an axiomatic informal system in an axiomatic formal system. The mathematical structures describe and detail the abstract logical-semantic core of the academic discipline in the form of a group of axiomatic systems. It is noted that the basic core of the content of academic discipline contains its basic concepts and judgments. This ensures a strictly logical transition from abstract general concepts and statements to the concepts and assertions of the lower level of universality and abstraction. It is noted that in order to accommodate the content of an academic discipline is advisable to develop a taxonomically oriented sub-strategy based on the multiple application of operations of general concept division. The mathematical structures allow for analysis of a generalized structure of interactions between the verbal level of the description of the academic discipline subject area, the formal level of description of the subject area and the description of the subject area at the level of computer ontology, which is implemented through the formalization, interpretation, encoding and decoding in the computer-ontology development environment. As an example of the application of the proposed axiomatic-deductive strategy, the elements of the glossary and taxonomies of the concepts of the discipline "Computer Logic", which are embodied in the Protégé environment with the help of OWL ontology description language have been developed.
Ranah Graduation dalam Sistem Appraisal yang Digunakan pada Harian Bali Post dan Tribun Bali

Abstract : This article focus on the use of one part of appraisal system that is graduation. Data were colected from Daily Newspaper Bali Post and Tribun Bali, criminal news in particular. The author used note technique and the method of observing. The collected data then were analized using appraisal system by White and finding the percentage to get the number of appearance and comparation. According to the result of analisys, it can be seen that the device graduation of each reporters are different, depending on the incidents that occur and the situation&#x0D; &#x0D; Keywords : Appraisal, graduation, criminal news.
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing

Supervised learning models are typically trained on a single dataset and the performance of these models rely heavily on the size of the dataset i.e., the amount of data available with ground truth.Learning algorithms try to generalize solely based on the data that it is presented with during the training.In this work, we propose an inductive transfer learning method that can augment learning models by infusing similar instances from different learning tasks in Natural Language Processing (NLP) domain.We propose to use instance representations from a source dataset, without inheriting anything else from the source learning model.Representations of the instances of source and target datasets are learned, retrieval of relevant source instances is performed using soft-attention mechanism and locality sensitive hashing and then augmented into the model during training on the target dataset.Therefore, while learning from a training data, we also simultaneously exploit and infuse relevant local instance-level information from an external data.Using this approach we have shown significant improvements over the baseline for three major news classification datasets.Experimental evaluations also show that the proposed approach reduces dependency on labeled data by a significant margin for comparable performance.With our proposed cross dataset learning procedure we show that one can achieve competitive/better performance than learning from a single dataset.