Reviewerly/exHarmony huggingface dataset:
Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study

Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another.It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists.Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages.Furthermore, we reproduce multisource projection (Tyers et al., 2018), in which dependency trees of multiple sources are combined.Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side.We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then training multi-treebank POS tagging and parsing models on the target side.
STUDENTS PERCEPTIONS TOWARD GRAMMAR IN ENGLISH CLASSROOM

The objective of this research focuses on students perceptions toward grammar in the English classroom. This research uses the qualitative descriptive method, the procedure for collecting data uses questionnaire analysis in google forms free online questionnaire. The population is 30 students in class TEI C at SMK TI Pembangunan Cimahi. The sample is 30 students. The results of the data analysis showed that 60% of students stongly agree that grammar in the English classroom important, while the other 40% agree. In addition 96% of students admit that their need to increase English grammar. The most dominant of students perceptions grammar showed to increase skills on grammar 96% of students choose to agree on statements. The findings revealed that students perceived differently toward grammar in the English classroom.CeivedKeywords: Students Perceptions, Grammar English.
The Comparative Study on Clustering Method Using Hospital Facility Data in Jakarta District and Surrounding Areas

The present study reports the findings of a comparative analysis conducted on health facility research (HFR) data, based on a clustering method with K-Means and K-Medoids algorithm.The K-Means algorithm consist of four steps: specifyng the centroid values, grouping data on the centroid, calculating centroid values, repeating grouping data on the centroid, and calculating the values until the cluster is stable (convergence), The K-Medoids algorithm consists of six steps: medoids initialization, data allocation to the nearest medoid, determinination of new medoids, calculation of data distance with medoid, calculation of deviation, and repeating the determination of new medoids, until the deviation to the convergence cluster is counted.The data utilized in this study are HFR data located in the Jakarta district and surrounding areas, from 2013 to 2018.Our results showed that the execution time from K-Medoids algorithm outperformed the K-Means algorithm.The K-Medoids algorithm speeded up the execution time, and at the same time improved the density value between clusters (silhouette).By utilizing the clustering method, health facilities in hospitals in Jabodetabek can be categorized based on their resources.
Factors influencing cloud computing adoption in higher education institution

&lt;table width="593" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="387"&gt;&lt;p&gt;There are few studies on factors influencing cloud computing adoption in higher education institutions. However, there are lacks of understanding of the cloud computing adoption issues in the university. The main objective of this study is to investigate factors influencing cloud computing adoption in a higher education institution. The research method involved using qualitative interviewing with relevant stakeholders and case study at one public university in Malaysia. The analysis was done by using Atlat.ti. There are eighteen factors that have been coded into three main categories of Technological, Organizational and Environmental. These are among factors to influence the decision of cloud computing adoption for a public university. The first category (Technological) consists of nine factors; (i) relative advantage, (ii) cost reduction, (iii) ease of use, (iv) compatibility, (v) operational requirement, (vi) security, (vii) sustainability, (viii) trialability and (ix) complexity, The second category (organizational) consists of four factors; (i) infrastructure readiness, (ii) top management, (iii) knowledge and IT skillset and (iv) financial. The third category (environmental) consists of five factors; (i) Cloud Service Provider, (ii) Geographical, (iii) Data Privacy, (iv) Guideline and Policy, (v) Service Level Agreement (SLA). The result may provide a reference for the adoption of cloud computing in the area of mobile learning or mobile computing. Future work involves conducting similar studies at other case studies including public and private universities in Malaysia.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
Enhancing Nutrition-Related Literacy, Knowledge and Behavior among University Students: A Randomized Controlled Trial

Background: Despite the key role of nutrition literacy and behavior in health, evidence suggests a low adherence to healthy nutritional behaviors. We sought to determine the influence of a group-based interactive training program on nutrition-related literacy, knowledge and behavior of students. Methods: Two-hundred and three undergraduate students were enrolled by multi-stage cluster sampling and were randomly divided into two groups of intervention (n=106) and control (n=97). Four questionnaires measured the demographic variables, nutrition-related literacy, knowledge, and behavior. The educational intervention was performed as workshop using a new educational style.  Results: There was no significant difference between the two groups regarding demographic variables. After educational intervention, a significant increase was found in the mean scores of nutrition literacy from 22.20±3.85 to 31.95±2.19, nutrition knowledge from 54.23±1.80 to 77.06±7.20, and nutritional behavior from 52.32±16.54 to 67.52±19.69 (p <0.001), but no significant difference was observed in the control group. The correlation coefficients significantly increased (from 0.167 to 0.552).  Conclusion: Our educational pattern was effective in improving nutritional literacy, knowledge and behavior and is recommended for students in their curriculum.
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models

Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics.One potential solution is for the search engine to generate multiple refined queries, each of which relates to a subset of the documents spanning the same topic.A preliminary step towards this goal is to generate a question that captures common concepts of multiple documents.We propose a new task of generating common question from multiple documents and present simple variant of an existing multi-source encoder-decoder framework, called the Multi-Source Question Generator (MSQG).We first train an RNN-based single encoder-decoder generator from (single document, question) pairs.At test time, given multiple documents, the Distribute step of our MSQG model predicts target word distributions for each document using the trained model.The Aggregate step aggregates these distributions to generate a common question.This simple yet effective strategy significantly outperforms several existing baseline models applied to the new task when evaluated using automated metrics and human judgments on the MS-MARCO-QA dataset.
English to Luganda SMT: Ganda Noun Class Prefix Segmentation for Enriched Machine Translation

Luganda or Ganda is a morphologically rich and low-resource language from Uganda.The morphological richness of Luganda sentences has an impact on the quality of translation and this work looks at improving machine translation (MT) for English to Luganda.Luganda sentence formation bases on 10 noun classes with a prefix for singular and plural.In various aspects, the interaction of these class prefixes in sentences usually enforces different words such as nouns, verbs, adverbs and adjectives to be in agreement with the subject in a given sentence.Such scenarios have resulted in various Luganda word inflectional and derivational tendencies because each noun, verb, adverb or adjective finds itself having to combine with a prefix from a class it belongs and thus creating new word forms.Using 6 statistical machine translation (SMT) models divided equally into base and morphology models, we propose a procedure to segment Luganda sentences from our English-Luganda parallel Bible corpus.Our morphological segmentation approach bases on Ganda Noun Class (GNC) prefixes and we design a tool we call GandaKIT to segment Luganda sentences at pre-processing stage and desegement them after translation at post processing stage.In experiments, we compare translation performance of SMT base models against systems trained with morphological segmentation at pre-processing stage.Our results show an improvement in MT performance over base models ranging by a difference of 1.58 BLEU points and 0.2257 NIST score for our best system.
On a Chatbot Providing Virtual Dialogues

We present a chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts that are extracted and selected from the documents.This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions that are automatically generated for these answers based on the initial text.
Development of Research Competence of Future Teachers in The Process of Their Professional Training

The research is important because of the complexity of the professional training of a creative-minded teacher based on the mastery of the professional research competence.This dictates the need to develop the research abilities of future teachers, whose successful activity implies a multifunctional research character.Psychological and pedagogical conditions for the development of research qualities of students are based on a system of pedagogical actions of intellectual significance in the aspect of individual and personal characteristics.These actions are aimed at creating a scientific and creative educational space using the developed mechanisms for self-activation, self-design, selfdetermination, self-development and self-study.The purpose of the study is to develop a multifunctional theoretically grounded and practice-oriented paradigm for the development of students' professional research competence and its approbation.Methods used are theoretical (theoretical analysis of pedagogical, psychological, scientific and methodical literature on the research problem), empirical (analysis, comparison, synthesis, content selection, observation, survey), pedagogical experiment (stating, forming, control stages of the experiment), method of expert assessments, statistical processing of research results.Students with different levels of intellectual, knowledge and professional-oriented qualification study in academic groups of the first course.The multifunctional, structurally informative paradigm for the development of research competence of future teachers was designed.
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering

This paper describes our system developed for the coreference resolution task of the CRAFT Shared Tasks 2019.The CRAFT corpus is more challenging than other existing corpora because it contains full text articles.We have employed an existing span-based state-of-theart neural coreference resolution system as a baseline system.We enhance the system with two different techniques to capture longdistance coreferent pairs.Firstly, we filter noisy mentions based on parse trees with increasing the number of antecedent candidates.Secondly, instead of relying on the LSTMs, we integrate the highly expressive language model-BERT into our model.Experimental results show that our proposed systems significantly outperform the baseline.The best performing system obtained F-scores of 44%, 48%, 39%, 49%, 40%, and 57% on the test set with B 3 , BLANC, CEAFE, CEAFM, LEA, and MUC metrics, respectively.Additionally, the proposed model is able to detect coreferent pairs in long distances, even with a distance of more than 200 sentences.
Characteristics of the corona discharge of polymer insulators based on solar-blind ultraviolet images

We present a method based on image pixel value to evaluate the characteristics of the corona discharge of polymer insulators. To capture the images of the solar-blind ultraviolet (SBUV) band, an ultraviolet (UV) imaging detector based on an image intensifier is designed. First, through the experiments’ results with the UV light source, the proposed method based on the image pixel value is proved to detect solar-blind signals more effectively than the photon counting method. Then, by conducting experiments with the insulator at a fixed observation distance, the corona characteristics of the insulator can be described through analyzing the images captured by the detector. Finally, the experiments at different observation distances are conducted, and the image pixel value ratios of the recorded images with certain observation distance are computed. The results reveal that the developing stages of different corona discharges can be uniquely described. Based on the method, the characteristics of a corona discharge in the SBUV band can be described accurately and objectively.
Research on Micro-Grid Group Intelligent Decision Mechanism under the Mode of Block-Chain and Multi-Agent Fusion

This paper aims to study the problems of surplus interaction, poor real-time performance, and excessive processing of information in the micro-grid scheduling and decision-making process. Firstly, the micro-grid dual-loop mobile topology structure is designed by using the method of block-chain and multi-agent fusion, realizing the real-time update of the decision-making body. Secondly, on the basis of optimizing the decision-making body, a two-layer model of intelligent decision-making under the decentralized mechanism is established. Aiming at the upper model, based on the theory of block-chain consensus mechanism, this paper proposes an improved evolutionary game algorithm. The maximum risk-benefit in the decision-making process is the objective function, which realizes the evaluation and optimization of decision tasks. For the lower layer model, based on the block-chain distributed ledger theory, this paper proposes an improved hybrid game reinforcement learning algorithm, with the maximum controllable load participation as the objective function, and realizes the optimal configuration of distributed energy in the micro-grid. This paper reveals the rules of group intelligent decision making in micro-grid under multi-task. Finally, the effectiveness of the proposed algorithm is verified by using Beijing Jin-feng Energy Internet Park data.
E-Voting in India Using Blockchain and It’s Modus Operandi

Creating an environment to fulfill the basic requirement of E-Voting is indeed a challenge for long time. Blockchain, distributed peer to peer technology has wide range of applications from financial to non-financial sectors. It can be utilized in E-Voting system as well. Electronic casting a ballot or e-casting a ballot has been in existence since seventies. The E-Voting system and its requirements are identified in this paper along with some limitations of the Blockchain in E-Voting system. The blockchain has obvious advantages of increased efficiency, increased productivity and less prone to error over paper based voting system. This paper presents the potential benefits of using the blockchain which can effectively be used in e-casting the votes. The proposed model is based on the fundamental requirements of e-casting to the verification of voters. The limitations of the blockchain with regards to E-Voting is also presented in this paper.
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including automatic speech recognition (ASR), natural language understanding (NLU), and entity resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time con-suming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by either errors in ASR, NLU, ER, or the application. In some cases, users may not properly formulate their requests (e.g., providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generates reformulations, and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns, and coupling it with a guardrail rewrite selection mechanism that reactively evaluates these fixes using feedback friction data. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win-loss ratio of 11.8 and effectively reduces the defect rate by more than 30 percent on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.
Gradient in spectral and color images: from the Di Zenzo initial construction to a generic proposition

In this article, we define a generic gradient for color and spectral images, considering a proposed taxonomy of the state of the art. A full-vector gradient, taking into account the sensor’s characteristics, is in compliance with the metrological properties of genericity, robustness, and reproducibility. Here, we construct a protocol to compare gradients from different sensors. The comparison is developed by simulating sensors using their spectral characteristics. We develop three experiments using this protocol. The first experiment shows the consistency of results for similar sensors; the second demonstrates the genericity of the approach, adapted to any kind of imaging sensors; and the third focuses on the channel inter-correlation considering sensors such as in the color vision deficiency case.
Identification of open quantum system: Via closed feedback control

For stochastic quantum systems with given dissipations, the identification method of Hamiltonian is given in this paper. First, the stability of the system is realized through designing a real-time feedback control method. Second, by using Routh–Hurwitz stability criterion, the intervals of the element values of the system Hamiltonians are given. Finally, the identification of Hamiltonian is realized by selecting the equilibrium state of the system equal to the desired target state. For two typical categories of noise: Purely dephasing decoherence and amplitude damping decoherence, we check the validity of the proposed estimation scheme.
DECISION SUPPORT SYSTEM OF EMPLOYEE ELECTION RECOMMENDATION ACCORDING TO PERSONALITY USING PROFILE MATCHING METHOD

In the current era of globalization, finding employees is no longer an easy thing.Strict competition makes each individual try to improve their quality.Especially in the search process of candidates, where in looking for candidates is not easy and often there is an error in the search process of the candidate itself if only relying on curriculum vitae.Therefore, personality is very necessary in dealing with the world of work, therefore the author wants to make an application of a decision support system that can adjust the criteria desired by the candidates who tested the accuracy by using the profile matching method.The results of this study are to add criteria other than curriculum vitae, namely the personality of candidates who are made based on the concept of knowledge management so that knowledge can be conveyed well to help job seekers in knowing their personality.After the application is finished, the writer does the test results of the system using the User Acceptance Test (UAT) method of 77% can be said to be good and can be applied in the candidate search process.
A Dynamic Hand Gesture Recognition Model Based on the Improved Dynamic Time Warping Algorithm

In order to recognize dynamic hand gestures with an effective and intelligent manner, this study proposes an integrated dynamic hand gesture recognition model based on the improved DTW (Dynamic Time Warping) algorithm that has a significant impact on the efficiency of dynamic trajectory analysis. The proposed model is divided into three operational steps: 1) using the three-frame difference method to track the gesture motion area by separating the dynamic motion region from the skin-like background; 2) applying the Hu-moment method to locate feature points from the extracted gesture motion region, and then use them to describe the motion trajectory; 3) exploring an improved DTW algorithm for gesture template match. Traditional DTW algorithm has excessive computational complexity and low operational efficiency. This study improved the running efficiency of DTW without reducing matching accuracy. Benchmarking experiments carried out on identifying the six classic dynamic gestures have yielded satisfactory classification results.
The Role of Model-View Controller in Object Oriented Software Development

Object Oriented Software Development (OOSD) is a design technique that is used before the development and design of a software. This design method makes the system appears as a collection of objects to communicate with other objects by passing messages. The Model-View-Controller (MVC) has been inherited from Object-Oriented Programming (OOP) with the integration of Graphical User Interface (GUI) and interactive program execution. The MVC is very useful for developing Interactive and Dynamic Web Applications and iOS. With MVC, developers can trust on design patterns that are widely accepted as solutions for recurring problems. MVC can be used to develop flexible, reusable and modular Software. Applying the MVC design pattern in object-oriented Software development a flexible, reliable, modular and scalable website can be built. So, it’s necessary for every developer to have the knowledge of software development using MVC design pattern.