Reviewerly/exHarmony huggingface dataset:
To study the prevalence of anaemia among school going children in rural area near Indore

Introduction: Anaemia is a global public health problem which affects both the developing and the developed countries. It is an indicator of poor nutrition and poor health with major consequences on human health, as well as for the social and economic development of a population. It has significant impact on cognitive function, behaviour and school performance. Anaemia is a common problem throughout the world and iron deficiency is the most prevalent nutritional deficiency in the world. It affects mainly the poorest segment of the population. The purpose of the study is to assess the prevalence of anaemia among school going children of rural area of three schools of Khudel Village in Indore District of Madhya Pradesh. Methods: This study was conducted in the three schools of rural area near Indore District, of MP. A cross-sectional household survey was conducted in rural Schools from January 2017 to June 2017 on 500 children, aged 5-16 years, selected through systematic random sampling method. Study subjects were school children (
Knowledge Distillation For Recurrent Neural Network Language Modeling With Trust Regularization

Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurrent Neural Network language model (RNNLM) or an ensemble of several RNNLMs is used. These models have large memory footprints and require heavy computation. In this paper, we examine the effect of applying knowledge distillation in reducing the model size for RNNLMs. In addition, we propose a trust regularization method to improve the knowledge distillation training for RNNLMs. Using knowledge distillation with trust regularization, we reduce the parameter size to a third of that of the previously published best model while maintaining the state-of-the-art perplexity result on Penn Treebank data. In a speech recognition N-bestrescoring task, we reduce the RNNLM model size to 18.5% of the baseline system, with no degradation in word error rate(WER) performance on Wall Street Journal data set.
A Study on Beta Analysis of Banking Sectors Listed in NSE

The Reserve Bank of India (RBI) is India's central bank.Though public sector banks currently dominate the banking industry, numerous private and foreign banks exist.India's government-owned banks dominate the market.Their performance has been mixed, with a few being consistently profitable.Several public sector banks are being restructured, and in some the government either already has or will reduce its ownership.India has an extensive banking network, in both urban and rural areas.All large Indian banks are nationalized, and all Indian financial institutions are in the public sector.The RBI has granted operating approval to a few privately owned domestic banks; of these many commenced banking business.Foreign banks operate more than 150 branches in India.
Introducing the Hearthstone-AI Competition

The Hearthstone AI framework and competition motivates the development of artificial intelligence agents that can play collectible card games. A special feature of those games is the high variety of cards, which can be chosen by the players to create their own decks. In contrast to simpler card games, the value of many cards is determined by their possible synergies. The vast amount of possible decks, the randomness of the game, as well as the restricted information during the player's turn offer quite a hard challenge for the development of game-playing agents. This short paper introduces the competition framework and goes into more detail on the problems and challenges that need to be faced during the development process.
Conflict as an Inverse of Attention in Sequence Relationship

Attention is a very efficient way to model the relationship between two sequences by comparing how similar two intermediate representations are. Initially demonstrated in NMT, it is a standard in all NLU tasks today when efficient interaction between sequences is considered. However, we show that attention, by virtue of its composition, works best only when it is given that there is a match somewhere between two sequences. It does not very well adapt to cases when there is no similarity between two sequences or if the relationship is contrastive. We propose an Conflict model which is very similar to how attention works but which emphasizes mostly on how well two sequences repel each other and finally empirically show how this method in conjunction with attention can boost the overall performance.
Accelerating Goal-Directed Reinforcement Learning by Model Characterization

We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learning. Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning. Built on such a novel observation, we design two new algorithms - Mean First Passage Time based Q-Learning (MFPT-Q) and Mean First Passage Time based DYNA (MFPT-DYNA), that have been fundamentally modified from the state-of-the-art reinforcement learning techniques. Preliminary results have shown that our hybrid approaches converge with much fewer iterations than their corresponding state-of-the-art counterparts and therefore requiring much fewer samples and much fewer training trials to converge.
Optimization of a solar irradiation forecasting tool based on artificial intelligence

In current electric markets, where many stakeholders can take part, power network operators need accurate predictions of the energy generated by intermittent renewable sources in order to control the whole system.Therefore, the capacity to accurately forecast solar irradiance is key when it comes to the large-scale integration of solar energy generators in the traditional network.One of the challenges, however, consists of providing accurate very short-term predictions (minutes ahead) due to the variability of solar irradiance caused by different meteorological phenomena.This study addresses this need for very short-term forecasts through the development of an irradiance prediction scheme for 10 minutes ahead.The irradiance prediction algorithm is based on a parallel combination of two different layer recurrent networks and has been trained with a two-year historical database of solar irradiance.The accuracy of the proposed tool has been validated through forecasting a whole year using data that is not in the database used in the training step.This tool was then used to forecast the irradiance in two Spanish locations with different weather conditions to analyse whether the accuracy changes.The accuracy between predicted and actual values demonstrates that this tool outperforms similar forecasters.
Automatic verification of heap-manipulating programs

Theoretical foundations of compositional reasoning about heaps in imperative programming languages are investigated. We introduce a novel concept of compositional symbolic memory and its relevant properties. We utilize these formal foundations to build up a compositional algorithm that generates generalized heaps, terms of symbolic heap calculus, which characterize arbitrary cyclic code segments. All states inferred by this calculus precisely correspond to reachable states of the original program. We establish the correspondence between inference in this calculus and execution of pure second-order functional programs.
Generation of Image by Sentence Based on Impression Words in Image

The use of social network services (SNS) have become widespread in recent years, and it is used to express the situation of individual users by using short sentences and images.Especially, young people prefer to express themselves by using short sentences on Twitter and pictures on Instagram.However, CAMPUS LIFE DATA 2017 suggested that young people do few read a long sentence in a newspaper, journal, and more.We think that it is important for people who have not read long sentences to express long sentences with images.Therefore, we aim to express images and short sentences in SNSs from a long sentence in a newspaper, journal, and other reading material in this research.Specifically, we extract the impression of a person looking at an image or after reading a sentence.We calculate the similarity of each extracted word and generate a new image similar to the impression of the sentence.The results indicate that the system could estimate the impression of human beings.However, it was found that a large number of impression words and images are required to improve accuracy.
Semi-Bagging Based Deep Neural Architecture to Extract Text from High Entropy Images

Extracting texts of various size and shape from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in natural scene, etc. The existing works (based on only CNN) often perform sub-optimally when the image contains regions of high entropy having multiple objects. This paper presents an end-to-end text detection strategy combining a segmentation algorithm and an ensemble of multiple text detectors of different types to detect text in every individual image segments independently. The proposed strategy involves a super-pixel based image segmenter which splits an image into multiple regions. A convolutional deep neural architecture is developed which works on each of the segments and detects texts of multiple shapes, sizes, and structures. It outperforms the competing methods in terms of coverage in detecting texts in images especially the ones where the text of various types and sizes are compacted in a small region along with various other objects. Furthermore, the proposed text detection method along with a text recognizer outperforms the existing state-of-the-art approaches in extracting text from high entropy images. We validate the results on a dataset consisting of product images on an e-commerce website.
Factors Influencing the Acceptance of Smart Homes in Jordan

Smart home services are one of the recent internet of things applications that aims to improve residents' quality of life. This study aims to investigate the factors that influence residents' acceptance and usage of smart home services in Jordan. Theoretically, this study extends the technology acceptance model. By following a quantitative method, responses were collected and statistically analysed from 258 household using the SEM-PLS approach. Results show that user awareness, trust, perceived enjoyment, personalisation, perceived usefulness and perceived ease of use significantly influence attitude towards smart home services which, with social influence, impact the intention to use.
A Theoretical Framework for Knowledge Level Assessment in Intelligent Tutoring Systems Using Machine Learning and Adaptive Testing

Machine learning, in general and knowledge level assessment, in particular are gaining momentum these days in the academic and industrial forums due to their immense scope and myriad applications. Ground breaking research has been carried out in the area of student modeling, performance prediction, difficulties faced by a student in a learning environment and so on. This paper attempts to propose only a theoretical framework wherein an intelligent tutoring system would be able to pinpoint a candidate’s knowledge level in a particular topic using various attributes such as question difficulty, time for solving and correctness of answers.
BLCU_NLP at SemEval-2019 Task 8: A Contextual Knowledge-enhanced GPT Model for Fact Checking

Since the resources of Community Question Answering are abundant and information sharing becomes universal, it will be increasingly difficult to find factual information for questioners in massive messages. SemEval 2019 task 8 is focusing on these issues. We participate in the task and use Generative Pre-trained Transformer (OpenAI GPT) as our system. Our innovations are data extension, feature extraction, and input transformation. For contextual knowledge enhancement, we extend the training set of subtask A, use several features to improve the results of our system and adapt the input formats to be more suitable for this task. We demonstrate the effectiveness of our approaches, which achieves 81.95% of subtask A and 61.08% of subtask B in accuracy on the SemEval 2019 task 8.
The Emergence of Internet Language and Its Influence on Chinese

Internet language, a kind of communicative language appeared on the internet along with the boom of the Internet, has drawn much attention for its conspicuous characteristics. From sociolinguistic perspective, this paper explores the social causes for language variation. Then it analyses the social causes of internet language variation.
THE INFLUENCE OF ONLINE READING ON FACEBOOK AND MOTIVATION TOWARD NARRATIVE READING ACHIEVEMENT OF THE EIGHTH GRADE STUDENTS OF MUHAMMADIYAH JUNIOR HIGH SCHOOL 7 OF PALEMBANG IN ACADEMIC YEAR OF 2013/2014

Reading text in a second or foreign language is more difficult than in the first language. The research questions in this research: Is there any significant difference in online reading on Facebook to ward narrative reading achievement that are thought by using guided reading comprehension? Is there any significant difference in online reading on Facebook toward narrative reading achievement in high motivation? Do online reading on Facebook and motivation toward narrative reading achievement affect to the studentsâ€™ narrative reading comprehension? The results of the research are: It was found the significant level (2tailed) was 0.000 since 0.000 &lt; 0.05 it means that the studentsâ€™ narrative reading text on Facebook and motivation in experimental group after applying guided reading increased significantly and was used effective to be taught in a certain group. And there is any influence, and improvement of online reading on Facebook and motivation toward narrative reading achievement. From the probability hypothesis table in SPSS Version 21 the output 0.000 &lt; 0.05 so the HO is ejected and Ha is accepted. Facebook can also be used for teaching other subjects such as speaking and structure. It is important that they must be able to motivate learners to think mentally and psychologically. Besides, the learners are supported to solve problems, to interact with each others, and to learn perpetually as well as consistently. Facebook give the effects and influences, as a strategy and also technique for the English teachers had positive and negative feedback for the students
Binary Document Classification Based on Fast Flux Discriminant with Similarity Measure on Word Set

Fast Flux Discriminant (FFD) is known as one of the high-performance nonlinear binary classifiers, and it is possible to construct a classification model considering the interaction between variables. In order to take account of the interaction between variables, FFD introduces the histogram-based kernel smoothing using subspaces including variable combinations. However, when creating a subspace, the original FFD should cover all variables including combinations of variables with low interaction. Therefore, the disadvantage is that the calculation amount increases exponentially as the dimension increases. In this study, we calculate the similarity between variables by using KL divergence. Then, among the obtained similarities, divisions are performed for each subspace with similar variables. Through this method, we try to reduce the amount of calculation while maintaining classification accuracy by using only combinations of variables that are likely to take high interaction. Through the simulation experiments with Japanese newspaper articles, the effectiveness of our proposed method is clarified.
NeuType: A Simple and Effective Neural Network Approach for Predicting Missing Entity Type Information in Knowledge Bases

Knowledge bases store information about the semantic types of entities, which can be utilized in a range of information access tasks. This information, however, is often incomplete, due to new entities emerging on a daily basis. We address the task of automatically assigning types to entities in a knowledge base from a type taxonomy. Specifically, we present two neural network architectures, which take short entity descriptions and, optionally, information about related entities as input. Using the DBpedia knowledge base for experimental evaluation, we demonstrate that these simple architectures yield significant improvements over the current state of the art.
Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing

Edit distance similarity search, also called approximate pattern matching, is a fundamental problem with widespread database applications. The goal of the problem is to preprocess $n$ strings of length $d$, to quickly answer queries $q$ of the form: if there is a database string within edit distance $r$ of $q$, return a database string within edit distance $cr$ of $q$. Previous approaches to this problem either rely on very large (superconstant) approximation ratios $c$, or very small search radii $r$. Outside of a narrow parameter range, these solutions are not competitive with trivially searching through all $n$ strings. In this work give a simple and easy-to-implement hash function that can quickly answer queries for a wide range of parameters. Specifically, our strategy can answer queries in time $\tilde{O}(d3^rn^{1/c})$. The best known practical results require $c \gg r$ to achieve any correctness guarantee; meanwhile, the best known theoretical results are very involved and difficult to implement, and require query time at least $24^r$. Our results significantly broaden the range of parameters for which we can achieve nontrivial bounds, while retaining the practicality of a locality-sensitive hash function. We also show how to apply our ideas to the closely-related Approximate Nearest Neighbor problem for edit distance, obtaining similar time bounds.
Pengembangan Lembar Kerja Peserta Didik (LKPD) Berbasis Website Menggunakan Notepad++ Pada Materi Bakteri UntukKelas X SMA

Education becomes the basis for the development and advancement of human life so that it is used as to develop their potential through the learning process. The success of the process in the learning activities also influenced by the teaching materials used. One of the teaching materials that can be used is LKPD. Utilization of LKPD based on the website becomes an attractive choice to be used in learning, wide access range and can overcome the limitations of space and time. The purpose of this research is to develop an LKPD based on the website using Notepad ++ on bacteria material for the tenth grade of senior high school, to find out LKPD base on website qualification, and to know the perceptions of teachers and students for the LKPD based on the website in bacteria material. The type of this research is development research using ADDIE model with stages of analysis, design, development, implementation, and evaluation. A subject of the test was two teachers and 12 students in SMA Negeri 8 Kota Jambi. Data in this research is qualitative data and quantitative data. The result of validation by material expert obtained by the percentage 95,31% with very good category and the result of validation by media expert obtained by percentage 93,75% with a very good category. The percentage on the perception of 2 teachers is 91,67% and 83,33% in the very good category. Percentage on the perception of a student is 89,58% with a very good category. Based on the results of the LKPD based on the website using Notepad ++ in bacteria material included in the category very well and can be used as an alternative teaching material to help learners understand the material of bacteria. Keyword: Bacteria, LKPD, Notepad++, Website
The Attributives Comparison between Chinese and Indonesian

The word order comparative analysis is important research in linguistics studies. It is assisting the second language learners or teachers in learning process. The research mainly shows the word order in noun phrase. Noun phrase consists of head and attributives, the head is noun and the attributives consist of any type of the word or group of words. The attributives order and the head as a core in noun phrase, it can show similarities and differences structures between Chinese Language and Indonesian Language. This article shows relation between head and attributives order in noun phrase. The order of Chinese Language attributives is placed before the head. The order of Indonesian language attributives (except: number-classifier, negation, restriction word) is placed after the head.
UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs

We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection.We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism.Among 33 participating teams, our submitted system ranks top 7 (65.3%accuracy) on the labels-by-publisher sub-task and top 24 out of 44 teams (68.3% accuracy) on the labels-by-article sub-task (65.3% accuracy).We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the labels-by-article sub-task.
Technical Health Check For Cloud Service Providers

Understanding the overall health of an IT Infrastructure is a key part of IT Service Management. Traditional approach to perform technical health check is by visiting customer's physical site and rigorously examining the IT infrastructure with Subject Matter Experts. Alternatively, periodic surveys are sent to Technical Architects who are responsible for the managed IT infrastructure. In essence, both site visits and surveys suffer from reactive nature, and subjective assessment. In this paper, we present technical health check for cloud providers, that monitors, assesses operational data and depicts the current health of an IT infrastructure in real time. We also discuss challenges and opportunities of technical health check in Hybrid Cloud Environment.
Quadratic Discriminant Analysis Metric Learning Based on Feature Augmentation for Person Re-Identification

The quadratic discriminant analysis (XQDA) method learns a general projection matrix for all cameras with its strong generalization ability, but it ignores the inherent properties of each camera itself and does not take feature of changes in each camera into account, causing each person under the camera to have a certain feature distortion problem which makes its discriminative ability worse. In this paper, feature augmentation is used to enhance the inherent properties of each camera. By ensuring the generalization ability of the camera, the feature of changes within each camera is taken into consideration and the final discriminative ability is improved. Finally, experiments on a challenging person re-identification dataset, VIPeR, show that the proposed method outperforms the state-of-the-art methods.
Software Development Process

The software process involves the steps necessary to produce a software product. Understanding the requirements for that software product, including regulatory, industry, and safety requirements, is fundamental before choosing a particular process. Such requirements may dictate the methods required within the software process, independence of development, or even the qualifications of the developers involved with a software product.
Notice of Removal: Comparison of the effectiveness of tree algorithms in the diagnosis of spongy tissue

Removed.