Reviewerly/exHarmony huggingface dataset:
Arabic Handwriting Recognition Model based on Neural Network Approach

Arabic language as the main language of more than millions of people all over the world has attracted researches in the handwriting script recognition field.Arabic scripts have many difficulties which make Arabic Handwriting recognition challenging.In this paper the model proposed aimed at recognizing Arabic words the IFN / ENIT dataset.The model is an OCR using Neural Network classifier preceded by a set of preprocessing techniques includes removing spaces between words, bolding the words, baseline estimation and correction and resizing the words images.The recognition rate of the proposed model is 70%.This result showed how much the proper selection of the preprocessing steps affects the recognition rate of handwritten words.The propo sed model differs from other suggested models in Arabic handwriting field, according, the unlike preprocessing steps applied on a model and a new approach in estimating and correcting words baseline.
Litescale: A Lightweight Tool for Best-worst Scaling Annotation

Best-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances.Studies have shown the efficacy of this methodology applied to NLP tasks in terms of a higher quality of the datasets produced by following it.In this system demonstration paper, we present LITESCALE, a free software library to create and manage BWS annotation tasks.LITESCALE computes the tuples to annotate, manages the users and the annotation process, and creates the final gold standard.The functionalities of LITESCALE can be accessed programmatically through a Python module, or via two alternative user interfaces, a textual console-based one and a graphical Web-based one.We further developed and deployed a fully online version of LITESCALE complete with multi-user support.
IoT Based Health Monitoring System

Normally, a health issue or an emergency needs to viewed in a multidisciplinary approach. And, thus a standard sensor-based diagnosis in the medical field, requires a greater number of sensors and human efforts if it’s processed on a large scale. To overcome this issue an IoT-based health care application is proposed in this research work. The proposed system consists of an online mobile application that supports continuous wireless monitoring of patients. The Main aim of the paper is to implement a low-cost system and send the patient vital parameters in case of emergency situations. Sensors such as ECG Monitor sensor, Heart Rate sensor, and temperature sensor are used to quantify the patient vital signs. The sensor data are collected and transferred to the cloud for storage of the IoT platform via a module which is connected to the micro controller. The information is processed within the cloud and the appropriate feedback steps are taken on the analyzed data which may be further analyzed by a doctor remotely in person. Remote viewing reduces the burden to doctors and also provides the precise health status of patients. If the patient needs urgent attention, then a message alerts is sent to the care taker or doctor. This kind of mechanism helps in taking precautions beforehand and also helps the doctor to analyze the patient in a better way. The setup is verified using simulation and various test experiments.
Theoretical and Practical Issues in the Semantic Annotation of Four Indigenous Languages

Computational resources such as semantically annotated corpora can play an important role in enabling speakers of indigenous minority languages to participate in government, education, and other domains of public life in their own language.However, many languagesmainly those with small native speaker populations and without written traditions -have little to no digital support.One hurdle in creating such resources is that for many languages, few speakers would be capable of annotating texts -a task which requires literacy and some linguistic training -and that these experts' time is typically in high demand for language planning work.This paper assesses whether typologically trained non-speakers of an indigenous language can feasibly perform semantic annotation using Uniform Meaning Representations, thus allowing for the creation of computational materials without putting further strain on community resources. 1
Efektivitas Penggunaan Ensiklopedia Berbasis Teknologi Sebagai Sumber Belajar di Sekolah Menengah Atas (SMA): Literature Review

Abstract : Objective: To determine the effectiveness of technology-based encyclopedias as learning resources to increase motivation and learning outcomes in the learning process. The method used in this article uses the literature review method, a source obtained on the Google Scholar website with problems in the past 5 years. From 8 articles, found the development of tenology-based encyclopedias as a very effective learning resource used in the learning process by using the ADDIE, 4-D development model, and Borg and Ball development models. Then in the use of technology-based learning resource encyclopedias this is very feasible to use in the learning process both in the class room and independently at home. So, the development of technology-based learning resource encyclopedia that is able to be a source of learning in addition to textbooks that are used by students to add insight, enrich information and understanding and can increase motivation and learning outcomes. Conclusion: This literature review found the use of technology-based encyclopedias as learning resources. Technology-based encyclopedias are one of the sources of appropriate and effective learning to increase motivation, learning outcomes, interest in reading to add insight, knowledge and understanding of students. Keywords : effectiveness, encyclopedia, learning resources
Researches Regarding the Development of a Virtual Reality Android Application Explaining Orientation Tolerances According to the Latest GPS Standards Using 3D Models

This paper presents a virtual reality application developed for engineering students aiming to help them develop their skills to correctly interpret the geometrical tolerances for orientation, like parallelism, perpendicularity and angularity, according to the latest Geometrical Product Specification (GPS) Standards. The previous studies focused on the impact of mobile learning on student achievement show that the method could be one promising educational method. The educational applications used on mobile devices develop a friendly environment and generate a positive effect on learning. Having spatial skills is essential for engineering students who need to imagine different shapes and orientations. The authors have discovered based on their experience in teaching courses like computer aided design and tolerances and dimensional control, that many students have difficulties with understanding 3D spatial geometry and the transitions between 2D drawings and 3D parts. This kind of skills can be developed through specific training with appropriate tools, like the virtual reality application developed and presented in this paper. Based on a long research the authors managed to develop this training application that offers the viewer a 360-degree view of different mechanical parts presenting 3D geometrical tolerances annotations according to the latest GPS standards. The developed application also presents 3D models of the meaning based on the skin model, the tolerance zones, the condition of conformity as well as the datum features.
Does BERT Understand Idioms? A Probing-Based Empirical Study of BERT Encodings of Idioms

Understanding idioms is important in NLP.In this paper, we study to what extent a pretrained BERT model is able to encode the meaning of a potentially idiomatic expression (PIE) in a certain context.We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification.Our experiment results suggest that BERT indeed is able to separate the literal and idiomatic usages of a PIE with high accuracy.It is also able to encode the idiomatic meaning of a PIE to some extent.
Relation Extraction using Multiple Pre-Training Models in Biomedical Domain

The number of biomedical documents is increasing rapidly.Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing.BERT-based models are known for their high performance in various tasks.However, it is often computationally expensive.A high-end GPU environment is not available in many situations.To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models.Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus.In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.
Urban Annual Electricity Consumption Prediction Method Based On Fuzzy Cognitive Map

Electricity has become an indispensable necessity for modern human society, and electricity consumption prediction is a necessary guarantee for stable social operation and healthy industrial development. In recent years, experts and scholars have put forward many models of electricity consumption prediction, but when considering complex systems and non-linear relations, there is no good explanation, and the prediction accuracy will be affected. Fuzzy Cognitive Map (FCM) is a soft computing method, which is the product of the combination of fuzzy logic and neural network. It has a strong ability of knowledge expression and reasoning in complex system modeling. In this paper, a method based on FCM is proposed to predict the annual electricity demand of the city. Taking the economy, industrial structure, population, climate, urban development and other factors into consideration, the electricity consumption of Jinan city is predicted. Experiments show that compared with traditional forecasting methods and neural network algorithms, the prediction accuracy is better, which can provide more accurate reference data for government decision-making, industrial development planning and other aspects.
Selected Webliographies on Indonesian Cultural Heritages

Indonesia is a country rich in cultural heritage. The diverse cultural heritages demand promotion through various media, such as the web, aimed primarily at the younger generation. This paper aims to present various links and descriptions of web-shaped information sources that participate in promoting the cultural heritage of the Nusantara. The method used is a qualitative descriptive method by searching websites through a search engine using predetermined keywords. The results showed that various websites were found to provide information and promote cultural heritage to the broader community. Dissemination was not only carried out to the Indonesian people but also to the international community. This can be seen from two things, namely the nature of web pages that can be accessed globally and the existence of websites using bilingual, namely Indonesian and English.
“Was it “stated” or was it “claimed”?: How linguistic bias affects generative language models

People use language in subtle and nuanced ways to convey their beliefs.For instance, saying claimed instead of said casts doubt on the truthfulness of the underlying proposition, thus representing the author's opinion on the matter.Several works have identified classes of words that induce such framing effects.In this paper, we test whether generative language models are sensitive to these linguistic cues.In particular, we test whether prompts that contain linguistic markers of author bias (e.g., hedges, implicatives, subjective intensifiers, assertives) influence the distribution of the generated text.Although these framing effects are subtle and stylistic, we find qualitative and quantitative evidence that they lead to measurable style and topic differences in the generated text, leading to language that is more polarised (both positively and negatively) and, anecdotally, appears more skewed towards controversial entities and events.
Comparative Analysis of the Application of Multilayer and Convolutional Neural Networks for Recognition of Handwritten Letters of the Azerbaijani Alphabet

Introduction. The implementation of information technologies in various spheres of public life dictates the creation of efficient and productive systems for entering information into computer systems. In such systems it is important to build an effective recognition module. At the moment, the most effective method for solving this problem is the use of artificial multilayer neural and convolutional networks. The purpose of the paper. This paper is devoted to a comparative analysis of the recognition results of handwritten characters of the Azerbaijani alphabet using neural and convolutional neural networks. Results. The analysis of the dependence of the recognition results on the following parameters is carried out: the architecture of neural networks, the size of the training base, the choice of the subsampling algorithm, the use of the feature extraction algorithm. To increase the training sample, the image augmentation technique was used. Based on the real base of 14000 characters, the bases of 28000, 42000 and 72000 characters were formed. The description of the feature extraction algorithm is given. Conclusions. Analysis of recognition results on the test sample showed: as expected, convolutional neural networks showed higher results than multilayer neural networks; the classical convolutional network LeNet-5 showed the highest results among all types of neural networks. However, the multi-layer 3-layer network, which was input by the feature extraction results; showed rather high results comparable with convolutional networks; there is no definite advantage in the choice of the method in the subsampling layer. The choice of the subsampling method (max-pooling or average-pooling) for a particular model can be selected experimentally; increasing the training database for this task did not give a tangible improvement in recognition results for convolutional networks and networks with preliminary feature extraction. However, for networks learning without feature extraction, an increase in the size of the database led to a noticeable improvement in performance. Keywords: neural networks, feature extraction, OCR.
Machine learning approaches to the QCD transition

We study the high temperature transition in pure(3) gauge theory and in full QCD with 3Dconvolutional neural networks trained as parts of either unsupervised or semi-supervised learning problems.Pure gauge configurations are obtained with the MILC public code and full QCD are from simulations of = 2 + 1 + 1 Wilson fermions at maximal twist.We discuss the capability of different approaches to identify different phases using as input the configurations of Polyakov loops.To better expose fluctuations, a standardized version of Polyakov loops is also considered.
AN ETHNOPRAGMATICS STUDY OF APOLOGIZE SPEECH ACTS IN JAVANESE (KAJIAN ETNOPRAGMATIK TINDAK TUTUR MEMINTA MAAF DALAM BAHASA JAWA )

Apologies are usually made by someone if they make a mistake. However, apologies for the speech community in Java are not only done after the speaker has made a mistake for a speech or action taken to the speaker, sometimes before doing or telling something that is considered wrong or taboo. In the Javanese speaking community, apologizing tends to be stated in advance even though what is actually said or done is not necessarily wrong. The focus of this research is the speech act form apologizing to the Javanese community in the area of Beringin Village, Lakarsantri District, Surabaya City. This study has a qualitative descriptive approach with an ethnopragmatic design. Research data in the form of situations, events, and communicative actions containing apologies obtained from 46 research subjects through observation and in-depth interviews. The data analysis method is the equivalent and split method with techniques for direct elements, such as missing techniques, dressing techniques, and expansion techniques. The results of this study: (1) the form of speech to apologize is different in modes and components; (2) The form of apologizing is componentially an open form, but in expression, in the same sentence, components that indicate the level of sincerity or other purposes are also inserted other than for the purpose of apologizing.
Synthetic Source Universal Domain Adaptation through Contrastive Learning

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more challenging for UDA to train models. I hypothesize that the degradation of trained models in the target domain is caused by the lack of direct training loss to improve the discriminative power of the target domain data. As a result, the target data adapted to the source representations is biased toward the source domain. I found that the degradation was more pronounced when I used synthetic data for the source domain and real data for the target domain. In this paper, I propose a UDA method with target domain contrastive learning. The proposed method enables models to leverage synthetic data for the source domain and train the discriminativeness of target features in an unsupervised manner. In addition, the target domain feature extraction network is shared with the source domain classification task, preventing unnecessary computational growth. Extensive experimental results on VisDa-2017 and MNIST to SVHN demonstrated that the proposed method significantly outperforms the baseline by 2.7% and 5.1%, respectively.
FAST: A carefully sampled and cognitively motivated dataset for distributional semantic evaluation

What is the first word that comes to your mind when you hear giraffe, or damsel, or freedom?Such free associations contain a huge amount of information on the mental representations of the corresponding concepts, and are thus an extremely valuable testbed for the evaluation of semantic representations extracted from corpora.In this paper, we present FAST (Free ASsociation Tasks), a free association dataset for English rigorously sampled from two standard free association norms collections (the Edinburgh Associative Thesaurus and the University of South Florida Free Association Norms), discuss two evaluation tasks, and provide baseline results.In parallel, we discuss methodological considerations concerning the desiderata for a proper evaluation of semantic representations.
Knowledge and Keywords Augmented Abstractive Sentence Summarization

In this paper, we study the knowledge-based abstractive sentence summarization.There are two essential information features that can influence the quality of news summarization, which are topic keywords and the knowledge structure of the news text.Besides, the existing knowledge-augmented methods have poor performance on sentence summarization since the sparse knowledge structure problem.Considering these, we propose KAS, a novel Knowledge and Keywords Augmented Abstractive Sentence Summarization framework.Tri-encoders are utilized to integrate contexts of original text, knowledge structure and keywords topic simultaneously, with a special linearized knowledge structure.Automatic and human evaluations demonstrate that KAS achieves the best performances. 1
RED: A Novel Dataset for Romanian Emotion Detection from Tweets

In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none.To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks.In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts.We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification.We also investigate more modern approaches like fastText, which makes use of subword information.Lastly, we finetune the Romanian BERT for text classification and our experiments show that the BERTbased model has the best performance for the task of Emotion Detection from Romanian tweets.
Research on Mobile English Learning System Based on iOS

In order to improve the use experience of mobile learning and reduce the response delay caused by the slow positioning of system resources, research on mobile English learning systems based on iOS is proposed. Firstly, the value of realizing mobile learning is objectively analyzed, and the practical significance of the research is clarified from the perspectives of social development and learning conditions. On this basis, taking the intelligent client equipped with an iOS operating system as the hardware condition of the system, the system architecture, including data access layer, business logic layer, and presentation layer, is constructed by MVC. In the model module, it is divided into ten difficulty levels by using the frequency of vocabulary, the length of words, and the number of syllables of a single word. The learning resources of the reading class are uniformly represented by the vector space model, and the improved TF-IDF is used to calculate the weight of eigenvalues in the resources. According to the calculation results, the resources are also divided into ten difficulty levels, and the divided resources correspond to their respective databases and use regular expressions to find the learning resources that match the access request. Finally, with the support of the iOS protocol, the feedback mechanism between each module is constructed by Express.js. By analyzing the access requests sent and received by the view, the timely call to the target database resources is realized to improve the response rate of the system. The test results show that the function of the designed system can be realized stably, and the module response delay time is stable within 1s; under the ideal network environment, the buffer delay is in the range of 0.4–0.6 s, and the loading delay is no more than 1.8 s when the network speed is 3 Mpbs, which has a good user experience.