Reviewerly/exHarmony huggingface dataset:
The problem occurred in students’ writing when using Picture Series technique in learning recount text

The aim of this study was to find out the problem occurred in students’ writing when using picture series technique in learning recount text. This research was a qualitative research. The subjects of this research were 36 students of SMAN 10 Bandar Lampung in academic year 2019/2020 at the second semester. Interview, analysing students’ worksheets and observation sheet were the instrument that used to collect the data. Before taking the data, the students had already been taught by picture series technique as a treatment. Furthermore, there is statistically significant improvement of students’ writing achievement on recount text written by the students after they were taught through picture series technique. After that, interview was done, as one of the instrument, in order to find out the result of this research. The result shows that one of the most common problems is the students still confused with the use of past tense and past continuous tense and lack of vocabulary on the students’ worksheets. Based on the analysis of the students worksheets it was found that some of the students still use English-Indonesian languange. In addition, it was found that the problems that the students made in their writing recount text worksheets are classified into four aspect of writing, namely organization, content, grammar, and vocabulary. Keywords: writing, problem, recount text, picture series technique.
Resources

Abstract Deep learning has been shown as a powerful method for a variety of artificial intelligence tasks, including some critical tasks in NLP. However, training a deep neural network is usually a very time-intensive process and requires lots of code to build related models. To alleviate these issues, some deep learning frameworks have been developed and released, which incorporate some existing and necessary arithmetic operators for neural network constructions. And these frameworks exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. Each framework has its advantages and disadvantages. In this chapter, we aim to exhibit features and running performance of these frameworks so that users can select an appropriate framework for their usage.
PENINGKATAN KEAHLIAN MENYIMAK SISWA DENGAN UJI MENYIMAK MELALUI TOEFL DI SMA DEK PADANG

As a medium of instruction, English does not easily develop in Indonesia. It is even becoming a problem in communication where many parties are not interested to use it in communication. On the contrary, world of Industries really demand the workers to be able to speak and write in English well. SMA DEK as one of the private school that its graduated students directly work in companies after graduation answers the problem. It provides its students with various English program such as english day, English zone, learning TOEFL and English Proficiency. By considering the quality of the students, these programs are expected running well. Emphasizing listening skill is the main purpose because it is the most activity used by students during English learning. So that, observation method, testing method and discussion method are implemented in order to find out the student qualification to answer the challenge. From the community service implementation, it is found that the students have been good on listening skill understanding. During the participant observation, they make and answer the question freely. While implementing the listening TOEFL test, they can answer the questions well and when the discussion method comes, they argue about the correct answer by giving their own reason. It finally concludes that, by learning the Listening TOEFL test, SMA DEK students are motivated to speak freely and give comment toward the given information.
An Optimality Theory Account Of English Loanwords In Pilibhit Hindi-Urdu (Uttar Pradesh) India

This paper contains the English Loanwords and their physical mechanism in Pilibhit Hind-Urdu that generalizes the different types of interchanging accounts of phonemes. The purpose of this paper is to present an account of English loanwords and their intervention in Urdu spoken in Pilibhit (U.P.), India within a framework of Optimality Theory (Prince and Smolensky, 1993). The groundwork of this study lead towards basically describe the facts and proposes theoretical account of alteration of phonemes in English Loanwords and how the native speakers of Pilibhit Hindi-Urdu substitute and break consonant cluster and use in communication. There are many conflicts in the intervention of phonemes as deletion, insertion, voicing, devoicing, assimilation, aspiration, deaspiration etc of English loanwords in Pilibhit Hindi-Urdu. We propose that such conflicts can be resolve by using Optimality Theoretic ranking of constraints and find out proper language specific facts expressed with parameters. This not only makes the grammar simpler, but it also allows the lower ranked constraints to have an effect in a non-conflict situation. This paper is about the nature of loanwords and more specially, the proper relationship between phonemes and candidate sets within the framework of Optimality Theory. We shall argue that the phonological information of phoneme is the best encoded in constraints rather than in representations. In this paper we examine some of the better-known arguments originally adduce in support of constraint rankings and argue that adoption of loanwords such a powerful mechanism and justified with proper evidence that create some effective processes at least in the cases discuses. Theoretical insights from OT enrich our understanding of Hindi-Urdu phonology, and data also reveal implicit figure prominently in the latest theoretical developments within OT.
Clinical Named Entity Recognition based on Deep Learning with Pre-trained Word Embedding for Colonoscopy Reports: Model Development and Performance Evaluation (Preprint)

<sec> <title>BACKGROUND</title> Colorectal cancer is a leading cause of cancer deaths. Several screening tests such as colonoscopy can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of the unstructured text data are still very low despite the large amounts of accumulated data. </sec> <sec> <title>OBJECTIVE</title> We aimed to develop a deep learning-based natural language processing (NLP) method for named entity recognition (NER) in colonoscopy reports. To the best of our knowledge, no previous studies on clinical NLP for colonoscopy reports have applied deep learning techniques. </sec> <sec> <title>METHODS</title> This study proposed a method to apply pre-trained word embedding to a deep learning-based NER model using large unlabeled colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of the Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared variants of the long short-term memory (LSTM) model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using a large unlabeled data (280,668 reports) to the selected model. </sec> <sec> <title>RESULTS</title> The NER model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 score for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. </sec> <sec> <title>CONCLUSIONS</title> In this study, clinical NER was applied to extract meaningful information from colonoscopy reports. We proposed a deep learning-based NER model with pre-trained word embedding. The proposed method in this study achieved promising results that demonstrate it can be applied to various practical purposes. </sec>
Economic Development, Political and Socio-Political Violence Crisis in the Sub-Saharan Africa: An Economic Panel Study

Social and political instability threatens the economic and social development of nations. Political persecution is therefore characterized by socio-political instability as a barrier to development that creates economic difficulties, limited public and private investments and hinders national development. This article describes political persecution and economic development through a close examination of the African ministry which covers key issues such as welfare and drug trafficking. We use various measures to measure political violence and economic development, and we use the crisis management strategies of Driscoll and Kraay and a comprehensive time-based approach to measure (GMM) for a sample of Sub Saharan African countries in the period 2000-2014. In the example of political persecution, we find that the combination of individual achievement, education, capture, and health has a negative impact on politics, but GINI, unemployment, drugs and homeless people that have a positive impact on violence. In the example of the economy, political violence, armed with bribery and corruption, has a negative impact on economic development, but population, security, employment, political participation, housing have a negative impact on production and on economic progress. Positive impact on economic development. The findings underscore the importance of implementing social policies and guidelines against political violence to increase economic growth and development, productivity, political participation and human security in the regions of Africa.
Ranking of energy consumption objects using the principal components method

Ensuring comfortable conditions in civil buildings requires the implementation of tasks of monitoring and forecasting the cost of energy resources, as well as energy-efficient management of heating engineering systems and its equipment. The implementation of appropriate automation and monitoring solutions allows the accumulation of a significant amount of data. To increase the informativeness of the analysis of energy efficiency in the operation of civil buildings a model of their information ranking was developed using correlation analysis and the principal component analysis. Based on the interdisciplinary methodology of data analysis (CRISP-DM), the basic indicators were determined for the accepted initial conditions on electricity and heat consumption of the university buildings and the matrix of correlation coefficients of their interrelation was estimated. Certain data (external volume and area of the building and average temperature values for this region according to the norm) are obtained from the technical documentation of buildings and available from open sources, others (amount of consumed heat and electricity, indoor temperature) are determined during operation and characterize the efficiency of energy resources in the building. At the initial stage, a correlation analysis of the relationship between the main parameters that characterize buildings and their consumption of energy resources. The principal component analysis was used to reduce the dimensionality of the feature set of data and to identify homogeneous groups of energy consumption objects. The obtained four components explain about 90% of the variance of the initial data and characterize the efficiency of energy use in terms of temperature, volume and coefficient of heating degree days of the heating season. The obtained results are recommended for implementation in modern systems of energy monitoring and municipal energy management as applied models for diagnosing abnormal situations and sound management decisions. Keywords – buildings; energy consumption; principal components; machine learning; data segmentation.
How is the Relationship between Reading Ability and Writing Ability in Language Learning?

In language learning there are four skills that must be mastered. These four skills are interrelated and have the ability to influence each other.
Administrasi Pendidik dan Tenaga Kependidikan (PTK) 1

This article contains the basic concepts of administration of the Administration of Educators and Education Personnel, Administrative Processes of the Administration of Educators and Education Personnel (procurement, placement, orientation), Prosperous Administration of Educators and Education Personnel (salaries, allowances, awards), Educator Administration and Education Personnel Administration.
ASSESSMENT OF THE INFORMATION OF MATHEMATICAL EXPECTATIONS OF EXPERIMENTAL SAMPLES OF MEASUREMENTS WITH UNKNOWN STATISTICAL REGULARITIES

Technologies for recognizing samples of experimental measurements with the use of empirical decision rules based on probability distribution functions have been developed and studied. Based on the results of computational experiments, conclusions are made about the effectiveness of the proposed decision rules when applying them to short samples of experimental measurements.
A STUDY ON VOCABULARY TEACHING STRATEGY FOR STUDENTS WITH HEARING IMPAIRMENT AT SPECIAL EDUCATION SCHOOL (SLB), MAKASSAR, INDONESIA

This research aims to find out (1) the vocabulary teaching strategy used by teacher for students with hearing impairment, (2) how to implement the strategy to teaching English vocabulary for students with hearing impairment.This research applied qualitative method.The participants of this research were one English teacher and four students of eight grades SMPLB at SLB Negeri 1 Makassar, Indonesia which were taken through purposive sampling technique.The data of this research were collected by observation, interview, and documentation.The obtained data were analyzed in four major phases namely data collection, data display, data condensation, and drawing conclusion.The result of this research revealed that, (1) there were five strategies used by an English teacher in teaching English vocabulary for students with hearing impairment namely rehearsal, sign language, gesture, writing language and discussion; (2) The teacher implemented the strategies by combining all of those strategies.The teacher showed the material such as explanation and vocabularies to students by writing it on the whiteboard.Next, the teacher explained it to the students by using sign language and gesture, when the teacher gave the students exercise, the students were allowed to discuss the exercise with their friends.All vocabularies the students had learned would be repeated until they were familiar and memorized the those vocabularies.
Elastic Technique for Load Balancing in Cloud Computing

The cloud computing is the architecture that is decentralized in nature due to which various issues in the network get raised which reduces its efficiency. The exchange of data over the network is also continuously increasing. New advanced technology, cloud computing is becoming popular because of providing the above services beneficially. Other vital technologies like virtualization and scalability by designing virtual machines in cloud computing. In cloud computing, web traffic and service provisioning are increasing day by day, so load balancing is becoming a big research issue in cloud computing. Cloud Computing is a new propensity emerging in the IT environment within huge requirements of infrastructure and resources. The load Balancing technique for cloud computing is a vital aspect of the cloud computing environment. Peerless Load balancing scheme ensures splendid resource utilization by provisioning resources to cloud users on-demand services basis in a pay-as-you-use manner. The technique of Load Balancing may further support prioritizing requests of users/clients by applying appropriate scheduling criteria. This paper presents various load balancing schemes in different cloud environments based on requirements specified in the Service Level Agreement (SLA).
Effective Detection of Weapons in Video Surveillance

Surveillance cameras also known as Closed Circuit Tele-Vision (CCTV) play a major part in the enforcement of the law and the administration of justice. The present level of industrial production technology cannot differentiate between a good and a bad, or even a bit worse, situation. As a consequence, additional crime scene investigation or law order maintenance work is needed, which takes a lengthy time. The suggested work is being utilized for a number of purposes, including surveillance, weapon monitoring and classification, live tracking, and more. Video input is permitted as a type of raw input in this project for monitoring and detecting abnormal events utilizing realtime detection techniques such as You Look Only Once Version 3 (YOLO V3). The proposed project's operations make use of a processing module for object recognition using convolutional neural networks such as YOLO V3, which predicts classes and bounding boxes for the entire image in a single run of the algorithm. The circular area will be watched by CCTV, which will automatically execute all operations and be controlled. Before implementing in such a setting and delivering optimal results, numerous samples and datasets will be examined to find accuracy in detection and classification. The planned effort aims to significantly reduce crime rates while simultaneously providing improved protection in specific areas and decreasing the time it takes to capture a criminal.
Comparison of Kidney Segmentation Under Attention U-Net Architectures

One of the most prominent machine learning advantages in the medical industry is the early detection of disease. Automatic kidney detection is of great importance for rapid diagnosis and treatment, where related diseases occupy over 73,750 new cases in the US in 2020 [1]. Today, the performance of diagnosis has been by highly trained radiologists. However, the complex structures contribute to speckle noise and inhomogeneous intensity profiles. Thus, there is a necessity to automate segmentation on kidney ultrasounds using U-Net Deep Learning architectures - an innovative solution for Medical Imaging Analysis. In this research, our focus is on the comparison of Attention U-Net in the context of different backbones such as VGG19, ResNet152V2, and EfficientNetB7. By providing this comparison, we will accomplish a survey for future researchers to more effectively decide on which Attention U-Net architecture to utilize for their segmentation projects.
Amazon

It is perhaps not readily apparent how an online retailer selling books and CDs came to give birth to the cloud computing industry. In previous chapters, you learned how several individuals and technology companies had hinted at cloud computing and built different pieces of it. Some even tried to market it, but it was not until Amazon put it all together and showed the vision for how to execute it that cloud computing materialized as a viable business. The history of Amazon is in many ways the history of the birth of cloud computing.
Integrating Resources Cataloging Workshop

SUMMARY. This pre-conference covered the first four sessions of the Serials Cataloging Cooperative Training Program’s Integrating Resources Cataloging Workshop, focusing on electronic resources. Steven Miller defined integrating resources and how to identify them. He then explained initial decisions to make when cataloging these resources. He covered the MARC fields and AACR2 rules that are unique for integrating resources and new to people accustomed to cataloging e-journals. Miller discussed how records should be updated to reflect changes in the resources, including how to update records created before the new rules. He concluded by discussing the difficulties of maintaining records for these resources. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;https://www.HaworthPress.com&gt;]
Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes (Preprint)

<sec> <title>BACKGROUND</title> Automated extraction of symptoms from clinical notes is a challenging task owing to the multidimensional nature of symptom description. The availability of labeled training data is extremely limited owing to the nature of the data containing protected health information. Natural language processing and machine learning to process clinical text for such a task have great potential. However, supervised machine learning requires a great amount of labeled data to train a model, which is at the origin of the main bottleneck in model development. </sec> <sec> <title>OBJECTIVE</title> The aim of this study is to address the lack of labeled data by proposing 2 alternatives to manual labeling for the generation of training labels for supervised machine learning with English clinical text. We aim to demonstrate that using lower-quality labels for training leads to good classification results. </sec> <sec> <title>METHODS</title> We addressed the lack of labels with 2 strategies. The first approach took advantage of the structured part of electronic health records and used diagnosis codes (International Classification of Disease–10th revision) to derive training labels. The second approach used weak supervision and data programming principles to derive training labels. We propose to apply the developed framework to the extraction of symptom information from outpatient visit progress notes of patients with cardiovascular diseases. </sec> <sec> <title>RESULTS</title> We used &amp;gt;500,000 notes for training our classification model with International Classification of Disease–10th revision codes as labels and &amp;gt;800,000 notes for training using labels derived from weak supervision. We show that the dependence between prevalence and recall becomes flat provided a sufficiently large training set is used (&amp;gt;500,000 documents). We further demonstrate that using weak labels for training rather than the electronic health record codes derived from the patient encounter leads to an overall improved recall score (10% improvement, on average). Finally, the external validation of our models shows excellent predictive performance and transferability, with an overall increase of 20% in the recall score. </sec> <sec> <title>CONCLUSIONS</title> This work demonstrates the power of using a weak labeling pipeline to annotate and extract symptom mentions in clinical text, with the prospects to facilitate symptom information integration for a downstream clinical task such as clinical decision support. </sec> <sec> <title>CLINICALTRIAL</title> <p /> </sec>