Reviewerly/exHarmony huggingface dataset:
A Comparative Study of IELTS Academic Writing Test in Paper Mode vs. Computer Mode among EFL IELTS candidates

espanolEl estudio actual es un intento para investigar si los estudiantes se desempenaron de manera diferente en la prueba de escritura del Sistema Internacional de Evaluacion del Idioma Ingles (IELTS) tanto por escrito como en computadora en terminos de respuesta/ logro de tareas, coherencia/cohesion, recurso lexico, rango gramatical y precision. Ademas, explora si la familiaridad con la computadora de los candidatos era diferente en los grupos por escrito y en computadora. Para este proposito, se selecciono un total de 108 candidatos de un total de 144 basandose en los resultados de la Prueba de Colocacion de Oxford (OPT) en la Universidad de Teheran, Iran. Para recopilar los datos, se administro una muestra de redaccion academica ya retirada del IELTS y un cuestionario de familiaridad con la computadora. Los participantes se dividieron en dos grupos iguales. En el grupo de Modo Escrito (PM), a los estudiantes se les dio la prueba para escribir en el modo de papel convencional. En el otro grupo Modo Computadora (CM), los estudiantes recibieron la misma prueba; sin embargo, se les pidio que escribieran la prueba en la computadora que se les proporciono en su clase. Ademas, todos los participantes tomaron el cuestionario de familiaridad con la computadora. Los datos recopilados se analizaron mediante la prueba t de muestras independientes. Los hallazgos revelaron diferencias significativas entre el modo escrito y el modo en computadora en ambas tareas de escritura. Ademas, el analisis del cuestionario mostro el impacto de la familiaridad con la computadora de los candidatos en su desempeno en la escritura. EnglishThe current study was an attempt to investigate whether learners performed differently in both paper and computer modes in IELTS writing tests in terms of Task response/achievement, coherence/cohesion, lexical resource, grammatical range and accuracy. In addition, it explored whether the candidates’ computer familiarity were different in paper and computer mode groups. To this end, a total number of 108 candidates were selected out of 144 candidates based on the results of the OPT in Tehran University, Iran. To gather the data a retired IELTS academic writing sample and a computer familiarity questionnaire were administered. The participants were divided into two equal groups. In the PM group, students were given the test to write in the conventional paper mode. In the other CM group, the students were given the same test; yet, they were asked to type the test in the computer provided for them in their class. Also, all the participants took the computer familiarity questionnaire. The gathered data were analyzed through the Independent samples t-test. The findings revealed significant differences between paper-based and computer-based modes in both writing tasks. Moreover, the analysis of the questionnaire showed the impact of the candidates’ computer familiarity on their writing performance.
KAJIAN OPERASIONAL SISTEM POLDER UNTUK PENANGANAN BANJIR DAN ROB DI KAWASAN GENUK SEMARANG

&lt;p&gt;The Kaligawe Genuk area is the main route for the North Coast (Pantura) of Java.&lt;br /&gt;The area is the gateway to the city of Semarang from the east direction. Since 2005,&lt;br /&gt;this area has frequently had floods caused by tides (tidal floods) and rainfall. The&lt;br /&gt;Central Government, supported by the Semarang City Government, built the Kali&lt;br /&gt;Tenggang and Sringin Polder systems from 2016 to 2019. In terms of capacity, this&lt;br /&gt;system is not yet perfect for dealing with floods, and it requires efforts so that the&lt;br /&gt;system can be operated optimally. This study activity aims to simulate rainfall flow&lt;br /&gt;rate, the pump capacity, and the retention pond. The simulation results will become&lt;br /&gt;the basis for the development of the polder system operational manual. The&lt;br /&gt;implementation method collects secondary rainfall data, reviewing the condition of&lt;br /&gt;a catchment area, retention pond, and pump station. Focus group discussion with&lt;br /&gt;stakeholders done in the Kaligawe Genuk area. The location of the activity is more&lt;br /&gt;focused on the Kali Sringin area. This study produces tables and graphs of pump&lt;br /&gt;capacity simulation, retention ponds, and an operational manual for the Sringin&lt;br /&gt;Polder system. It is hoped that this guideline will become the basis for the polder&lt;br /&gt;system's sustainable operations.&lt;/p&gt;
Study Guides: Music & Sound Design: Home

This guide will help Music & Sounds Design students find and evaluate key resources needed to successfully complete their assessments.
Applying Abstract Argumentation to Normal-Form Games

Game theory is the most common approach to studying strategic interactions between agents, but it provides little explanation for game-theoretical solution concepts. In this paper, we use a game-based argumentation framework to solve normal-form games. The result is that solution concepts in game theory can be interpreted by extensions of a game-based argumentation framework. We can use our framework to solve normal-form games, providing explanation for solution concepts.
Business Ethical Values as a Mechanism Linking CSR and Internal Outcomes on Job Performance

This research aims to examine Business Ethical Values (BEV) important role on CSR and outcomes related to work, such as Organizational Citizenship Behaviour (OCB), Organizational Commitment (OC) and Job Performance (JP) relationship of Indonesian ports-logistic management organizations. An empirical validated formal structured questionnaire was used to perform survey research. Two dimensions (i.e., environmental and social) were used to measure employees' CSR perceptions. The PLS-SEM analysis confirms the study theoretical framework. Precisely, the findings show the relationship between dimensions of CSR and OCB and BEV. Nevertheless, the link between CSR and Organizational Commitment (OC) is mediated by BEV. Therefore, the effect of CSR on OC can only be attained through the concrete BEV. Likewise, the influence of BEV on JP through OCB and OC.
8 субнормальных подгрупп классических интертипных отношений, которые могут породить 16 тетрахотомий социона: проблема взаимодействия подмножеств

This is the twenty-second article in a series of publications devoted to exploring the mathematical properties of socionics. This article focuses on the eight tetrachotomies that can be obtained using left cosets generated from fourth-order subnormal classical intertype relation subgroups. It is shown that the use of left cosets, in contrast to right cosets, leads to tetrachotomies that have deterministic intertype relationships between each of its quarters. Four of these tetrachotomies can be defined with the Augusta-Reynin traits, and four can be defined with the Jung-Minaiev traits. In both cases, tetrachotomies are “two-formula”. “Formulas” are the three nontrivial classical intertype relation operators shared between the four types of each tetrachotomy quarter. For the four “two-formula” tetrachotomies that can be defined with the Augusta–Reynin traits, the quarters in the irrational half will have a different formula from the quarters in the rational half. For the four “two-formula” tetrachotomies that can be defined with the Jung–Minaiev traits, the quarters in the democratic half will have a different formula from the quarters in the aristocratic half.
Discovery of Personal Name From its Aliases Using Lexical Pattern-Based Approach

Every individual is recognized by its personal name on web. When the web related task is done such as retrieval of some information and some sentimental analysis then such aliases of personal name are identified correctly. These tasks are useful in detecting the personal name from its aliases. This paper presents an OERPN method is which will extract personal name from its aliases. For this alias name is to be given and we criticize which will extract a set of urls in which that alias name is present. We criticize a method which extract lexical pattern automatically. If we will extract the set of personal names from given alias name using online search engine task then in such case lexical pattern based method is used. This will happen using the following OERPN method having following steps such as online search engine task, extracting urls from the web, removing lexical pattern from snippet, pattern extraction and the name extraction. This paper presents a method which is based on real time whereas existing method was not based on real time which was that drawback.
Conditionally Independent Data Generation

Conditional independence (CI) is a fundamental concept with wide applications in machine learning and causal inference. Although the problems of testing CI and estimating divergences have been extensively studied, the complementary problem of generating data that satisfies CI has received much less attention. Given samples from an input data distribution, we formulate the problem of generating samples from a distribution that is close to the input distribution and satisfies CI. We establish a general characterization of CI in terms of a divergence identity that holds for a large class of divergences satisfying separability and strict convexity properties. Based on the Jensen-Shannon version of this identity, an architecture is proposed that leverages the capabilities of generative adversarial networks (GANs) to enforce CI in an end-to-end differentiable manner. As one illustration of the problem formulation and architecture, we consider applications to notions of fairness that can be written as enforcing CIs. We demonstrate the generation of data to trade off adherence to fairness criteria against classification accuracy.
Handwritten Ancient Tamil Character Recognition Using Generative Adversarial Network (Gan)

Recognition Of Handwritten English Characters Has Been Done By Peoples Of Various Parts Of The Geographical Location. Although  The  Recognition  Of  These Characters  Has  Been  Achieved  For  Many  Languages  Other  Than  English,  It  Has  Not  Yet  Been  Achieved For Indian Languages Such As Tamil, Kannada, Telugu And Etc. Ancient Period Tamil Characters Are Different From Current Tamil Characters. People Have Shaped Their Discoveries In Fields Such As Medicine, Astronomy, Art And Science. The People Who Have Knowledge In Recognizing Ancient Characters Are Few. It Made The Situation To Recognize Characters That Are Written In Various Sculptures And Temples. We Will Learn About Their Lifestyle By Recognising The Letters. This Model Helps In Recognizing Tamil Characters Used In The Period Of A.D 3rd And 4th Century. The Aim Of The Proposed System Is To Recognize Handwritten Ancient Period Tamil Characters Using Gan Based Convolutional Neural Networks (Cnn). Generative Adversarial Networks (Gans) Are An Exciting Recent Innovation In Deep Learning. Gan Contains Generative Model Which Create New Data Instances That Resemble The Training Data. Convolution Neural Network Is Deep Learning Models Which Takes Input Generated By Gan And Process It With Its Multiple Layers To Recognize The Characters. The Implementation Of Such Training Model Results With The Accuracy Of 96 %.
Advances in Debating Technologies: Building AI That Can Debate Humans

Roy Bar-Haim, Liat Ein-Dor, Matan Orbach, Elad Venezian, Noam Slonim. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts. 2021.
Identification of Fraud Investigator in Digital Forensics Investigation Using Blockchain

Crime is on the rise in today's world and with the emergence of the digital world, a whole new aspect to crime is open. With the rise in crime, a number of investigations at various fronts is necessary to bring the culprits to justice. Digital forensic investigation is one such branch of these investigations that has been increasingly popular and mandatory for providing justice. One of the problems that hinders the efficient execution of the investigation is the possible tampering of evidence. In digital forensics, it is digital evidence. Tampering of such evidence can lead to wrong judgments and erroneous accusations and can lead the investigation to be inaccurate. It is a serious offence and the culprit needs to be identified before being dealt with.The current system in place is able to identify the team or the agency responsible for the discrepancy but recognizing the individual that is the true culprit is almost impossible. The solution we propose is to integrate Blockchain to record and store the changes during an ongoing investigation. The chain of custody can be determined easily if only authorized people are allowed to access and the records of the access are being stored. This helps in maintaining the integrity of the digital evidence that is vital to an investigation. The creator creates the blockchain after collecting the necessary evidence. The investigators of the case are allowed to access this evidence for further analysis after providing their ID for recording who is accessing the evidence.Only the creator will be allowed to delete the evidence. The integrity of the evidence is preserved because Blockchain is immutable.This solution aims to help aid investigations to move on the right track with the accurate evidence required to prove the accused guilty or not guilty. It helps to supervise and observe that none of the investigators are indulging in any malpractices.
Medical Image Segmentation Of Wbc Using Improved Dual Threshold Method

segmentation of medical images is a critical method for the proper identification and diagnosis of diseases. Because of the distinction between them, the segmentation of the WBCs is an important issue.The automatic diagnosis of multiple white blood cell or leukocyte disorders plays a significant role. The suggested work is an Improved Dual-Threshold approach based on a variation of white blood cell (WBC) segmentation of different color spaces. The improved dual-threshold segmentation method consists of three stages: the pre-processing stage, the segmentation step of the threshold, and the post-procurement phase. For additional handling, two images are obtained: one is a contrast-stretched grey image and the other is an H part image from the altered colour space of YCbCr in the pre-processing step. The three main steps comprising the threshold segmentation are context separation, red blood cell extraction, and the finest threshold selection.The single-threshold method in RGB colour space is involved in the process of separation of Red Blood Cells (RBC), the H channel image is used in the step of background extraction. The Golden Section search method is used for the best threshold selection item. Finally, to eliminate the noise and the unfinished&#x0D; WBCs, median filtering and arithmetical morphology are used in the post-processing stage. The image data collection for Acute Lymphoblastic Leukaemia (ALL) is used to test and segment irregular cells from test data using an overall accuracy. Compared to the current segmentation outcome, the implemented segmentation approach shows better precision performance.
Метод синхронного видео как экспериментальный способ проверки на тождество

The essence of the hypothesis: if you analyze the video of two people (who communicate in a relaxed atmosphere), time intervals are found, at least 30 seconds, where they show similar movements, speech, intonations, pauses, etc. — it means that you found identical types.
Noobs at Semeval-2021 Task 4: Masked Language Modeling for abstract answer prediction

This paper presents the system developed by our team for Semeval 2021 Task 4: Reading Comprehension of Abstract Meaning.The aim of the task was to benchmark the NLP techniques in understanding the abstract concepts present in a passage, and then predict the missing word in a human written summary of the passage.We trained a Roberta-Large model trained with a masked language modeling objective.In cases where this model failed to predict one of the available options, another Roberta-Large model trained as a binary classifier was used to predict correct and incorrect options.We used passage summary generated by Pegasus model and question as inputs.Our best solution was an ensemble of these 2 systems.We achieved an accuracy of 86.22% on subtask 1 and 87.10% on subtask 2.
UNIVERSITY EDUCATION DURING THE SECOND WAVE OF THE CORONA CRISIS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 10747-10756ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2237Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Cadlaws – An English–French Parallel Corpus of Legally Equivalent Documents

This article presents Cadlaws, a new English–French corpus built from Canadian legal documents, and describes the corpus construction process and preliminary statistics obtained from it. The corpus contains over 16 million words in each language and includes unique features since it is composed of documents that are legally equivalent in both languages but not the result of a translation. The corpus is built upon enactments co-drafted by two jurists to ensure legal equality of each version and to re­flect the concepts, terms and institutions of two legal traditions. In this article the corpus definition as a parallel corpus instead of a comparable one is also discussed. Cadlaws has been pre-processed for machine translation and baseline Bilingual Evaluation Understudy (bleu), a score for comparing a candidate translation of text to a gold-standard translation of a neural machine translation system. To the best of our knowledge, this is the largest parallel corpus of texts which convey the same meaning in this language pair and is freely available for non-commercial use.
VANDALISME KOLEKSI DI PERPUSTAKAAN SMA NEGERI 8 PEKANBARU

This study aims to determine how the collection vandalism acts that occur in the library of SMA Negeri 8 Pekanbaru. The research method used is quantitative methods, with data collection techniques through observation, interviews, literature study, and documentation. The research sample was 99 books. The results showed that vandalism to cross certain pages was the most prevalent vandalism, amounting to 38.26%. Then the page folding vandalism was 26.95%. Hacking vandalism on certain pages 24.35%. Book vandalism that is not returned, vandalism marking sentences with 4.35% coloring. Vandalism wrote a book margin of 1.74%. Vandalism in the type of image cutting, page cutting, etc. was not found in vandalism, so the percentage was 0%.
Comparison of Moora, Waspas and SAW Methods in Decision Support Systems

Education is a process in the framework that can adapt to the environment and thus will cause changes in themselves that enable it to involve all that is in society. In this case, the process of selecting the best teachers and staff in data processing is still done manually by conducting tests through several criteria that have been made. Criteria used for the process. There are 19 criteria. The selection process carried out manually has several weaknesses so that it will result in errors in data processing. For this reason, a Decision Support System is needed that can be used in the process of selecting the best Teachers and Staff. In this study, using the calculation method calculation methods Moora, SAW, and WASPAS with scholarship recipients. The results obtained from the Moora Method are 3 (three) highest values at V1 = 1.89; V8 = 1.81; and V2 = 1.80, the SAW Method was 3 (three) highest values at V1 = 10.38; V8 = 9.90; and V2 = 9.87. The WASPAS method that 3 is highest at V1 = 5.07; V8 = 4.72; and V2 = 4.68. The system created using the SAW method because it provides the Best Alternative value and provides the best ranking results
Seeing in the dark: Testing deep neural network and analysis-by-synthesis accounts of 3D shape perception with highly degraded images

Author(s): Yilmaz, Hakan; Singh, Gargi; Egger, Bernhard; Tenenbaum, Josh; Yildirim, Ilker | Abstract: The visual system does not require extensive signal in its inputs to compute rich, three-dimensional (3D) shape percepts. Even under highly degraded stimuli conditions, we can accurately interpret images in terms of volumetric objects. What computations support such broad generalization in the visual system? To answer, we exploit two degraded image modalities – silhouettes and two-tone “Mooney” images – alongside regular shaded images. We test two distinct approaches to vision: deep networks for classification and analysis-by-synthesis for scene inference. Deep networks perform substantially sub-human even after training on 18 times more images per category compared to the existing large-scale image sets for object classification. We also present a novel analysis-by-synthesis architecture that infers 3D scenes from images via optimization in a differentiable, physically-based renderer. This model also performs substantially sub-human. Nevertheless, both approaches can explain some of the key behavioral patterns. We discuss the insights these results provide for reverse-engineering visual cognition.
Semi-Supervised Semantic Dynamic Text Clustering Algorithm

In the traditional dynamic text clustering, the similar texts with different descriptions are divided into different groups; and the difference between the number of cluster categories and the number of real categories is obvious. Aiming at these problems, this paper proposes a semi-supervised semantic dynamic text clustering algorithm (SDCS). The algorithm captures the semantic relationship between texts by semantically representing the text, and dynamically learns the category semantics during the clustering process, so that the text can be accurately clustered according to semantics. At the same time, the algorithm uses the semi-supervised clustering algorithm to supervise the generation of new classes, and produces clustering results that are consistent with the actual situation. The experimental results show that the proposed algorithm is effective and feasible.
Allocation and Migration of Virtual Machine using ABC and SVM based Optimization Strategy

Cloud computing is one of the widely used technology to handle massive data generated at the cloud data center. To analysis this big data number of VMs are generated at the server end to balance load of PMs. When VMs are allocated to PMs resources are utilized to power this system. This process at times involves wastage of resources such as energy and Service Level Agreement (SLA) that drastically increases when unnecessary migrations occur to balance the overloaded PMs. Therefore, in the paper author proposes an energy efficient VM allocation and migration framework that is wise enough to minimize the number of migrations and reduces the instances of SLA violations. The idea here is to rank the available VMs according to their current load while considering the dynamics of energy and SLA violations using hybrid optimization involving Artificial Bee Colony (ABC) and Support Vector Machine (SVM) followed by machine learning architecture. The simulation analysis using 1000 iterations demonstrated reduced energy consumption and SLA violations achieved by minimizing the required number of migrations for load balancing of the PMs. This proved to achieve an effective resource management of cloud data centers.