Reviewerly/exHarmony huggingface dataset:
A Study Of Machine Translation Theories And Methods

The demand for language translation has greatly increased in recent times due to increasing cross-regional communication and the need for information exchange. Most material needs to be translated, including scientific and technical documentation, instruction manuals, legal documents, textbooks, publicity leaflets, newspaper reports etc. Some of this work is challenging and difficult but mostly it is tedious and repetitive and requires consistency and accuracy. It is becoming difficult for professional translators to meet the increasing demands of translation. In such a situation the machine translation can be used as a substitute. This paper intends to study methods and techniques of Machine Translation (MT). Through the following points: History of MT, Statistical MT, Types of MT, and evaluation of MT.&#x0D;
ENGLISH TRAINING PROGRAM THROUGH DEBATE METHOD FOR TEACHERS OF SMKN 1 SINGOSARI MALANG

The aim of this training is to teach English language teachers of Vocational High School/SMKN 1 Singosari Malang. The absolute role of English as a medium of communication and learning for vocational students is undeniable. Vocational High School is dedicated to produce technical worker which they are prepared to be readily working after graduated without continuing to study on college degree. By having English language as their fluent second language, alumnus will have big weapon to compete in the current bothersome job demand. Problem of teaching-learning English is the one background of this training. English Debate method is a new method in SMKN 1 which they are introduced various techniques to learn English. By this method, teachers will be talented to teach English to students actively and innovatively. The result of this training told us that English Debate is effective method to be applied on teaching-learning English especially on speaking.
Online Hate Speech: A Critical Appraisal of law relating Electronic Evidence in India

We are living in the era of technology and internet is gift of technology. The advantage of internet is blessings but misuse of internet results into the headache. Online hate speech is one of the issues of the modern era. The legal mechanism is also going through big challenge regarding the protection of online hate speech in India. Section 66 A of the Information Technology Act, 2002, was struck down by the Hon’ble Supreme Court and it created challenge for the protection of online hate speech. We have seen number of incidence where fake news/objectionable materials/hate speech/rumors are put through computer resources and communication devices. It had spread hate and violence in the large number of cases. The researcher put light on the notion of hate speech and discuss in detail the international law, domestic law and judicial pronouncements. Civilized Society is facing enormous crisis to deal with online hate speech. The researcher is examining the existing legal mechanism on online hate speech with the relevant provisions of the law and judgments. Electronic evidence is vital to judge the online hate speech. The legal mechanism and judicial pronouncements on the electronic evidence are scrutinized in
Deep Density-Aware Count Regressor.

We seek to improve crowd counting as we perceive limits of currently prevalent density map estimation approach on both prediction accuracy and time efficiency. We leverage multilevel pixelation of density map as it helps improve SNR of training data and therefore, reduce prediction error. To achieve a better model, we introduce multilayer gradient fusion for training a density-aware global count regressor. More specifically, on training stage, a backbone network receives gradients from multiple branches to learn the density information, whereas those branches are to be detached to accelerate inference. By taking advantages of such method, our model improves benchmark results on public datasets and exhibits itself to be a new solution to crowd counting problems in practice.
Correntropy based robust sparse representation for erratic noise suppression

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Correntropy based robust sparse representation for erratic noise suppressionAuthors: Weiwei XuYanhui ZhouXiaokai WangWenchao ChenWeiwei XuXi’an Jiaotong UniversitySearch for more papers by this author, Yanhui ZhouXi’an Jiaotong UniversitySearch for more papers by this author, Xiaokai WangXi’an Jiaotong UniversitySearch for more papers by this author, and Wenchao ChenXi’an Jiaotong UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3427544.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRandom noise in seismic data severely corrupts the useful signal and can often lead to the misinterpretation. Conventional sparsity-based methods using the mean square error (MSE) criterion depend on the Gaussian assumption of the noise distribution and have demonstrated their effectiveness in suppressing Gaussian random noise. However, erratic noise, that has the high-amplitude feature and non-Gaussian distribution, may lead to the performance degradation. In this abstract, a novel correntropy based robust sparse representation method, is presented to improve the erratic noise suppression. Specifically, the correntropy induced metric (CIM) is utilized to adaptively assign weights for noisy data depending on the erratic noise level. And also, the sparse prior is imposed on the CIM to learn robust and sparse representations. Using the Half-Quadratic optimization technique, the correntropy based optimization can be transformed into an 1 l -constrained quadratic problem, which can be efficiently solved by a standard optimization method. Experiments on synthetic and field data show the better results of our presented method in comparison with conventional approach.Presentation Date: Tuesday, October 13, 2020Session Start Time: 1:50 PMPresentation Time: 3:30 PMLocation: Poster Station 11Presentation Type: PosterKeywords: noise, signal processing, attenuation, optimization, algorithmPermalink: https://doi.org/10.1190/segam2020-3427544.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Weiwei Xu, Yanhui Zhou, Xiaokai Wang, and Wenchao Chen, (2020), "Correntropy based robust sparse representation for erratic noise suppression," SEG Technical Program Expanded Abstracts : 2883-2887. https://doi.org/10.1190/segam2020-3427544.1 Plain-Language Summary Keywordsnoisesignal processingattenuationoptimizationalgorithmPDF DownloadLoading ...
Удосконалення документаційого забезпечення управління підприємством на основі використання інформаційних технологій.

The article is devoted to the development of mechanisms for improving the workflow of a higher educational institution and their application. One of the most important reasons for the effective and streamlined work of the university is the correct and structured organization of its workflow. And with the advent of electronic documentation, the number of problems and tasks in this direction has only increased. In this regard, this study provides information on the development of such mechanisms in which: a) document management at the university will be an established system that combines both paper and electronic documents; b) there will be automation of processes for working with all three types of documentation; c) the problem of storing electronic documents will be solved. Priority methods of work are scientific, analysis and synthesis, generalization and abstraction; observation.
A unifying framework for continuity and complexity in higher types

We set up a parametrised monadic translation for a class of call-by-value functional languages, and prove a corresponding soundness theorem. We then present a series of concrete instantiations of our translation, demonstrating that a number of fundamental notions concerning higher-order computation, including termination, continuity and complexity, can all be subsumed into our framework. Our main goal is to provide a unifying scheme which brings together several concepts which are often treated separately in the literature. However, as a by-product, we also obtain (i) a method for extracting moduli of continuity for closed functionals of type $(\mathbb{N}\to\mathbb{N})\to\mathbb{N}$ definable in (extensions of) System T, and (ii) a characterisation of the time complexity of bar recursion.
Complex event processing on real-time video streams

Cameras are ubiquitous nowadays and video analytic systems have been widely used in surveillance, traffic control, business intelligence and autonomous driving. Some applications, e.g., detecting road congestion in traffic monitoring, require continuous and timely reporting of complex patterns. However, conventional complex event processing (CEP) systems fail to support video processing, while the existing video query languages offer limited support for expressing advanced CEP queries, such as iteration, and window. In this PhD research, we aim to develop systems and methods to alleviate these issues. In this paper, we first identify the need for an expressive CEP language which allows users to define queries over video streams, and receive fast, accurate results. To evaluate CEP queries on videos in real-time and with high accuracy, we explain how a streaming query engine can be designed to provide native support of machine learning (ML) models for fast and accurate inference on video streams. In addition, we describe a set of optimization problems that arise when ML models, with trade-offs in speed, accuracy, and cost, are part of a query plan. Finally, we describe how query plans on real-time videos can be optimized and deployed on edge devices with limited computational and network capabilities.
Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma

TFE3 Xp11.2 translocation renal cell carcinoma (TFE3-RCC) generally progresses more aggressively compared with other RCC subtypes, but it is challenging to diagnose TFE3-RCC by traditional visual inspection of pathological images. In this study, we collect hematoxylin and eosin- stained histopathology whole-slide images of 74 TFE3-RCC cases (the largest cohort to date) and 74 clear cell RCC cases (ccRCC, the most common RCC subtype) with matched gender and tumor grade. An automatic computational pipeline is implemented to extract image features. Comparative study identifies 52 image features with significant differences between TFE3-RCC and ccRCC. Machine learning models are built to distinguish TFE3-RCC from ccRCC. Tests of the classification models on an external validation set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894. Our results suggest that automatically derived image features can capture subtle morphological differences between TFE3-RCC and ccRCC and contribute to a potential guideline for TFE3-RCC diagnosis. Translocation renal cell carcinoma is an aggressive form of renal cancer that is often misdiagnosed to other subtypes. Here the authors demonstrated that by using machine learning and H&E stained whole-slide images, an accurate diagnose of this particular type of renal cancer can be achieved.
Risk Bounds for Low Cost Bipartite Ranking

Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent the prohibitive sample cost, many recent work focus on stochastic gradient-based methods. In this paper we consider an alternative approach, which leverages the structure of the widely-adopted pairwise squared loss, to obtain a stochastic and low cost algorithm that does not require stochastic gradients or learning rates. Using a novel uniform risk bound---based on matrix and vector concentration inequalities---we show that the sample size required for competitive performance against the all-pairs batch algorithm does not have a quadratic dependence. Generalization bounds for both the batch and low cost stochastic algorithms are presented. Experimental results show significant speed gain against the batch algorithm, as well as competitive performance against state-of-the-art bipartite ranking algorithms on real datasets.
IMPROVING STUDENTS’ SPEAKING ABILITY THROUGH TWO STAY – TWO STRAY OF FPBS IKIP MATARAM

This research is aimed at knowing whether students’ speaking ability can be improved by using Two Stay - Two Stray and what are the strengths and weaknesses of Two Stay - Two Stray in teaching speaking in the second semester students of FPBS IKIP Mataram in the academic years of 2013/2014. This research uses Classroom Action Research (CAR) which is taken from Kemmis and McTanggart’s design; it consists of 2 cycles. Every cycle consists of four phases those are: planning, acting, observing, and reflecting. The subject of this research is the students in IId of FPBS IKIP Mataram. Field note, questionnaire, and test are used in collecting the data. There are three tests used in this research, they are: pre-test, post-test 1, and post-test 2 with the standard of students’ successful was 65. The findings of the research showed that (1) The students’ speaking ability improved; (2) Related to the field note results showed that the students were more confident and enthusiast in speaking. It can be seen from their participation in the class, in the conversation, and their performance in group work; and (3) Related to the questionnaire result, it is proved that the response of the students toward Two Stay – Two Stray in teaching speaking is 96%. Based on all those findings, it can be concluded that the application of Two Stay - Two Stray can improve the students’ speaking ability.
Development of convergence courses centered on mathematics subjects

이 논문의 목적은 창의·융합 인재 양성을 위한 중요과목 중의 하나인 수학 교과목의 효과적인 학습 체계 수립을 위한 방법을 제시하는 것이다. 이 연구의 내용은 수학 개념을 포함한 자연과학과 문학작품 및 대중문화를 이용한 글쓰기를 접목한 융합교과목을 개발하는 것이다. 교과목 개발 절차는 분석, 설계, 개발, 실행, 평가의 5단계로 구성된 대표적인 교수설계모형인 ADDIE 모형에 따라 설계하였다. 개발된 융합교과목의 실행은 공식의 암기와 문제 풀이형식의 수학 교과목 학습 방법에 지친 학습자에게 새로운 접근 방법을 제시함으로 교과목의 흥미를 유발하여 적극적으로 학습하는 자기주도 학습을 가능하게 했다.The purpose of this paper is to present a method for establishing an effective learning system for mathematics, which is an important subject for creative and convergence talent training. A convergence course that combines natural science and writing was developed for this study. The course development process was designed according to the ADDIE model, which is a representative teaching design model composed of five steps. The developed convergence subjects stimulated interest and made self-directed learning possible by presenting new approaches to learners.
Leyendo y pensando: una estrategia pedagógica para fortalecer el pensamiento crítico en adolescentes

This research focused on strengthening critical thinking in a group of adolescents, through a critical reading strategy based on the disagreements, needs, and concerns generated by their realities in different contexts. The project emerged from a resignification of popular education from practices in the classroom, specifically in reading spaces. The research was based on the critical socio-critical paradigm and action research methodologies, which established the following categories of analysis: adolescents, critical thinking, and critical reading. The analysis process carried out in this project was implemented through interpretation matrices, where the data collected at each stage were classified. Therefore, reading meanings were found from the participants' visions that are not governed by any specific theory, but in the way young people appropriate reading their environments. With the established strategy, spaces were created that allow the strengthening of critical thinking. The research resulted in the rethinking of a critical reading strategy in popular education environments; where the concept of this pedagogical model must be re-signified from a permanent training of teachers who practice their profession in these contexts. Likewise, efforts should be made to transform the role of teachers from a more mediator aspect to give a change to the traditional teacher. Finally, young people must be more engaged and leadership to meet the objectives of the popular educational model.
예비교사 인식을 통한 초등영어 CLIL 수업 적용 가능성 탐색

This paper intends to explore the possibility of applying the Content and Language Integrated Learning (CLIL) approach in elementary school English...
Perspectives in Talent Management Strategies for Cybersecurity Job Roles in Public Safety and Health in Government Organizations

US government organizations struggle to broaden an entire of government approach to recruit and keep younger cybersecurity talent at a time when national security and intelligence organizations and emergency response agencies are extremely concerned that threats to federal structures and networks from cyber-attacks have never been higher for the state, local, and federal agencies. This study looks to explore innovative options for these agencies to address these employee talents and retention shortages for young information security and information technology professionals.
General supervision via probabilistic transformations

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized robust risk minimization (GRRM). The paper shows how current and novel supervision schemes can be addressed under the proposed framework by representing the relationship between examples at test and training via probabilistic transformations. The results show that GRRM can handle different types of training data in a unified manner, and enable new supervision schemes that aggregate general ensembles of training data.
Penerapan Algoritma Venigmare Cipher dan Vernam Cipher Dalam Pengamanan Data Teks

Data security and confidentiality is one very important aspect in today's information system. The emergence of internet and multimedia technology has prompted various efforts to protect, secure, and hide data on digital files from unauthorized parties to access these files. One of the efforts to secure data and information includes using the Venigmare Cihper and the Vernam Cihper. Venigmare is an encryption that can reduce the correlation between the frequency of letters in plaintext and Ciphertext. But unfortunately this method still leaves a repeating pattern, especially if the encrypted plaintext is long enough. In this case, the role of the data encryption technique known as cryptography is very important. Cryptography is a technique for encoding data through a process of encryption and decryption with a specific key to produce encrypted data that is unknown to others. This paper will use the Vernam Cipher algorithm.
Критическая муза Вяч. Иванова в современной русской литературе. Рец. на кн.: Иванов В. И. По звездам. Опыты философские, эстетические и критические: статьи и афоризмы / Вячеслав Иванов ; редкол.: А. В. Лавров (гл. ред.) [и др.] ; Рос. акад. наук, Ин-т рус. литературы (Пушкинский Дом). СПб. : Пушкинский Дом, 2018. Кн. 1 : Тексты. 430 с. Кн. 2 : Примечания. 670 с.

This review considers the academic publication of the book By the Stars (1909) by Vyacheslav Ivanov prepared by a team of researchers and published by the Pushkin House Publishing in 2018. The reviewer analyses the principles of publication (design of the cover, Ivanov’s own graphics and spelling, etc.). Special attention is paid to the multistep comments on Ivanov’s articles. The comments in the book are compared with the notes and comments in other editions of Ivanov’s articles. The reviewer points out the inconsistency between the table of contents in the analysed reissue of the book By the Stars and the table of contents in the original book released in 1909: in the modern version, there is no division of articles into sections or their numbering. The reviewer emphasises that the book By the Stars (2018) is of great importance both for the study of the creative heritage of Ivanov and for modern publishing practice as a whole.
Research on image recognition algorithm of Agricultural products based on edge calculation

In this paper, the problem of agricultural product image recognition is studied. Due to the difficulty and time consuming in human eye identification of agricultural product image recognition, an algorithm of agricultural product image recognition based on edge calculation is proposed, which can be applied to the problem of agricultural product image recognition in traceability. Firstly, the advantages of edge calculation and image feature extraction methods are analyzed, and the layered discriminant analysis algorithm is proposed. The experimental results show that the layered discriminant analysis algorithm has high image recognition accuracy, reduces the response time of image recognition, and has better flexibility and practicability.
Rewrite or Not Rewrite? ML-Based Algorithm Selection for Datalog Query Answering on Knowledge Graphs.

Query-driven reasoning techniques with Datalog rules, like Magic Sets (MS), are ideal for implementing query answering on Knowledge Graphs (KGs). For some queries, executing a rewriting procedure like MS is the best choice, but for others a non-rewriting procedure like Query-subquery (QSQ) can be faster. Choosing beforehand which procedure should be used is not trivial and mistakes can be costly. To address this problem, we describe a first-of-its-kind method that builds a Machine Learning (ML) model to predict whether a query should be answered with MS or with QSQ. Experiments on several well-known KGs show that our method can return accurate predictions, and this leads to a significant reduction of the response time of query answering.
bOWL: A Pluggable OWL Browser (Short Paper)

The Web Ontology Language (OWL) is a World Wide Web Consortium standard, based on the Resource Description Format standard. It is used to define ontologies. While large ontologies are useful for different applications, some tools require partial ontologies, based mostly on a hierarchical relationship of classes. In this article we present bOWL, a basic OWL browser, with the main goal of being pluggable into others, more significant, web applications. The tool was tested through its integration on LeXmart, a dictionary editing tool.
Robust Multi-user Detection Based on Hybrid Grey Wolf Optimization

The search for an effective nature-inspired optimization technique has certainly continued for decades. In this paper, a novel hybrid Grey wolf optimization and differential evolution algorithm robust multi-user detection algorithm is proposed to overcome the problem of high bit error rate (BER) in multi-user detection under impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER.