Reviewerly/exHarmony huggingface dataset:
IRLab@IITBHU at WNUT-2020 Task 2: Identification of informative COVID-19 English Tweets using BERT

This paper reports our submission to the shared Task 2: Identification of informative COVID-19 English tweets at W-NUT 2020.We attempted a few techniques, and we briefly explain here two models that showed promising results in tweet classification tasks: Distil-BERT and FastText.DistilBERT achieves a F 1 score of 0.7508 on the test set, which is the best of our submissions.
Specifics of Using Image Processing Techniques for Blood Smear Analysis

The process of medical diagnosis is an important stage in the study of human health. One of the directions of such diagnostics is the analysis of images of blood smears. In doing so, it is important to use different methods and analysis tools for image processing. It is also important to consider the specificity of blood smear imaging. The paper discusses various methods for analyzing blood smear images. The features of the application of the image processing technique for the analysis of a blood smear are highlighted. The results of processing blood smear images are presented.
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors

Most recent improvements in NLP come from changes to the neural network architectures modeling the text input.Yet, state-of-theart models often rely on simple approaches to model the label space, e.g.bigram Conditional Random Fields (CRFs) in sequence tagging.More expressive graphical models are rarely used due to their prohibitive computational cost.In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling.Our approach is the natural adaptation of SampleRank (Wick et al., 2011) to neural models, and is widely applicable to tasks beyond sequence tagging.We apply our approach to named entity recognition and present a neural skipchain CRF model, for which exact inference is impractical.The skip-chain model improves over a strong baseline on three languages from CoNLL-02/03.We obtain new state-of-the-art results on Dutch. 1
University digital educational model

This work was carried out in accordance with the Digital Economy program of the Russian Federation and is therefore relevant. The program posed the following tasks for universities: the digital transformation of universities and the training of specialists of the digital economy. The Ministry of Education has articulated these issues as the creation of the Digital University. The paper proposes a model that can be laid the foundation of the Digital University. The model was developed on the basis of an analysis of the development of education in the fi of digitalization over the past 20 years, an assessment of the readiness of the leading universities of the Russian Federation to transform at present, and their own experience in using digital technologies. The proposed model satisfies the following requirements: openness of the system for students and teachers; the student’s participation in the formation of the trajectory of his own learning; the ability to access additional resources, courses and choose the places for practical training; formation and analysis of the digital trace; the use of intelligent methods for assessing acquired knowledge and competencies. The main blocks of the model are identified and the tasks for each block are formulated, which must be solved at the stage of creating the Digital University. The paper proposes approaches to their solution, technologies that the authors tested, evaluated and published the results in their previous works. In conclusion, we note that to create a Digital University, first of all, it is necessary: expand the base of control and training materials, modify, improve their forms; implement software smart systems for training and control; form a digital student trace, digital passports of information resources and, on their basis, form an individual learning path, an image of a specialist; develop learning scenarios for different images of students, goals, objectives; integrate intellectual resources of science and education; automate the management of the learning and control process as much as possible.
Virtual Teaching and Learning of Accounting in Technical Universities (TUs) in Ghana amid the COVID-19 Pandemic

The novel COVID-19 pandemic has affected every facet of life including education.Governments the world over adopted several measures and protocols, including shutting schools, introducing online teaching and learning on campuses as an alternative to the traditional face to face mode of delivery to minimise the impact of the pandemic.In this study, the effectiveness of the virtual teaching and learning of accounting adopted by authorities in Ghanaian TUs is explored.The Technology Acceptance Model (TAM) was used to formulate the hypotheses of this paper.The online survey was adopted to collect data from 315 respondents sampled purposively.In assessing both the measurement and structural models, Partial Least Square-Structural Equation Model (PLS-SEM) approach was adopted.The findings of the study prove that both mode of delivery and assessment, and period of delivery and assessment, have significant effects on the virtual teaching and learning of accounting in TUs in Ghana.This study is among the first papers to make significant attempts to explore the impact of the COVID-19 pandemic on teaching and learning in technical universities in Africa.This paper offers contributions to policymakers in tertiary institutions to have a better perspective of the effective virtual teaching and learning of accounting in TUs by both teachers and students.The researchers recommend that, the appropriate training opportunities for both learners and teachers on the virtual teaching and learning of accounting.This would go a long way to help students and teachers to discover the comprehensive and effective attributes of these virtual learning platforms to facilitate their effective usage.
Cloud Resource Optimization for Processing Multiple Streams of Visual Data

Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational resources and the demands may vary over time. Cloud computing shows the most promise to provide the needed resources on demand. In this paper, we investigate how to allocate cloud resources when analyzing real-time data streams from network cameras. A resource manager considers many factors that affect its decisions, including the types of analysis, the number of data streams, and the locations of the cameras. The manager then selects the most cost-efficient types of cloud instances (e.g., central processing unit versus general-purpose graphics processing units) to meet the computational demands for analyzing streams. We evaluate the effectiveness of our approach using Amazon Web Services. Experiments demonstrate more than 50% cost reduction for real workloads.
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning

In this paper, we propose a meta-learning based semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation, denoted as MEDST.Our main motivation is to further bridge the chasm between the need for high accuracy dialogue state tracker and the common reality that only scarce annotated data is available for most real-life dialogue tasks.Specifically, MEDST has two core steps: meta-training with adequate unlabelled data in an automatic way and meta-testing with a few annotated data by supervised learning.In particular, we enhance SEDST via entropy regularization, and investigate semi-supervised learning frameworks based on model-agnostic meta-learning (MAML) that are able to reduce the amount of required intermediate state labelling.We find that by leveraging un-annotated data in meta-way instead, the amount of dialogue state annotations can be reduced below 10% while maintaining equivalent system performance.Experimental results show MEDST outperforms SEDST substantially by 18.7% joint goal accuracy and 14.3% entity match rate on the KVRET corpus with 2% labelled data in semi-supervision.
Analysis and Design of Decision Support System for Employee Performance Appraisal with Simple Additive Weighting (SAW) Method

The decision support system (DSS) for employee performance appraisal is a decision support system used in the employee performance appraisal process at PT. Surya Toto Tbk. Currently, the employee performance appraisal process still done manually (paper-based) so that the reports produced were not real-time. From the existing problems, this research aimed to analyze and design a decision support system according to the existing criteria using the Simple Additive Weighting (SAW) method to be able to develop into a Web-based DSS. The method used began with communication with management, especially the perpetrators of employee performance appraisals. Furthermore, planning, the process of data collection, analysis, design, and finally making reports carried out. The conclusion of this study provided a suggestion to use the criteria that had given two more criteria. By giving weights and calculations carried out for three employees, the value obtained for employee C got the highest score (0.98) followed by employee B, and the lowest score (0.85) was employee A.
Context-aware Stand-alone Neural Spelling Correction

Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.On the contrary, humans can easily infer the corresponding correct words from their misspellings and surrounding context.Inspired by this, we address the standalone spelling correction problem, which only corrects the spelling of each token without additional token insertion or deletion, by utilizing both spelling information and global context representations.We present a simple yet powerful solution that jointly detects and corrects misspellings as a sequence labeling task by fine-turning a pre-trained language model.Our solution outperform the previous state-ofthe-art result by 12.8% absolute F 0.5 score.
Penggunan Media Pembelajaran Video untuk Solusi Penurunan Pemahaman Materi Pembelajaran Ketika Belajar Online Akibat Pandemic Covid-19

Recent COVID-19 pandemic has forced people to minimize outdoor activities to avoid increasingly massive spread of the virus, including schools in Indonesia. Government has established a policy that learning activities must be carried out online. In fact, based on a literature review conducted by researchers, online learning is a new thing for some students and teachers in Indonesia, which might cause learning materials are not maximally delivered. Students are very likely to feel bored which presumably have an impact on decreased understanding of learning materials. One of alternative choices for teachers is using learning videos as interesting learning media which are then followed by teacher explanations so that students are able to understand materials provided better. Video is a media or tool that provides audio and visuals that contain learning messages covering concepts, principles, procedures, and theories of knowledge application. In addition, video is one of the excellent media in delivering materials in the context of online learning nowadays. It is expected to increase understanding of materials whose process takes place asynchronously, in which students can learn at any time.
Deconstructing word embedding algorithms

Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.Uncontextualized word embeddings are used in many NLP tasks today, especially in resourcelimited settings where high memory capacity and GPUs are not available.Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms.In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the common conditions that seem to be required for making performant word embeddings.We believe that the theoretical findings in this paper can provide a basis for more informed development of future models.
Cybercrime in Online Dating Site: Pornography Business in the Virtual World

Online dating sites have been a common tool for individuals to find and meet new companions and potential mates.Although it has the potential to bring great happiness into their lives, many individuals have been slow to recognize the cyber risk connected to these dating sites such as cybercrime and cyber pornography.The researcher has performed an analysis on an online dating site to know how cybercrime and pornography developed in the virtual world.The researcher used the case study method.Interviews were conducted with five key informants who had been an active member of the dating site to provide information.The result shows that there are three kinds of cybercrime activities which involve pornography content at the dating site.The first is hacking; the second is sextortion, and the third one is online prostitution.The result also shows a cybercrime activity that does not involve pornography content called doxxing.
Detecting Images That Have a Destructive Impact on Users on the Internet

This article deals with the current problem of automated detection of facts of destructive influence of images on the Internet on the person.It should be noted that detecting aggressive content in images is more difficult than detecting an object and defining its category.The reason for this is that aggressive content has no specific color or object parameters, there are no common features for classification.In this case the use of convolutional neural networks with pre-learning is a successful solution.Due to increase in aggressive content on the Internet, the problem of identifying such objects in order to minimize its impact on users is acute.The paper presents the developed architecture of the neural network for solving the problem of image with aggressive content recognition.Experiments during 180 epochs showed that at the number of epochs ~ 95-100 during training and ~ during testing it is possible to achieve the classification accuracy 91.8% taking into account the top-5 results.
PERMAINAN MONOPOLI BERBASIS METODE STAD (STUDENT TEAMS ACHIEVEMENT DIVISION) SEBAGAI MEDIA PEMBELAJARAN BIOLOGI MATERI SISTEM PENCERNAAN

Learning media is an intermediary used for student learning tools. Students inthe learning process feel bored with the delivery of material. Submission ofmaterial that is less varied causes students to be less active so that theunderstanding of the material is not optimal. The teacher uses torso media inlearning but the learning outcomes are still less than KKM (Minimumcompleteness criteria). One way to overcome this is by developing monopolygames as biology learning media. Monopoly game is a type of board gamethat aims to accumulate wealth and control complexes on a monopoly boardgame. The teacher can modify the shape of this monopoly board game (astuti:2014). The use of instructional media appropriately and varied can overcomethe passive attitudes of students and overcome boredom during the learningprocess. Monopoly Game Development as a Biology Learning Media Materialof Digestive System Based on STAD (Student Teams Achievement Division)Method is interesting to be studied.The purpose of this research is to producea media product in the form of a decent monopoly game for learning biologyof the human digestive system. This type of research is the development of the4D model (Define, Design, Develop, Desseminat or 4 P called defining,planning, developing and disseminating. Monopoly Game Development as aBiological Learning Media for Digestive System Material using the STAD(Student Teams Achievement Division) method. Media development wasvalidated by biology lecturers and subject teachers as media design expertsand the material was subsequently tested on grade XI students.The results ofrecapitulation of expert validation and student trials showed very goodcriteria, namely: (a) Aspects of media design reached an average score of88.7% (b) Aspects of material reached an average score of 89.1% and (c)trials learners reach a score of 90.25%. This study can be concluded that theMonopoly Game as a Learning Media for Biology of the Digestive SystemMaterial Based on the STAD Method (Student Teams Achievement Division) issuitable for the learning process facilities.
MULTIMEDIA AS A MEDIA FOR ANALYZING STUDENT LEARNING OUT-COMES WITH THE CONCEPT OF BLENDED LEARNING

in the world of education innovation is needed to improve the quality of education. Especially in increasingly rapid technological developments. The use of technology in the education sector makes the quality of education increasingly developed. In this study the authors made an online analytical medium that was used to test students' understanding of the lessons taught. By applying an online test at the end of the meeting. This application proves the level of ability of students in real and real time. This multimedia application also provides results directly to the participants. So that they can find out the results of their competency test directly
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling

Semantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence.Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages.In fact, even a simple combination of data has been shown to be effective with polyglot training by representing the distant vocabularies in a shared representation space.Meanwhile, despite the dissimilarity in argument annotations between languages, certain argument labels do share common semantic meaning across languages (e.g.adjuncts have more or less similar semantic meaning across languages).To leverage such similarity in annotation space across languages, we propose a method called Cross-Lingual Argument Regularizer (CLAR).CLAR identifies such linguistic annotation similarity across languages and exploits this information to map the target language arguments using a transformation of the space on which source language arguments lie.By doing so, our experimental results show that CLAR consistently improves SRL performance on multiple languages over monolingual and polyglot baselines for low resource languages.
Certification of a functionality in a quantum network stage

Abstract We consider testing the ability of quantum network nodes to execute multi-round quantum protocols. Specifically, we examine protocols in which the nodes are capable of performing quantum gates, storing qubits and exchanging said qubits over the network a certain number of times. We propose a simple ping-pong test, which provides a certificate for the capability of the nodes to run certain multi-round protocols. We first show that in the noise-free regime the only way the nodes can pass the test is if they do indeed possess the desired capabilities. We then proceed to consider the case where operations are noisy, and provide an initial analysis showing how our test can be used to estimate parameters that allow us to draw conclusions about the actual performance of such protocols on the tested nodes. Finally, we investigate the tightness of this analysis using example cases in a numerical simulation.
A Bayesian Binomial Regression Model with Latent Gaussian Processes for Modelling DNA Methylation

Epigenetic observations are represented by the total number of reads from a given pool of cells and the number of methylated reads, making it reasonable to model this data by a binomial distribution. There are numerous factors that can influence the probability of success in a particular region. Moreover, there is a strong spatial (alongside the genome) dependence of these probabilities. We incorporate dependence on the covariates and the spatial dependence of the methylation probability for observations from a pool of cells by means of a binomial regression model with a latent Gaussian field and a logit link function. We apply a Bayesian approach including prior specifications on model configurations. We run a mode jumping Markov chain Monte Carlo algorithm (MJMCMC) across different choices of covariates in order to obtain the joint posterior distribution of parameters and models. This also allows finding the best set of covariates to model methylation probability within the genomic region of interest and individual marginal inclusion probabilities of the covariates.
Shallow EDSLs and Object-Oriented Programming: Beyond Simple Compositionality

Context: Embedded Domain-Specific Languages (EDSLs) are a common and widely used approach to DSLs in various languages, including Haskell and Scala. There are two main implementation techniques for EDSLs: shallow embeddings and deep embeddings. Inquiry: Shallow embeddings are quite simple, but they have been criticized in the past for being quite limited in terms of modularity and reuse. In particular, it is often argued that supporting multiple DSL interpretations in shallow embeddings is difficult. Approach: This paper argues that shallow EDSLs and Object-Oriented Programming (OOP) are closely related. Gibbons and Wu already discussed the relationship between shallow EDSLs and procedural abstraction, while Cook discussed the connection between procedural abstraction and OOP. We make the transitive step in this paper by connecting shallow EDSLs directly to OOP via procedural abstraction. The knowledge about this relationship enables us to improve on implementation techniques for EDSLs. Knowledge: This paper argues that common OOP mechanisms (including inheritance, subtyping, and type-refinement) increase the modularity and reuse of shallow EDSLs when compared to classical procedural abstraction by enabling a simple way to express multiple, possibly dependent, interpretations. Grounding: We make our arguments by using Gibbons and Wu's examples, where procedural abstraction is used in Haskell to model a simple shallow EDSL. We recode that EDSL in Scala and with an improved OO-inspired Haskell encoding. We further illustrate our approach with a case study on refactoring a deep external SQL query processor to make it more modular, shallow, and embedded. Importance: This work is important for two reasons. Firstly, from an intellectual point of view, this work establishes the connection between shallow embeddings and OOP, which enables a better understanding of both concepts. Secondly, this work illustrates programming techniques that can be used to improve the modularity and reuse of shallow EDSLs.
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts

Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools.We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts.It builds on 205 automatically induced high-quality type systems for popular fictional domains, and provides recommendations towards reference type systems for given input texts.Users can exploit the richness and diversity of these reference type systems for fine-grained supervised typing, in addition, they can choose among and combine four other typing modules: pre-trained real-world models, unsupervised dependency-based typing, knowledge base lookups, and constraint-based candidate consolidation.
The Legacy of George Sudarshan

Ennackal Chandy George Sudarshan was born on 16 September 1931 in Pallam, a small village in the Kottayam District of Kerala State, South India.
Concerning the History of the Writers’ Publishing House in Leningrad (1927–1934): Unknown Episodes

The article analyzes Writers’ Publishing House in Leningrad as the publishing base of writersfellow travelers (pisateli-poputchiki). Based on archival documents, the history of its official registration and reorganization in the early 1930s is being reconstructed.