Reviewerly/exHarmony huggingface dataset:
Water Quality Data Outlier Detection Method Based on Spatial Series Features

Outlier detection is one of the major branch in data mining which has been applied in different fields. Researchers have focused on the outlier detection in time series, but rarely spatial series. In this paper, we propose a new outlier detection method based on k-nearest neighbour (KNN) and Mahalanobis distance, which is first applied to the water field. Experimental results verify that the algorithm has good accuracy and effectiveness in outlier detection for water quality spatial series dataset.
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles

Pedestrian action recognition and intention prediction is one of the core issues in the field of autonomous driving. In this research field, action recognition is one of the key technologies. A large number of scholars have done a lot of works to improve the accuracy of the algorithm for the task. However, there are relatively few studies and improvements in the computational complexity of algorithms and system real-time. In the autonomous driving application scenario, the real-time performance and ultra-low latency of the algorithm are extremely important evaluation indicators, which are directly related to the availability and safety of the autonomous driving system. To this end, we construct a bypass enhanced RGB flow model, which combines the previous two-stream algorithm to extract RGB feature information and motion feature information respectively. In the training phase, the two streams are merged by distillation method, and the bypass enhancement is combined in the inference phase to ensure accuracy. The real-time behavior of the action recognition algorithm is significantly improved on the premise that the accuracy does not decrease. Experiments confirm the superiority and effectiveness of our algorithm.
CORPUS-DRIVEN BAMBARA SPELLING DICTIONARY

A model for the development of a corpus-driven spelling dictionary for the Bambara language is described. First, a list of about 4,000 lexemes characterized by spelling variability is extracted from an electronic BambaraFrench dictionary. At the next stage, a script is applied to determine the number of occurrences of each spelling variant in the Bambara Reference Corpus, separately for the entire Corpus (more than 11 million words) and for its disambiguated subcorpus (about 1.5 million words). Statistics on the diversity of sources and authors are also obtained automatically. The statistical data are then sorted manually into two lists of lexemes: those whose standard spelling can be established statistically, and those requiring evaluation by expert linguists. Some difficult cases are discussed in the paper. At the final stage, a representative expert commission will discuss all those lexemes for which statistical data alone do not suffice to define a standard spelling variant, before taking a final decision on each. The resulting Bambara spelling dictionary will be published electronically and on paper.
Augmented Lagrangian method for a total variation-based model for demodulating phase discontinuities

In this work, we reformulate the method presented in App. Opt. 53:2297 (2014) as a constrained minimization problem using the augmented Lagrangian method. First we introduce the new method and then describe the numerical solution, which results in a simple algorithm. Numerical experiments with both synthetic and real fringe patterns show the accuracy and simplicity of the resulting algorithm.
A Survey on Cloud Computing and Security Challenges to Cloud Computing

Cloud Computing is a transformative computing paradigm that involves delivering applications and services over the internet using standard access mechanisms through the use of workstations, laptops, smart phones etc [1].Due to this technology, the expense of computation, application facilitating, content storage and conveyance have been reduced.This paper presents the advantages of using cloud computing, reviewing the several cloud deployment and service models.This paper also analyzes the key challenges and security issues to cloud computing as security is one of the major issues which hamper the growth of cloud.It also provides cryptographic algorithms of data security and a comparative analysis of data security algorithms.It also addresses some methods to minimize these challenges.
ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings

This paper presents Iswara's participation in the WNUT-2020 Task 2 "Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings",which tries to classify whether a certain tweet is considered informative or not. We proposed a method that utilizes word embeddings and using word occurrence related to the topic for this task. We compare several models to get the best performance. Results show that pairing BERT with word occurrences outperforms fastText with F1-Score, precision, recall, and accuracy on test data of 76%, 81%, 72%, and 79%, respectively
Provably Robust Metric Learning

Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied. In this paper, we show that existing metric learning algorithms, which focus on boosting the clean accuracy, can result in metrics that are less robust than the Euclidean distance. To overcome this problem, we propose a novel metric learning algorithm to find a Mahalanobis distance that is robust against adversarial perturbations, and the robustness of the resulting model is certifiable. Experimental results show that the proposed metric learning algorithm improves both certified robust errors and empirical robust errors (errors under adversarial attacks). Furthermore, unlike neural network defenses which usually encounter a trade-off between clean and robust errors, our method does not sacrifice clean errors compared with previous metric learning methods. Our code is available at this https URL.
Analyzing the Complex Nature of Emotions From Written Text Using Parrott Theory of Emotions

Emotions are the main component of a person's character and personality.They are used to communicate messages and information.Emotions are represented in various ways, such as by physical actions, verbal tone, written text, etc.We are interested in identifying the emotions presented in the written text and then to analyze them.Emotions are complex in nature.An emotion exhibits its own nature and characteristics.It may represent another emotion in a written text.The complex property of emotions is similar to the characteristics of complex numbers, which are represented by their real and imaginary parts.Various algorithms are used to identify and analyze emotions in the text.These techniques usually considered each emotion as a separate element with its own characteristics.The relationship between different emotions and their influence on the overall polarity of the text has not received much attention from researchers over a period.In this work, the main idea is to analyze the complex characteristics of emotions by identifying the relationships and dependencies that exist between them.To support this idea, theories of emotions such as Plutchik's wheel of emotions and Parrott's theory of emotions are utilized.According to these theories, emotions are classified into different groups and levels depending on their intensities and dependencies.The results of the research are described in detail in the later sections of the article.
Deeply Embedded Knowledge Representation &amp; Reasoning For Natural Language Question Answering: A Practitioner’s Perspective

Successful application of Knowledge Representation and Reasoning (KR) in Natural Language Understanding (NLU) is largely limited by the availability of a robust and general purpose natural language parser.Even though several projects have been launched in the pursuit of developing a universal meaning representation language, the existence of an accurate universal parser is far from reality.This has severely limited the application of knowledge representation and reasoning (KR) in the field of NLP and also prevented a proper evaluation of KR based NLU systems.Our goal is to build KR based systems for Natural Language Understanding without relying on a parser.Towards this we propose a method named Deeply Embedded Knowledge Representation & Reasoning (DeepEKR) where we replace the parser by a neural network, soften the symbolic representation so that a deterministic mapping exists between the parser neural network and the interpretable logical form, and finally replace the symbolic solver by an equivalent neural network, so the model can be trained end-to-end.We evaluate our method with respect to the task of Qualitative Word Problem Solving on the two available datasets (QuaRTz and QuaRel).Our system achieves same accuracy as that of the state-of-the-art accuracy on QuaRTz, outperforms the state-of-the-art on QuaRel and severely outperforms a traditional KR based system.The results show that the bias introduced by a KR solution does not prevent it from doing a better job at the end task.Moreover, our method is interpretable due to the bias introduced by the KR approach.
End-to-end NLP Pipelines in Rust

The recent progress in natural language processing research has been supported by the development of a rich open source ecosystem in Python. Libraries allowing NLP practitioners but also non-specialists to leverage state-of-the-art models have been instrumental in the democratization of this technology. The maturity of the open-source NLP ecosystem however varies between languages. This work proposes a new open-source library aimed at bringing state-of-the-art NLP to Rust. Rust is a systems programming language for which the foundations required to build machine learning applications are available but still lacks ready-to-use, end-to-end NLP libraries. The proposed library, rust-bert, implements modern language models and ready-to-use pipelines (for example translation or summarization). This allows further development by the Rust community from both NLP experts and non-specialists. It is hoped that this library will accelerate the development of the NLP ecosystem in Rust. The library is under active development and available at https://github.com/guillaume-be/rust-bert.
One of these words is not like the other: a reproduction of outlier identification using non-contextual word representations

Word embeddings are an active topic in the NLP research community.State-of-the-art neural models achieve high performance on downstream tasks, albeit at the cost of computationally expensive training.Cost aware solutions require cheaper models that still achieve good performance.We present several reproduction studies of intrinsic evaluation tasks that evaluate non-contextual word representations in multiple languages.Furthermore, we present 50-8-8, a new data set for the outlier identification task, which avoids limitations of the original data set, such as ambiguous words, infrequent words, and multi-word tokens, while increasing the number of test cases.The data set is expanded to contain semantic and syntactic tests and is multilingual (English, German, and Italian).We provide an in-depth analysis of word embedding models with a range of hyperparameters.Our analysis shows the suitability of different models and hyper-parameters for different tasks and the greater difficulty of representing German and Italian languages.
RUSSIAN LANGUAGE AND CORPUS DIVERSITY

This paper discusses the use of most widely-known Russian corpora, namely Russian National Corpus, ruTenTen, General Internet Corpus of Russian, and Araneum Russicum Maximum, for the theoretical study of Russian language. Based on a sample of papers from 2019, I demonstrate that scholars, especially theoretical linguists, tend to ignore the opportunities provided by a wide range of Web corpora, even though these resources are well-known to the NLP community. I present a selection of case studies to show that data from “non-classical” corpora can be used for studying various linguistic phenomena, such as: 1) variation in morphology and syntax; 2) word formation and lexical change; 3) construction grammar. I also claim that the underuse of non-classical corpora is partly due to the fact that they are (perceived as) not quite user-friendly.
Sound Natural: Content Rephrasing in Dialog Systems

We introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent-slot tagging and the slot values are directly passed as-is to the execution engine. However, this setup fails in some scenarios such as messaging when the query given by the user needs to be changed before repeating it or sending it to another user. For example, for queries like ‘ask my wife if she can pick up the kids’ or ‘remind me to take my pills’, we need to rephrase the content to ‘can you pick up the kids’ and ‘take your pills’. In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query. We show that BART, a pre-trained transformers-based masked language model, is a strong baseline for the task, and show improvements by adding a copy-pointer and copy loss to it. We analyze different trade-offs of BART-based and LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the best practical model
Neural Dialogue State Tracking with Temporally Expressive Networks

Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to improve the accuracy of turn-level-state prediction and the state aggregation.
Brief report Robustness of coherence for multipartite quantum states

In this brief report, we prove that the robustness of coherence (ROC), in contrast with many popular quantitative measures of quantum coherence derived from the resource-theoretic framework of coherence, may be a subadditive for a specific class of multipartite quantum states. We investigate how the subadditivity is affected by admixture with other classes of states for which ROC is superadditive. We show that pairs of quantum states may have different orderings with respect to relative entropy of coherence, $$l_{1}$$ -norm of coherence and ROC and numerically study the difference in ordering for the chosen pairwise coherence measures.
THINK-PAIR-SHARE: ENGAGING STUDENTS IN SPEAKING ACTIVITIES IN CLASSROOM

Think-Pair-Share technique is one of the cooperative learning strategies that promote student involvement in the classroom. This strategy makes students engaged in in-class activities such as discussions and provides opportunities to share their ideas with others. The purpose of this study was to describe the implementation of the Think-Pair-Share strategy in teaching speaking skill for secondary students. A qualitative method with a descriptive analysis was undertaken in this study. The sample was twenty students of eighth grade. The data was collected through interviews, observation, and document analysis. The data analysis technique used was presented in a descriptive form. The results indicated the implementation of the Think-Pair-Share technique during the learning process took place following the stages that have been stated in the learning process plan made by the teacher. Appreciation for the implementation of the Think-Pair-Share technique both from students is quite good. Students stated that they can overcome the constraints in the learning process of speaking activity through Think-Pair-Share.
THE ANALYSIS OF QUALITY STANDARDS OF ELECTRONIC SERBIAN LANGUAGE TEXTBOOK FOR THE SECOND GRADE OF PRIMARY SCHOOL

Electronic textbooks have a potential to combine the teaching values ​​of digital technology with didactic-methodological requirements that a textbook should meet and thus become a means of teaching capable of meeting the needs of today’s students and achieving modern educational goals. In order for an electronic textbook to have its ex­pected effect, it is necessary that its concept takes into account certain theoretical and practical knowledge synthesized in the so-called quality standards. Although the theoretical and empirical research of electronic textbooks is at its very beginning and there is not even closely a rich body of knowledge as in the case of printed textbooks, certain postulates do exist and they need to be taken into account when creating and evaluating electronic textbooks. This paper uses the content analysis technique to examine the extent to which the electronic textbook On Language, published by Klett, meets the specific requirements of electronic textbooks. The results show that the analysed electronic textbook is between low and medium level of interactivity, contains a small number of multimedia elements and does not use the possibility of hyper textual links, but relies exclusively on a linear structure in the content presentation.
Parameter-dependent unitary transformation approach for quantum Rabi model

Abstract Quantum Rabi model has been exactly solved by employing the parameter-dependent unitary transformation method in both the occupation number representation and the Bargmann space. The analytical expressions for the complete energy spectrum consisting of two double-fold degenerate sub-energy spectra are presented in the whole range of all the physical parameters. Each energy level is determined by a parameter in the unitary transformation, which obeys a highly nonlinear equation. The corresponding eigenfunction is a convergent infinite series in terms of the physical parameters. Due to the level crossings between the neighboring eigenstates at certain physical parameter values, such the degeneracies could lead to novel physical phenomena in the two-level system with the light–matter interaction.
Implementation of controllable universal unital optical channels

We show that a configuration of four birefringent crystals and wave-plates can emulate almost any arbitrary unital channel for polarization qubits encoded in single photons, where the channel settings are controlled by the wave-plate angles.The scheme is applied to a single spatial mode and its operation is independent of the wavelength and the fine temporal properties of the input light.We implemented the scheme and demonstrated its operation by applying a dephasing environment to classical and quantum single-photon states with different temporal properties.The applied process was characterized by a quantum process tomography procedure, and a high fidelity to the theory was observed.
Automated Assessment of Noisy Crowdsourced Free-text Answers for Hindi in Low Resource Setting

The requirement of performing assessments continually on a larger scale necessitates the implementation of automated systems for evaluation of the learners' responses to free-text questions.We target children of age group 8-14 years and use an ASR integrated assessment app to crowdsource learners' responses to free text questions in Hindi.The app helped collect 39641 user answers to 35 different questions of Science topics.Since the users are young children from rural India and may not be well-equipped with technology, it brings in various noise types in the answers.We describe these noise types and propose a preprocessing pipeline to denoise user's answers.We showcase the performance of different similarity metrics on the noisy and denoised versions of user and model answers.Our findings have large-scale applications for automated answer assessment for school children in India in low resource settings.
NATIVE LANGUAGE IDENTIFICATION FOR RUSSIAN USING ERRORS TYPES

The task of recognizing the author’s native (Native Language Identification—NLI) language based on a texts, written in a language that is non-native to the author—is the task of automatically recognizing native language (L1). The NLI task was studied in detail for the English language, and two shared tasks were conducted in 2013 and 2017, where TOEFL English essays and essay samples were used as data. There is also a small number of works where the NLI problem was solved for other languages. The NLI problem was investigated for Russian by Ladygina (2017) and Remnev (2019). This paper discusses the use of well-established approaches in the NLI Shared Task 2013 and 2017 competitions to solve the problem of recognizing the author’s native language, as well as to recognize the type of speaker—learners of Russian or Heritage Russian speakers. Native language identification task is also solved based on the types of errors specific to different languages. This study is data-driven and is possible thanks to the Russian Learner Corpus developed by the Higher School of Economics (HSE) Learner Russian Research Group on the basis of which experiments are being conducted.
Penggunaan sistem informasi geografis untuk pemetaan sebaran kasus dan faktor risiko kejadian penyakit tb. Paru bta (+) di kabupaten majene

Background: Tuberculosis is a chronic infection disease caused by Mycobacterium tuberculosis, its health problem especially in developing countries.In the Global TB Report 2015, there were 9.6 million people sick with tuberculosis, 1.5 million people died, consisting of 890,000 men, 480,000 women and 140,000 children, including 0.4 million people positive for HIV / AIDS.Majene regency is the district with the highest rates of pulmonary TB cases in West Sulawesi Province for 5 years, from 2010 until 2014.Methode:.This research use cross sectional design.Using GIS for mapping and knowing pattern of smear-positive TB.Sample consisted of 240 cases of smear-positive TB that was recorded in the health services during January to December 2016 to mapping and 244 suspected TB that was recorded in the health services during January to December 2016 to determine the association of smearpositive TB with risk factors.Using Geoda and stata 13 to determine the association of risk factors with the incidence smear-positive TB and Purely spatial scan statistic using SaTScan to determine cluster case.Result: There is a positive association between ventilation (p = 0,011), lighting (p = 0,013), housing density (p = 0,001), rainfall (p = 0,05 dan p = 0,001), poor nutritional status (p = 0,000) and poverty (p = 0,000) with the incidence of smear-positive TB.There are 3 clusters (p value 0,026, 0,027 and 0,029) in areas with high poor housing , poor nutritional status and high poverty. Conclusion: The incidence of smear-positive TB in Majene Regency has a positive association with environmental factors and socio-economic factors.There are clusters in areas with high risk factors.Case mapping, risk factors and cluster identification are expected to support the control program of TB.
Test statistics based on independence processes

Paper is devoted to investigating classical normalized empirical process of independence. Processes are investigated by using strong approximation methods with best rate of convergence. We also consider the problems of finding of limit distributions of certain classes of statistics for testing the hypothesis of independence of random variable and event. The application to random censoring model also considered.