Reviewerly/exHarmony huggingface dataset:
Leveraging HTML in Free Text Web Named Entity Recognition

HTML tags are typically discarded in free text Named Entity Recognition from Web pages.We investigate whether these discarded tags might be used to improve NER performance.We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models.We find an increased F1 performance for Text+Tags of between 0.9% and 13.2% over all datasets, variants and models.This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable.These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.
Developing a Design Inquiry Method for Data Exploration

The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the findings in design methodology literature and motivating data exploration principles for design inquiry.The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-an-end, and designers being part of the data collection.
Anomaly detection of android malware using One-Class K-Nearest Neighbours (OC-KNN)

The advent of the Android Operating System has recorded a remarkable ground-breaking opportunities in the Technological world. However, this great breakthrough also has a very dark side – an uncontrollable rapid continuous releases of malware in the wild, targeted at the platform and all its information and human assets. The misuse-based approaches adopted by many detection systems do no longer have the rigidity and the tenacity to accommodate the rapid successive releases of malware that come in great volume in order to keep up with active defenses against unknown and novel attacks. Systems that are capable of offering anomaly protection are thus in dire need. This study developed a normality model that is based on One-Class K-Nearest Neighbour (OC-kNN) Machine Learning approach for anomaly detection of Android Malware. The OC-kNN was trained, using WEKA 3.8.2 Machine Learning Suite, through a semi-supervise procedure that contained mostly benign and a very few outliers Android application samples. The OC-kNN had 88.57% true performance accuracy for normal instances while 71.9% was recorded as true performance accuracy for outliers (unknown) instances. The false alarm rates for both normal and outlier’s instances were recorded as 28.1% and 11.5%. The study concluded that a One-Class Classification model is an effective approach to be used for the detection of unknown Android malware.&#x0D; Keywords: Android; Machine Learning, Malware, One-Class Classification, Anomaly Detection, Outlier Detection, Novelty Detection, Concept Learning, k-NN
THE IMPACT OF SECURITIES MARKETS ON ECONOMIC GROWTH IN ZIMBABWE

The paper examines the impact of securities exchange on economic growth in Zimbabwe for the period 1980 to 2017. The study adopted the endogenous growth model on which magnitude and fluidity of securities were deemed to be the main drivers of the economy. The ordinary least squares regression technique was applied as the main method of analysis and as the maximum likelihood estimator. The findings of the study showed that securities exchange has blended impacts on economic expansion. It was observed that the market size (market capitalisation) and foreign direct investment (FDI) have a significant positive impact on the economic growth.
A Traceable and Reliable Electronic Supply Chain System Based on Blockchain Technology

Electronic supply chain (ESC) is a network among the parties of a supply chain system, such as manufacturers, suppliers, and retailers. It recodes all the processes involved in the distribution of specific products until transported to final customers. Blockchain (BC) technology is a decentralized network that records all the transactions in real-time and is used in many areas such as cryptocurrency. In this paper, we work on an ESC system that records all the transactions based on BC technology using a drug supply chain system as a case study. The recording of the transactions consists of three main stages. First, all the parties of the ESC system are represented in the BC network as clients with unique identities. Second, all the information related to a specific drug is recorded inside the transaction and each transaction has its own signature. Finally, all the transactions of the drug from the manufacture to the patient are recorded inside a block with a unique identity for each block. These steps inside the BC are performed based on security cryptography mechanisms, such as rivest-shamir-adleman (RSA) and secure hash algorithm SHA. The results illustrate that the proposed approach protects the drugs from counterfeiting, ensures the reliability, and provides a real-time tracking system for the transactions that have occurred among ESC parties.
SMARTAJWEED Automatic Recognition of Arabic Quranic Recitation Rules

Tajweed is a set of rules to read the Quran in a correct Pronunciation of the letters with all its Qualities, while Reciting the Quran. which means you have to give every letter in the Quran its due of characteristics and apply it to this particular letter in this specific situation while reading, which may differ in other times. These characteristics include melodic rules, like where to stop and for how long, when to merge two letters in pronunciation or when to stretch some, or even when to put more strength on some letters over other. Most of the papers focus mainly on the main recitation rules and the pronunciation but not (Ahkam AL Tajweed) which give different rhythm and different melody to the pronunciation with every different rule of (Tajweed). Which is also considered very important and essential in Reading the Quran as it can give different meanings to the words. In this paper we discuss in detail full system for automatic recognition of Quran Recitation Rules (Tajweed) by using support vector machine and threshold scoring system.
A Blockchain Based Lifelong Learning Platform: The Smart University

According to today's education system, the content, applicability, quality and performance measurement criteria of the courses may vary according to countries, educational institutions and educators. Because of incorrect performance measurement, hidden skills disappear before they appear. Thanks to the graduation diploma it is observed that people with less than professional knowledge and skills come to better places than those who are more knowledgeable and skilled. In addition, due to the lack of a proper training plan according to the target learning community, boredom and financial problems may arise due to long periods of education. In this study, while trying to avoid material and temporal problems, a blockchain-based lifelong learning platform whose content, processing, reliability, decentralized, open to continuous and rapid accessibility, and evaluation criteria and method is completely objective is proposed. Thanks to this platform, the diplomas to be received by the students who have successfully passed the end-of-education exams will be time-stamped, immutable, safe and traceable. At the same time, all these documents, storage of training contents, diplomas, certificates, post-training exams and all related files , will be kept accessible, secure and immutable thanks to IPFS, the Inter Planetary File System. Achievements, employer and employee pools to be created according to the achievements, and a recruitment contract to be realized will be controlled by smart contracts and absolute reliability and objectivity will be ensured. All these concepts will be made available through a decentralized application (DApp) which will run on a private blockchain network that requires a partial permission to be established.
Probabilistic Model of Interaction of Necessary and Sufficient Conditions for Mass Morbidity of the Population, Taking into Account the Scale-Population Factor Materials

It is proposed to use the phenomenology of the well-known «scale strength factor» to construct a hardware platform for probabilistic modeling of the interaction kinetics of necessary and sufficient conditions for the occurrence of epidemics or pandemics. Exponential-plastic models have been obtained that reflect the characteristic features of these mass-population phenomena, which provide the possibility of probabilistic forecasting of various scenarios of their course. Examples of calculations based on the proposed probabilistic model are given and their correspondence to real statistical data on the development of morbidity in different countries is shown.
Changemaking and English Language Learners (Els): Language, Content and Skill Development through Experiential Education

The idea of offering all children and youth an education that is experiential, student centered, engaging, and relevant to life is not a new concept (Dewey, 1938; Kolb, 1981). Preparing students with the competencies, skills, and character for full participation in the 21st century has become the vision of schools, educators, and organizations around the world (NEA, 2020; Geisinger, 2016; Trilling, B. &amp;amp; Fadel, C. 2009). Changemaker teachers, staff, and administrators believe in facilitating children and youth development as citizens for the 21st century. These educators also guide them as agents of change who empathize with others and solve real life problems for the greater good. These children and youth are what Ashoka calls &amp;ldquo;Changemakers&amp;rdquo;. (Ashoka, 2020). This article explores the potential for facilitating the development of English Learners (Els) as Changemakers by using effective Second Language Acquisition (SLA) approaches in combination with experiential approaches. The intent is to contribute a theoretical framework and curriculum ideas for effective practice to help English language learners develop language, access content, and develop 21st century skills as Changemaker attributes.
Cells Detection and Segmentation in ER-IHC Stained Breast Histopathology Images

In this paper, we present our recent work on cells detection and segmentation in estrogen receptor immunohistochemistry (ER-IHC)-stained breast carcinoma images. The proposed cell detection and segmentation is very useful in the predictive scoring of hormone receptor status in ER-IHC stained whole-slide images, which helps pathologists to decide whether a patient should be offered hormonal therapy or other treatments. The proposed method is based on deep convolutional neural network, followed by watershed-based post-processing. The cell detection results are compared and evaluated objectively against the ground truth provided by our collaborating pathologists. The cell segmentation results, on the other hand, are evaluated visually by overlaying the computer segmented boundaries on the ER-IHC images for comparison. The automated cell detection algorithm recorded precision and recall rates of 95% and 91% respectively. The very promising performances for both the detection and segmentation paves the way for an automated system for hormone receptor scoring in ER-IHC stained whole-slide breast carcinoma images.
IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language

Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C).This paper presents the results our classifiers obtained for the English language in the 3 subtasks.The classifiers used by us were BERT and BiLSTM.On the test set, our BERT classifier obtained a macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained a macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers.We conjecture that the presence of a few misleading instances in the dataset is affecting the performance of the classifiers.Our analysis also discusses the need for temporal context and world knowledge to determine the offensiveness of a few comments.
CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights Based Counterfactual Detection

In this paper, we describe an approach for modelling causal reasoning in natural language by detecting counterfactuals in text using multi-head self-attention weights.We use pre-trained transformer models to extract contextual embeddings and self-attention weights from the text.We show the use of convolutional layers to extract task-specific features from these self-attention weights.Further, we describe a fine-tuning approach with a common base model for knowledge sharing between the two closely related sub-tasks for counterfactual detection.We analyze and compare the performance of various transformer models in our experiments.Finally, we perform a qualitative analysis with the multi-head self-attention weights to interpret our models' dynamics.
The Construction and Implementation of Online Teaching Platform in Colleges and Universities

After the Spring Festival in 2020, the teaching work across the country has been greatly affected by the COVID-19 epidemic.How to solve the problem of "suspended class, ongoing learning" is in front of the teaching staff.Meanwhile, with the investment and construction in the field of education in China, informatization has been widely applied and can be used as a variety of online teaching tools.According to the actual needs of online teaching of "Introduction to UAV System" course, combined with the characteristics of this course, this paper first constructed the teaching platform of "DingTalk + WeChat + E-mail".At the same time, focusing on the teaching process of "online lesson preparation + online teaching + online homework + online test", the specific characteristics and detailed practices of pre-class preparation, classroom organization, after-class consolidation and unit test are expounded in depth.In the teaching organization, I organized online teaching meetings for many times, listened to students' opinions and Suggestions, and organized online examinations for students after the end of the chapter, and analyzed the test results.Finally, the author points out the problems in online teaching, such as insufficient accumulation, inadequate response and inadequate supervision, and puts forward some countermeasures and Suggestions.
Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning

The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility.In most applications, users are not able to provide the correct answer to the system, but they are able to provide binary (correct, incorrect) feedback.In this paper we propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback.We perform simulated experiments on document classification (for development) and Conversational Question Answering datasets like QuAC and DoQA, where binary user feedback is derived from gold annotations.The results show that our method is able to improve over the initial supervised system, getting close to a fully-supervised system that has access to the same labeled examples in in-domain experiments (QuAC), and even matching in out-of-domain experiments (DoQA).Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.
SOCIO-AFFECTIVE STRATEGIES EMPLOYED BY STUDENTS IN SPEAKING CLASS

This research is aiming at scrutinizing the socio-affective strategies used by students in speaking class at SMP Ma’arif 1 Ponorogo. This research employed qualitative approach, and the design was case study was conducted on the Eighth Grade B class of SMP Ma’arif 1 Ponorogo, and focused on the description of using socio-affective strategies in teaching speaking at the Eighth Grade B class of SMP Ma’arif 1 Ponorogo. The data in this research is collected through interview, observation, reviewing the documents. The data were analyzed by using data Mattew B Miles and Michael Hubberman’s view of data reduction, data display and drawing conclusion. The result of this research revealed that (1) the implementation of socio-affective starategies is devided into three activities namely; Pre activities, main activities, and the last activities. In pre activities there are two activities, greeting and apperception. In main activities, teacher applied “socio-affective stratgies. The findings that. In the last activities the teacher gave an evaluation and motivation. In evaluation activity a teaching and learning process need to be evaluated. (2) Those the role of the teacher is as follow in supporting the implementation of socio-affective strategies at Eighth Grade B Class of SMP Maarif 1 Ponorogo is very important because as a teacher holds an important position in the learning process. The roles teachers play in English Language Teaching (ELT) field are mostly of an assessor, organizer, facilitator, prompter, motivator, participant, monitor, model, etc.
Analyzing partially paired data: when can the unpaired portion(s) be safely ignored?

Partially paired data, either with incompleteness in one or both arms, are common in practice. For testing equality of means of two arms, practitioners often use only the portion of data with complete pairs and perform paired tests. Although such tests (referred as ‘naive paired tests’) are legitimate, their powers might be low as only partial data are utilized. The recently proposed ‘P-value pooling methods’, based on combining P-values from two tests, use all data, have reasonable type-I error control and good power property. While it is generally believed that ‘P-value pooling methods’ are superior to ‘naive paired tests’ in terms of power as the former use more data than the latter, no detailed power comparison has been done. This paper aims to compare powers of ‘naive paired tests’ and ‘P-value pooling methods’ analytically and our findings are counterintuitive, i.e. the ‘P-value pooling methods’ do not always outperform the naive paired tests in terms of power. Based on these results, we present guidance on how to select the best test for testing equality of means with partially paired data.
Digital university: rethinking the model framework within stakeholder theory

The COVID-19 pandemic, which triggered an avalanche-like transition of universities to a remote work format, not only identified new conditions for organizing pedagogical and administrative processes in universities, but also updated the topic of digital technologies in research projects and publications. The framework of the “digital university” model, for the implementation of which the state provides subsidies, consists of four blocks: management systems based on data; digital educational technologies; individual educational trajectories; competence of the digital economy. In general, the development of a framework for a digital university model is aimed at solving specific problems that are fully justified, and their solution seems to be extremely necessary, but in a sense they can represent only the first stage in the development of a digital university as a phenomenon of a new era. The introduction of technological innovation into the ritualized practices of universities is, in fact, a response to past challenges. To rethink the framework of the model, its enrichment and refinement, the theory of stakeholders (stakeholders) was used. The main stakeholders are: the state; University management; administrative staff; teaching staff; researchers and laboratory assistants; educational support staff; technical staff; students; parents and legal representatives of students; applicants; parents and legal representatives of applicants; foreign universities and their representatives in the university administration; foreign faculty; foreign students, graduate students and doctoral students; employers; investors; mass media (primarily regional); public organizations. The needs of stakeholders are considered, as well as possible digital solutions. The article substantiates that the traditional use of a digital device without the actual use of innovative digital technologies does not make higher education digital in the literal sense of the word.
Krylov–Bogolyubov averaging

We present the modified approach to the classical Bogolyubov-Krylov averaging, developed recently for the purpose of PDEs. It allows to treat Lipschitz perturbations of linear systems with pure imaginary spectrum and may be generalized to treat PDEs with small nonlinearities.
Promoting Interest in Learning YorÃ¹bÃ¡ Language Using Mobile Game

This paper describes acute areas in which technology plays a role in language and culture revitalization. It was discovered that in order for people to learn a new language, they must express interest in that language. This work presents a new way of arousing the interest of people in learning YorÃ¹bÃ¡ language through the use of mobile game thereby promoting and revitalizing YorÃ¹bÃ¡ language and culture. The mobile application was evaluated using questionnaire to selected participants who have the mobile game developed installed on their phones and explored the application, and then rated based on some criteria such as extensibility, ease of use and user interest in learning YorÃ¹bÃ¡ Language after playing the game. The results showed that 76% of respondents rated the game ease of use as above average, 70% and 90% of the respondents rated the extensibility of the game and interest in learning YorÃ¹bÃ¡ after playing game above average respectively. This technology-based application will serve as an interesting and fun-filled approach of getting people to express interest in learning native indigenous language individually and as a group.
Understanding Unnatural Questions Improves Reasoning over Text

Complex question answering (CQA) over raw text is a challenging task.A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions and the interpreter then executes these actions on the raw text.Learning an effective CQA model requires large amounts of human-annotated data, consisting of the ground-truth sequence of reasoning actions, which is time-consuming and expensive to collect at scale.In this paper, we address the challenge of learning a high-quality programmer (parser) by projecting natural human-generated questions into unnatural machinegenerated questions which are more convenient to parse.We firstly generate synthetic (question, action sequence) pairs by a data generator, and train a semantic parser that associates synthetic questions with their corresponding action sequences.To capture the diversity when applied to natural questions, we learn a projection model to map natural questions into their most similar unnatural questions for which the parser can work well.Without any natural training data, our projection model provides high-quality action sequences for the CQA task.Experimental results show that the QA model trained exclusively with synthetic data outperforms its state-of-the-art counterpart trained on human-labeled data.