Reviewerly/exHarmony huggingface dataset:
Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

Aim: The aim of this research is to evaluate the factors that affect the adoption intention of AI and ML in the context of Jordan’s retail industry&#x0D; Method: For this research paper, primary data was collected with the help of surveying different retail companies that are operational in Jordan with a sample of 400 participants. The survey questionnaire was based on a Likert scale where five points ranging from strongly agree to strongly disagree were provided to the participants. Structural Equation Modelling (SEM) used to analyse the impact and significance of the different factors on the adoption of AI and ML in Jordanian retail sector.&#x0D; Results: It has been concluded from this research paper that communication, government regulations, market structure, and technological infrastructure are important factors that influence the adoption of AI and ML in the retail industry of Jordan. However, the results of this research have pointed out that managerial support and vendor relationship do not have a significant influence on the adoption of AI and ML.&#x0D; Limitations: The scope of the research is restricted to the context of the retail industry only. This research has been carried out in the context of Jordan thus it cannot be applied on to other geographical backgrounds. Due to the time and scope limitations, there are restricted factors considered in the framework.
Earthquake Damage Prediction Using Random Forest and Gradient Boosting Classifier

Earthquake is a major natural disaster that causes casualties in millions and leaving many more in trauma.Analyzing the consequences of such consequences gives one a better stand-in for potential catastrophe occurrences.It is important to establish a methodology that can assist in forecasting these earthquakes, as they can help prevent the severity of the damage.This paper discusses a machine learning model that can predict the damage grade severity caused by lifethreatening earthquake that hit Nepal in the year 2015.The dataset is derived from the live competition hosted by Driven Data.The data was collected through the surveys conducted by the Kathmandu Living Labs and the Central Bureau of Statistics, which operates under the National Planning Commission Secretariat of Nepal.To accomplish the defined goal, we used the Random Forest Classifier and Gradient Boosting Classifier.The Random Forest Classifier algorithm demonstrated in this study was outperformed by the Gradient Boosting Classifier.With necessary parameter tuning using the Random Forest Classifier, the F1-Score achieved was 72.95%.The next technique was to perform winsorization on some attributes to handle outliers which improved the F1-score to 74.33% along with gradient boosting classifier.The last techniqueinvolved only hyper-parameter tuning with gradient boosting classifier achieved the best F1-Score of 74.42%.
Gated Recurrent Networks for Video Super Resolution

Despite the success of Recurrent Neural Networks in tasks involving temporal video processing, few works in Video Super-Resolution (VSR) have employed them. In this work we propose a new Gated Recurrent Convolutional Neural Network for VSR adapting some of the key components of a Gated Recurrent Unit. Our model employs a deformable attention module to align the features calculated at the previous time step with the ones in the current step and then uses a gated operation to combine them. This allows our model to effectively reuse previously calculated features and exploit longer temporal relationships between frames without the need of explicit motion compensation. The experimental validation shows that our approach outperforms current VSR learning based models in terms of perceptual quality and temporal consistency.
The Effect of Using Artificial Intelligence on the Quality of Decision-Making in Various Organizations: A Critical Survey Study

Over the past few years Artificial Intelligence has started changing the dynamics of the business world.Significantly the interactions of Artificial intelligence with the Organizations performance is expected to improve the level of global competency for the company.AI is expected to help the organization understand its employees, customer, target audience or potential target audience.AI could make life better by making things more convenient and personalized, organizations will have the ability to personalize and do things at an upper scale, in a way that is currently limited by humans-only.Organizations have a series of requirements and challenges everyday on; what to do, How to do, When to do, Whom should do, The cost that is involved, The time that is involved, The resources that are required, The current market trend, The current state of the economy, The change in the preference of consumers etc.All these are expected to be analyzed by Humans based on past data, with a constrained duration of time it could be challenging and difficult for both the organization and the employees.Each of these situations is an instance for where artificial intelligence could be applied.AI could be implied in every department of an Organization.The research design followed is descriptive in nature.The target sample size on which the study was conducted and analyzed is 100.Bar graphs and pie-charts were used as research tool to present the findings.The key players involved in this study were Organization Employees.
NTUAAILS at SemEval-2020 Task 11: Propaganda Detection and Classification with biLSTMs and ELMo

This paper describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles.This task comprises of two different sub-tasks, namely A: Span Identification (SI), B: Technique Classification (TC).The goal for the SI sub-task is to identify specific fragments, in a given plain text, containing at least one propaganda technique.The TC sub-task aims to identify the applied propaganda technique in a given text fragment.A different model was trained for each sub-task.Our best performing system for the SI task consists of pre-trained ELMo word embeddings followed by residual bidirectional LSTM network.For the TC sub-task pre-trained word embeddings from GloVe fed to a bidirectional LSTM neural network.The models achieved rank 28 among 36 teams with F1 score of 0.335 and rank 25 among 31 teams with 0.463 F1 score for SI and TC sub-tasks respectively.Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand.
Correlation Analysis of Airline Customer Satisfaction using Random Forest with Deep Neural Network and Support Vector Machine Model

There are many airline customer evaluation data, but they are insufficient in terms of predicting customer satisfaction in practice. In particular, they are generally insufficient in case of verification of data value and development of a customer satisfaction prediction model based on customer evaluation data. In this paper, airline customer satisfaction analysis is conducted through an experiment of correlation analysis between customer evaluation data provided by Google's Kaggle. The difference in accuracy varied according to the three types, which are the overall variables, the top 4 and top 8 variables with the highest correlation. To build an airline customer satisfaction prediction model, they are applied to three classification algorithms of Random Forest, SVM, DNN and conduct a classification experiment. They are divided into training data and verification data by 7:3. As a result, the DNN model showed the lowest accuracy at 86.4%, while the SVM model at 89% and the Random Forest model at 95.7% showed the highest accuracy and performance.
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?

We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs).With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences.
YNU-oxz at SemEval-2020 Task 4: Commonsense Validation Using BERT with Bidirectional GRU

This paper describes the system and results of our team participated in SemEval-2020 Task4: Commonsense Validation and Explanation (ComVE), which aim to distinguish meaningful natural language statements from unreasonable natural language statements.This task contains three subtasks: Subtask A-Validation, Subtask B-Explanation (Multi-Choice), and Subtask C-Explanation (Generation).In these three subtasks, we only participated in Subtask A, which aims to distinguish whether a given two natural language statements with similar wording are meaningful.To solve this problem, we proposed a method using a combination of BERT with the Bidirectional Gated Recurrent Unit (Bi-GRU).Our model achieved an accuracy of 0.836 in Subtask A (ranked 27/45).
A BERT-based Dual Embedding Model for Chinese Idiom Prediction

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional.The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank.We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms.Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context.We then match the embedding of each candidate idiom with the hidden representations of all the tokens in the context thorough context pooling.We further propose to use two separate idiom embeddings for the two kinds of matching.Experiments on a recently released Chinese idiom cloze test dataset show that our proposed method performs better than the existing state of the art.Ablation experiments also show that both context pooling and dual embedding contribute to the improvement of performance.
New machine learning based method for identifying objects in biomedical images obtained by photon counting detectors.

The article discusses a new approach to the problem of recognition, or rather the identification of objects by the shape of their intensity in images formed by photon counting detectors or those close to them. The main problem analyzed within the framework of the approach proposed is related to the quantitative estimation of similarity between the form of radiation intensity of the object in the image and the forms of intensity of previously observed objects (precedents), providing that all of them are given by the registered sets of discrete photocounts (~ photons). It is shown that when the intensity shape is approximated by a mixture of components of an exponential family, for the implementation of the approach proposed in this work a recurrent PM algorithm for estimating the parameters of the intensity shape from a finite sample of counts associated with radiation can be synthesized. The PM algorithm turns out to be a close analogue of the well-known K-means algorithm, which is one of the most popular in the field of machine (statistical) learning. The main features of the method and its implementation are illustrated by the example of applying the method to the problem of image fragments identification for the COVID ‒ CT ‒ Dataset database.
Supplemental Material: Provenance and tectonic setting of late Paleozoic sedimentary rocks from the Alxa Tectonic Belt (NW China): Implications for accretionary tectonics of the southern Central Asian Orogenic Belt

Whole-rock geochemical and zircon U-Pb-Hf data.
Academic Writing In The Modern University Curriculum

Today, Russian universities face the administrative demand for boosting research publication rates, which presupposes the acquisition of appropriate academic writing skills.The discipline of Academic Writing appeared in the curricula of separate Russian universities only in the last decade.Feedback from the teachers shows that there are still many questions that need to be answered to move the learning outcomes to a satisfactory extent.Problems arising with teaching academic writing range from the absence of academic writing tradition in the national education system to the lack of students' motivation for learning.Teachers confess they are stepping blindly in the darkness as the methodology of academic writing is still developing, and there are many unresolved questions.The matter is the discipline was borrowed from the Western and American education and needs to be adopted to the realities of Russian academic and social discourse.This proves to be a challenging task as the development of writing skills has lately been paid little attention to by our education system.Students meet serious difficulties on their way to the creation of academic texts following the norms of the international academic format and the requirements of scholarly journals.
Clustering with ε-Hyperballs Based Simplification of Fuzzy Rules to Support the Assessment of Fetal State

CardioTocoGraphy (CTG) is a fundamental procedure to support fetal diagnosis during pregnancy and labor, which involves registration and analysis of fetal heart rate and uterine contraction signals. As the fetal state assessment based on visual evaluation of the signal printouts is demanding, methods supporting the diagnostic decision are explored. To provide the qualitative assessment of the fetal condition, a fuzzy classifier can be applied. Its main advantages include the knowledge base (fuzzy rule base) linguistic interpretability. As fuzzy models involving fewer rules are easier to interpret, procedures of Rule Base Simplification (RBS) are the topic of research. This work describes the RBS method based on the fuzzy clustering with eps-hyperballs integrated with the evolutionary strategy framework. Its objective is to decrease a rule base size of zero order Takagi-Sugeno-Kang (TSK) fuzzy classifier while maintaining high CTG signals classification quality. The efficiency of the proposed RBS procedure was confirmed based on the evaluation of CTG signals from the benchmark CTU-UHB database.
Strategy Analysis of Network Information Technology in College English Micro-lesson Teaching

Modern network information technology has not only changed people's life, but also changed the pattern of education. With the help of network information technology, micro class, a new teaching mode in college English teaching, has been widely used. This paper mainly focuses on the following aspects: an overview of the connotation of micro class, the analysis of the relationship between network information technology and micro class teaching, and the strategy exploration of network information technology in college English micro class teaching, hoping to bring some new inspiration to the majority of college English educators and further improve the quality of college English teaching in China.
An Ensemble Based Offline Handwritten Signature Verification System

In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person. In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed system, three classifiers, viz, one unsupervised, viz. Fuzzy C-Means (FCM) and two supervised classifiers, viz. Naive Bayes (NB) and Support Vector Machine (SVM) are used as base classifiers. An attempt is made to compare the different approaches. We attempt both the categories of classification not only because both of them are applicable in this particular case but also with an objective of finding out which performs better. In most cases it is observed that Naive Bayes and Ensemble are comparable as they exhibit better performance than the other two. But among them, in most of the cases Ensemble classifier performs better than the Naive Bayes and consequently we have taken the Ensemble as a final classifier.
VibAuth: Enabling Accurate, Usable, and Deployable User Authentication by Leveraging Vibration

User authentication is essential to prevent the leakage of a user's sensitive information in mobile and IoT devices. Several attempts have been made for supporting accurate user authentication, but compromising usability and deployability. In this work, we propose a novel user authentication method, called VibAuth, that is accurate, usable, and deployable, especially by leveraging vibration. It simply generates vibrations by using wrist-wearable devices (e.g., smartwatches). The vibrations are transmitted through a user's hand and eventually captured by motion sensors (e.g., accelerometers). VibAuth then extracts user-specific features and authenticates users. Our experiments with a prototype of VibAuth demonstrate that we can support a reasonable accuracy in user authentication, by using only commodity and easily-accessible devices.
Cross-Lingual Document Retrieval with Smooth Learning

Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language.In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages.This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective.We further provide theoretical guarantee on the generalization error bound for the proposed framework.We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.
The Harmonia Corpus – A Dialogue Corpus for Automatic Analysis of Phonetic Convergence

The work presents the creation of a dialogue corpus for analysis and formal evaluation of phonetic convergence in spoken dialogues in human-human and human-machine communication, with the goal of comparing dialogue features at all levels of language use. The Harmonia corpus was created within a project which aims at (1) extracting phonetic features which can be mapped on a synthetic signal, (2) creating dialogue models applicable in a human-machine interaction and (3) practical evaluation of the convergence. For the corpus the following language groups were recorded: 16 pairs of Polish speakers speaking Polish (native speech), 10 pairs of German speakers speaking German (native speech), 12 pairs of German and Polish speakers speaking Polish (non-native speech), and 10 pairs of Polish and German speakers speaking German (non-native speech). The speakers could hear each other, but could not see each other. The recording scenarios consisted of controlled, neutral and expressive tasks and provided over 27 h of speech. This scenario combination is novel and promises to provide an empirical foundation for both linguistic and computational dialogue modelling of both face-to-face and man-machine dialogue.
Determinants of Cryptocurrency Market: An Analysis for Bitcoin, Ethereum and Ripple

One of the most important innovations brought by digitalization is crypto money known as virtual money.Cryptocurrencies, which have been discussed in recent years and especially a new portfolio for investors, are very popular.Bitcoin is the most well-known of these cryptographic systems, which do not depend on a central authority and have maximum reliability.The effects of various financial indicators on cryptoparas were examined in this study.The model includes a daily database in between April 3, 2018 to December 31, 2019.Initially stationarity is tested with unit root tests.Then cointegration and causality tests are employed.Impulse response is also implemented and analysed.
Analysis of the Naïve Bayes Method in Classifying Formalized Fish Images Using GLCM Feature Extraction

Fish is one of the foods that are high in protein so that many Indonesians consume fish as protein intake for health. Fish can be found in any waters including Indonesian marine waters, so that some of the Indonesian people work as fishermen. This causes the number of fish catches to increase and the fishermen have to sell the fish quickly in at least one day because the fish will rot easily if not consumed immediately. This has led some traders to cheat by mixing formaldehyde with fish that are not sold out. This action is very detrimental to consumers, so they must be more vigilant in choosing or buying fish on the market. One way for consumers to recognize formaldehyde fish is a technology that can distinguish fresh fish or formalin fish based on the image of the fish, Naive Bayes and GLCM (Gray Level Co-Occurrence Matrix) by using this method the accuracy of this system can reach up to 70%.
Exploiting a lexical resource for discourse connective disambiguation in German

In this paper we focus on connective identification and sense classification for explicit discourse relations in German, as two individual sub-tasks of the overarching Shallow Discourse Parsing task.We successively augment a purely-empirical approach based on contextualised embeddings with linguistic knowledge encoded in a connective lexicon.In this way, we improve over published results for connective identification, achieving a final F 1 -score of 87.93; and we introduce, to the best of our knowledge, first results for German sense classification, achieving an F 1 -score of 87.13.Our approach demonstrates that a connective lexicon can be a valuable resource for those languages that do not have a large PDTB-style-annotated coprus available.
Implementation Strategies for Improving the Teaching Quality of Foreign Language Courses

The teaching quality of foreign language courses is constrained by various factors. It is no easy task to improve the teaching quality or evaluate it in a reliable manner. To solve the problem, this paper attempts to design feasible strategies for improving the teaching quality of foreign language courses. Firstly, theoretical analysis was carried out on the development of foreign language teaching and the factors affecting the teaching quality, revealing the key obstacles to improving the teaching quality. On this basis, several strategies and paths were put forward to improve the quality of foreign language teaching. In addition, a novel evaluation indicator system and a multi-attribute fuzzy evaluation model were established for the teaching quality of foreign language courses. The research results provide strong supports to the teaching optimization of foreign language courses.
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation

The topic of this paper is neural multi-task training for text style transfer.We present an efficient method for neutral-to-style transformation using the transformer framework.We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus.We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora.Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models.We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.