Reviewerly/exHarmony huggingface dataset:
The use of Global Biodiversity Information Facility (GBIF)-mediated data in publications written in Chinese

With the development of biodiversity informatics, more and more data repositories have emerged, providing massive stores of open scientific data. The use of open data is increasingly valuable for scientific research and related decision making but little is known about which topics are addressed, especially in non-English publications. Further, how this data should be cited has been called into question. The Global Biodiversity Information Facility (GBIF) is the world’s largest and most widely-used open data access platform on the occurrence of species in nature. Here, we analyze the use of GBIF-mediated data in Chinese language publications. From 623 publications containing the word “GBIF”, we obtained 324 peer-reviewed publications. Among these publications, there were 237 journal papers, 17 doctoral theses, 66 master theses, and 4 conference papers. The publications came from 114 journals and 49 research institutes and universities. We found that the most common topics addressed by these publications were species distribution, climate change, biological invasion and medicinal plants. For data citation, multiple forms coexist. 295 publications (90% of publications) only cited the name of “GBIF” in the main text, without a Digital Object Identifier (DOI). Among the 29 publications that cited GBIF in the references, 17 cited the GBIF website, 8 publications listed the URL of a species page in GBIF, and only 4 publications cited a DOI, which is what GBIF recommends. Based on a discussion on the use of GBIF-mediated data and the resulting data citation issues, this study makes three suggestions to improve data citation practice, which are applicable to data citation practice in other non-English publications.
Explicitation in Neural Machine Translation

This paper is concerned with the following question: to what extent does neural machine translation (NMT) – a relatively new approach to machine translation (MT), which can draw on richer contextual information than previous MT architectures – perform explicitation shifts in translation and how are these shifts realised in linguistic terms? In order to answer this question, the paper attempts to identify instances of explicitation in the machine-translated version of a research report on carbon dioxide capture and storage. The machine-translated text was created using the publicly available generic NMT system DeepL . The human translation of the research report was analysed in a prior research project for instances of explicitation and implicitation (Krüger 2015). After a brief quantitative di scussion of the frequency and distribution of explicitation shifts identiﬁed in the DeepL output as compared to the shifts identiﬁed in the human translation of the research report, the paper analyses in detail several examples in which DeepL performed explicitation shifts of various kinds. The quantitative and qualitative analyses are intended to yield a tentative picture of the capacity of state-of-the art neural machine translation systems to perform explicitation shifts in translation. As explicitation is understood in this article as an indicator of translational text–context interaction, the explicitation performance of NMT can – to some extent – be taken to be indicative of the “contextual awareness” of this new MT architecture.
NAYEL at SemEval-2020 Task 12: TF/IDF-Based Approach for Automatic Offensive Language Detection in Arabic Tweets

In this paper, we present the system submitted to "SemEval-2020 Task 12".The proposed system aims at automatically identify the Offensive Language in Arabic Tweets.A machine learning based approach has been used to design our system.We implemented a linear classifier with Stochastic Gradient Descent (SGD) as optimization algorithm.Our model reported 84.20%, 81.82% f1-score on development set and test set respectively.The best performed system and the system in the last rank reported 90.17% and 44.51% f1-score on test set respectively.
Wireless Head Gesture Controlled Robotic Wheel Chair for Physically Disable Persons

A robotic wheelchair is assumed to be capable of doing tasks like navigation, obstacle detection, etc. using sensors and intelligence.The initial part of the work was development of a cap controlled wheelchair to test and verify the gesture operation.Following that, a real time operating wheelchair was developed consisting of mode changing option between joystick control mode and head gesture control mode as per as the user's requirement.The wheelchair consists of MPU6050 sensor, joystick module, RF module, battery, dc motor, toggle switch and Arduino.The movement of the head is detected by MPU6050 and the signal is transmitted to the microcontroller.Then the signal is processed by controller and motion of wheelchair is enabled for navigation.The wheelchair was capable of moving left, right, forward and backward direction.The speed of the wheelchair was 4.8 km/h when tested.Design objective of the wheelchair included cost effectiveness without compromising safety, flexibility and mobility for the users.
Token Drop mechanism for Neural Machine Translation

Neural machine translation with millions of parameters is vulnerable to unfamiliar inputs.We propose Token Drop to improve generalization and avoid overfitting for the NMT model.Similar to word dropout, whereas we replace dropped token with a special token instead of setting zero to words.We further introduce two self-supervised objectives: Replaced Token Detection and Dropped Token Prediction.Our method aims to force model generating target translation with less information, in this way the model can learn textual representation better.Experiments on Chinese-English and English-Romanian benchmark demonstrate the effectiveness of our approach and our model achieves significant improvements over a strong Transformer baseline 1 .
News Editorials: Towards Summarizing Long Argumentative Texts

The automatic summarization of argumentative texts has hardly been explored.This paper takes a further step in this direction, targeting news editorials, i.e., opinionated articles with a well-defined argumentation structure.With Webis-EditorialSum-2020, we present a corpus of 1330 carefully curated summaries for 266 news editorials.We evaluate these summaries based on a tailored annotation scheme, where a high-quality summary is expected to be thesis-indicative, persuasive, reasonable, concise, and self-contained.Our corpus contains at least three high-quality summaries for about 90% of the editorials, rendering it a valuable resource for the development and evaluation of summarization technology for long argumentative texts.We further report details of both, an in-depth corpus analysis, and the evaluation of two extractive summarization models.1. We define an annotation scheme tailored to editorial summaries, requiring a high-quality summary to be thesis-indicative, persuasive, reasonable, concise, and self-contained.2. We create a corpus of 1330 summaries for 266 news editorials (five summaries each), manually acquired and evaluated by operationalizing the proposed annotation scheme.3. We analyze each summary of the corpus with respect to content overlap, distribution of evidence types, adherence to the editorial structure, and annotator indications regarding summary quality.
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is a challenging topic in computer vision. It is a problem characterized by large intra-class differences and subtle inter-class differences. In this paper, we tackle this problem in a weakly supervised manner, where neural network models are getting fed with additional data using a data augmentation technique through a visual attention mechanism. We perform domain adaptive knowledge transfer via fine-tuning on our base network model. We perform our experiment on six challenging and commonly used FGVC datasets, and we show competitive improvement on accuracies by using attention-aware data augmentation techniques with features derived from deep learning model InceptionV3, pre-trained on large scale datasets. Our method outperforms competitor methods on multiple FGVC datasets and showed competitive results on other datasets. Experimental studies show that transfer learning from large scale datasets can be utilized effectively with visual attention based data augmentation, which can obtain state-of-the-art results on several FGVC datasets. We present a comprehensive analysis of our experiments. Our method achieves state-of-the-art results in multiple fine-grained classification datasets including challenging CUB200-2011 bird, Flowers-102, and FGVC-Aircrafts datasets.
Impact of Inclusive Leadership on Project Success

The objective of the present study is to investigate the impact of inclusive leadership on project success with the mediating role of innovative work behavior and moderating role of knowledge sharing. Data were collected from information technology projects in Pakistan. After validating the proposed model, path analysis was conducted. The findings revealed that inclusive leadership is significantly related with innovative work behavior and project success. Further, the findings showed that innovative work mediates the relationship between inclusive leadership and project success. Moreover, knowledge sharing doesn't moderate the relationship between inclusive leadership and innovative work behavior. The theoretical and practical implications and limitations of the study are discussed in detail.
Probabilistic Object Detection via Deep Ensembles

Probabilistic object detection is the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detection. Measuring uncertainty is important in robotic applications where actions based on erroneous, but high confidence visual detections, can lead to catastrophic consequences. We introduce an approach that employs deep ensembles for estimating predictive uncertainty. The proposed framework achieved 4th place in the ECCV 2020 ACRV Robotic Vision Challenge on Probabilistic Object Detection.
RETRACTED: Practical research on college English vocabulary teaching with mobile technology

With the development of mobile technology, the intellectualization and intellectualization of mobile learning technology have greatly expanded the dimension of time and space of learning, and become a useful supplement to the traditional teaching mode. Taking College English vocabulary teaching as an example, this paper studies college English vocabulary teaching under the support of mobile technology, in order to provide new ways and methods to meet the individual learning needs of different learners. This paper designs College English vocabulary teaching based on mobile technology and puts forward a basic framework of mobile learning for college students’ English vocabulary learning. In this paper, English vocabulary technology is applied to college English vocabulary teaching. Through experiments, it promotes college students’ English vocabulary memory level, vocabulary use level and interest in English learning, respectively, to verify the effectiveness of College English vocabulary teaching mode based on mobile technology. The experimental results show that the flexibility of mobile learning can take into account the different learning needs of students at different levels of the same group, categorize and categorize the individual needs of students, and adjust different learning content and learning difficulty ladder to a certain extent. The innovation of this paper is to fully combine mobile technology with college English vocabulary teaching, solve practical application problems, and improve the application value of mobile technology in college teaching.
An Improved Model for Breast Cancer Classification Using Random Forest with Grid Search Method

An unhealthy lifestyle today is becoming the main cause of increase in disease cases among humans. One such disease has got a rapid increase among women that is breast cancer. Benign and malignant are two in which breast cancer is mainly classified. Benign indicates non-cancerous which means spreading through other parts of the body does not happen, whereas malignant is cancerous and therefore it can spread through different parts of the body but this can be treated if detection is done at the early stages. In this paper, for breast cancer classification into two categories malignant and benign, we are going to use machine learning algorithms and develop an improvised model for RF using grid search method . Here, we are using the Wisconsin breast cancer openly available data set and will apply random forest algorithm using Python, train the data set for classification and analyze their outcomes subsequently.
Agility of the Post COVID-19 Strategic Plan on Distance Learning at Cadi Ayyad University. An Opportunity Towards a Total Digital Transformation of the University

In early March 2020, Morocco, like most countries, decided to impose restrictive measures to limit the spread of the SARS-CoV-2 virus. Among these measures, all schools and universities, including Cadi Ayyad University (UCA), have been closed so far, forcing students to take distance learning courses. Previously, students and professors were reluctant to use online platforms, but the new closure situation has forced everyone to experiment with these strategies. 14 UCA higher education institutions have joined forces to offer students e-learning as an alternative to overcome university closures and ensure that learning never stops. 15852 educational resources have been produced to date, covering all UCA programs, with about 70% of the resources produced since the beginning of the pandemic. For this first stage, the university has recovered the bulk of the educational needs. The second phase of the non-disruptive learning process, consists of strengthening interactive online learning with learners to motivate them to prepare for their exams, which have been postponed to September 2020. The last stage of the distance learning process is devoted to the redesign of post-COVID-19 education, taking into account the challenges and experiences gained in the first and second stages. The objective of this paper is to analyze the measures taken to ensure non-disruptive learning in the context of COVID-19. This study will help higher education institutions and the Ministry of Higher Education to compare the different experiences in the country, as well as in other faculties around the world.
Abstractive Text Summarization Using Artificial Intelligence

Text summarization is the process of creating concise summary of text. There are two main approaches to summarization namely extractive and abstractive method. Most of the system summaries use extractive method. Amongst few abstractive models available there are two models namely sequence to sequence and LSTM bidirectional model. In this work,we compare the performance of above two models using ROUGE and BLEU score on Amazon reviews and CNN news dataset.
Blockchain Disruption and Decentralized Finance: The Rise of Decentralized Business Models

Block-chain technology can reduce transaction costs, generate distributed trust, and empower decentralized platforms, potentially becoming a new foundation for decentralized business models. In the financial industry, block-chain technology allows for the rise of decentralized financial services, which tend to be more decentralized, innovative, inter-operable, border-less, and transparent. Empowered by block-chain technology, decentralized financial services have the potential to broaden financial inclusion, facilitate open access, encourage permission-less innovation, and create new opportunities for entrepreneurs and innovators. In this article, we assess the benefits of decentralized finance, identify existing business models, and evaluate potential challenges and limits. As a new area of financial technology, decentralized finance may reshape the structure of modern finance and create a new landscape for entrepreneurship and innovation, showcasing the promises and challenges of decentralized business models.
Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.
Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing

Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development footprints, the manuscript describes three development stages and key technologies of cognitive computing through burst study and step-by-step clustering. Finally, this study proposed influencing framework of cognitive computing on TEL and some research trends. This work provides an advanced background of TEL and a systemic review of cognitive computing, contributing to theory development and application of cognitive computing in TEL.
Classic McEliece Implementation with Low Memory Footprint

The Classic McEliece cryptosystem is one of the most trusted quantum-resistant cryptographic schemes. Deploying it in practical applications, however, is challenging due to the size of its public key. In this work, we bridge this gap. We present an implementation of Classic McEliece on an ARM Cortex-M4 processor, optimized to overcome memory constraints. To this end, we present an algorithm to retrieve the public key ad-hoc. This reduces memory and storage requirements and enables the generation of larger key pairs on the device. To further improve the implementation, we perform the public key operation by streaming the key to avoid storing it as a whole. This additionally reduces the risk of denial of service attacks. Finally, we use these results to implement and run TLS on the embedded device.
On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method

In this paper, a novel device identification method is proposed to improve the security of Visible Light Communication (VLC) in 5G networks. This method extracts the fingerprints of Light-Emitting Diodes (LEDs) to identify the devices accessing the 5G network. The extraction and identification mechanisms have been investigated from the theoretical perspective as well as verified experimentally. Moreover, a demonstration in a practical indoor VLC-based 5G network has been carried out to evaluate the feasibility and accuracy of this approach. The fingerprints of four identical white LEDs were extracted successfully from the received 5G NR (New Radio) signals. To perform identification, four types of machine-learning-based classifiers were employed and the resulting accuracy was up to 97.1%.
A Systematic Literature Review of Graphical Password Schemes

Graphical passwords are an alternative to traditional alphanumeric passwords and can similarly be used to secure online accounts. The widely used alphanumeric passwords have memorability issues and users often find it difficult to memorize a large number of unique passwords. Since 1996, researchers have implemented different graphical password schemes (GPSs) to address such security and usability issues. There are a wide variety of such schemes available. To initiate a study in this domain, it is necessary for a researcher to have a good understanding of the existing research. There are a number of existing review articles, but no systematic literature review (SLR). Additionally, the existing reviews have not covered recent papers. This paper aims to fill in these gaps by reviewing existing GPSs, and intends to address their contributions, limitations, the contexts in which they are used, and the relevant algorithms/techniques. To this end, we conducted an SLR of empirical studies on a number of GPSs published from 1996 to 2019. This article also identifies the security threats that the reviewed schemes are resilient against. A number of schemes have been found to have greater resiliency against different attacks, but not a single scheme is completely resistant to all known attacks.
RETRACTED: Development and Innovation of Music Course Teaching Mode Based on Big Data

Abstract The development direction of online teaching is personalized and customized teaching based on user needs. Applying big data technology to the online network teaching platform can effectively solve the problems of the huge and chaotic number of teaching resources, the difficulty of user selection, the weak management of the network learning process, the inefficient use of teaching resources, and the lack of user role sense. The purpose of this article is to explore the development and innovation of music curriculum teaching models based on big data. This paper studies the modern network music teaching platform based on big data, realizes the user demand discovery of music teaching platform and optimizes the push of music teaching resources, and realizes the full use of music teaching resources. Starting from five dimensions of user attributes, learning roles, learning process, learning objects and learning tasks, this paper analyzes the user portraits of modern online music teaching platforms based on big data and expresses them based on ontology technology. This article is based on current scholars’ theoretical foundation and practical application research on education big data and smart classroom model, through the previous literature review and analysis, research on the popularization of smart classroom model and actual observation of existing smart classroom models. For the verification of the practical effects of music teaching cases using this model, the author used questionnaire and interview methods to investigate teachers and students, and conducted investigations on teachers from basic information, classroom application and effects, and classroom satisfaction.. Research shows that 29% of teachers are very satisfied with the application of this model, and 16% of teachers are generally satisfied. It can be known that most teachers are quite satisfied with the “smart classroom” teaching model under the big data environment.