Reviewerly/exHarmony huggingface dataset:
Impact of low visual contrast on posture, motion sickness, and performance in VR

Virtual Reality (VR) technology lets users train for high-stakes situations in the safety of a virtual environment (VE). Yet user movement through such an environment can cause postural instability and motion sickness. These issues are often attributed to how the brain processes visual self-motion information in VEs. Low-contrast conditions, like those caused by dense fog, are known to affect observers’ self-motion perception, but it is not clear how posture, motion sickness, and navigation performance are affected by this kind of visual environment degradation. Ongoing work using VR focuses on three aspects of this problem. First, we verify the effects in VR of low contrast on visual speed estimates. Second, we test how contrast reduction affects posture control, motion sickness, and performance during a VR navigation task. Third, we examine whether it is useful to augment low-contrast conditions with high-contrast visual aids in the environment.
Research Status and Countermeasures of Teaching Incentive Mechanism for University Teacher

Colleges and universities shoulder the important task of talent training.They are the main carrier of "establishing moralities and cultivating people", and it is also the key force for cultivating qualified builders and reliable successors.Under the background that colleges and universities are paying more and more attention to the construction of teachers and the improvement of teachers' teaching level, perfecting the teaching incentive mechanism of colleges and universities has theoretical and practical significance for improving teachers' initiative in teaching and improving the quality of talents.This article uses research methods of literature and logical analysis to analyze the existence of the phenomenon of "emphasizing scientific research and neglecting teaching" in colleges and universities, the insufficient teaching management model, and the imperfect teaching assessment system to analyze the existence of teaching incentives in Chinese colleges and universities.This article proposed measures such as reforming the professional title evaluation system, improving the teaching management model, and building a diversified teaching evaluation system for teachers in colleges and universities to improve the teaching level of teachers.
RETRACTED: Judgment and Coping Strategies of IAPE Based on Big Data Analysis

Abstract With the rapid development of Internet technology, we ushered in a new era of data. The advantages of Internet technology have made it widely and deeply applied in all fields of society. IAPE (IAPE) is a key issue in today’s education field, and it is urgent to solve the current IAPE problem in schools. This article combines the advantages of current Internet technology and data technology, and takes two universities as examples to explore the differences between IAPE and traditional IAPE in the era of big data, and analyze the advantages of the former. The experiment shows that the former can improve the quality of IAPE’s teaching work, and when compared with the two teaching modes, 31% of the participants in the experimental group are satisfied, and 36% of them are very satisfied, while only 25% of the control group were satisfied. Therefore, in addition to expounding the problems of IAPE in the era of big data, this article also conducts some research and discussion on the methods and strategies to deal with this problem on this basis.
Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection

The 2020 US Elections have been, more than ever before, characterized by social media campaigns and mutual accusations. We investigate in this paper if this manifests also in online communication of the supporters of the candidates Biden and Trump, by uttering hateful and offensive communication. We formulate an annotation task, in which we join the tasks of hateful/offensive speech detection and stance detection, and annotate 3000 Tweets from the campaign period, if they express a particular stance towards a candidate. Next to the established classes of favorable and against, we add mixed and neutral stances and also annotate if a candidate is mentioned with- out an opinion expression. Further, we an- notate if the tweet is written in an offensive style. This enables us to analyze if supporters of Joe Biden and the Democratic Party communicate differently than supporters of Donald Trump and the Republican Party. A BERT baseline classifier shows that the detection if somebody is a supporter of a candidate can be performed with high quality (.89 F1 for Trump and .91 F1 for Biden), while the detection that somebody expresses to be against a candidate is more challenging (.79 F1 and .64 F1, respectively). The automatic detection of hate/offensive speech remains challenging (with .53 F1). Our corpus is publicly available and constitutes a novel resource for computational modelling of offensive language under consideration of stances.
Using Recurrent Neural Networks to Judge Fitness in Musical Genetic Algorithms

We used a recurrent neural network as a fitness function for a genetic algorithm to generate monophonic solos. The genetic algorithm is based on GenJam as described in Biles (1994). We conducted training sessions with human participants in order to compare and quantify some of the differences between human-feedback and RNN fitness functions. We found that the RNNs can effectively play the role of human fitness feedback, but still suffer in many areas. Our results suggest that certain types of recurrent neural networks can address the issues with human feedback, and thus should be explored in future research.
Mid-wave infrared 3D sensor based on sequential thermal fringe projection for fast and accurate shape measurement of transparent objects

Structured light sensors based on diffuse reflection of projected patterns are widely used to measure the 3D shape of objects, e.g., in industry, medicine, or cultural heritage. Unfortunately, there exist many objects made of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent, which cannot be measured reliably by this measurement technique. In the last years, we presented a two-step method based on thermal pattern projection which allows the determination of the object surface of these uncooperative objects. In the first step, a multi-fringe thermal pattern is projected onto the measurement object. In the second step, a mid-wave infrared (MWIR) stereo camera records the thermal radiation that is absorbed and re-emitted by the object surface. This system allows us to measure transparent objects. However, the measurement time is with tens of seconds up to minutes quite long and the measurement accuracy should be improved. In this contribution, we present a projection principle of a fast sequentially scanning fringe leading to a significant reduction in measurement periods down to the second range or even below while increasing the measurement accuracy. The work includes a characterization of our MWIR 3D setup for both projection principles, the multifringe and sequential fringe one, regarding the measurement accuracy and speed. We show measurement results obtainted with both projection techniques for an object made of different material classes.
Improving of Educational Programs for the Formation of Information and Communication Competence of Teachers

An assessment of the readiness of Ukrainian teachers to provide online education has been made in the context of a world coronavirus pandemic. According to world experts, the pandemic has made radical and irreversible changes in modern ideas about learning, the role of teachers in the educational process, the set of competencies of teachers in the XXI century.
RETRACTED: Research on the Integration of ESP and Big Data in Modern College English Teaching

Abstract In the context of big data, it analyzes ESP-based college English teaching. First, it explains the big data theory in ESP, and then discusses the resource discovery service, information storage service, resource organization service, and information retrieval service of English teaching. On the basis of the service content of ESP in college English teaching, it expounds the self-learning mode of college English based on ESP theory from the aspects of one-stop resource service, subject knowledge service, information visualization service and smart service.
Active Forgery Detection in Grayscale Images using CRC-8-based Fragile Watermarking

This chapter presents an error-detection code-based active forgery detection mechanism which is realised using a block-level fragile. The fragile watermark embedding and forgery detection algorithms were tested on a large number of grayscale images. The outcome of this forgery detection scheme is formally a classification of the image blocks as tampered and pure which is further responsible for the localisation of the tampered regions when mapped on a map image. The chapter explains the essence of the various steps involved in this proposed fragile watermarking algorithm. It describes the pre-requisite topics of the work proposed here, including the concepts of cyclic redundancy check and logistic maps in brief. Watermarking scheme based on CRC-8 and logistic maps. The tamper detection and localisation algorithm is responsible for analysing the received image which was earlier known to have been watermarked. The performance of the watermark embedding procedure is analysed using parameters like Peak Signal to Noise Ratio, Structural Similarity Index and Entropy Measure.
Differentiated Teaching Based on Standardized Metrics Integrating Fuzzy Logic Type 2 Detection Theory: High School Case—PrepaTec, Mexico

Universities and high schools constantly research and develop educational methods to improve the student learning process. This paper presents a novel educational methodology for students to obtain better learning results in Spanish grammar through an intervention that fuses differentiated instructions, standardized evaluation, and a Fuzzy Logic Type 2 system. This successful case study in a Mexico City high school reports improved Spanish grammar outcomes after the intervention. Before then, 79% of the students did not obtain satisfactory scores in a national Spanish evaluation. This educational methodology uses a flexible intervention plan that could be replicated or tailored for various educational scenarios and topics using the same framework.
FORMATION OF FUTURE SOCIAL TEACHERS ' READINESS FOR PROFESSIONAL ACTIVITY

One of the important indicators of the development of modern Kazakhstan society is the training of professional teaching staff. Traditionally, the training of social educators focused on personal and social development of students, formation of their General culture, on the harmonization of the social sphere in educational institutions, the willingness to protect the life and health of pupils in the educational process. Professional activity of social pedagogue influenced by his personality, and how to be prepared for practice, will largely depend on social welfare, social immunity and stability of the functioning of contemporary society. The author of the article considers the formation of the readiness of future social teachers for professional activity as a socio-psychological phenomenon. Within the framework of this article, the concept of "readiness for professional activity of future social teachers" will be clarified and its main characteristics will be considered. The results of the research, theoretical conclusions and practical developments in the process of forming the professional readiness of social teachers can be used in the field of social pedagogy, psychology, pedagogy, and social work.
Reviewing Natural Language Processing Research

The reviewing procedure has been identified as one of the major issues in the current situation of the NLP field.While it is implicitly assumed that junior researcher learn reviewing during their PhD project, this might not always be the case.Additionally, with the growing NLP community and the efforts in the context of widening the NLP community, researchers joining the field might not have the opportunity to practise reviewing.This tutorial fills in this gap by providing an opportunity to learn the basics of reviewing.Also more experienced researchers might find this tutorial interesting to revise their reviewing procedure.
Structure-Preserving Diagram Operators

Theory operators are meta-level operators in logic that map theories to theories. Often these are functorial in that they can be extended to theory morphisms and possibly enjoy further valuable properties such as preserving inclusions. Thus, it is possible to apply them to entire diagrams at once, often in a way that the output diagram mimics any morphisms or modular structure of the input diagram. Our results are worked out using the Mmt language for structured theories instantiated with the logical framework LF for basic theories, but they can be easily transferred to other languages. We investigate how Mmt/LF diagram operators can be defined conveniently and give multiple examples.
Machine Learning for Software Engineering: a large-scale empirical study

Refactorings tackle the challenge of architectural degradation of object-oriented software projects by improving its internal structure without changing the behavior. Refactorings improve software quality and maintainability if applied correctly. However, identifying refactoring opportunities is a challenging problem for developers and researchers alike. In a recent work, machine learning algorithms have shown great potential to solve this problem. This thesis used RefactoringMiner to detect refactorings in open-source Java projects and computed code metrics by static analysis. We defined the refactoring opportunity detection problem as a binary classification problem and deployed machine learning algorithms to solve it. The models classify between a specific refactoring type and a stable class using the metrics as features. Multiple machine learning experiments were designed based on the results of an empirical study of the refactorings. For this work, we created the largest data set of refactorings in Java source code to date, including 92800 open-source projects from GitHub with a total of 33.67 million refactoring samples. The data analysis revealed that Class- and Package-Level refactorings occur most frequently in early development stages of a class, Method- and Variable-Level refactorings are applied uniformly during the development of a class. The machine learning models achieve high performance ranging from 80\% to 89\% total average accuracy for different configurations of the refactoring opportunity prediction problem on unseen projects. Selecting a high Stable Commit Threshold (K) improves the recall of the models significantly, but also strongly reduces the generalizability of the models. The Random Forest (RF) classifier shows great potential for the refactoring opportunity detection, it can adapt to various configurations of the problem, identifies a large variety of relevant metrics in the data and is able to distinguish different refactoring types. This work shows that for solving the refactoring opportunity detection problem a large variety of metrics is required, as a small set of metrics cannot represent the complexity of the problem.
Fingerprint Door Lock System with Temperature Sensor

Abstract This paper describes an advanced security system designed for the purpose of door locking. Keys can be stolen but not our identity. Also, overriding a fingerprint sensor door lock is really tough so the system guarantees high security. Technology is getting upgraded day by day and is accessible by any educated and technical person with minimum of computing skills. Now technology is nothing but advancement of the particular field of science irrespective of the fact whether it is related to computer or not. With the advancement of technology, our security is at constant threat to get away with this problem, we developed this security system completely based on biometric authentication. For implementing this project, we will be using the Arduino UNO, a fingerprint sensor, a servo motor, and some other hardware devices. Alongside the working of this fingerprint-based system, we shall also discuss about how our project can be helpful to the society during this epidemic.
Application of Data Mining Technology in the Analysis of E-commerce Emotional Law

Abstract The rapid development of the Internet has brought opportunities for enterprises. Data mining is becoming more and more important in e-commerce websites. In e-commerce, data mining is helpful to discover the development trend of the industry and help enterprises make correct decisions. This paper summarizes the current data mining methods of e-commerce, describes the application of e-commerce website in some data mining applications, classifies its objects, and analyzes the functions of network data mining. It is an important tool to support the experimental research and design of e-commerce system oriented to emotion. The technology of Intelligent Emotion Recognition Based on modern mathematical model and the simulation model of emotion oriented intelligent e-commerce system based on computational intelligence are proposed. For the future of e-commerce practice to provide a reference, especially the development and application of software. The progress in this field is outstanding.
AraFacts: The First Large Arabic Dataset of Naturally Occurring Claims

We introduce AraFacts, the first large Arabic dataset of naturally occurring claims collected from 5 Arabic fact-checking websites, e.g., Fatabyyano and Misbar, and covering claims since 2016. Our dataset consists of 6,121 claims along with their factual labels and additional metadata, such as fact-checking article content, topical category, and links to posts or Web pages spreading the claim. Since the data is obtained from various fact-checking websites, we standardize the original claim labels to provide a unified label rating for all claims. Moreover, we provide revealing dataset statistics and motivate its use by suggesting possible research applications. The dataset is made publicly available for the research community.
Robot autonomy in complex environments

The self-driving car industry has made great autonomy advances, but mostly for well-structured and highly predictable environments. In complex militarily-relevant settings, robotic vehicles have not demonstrated operationally relevant speed and aren’t autonomously reliable. While vehicle platforms that can handle difficult terrain exist, their autonomy algorithms and software often can’t process and respond to changing situations well enough to maintain necessary speeds and keep up with soldiers on a mission. DARPA’s Robotic Autonomy in Complex Environments with Resiliency (RACER) program aims to make sure algorithms aren’t the limiting part of the system and that autonomous combat vehicles can meet or exceed soldier driving abilities. RACER will demonstrate game-changing autonomous UGV mobility, focused on speed and resiliency, using a combination of simulation and advanced platforms. It tests algorithms in the field at DARPA-hosted experiments across the country on a variety of terrain. The program provides UGV platforms that research teams can use to develop autonomous software capabilities through repeated cycles of tests on unstructured off-road landscapes. Goals include not only autonomy algorithms, but also creation of simulation-based approaches and environments that will support rapid advancement of self-driving capabilities for future UGVs.
Evaluating the Multiple Choice Questions of Higher Secondary School English through Item Analysis

Testing and evaluation are important part of education system. The educators are exploring various methods to elevate the validity and reliability of the syllabus and materials that can make authentic materials for the learners. Considering these aspects the researchers intended to evaluate the difficulty index of the multiple choice questions of intermediate English part 1 compulsory. The core ideology behind this research is to provide authentic and valid learning materials than is evaluated through an authentic process. For the said purpose the whole Punjab public sector colleges were taken as a population where 2511 learners from 9 different cities of Punjab were chosen through convenient sampling methods. Each learner was provided with a book of MCQs containing 3087 MCQs for attempting it. After completion the books were collected and the results were obtained. The outcomes of this study clearly suggested that all these MCQs are varied in terms of their difficulty level. The item analysis process segregated the difficult average and easy MCQsand evaluated the intermediate MCQs for the Pakistani learners. This research can be helpful for the local learners for better comprehension of MCQ based questions of intermediate English part 1.
KEOPS: Knowledge ExtractOr Pipeline System

The KEOPS platform applies text mining approaches (e.g. classification, terminology and named entity extraction) to generate knowledge about each text and group of texts extracted from documents, web pages, or databases. KEOPS is currently implemented on real data of a project dedicated to Food security, for which preliminary results are presented.
Combining Sequence Learning and Dilated Convolution Network for Hippocampus Segmentation

In the volume image of brain MRI, the volume of hippocampus is small, the boundary between hippocampus and surrounding tissue is fuzzy. In order to improve the accuracy of hippocampal segmentation, a new three-dimensional convolutional network is proposed which combines sequence learning and dilated convolution. The channel number of the convolution layer in the network is distributed in a downward increasing way, which effectively reduces the size of the parameters. Secondly, three-dimensional dilated convolution is used as the cascade convolution operation, which effectively combines the deep and shallow features of brain MRI, expands the receptive field of convolution layer and obtains multi-scale feature information, thus improving the segmentation performance of the network and greatly improving the segmentation accuracy. Experiments are carried out on the ADNI dataset, using dice similarity coefficient as evaluation indexes, and the accuracy reaches 89.23%. Experimental results show that the proposed network model makes full use of the three-dimensional spatial information of brain MRI images, and has better feature expression ability, thus greatly improving the segmentation accuracy.
Fake Product Monitoring and Removal for Genuine Product Feedback

A customer’s decision to purchase a product or service are primarily influenced by online reviews. Customers use online reviews, which are valuable sources of information to understand the public opinion on products and/or services. Dependability on online reviews can give rise to the potential concern that violator could give deceitful reviews in order to synthetically promote or decry products and services. This practice is known as Opinion Spam, where spammers manipulate reviews by making fake, untruthful, or deceptive reviews to get profit and boost their products, and devalue a competitor’s products. In order to tackle this issue, we propose to build a fraud risk management system and removal model. This captures fraudulent transactions based on user behaviors and network, analyses them in real-time using Data Mining, and accurately predicts the suspicious users and transactions. In this system, we use two algorithms NLP and TF-IDF to differentiate between fake and genuine reviews or feedback received by the customers