Reviewerly/exHarmony huggingface dataset:
Gaze-Controlled Instructions for Manual Assembly Tasks – A Usability Evaluation Study

People interact with technical systems every day, making use of manifold input methods. One possible but not yet very established input method is eye gaze. The present article investigates a gaze-controlled interface in the context of manual assembly tasks, where it provides a language-free and at the same time hands-free input alternative. To this end, we implemented a gaze-controlled instruction prototype and compared its efficiency, usability, and user experience to that of an established paper manual. Both instruction forms were assessed on subjective measures (NASA-TLX, UEQ, and USE) as well as on an objective measure (assembly time). Albeit being prototypical and novel to the participants, the usability of the gaze-based instruction form was at least comparable to that of the paper manual and on some scales even better. Further, the gaze-based interface yielded similar assembly times and was rated preferable in terms of user experience. Taken together, our results suggest that gaze-based instructions can be a valuable alternative to previously used instruction forms in the work context.
“Who Is the FAIRest of Them All?” Authors, Entities, and Journals Regarding FAIR Data Principles

The perceived need to improve the infrastructure supporting the re-use of scholarly data since the second decade of the 21st century led to the design of a concise number of principles and metrics, named FAIR Data Principles. This paper, part of an extended study, intends to identify the main authors, entities, and scientific journals linked to research conducted within the FAIR Data Principles. The research was developed by means of a qualitative approach, using documentary research and a constant comparison method for codification and categorization of the sampled data. The sample studied showed that most authors were located in the Netherlands, with Europe accounting for more than 70% of the number of authors considered. Most of these are researchers and work in higher education institutions. These entities can be found in most of the territorial-administrative areas under consideration, with the USA being the country with more entities and Europe being the world region where they are more numerous. The journal with more texts in the used sample was Insights, with 2020 being the year when more texts were published. Two of the most prominent authors present in the sample texts were located in the Netherlands, while the other two were in France and Australia.
Pedagogy Modeling

This chapter introduces the detailed design of SeisTutor and the adaptive features incorporated in the pedagogy model. This is followed by an in-depth discussion of the adaptive features: Custom-Tailored Curriculum Sequencing module, Tutoring Strategy recommendation module, and Learner Performance Analyzer module (Emotion Recognition Module and the degree of understandability). The Emotion Recognition Module is used to keep track of learner emotions. In contrast, the degree of understandability module is used to determine the learner’s degree of understanding after completing the learning session. The overall performance and activity of the learner are visualized in the form of a progress report.
Performance Metrics: Intelligent Tutoring System

This chapter describes the evaluation of the SeisTutor. This section describes the statistical methods and their application on the learner’s performance parameters, captured during ongoing tutoring through SeisTutor.
An Exploratory Study on the Author Keyphrases Selection Behaviours in Scientific Articles

This study analyzes the keyword selection behavior of authors in scientific articles from multiple perspectives. It first investigates to what extent author-provided keyphrases are retrievable (i.e., abstract coverage) from the abstracts of scientific publications based on phrase-level matching. Afterward, the relations between the abstract coverage with the abstract length and readability scores are investigated. Then, it inspects the performance of domain-independent statistical and co-occurrence-based keyword extraction methods for automatically identifying the author-provided keywords from the abstracts of articles. Finally, it analyzes the agreements between various unsupervised keyphrase extraction methods. It is noticed that author-provided keyphrases are often not present in the abstract. The abstract coverage of around 30% is observed in three different datasets with no perceptible relationships with the abstract length and readability. It is found that unsupervised approaches yield limited accuracy in automatically determining keywords from the abstract (even if they exist in the abstract); They achieve coverage of less than 20%. Besides, the low agreements between various keyword extraction techniques for the automatic keyphrase determination suggest that author keyword selection behavior is not much connected to the statistical attributes (e.g., frequency and co-occurrence of words) or syntactic attributes (i.e., word/sentence length, syllables) of the abstracts.
Domain Modeling

Acquiring and representing data in ITS is a time-consuming and challenging endeavor that requires considerable expertise. This has been the subject of a flurry of studies on artificial intelligence. It is the purpose of this chapter to examine various approaches and procedures. As a result of domain knowledge engineering, a major epistemological dilemma has arisen, which is discussed in detail in the first section of this work. Following that, a range of knowledge representation languages considers factors such as expressiveness, inferential power, cognition plausibility, and pedagogical emphasis.
A blockchain anonymity solution to prevent location homogeneity attacks

Summary Location‐based services currently face two critical issues: an insufficient number of anonymous users and the problem of location semantic homogeneity. To prevent location homogeneity attacks, we suggest a blockchain‐based anonymization approach. This scheme introduces blockchain to store the anonymous process of the requesting user and collaborating user as evidence, establishes an incentive mechanism to promote cooperation between the two parties, and then selects users who meet the semantic threshold through the location semantic tree to construct the final anonymous set. The security analysis and simulation experiments demonstrate that the scheme suggested in this article can effectively motivate and constrain each user. The semantic security value is close to the maximum value of 1, preventing homogeneity attacks caused by location semantics and protecting users' location privacy.
Improved the Image Enhancement Using Filtering and Wavelet Transformation Methodologies

The use of a person's fingerprints as a method of identification is both one of the earliest methods and one of the most common methods used today. The process of recognizing a person's fingerprints can be broken down into two primary groups: image-based and minutiae-based. An approach to fingerprint identification that is based on photographs is provided in this piece of research. After finding the central point and finishing the pre-processing of the image, the next step is feature extraction, which can be done with the help of a variety of different methods, including the Fast Fourier Transform (FFT), the Discrete Cosine Transform (DCT), the Discrete Wavelet Transform (DWT), and the Gabor Filter. This list is comprised of the mean energy, the standard deviation, and the Shannon entropy. In this study, a comparison of all four transforms reveals that the DCT and DFT are more effective than the DWT and the Gabor filter.
Towards a better blockchainification of supply chain applications

A supply chain ecosystem is a collection of complex asynchronous events. Blockchain has already found commercial applications in the SC domain, particularly in product tracing and verification. However, there is a lack of uniformity in these approaches. Application-generated data cannot be accessed across the supply chain ecosystem, resulting in data silos. Data silos reduce the opportunity for supply chain process optimizations. This paper does not propose any supply chain solution but a generic framework primarily aimed at reducing the communication gaps among the stakeholders and application developer(s) to build quality solutions. The ideal readers are who want to blockchanify their existing supply chain applications. The proposed framework can add real value to the organization by developing effective SC solutions satisfying application requirements. The framework consists of four stages. In the first stage, it extracts the application requirements and then maps on blockchain following an asynchronous mode of communication among the stakeholders and application developer(s). Next, it discusses how it can combine technologies to achieve the requirements stated in the first stage. Later, it discusses how to perform effective data management. Finally, it proposes a four-stage software build method that can lead to an efficient SC solution. The primary aim of this framework is to reduce communication gaps during solution development and ensure smooth operational data movement across the SC ecosystem, thanks to blockchain. The software development process also embeds eight essential features for a quality solution. The paper is concluded by discussing the technical challenges.
Benign Overfitting and Noisy Features

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Feedforward Neural Network-Based Chaos Encryption Method for Polarization Division Multiplexing Optical Ofdm/Oqam System

Optical orthogonal frequency division multiplexing offset quadrature amplitude modulation （O-OFDM/OQAM), as a technique that has a great potential to provide high spectral efficiency optical transmission in physical layer for future communication systems, faces challenges in data transmission security like malicious eavesdropping. In this letter, a novel twice encryption method based on chaotic mapping and feedforward neural network (FNN) is proposed to achieve the capability of anti-eavesdropping and security enhancement for polarization division multiplexing (PDM) O-OFDM/OQAM system. In the proposed method, the symbols in an OFDM/OQAM block are first used for symbol substitution by the chaotic sequences generated by Lorenz mapping and then permutated by chaotic scrambling vectors generated by FNN. The simulation results show that the FNN-based chaos encryption (FNNCE) method can strengthen the security of data transmission and realize the large key space simultaneously, and without apparent bit error rate (BER) deterioration in comparison with the original system.
Does Absorptive Capacity Matter in the Impact of Immigration on Innovation in Africa? Evidence from Panel Quantile Regression with Nonadditive fixed Effects

This study aims to assess the role of absorptive capacity in the relationship between immigration and domestic innovation in sending countries using five average period data points from a sample of 35 African countries over the period 1995-2019. The results based on panel quantile regression with nonadditive fixed effects support the brain drain hypothesis, implying that immigration has negative heterogeneous effects on the innovation performance of sending countries. Our findings also revealed that absorptive capacity mitigates the negative effects of immigration when countries’ absorptive capacity reaches a certain threshold. These results suggest that strengthening the quality of institutions and physical infrastructures could mitigate the brain drain effect of immigration in Africa.
Organization of research activities of students as a mechanism for the formation of a future specialist

Currently, the speed of development and the volume of use of diversified technologies require from modern education a competent, active, competitive specialist who is able to think freely and creatively, create, expand and improve skills and abilities through research, scientific work; able to navigate and adapt to production activities. It is necessary that the future specialist be able to cope with any difficulties that arise. In this regard, it is extremely important to reorganize modern education by introducing an additional load on students, improving the methods and methods of teaching, choosing educational disciplines and their content by coordinating the variable part with interested employers, through the introduction of technologies aimed at forming a complex of general and professional competencies…. An important aspect of the formation of a future specialist is participation in research activities. This article defines the role and significance of the research work of students, considers the main stages of the formation of the system of educational and research work in the conditions of mastering the main professional educational program of secondary vocational education, gives the goals of the formation of social and personal qualities for solving problems in the field of future professional activities implemented by introducing an experimental methodology for the formation of a future specialist, which consists in dividing general competencies into functional blocks, methods for their implementation and assessment are given.
Foreign Direct Investment, Stock Market Development, and Inclusive Growth in Selected Sub-Saharan African Countries

This paper examines the relationship between Foreign Direct Investment (FDI), Stock Market Development (SMD), and Inclusive Growth (IG) for 11 Sub-Saharan African countries for the period 1992 to 2019. We utilise dynamic panel data models using the Vector Error Correction Model (VECM) Granger causality framework and find that FDI has a positive effect on SMD. However, we do not find evidence suggesting that FDI and SMD exert a significant positive influence on IG. Also, IG, FDI, and SMD tend not to converge to their long-run equilibrium path in response to changes in any of the variables. However, the robustness tests estimated from panel models confirm the results from the VECM model. Also, the panel models show that human capital and the female labour force play a significant role in fostering IG. We further show that the availability of natural resources and domestic infrastructure attract FDI, while savings exert a significant negative influence on FDI.
A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification

As the amount of data generated by monitoring the condition of rolling bearings is increasing, matrix factorization-based collaborative filtering can effectively dig out valuable fault information from it. However, in practice, the amount of data generated by the normal state of the bearing is much larger than the amount of data of the bearing fault. As the total amount of data increases, this imbalance will become more and more and more severe, bearing fault information is often overwhelmed in it. In response to this problem, this paper starts from the perspective of mathematical statistics, a method of mean conjugate prior is proposed for the bearing normal condition data of bearing score matrix, from which the prior distribution of the probability distribution parameters of the bearing fault data is obtained. Then combined with the Bayesian method, we get the posterior distribution. According to the distribution, the random number is used to construct the Bayesian probabilistic scoring matrix (BPSM). Relying on BPSM, the collaborative filtering recommendation algorithm is used to identify different types of faults in rolling bearings. Under unbalanced data, comparing with the identification under a conventional joint score matrix (CJSM), the model built based on BPSM has a better identification effect on bearing fault state.
Missing Person Identification System Using Deep Learning Algorithm (CNN) and Machine Learning Classifiers

Missing our loved ones is the most painful situation. India, as the world’s second most populated country, reports a countless number of missing person cases and many of those remain untraced. With the use of deep learning methods, a novel strategy is employed to locate individuals who have gone missing. People can upload images of shady people along with the necessary information to a common webpage. The submitted image is immediately verified to the registered images of missing people. The given input person image goes through a categorization process, and the image with the perfect match is chosen from the missing person database. Deep learning type is trained to accurately recognize the missing person photo in order to execute the selection task. Face recognition is performed using the convolutional NN and a robust deep learning method. Face descriptors are derived from photos using VGG-face deep architecture. The retrieved descriptors are matches with nearest missing person testing photos and classified using the KNN and SVM classifiers. The convolutional algorithm, unlike other deep learning methods, is utilized as a high-level feature extractor. The obtained outcomes are invariant to noise, light, contrast, occultation, pose, and the person’s age because the best-performing algorithms were chosen. As a consequence, the results are obtained with a high degree of precision.
Comparative Analysis of Face Mask Detection Models

With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean.
Wind Turbine Condition Monitoring Based on SCADA Data Co-integration Analysis

A wind turbine condition monitoring method based on cointegration analysis is proposed. The co-integration residual obtained by the co-integration process of the SCADA data of the wind turbine is used for monitoring the operation state of the wind turbine. Take the experimental data of a 1.5 MW doubly-fed wind turbine from Jinjie Company in Baotou City, Inner Mongolia, under different environmental and operating conditions, and conduct experiments on the proposed method. The method was tested with known failure cases. The results show that this method can effectively monitor the running status of wind turbines.
Review of Sentiment Analysis on COVID-19 and Lockdown Twitter Data: Novel Techniques

Novel coronavirus (COVID-19) saw a daily major rise after August 2021. Health ministry of India shows more than 1000 deaths in 24 h. Reviewing this pandemic situation, there are again possibilities of lockdown in India. Daily updates of vaccinations, government permissions are changing day by day. Live updates and its insights should be reached to the common man. People are creating rumors about COVID-19. Social media like Twitter can ease the lifestyle in pandemic situations. The survey of COVID-19 sentiment analysis of Twitter data included in this paper. Novel techniques like BERT model, TextBlob, and VADER libraries are also discussed.
A Robust Gender Identification System for Speaker Recognition Using Linear Discriminant Analysis Stepwise Dimension Reduction

Gender identification is one of the important aspects to reduce computational time in speaker related applications because prior information of gender limits the search space. In this chapter, a robust gender identification system has been presented using a novel dimension reduction algorithm called Linear Discriminant Analysis Stepwise Dimension Reduction (LDASDR) which is based on a well-established Feature Selection algorithm called Linear Discriminant Analysis Stepwise Feature Selection (LDASFS). Three different feature sets namely, acoustic, cepstral and their combination have been separately used for the Neural Network classifier’s input. For carrying out the task of gender identification, benchmark database named English Language Speech Database for Speaker Recognition (ELSDSR) has been used. The experiments have been performed both for the small and large databases. The results obtained with large training samples are better as compared with the results achieved with small number of samples. However, on applying the proposed algorithm on small sample size the classification accuracy of the system improves by 12% (approx.).
Machine Learning-Based Analytical and Predictive Study on Formula 1 and Its Safety

Formula 1, in short F1, is an international racing sport. It is of the highest class among the category. It is a premier sporting event right from the time it started in 1950. It consists of a series of races, known as Grand Prix, taking place across the world on a wide range of circuits. There is enormous amount of data being captured, analyzed, and used to design, build, and drive the F1 cars. The primary goal of this paper is to increase the safety in F1 races by predicting if a race can result in accidents based on the conditions and factors under which the race will be held. This paper also proposes to use different racing datasets from various sources in order to analyze, visualize, and make fruitful predictions along with some kind of analysis using different types of plots, based on the requirements. The data are visualized using different plots after doing required preprocessing. A few conclusions were made based on the patterns observed through comparing different plots. This paper also presents the comparative performance analysis of cars over the years. The performance measurement considered in this study (based on the race results) is the time taken for laps/races. Apart from this, a comparative analysis of two drivers on different race tracks was depicted using plots. Finally, the paper presents a machine learning model to predict the winner of the next race based on the previous results including important factors like the weather conditions.