Reviewerly/exHarmony huggingface dataset:
Projection Grid Cues: An Efficient Way to Perceive the Depths of Underground Objects in Augmented Reality

Augmented Reality is increasingly used for visualizing underground networks. However, standard visual cues for depth perception have never been thoroughly evaluated via user experiments in a context involving physical occlusions (e.g., ground) of virtual objects (e.g., elements of a buried network). We therefore evaluate the benefits and drawbacks of two techniques based on combinations of two well-known depth cues: grid and shadow anchors. More specifically, we explore how each combination contributes to positioning and depth perception. We demonstrate that when using shadow anchors alone or shadow anchors combined with a grid, users generate 2.7 times fewer errors and have a 2.5 times lower perceived workload than when only a grid or no visual cues are used. Our investigation shows that these two techniques are effective for visualizing underground objects. We also recommend the use of one technique or another depending on the situation.
Person Identification in UAV Shot Videos by Using Machine Learning

Footage that is captured through unmanned aerial vehicles (UAVs) provides the sufficient information for identification of a person. This idea gives limelight in the direction of surveillance with the help of drones. Considering the areas which are inaccessible for humans in an easy way, UAVs or so-called drones can make a great impact by making it easy for humans to reach those particular areas. In this chapter, two algorithms for face detections are used which are Viola Jones and Tine Face. For face recognition, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP),VGG-Face and Commercial-Off-The-Shelf System (COTS) are used. According to the result, tiny faces give better precision and recall as compared to viola jones. This result is justifiable because the tiny face detector is trained for low-resolution data which increases its performance. Viola Jones does not give good precision and recall because it needs a proper frontal view of the face. For face recognition, VGG-Face algorithm gave the best results with 14.36% in identification performance. The LBP model gave the accuracy of 5.08%; on the other hand, 5.08% accuracy was achieved with the HOG algorithm. The COTS algorithm gave the worst results in comparison to others by giving the accuracy of less than 7%. Current face recognition techniques do not perform well in the given data set.
Encouraging Chemistry Learning Through an Augmented Reality Magic Game

Chemistry is often labelled as a difficult subject, which challenges come mostly from topics that require abstract thinking, leading to a negative attitude by the learners. Studies show that augmented reality (AR) is potentially useful for education purposes, particularly in the representation of abstract concepts by depicting them through virtual objects. This paper presents an AR educational mobile game, named “Magic Elements”, targeting children from 9 to 12 years old. The game was designed to promote a positive attitude towards chemistry, teaching children basic concepts while creating climate change awareness. As climate change is having a profound impact on Earth and urgent actions need to be taken, the game also addresses this topic relating chemistry with the real world and promoting sustainable habits among children from an early age. Tangibles and storytelling are explored to engage and sensitize children. Our findings show a positive outcome, as well as a considerable increase in the acquired chemistry knowledge and awareness towards climate change.
Machine learning based anomaly detection and diagnosis method of spinning equipment driven by spectrogram data

Anomaly detection of spinning equipment monitors the running state using spectrogram based on quality data of sliver or yarn, thus detecting worn components, equipment faults or incorrect drafting parameters. In order to improve the detection efficiency and accuracy, an assembling anomaly detection method for spectrogram data is proposed in this study that combines traditional spectrum analysis method and machine-learning method. Because of the characteristics of wavelength-spectrum data, this method integrates a data dimensionality reduction method based on extended variable selection, and an isolated forest anomaly detection method, to split out wavelength-spectrum data when a fault occurs. Experimental analysis in study cases from the drawing frame and spinning frame is given to verify the effectiveness of the proposed method, as well as to explain parameter selection for improving detection accuracy.
The essence of formative assessment and its role in the foreign language teaching

We consider the main principles of constructivism (taking into account and developing the cognitive interests of students, encouraging mutual learning and activating peers as sources of learning), which underlie modern pedagogy and linguodidactics,. A comparison of a traditional lesson and a lesson based on the principles of constructivism is carried out, it is shown how the es-sence of assessment and the attitude towards it change at the present stage of development of education and in theory. Particular attention is paid to the problem of formative assessment, in particular, its specifics are discussed at different stages of teaching and the roles that are assigned in it to participants in the educational process – teacher, student and classmates. The definition of formative assessment is clarified and its various aspects are considered: the involvement of students in the process of goal-setting, self- and mutual control; the quality of the questions used by the teacher in the classroom and the quality of the feedback; subsequent use of the results of the final control for teaching purposes. The functions of formative assessment are revealed. The levels of formative assessment (low, medium and high) are analyzed, the choice of which depends on the learning context. The relevance of formative assessment to educational results with an emphasis on the development of subject and meta-subject skills in accordance with the Federal State Educational Standard of General Secondary Education is demonstrated.
New Results on Optimal Multichannel Conflict-Avoiding Codes

A multichannel conflict-avoiding code of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> and weight <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$w$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> orthogonal channels, denoted by MC-CAC(M, L, w), is used for deterministic multiple-access without feedback. When the number of simultaneously active users is less than or equal to w, an MC-CAC(M, L, w) is able to provide a hard guarantee that each active user has a successful transmission within every consecutive <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> time slots. An upper bound on the number of potential users, or codewords, that an MC-CAC(2, L, 3) can support is derived in this paper. By means of (hooked) Skolem sequences, we propose a new construction that can provide a series of optimal MC-CAC(2, L, 3)s in the sense that the code sizes achieve the obtained upper bounds.
NB vs. SVM: A Contrastive Study for Sentiment Classification on Two Text Domains

Thanks to the availability of websites like Twitter, user-generated content is being published on the Internet every second. Sentiment Classification is one of the most attractive fields in text mining, which classifies reviews into positive and negative classes. Pre-processing is an important goal when these textual contexts are employed through machine learning techniques. Without effective pre-processing methods, inaccurate results will be achieved. This article aims to investigate the role of pre-processing in the Sentiment Classification problem. The main idea in this paper comes from using sampling techniques. This paper suggests classifying the tweets and reviews using supervised classifiers. We applied a set of pre-processing stages consisting of n-grams and samplings on two well-known datasets. Our results are worthwhile for companies to monitor the people's sentiment about their brands and for many other applications. We have provided further evidence to confirm the superiority of our model. Experimental results reveal that the proposed model outperforms the existing methods and can improve the performance of Sentiment Classification in terms of accuracy, precision, recall, and F1 criteria.
Teaching of bachelors of pedagogical education to the implementation of design and research activities in the conditions of digitalization of the school

Problem and goal. In the context of digitalization of school education and update of requirements of Federal State Education Standards to implementation of project and research activities of school children the relevance of development of professional training of bachelors of pedagogical education in higher education institutions is substantiated. Methodology. As a summary and systematization of the accumulated practical experience of project-based teaching as well as its scientific and methodological analysis, training of bachelors of pedagogical education for project and research activities based on cross-subject interaction is described. Results. The examples for training of future teachers in project and research activities by use of cross-subject interaction in higher education institution are provided. Conclusion. The need to develop bachelors of pedagogical education - future teachers - in fundamental, technological skills to solve subject and cross-subject tasks using means of digitalization and digital educational resources in the context of professional interaction between subject teachers and computer science teacher is proved.
AR visualization of automated access path planning for percutaneous interventions

Abstract Minimally invasive interventions, e.g., percutaneous needle interventions, have many advantages compared to traditional surgery. However, they may require complex and time-consuming planning with experience-dependent success. Automated access path planning is faster and more consistent but individual preferences and situational circumstances are not considered. To this end, displaying the path planning results directly on the patient’s skin, using projector-based augmented reality (AR), was investigated. A constraint-based path planning was implemented to evaluate the quality of every path, taking into account risk structures and path length. A visualization was developed to display the results on the skin and to allow for path selection. The choice of the path followed by a navigated insertion was evaluated in a pilot study (n=5), considering four levels of the visualization with different amounts of displayed insertion points. Participants stated that they preferred to have multiple potential puncture points displayed. However, the results for the considered variables show only small differences. Overall, it has been shown that projectorbased AR visualization of automated access path planning is possible and enables individual, situation-adapted insertion point selection. More research is required to further explore optimal display of paths.
ANALYSIS OF COGNITIVE ABILITIES FOR STEM LEARNING: TWO VIEWPOINTS, TWO OPINION

Background: Cognition-focused approach to education is one of the foundational factors for preparing the younger generation to further mastering the educational and informational space. Aim: The purpose is to determine the types of cognitive difficulties in students of STEM disciplines (science, technology, engineering, and mathematics) in school, to determine a connection between cognitive abilities and skills, and to determine their significance for both sides of the educational process – the teachers, and the students. Methods: The basic method of the study is polling the respondents, using the questionnaire created by the study’s author, then analyzing and statistically processing the results. The study included three stages: identifying significant difficulties of students in performing educational activities within a specific discipline, identifying the most demanded cognitive abilities and skills, searching for connections, and determining the correspondence of cognitive abilities and skills. Results and Discussion: Types of studies that presented the most difficulties to students were identified and the interconnection between the cognitive competencies. Differences were revealed when comparing the answers of both groups of respondents about the complexity and importance of educational actions in the study of physics and mathematics and the attitude type of schoolchildren to the reproductive level of education or the clearest and memorable algorithms of actions. Competencies most significant to the success of the educational process were extrapolated. Correlation analysis in pairs of cognitive ability/cognitive skill made it possible to correct the initial ideas about their relationship. Conclusions: The data collected allowed establishing the complexity of the educational activities and significant cognitive subject-applied abilities and skills. Systematizing the results of studying the subjective opinions of students and teachers about the importance of cognitive competencies for STEM makes it possible to purposefully transform the educational process, taking into account the needs of its participants and society.
Cybersecurity and Fatigue: Does fatigue from visual contrast impact our ability to correctly classify emails?

Email phishing schemes represent a constant threat to personal and organizational security. To combat this threat, it is critical to develop a firm understanding of the factors that affect email classification performance. One such factor might be fatigue. Work performance research shows that fatigue impairs information processing, shortens attention span, and slows reaction times. Studies have demonstrated that elevated screen use can induce fatigue that leads to such impairments (Jeong, 2012; Lin et al., 2008). Specifically, Bhattacharyya et al. (2014) showed that changes in text-background contrast induce fatigue. The current study examined whether fatigue impacts email classification performance. Participants first read a series of text-excerpts on a computer screen and answered comprehension questions, after which they classified 100 emails (4 blocks; 25 emails per block) as either legitimate or non-legitimate (50% legitimate; see Sarno et al., 2020). Additionally, we manipulated the text-background contrast of the initial reading task; the text was either black (high contrast/low-fatigue) or yellow text (low contrast/high-fatigue) on a white background. Somewhat surprisingly, we found that the high-fatigue group had higher accuracy (75.6%) than the low-fatigue group (66.4%; pholm= 0.002). Participants performed better when classifying legitimate emails (p< 0.001). Additionally, the high-fatigue group had higher accuracy (75.3%) for non-legitimate emails than the low-fatigue group (48.7%; pholm< 0.001). No group difference was found for legitimate emails (pholm= 0.285). The low-fatigue group also misclassified non-legitimate emails as legitimate at a higher rate (50.6%) than the high-fatigue group (29.5%; p= 0.005). The data support fatigue’s role in email classification performance, but in an unexpected way. Fatigue improves email classification accuracy, particularly for non-legitimate emails. We speculate that the high-fatigue task required participants to actively focus, which increased stress and boosted performance. It is also possible that the high-fatigue condition made participants more cautious, and subsequently changed participants’ email classification criterion.
A Novel Methodology for Automating Spatio-Temporal Data Classification in Basketball Using Active Learning

The use of machine learning on spatio-temporal datasets has generated significant interest in a range of applications, including vehicular traffic modelling and urban planning. One of the most prolific application domains is sports analytics due to the availability of real-world multi-agent datasets, where such techniques are used to recognize and predict offensive and defensive strategies in a range of team sports. However, the use of advanced machine learning techniques requires the large datasets to be annotated by domain experts, which is a time-consuming task. Active learning is a methodology that significantly cuts down the data-annotation time on large datasets. In this paper, we investigate active learning strategies to annotate spatio-temporal datasets for the purpose of classification model building. The proposed algorithms are demonstrated on a dataset obtained from professional basketball games to classify an offensive strategy known as ‘Pick-and-Roll’. Several neural network architectures are investigated for the classification of more than 900 segments of basketball plays. The results obtained suggest that the proposed, preferred, methodology is well suited for annotating large spatio-temporal datasets and has the potential to be applicable across a range of team sports and non-sports usage scenarios.
Perceptual and Affective Reverberation Components

The perceptual and affective reverberation phases of the empathic process describe how authors gather data about the affective state of a person, and, having gathered that data, how they assimilate it into a distinguishable affective production within themselves. “Reverberation” is given by one desk dictionary as “the persistance of a sound after its source has stopped, caused by multiple reflections of the sound within a closed space.” Several sources of contamination of this process may be described. Simply not wanting to feel what the other person is feeling would suffice to motivate terminating the process. An analysis of several methods that purport to enhance the affective reverberation component of empathic ability must begin with an appraisal of the concept of motor mimicry. The role of motor mimicry in empathy has a long history and continues to receive popular currency.
Improving the accuracy of Neural Networks through Ensemble Techniques

Neural networks usually suffer from model performance problems due to high variance. Ensemble techniques refer to the method of combining multiple models resulting in improving the overall performance of the model. The overall performance can be improved by combining the predictions from multiple models having a good accuracy instead of highly tuning the models, to reduce the variance of predictions and reduce the error due to generalization. In this paper, the different types of ensemble techniques such as stacking ensemble, horizontal voting, and weight average ensemble are being discussed which can be used to improve the model accuracy. The numerous disadvantages of hyperparameter tuning like the wrong choice of parameters, overfitting, and other inefficient optimizing strategies can be overcome using ensemble techniques. The ensemble techniques have numerous advantages such as improving the model performances, reduction of model variance, and so on. Accuracy is improved because results are obtained using the mean predictions of the number of sub-models and the performance is improved with the learning weights of each of the models.
Adaptive Mesh Refinement in Deformable Image Registration: A Posteriori Error Estimates for Primal and Mixed Formulations

Adaptive Mesh Refinement in Deformable Image Registration: A Posteriori Error Estimates for Primal and Mixed Formulations
Online Fraud Detection using Deep Learning Techniques

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of user’s concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing
HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS

Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters &amp; special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.
Random effect models for multivariate mixed data: A Parafac-based finite mixture approach

We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can be further simplified by using the Parafac model. A case study illustrates the proposal.
Virtual learning environments and metacognition: a bibliometric study in the Latin American context

Information and Communication Technologies started to be used as Virtual Learning Environments (VLE), in the nineties and have become a regular resource in the educational field. Its accelerated development and variety of tools make them high potential instruments able to transform an educational system designed for the industrial society and inconsistent with the requirements of the XXI century, characterized by its emphasis on knowledge and creativity as first order resources. The purpose of the current article is to present the results of a bibliometric study about the Latinamerican research production in the area of Virtual Learning Environments and metacognition, developed in the last eight years and published in three different databases: SciElo, Ebsco y Proquest. Data about number of articles, types of VLE, educational levels and fields in which these researches have been done. Finally, the contributions and future directions of these studies are exposed.