Reviewerly/exHarmony huggingface dataset:
Students' Attitudes toward the Application of Mobile Augmented Reality in Higher Education

Virtual (VR) and augmented reality (AR) have emerged from the framework of academic and industrial laboratories and have acquired global attention. Currently, the focus shifted from the technologies themselves to finding adequate teaching and learning applications. In this paper, the students' attitudes toward the application of mobile AR (MAR) in higher education (HE) were researched (with a focus on augmented textbooks). The results showed that the students have a mostly positive attitude, and it is concluded that there was no statistically significant difference between the STEM (science, technology, engineering, and mathematics) and non-STEM students' opinions regarding this topic. Based on the results, the further research and integration of this technology into HE settings can be suggested.
IDA: Improved Data Augmentation Applied to Salient Object Detection

In this paper, we present an Improved Data Aug-mentation (IDA) technique focused on Salient Object Detection (SOD). Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited generalization. Our method combines image inpainting, affine transformations, and the linear combination of different generated background images with salient objects extracted from labeled data. Our proposed technique enables more precise control of the object's position and size while preserving background information. The background choice is based on an inter-image optimization, while object size follows a uniform random distribution within a specified interval, and the object position is intra-image optimal. We show that our method improves the segmentation quality when used for training state-of-the-art neural networks on several famous datasets of the SOD field. Combining our method with others surpasses traditional techniques such as horizontal-flip in 0.52% for F-measure and 1.19% for Precision. We also provide an evaluation in 7 different SOD datasets, with 9 distinct evaluation metrics and an average ranking of the evaluated methods.
Crowd Size Estimation Using CommSense Instrument for COVID-19 Echo Period

The period after the COVID-19 wave is called the Echo-period.Estimation of crowd size in an outdoor environment is essential in the Echo-period.Making a simple and flexible working system for the same is the need of the hour.This article proposes and evaluates a nonintrusive, passive, and cost-effective solution for crowd size estimation in an outdoor environment.We call the proposed system as LTE communication infrastructure based environment sensing or LTE-CommSense.This system does not need any active signal transmission as it uses LTE transmitted signal.So, this is a power-efficient, simple, low-footprint device.Importantly, the personal identity of the people in the crowd cannot be obtained using this method.First, the system uses practical data to determine whether the outdoor environment is empty or not.If not, it tries to estimate the number of people occupying the near range locality.Performance evaluation with practical data confirms the feasibility of this proposed approach.& WITH THE OUTBREAK of COVID-19 virus, we are forced to rethink visiting public areas 1-4 .After the current primary wave of infection improves, we will be living in a period when we will need to run business "almost" as usual without a vaccine.This phase is sometimes called the Echoperiod. 5In this scenario, a device to measure the crowd size is urgently required.Crowd sensing is an active area of research because of its diverse applications.
Creative and diverse artwork generation using adversarial networks

Existing style transfer methods have achieved great success in artwork generation by transferring artistic styles onto everyday photographs while keeping their contents unchanged. Despite this success, these methods have one inherent limitation: they cannot produce newly created image contents, lacking creativity and flexibility. On the other hand, generative adversarial networks (GANs) can synthesise images with new content, whereas cannot specify the artistic style of these images. The authors consider combining style transfer with convolutional GANs to generate more creative and diverse artworks. Instead of simply concatenating these two networks: the first for synthesising new content and the second for transferring artistic styles, which is inefficient and inconvenient, they design an end‐to‐end network called ArtistGAN to perform these two operations at the same time and achieve visually better results. Moreover, to generate images of higher quality, they propose the bi‐discriminator GAN containing a pixel discriminator and a feature discriminator that constrain the generated image from pixel level and feature level, respectively. They conduct extensive experiments and comparisons to evaluate their methods quantitatively and qualitatively. The experimental results verify the effectiveness of their methods.
Investigation of Students’ and Parents’ Perceptions of Authentic Contextual Learning at Home and Their Mutual Influence on Technological and Pedagogical Aspects of Learning under COVID-19

During the COVID-19 pandemic, students have been forced to learn at home. Authentic contexts are essential to designing useful learning activities. Therefore, this study used mobile technology, namely Ubiquitous Geometry (UG), to merge authentic contexts into learning activities and investigate the influence of authentic contextual learning (ACL) on students’ and parents’ perceptions. This is because parents inevitably have an effect on students’ learning at home, which has not been clearly addressed in past studies. This study investigated students’ and parents’ perceptions in terms of technological and pedagogical aspects of the implementation of ACL supported by UG while learning at home. We conducted one experiment on 20 fifth-grade students and their parents. In the students’ acceptance model, the results indicated that students’ ease of use and usefulness significantly influenced their positive attitude toward ACL supported by UG, and the positive attitude also significantly influenced intention to continue using our proposed system in both the technological and pedagogical aspects. In the mutual influence between students’ and parents’ acceptance model, it was found that parents significantly influenced their children’s perceptions of ACL at home. In the mediation analysis, we found parents’ ease of use and intention to use could mediate the relation between students’ positive attitude and intention to use in the pedagogical aspect. From the interview, we found that parents thought that the learning activity in authentic contexts was useful and encouraged their children to do more engagement. However, in the technological aspect of the mutual influence between students’ and parents’ acceptance model, no mediation existed. This might be because parents worried their children were overusing mobile devices.
Towards a Collaborative Omnidirectional Mobile Robot in a Smart Cyber-Physical Environment

This paper presents current progress in a three-fold research project, focusing on increasing the movement flexibility in smart cyber-physical environments, which are becoming more prevalent with industry 4.0. The objective is to design and develop a new research platform in the form of a highly flexible omnidirectional mobile manipulator with a full kinematics model. The research platform entails an omnidirectional autonomous mobile platform with an integrated collaborative manipulator, making it a single dynamic system. This task is split into three parts: 1) designing and constructing a flexible low cost omnidirectional mobile platform, 2) the addition of a collaborative robot arm to add task execution capabilities, 3) design the 9 DoF model for the system. This paper presents the first step towards the overall goal of collaborative omnidirectional mobile manipulators. The constructed mobile robot was able to generate and navigate an obstacle free path, using sensor data, from an initial pose to a goal pose.
Why Individual Employees Commit Malicious Computer Abuse: A Routine Activity Theory Perspective

Prior information security studies have largely focused on understanding employee security behavior from a policy compliance perspective. We contend that there is a pressing need to develop a comprehensive understanding of the circumstances that lead to employee commitment of deliberate and malicious acts against organizational digital assets. Drawing on routine activity theory (RAT), we seek to establish a comprehensive model of employee-committed malicious computer abuse (MCA) by investigating the motivations of the offenders, the suitability of the desired targets, and the effect of security guardianship in organizational settings. Specifically, we delineate the effects of the individual characteristics of self-control, hacking self-efficacy, and moral beliefs, as well as the organizational aspects of deterrence based on the routine activity framework of crime. We tested this research model using research participants holding a wide range of corporate positions and possessing varying degrees of computer skills. Our findings offer fresh insights on insider security threats, identify new directions for future research, and provide managers with prescriptive guidance for formulating effective security policies and management programs for preventing MCA in organizations.
Digital quantum simulation framework for energy transport in an open quantum system

Abstract Quantum effects such as the environment assisted quantum transport (ENAQT) displayed in photosynthetic Fenna–Mathews–Olson (FMO) complex has been simulated on analog quantum simulators. Digital quantum simulations offer greater universality and flexibility over analog simulations. However, digital quantum simulations of open quantum systems face a theoretical challenge; one does not know the solutions of the continuous time master equation for developing quantum gate operators. We give a theoretical framework for digital quantum simulation of ENAQT by introducing new quantum evolution operators. We develop the dynamical equation for the operators and prove that it is an analytical solution of the master equation. As an example, using the dynamical equations, we simulate the FMO complex in the digital setting, reproducing theoretical and experimental evidence of the dynamics. The framework gives an optimal method for quantum circuit implementation, giving a log reduction in complexity over known methods. The generic framework can be extrapolated to study other open quantum systems.
Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction

Automatic facial expression recognition, which has many applications such as drivers, patients, and criminals' emotions recognition, is a challenging task. This is due to the variety of individuals and facial expression variability in different conditions, for instance, gender, race, colour and changing illumination. In addition, there are many regions in a face image such as forehead, mouth, eyes, eyebrows, nose, cheeks and chin, and extracting features of all these regions are expensive in terms of computational time. Each of the six basic emotions of anger, disgust, fear, happiness, sadness and surprise affect some regions more than the other regions. The goal of this study is to evaluate the performance of enhanced local binary pattern, pyramid histogram of oriented gradients feature-extraction algorithms and their combination in terms of recognition accuracy, feature vector length and computational time on one, two and three combined regions of a face image. Our experimental results show that the combination of both feature-extraction algorithms yields an average recognition accuracy of 95.33% using three regions, that is, the mouth, nose and eyes on Cohn–Kanade dataset. Besides, the mouth region is the most important part in terms of accuracy in comparison to eyes, nose and combination of both eyes and nose regions.
Guessing about Guessing: Practical Strategies for Card Guessing with Feedback

In simple card games, cards are dealt one at a time and the player guesses each card sequentially. We study problems where feedback (e.g., correct/incorrect) is given after each guess. For decks with repeated values (as in blackjack where suits do not matter), the optimal strategy differs from the “greedy strategy” (of guessing a most likely card each round). Further, both optimal and greedy strategies are far too complicated for real-time use by human players. Our main results show that simple heuristics perform close to optimal.
The educational potential of technical university educational space: system-activity aspect

Purposes of the work: 1) to reveal the educational potential of technical university educational space; 2) to consider the transfer mechanisms of cultural and historical experience from the point of view of the system-activity approach. Methodology. The educational potential of a technical university is revealed with the help of the methodological tools of social and psycho-pedagogical disciplines. Results. The article substantiates the need to present new requirements for the formation of an upbringing educational environment in a higher educational institution. The importance of creating favorable psychological and pedagogical conditions in the educational space of the university, influencing the creation of a model of a spiritual and moral personality. Reconstruction of such a model is possible with the interaction of the main psychological and physiological factors: intellectual, emotional-volitional, efficient-practical. Conclusions. Recommendations. The fundamental factor in the development of the emotional sphere of a modern student is upbringing activity of an educational institution, one of the main tasks of which is the formation of a system of continuous spiritual and moral development of the individual. The implementation of the main social functions within the framework of the system-activity approach in teaching and upbringing increases the social responsibility and civic activity of students.
Hybrid SCA-CS optimization algorithm for feature selection in classification problems

This paper presents a new hybrid optimization algorithm to be used in feature selection problem. The proposed algorithm utilizes the power of sine cosine algorithm and Cuckoo search to both exploration and exploitation the search space to reach at best promising solution. The empirical results are evaluated on four datasets, named Breast, Leukemia, Heart and Iris that obtained from UCI repository and compared with the native sine cosine and other feature selection algorithms include Particle swarm optimization and Antlion algorithm. The results of the experiments clearly showed the efficiency of the hybrid algorithm to exploration and exploitation the features space for finest features and improved high classification rate with low run time on all used datasets.
An Academic Publishers’ GO FAIR Implementation Network (APIN)

Presented here is a proposal for the academic publishing industry to get actively involved in the formulation of protocols and standards that make published scientific research material machine-readable in order to facilitate data to be findable, accessible, interoperable, and re-usable (FAIR). Given the importance of traditional journal publications in scholarly communication worldwide, active involvement of academic publishers in advancing the more routine creation and reuse of FAIR data is highly desired.
Quality of education as a requirement of modern society

The aim of the work is to study the compliance of the quality of education in Russia with the requirements determined by the needs of the Russian society. The article reveals the genesis of such categories as" quality of education "and"quality of products and services". The features of educational services as a public good in the forms of market relations are revealed. The structure and role of various consumers of educational services are defined separately, and a classification of the needs and requirements of consumers to the quality of education is proposed. In the course of the research, such methods as analysis and synthesis, induction and deduction, and statistical methods for studying the economic situation were used. The empirical base of the research is scientific works of Russian and foreign authors on the problems of education quality, National standards of the Russian Federation and International standards in the field of quality, the Federal law "on education in the Russian Federation", Federal state educational standards of the Russian Federation, official data of the Federal Treasury and the Federal state statistics service of the Russian Federation.
Photon-photon correlations from a pair of strongly coupled two-level emitters

We investigate two-color photon correlations in the light emitted by strongly coupled two-level emitters. Spectral filtering allows us to manipulate the collected light statistics and we show that the resonances induced by dipole-dipole interactions give rise to specific correlations, where the time-symmetry of the correlations is broken. Based on the collective dressed states, our study encompasses both the case of real processes, where the photons are associated with specific resonances and classical correlations between each other, and virtual processes, where pairs of photons are emitted with non-classical correlations.
Fully Autonomous UAV-Based Action Recognition System Using Aerial Imagery

Human action recognition is an important topic in artificial intelligence with a wide range of applications including surveillance systems, search-and-rescue operations, human-computer interaction, etc. However, most of the current action recognition systems utilize videos captured by stationary cameras. Another emerging technology is the use of unmanned ground and aerial vehicles (UAV/UGV) for different tasks such as transportation, traffic control, border patrolling, wild-life monitoring, etc. This technology has become more popular in recent years due to its affordability, high maneuverability, and limited human interventions. However, there does not exist an efficient action recognition algorithm for UAV-based monitoring platforms. This paper considers UAV-based video action recognition by addressing the key issues of aerial imaging systems such as camera motion and vibration, low resolution, and tiny human size. In particular, we propose an automated deep learning-based action recognition system which includes the three stages of video stabilization using the SURF feature selection and Lucas-Kanade method, human action area detection using faster region-based convolutional neural networks (R-CNN), and action recognition. We propose a novel structure that extends and modifies the InceptionResNet-v2 architecture by combining a 3D CNN architecture and a residual network for action recognition. We achieve an average accuracy of 85.83% for the entire-video-level recognition when applying our algorithm to the popular UCF-ARG aerial imaging dataset. This accuracy significantly improves upon the state-of-the-art accuracy by a margin of 17%.
Research on ensemble model of anomaly detection based on autoencoder

In the fields of technology such as aerospace, anomaly detection is critical to the overall system. With the large increase in data volume and dimensions, the traditional detection methods have great limitations, and thus anomaly detection algorithms based on deep learning have received widespread attention. In this paper, based on autoencoder: standard autoencoder, denoising autoencoder, and sparse autoencoder, an ensemble detection model that can extract more feature information is proposed. To make more use of these feature information, inspired by the idea of pooling layer of the CNN, two feature fusion methods are proposed. Finally, the experiment verifies that the result of this model is better than the single autoencoder model.
Unitary matrix decompositions for optimal and modular linear optics architectures

Abstract We introduce procedures for decomposing N × N unitary matrices into smaller M × M unitary matrices. Our procedures enable designing modular and optimal architectures for implementing arbitrary discrete unitary transformations on light. Such architectures rely on systematically combining the M -mode linear optical interferometers together to implement a given N -mode transformation. Thus this work enables the implementation of large linear optical transformations using smaller modules that act on the spatial or the internal degrees of freedom of light such as polarization, time or orbital angular momentum. The architectures lead to a rectangular gate structure, which is optimal in the sense that realizing arbitrary transformations on these architectures needs a minimal number of optical elements and minimal circuit depth. Moreover, the rectangular structure ensures that each of the different optical modes incurs balanced optical losses, so the architectures promise substantially enhanced process fidelities as compared to existing schemes.
Multi-UAV Mission Planning Method

Compared with the single UAV system, multi-UAV cooperative system has the advantage in multiple complex tasks. This paper mainly focuses on the modeling of the constraints and studies the algorithms in the cooperative mission planning problem. Firstly, based on the constraint of time, UAV attributes and target attributes, the cooperative task assignment and cooperative resource allocation are modeled respectively. Then the genetic algorithm which solves the task assignment problem and the simulated annealing algorithm which solves the resource allocation problem are proposed. Finally, the simulation of cooperative mission planning is carried out, and the results show that the two algorithms can be used to solve the multi-UAV mission planning problem with complex space-time constraints.
Methods for improving the educational potential of students

This article is devoted to increasing students ' motivation to learn by means of internal settings set by the teacher to himself, aimed at establishing a common frequency with students. Along with numerous existing methods, the internal energy of the teacher plays a significant role. Research has shown that in the absence of this source for the teacher, the role of other methods is sufficiently reduced. If the flow of energy from the teacher finds its common channel with the flow radiated by students, and it will be on the same frequency, then the phenomenon of resonance occurs, the effect of the lesson will increase at times. Students under these conditions show results that exceed their own capabilities. A statistical study of the hypothesis put forward about the influence of waves of a certain frequency coming from the teacher on the thinking and motivational processes in learning is carried out. The statistical distribution of the sample is given. Numerical characteristics are calculated: sample average and sample variance. The law of distribution of performance indicators according to the point-rating system is established. The article proposes a twelve-level system of cognition with a core in the innovation.