Reviewerly/exHarmony huggingface dataset:
Head-Movement Analysis of 360° Affective Experience

The current study aims to analyse VR measures in correspondence with 360° virtual affective experiences to understand the underlying spatio-temporal attention processes. We selected extreme pleasant and extreme unpleasant emotional videos from the Stanford affective database and displayed to participants on HMD VR. The participants' task was to explore the video and report their affective experience using the Self-Assessment Manikin (SAM) scale. The exploration behaviour was analysed using head-tracking parameters, such as standard deviation of all the three head-rotation axes (yaw, pitch and roll), angular speed and region-wise analysis. We observed a positive correlation between standard deviation of yaw and valence, and angular speed and valence. The result suggests that affective experience not only increases the scope of attention spatially (wide scanning), it also motivates to seek more information that leads to more head-movement during exploration. In region-wise analysis, we observed front field-of-view was primarily explored, and rear field-of-view was least explored, showing natural responses similar to real environments. In addition, we used Beck Depression Inventory-II (BDI) to assess participants' emotional state, specifically depression symptoms. The relation between virtual affective experience and 360° exploration behaviour is discussed in light of the BDI categories. The study findings broaden the scope for VR affective research by enabling psychomotor assessment in more ecologically valid settings.
Personal cyber security in email communication

The article presents a description and analysis of phishing e-mails that were sent to one of the authors within two months. The article deals with social engineering, of which phishing attacks are a part, and describes the philosophy and tactics of attackers. Attention is focused on a detailed analysis of received e-mails and their decomposition from the point of view of the companies behind which attackers hide, analysis of offers and promises, frequency of receiving phishing e-mails and also on whether attackers hide behind men or women and where emails are most often sent.
Bayesian bootstrapping for symmetric distributions

In this paper, we describe a Bayesian nonparametric approach to make inference for a spherically symmetric distribution. We consider a Dirichlet invariant process prior on the set of all spherically symmetric distributions and we derive the Dirichlet invariant process posterior. Indeed, our approach is an extension of the Dirichlet invariant process to a spherically symmetric distribution. In addition, we obtain the Dirichlet invariant process posterior for the infinite transformation group and we prove that it approaches the mixtures of Dirichlet processes. Moreover, we develop our approach to obtain the Bayesian nonparametric posterior distribution for functionals of the distribution's support when the support is symmetric with respect to the parallel lines of axes. This suggests a Bayesian nonparametric bootstrapping scheme. The estimates can be derived based on posterior averaging. Then, our simulation results demonstrate that our suggested bootstrapping technique improves the accuracy of the estimates.
Effective semi-supervised learning for structured data using Embedding GANs

The semi-supervised learning(SSL) was proposed to deal with the situation that only a few samples were labeled, so how to make the most use of the existing samples is crucial. In general, we apply data augmentation methods, like Generative Adversarial Networks(GAN), to increase the number of data when facing unstructured data. However, things get totally different with structured data, the continuity of neural networks limits their use in category variables. In this paper, we propose a semi-supervised Embedding GAN(EmGAN) to solve that problem. We add an embedding layer before the discriminator to better characterize category features and design a new loss function to further train our model. Moreover, the structures of the generator and discriminator are modified to match structured data. With the experiments of nonparametric statistical test, EmGAN shows its advantages in processing structured samples.
Emojional: Emoji Embeddings

Emojis are intended to illustrate or replace words in natural language. Although they are understandable across linguistic barriers, their implications are ever-evolving, and change depending on who we are talking to, and when. The underlying emotion is key. This is evident in language online, highlighted recently by the rise of cancel culture via online shaming. For example, the use of the clown emoji to signify someone is making a fool of themself, or the collective spamming of the snake emoji to "cancel" someone, both show seemingly innocent emojis being used as clear forms of aggression online. To capture these nuances, we have created novel emoji embeddings trained on their emotional content. The subsequent emoji embeddings are generally more accurate than the state-of-the-art embeddings on the task of sentiment analysis. These embeddings can be found in our GitHub repository: https://github.com/elenabarry/emojional .
Learning Deeper Non-Monotonic Networks by Softly Transferring Solution Space

Different from popular neural networks using quasiconvex activations, non-monotonic networks activated by periodic nonlinearities have emerged as a more competitive paradigm, offering revolutionary benefits: 1) compactly characterizing high-frequency patterns; 2) precisely representing high-order derivatives. Nevertheless, they are also well-known for being hard to train, due to easily over-fitting dissonant noise and only allowing for tiny architectures (shallower than 5 layers). The fundamental bottleneck is that the periodicity leads to many poor and dense local minima in solution space. The direction and norm of gradient oscillate continually during error backpropagation. Thus non-monotonic networks are prematurely stuck in these local minima, and leave out effective error feedback. To alleviate the optimization dilemma, in this paper, we propose a non-trivial soft transfer approach. It smooths their solution space close to that of monotonic ones in the beginning, and then improve their representational properties by transferring the solutions from the neural space of monotonic neurons to the Fourier space of non-monotonic neurons as the training continues. The soft transfer consists of two core components: 1) a rectified concrete gate is constructed to characterize the state of each neuron; 2) a variational Bayesian learning framework is proposed to dynamically balance the empirical risk and the intensity of transfer. We provide comprehensive empirical evidence showing that the soft transfer not only reduces the risk of non-monotonic networks on over-fitting noise, but also helps them scale to much deeper architectures (more than 100 layers) achieving the new state-of-the-art performance.
HOW GAME AND GAME PRINCIPLES FACILITATE THE CO-DESIGN PROCESSES: REFLECTIONS ON TWO CASE STUDIES

Abstract The increasing need to motivate and engage participants in the co-design has led to the trend of employing game elements in the early phase of the design process to facilitate the activities. However, most have described the gamified co-design by its methods, purposes, and functions. How games theoretically contribute to achieving those goals is still uncovered. This research aims to bridge this theoretical-practical gap by first defining the game principles from the literature. Then investigate how those principles manifested in two case studies with detailed game elements. Finally, we present a framework that links the game principles and purposes/functions by reflecting the cases.
A Reliable and Fast Detection Technique for Lung Cancer Using Digital Image Processing

This paper depicts a computer-based algorithm that analyzes different normal and cancer cell affected images. Several image segmentation and enhancement algorithms have been used in this proposed system—many images collected from a local hospital and from open access internet database. Histogram equalization has been used in the end stage and compared with the original normal and affected cancer cell image curve. The novelty and uniqueness of this paper are that the output curve of the proposed system has to match with two sets of algorithms (AHE, watershed segmentation) to ensure lung cancer. If both two curves are matched similarly concerning the original images we collect, a conclusion can be drawn. Results were 82% same after using a variety of lung images.
Software Craftsmanship Skill using Extreme Programming for Quality Improvement: A Case of Very Small Software Organization

Software product release often sacrifices quality concerns and produces messy code to reach the market quickly. At some point, the software becomes difficult to maintain, and reengineering is an available option to improve software quality. By finding a way to improve our software product quality effectively, we explore the available software reengineering methods that match the business needs. This paper presents our case, a product development improvement effort in a very small software organization with limited resources and a tight development schedule. Our research shows that with careful software redesigning and implementing XP practices, supported by a well-crafted software manifesto, we can ensure software code quality improvement is achieved efficiently.
PROCEDURES FOR ASSESSING COGNITIVE SKILLS OF PROSPECTIVE LANGUAGE TEACHERS

The purpose of the research was to find out how the procedures for measuring students’ cognitive skills could be incorporated into the university course Language Teaching Methodology. The study was organised within a framework of Anderson’s theory of cognitive skills development and Glaser’s taxonomy of dimensions for assessing achievement. We developed the instrument, which encompassed two empirical questionnaires for treatment groups. Both questionnaires comprised an equal number of tasks but differed in the content of procedures for measuring knowledge acquisition and structure as one of the dimensions of cognitive assessment. Empirical Questionnaire 1, based on a traditional approach to assessment, included multiple-choice questions related to the lecture material. Empirical Questionnaire 2 comprised both traditional and unconventional measures, such as a SVT test; constructed and conversation-based responses; simple and high order rule tasks. Thirty-four third-year students of the Department of Foreign Languages, V.N. Karazin Kharkiv National University participated in three-stage research-oriented teaching, which lasted ten weeks. Both qualitative and quantitative data analysis methods were employed for the evaluation of learning outcomes. After final testing, we compared the results obtained from the students. The group mean difference (EG vs. CG) was 0.12 points, 95% confidence interval (0.07 - 0.17), two-sample t-test p &lt; 0.0001. Findings suggest that cognitive skills assessment considerably affects and improves student learning. The implications relate to final grades assessment and curriculum design and contribute to expanded uses for cognitive skills testing.
Integration of multi-channel video and GIS based on LOD

With the deepening application of digital technology in the field of security, the integration of 3D-GIS and surveillance video system has become a hot research issue. In order to solve the problems of separate distribution, poor correlation and low utilization of traditional video surveillance, a massive video scheduling method based on octree spatial index and pixel mode LOD is proposed. Meanwhile, the fusion method of dynamic video and 3DGIS scenes is presented, which added the temporal information of video in 3D space. This method realizes the splicing fusion of video image and virtual scene model, and 40 channel video scheduling in large-scale 3DGIS is realized. Compared with the direct projection method, and the results show that our fusion method is better than the direct projection method.
Actionable XAI for the Fuzzy Integral

The adoption of artificial intelligence (AI) into domains that impact human life (healthcare, agriculture, security and defense, etc.) has led to an increased demand for explainable AI (XAI). Herein, we focus on an under represented piece of the XAI puzzle, information fusion. To date, a number of low-level XAI explanation methods have been proposed for the fuzzy integral (FI). However, these explanations are tailored to experts and its not always clear what to do with the information they return. In this article we review and categorize existing FI work according to recent XAI nomenclature. Second, we identify a set of initial actions that a user can take in response to these low-level statistical, graphical, local, and linguistic XAI explanations. Third, we investigate the design of an interactive user friendly XAI report. Two case studies, one synthetic and one real, show the results of following recommended actions to understand and improve tasks involving classification.
A Novel Concept of Analysing Performance of Deaf Students using Neural Networks

Deaf students need modified curriculum reflecting their special needs as exceptional learners. The barriers related to their disability should be identified and recommendations to overcome these barriers should be made. This paper introduces a novel concept of feature engineering in analyzing the performance assessment of deaf students. In this paper an attempt is made to identify the features specific to deaf students and develop a dataset. The classification models are used to classify the students according to their performance. On comparison, the classification accuracy of ANN is found to be better and the performance of this model is evaluated using various parameters - Accuracy, Precision, Recall, F1-Score and AUROC.
Dynamic Scene Video Deblurring Using Robust Incremental Weighted Fourier Aggregation

Motion blur is an inevitable problem when shooting a video in motion, and it will cause serious degradation of images. In this letter, we propose a new robust incremental weighted Fourier aggregation algorithm for dynamic scene video deblurring. This is motivated by the fact that existing multiframe aggregation methods usually require a tedious iterative process to obtain sharp information in distant frames. We propose to perform alignment and aggregation once for each frame using the current frame and the previous frame result, which includes the sharp information of all the previous frames. Moreover, a criterion for evaluating the alignment of blurred images is proposed. Based on this, a robust aggregation approach is also proposed to help reduce artifacts caused by image patch alignment failure. Specifically, we decompose the current frame into a series of image patches, find consistent image patches in the deblurring results of the previous frame by a coarse-to-fine alignment algorithm, and aggregate them in the Fourier domain. Experiments show that our method can obtain improved deblurring results and reduce the computational complexity compared to the traditional aggregation method.
The Creation of Multi Intelligence Music Classroom in Children's Enlightenment Stage Based on Virtual Reality Technology

With the development of virtual reality technology, the real realization of virtual reality will cause great changes in human life and development. VR is the abbreviation of virtual reality, which means virtual reality in Chinese. Virtual reality is the ultimate application form of multimedia technology. It is the crystallization of the rapid development of computer hardware and software technology, sensor technology, robot technology, artificial intelligence and behavioral psychology. Because of this, the combination of VR and education classroom will become the inevitable trend of future education development. In the context of the development of traditional education, the enlightenment and influence of music education classroom on children is point like, and through the intervention and influence of VR technology, the influence of VR on music education begins to appear face like, and can maximize the mental and thinking ability of preschool children. Through the above analysis, this paper concludes the feasibility and importance of introducing VR technology into the current music education classroom, and will explore for the development of multiple intelligence education.
Face Detection and Recognition Method Based on Improved Convolutional Neural Network

with rapid development of deep learning technology, face recognition based on deep convolutional neural network becomes one of the main research methods. In order to solve the problems of information loss and equal treatment of each element in the input feature graph in the traditional pooling method of convolutional neural network, a face recognition algorithm based on convolutional neural network is proposed in this paper. First, MTCNN algorithm is used to detect the faces and do gray processing, and then a local weighted average pooling method based on local concern strategy is designed and a convolutional neural network based on VGG16 to recognize faces is constructed which is finally compared with common convolutional neural network. The experimental results show that this method has good face recognition accuracy in common face databases.
Russian Students’ Readiness for Distance Learning: Current Situation and Future Challenges

In the context of the COVID pandemic, there has dramatically increased the significance of distance learning technologies. Higher education will most probably increase their usage even after overcoming the coronavirus. This paper aims at assessing Russian university students’ readiness to exercise distance learning technologies. The survey within Rostov-on-Don universities provided data on 428 students’ skills in using Internet technologies when studying. It is shown that in the pre-pandemic period, no more than a quarter of students had the necessary skills to participate in video conferences, and about 16 % of students took online courses autonomously. Only 6,5 % of the respondents could manage both technologies that comprise distance learning. The results obtained on the relationship between academic performance and self-participation in online courses, as well as on the relationship of these indicators with general digital literacy and immersion in social networks, should be taken into account within wide computerization of education during the pandemic.
Arbitrary-Oriented Object Detection on High Resolution Images Based on Differentiable Architecture Search

High-resolution images exhibit wide field of vision, high background complexity, special angle of view, rotation, and small objects, thus making automatic object detection a challenging problem. Recently, this problem has been studied by many researchers through the application of deep learning methods, and good detection results have been achieved. However, most of the current networks for object detection on remote sensing images are designed manually, which is not necessarily the optimal structure. Therefore, DARTS-FPN, a network based on differentiable architecture search, has been constructed to improve the accuracy of object detection on high resolution images. The DARTS algorithm is first used to search the remote sensing images data set. The neural architecture search technique of NAS-FPN is then integrated into this network and merged with RetinaNet, a single-stage rotating network for object detection. Experiments are conducted on the DOTA data set to evaluate the performance of DARTS-FPN. Compared with existing classical networks, DARTS-FPN achieves 55.86%, 45.06%, 5.78%, and 2.19% higher mean average precision than the SSD, YOLOv2, R2CNN, and DM algorithms, respectively.
MK-RRT*: Multi-Robot Kinodynamic RRT Trajectory Planning

This paper introduces MK-RRT*: a Multi-robot Kinodynamic RRT*-based framework for trajectory planning of multiple dynamically-modeled robots. The framework includes both tightly-coupled and loosely-coupled methods for planning. The simultaneous, tightly-coupled, method provides an asymptotically optimal solution to the multi-robot trajectory planning problem. A sequential, loosely-coupled, method is also developed to compare costs of the generated trajectories and computational complexity of both methods. An application of this framework to multi-UAV trajectory planning is presented and experimentally evaluated using a team of Ryze Tello EDU UAVs.
Making more research count: a blockchain enabled one-stop shop for immutable behavioral research

Purpose The purpose of this paper is to offer a panoramic view at the credibility issues that exist within social sciences research. Design/methodology/approach The central argument of this paper is that a joint effort between blockchain and other technologies such as artificial intelligence (AI) and deep learning and how they can prevent scientific data manipulation or data forgery as a way to make science more decentralized and anti-fragile, without losing data integrity or reputation as a trade-off. The authors address it by proposing an online research platform for use in social and behavioral science that guarantees data integrity through a combination of modern institutional economics and blockchain technology. Findings The benefits are mainly twofold: On the one hand, social science scholars get paired with the right target audience for their studies. On the other hand, a snapshot of the gathered data at the time of creation is taken so that researchers can prove that they used the original data set to peers in the future while maintaining full control of their data. Originality/value The proposed combination of behavioral economics with new technologies such as blockchain and AI is novel and translated into a cutting-edge tool to be implemented.
An Intelligent Mobile App for Fall Detection

Falling is one of the most prominent external causes of unintentional injury. On the other hand, the number of elderly people living alone has been continuously growing worldwide. This independence comes with the risk of not receiving a prompt attention if an accident occurs. Lack of attention in the first hour of the accident increases the risk of death and chronic affections. In this paper, a real-time, lowcomplexity mobile fall detection and alert system was developed for helping -but not limited to- elderly people. The system uses smartphones as a platform for fall detection. The fall detection approach is based on processing accelerometer data which create a recognizable pattern during a fall. The proposed system uses Neural Network as a classifier for fall detection, and voice recognition for capturing and confirming the need of sending help alerts. Deep Neural Network was also implemented to see its effect on performance. Experimental results show that the system can detect falls with high accuracy (up to 96%) while consuming reasonable amount of power.
A GA approach to Optimization of Convolution Neural Network

In recent days a lot of activities in Deep Learning demonstrated ability to produce much better than other Machine Learning techniques. Much of the challenge in the Deep Learning is about optimizing the weights and several hyper parameters as it takes lot of computation and time to do. Gradient descent has been most popular technique currently in its weights optimization for back propagation. Most of the existing implementation of Convolution Neural Networks/Deep Learning Networks plays pivotal role in image processing. Though being scientifically regressive, BP and GD is slowly converging and getting easily trapped in local minima these are inherent disadvantages. For this reason, we explored another optimization with Meta Heuristic Algorithms such as Genetic Algorithm in the Deep Learning algorithm.