Reviewerly/exHarmony huggingface dataset:
Virtual Reality in Process for Children With Autism Disability

For more than half a century, educators for normal as well as special children have used virtual reality in the classroom. Systematic analysis and metanlysis of virtual reality in teaching were conducted in order to understand these difficulties and what educators want to gain from employing these innovations in the first place. The researcher picked 250 articles for systematic literature review and select 6 using Prism flowchart. For the purpose of metanlysis, compare the results with six relevant articles. Looked into the potential uses and reported motivations for creating virtual reality learning environments supplied by educators in scholarly papers, and then looked into the potential drawbacks of doing so. Most academics employ virtual reality to boost students' intrinsic motivation, and they focus on a few characteristics, such as instructional practices, engagement, and ramification, in their design of experience, according to these findings. The great majority of instructional virtual reality deployments are found in significant number of educational fields.
The Reality of Employing the Microsoft Application Teams in Learning Mathematics Remotely During the COVID-19 Pandemic in Palestine

This study aimed to investigate the reality of using the Microsoft application Teams in learning mathematics remotely during the COVID-19 pandemic in the governorates of northern Palestine. To achieve the objectives of this study, the researchers used the mixed (quantitative and qualitative) approach, where a questionnaire was designed, consisting of 27 items. To collect information about the reality of employing the Microsoft Teams application from the study sample, which included 1035 male and female students, from the two primary and secondary stages in Palestine, interviews were conducted with 27 teachers of mathematics working in the Ministry of Education. The results showed that using Microsoft Teams in distance learning mathematics, during the COVID-19 pandemic in the governorates of northern Palestine, was significantly high. In general, the results also showed no statistically significant differences due to the place of residence. At the same time, there were statistically significant differences due to the two variables: gender and school stage.
Global Disaster Tracker

The Global Disaster Tracker will be an application which will be used to monitor the disasters happening around the world. The goal of our project is to create an application which can track disasters accurately and update new information in near real time. The dataset which will be used is using NASA’s EONET which stands for Earth Observatory Natural Event Tracker. EONET is a repository of metadata about natural events. EONET is accessible via web services.
A Survey on the Impact of Digital Transformation on NPQEL Training in Institute of Aminuddin Baki

This study aims to identify the strengths and drawbacks of the fully DT of NPQEL training 2021 and secondly to probe opinions on how to improve the quality of the training. A mixed mode research was administered with a set of questionnaires adopting the Sloan-C Five Pillars for Quality Online Education Model via Google Forms and interviews of 1026 NPQEL respondents. The results disclosed intriguing information; despite the blessings, they perceived DT as a bane. Apparently, participant gender had no significant effect on their perceptions; however, age factor influenced their preferences on blended mode and face to face. Various challenges were examined: (1) equity and accessibility, (2) technical challenges related to participants' digital literacy, (3) psychological factor and personal health disputes. Overall, three of the five pillars of the Sloan-C model in terms of respondent satisfaction, scale, and faculty satisfaction were received well. The findings present several suggestions for increasing satisfaction to improve the online learning experience post COVID-19.
Cross Domain Heterogeneous Signcryption Scheme with Equality Test for WBAN

The rapid development of wireless sensors has accelerated the popularity of wireless body area network (WBAN). WBAN use multiple sensors to collect the patient’s body data, and the data is transferred to the medical cloud for processing and analyzing. In order to protect the data in the medical cloud, some heterogeneous signcryption schemes that support equality test have been proposed. However, we observe that these schemes use the same cryptographic parameters in different cryptographic systems. In addition, most of these schemes cannot resist the replay attack (RRA) or know session temporary key attack (RKSTKA). To deal with these problems, this paper presents a cross domain heterogeneous signcryption scheme with equality test (CDSCET) for WBAN. In CDSCET, the ciphertexts are from certificateless cryptographic system to public key infrastructure, where two different cryptosystems use different cryptographic parameters. CDSCET can realize confidentiality, integrity, authentication, RRA and RKSTKA. Moreover, compared with three latest schemes, CDSCET has reduced the total computation cost by at least 56.46%.
Ambient Assisted Living (AAL) Systems to Help Older People

There is significant interest in the development and implementation of smart and intelligent ambient assisted living (AAL) systems that can give daily support to help older people live independently in their homes. Additionally, such systems will lower the expense of healthcare that governments must bear in order to provide support for this group of residents. It also relieves families of the burden of constant and often tedious round-the-clock surveillance of these individuals, allowing them to focus on their own lives and commitments. As a result, recognition, classification, and decision-making for such people's daily activities are critical for the development of appropriate and successful intelligent support systems capable of providing the essential assistance in the correct manner and at the right time.
Enhancing Brain Imaging Using Super Resolution

Super-resolution reconstruction creates one or more high-resolution images from a collection of low-resolution frames. This chapter examines a number of super-resolution methods proposed over the last two decades and provides an overview of the contributions made recently to the broad super-resolution problem. During the procedure, a thorough examination of numerous crucial elements of super-resolution is presented, which are frequently overlooked in the literature. The authors have also outlined various advancements and studies that have been done in the particular domain. The prime focus of this chapter is to highlight the importance and application of super resolution in brain MRI and explore all the work that has been done in the field so far. The experiments on simulated and actual data are used to support novel strategies for tackling the difficulties faced while implementing the technique. Finally, several prospective super-resolution difficulties are identified, and methodologies are presented.
A novel ensemble estimation of distribution algorithm with distribution modification strategies

Abstract The canonical estimation of distribution algorithm (EDA) easily falls into a local optimum with an ill-shaped population distribution, which leads to weak convergence performance and less stability when solving global optimization problems. To overcome this defect, we explore a novel EDA variant with an ensemble of three distribution modification strategies, i.e., archive-based population updating (APU), multileader-based search diversification (MSD), and the triggered distribution shrinkage (TDS) strategy, named E 3 -EDA. The APU strategy utilizes historical population information to rebuild the search scope and avoid ill-shaped distributions. Moreover, it continuously updates the archive to avoid overfitting the distribution model. The MSD makes full use of the location differences among populations to evolve the sampling toward promising regions. TDS is triggered when the search stagnates, shrinking the distribution scope to achieve local exploitation. Additionally, the E 3 -EDA performance is evaluated using the CEC 2014 and CEC 2018 test suites on 10-dimensional, 30-dimensional, 50-dimensional and 100-dimensional problems. Moreover, several prominent EDA variants and other top methods from CEC competitions are comprehensively compared with the proposed method. The competitive performance of E 3 -EDA in solving complex problems is supported by the nonparametric test results.
On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques.
Preliminary monitoring and performance evaluation of photovoltaic system in rural buildings in severe cold area

In recent years, the photovoltaic (PV) systems of rural buildings have been demonstratively applied in severe cold areas of China. However, the lack of power generation performance and economic evaluation results is not conducive to the PV promotion. This study innovatively presented the performance and economic evaluation of the grid-connected rural building applied PV (BAPV) system in Hailar, China from the predicted and measured data. The measured monthly PV outputs were counted and analyzed from the year 2020 to 2021. The ambient temperature, solar radiation and predicted output were obtained by the software of PVSYST. The performance ratio (PR), efficiency and loss were investigated, and their relationship with temperature and sunshine was analyzed. According to the measured data and theoretical calculation, the payback periods and profits were calculated. The results indicate that the measured output yields are 14,123.70 kWh in 2020 and 13,265.10 kWh in 2021, which are 6.05%~11.76% lower than the predicted value due to snow cover in the cold months. The monthly predicted array output is about 3.4% higher than the energy output. The array capture loss (LC) is 14.99–34.80 kWh and the system loss (LS) is 29.6–5.81 kWh. The PR is 83.30%-99.80%. The average system efficiency and array efficiency are 16.15% and 15.62%. The lower PR and efficiency are caused by the high temperature. The measured and theoretical payback periods are 6.7 and 5.6 years, respectively. The theoretical annual rate of income is 4828.15 Chinese yuan (CNY). These results provide an important reference for the promotion of rural residential BAPV in severe cold areas.
Pushing the Limits of Clingo’s Incremental Grounding and Solving Capabilities in Practical Applications

Incremental techniques aim at making it possible to improve the performance of the grounding and solving processes by reusing the results of previous executions. Clingo supports both incremental grounding and incremental solving computations. In order to leverage incremental computations in clingo, the incremental fragments of ASP programs must satisfy certain safety-related conditions. In a number of problem domains and reasoning tasks, these conditions can be satisfied in a fairly straightforward way. However, we have observed that in certain practical applications, satisfying the conditions becomes more challenging, to the point that it is sometimes unclear how or even if it is possible to leverage incremental computations. In this paper, we report our findings, and ultimate success, with the use of incremental grounding and solving techniques in one of these challenging cases. We describe the domain, which is linked to a large practical application, discuss the challenges we faced in attempting to leverage incremental computations, and then describe the techniques that we developed, in particular at the level of methods for encoding the domain knowledge and of algorithms supporting the intended interleaving of grounding and solving. We believe that our findings may provide valuable information to practitioners facing similar challenges and ultimately increase the adoption of clingo’s incremental capabilities for complex practical applications.
Research on Applying the “Shift” Concept to Deep Attention Matching

The main purpose of chat robots is to realize intelligent interaction between human beings and chat robots. Generally, a complete conversation involves chat contexts. Before responding, human beings need to extract information from their chat contexts, and human beings are very good at extracting information. However, how to make the chatbots more complete in extracting appropriate contextual information has become a key issue for the chatbots in multi-round dialogues. Most research in recent years has paid attention to the point-to-point matching of responses and utterances at reciprocal granularity, such as SMN (Sequential Matching Network) and DAM (Deep Attention Matching Network), for which these parsing methods affect the effectiveness of the above networks to some extent. For example, DAM introduces an attention mechanism, which can obtain good results by parsing five levels of granularity of response and utterance through a self-attention module, and then matching the same level of granularity to mine similar spatial information in it. Based on the structure of DAM, this paper proposes an information-mining idea for improving DAM, which applies to those models with a multi-layer matching structure. Therefore, based on this idea, this paper presents two improved methods for DAM, so as to improve the accuracy of the information extraction from the contexts of the multiple round chats. The experiments show that the improved DAM has better results than that of the unimproved DAM, which are, respectively, R_2@1 increased by 0.425%, R_10@1 increased by 0.515%, R_10@2 increased by 0.341%, MAP (Mean Average Precision) increased by 0.358% in Douban data set, P@1 increased by 1.16%, R_10@1 increased by 1.17%, and these results are better than those state-of-the-art methods, and the improved methods presented in this paper can be used not only in DAM but also in other models with similar the point-to-point matching structures.
Online Learning-Based Hybrid Tracking Method for Unmanned Aerial Vehicles

Tracking unmanned aerial vehicles (UAVs) in outdoor scenes poses significant challenges due to their dynamic motion, diverse sizes, and changes in appearance. This paper proposes an efficient hybrid tracking method for UAVs, comprising a detector, tracker, and integrator. The integrator combines detection and tracking, and updates the target's features online while tracking, thereby addressing the aforementioned challenges. The online update mechanism ensures robust tracking by handling object deformation, diverse types of UAVs, and changes in background. We conducted experiments on custom and public UAV datasets to train the deep learning-based detector and evaluate the tracking methods, including the commonly used UAV123 and UAVL datasets, to demonstrate generalizability. The experimental results show the effectiveness and robustness of our proposed method under challenging conditions, such as out-of-view and low-resolution scenarios, and demonstrate its performance in UAV detection tasks.
Estimating Optical Flow with Streaming Perception and Changing Trend Aiming to Complex Scenarios

Most of the recent works on optical flow estimation focus on introducing more effective CNNs or even the Transformer architecture that is quite popular in today’s CV field, but they all still follow the traditional two-frame input structure. In this paper, we argue that optical flow estimation should be more focused on the dynamic information in the image sequence. To that end, we present a straightforward but effective streaming framework equipped with a novel prediction module for capturing optical flow trends, which is a point that has not yet been discussed in previous works. By employing the backbone of the strong baseline to extract the stream of features from three consecutive frames as inputs, this module can predict the current state and update the previous changing trend of optical flow for feature fusion at the next stage. Additionally, we redesign the training loss, providing two new cost functions, in particular, to measure the variations in the optical flow trend. At the forward stage, we deploy a historical buffer to maintain the inference speed so that it is unaffected by additional input frames. Compared with the strong baseline, our method improves the average end-point error by 21.12% on Sintel Clean and 12.94% on Sintel Final. In addition, it also outperforms existing methods in real-world scenarios with low brightness or fast-moving targets.
Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows

Scene text detection has become a popular topic in computer vision research. Most of the current research is based on deep learning, using Convolutional Neural Networks (CNNs) to extract the visual features of images. However, due to the limitations of convolution kernel size, CNNs can only extract local features of images with small perceptual fields, and they cannot obtain more global features. In this paper, to improve the accuracy of scene text detection, a feature enhancement module is added to the text detection model. This module acquires global features of an image by computing the multi-headed self-attention of the feature map. The improved model extracts local features using CNNs, while extracting global features through the feature enhancement module. The features extracted by both of these are then fused to ensure that visual features at different levels of the image are extracted. A shifted window is used in the calculation of the self-attention, which reduces the computational complexity from the second power of the input image width-height product to the first power. Experiments are conducted on the multi-oriented text dataset ICDAR2015 and the multi-language text dataset MSRA-TD500. Compared with the pre-improvement method DBNet, the F1-score improves by 0.5% and 3.5% on ICDAR2015 and MSRA-TD500, respectively, indicating the effectiveness of the model improvement.
Robust one-class classification with support vector data description and mixed exponential loss function

Support vector data description (SVDD) has received a lot of attention due to its outstanding performance to perform one-class classification or novelty detection tasks. However, the same weight is directly imposed on all slack variables in the process of modeling, which may result in degraded learning performance when training data are contaminated by some outliers or mislabeled observations. In this paper, an extended SVDD model is therefore proposed by reformulating the original optimization problem of SVDD with a mixed exponential loss function. Since this loss function can emphasize the importance of the samples that tend to be the target class, and weaken the influence of those tending to be outliers, it can be viewed as a weighted SVDD. However, the weights in the new model are automatically calculated rather than being calculated in advance using some specific manners. To solve the optimization problem of the proposed model effectively, the half-quadratic optimization technique has been adopted to perform the optimization, generating a dynamic optimization algorithm. Meanwhile, the convergence and computational complexity of this dynamic optimization algorithm are analyzed from a theoretical respective. Experimental results on a synthetic data set and some publicly available real world data sets are reported to demonstrate the performance superiority of the new method in comparison with SVDD and other competitive SVDD variants.
The Moderating Effect of Flexible Work Option on Structural Empowerment and Generation Z Contextual Performance

Leading and managing Generation Z can be a daunting task due to the distinct expectations, behaviours, and preferences they bring with them compared to prior generations. As such, when managing Gen Z workers, it is essential that leaders are aware of these variations to effectively manage their teams. Hence, this research endeavours to investigate the role of flexible work options on strengthening the relationship between structural empowerment and contextual work performance among Generation Z. This study employed a quantitative approach via an online questionnaire distributed to full- or part-time employed Gen Z workers in Jeddah, Saudi Arabia working within the wholesale and retail sectors. The purpose of this study is, therefore, to determine the impact of the moderating effect of flexible work options on the relationship between structural empowerment and Gen Z employee work performance. The findings suggest that H1, the moderating effect of the flexible work option on the opportunity and contextual work performance relationship, is not supported, while H2 and H3, the moderating effect of the Flexible work option on the relationships of support as well as information and contextual work performance, are both supported. However, it has a negative effect on the relationship between access to support and Gen Z contextual work performance and a positive effect on the relationship between access to information and Gen Z contextual work performance. The study’s findings highlight the necessary structural empowerment for enhancing Generation Z’s contextual work performance, offering useful information to management, policy makers, and the business as a whole.
One Step Entropy Variation in Sequential Sampling of Species for the Poisson-Dirichlet Process

We consider the sequential sampling of species, where observed samples are classified into the species they belong to. We are particularly interested in studying some quantities describing the sampling process when there is a new species discovery. We assume that the observations and species are organized as a two-parameter Poisson-Dirichlet Process, which is commonly used as a Bayesian prior in the context of entropy estimation, and we use the computation of the mean posterior entropy given a sample developed in Archer et al. (J. Mach. Learn. Res. 15(1):2833–2868, 2014). Our main result shows the existence of a monotone functional, constructed from the difference between the maximal entropy and the mean entropy throughout the sampling process. We show that this functional remains constant only when a new species discovery occurs.