Reviewerly/exHarmony huggingface dataset:
Review of Effects of the Virtual Reality in Education

Virtual Reality (VR) have been shown to be effective instruments for teaching students, particularly in some disciplines such as Science, Technology, Engineering and Mathematics (STEM) fields that need more learners’ engagement as an immersive, whole-body interactive simulation with environment components for experiments, this will affect students’ interactions and conceptual understanding. In this article, we focus on the effects of the using of VR on learning Biology and Medicine courses. The paper summarizes the outcomes of some related articles; it presents a comparison between some methods that are used to support the educational process. Furthermore, it discusses some challenges of the use of VR in different fields of education.
Artificial Intelligence Techniques for Conflict Resolution

Abstract Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.
Introducing Data Wrangling using Graphical Subgoals - Findings from an e-Learning Study

Students across disciplines are increasingly wrangling their data programmatically. Previous research indicates that graphical, instructor-provided subgoals could help novices solve data wrangling tasks, and that graphical thumbnails could make the syntax documentation more usable. Using a 2x2 design, we trialled different versions of a web application for teaching data wrangling, which contains both programmatic and non-programmatic exercises. Half of all participants received graphical subgoals to visualise data wrangling solutions instead of just textual subgoals, and half received graphical thumbnails. Performance and time on task are reported, but did not result in any significant effect. We discuss possible reasons why.
On applying the lackadaisical quantum walk algorithm to search for multiple solutions on grids

Quantum computing promises to improve the information processing power to levels unreachable by classical computation. Quantum walks are heading the development of quantum algorithms for searching information on graphs more efficiently than their classical counterparts. A quantum-walk-based algorithm standing out in the literature is the lackadaisical quantum walk. The lackadaisical quantum walk is an algorithm developed to search graph structures whose vertices have a self-loop of weight l. This paper addresses several issues related to applying the lackadaisical quantum walk to search for multiple solutions on grids successfully. Firstly, we show that only one of the two stopping conditions found in the literature is suitable for simulations. In the most discrepant case shown here, a stopping condition is prematurely satisfied at the step T=288 with a success probability Pr=0.593276, while the suitable condition captures the actual amplification that occurred until T=409 with Pr=0.878178. We also demonstrate that the final success probability depends on both the space density of solutions and the relative distance between solutions. For instance, we show here that decreases in the density of solutions can even take a success probability of 0.849178 to 0.961896. In contrast, increases in the relative distances can even take a success probability of 0.871665 to 0.940301. Furthermore, this work generalizes the lackadaisical quantum walk to search for multiple solutions on grids of arbitrary dimensions. In addition, we propose an optimal adjustment of the self-loop weight l for such d-dimensional grids. It turns out other fits of l found in the literature are particular cases. Our experiments demonstrate that successful searches for multiple solutions with higher than two dimensions are possible by achieving success probabilities such as 0.999979, with the value of l proposed here, where it would be 0.637346, with the value of l proposed in previous works. Finally, we observe a two-to-one relation between the steps of the lackadaisical quantum walk and Grover’s algorithm, which requires modifications in the stopping condition. That modified stopping condition can escape intermediary fluctuations that would produce premature stops at T=6 with Pr=0.000878 where the system can evolve until T=354 with Pr=0.99999, as an example that we show here. In conclusion, this work deals with practical issues one should consider when applying the lackadaisical quantum walk, besides expanding the technique to a broader range of search problems.
Feasibility study of a solar system learning media based on merge cube augmented reality to embedding problem solving skills

Abstract Science learning activities in the era of the industrial revolution 4.0 need to be directed towards attitudes, skills, and mastery of all material in a comprehensive manner that equips students with 21st century skills which are very important, including problem solving skills. This research aims to determine the feasibility of learning media for solar system based on merge cube augmented reality to embedding problem solving skills. Research and Development (R and D) the ADDIE model has applied to achieve the objectives of this research. This research stage is planned through the steps of Analysis, Design, Development, Implementation and Evaluations. This research has produced a solar system learning media based on Merge Cube Augmented Reality (AR) to embedding problem solving skills. The results of validation of material experts were 82%, validation of media and technology experts was 86% and validation or response of teachers was 82%. The three validations reached the very feasible criteria. The average percentage of the validation score is 83.33% or the criteria are very feasible. It was concluded that the learning medio of solar system based on Merge Cube Augmented Reality (AR) was feasible to embedding problem solving skills.
Intelligent Tutoring System: Learning Math for 6th-Grade Primary School Students

This paper proposes a web-based application designed to help elementary school students who have difficulty learning online independently and also their parents who are currently having difficulty teaching their children to study at home online, especially at this time of difficulty with a pandemic outbreak like COVID-19; this time does not allow for physical meetings for the learning process in primary schools. In this paper, we only focus on mathematics because based on several other studies, it is very difficult and important to learn mathematics at the beginning of educational activities such as at the elementary school level. In this paper, the system is modeled using the Unified Modeling Language (UML) tool in the form of a use case diagram which is used to describe the proposed business process and uses class diagrams to describe the database model diagram. In this case, the class diagram is used to describe the data in the class diagram where each class refers to a table in the database. The web-based application user interface is shown at the end to show the communication between users and applications, where this web-based application is implemented using Personal Home Pages (PHP) as server programming and using MySQL to store database model designs. Moreover, for the Intelligent Tutoring System (ITS), content was created using the Cognitive Tutor Authoring Tools (CTAT) which is an authoring tool for learning mathematics created by Carnegie Mellon University. In the end, this web-based application is expected to be used and support teachers as a complement to online mathematics learning, especially during difficult times such as during the COVID-19 pandemic.
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning

Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and DKL. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the Polya Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate the effectiveness of our method against other Gaussian process training baselines, and we show how our general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks.
Distillation-Guided Residual Learning for Binary Convolutional Neural Networks

It is challenging to bridge the performance gap between binary convolutional neural network (BCNN) and floating-point CNN (FCNN). This performance gap is mainly caused by the inferior modeling capability and training strategy of BCNN, which leads to substantial residuals in intermediate feature maps between BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This intuition leads to a more effective training strategy for BCNN, i.e., optimizing each binary convolutional block with blockwise distillation loss derived from FCNN. The goal of minimizing the residuals in intermediate feature maps also motivates us to update the binary convolutional block architecture to facilitate the optimization of blockwise distillation loss. Specifically, a lightweight shortcut branch is inserted into each binary convolutional block to complement residuals at each block. Benefited from its squeeze-and-interaction (SI) structure, this shortcut branch introduces a fraction of parameters, e.g., less than 10% overheads, but effectively boosts the modeling capability of binary convolution blocks in BCNN. Extensive experiments on ImageNet demonstrate the superior performance of our method in both classification efficiency and accuracy, e.g., BCNN trained with our methods achieves the accuracy of 60.45% on ImageNet, better than many state-of-the-art ones.
A Study of the Impact of Managerial Innovation on SME Performance in Africa

Purpose. Managerial innovation, if it constitutes a real lever for transformation and performance of companies in developed countries, in Africa, only a tiny part of SME managers make it a priority. At the same time, most African economies continue to be at the forefront of the adoption of global technological innovations. Given the fact that managerial innovation has proven itself in the Western context, and that the context of Sub-Saharan Africa is still unclear, it is important to develop management methods in this context by adapting them to new ones economic models, new objectives, new processes in order to see its impact on improving the productivity and performance of SMEs. Design/Methodology/Approach. A survey instrument based on the questionnaire was used to collect quantitative data to explain the performance of SMEs through the adoption of managerial innovation. For data analysis, multiple linear regression analysis was used. Findings and implications. The findings indicate that, managerial innovation, through its two main components, "change in management practices" and "change in organizational structure", make it possible to increase market share, production efficiency, the bottom line and, in turn, improve the overall performance of the business. Overall, the results of the study show that the fit model is of good quality and can be used to explain the theory. Limitations. The results of this study may not be generalisable to all African SMEs because they are based only on a sub-Saharan African country and the sample size therefore remains small. Originality. The contribution of this article is manifold: it supports the theories of contingency and resource dependence that organizations are adaptive systems that introduce changes to function effectively and improve their performance. Second, it allows SME managers to optimize the chances of sustainability for their businesses, because managerial innovation allows them to: differentiate themselves from their competitors by inventing new offers. Finally, it allows SME managers to no longer confine themselves to the technological aspect of innovation (products, processes) whose lifespan is constantly shortened. Studies of this nature can lead to stimulating managerial innovation in emerging and developing countries, by developing horizontal or networked organizational structures and no longer vertical and pyramidal structures which no longer meet current requirements.
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling

Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established. This paper provides the first such convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning that incorporate AMSGrad updates (a standard alternative of Adam in theoretical analysis), referred to as PG-AMSGrad and TD-AMSGrad, respectively. Moreover, our analysis focuses on Markovian sampling for both algorithms. We show that under general nonlinear function approximation, PG-AMSGrad with a constant stepsize converges to a neighborhood of a stationary point at the rate of O(1/T) (where T denotes the number of iterations), and with a diminishing stepsize converges exactly to a stationary point at the rate of O(log^2 T/√T). Furthermore, under linear function approximation, TD-AMSGrad with a constant stepsize converges to a neighborhood of the global optimum at the rate of O(1/T), and with a diminishing stepsize converges exactly to the global optimum at the rate of O(log T/√T). Our study develops new techniques for analyzing the Adam-type RL algorithms under Markovian sampling.
Parallax‐based second‐order mixed attention for stereo image super‐resolution

Stereo image pairs can effectively enhance the performance of super-resolution (SR) since both intra-view and cross-view information can be used. However, exploiting cross-view information accurately is extremely challenging. Most recent methods use the attention mechanism to get stereo correspondence. But these methods ignore the high-frequency information since they only utilise first-order statistics, which leads to reducing the discriminative ability of the network. To address this issue, in this work, a parallax-based second-order mixed attention stereo SR network (PSMASSRnet) is proposed to integrate the cross-view information from a stereo image pair for SR. Specifically, a novel parallax-based second-order mixed attention module (PSMAM) is developed to combine second-order channel features and spatial features to obtain more discriminative representations. Furthermore, a dense cross-atrous spatial pyramid pooling (ASPP) module is also presented, which can effectively explore the local and the multi-scale features with different dilation rates to extract more discriminative features with fewer parameters and less execution. The extensive experiments on the KITTI2012, KITTI2015 and Middlebury datasets have demonstrated the superiority of the proposed PSMASSRnet over the state-of-the-art methods in the aspects of both the quantitative metrics and the visual quality.
Autonomous Drone for Social Distancing Surveillance

Drones being capable of doing things that are not even remotely possible for a human have a major role to play in our society. These unmanned aerial vehicles have endless applications in various fields such as photography, surveillance, farming and communication systems. These drones due to such extraordinary capabilities pose a serious threat to privacy of the general population. However in desperate situations privacy is overtaken by safety and security in terms of priority. In this project the application of unmanned aerial vehicles in the surveillance field are explored while walking the thin line between invasion of privacy and safety concerns. An autonomous drone with a camera module with an intelligent scene analysis model is used to automate the surveillance of vast areas in a single go while keeping maintenance cheap and operability easy. The primary focus would be to surveil the overall population for honouring the “social distancing protocols” and then based on the results, a tool with a wider application in human behaviour analysis is to be developed.
Geometrically Invariant NSCT–GPCET-Based Image Watermarking with Accurate Moment Calculation

Generic polar complex exponential transform (GPCET), as continuous orthogonal moment, has the advantages of computational simplicity, numerical stability, and resistance to geometric transforms, which make it suitable for watermarking. However, errors in kernel function discretization can degrade these advantages. To maximize the GPCET utilization in robust watermarking, this paper proposes a secondary grid-division (SGD)-based moment calculation method that divides each grid corresponding to one pixel into nonoverlapping subgrids and increases the number of sampling points. Using the accurate moment calculation method, a nonsubsampled contourlet transform (NSCT)–GPCET-based watermarking scheme with resistance to image processing and geometrical attacks is proposed. In this scheme, the accurate moment calculation can reduce the numerical error and geometrical error of the traditional methods, which is verified by an image reconstruction comparison. Additionally, NSCT and accurate GPCET are utilized to achieve watermark stability. Subsequent experiments test the proposed watermarking scheme for its invisibility and robustness, and verify that the robustness of the proposed scheme outperforms that of other schemes when its level of invisibility is significantly higher.
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses.

Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF where agents are constantly engaged with new goal locations, such as in large-scale warehouses. We propose a new framework for solving lifelong MAPF by decomposing the problem into a sequence of Windowed MAPF instances, where a Windowed MAPF solver resolves collisions among the paths of the agents only within a finite time horizon and ignores collisions beyond it. Our framework is particularly well suited to generating pliable plans that adapt to continually arriving new goal locations. Theoretically, we analyze the advantages and disadvantages of our framework. Empirically, we evaluate our framework with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents, significantly outperforming existing methods.
Gaussian noise level estimation for color image denoising

Noise level is an important parameter in many visual applications, especially in image denoising. How to accurately estimate the noise level from a noisy image is a challenging problem. However, for color image denoising, it is not that the more accurate the noise level is, the better the denoising performance is, but that the noise level higher than the true noise can achieve a better denoising result. For better denoising, we propose a statistical iterative method based on low-rank image patches. We select the low-rank patches in the image and calculate the eigenvalues of the covariance matrix of these patches. Unlike the existing methods that take the smallest eigenvalue as the estimated noise level, the proposed method analyzes the relationship between the median value and the mean value of the eigenvalue according to the statistical property and selects an appropriate number of eigenvalues to average as the estimated noise level. Extensive experiments are conducted, demonstrating that our estimated noise level reaches the highest value of all comparison methods. And the denoising results on color images of our method outperform all the state-of-the-art methods and the true noise level.
The Effectiveness of Ice Breaking to Increase Students’ Motivation in Learning English

This study was generally attempted to find the effect of ice breaking to increase students' motivation in English learning at the first grade of SMP Nurul Jadid. This research used pre-experimental design by using one shot case study pre-test and post-test. Before the treatment, the researcher gave the pre-test in one class. Moreover, the researcher taught English by using ice breaking in the experimental class. The last, the researcher conducted the post-test to know whether an ice breaking was effective for teaching English in experimental class. Based on the data analysis, the data t-test (2.77) was higher than t-table value (2.042). The result analyzing showed that there was significant difference on the students’ motivation taught by using ice breaker. The students’ mean score of the pre-test of the experiment class was 60. Then, in the post-test, the mean score of experiment class was 66.1. Therefore, hypothesis H0 was rejected and H1 was accepted. It concluded that ice breaking was effective in teaching English learning toward the students’ motivation for the first grade of SMP Nurul Jadid. Thus, this research make students be enjoy, be active, energize, feeling happiness, interactive in the classroom, stay in the class during learning process, and focus on the material.
IgSEG: Image-guided Story Ending Generation

In this work, we propose a new task called Image-guided Story Ending Generation (IgSEG).Given a multi-sentence story plot and an ending-related image, IgSEG aims to generate a story ending that conforms to the contextual logic and the relevant visual concepts.In contrast to the story ending generation task, which generates open-ended endings, the major challenges of IgSEG are to comprehend the given context and image sufficiently, and mine the appropriate semantics from the image to make the generated story ending informative, reasonable, and coherent.To address the challenges, we propose a Multi-layer Graph convolution and Cascade-LSTM (MGCL) based model which mainly comprises of two collaborative modules: i) a multi-layer graph convolutional network to learn the dependency relations of sentences and the logical clue of the context; ii) a multiple context-image attention module to generate the story endings by gradually incorporating textual and visual semantic concepts.Our MGCL is thus capable of building logically consistent and semantically rich story endings.To evaluate the proposed model, we modify the existing VIST dataset to obtain the VIST-Ending dataset.Empirically, our MGCL outperforms all the strong baselines on both automatic and human evaluation.
Utilizing Cloud Capabilities for Face Detection and Face Recognition During COVID-19: Comparative Analysis

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI