Reviewerly/exHarmony huggingface dataset:
Augmented Reality Applications in Education and Examining Key Factors Affecting the Users’ Behaviors

Augmented Reality (AR) is increasingly influential in education. AR technology allows users to learn and practice in a simulated environment that enables repetition, correction, and failure without risk. The present study evaluated users’ attitudes towards using AR for learning complex tasks. The users are asked to interact with an AR Piling (ARP) application that shows various steps of a construction process. A set of selected practitioners and students used the application, and the evaluation involved various participants of different genders and backgrounds. A questionnaire was designed and data was collected through an online survey based on the Technology Acceptance Model (TAM). The model is modified considering education practices and adjusted to an AR app for learning purposes. The novelty of the model lies in various constructs such as technical quality, social influence, perceived immersion, learning, and perceived enjoyment. 200 responses were obtained and used for evaluating the proposed model. The attitude toward using AR and the perceived usefulness of AR were the two factors that determined the participants’ behavioral intention to use ARP. Respondents showed a high level of acceptance for AR. In education and higher learning contexts, the findings of this study contribute to a deeper understanding of how AR is accepted in complex learning environments. The study allows us to extend the TAM by examining how AR technology can be applied to teaching in universities and unpack the ways in which gender influences learning through AR application.
Analysis and Optimization of Landscape Preference Characteristics of Rural Public Space Based on Eye-Tracking Technology: The Case of Huangshandian Village, China

As an important part of rural space, the public space landscape has a positive influence on tourists and local residents, and also has an important guiding significance for the sustainable development of rural areas. This study expands the previous research on rural public space, taking the “user-oriented” landscape preference as an important entry point, taking Beijing rural public space as the research object, using eye-tracking technology to objectively reflect the degree of users’ preference for the landscape, making quantitative analysis through eye-tracking objective data and subjective satisfaction evaluation to explore the subconscious and aesthetic laws of the subjects, and summarizing the internal driving factors leading to its evolution from the summary of phenomena. This paper explores the visual behavior information and significance behind the “subjective space” created by the countryside for the users, intending to provide strategies for landscape optimization and the management of rural public space in an effort to aid the sustainable development of the countryside.
6G IoT Tracking- and Machine Learning-Enhanced Blockchained Supply Chain Management

The 6G Internet of Things (IoT) is of utmost importance when it comes to running and controlling contemporary supply chains. Blockchain and machine learning (ML) are two upper-layer technologies that can assist with securing and automating the IoT. First, we propose integrating blockchain technology into modern supply chains to facilitate effective communication among all partners. Second, for inbound logistics task prediction, we develop Multi-Head Attention (MHA)-Based Gated Recurrent Unit (GRU). Finally, numerical findings demonstrate that the MHA-Based GRU model has satisfying fitting efficiency and prediction precision compared to its competitors.
Think Twice: First for Tech, Then for Ed

The embodiment of technology in education can make learning easier, more enjoyable, and more accessible. From Learning Machines to artificial intelligence (AI), educational technology has repeatedly tested its strength as an aider or a substitute to in-person teaching. During the COVID-19 pandemic international organisations promoted the idea of the transformation of education using technology. Comparison of their texts published in 2020 with texts published in 2021 indicates that much of the early enthusiasm concerning the transition from in-person to remote learning gave its position to more thoughtful accounts after considering the learning losses and students' disappointment from the disruption of in-person relationships. This publication highlights aspects of education technology usually overlooked in futuristic accounts of education. Adopting a non-deterministic view of technology attempts to contribute to the more human-centred incorporation of technologies in education.
MLP-TLBO: Combining Multi-Layer Perceptron Neural Network and Teaching-Learning-Based Optimization for Breast Cancer Detection

Symptoms and types of breast cancer, like most other diseases, vary from person to person. This is while some infected people may not have any symptoms. Breast cancer is one of the common cancers among women, of course, some types of it also affect men. Basically, timely and early diagnosis before the progress of the disease can significantly provide treatment grounds. There are many approaches to breast cancer diagnosis. Classification of patients in terms of benign or malignant cancer using data mining techniques has attracted the attention of many researchers. Data mining can provide a medical assistant system with very low cost and high accuracy for early detection of breast cancer. In this regard, this study proposes a data mining-based method by a combining Multi-Layer Perceptron (MLP) neural network and an evolutionary approach for breast cancer diagnosis. We aim to tune MLP parameters using an evolutionary approach. Here, Teaching-Learning-Based Optimization (TLBO) is used as a new evolutionary approach. However, the capabilities of approaches such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Open-Source Development Model Algorithm (ODMA) are analyzed compared to TLBO. The proposed method named MLP-TLBO can simultaneously adjust all MLP parameters (i.e., effective features and their number, number of hidden layers, number of neurons in each hidden layer, and weights of links). To improve the classification process, MLP-TLBO develops an ensemble classification mechanism in which multiple MLPs simultaneously model the training data. The evaluation of the proposed method is done on the Wisconsin Breast Cancer Database. We use three common datasets from Wisconsin including WBCD, WDBC and WPBC for simulations. Based on the results, the proposed method has promising results and averages between 7% and 26% better performance compared to similar methods.
Geometry-aware view reconstruction network for light field image compression

Light Field (LF) imaging empowers many attractive applications by simultaneously recording spatial and angular information of light rays. In order to meet the challenges of LF storage and transmission, many view reconstruction-based LF compression methods are put forward. However, occlusion issue and under-exploitation of LF rich structure information limit the view reconstruction qualities, which further influence LF compression efficiency. In order to alleviate these problems, in this paper, we propose a geometry-aware view reconstruction network for LF compression. In our method, only sparsely-sampled LF views are encoded, which are further used as priors to reconstruct the un-sampled LF views at the decoder side. The proposed reconstruction process contains two stages including geometry-aware reconstruction and texture refinement. The geometry-aware reconstruction stage utilizes a multi-stream framework, which can fully explore LF spatial-angular, location and geometry information. The texture refinement stage can adequately fuse such rich LF information to further improve LF reconstruction quality. Comprehensive experimental results validate the superiority of the proposed method. The rate-distortion performance and the perceptual quality of reconstructed views further demonstrate that the proposed method can save more bitrate while increasing LF reconstruction quality.
Development of a multi-objectives integer programming model for allocation of anti-fraud capacities during cyberfraud mitigation

Purpose This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots. Design/methodology/approach From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints. Findings The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives. Practical implications The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources. Originality/value The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.
Does the slanted-edge method provide the true value of spatial frequency response?

The slanted-edge method is commonly used for measuring the spatial frequency response (SFR) of digital imaging systems. To investigate the intrinsic capability of the slanted-edge method, this study formulated a procedure for calculating the SFR and proposed a numerical method for evaluating the accuracy of the estimated SFR relative to the theoretical value. It is shown that the slanted-edge method yields an accurate value of the SFR under ideal conditions and is a rational and consistent tool for determining the true resolution characteristics. However, under practical conditions, this includes some estimation errors. Based on this analysis, it is possible to identify the parameter that influences the estimation accuracy in the SFR computation.
DRONE: An Efficient Distributed Subgraph-Centric Framework for Processing Large-Scale Power-law Graphs

Nowadays, the ever-increasing volume of graph-structured data such as social networks, graph databases and knowledge graphs requires to be processed efficiently and scalably. These natural graphs commonly found in the real world have highly skewed power-law degree distribution and are called power-law graphs. The subgraph-centric programming model is a promising approach applied in many state-of-the-art distributed graph computing frameworks. However, the performance of subgraph-centric frameworks is limited when processing large-scale power-law graphs. When deployed to the subgraph-centric framework, existing graph partitioning algorithms are not suitable for power-law graphs. In this paper, we present a novel distributed graph computing framework, DRONE (Distributed gRaph cOmputiNg Engine), which leverages the subgraph-centric model and the vertex-cut graph partitioning strategy. DRONE also supports the fault tolerance mechanism to accommodate the increasing scale of machines with negligible overhead (6.48% on average). We further study the execution workflow of DRONE and propose an efficient and balanced graph partition algorithm (EBV) for DRONE. Experiments show that DRONE reduces the running time on real-world graphs by 25.6%, on average, compared to the state-of-the-art distributed graph computing frameworks. In addition, the EBV graph partition algorithm reduces the replication factor by at least 21.8% than other self-based partition algorithms. Our results indicate that DRONE has excellent potential in processing large-scale power-law graphs.
Deep Learning-Based Path Loss Model in Urban Environments Using Image-to-Image Translation

Ray-tracing techniques offer accurate predictions on path loss but suffer from high computational complexity. To have a fast and accurate path loss prediction, this article applies a deep learning-based image-to-image translation technique to construct a path loss model in urban environments. The proposed method combines a variational autoencoder with a generative adversarial network to translate images from the domain of street maps to the domain of path loss. It is trained in a supervised manner using paired samples, where the input is the street map with 3-D building information and the output is the path loss in the area obtained from the ray-tracing model. Based on a realistic digital map of urban Taipei city, simulation results show that the proposed model outperforms conventional ones when operating at the 3.5 GHz frequency band. The standard deviation of prediction error is reduced by over 62%. Besides prediction accuracy, the proposed model has the advantage of low computational complexity over ray-tracing techniques. Hence, it has great potential for the deployment of unmanned aerial vehicle-mounted base stations (UAV-BSs) for future communication systems. In this future work, the optimal UAV mobility can be determined upon the rapid evaluation of the UAV-BS coverage using the proposed model.
Classification of Salt Quality based on Salt-Forming Composition using Random Forest

Abstract Salt is part of the chemical that can be used and needed by humans in the field of consumption or industry. The formation of salt can be done in several ways, namely with seawater or lake water that is evaporated to produce salt crystals or through the process of mining rock salt. The results of the salt obtained will have a different composition depending on the process of formation, the difference in composition can affect the quality of the salt produced, so not all salt results are suitable for consumption. Generally, the salt quality classification process is still done manually, but this method takes a long time and is less effective. So, to overcome this problem, this research utilizes data mining science in classifying salt quality automatically using the Machine Learning algorithm, namely Random Forest. The data used in this study is a salt dataset with 7 attributes and 4 target classes totaling 349 data where the data is divided into training data and test data using k-fold cross validation with different k-fold values, namely 5-fold, 10-fold, and 20-fold. folds. The test results obtained indicate that the value of k = 10 has the best performance by achieving an AUC value of 96.1%, then for the classification accuracy is 87.7%, f1 score is 87.6%, precision is 87.7% and recall is 87.7%.
A Vision and GPS Based System for Autonomous Precision Vertical Landing of UAV Quadcopter

Abstract This research aims to develop an algorithm for the precision vertical landing process on a quadcopter using GPS sensors and computer vision. This research consists of 2 main parts: GPS-based control used by the quadcopter to return to the home area and computer vision-based control in the form of ArUco marker detection, which is used by the quadcopter in order to land precisely. Detection of the ArUco marker that is used goes through 2 stages: the application of threshold and ratio and the process of calculating the black and white area, which can then be searched for four corners of the marker. The algorithm designed works well on the Webots simulation program with mid-computer specifications. The ArUco marker detection process is fast and reliable, so it does not overload the processor unit so that the simulation process can run well. From 30 tests with different initial positions, the average error of the landing position of the quadcopter at coordinate X is 0.02 meters, and coordinates Y is 0.03 meters. All test results show that the quadcopter can land inside the landing pad area without any parts coming out.
Technical agonism: embracing democratic dissensus in the datafication of education

This paper interrogates whether technical democracy is well-suited to contend with possible technical controversies, including the increasing prevalence of EdTech in public schooling. Drawing across Habermasian ‘deliberative democracy’ that seeks consensus-derived resolution, as well as more agonistic approaches that embrace conflict as the means for pursuing pluralistic values, we use this paper to problematise technical democracy for its potential to produce agonistic and pluralistic im/possibilities. Drawing on case studies developed from our previous research into digital platforms and infrastructures, we consider what an alternative to technical democracy might offer towards realising democratic ideals through what we call technical agonism. Using these thinking tools, we argue that a move towards technical agonism would centre dissension and scepticism as fundamental features of liberal democratic institutions, while also providing opportunities for schooling to be reimagined in ways that are more democratic and ethical.
Framework for the Application of Blockchain Technology to Delay Analysis

Delay analysis and all its relevant techniques is an eminently sought-after area of study; in fact, delay analysis within the construction industry is currently a field that lies under the umbrella of ongoing progressiveness. Its necessity lies within the need for contractors to substantiate their right with regards to qualitative entitlement to an extension of time, which proves to be a tedious process. Problems faced in the delay analysis submission process mostly constitute a skewed advantage point to the contractor, which eventually leads the engineer to face a tedious task of tracing the steps conduced or even dealing with manipulated schedule submissions. Such gives rise to the need of combining forthcoming technology with prominent delay analysis techniques to establish a sense of security between parties. Despite previous research efforts in exploring blockchain technology within the project management realm, nothing addressed its usage in depth with delay analysis. Therefore, this paper aims to explore a framework of implementing delay analysis techniques through blockchain technology to allow for not only facilitating interactions between parties within the delay analysis implementation process, but also creating a secure and updated reserve to refer to. At the outset, the framework shall include a study of its applicability by analyzing the availability of required technology. To proceed, a structured work flow for the entire process was mapped out to bring the pairing of blockchain technology and delay analysis one step closer for practitioners’ implementation within the construction industry.
Research on energy‐saving virtual machine migration algorithm for green data center

Abstract The cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service.
Optimal Extraction Method of Feature Points in Key Frame Image of Mobile Network Animation

Abstract In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.
Blockchain-as-a-Service and Blockchain-as-a-Partner: Implementation options for supply chain optimization

Smart contracts show a high potential to make supply chain management strategies epochally leap towards higher levels of productivity, not only in the functioning of production processes but also in terms of product innovation and overall economic returns. This article illustrates the principle of Income Sharing as a highly performing economic strategy for supply chains with a natural implementation in blockchain smart contracts. It proposes a blockchain-based architecture that uses smart contracts to implement various algorithmic versions of the Income Sharing principle among companies participating in a supply chain. The formation of the total income and its consequent redistribution are calculated taking into account the role of the technological platform automating these procedures, which therefore becomes a party to the inter-company business project of a supply chain in the alternative roles, as feasible in business practice, of Blockchain-as-a-Service and Blockchain-as-a-Partner. The approach is implemented on Hyperledger Fabric, the most widespread platform for private and consortium blockchains. We compare and justify this design choice with the alternative given by public blockchains, with specific attention to Ethereum.