Reviewerly/exHarmony huggingface dataset:
A core reference ontology for steelmaking process knowledge modelling and information management

Following the trend of Industry 4.0, the business model of steel manufacturing is transforming from a historical inwardly focused supplier/customer relationship to one that embraces the wider end-to-end supply chain and improves productivity more holistically. However, the data and information required for supply chain planning and steelmaking process modelling are normally distributed over scattered sources across organisation boundaries and research communities. This leads to a major problem concerning semantic interoperability. To address this issue, this paper introduces a Common Reference Ontology for Steelmaking (CROS). CROS serves as a shared steelmaking resource and capability model that aims to facilitate knowledge modelling, knowledge sharing and information management. In contrast to most of the existing steelmaking ontologies which merely focus on conceptual modelling, our work pays special attention to the real-world implementation and utilisation aspects of CROS. The functionality and usefulness of CROS is evaluated and tested on a real-world condition-based monitoring and maintenance task for cold rolling mills at Tata Steel in the United Kingdom.
An Intelligent Vaccine Distribution Process in COVID-19 Pandemic through Blockchain-SDN Framework from Bangladesh Perspective

Currently, vaccine distribution is the most concerning issue of Coronavirus (SARS-CoV-2) diseases in every stage of peoples properly. There are several strategies is proposed by the researchers. Still, the public is facing various difficulties in taking the vaccine. However, the current technologies such as the Internet of Things (IoT), Software-Defined Networking (SDN), and Blockchain (BC) are the intelligent solution for vaccine distribution efficiently. In light of these considerations, in this paper, we propose the “DistVAC-COVID” architecture incorporating a distributed Blockchain-based SDN-IoT framework for COVID-19's vaccine distribution considerably. In the presented system, the SDN benefits to divide the entire network into two distinct planes such as the data and control plane for distributing COVID data smartly. Further, a distributed Blockchain provides the desired system to be prominently secured and more confidential. Therefore, the “DistVAC-COVID” model combining SDN, IoT, and BC can be handled by several COVID-19 zones-red, hazard, and green securely. Finally, we evaluate the “DistVAC-COVID” architecture via an experimental evaluation process and show the effectiveness of the suggested solution in comparison with baseline system.
Human Action Recognition in Smart Cultural Tourism Based on Fusion Techniques of Virtual Reality and SOM Neural Network

Smart cultural tourism is the development trend of the future tourism industry. Virtual reality is an important tool to realize smart tourism. The reality of virtual reality mainly comes from human-computer interaction, which is closely related to human action recognition technology. Therefore, the research takes human action recognition as the research direction, uses a self-organizing mapping network (SOM) neural network to extract the key frame of action video, combines it with multi-feature vector method to recognize human action, and compares the recognition rate and user satisfaction of different recognition methods. The results show that the recognition rate of multi-feature voting human action recognition algorithm based on SOM neural network is 93.68% on UT-Kinect action, 59.06% on MSRDailyActivity3D, and the overall action recognition time is only 3.59 s. Within six months, the total profit of human-computer interactive virtual reality tourism project with SOM neural network multi-eigenvector as the core algorithm reached 422,000 yuan, and 88% of users expressed satisfaction after use. It shows that the proposed method has a good recognition rate and can give users effective feedback in time. It is hoped that this research has a certain reference value in promoting the development of human motion recognition technology.
When Tether says “JUMP!” Bitcoin asks “How low?”

While stablecoins such as Tether closely track the peg, there is some evidence for recurring spikes in stablecoins' intraday volatilities rendering stablecoin volatilities unstable (Grobys et al., 2021). Using the Barndorff-Nielsen and Shephard (2006a) methodology, the purpose of our study is to examine whether jumps in Tether have an impact on (subsequent) Bitcoin returns. We retrieve hourly data for Bitcoin and Tether from Bitfinex covering the November 2018 to June 2021 period and encode the binary choice (1 – 'jump' and 0 – 'no jump') using bi-power variation based on asymptotic distribution theory at 5% significance level for each trading day. Our results show that the joint effect of positive jumps in Tether in association with an 1% increase in Tether returns on the prior day significantly predict negative prices changes in Bitcoin ranging from -3.65% to -8.49% in daily terms. Our results remain robust even after controlling for various other variables.
Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality

Coronavirus disease 2019 (COVID-19) has highlighted the need for the timely collection and sharing of public health data. It is important that data sharing is balanced with protecting confidentiality. Here we discuss an innovative mechanism to protect health data, called differential privacy. Differential privacy is a mathematically rigorous definition of privacy that aims to protect against all possible adversaries. In layperson's terms, statistical noise is applied to the data so that overall patterns can be described, but data on individuals are unlikely to be extracted. One of the first use cases for health data in Australia is the development of the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER), which provides proof of concept for the use of this technology in the health sector. If successful, this will benefit future sharing of public health data.
Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation

Technological advances enable the design of systems that interact more closely with humans in a multitude of previously unsuspected fields. Martial arts are not outside the application of these techniques. From the point of view of the modeling of human movement in relation to the learning of complex motor skills, martial arts are of interest because they are articulated around a system of movements that are predefined, or at least, bounded, and governed by the laws of Physics. Their execution must be learned after continuous practice over time. Literature suggests that artificial intelligence algorithms, such as those used for computer vision, can model the movements performed. Thus, they can be compared with a good execution as well as analyze their temporal evolution during learning. We are exploring the application of this approach to model psychomotor performance in Karate combats (called kumites), which are characterized by the explosiveness of their movements. In addition, modeling psychomotor performance in a kumite requires the modeling of the joint interaction of two participants, while most current research efforts in human movement computing focus on the modeling of movements performed individually. Thus, in this work, we explore how to apply a pose estimation algorithm to extract the features of some predefined movements of Ippon Kihon kumite (a one-step conventional assault) and compare classification metrics with four data mining algorithms, obtaining high values with them.
Exploration of the spatially varying relationships between lead and aluminium concentrations in the topsoil of northern half of Ireland using Geographically Weighted Pearson Correlation Coefficient

The investigation of spatially varying relationships provides a novel and effective way to identify the influencing factors and potential sources of potentially toxic elements (PTEs). The varying relationships for PTEs at different sampling locations can be associated with the different controlling factors from geological units or pollution sources, which benefits environmental studies and management for local government. Generally, the concentrations of lead (Pb) and aluminium (Al) in the soil are reported as being positively correlated under most natural conditions, due to Pb and Al being enriched in clay minerals. However, the original positive relationship can be masked by external influences at the local level, thus the resulting varying relationships can be observed and associated with the potential pollution sources. In this study, Geographically Weighted Pearson Correlation Coefficient (GWPCC) was applied to explore the spatial relationships between Pb and Al in soils. Both positive and negative correlation coefficients were observed from the results of GWPCC at different scales with bandwidths ranging between 43 and 300 nearest neighbours, suggesting the relationships between Pb and Al are spatially varying at the regional scale. Negative correlations were observed in more than 35% of the study area, mainly clustered in the western and north-eastern of Ireland. The positive correlations were found in the central-western and midlands, while comparatively mixed correlations occurred in the eastern coastal areas. The spatial patterns for the majority of negative correlations showed a clear association with blanket peat, which likely can be attributed to long-distance transportation of Pb through atmospheric deposition. Moreover, anthropogenic activities weakened the relationships in the eastern coastal areas. These results demonstrated the efficiency of GWPCC in exploring the spatially varying relationships between environmental variables and identifying associations with related influencing factors, which could be hardly achieved by traditional techniques.
Optimizing the transfer‐learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning-based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.
Longitudinal Effects in the Effectiveness of Educational Virtual Field Trips

Virtual and immersive virtual reality, VR and iVR, provide flexible and engaging learning opportunities, such as virtual field trips (VFTs). Despite its growing popularity for education, understanding how iVR compared to non-immersive media influences learning is still challenged by mixed empirical results and a lack of longitudinal research. This study addresses these issues through an experiment in which undergraduate geoscience students attended two temporally separated VFT sessions through desktop virtual reality (dVR) or iVR, with their learning experience and outcomes measured after each session. Our results show higher levels of enjoyment and satisfaction as well as a stronger sense of spatial presence in iVR students in both VFTs compared to dVR students, but no improvement in learning outcomes in iVR compared to dVR. More importantly, we found that there exists a critical interaction between VR condition and repeated participation in VFTs indicating that longitudinal exposure to VFTs improves knowledge performance more when learning in iVR than through dVR. These results suggest that repeated use of iVR may be beneficial in sustaining students’ emotional engagement and compensating the initial deficiency in their objective learning outcomes compared to other less immersive technologies.
Mapping HCI research methods for studying social media interaction: A systematic literature review

In the last decades, researchers in Human-computer interaction (HCI) took numerous efforts to investigate people's social media interaction. To understand how researchers in HCI have studied social media interaction, we examined 149 peer-reviewed articles published between 2008 and 2020 in major HCI conference proceedings and journals. We systematically reviewed the methodologies HCI researchers applied, the research topics these methods covered, and the types of data collected. Through the analysis, we make three contributions: We (1) pinpoint the topic trends by identifying three phases in the study of social media interaction in HCI. Namely, the early phase (2008–2012) focused on user behavior, the growing phase (2013–2016), focused on privacy and health, and the latest phase (2017–2020) focused on design. (2) We map methodological trends in the study of social media interaction in HCI. We also illustrate the trends in relation to the types of data collected in the selected works and, (3) identify underexplored study areas.
Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation

Virtual reality (VR) and 3D modeling technologies have become increasingly powerful tools for multiple fields, such as education, architecture, and cultural heritage. Museums are no longer places for only placing and exhibiting collections and artworks. They use such technologies to offer a new way of communicating art and history with their visitors. In this paper, we present the initial results of a proposed workflow towards highlighting and interpreting a historic event with the use of an immersive and interactive VR experience and the utilization of multiple senses of the user. Using a treadmill for navigating and haptic gloves for interacting with the environment, combined with the detailed 3D models, deepens the sense of immersion. The results of our study show that engaging multiple senses and visual manipulation in an immersive 3D environment can effectively enhance the perception of visual realism and evoke a stronger sense of presence, amplifying the educational and informative experience in a museum.
Learning edge-preserved image stitching from multi-scale deep homography

Image stitching is a classical and challenging technique in computer vision, which aims to generate an image with a wide field of view. The traditional methods heavily depend on feature detection and require the feature points to be dense and evenly distributed in the image, leading to poor robustness in low-texture scenes. Learning methods are rarely studied due to the unavailability of ground truth stitched results, showing unreliable performance on real-world datasets. In this paper, we propose an image stitching learning framework, which consists of a multi-scale deep homography module and an edge-preserved deformation module. First, we design a multi-scale deep homography module to estimate the accurate homography progressively from coarse to fine. After that, an edge-preserved deformation module is designed to learn the deformation rules of image stitching from edge to content, generating the stitched image with artifacts eliminated. Besides, the proposed supervised learning framework can stitch images of arbitrary resolutions and demonstrate good generalization capability in real-world images. Experiments show that our superiority to the existing homography solutions and image stitching algorithms.
Optimal qubit mapping with simultaneous gate absorption

Before quantum error correction (QEC) is achieved, quantum computers focus on noisy intermediate-scale quantum (NISQ) applications. Compared to the well-known quantum algorithms requiring QEC, like Shor's or Grover's algorithm, NISQ applications have different structures and properties to exploit in compilation. A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer, which has been shown to be an NP-hard problem. In this paper, we present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption during qubit mapping, which we show to be a very effective optimization technique for NISQ applications. For the class of quantum approximate optimization algorithm (QAOA), an important NISQ application, OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods, which translates to 55.9% fidelity improvement. The solution optimality of OLSQ-GA is achieved by the exact SMT formulation. For better scalability, we augment our approach with additional constraints in the form of initial mapping or alternating matching, which speeds up OLSQ-GA by up to 272X with no or little loss of optimality.
Rates of approximation by neural network interpolation operators

We construct neural network interpolation operators with some newly defined activation functions, and give the approximation rate by the operators for continuous functions. By adding some smooth assumptions on the activation function, we establish two important inequalities of the derivative of the operators. With these two inequalities, by using K-functional and Berens–Lorentz lemma in approximation theory, we obtain the converse theorem of approximation by the operators. To approximate smooth functions, we further introduce special combinations of the operators, which can be regarded as FNNs with four layers, and can approximate the object function and its derivative simultaneously. Finally, we introduce a Kantorovich type variant of the operators. We establish both the direct theorem and the converse theorem of approximation by the Kantorovich type operators in Lp spaces with 1≤p≤∞.
3D Volume Reconstruction from MRI Slices based on VTK

In today's fast-advancing world, Deep learning brought the huge potential to the healthcare system and it still undergoes different amazing new techniques. New automatic brain tumor segmentation models have been realized. As a result, it is being much more affordable and faster to save lives. However, most of the tumor detection works are still being conducted with 2D single slices of brain image, although, there are new 3D CNN [1] models with more benefits. Those 3D models enable to scan of brain images in 3d volume. 2D models accept only single slices as input and they innately fail to use context from neighboring slices. Missed voxel data from contiguous slices might affect the detection of tumors and decrease the accuracy of the model. 3D models address this issue by utilizing 3D convolutional kernels to make predictions from volumetric inputs. The capacity to use interslice features can increase the further performance of the model. Therefore, in practice, 3D volumes enable to obtain much more efficient and clear diagnoses. İn this paper we purpose our new 3D MRI reconstruction algorithm based on VTK toolkit [3].
Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models

The human attention mechanism can be understood and simulated by closely associating the saliency prediction task to neuroscience and psychology. Furthermore, saliency prediction is widely used in computer vision and interdisciplinary subjects. In recent years, with the rapid development of deep learning, deep models have made amazing achievements in saliency prediction. Deep learning models can automatically learn features, thus solving many drawbacks of the classic models, such as handcrafted features and task settings, among others. Nevertheless, the deep models still have some limitations, for example in tasks involving multi-modality and semantic understanding. This study focuses on summarizing the relevant achievements in the field of saliency prediction, including the early neurological and psychological mechanisms and the guiding role of classic models, followed by the development process and data comparison of classic and deep saliency prediction models. This study also discusses the relationship between the model and human vision, as well as the factors that cause the semantic gaps, the influences of attention in cognitive research, the limitations of the saliency model, and the emerging applications, to provide new saliency predictions for follow-up work and the necessary help and advice.
Reconstruction of a Three-Dimensional Scene from its Projections in Computer Vision Systems

The problem of reconstructing a three-dimensional scene from its projections is one of the most urgent and studied among the problems solved by computer vision methods. Most often, such systems are implemented by creating geographically distributed complexes that require the transfer of a large amount of data between their components, which creates a large load on the transmission lines. Scene reconstruction using projections obtained from several observation points is a special case and is not always possible, especially when used in unmanned aerial vehicles or autonomous vehicles. To resolve these limitations, the possibility of using a computer stereoscopic vision system and a three-dimensional convolutional neural network capable of reconstructing a complete scene from a single image containing subjective characteristics of scene objects, such as color and depth, is considered in this work, creating its three-dimensional representation. The article describes the architecture and principle of operation of a neural network, and also builds a model of a computer stereo vision stand for image registration. An image formation algorithm is proposed for this model.
Randomized neural networks for multilabel classification

Multilabel classification is a supervised learning problem in which input instances belong to multiple output labels. In this paper, we propose noniterative randomization-based neural networks for multilabel classification. These multilabel neural networks are named as Multilabel Random Vector Functional Link Network (ML-RVFL), Multilabel Kernelized Random Vector Functional Link Network (ML-KRVFL), Multilabel Broad Learning System (ML-BLS), and Multilabel Fuzzy Broad Learning System (ML-FBLS). The output weights of these neural networks are computed using pseudoinverse. At the output layer, multilabel classification is performed by using an adaptive threshold function. The computation of output weights using pseudoinverse retains the faster computation power of these algorithms compared to iterative learning algorithms. The adaptive threshold function used in the proposed approach can consider the correlation among the output labels and the whole dataset for threshold computation. Five multilabel evaluation metrics evaluate the proposed multilabel neural networks on 12 benchmark datasets of various domains such as text, image, and genomics. The ML-KRVFL provides the overall best Friedman rankings on five evaluation metrics followed by ML-RVFL, ML-FBLS, and ML-BLS, respectively. Based on the experimentation results, the proposed ML-KRVFL, ML-RVFL, ML-FBLS, and ML-BLS perform better than other relevant multilabel approaches in the mentioned order.The proposed approaches are faster than other state-of-the-art iterative approaches and noniterative approaches in terms of running time.