Reviewerly/exHarmony huggingface dataset:
Research on Super-Resolution Relationship Extraction and Reconstruction Methods for Images Based on Multimodal Graph Convolutional Networks

This study constructs a multimodal graph convolutional network model, conducts an in-depth study on image super-resolution relationship extraction and reconstruction methods, and constructs a model of image super-resolution relationship extraction and reconstruction methods based on multimodal graph convolutional networks. In this study, we study the domain adaptation model algorithm based on chart convolutional networks, which constructs a global relevance graph based on all samples using pre-extracted features and performs distribution approximation of sample features in two domains using a diagram convolutional neural network with maximum mean difference loss; with this approach, the model effectively preserves the structural information among the samples. In this study, several comparison experiments are designed based on the COCO and VG datasets; the image space information-based and knowledge graph-based target detection and recognition models substantially improve recognition performance over the baseline model. The super-pixel-based target detection and recognition model can also effectively reduce the number of floating-point operations and the complexity of the model. In this study, we propose a multiscale GAN-based image super-resolution reconstruction algorithm. Aiming at the problems of detail loss or blurring in the reconstruction of detail-rich images by SRGAN, it integrates the idea of the Laplace pyramid to complete the task of multiscale reconstruction of images through staged reconstruction. It incorporates the concept of a discriminative network with patch GAN to effectively improve the recovery effect of graph details and improve the reconstruction quality of images. Using Set5, Set14, BSD100, and Urban100 datasets as test sets, experimental analysis is conducted from objective and subjective evaluation metrics to effectively validate the performance of the improved algorithm proposed in this study.
Fuzzy Combination of Moth-Flame Optimization and Lightning Search Algorithm with Fuzzy Dynamic Parameter Adjustment

In general, this paper is focused on creating a fuzzy combination of two optimization algorithms. In this case, the algorithms work with populations and allow us to migrate between them every certain number of iterations. On the other hand, fuzzy logic is responsible for the dynamic adjustment of parameters within each of the algorithms since the variables are different in each algorithm. In previous works, a combination between genetic algorithm and particle swarm optimization was developed, which motivated us to create this combination expecting to obtain better results when compared to the previous works. The moth-flame optimization and lightning search algorithm were combined to obtain a powerful hybrid metaheuristic combining the advantages of both individual algorithms.
Application of Artificial Neural Network in Solar Energy

Accurate prediction of system performance is very important for the optimal planning of solar energy systems. The latest research of artificial neural network (ANN) technology for predicting the efficiency of solar thermal systems and the performance of photovoltaic system is reported here. Application of ANN to performance assessment of solar collectors is briefly reviewed including novel all-glass straight-through evacuated tube collectors. An overview of the most recent work of ANN for combined photovoltaic/thermal panels (PV/T) and concentrating photovoltaic collectors is also provided.
Performance analysis of all optical-based quantum internet circuits

A Quantum dot implanted within a double-sided optical microcavity is considered and investigated as a critical component for all optical-based quantum internets. Due to the duality as the photonic quantum transistor and gate, the QD cavity system repsents a sturdy base for the future photonic quantum network. With the help of the analytical investigation and review presented in this paper, a quantum dot cavity unit can be developed for implementing deterministic quantum gates and transistors. The maximum fidelity observed for quantum diode, router, and storage is 95.24, 62.06, and 90.42%without considering a noisy environment, respectively, and 62.12, 60.36, and 43.66% under a noisy environment, respectively. Fidelity is also calculated with varying coupling conditions (strong and weak coupling), and optimized cavity parameters are calculated.
FlowChain: The Playground for Federated Learning in Industrial Internet of Things Environments

The Industrial Internet of Things (IIoI) lays the foundation for a new industrial revolution, the so-called Industry 4.0, in which every element, from machines to processes, is interconnected and fully automated, producing a huge amount of valuable data, crucial for making industrial processes more efficient and profitable. For this reason, it is common to run machine learning algorithms in order to extract useful information from these data. At the same time, due to bandwidth and privacy issues, it is often infeasible to transfer all these large-scale data to a centralized location where these algorithms can be performed. To address these scenarios, federated learning (FL) has been gaining ground in recent years. FL leaves the training data on the devices where they are produced and builds a global model by aggregating locally computed models, preserving user privacy and avoiding overwhelming the network with unnecessary raw data. However, there are still important challenges and limitations to the application of FL in Industry 4.0, mainly due to security issues and the fact that many solutions still suffer from single points of failure and bottlenecks. In this article, we present FlowChain, a framework that integrates FL with blockchain technology and decentralized identifiers (DIDs) to create an infrastructure that offers the possibility to easily exploit FL in Industry 4.0 scenarios. By using smart contract technology to automate the aggregation of partial models, it is possible to make FL fully decentralized. In addition, the fact that the blockchain is immutable and every transaction is verified and traceable makes FL secure. Furthermore, we exploit DIDs to be able to uniquely identify each element participating in industrial processes. This allows finer-grained control of authorizations so that only IIoT devices with a known and authorized identity can actually participate in the FL training process. Finally, we present some preliminary results that demonstrate the feasibility of the proposed approach.
Equilibrium optimizer with divided population based on distance and its application in feature selection problems

Effective machine learning relies on feature selection (FS) to preprocess the data to search for the best feature subset among all feature combinations, which is a global optimization problem (GOPs). The equilibrium optimizer (EO) is a physical-based meta-heuristic that is promising in solving global optimization problems but still exhibits unbalanced exploration and exploitation; low convergence accuracy; and can easily fall into local optima. This paper proposes an equilibrium optimizer with divided population based on distance (DEO) to improve EO’s global optimization abilities and to use it to solve FS problems. First, we propose an adaptive population division strategy with beta distribution, which makes DEO divide populations by particles’ distance weights and dynamic adjustment of the number of particles in the population, facilitating the algorithm to achieve a balance between exploration and exploitation. Second, we add distance factor to the exploitation population update to control the step size, increasing convergence accuracy. Third, the exponential term in the EO algorithm is introduced into the exploration population update as a disturbance factor to enhance the ability to jump out of a local optimum. Finally, twenty benchmark functions are used to verify the effectiveness of the improved strategy. Numerical experiments based on CEC’17 and FS experiments based on 14 UCI datasets are conducted to validate DEO’s global optimization and feature selection capabilities, respectively. Experimental results demonstrate that the proposed DEO significantly outperforms seven state-of-the-art algorithms and five other modified algorithms in terms of the optimization-seeking accuracy and convergence speed. Additionally, DEO also obtains overall better fitness as well as shorter running time than the other seven algorithms in FS problems.
Ensemble learning via supervision augmentation for white matter hyperintensity segmentation

Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertain. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image. However, multiple annotations are often not available in a real-world scenario. To mitigate the issue, this paper proposes a supervision augmentation method (SA) and combines it with ensemble learning (SA-EN) to improve the generalization ability of the model. SA can obtain diverse supervision information by estimating the uncertainty of annotation in a real-world scenario that per image have only one ambiguous annotation. Then different base learners in EN are trained with diverse supervision information. The experimental results on two white matter hyperintensity segmentation datasets demonstrate that SA-EN gets the optimal accuracy compared with other state-of-the-art ensemble methods. SA-EN is more effective on small datasets, which is more suitable for medical image segmentation with few annotations. A quantitative study is presented to show the effect of ensemble size and the effectiveness of the ensemble model. Furthermore, SA-EN can capture two types of uncertainty, aleatoric uncertainty modeled in SA and epistemic uncertainty modeled in EN.
Continual Learning for Human State Monitoring

Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications.We propose two new CL benchmarks for Human State Monitoring.We carefully designed the benchmarks to mirror real-world environments in which new subjects are continuously added.We conducted an empirical evaluation to assess the ability of popular CL strategies to mitigate forgetting in our benchmarks.Our results show that, possibly due to the domainincremental properties of our benchmarks, forgetting can be easily tackled even with a simple finetuning and that existing strategies struggle in accumulating knowledge over a fixed, held-out, test subject.
Augmented and virtual reality effects on social responsibility in retail

The purpose of this research is to understand how knowledge of social responsibility within the apparel industry creates a sense of social responsibility concern, which would lead to purchase intention, while being moderated with the use and perception of virtual reality (VR) and/or augmented reality (AR). An online survey of 214 Gen Z respondents was conducted to understand participants' thoughts and familiarity with AR/VR. Overall, it was found that AR and VR are tools Gen Z is willing to use within their shopping experience, if given more experience with it.
Deep exploration of Jiaodong type gold deposit, taking Shanhou gold deposit, southern part of Zhaoping fault as an example

Crustal response induced by the subduction of Paleo-Pacific Plate, is crucial to study the metallogenic law and prediction of Jiaodong type gold deposit. Gold deposits are preferentially hosted in the lithospheric-scale second-order faults, which provide migration channels, mineralization corridors, and the physicochemical conditions. Super-large gold deposits in northwestern Jiaobei Terrane are generally controlled by regional linear faults, such as Sansandao, Jiaojia, Zhaoping and Qixia faults. However, Zhaoping fault is exposed sporadically (in Xiadian and Jiangjiayao gold deposits) beneath a thick cover of Quaternary basalts, which indicating that limited information can be obtained by traditional geological survey. In this study, high-precision geophysical surveys such as gravity and controlled source audio frequency magnetotellurics (CSAMT) are conducted in the Shanhou gold deposit, which hosted in southern part of Zhaoping fault. Three integrated geophysical profiles results further identified the spatial distribution of second-order oblique-slip faults of Zhaoping fault and established the relationships between brittle deformation and mineralization. The spatial variability of stratum (high density 2.82 g/cm 3 and intermediate to high specific resistivity 4000 Ω m), brittle-slip faults (intermediate density 2.73 g/cm 3 and low specific resistivity 200 Ω m) and magmatite (low density 2.57 g/cm 3 high specific resistivity 8000 Ω m) are identified by two inversion techniques, including the man-machine interactive inversion technology and nonlinear conjugate gradient technology. In addition, the geophysical inversion pseudo sections delicately portray a smooth-out waveform low specific resistivity anomaly, which strongly couples with the spatial ore-hosted locations and enrichment places. The gold mineralized alteration zones generally dips to southeast and the angle changing from 45° to 30°, which are strong coupling with the large-scale acid magmatic intrusion and extensional tectonics occurred in the late Mesozoic. Finally, according to the deep prospecting prediction, a gold ore body with 10 m in thickness was revealed by drilling below the depth of 1,015 m, which is hosting in the brittle deformation of Zhaoping fracture zone.
THE RESULTS OF REFORMS IN THE FIELD OF TRAINING HIGHLY QUALIFIED PERSONNEL IN THE LATE XX- EARLY XXI CENTURY: SCIENTIFIC PERSONNEL

Aim - to study the last 25 yeas dynamics in the system of Russian postgraduate education. The research is based on institutional analysis of the system of postgraduate education, as well as the resource approach in assessing the human potential of scientific personnel, postgraduate students and doctoral students. A comparative approach allowed us to analyze the dynamics of changes in the numbers, gender, age characteristics of researchers, doctoral students and graduate students for the last decades. Other methods used include analysis and systematization of statistical data, periodization method, analysis of theoretical sources, secondary analysis of sociological data. Results: the article presents the overview of Russian postgraduate education system evolution and analyses characteristics of its present state. The authors introduce their own periodization of the postgraduate education system development. The study presents human capacity, age and gender profiles of scientific personnel. The study reveals the trend in reducing the number of organizations providing postgraduate studies, as well as the decrease in the number of postgraduate and doctoral students and researchers. The authors came to the conclusion that the present situation is characterized by the contradiction between government regulation to the system of postgraduate education and its possibilities to ensure these requirements. The crisis in the system of Russian postgraduate education is evident. The presented results evaluate the process of training highly qualified educational personnel, the effectiveness of the institute of postgraduate and doctoral studies, as well as the qualitative and quantitative characteristics of scientific personnel in Russia. The data presented in the article will contribute to the development of the state educational policy and bring the process of training highly qualified educational personnel in accordance with the requirements and demand for highly qualified specialists in modern industry and science.
A Hybrid Clustering Approach Based on Fuzzy Logic and Evolutionary Computation for Anomaly Detection

In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initially use a portion of labelled data from known classes to divide them into a given number of clusters, and then attempt to determine if the new instances, which may be unlabelled, belong to the known or novel classes or if they are anomalies, namely if they are extreme values that deviate from other observations, indicating noise or errors in measurement. However, for each class in HPFuzzNDA clusters are designed by using the new evolutionary algorithm NL-SHADE-RSP, the latter is a modification of the well-known L-SHADE approach. Additionally, the number of clusters for all classes is automatically adjusted in each step of HPFuzzNDA to improve its efficiency. The performance of the HPFuzzNDA approach was evaluated on a set of benchmark problems, specifically generated for novelty and anomaly detection. Experimental results demonstrated the workability and usefulness of the proposed approach as it was able to detect extensions of the known classes and to find new classes in addition to the anomalies. Moreover, numerical results showed that it outperformed PFuzzND. This was exhibited by the new mechanism proposed for cluster adjustments allowing HPFuzzNDA to achieve better classification accuracy in addition to better results in terms of macro F-score metric.
NEXAS: A Visual Tool for Navigating and Exploring Argumentation Solution Spaces

Recent developments on solvers for abstract argumentation frameworks (AFs) made them capable to compute extensions for many semantics efficiently. However, for many input instances these solution spaces can become very large and incomprehensible. So far, for the further exploration and investigation of the AF solution space the user needs to use post-processing methods or handcrafted tools. To compare and explore the solution spaces of two selected semantics, we propose an approach that visually supports the user, via a combination of dimensionality reduction of argumentation extensions and a projection of extensions to sets of accepted or rejected arguments. We introduce the novel web-based visualization tool NEXAS that allows for an interactive exploration of the solution space together with a statistical analysis of the acceptance of individual arguments for the selected semantics, as well as provides an interactive correlation matrix for the acceptance of arguments. We validate the tool with a walk-through along three use cases.
(Re-)connecting with Nature in Urban Life: Engaging with Wildlife via AI-powered Wearables

Connecting with nature is essential for our health and well-being. However, we are getting less connected with nature today because of urbanization. Considering urban is where human and other species co-habit, we believe there is an opportunity to design interactive technologies to engage urban residents with those wild species to deepen the residents' connection with nature in their daily life. In this paper, we present GoChirp, an always-available AI-powered wearable device that connects users with nature by engaging them with surrounding wild birds. GoChirp embeds machine learning techniques to detect surrounding birds by recognizing birds' chirping sound. The device brings the birds to users' attention via haptic feedback that mimics the bird flapping. GoChirp comes with a mobile APP that supports users to take photos and identifying the species of the bird showing in the photo. With GoChirp, we hope to further the understanding of interaction design for connecting people with nature and inspire designers to consider the role of always-available technology in supporting human-nature interaction.
Assessing SMEs’ cybersecurity organizational readiness: Findings from an Italian survey

The Small and Medium-sized Enterprises’ (SMEs) level of organizational cybersecurity readiness has been poorly investigated to date. Currently, all SMEs need to maintain an adequate level of cybersecurity to run their businesses, not only those wishing to fully exploit digitalization’s benefits. Unfortunately, due to their lack of resources, skills, and their low level of cyber awareness, SMEs often seem unprepared. It is essential that they address the digital threats that they face by using technology and complementary (and not alternative) factors, such as guidelines, formal policies, and training. All these elements trigger development processes regarding skills, awareness, the organizational cybersecurity culture, and the organizational resilience. This paper describes Italy’s first multidisciplinary attempt to assess its SMEs’ overall cybersecurity readiness level. We used a survey as its initial quantitative assessment approach, although SMEs can also use it as a cyber self-assessment tool, which prepares them better to navigate the digital ecosystem. Thereafter, we held semi-structured interviews to explore the critical points that had emerged from the study’s first phase. The overall results show that SMEs have not yet achieved high levels of organizational readiness. SMEs are currently starting to set the stage for their organizational cyber readiness and will, therefore, have to take many more proactive steps to address their cyber challenges.
Virtual Reality in Education: Reviewing Different Technological Approaches and Their Implementations

Virtual Reality is a beneficial and appealing supplement to traditional education. VR has been a broad term used to designate a variety of educational systems throughout the years, including desktop-based Virtual Reality, CAVE-based Virtual Reality, stereoscopic glasses-based Virtual Reality and custom-developed Virtual Reality systems. This study investigates the many applications of the term “Virtual Reality” for educational purposes, with the objective of serving as a map to differentiate earlier uses of the term “Virtual Reality” as it is currently interwoven with Head Mounted Displays. The study also discusses the features and advantages of modern Head Mounted Displays that provide a complete VR learning experience that impact the user’s experience. More degrees of freedom (DoF), increased display resolution, greater refresh rate, hand and head tracking and a larger field of vision (FoV) are just a few of the hardware advancements that are promoting the use of VR as a technological method for education in the classroom.
Expanding Domain Knowledge Elements for Metro Construction Safety Risk Management Using a Co-Occurrence-Based Pathfinding Approach

Knowledge is a contribution factor leading to more effective and efficient construction safety management. Metro construction practitioners always find it difficult to determine what specialized knowledge is needed in order to lead to better safety risk management. Currently, domain knowledge elements are generally determined by experts, which is coarse-grained and uncomprehensive. Therefore, this paper aims to provide a structure of domain knowledge elements, using an automatic approach to expand domain knowledge elements (DKEs) from a big dataset of unstructured text documents. First, the co-word co-occurrence network (CCN) was used to find the connected knowledge elements, and then the association rule mining (ARM) was compiled to prune the weakly related subnetworks, leaving the strong associated elements. Finally, a list of DKEs in the metro construction safety risk management was obtained. The result shows that the obtained DKEs are more comprehensive and valuable compared to previous studies. The proposed approach provides an automatic way to expand DKEs from a small amount of known knowledge, minimizing the expert bias. This study also contributes to building a fine-grained knowledge structure for metro construction safety risk management. The structure can be used to guide safety training and help knowledge-based safety risk management.