Reviewerly/exHarmony huggingface dataset:
RepECN: Making ConvNets Better Again for Efficient Image Super-Resolution

Traditional Convolutional Neural Network (ConvNet, CNN)-based image super-resolution (SR) methods have lower computation costs, making them more friendly for real-world scenarios. However, they suffer from lower performance. On the contrary, Vision Transformer (ViT)-based SR methods have achieved impressive performance recently, but these methods often suffer from high computation costs and model storage overhead, making them hard to meet the requirements in practical application scenarios. In practical scenarios, an SR model should reconstruct an image with high quality and fast inference. To handle this issue, we propose a novel CNN-based Efficient Residual ConvNet enhanced with structural Re-parameterization (RepECN) for a better trade-off between performance and efficiency. A stage-to-block hierarchical architecture design paradigm inspired by ViT is utilized to keep the state-of-the-art performance, while the efficiency is ensured by abandoning the time-consuming Multi-Head Self-Attention (MHSA) and by re-designing the block-level modules based on CNN. Specifically, RepECN consists of three structural modules: a shallow feature extraction module, a deep feature extraction, and an image reconstruction module. The deep feature extraction module comprises multiple ConvNet Stages (CNS), each containing 6 Re-Parameterization ConvNet Blocks (RepCNB), a head layer, and a residual connection. The RepCNB utilizes larger kernel convolutions rather than MHSA to enhance the capability of learning long-range dependence. In the image reconstruction module, an upsampling module consisting of nearest-neighbor interpolation and pixel attention is deployed to reduce parameters and maintain reconstruction performance, while bicubic interpolation on another branch allows the backbone network to focus on learning high-frequency information. The extensive experimental results on multiple public benchmarks show that our RepECN can achieve 2.5∼5× faster inference than the state-of-the-art ViT-based SR model with better or competitive super-resolving performance, indicating that our RepECN can reconstruct high-quality images with fast inference.
An image inpainting method based on generative adversarial networks inversion and autoencoder

Abstract Image inpainting aims to repair the damaged region according to the known content in the damaged image. Recently, image inpainting methods have poor effects on high‐resolution damaged images, and the research on the inpainting of large‐area damaged images is limited. Therefore, this paper proposes an image inpainting method based on Generative Adversarial Networks (GAN) inversion and autoencoder. This work consists of two phases: first, the authors design an autoencoder‐based GAN, which learns the mapping from noise to low‐dimensional feature maps by training a generator, and then converts the generated feature maps into high‐resolution images. Thus, the difficulty of learning the mapping relationship is reduced. Second, the authors adopt the learning‐based GAN inversion to infer the closest latent code. The trained GAN is then used to reconstruct the complete image. Finally, the authors compare their method with other classical methods on the CelebAMask‐HQ, Flickr‐Faces‐HQ, and ImageNet datasets. According to the quantitative comparison, when the mask range is large, in other words, when the image has a large area of damage, the authors’ method is superior to the comparison methods. According to the qualitative comparison, the structure of the high‐resolution image inpainted by the authors’ method is more reasonable and the texture details are more realistic.
Depthwise channel attention network (<scp>DWCAN</scp>): An efficient and lightweight model for single image super‐resolution and metaverse gaming

Abstract Single image super‐resolution (SISR) has gained significant attention in image processing and computer vision, driven by deep learning‐based models like convolutional neural networks (CNN). Yet, the resource‐intensive nature of these models poses challenges when deploying them on edge devices. To address this issue, resource‐constrained models need to be developed. While recent models like the information distillation network (IDN), the information multi‐distillation network (IMDN), the residual feature distillation network (RFDN), and so on, have attempted to reduce parameters and computational complexity, further optimization remains vital. Therefore, this paper presents an approach to enhancing the efficiency and lightweight nature of the SISR. We introduce a novel lightweight SR model by building upon the RFDN architecture, the winner of the AIM2020 and NTIRE2022 SR challenges. The proposed depthwise channel attention network (DWCAN) model makes some key changes to RFDN. First, it replaces the main residual feature distillation block (RFDB) with a depthwise channel attention block (DWCAB). Additionally, DWCAN includes a shallow residual block (SRB) with depthwise separable convolution (DW) and a channel attention (CA) block. The primary goal of our work is to significantly reduce model parameters, computational operations, inference time, and memory size while maintaining or improving a peak signal‐to‐noise ratio (PSNR) of 29 dB. The experimental results demonstrate the effectiveness of the proposed model. By applying our modifications, we achieve a notable reduction in model complexity, leading to an improved PSNR of 29.07 dB, up from RFDN's 29.04 dB on a diverse 2 K resolution (DIV2K) dataset. This underscores the potential of our lightweight model to balance computational efficiency and SR quality. Additionally, the proposed work is essential for the metaverse for two key reasons: (1) Enhancing visual quality by adding complex details to textures and objects, making the digital world feel more like reality. (2) Ensuring device compatibility across a range of gadgets, from smartphones to VR headsets, optimizing the metaverse experience for all users. In short, a lightweight single image super‐resolution model for image reconstruction is proposed in this paper.
A Innovative Methodology to Fuzzy-Neural System Modeling

We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce "fuzzy curves" and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network.
Formation of Students' Readiness for Research Activities in the Context of STEM-Education

The article presents the results of a theoretical and empirical study of the problem of students' readiness for research activities, gender peculiarities of this issue. Research activity is considered as a special type of creative activity that arises as a result of search activity and involves independent research aimed at theoretical and experimental study of phenomena and processes, substantiation of facts, identification of patterns using scientific methods of knowledge.&#x0D; STEM education is aimed at developing a personality through the formation of competencies, a natural science picture of the world, and life values using a transdisciplinary approach to learning based on the practical application of scientific, mathematical, technical, and engineering knowledge and skills to solve practical problems for their further use in professional activities and science. The authors assume that the introduction of the main forms of STEM education (STEM lesson, STEM project, STEM quest, STEM hackathon) will contribute to the formation of students' readiness for research activities.&#x0D; In order to study the main features of the formation of students' readiness for research activities, the authors of the article used theoretical and empirical research methods: theoretical analysis and generalization of psychological and pedagogical practice; survey; method of expert evaluation: longitudinal research method. To process the empirical data, the procedures of qualitative and quantitative analysis were used. The research work covered 620 7th grade students of general secondary education institutions from 5 regions of Ukraine during the 2021-2022 academic year.&#x0D; The research findings do not claim to be an exhaustive solution to the problem, but contain significant conclusions. It is necessary to further study the structure of students' readiness for research work, as well as the use of the latest forms, methods and means of its formation in the learning process.
Student Polymotivation as a Factor of Personal Professional Growth

The article studies the peculiarities of the students' motivational and need sphere. The concept of polymotivation, which contains interpretations of the internal consistency of the motivational sphere, ways of eliminating contradictions in it regarding personal and social choices, is considered. In the context of professional development, polymotivation appears as a significant structural force and a meaningful center of personal professional growth. Due to the need to study effective mechanisms that will contribute to the development of an individual subject, modern approaches to the interpretation of the motivation of personal growth are analyzed, which allows the individual to direct efforts to mastering the chosen professional activity, desired and, at the same time, socially demanded social role; to adequately assess the discrepancy between where a young person is now and where he wants to be in order to realize himself and enjoy his life. The purpose of the study was to identify the impact of value orientations of student youth on the structure of their motivational and need sphere. To solve the set goal, the following methods were used: T. Ehlers' personality diagnosis method for success motivation and S. S. Bubnov's method for diagnosing the real structure of the value orientations of the individual. The subjects were full-time students of the National Aviation University. The obtained data showed that most of the subjects have high and moderately high indicators of motivation to achieve success and socially significant value orientations. The highest scores were obtained on the scales that determine the value of prosocial activities: "High social status and people management", "Social activity to achieve positive changes in society", "Communication". It has been proven that the intrapersonal and social determinants of the educational environment influence the formation of the structural integrity of the personal orientation of students, contribute to the development of self-regulatory processes within the educational activity at the level of the value orientations determined by them. We consider the selection of educational and narrowly personal factors that will influence the internal consistency of the motivational-needs sphere of student youth to be a further promising direction of research.
Investigation of Readiness of Pedagogical Teams for Implementation of an Innovative Educational Project «Organizational-Pedagogical Conditions For the Functioning and Development of а Safe School in Wartime/Post-War Period»

The article reveals the readiness of the administration, pedagogical teams of experimental institutions of general secondary education, and scientific-pedagogical workers of regional institutes of postgraduate pedagogical education for the implementation of innovative activities within the framework of the implementation of an innovative educational project at the national level on the topic «Organizational-pedagogical conditions for the functioning and development of a safe school in wartime/post-war period» (2023–2027). The study of the readiness of educators and researchers for innovative activities is considered as one of the important tools for obtaining more effective results in the implementation of the innovative educational project.&#x0D; The necessity of a comprehensive approach is substantiated, which positively influences the study of the readiness of project participants for innovative activities. Personal factors related to the competencies of the direct executors of the innovative educational project – teachers and scientific-pedagogical workers, heads of general secondary education institutions – are highlighted.&#x0D; The article reveals the content of the concepts «readiness», «innovative activity», «teacher's readiness for innovative activity». Criteria, indicators, and levels of teachers' readiness for innovative activities are determined. Attention is drawn to the fact that innovative activity in the modern educational environment requires not only high professional qualities from the teacher but also the ability to effectively use tools and resources to achieve specific pedagogical goals.
The Metaverse in the Workplace: Possibilities and Implications for Human Resource Development

This study reviews the potential applications of the metaverse to various areas of human resource development (HRD) and reflects on the applications’ significance for employee and organizational development. Adopting a narrative review approach to this study, we selected and reviewed 34 cases. From our review, we found several findings that contributes to the HRD field in several ways. First, it draws the attention of HRD researchers and practitioners to a critical emerging trend that has been overlooked in the field. Second, it offers an analytical view of the metaverse in comparison to other training or learning technologies, assessing its effectiveness in employee development. Third, this study enhances the perspectives of HRD researchers and practitioners regarding this new technology by providing practical and theoretical insights into how metaverse-based interventions can reshape organizational culture and improve employee performance in the workplace. Lastly, it addresses compelling theoretical and practical questions related to HRD issues arising from the metaverse.
MBMF: Constructing memory banks of multi‐scale features for anomaly detection

Abstract In industrial manufacturing, how to accurately classify defective products and locate the location of defects has always been a concern. Previous studies mainly measured similarity based on extracting single‐scale features of samples. However, only using the features of a single scale is hard to represent different sizes and types of anomalies. Therefore, the authors propose a set of memory banks of multi‐scale features (MBMF) to enrich feature representation and detect and locate various anomalies. To extract features of different scales, different aggregation functions are designed to produce the feature maps at different granularity. Based on the multi‐scale features of normal samples, the MBMF are constructed. Meanwhile, to better adapt to the feature distribution of the training samples, the authors proposed a new iterative updating method for the memory banks. Testing on the widely used and challenging dataset of MVTec AD, the proposed MBMF achieves competitive image‐level anomaly detection performance (Image‐level Area Under the Receiver Operator Curve (AUROC)) and pixel‐level anomaly segmentation performance (Pixel‐level AUROC). To further evaluate the generalisation of the proposed method, we also implement anomaly detection on the BeanTech AD dataset, a commonly used dataset in the field of anomaly detection, and the Fashion‐MNIST dataset, a widely used dataset in the field of image classification. The experimental results also verify the effectiveness of the proposed method.
Evolutionary Optimization of Convolutional Extreme Learning Machine for Remaining Useful Life Prediction

Abstract Remaining useful life (RUL) prediction is a key enabler for making optimal maintenance strategies. Data-driven approaches, especially employing neural networks (NNs) such as multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs), have gained increasing attention in the field of RUL prediction. Most of the past research has mainly focused on minimizing the RUL prediction error by training NNs with back-propagation (BP), which in general requires an extensive computational effort. However, in practice, such BP-based NNs (BPNNs) may not be affordable in industrial contexts that normally seek to save cost by minimizing access to expensive computing infrastructures. Driven by this motivation, here, we propose: (1) to use a very fast learning scheme called extreme learning machine (ELM) for training two different kinds of feed-forward neural networks (FFNNs), namely a single-layer feed-forward neural network (SL-FFNN) and a Convolutional ELM (CELM); and (2) to optimize the architecture of those networks by applying evolutionary computation. More specifically, we employ a multi-objective optimization (MOO) technique to search for the best network architectures in terms of trade-off between RUL prediction error and number of trainable parameters, the latter being correlated with computational effort. In our experiments, we test our methods on a widely used benchmark dataset, the C-MAPSS, on which we search such trade-off solutions. Compared to other methods based on BPNNs, our methods outperform a MLP and show a similar level of performance to a CNN in terms of prediction error, while using a much smaller (up to two orders of magnitude) number of trainable parameters.
Identification of iron ores in Sierra Leone, Africa by using remote sensing techniques

Remote sensing is used in this work as a geological reconnaissance technique, demonstrating that it is profitable and effective in providing valuable information for distant regions of mining interest. Due to the Civil War that affected Sierra Leone from 1991 to 2002, Sierra Leone&#39;s mining resurgence has focused only on restoring closed mines and the exploitation of previously proven reserves. Thus, the main objective of this work is to locate new iron ores in the districts of Marampa and Tonkolili, located in areas outside the mining licensee sites where previous exploratory studies have not been carried out. To do this, Landsat 5 TM multispectral satellite images were used. Two different spectral enhancement methodologies were applied: Colour composition with band ratios and principal component analysis (PCA) applied to band ratios. The results were integrated to generate a map that delimits areas with exposed mineralization of iron oxides, which allowed us to associate them with the regional geology. Finally, access roads are included for field checking and detailed exploration. Key words: Remote sensing, iron ores, PCA, spectral signature, Landsat 5 TM.
Direction of material creep during the deformation phase in shear zones, role in mining prospecting: A case of the Nassara-Torkera gold deposits in the Gaoua region, Burkina Faso, West Africa

Mineralization at the Nassara and Torkera gold deposits is situated at the contact between volcanic rocks (basalt-andesite) and volcanosedimentary rocks (pyroclastite, black shale) within the echelon faults of the large West Bati&eacute; shear zone (WBZ). Along the mineralized body, shear deformation is intense, accompanied by significant hydrothermal fluid circulation. The objective of this study is to determine the direction of hydrothermal fluid creep in the Nassara-Torkera shear zone. To achieve this, we have integrated direct field measurements with Anisotropy of Magnetic Susceptibility (AMS) measurements and microstructure analysis. Magnetic foliation data align with direct field measurements. Additionally, the lineation data indicate that, during the deformation phase, the material or mineralizing fluids exhibit a southeastward creep, following the contemporaneous structures of deformation. These structures are observed to govern the gold mineralization. Furthermore, the gold content increases in the vicinity of lamprophyre, dacite, and diorite dykes. This observation suggests that the mineralizing fluid and the dykes were emplaced along the same structures of deformation. The drainage of the mineralizing fluid to the southeast explains the occurrence of the seven gold deposits (Djikando, Poni, Nassara, Torkera, Wadaradoo, Konkera and Napelepera) identified along the shear corridor. Identifying the direction of material creep in shear zones serves as a potent prospecting guide for mining explorers, enabling them to strategically position various drill holes efficiently. Key words: Nassara-Torkera, gold deposit, material creep direction, Anisotropy of Magnetic Susceptibility (AMS), dykes.
EASE: An effort-aware extension of unsupervised key class identification approaches

Key class identification approaches aim at identifying the most important classes to help developers, especially newcomers, start the software comprehension process. So far, many supervised and unsupervised approaches have been proposed; however, they have not considered the effort to comprehend classes. In this paper, we identify the challenge of “ effort-aware key class identification ”; to partially tackle it, we propose an approach, EASE , which is implemented through a modification to existing unsupervised key class identification approaches to take into consideration the effort to comprehend classes. First, EASE chooses a set of network metrics that have a wide range of applications in the existing unsupervised approaches and also possess good discriminatory power . Second, EASE normalizes the network metric values of classes to quantify the probability of any class to be a key class, and utilizes Cognitive Complexity to estimate the effort required to comprehend classes. Third, EASE proposes a metric, RKCP , to measure the relative key-class proneness of classes and further uses it to sort classes in descending order. Finally, an effort threshold is utilized, and the top-ranked classes within the threshold are identified as the cost-effective key classes. Empirical results on a set of eighteen software systems show that i) the proposed effort-aware variants perform significantly better in almost all (≈ 98.33%) the cases, ii) they are superior to most of the baseline approaches with only several exceptions, and iii) they are scalable to large-scale software systems. Based on these findings, we suggest that i) we should resort to effort-aware key class identification techniques in budget-limited scenarios; and ii) when using different techniques, we should carefully choose the weighting mechanism so as to obtain the best performance.