Reviewerly/exHarmony huggingface dataset:
Automated mapping of faults in the area of Poyasok Isthmus (Sakhalin) by remote sensing data

Automated mapping of disturbances in the earth’s crust allows you to quickly identify areas of development of dangerous geological processes, and to determine the measures that should be taken to organize the safe operation of extended linear structures (roads and railways, pipelines, power lines) that run through areas with difficult natural conditions. The main trigger mechanism causing the activation of dangerous geological processes is displacements in zones of active faults in the earth’s crust. Monitoring the state of fault zones and timely detection of manifestations of hazardous processes are urgent tasks for ensuring the sustainable development of regional infrastructure. The area of the Poyasok isthmus (Sakhalin Island) was chosen as the object of research, along the territory of which there are automobile and railway roads, power lines, and the main pipeline of the Sakhalin-2 project was laid along the isthmus coast. Lineament analysis of space images and SRTM data using the LEFA software package by binary morphological erosion method to highlight boundaries, as well as made selection long lines by performing mathematical operations using Canny algorithms, with subsequent Hough transformations, were carried out to identify faults of research territory. The obtained intermediate data were processed using the tools of the QGIS software package and made it possible to compile a map of disjunctive disturbances for the area under study. Lineament analysis made it possible to identify faults that bound the block megastructure of the Poyasok isthmus from the north and south. The saddle of Poyasok isthmus separates the large Central Sakhalin and South Sakhalin segments of the Hokkaido-Sakhalin system of island uplifts. Structures of the Central-Kamyshovy and South-Kamyshovy lifted megablocks (3 orders) in the West-Sakhalin Mountains adjoin directly to the saddle. The southern part of the Central-Kamyshovy mega-uplift is represented by a series of uplifts separated by narrow depressions bounded by faults. The number of uplifts increases with the distance to the north from the Poyasok isthmus. The northern part of the South-Kamyshovy mega-uplift is divided by faults of the northern-east strike into block structures. The obtained data are suitable for performing detailed seismic zoning.
Fit For Purpose

Different types of declarative models passed the revue, yet all of them had limitations. Is there one ‘best’ among mind maps, biological models, conceptual data models, ontologies, and theories in Ontology? What if you didn’t like either one fully, could you design your own one, and if so, how? Do any of them have ethical issues in their design or use, alike the ‘AI ethics’ fails? No. Yes. And sort of. We begin this chapter with a feature comparison of the types of models we covered and add two example-based comparisons to that, on all those models about dance and a task-based evaluation on learning from a textbook passage about labour migration. Second, the notion of ethics is addressed in the sense of both professions practice and potential pitfalls in modelling that would be explicit biases once you know what to look out for. Third, we’ll step through a procedure how you could develop a new modelling language for the purpose you like most.
Pruning and Quantization for Deeper Artificial Intelligence (AI) Model Optimization

Artificial intelligence (AI) models are being produced and used to solve a variety of business and technical problems. AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. Due to deeper automation, the number of features being used for model generation is numerous and hence the resulting AI models are bulky. Therefore, AI researchers have come out with a number of powerful optimization techniques and tools to compress AI models. This paper explores a suite of pioneering methods for achieving AI model compression. Pruning and quantization techniques are being used together to reduce the size of complex AI model architectures and make them optimized and performant so that they can be easily deployed in the IoT edge devices. We have elaborated on three different methods to compress an AI model and drawn inferences on the same. Further, proper comparisons were conducted between model metrics on the basis of minimal loss in accuracy and precision. To identify the most accurate and compressed model, we utilized pruning techniques and dived deeper by comparing performances using right metrics. We have implemented two types of pruning for the purposes of this paper: weight pruning and unit pruning. These pruning techniques were implemented with 0 to 100 percent sparsity to create a network model that is lightweight and does not significantly affect performance metrics. We have also reduced the size of the model by removing weights and neurons that were used more than once. We have compared pruning and quantization compression methods, which are implemented using the Tensor flow lite library. We have found that Tensor flow lite model is better for edge deployment. Additionally, a practical survey of network compression techniques is also included.
Soundness Does Not Come for Free (If at All)

We respond to some of the points made by Bennet and Blanck (2022) concerning a previous publication of ours elsewhere (2021).
Histopathological Colorectal Cancer Image Classification by Using Inception V4 CNN Model

It is crucial to analyze colorectal cancerColorectal Cancer histological pictures with objectivity. One of the main causes of mortality globally is colorectal cancerColorectal Cancer (CRC), sometimes referred to as bowel cancer. Early diagnosisDiagnosis has become crucial for a treatment to be effective. A cutting-edge deep convolutional neural networkConvolutional Neural Network (CNN) with transfer learning can classify several images of CRC into a variety of categories of artificial intelligenceArtificial Intelligence (AI). We have adjusted the Inception V4Inception V4 model to classify the CRC histopathology images in this study's experiment. We also use transfer learning (TL) and modification techniques for increasing accuracy. Inception V4Inception V4 net is currently one of the finest CNN designs for identifying CRC histopathology pictures from the National Center for Tumor Diseases (NCT) Biobank an open-source collection, according to the findings of our experiment. Additionally, we are able to achieve 97.7% accuracy using TL on the validation dataset, outperforming all prior values we could find in the literature.
Meboya’s Meaning in Strengthening the Influence of the Peer Attitudes on Student’s Professional Skepticism

This study examines the effect of peer attitudes on the professional skepticism (PS) of accounting students in the Buleleng Regency. We predict that peer attitudes representing strong (weak) pressure on PS will encourage accounting students to be more (less) skeptical. Using a between-subject experimental procedure, our hypothesis was significantly supported. Furthermore, this study identifies differences in the influence of peer attitudes towards PS among students who have a professional (non-professional) meaning on local genius meboya. In this study, the professional (non-professional) meaning of local genius meboya is a proxy for the higher (lower) inherent skepticism. The results of the differential test between groups indicate that the meaning of certain cultural values can strengthen the influence of peer attitudes towards PS. Our results identify the importance of developing human resource policies in accounting firms, particularly the professional skepticism of accountants and auditors.
Residual strength estimation of damaged steel tubular columns using digital image correlation

Tubular structural steel members are extensively used in modern constructions due to their outstanding structural efficiency, improved capacity against torsion and buckling, and aesthetic appearance. Steel tubular members may deform due to unanticipated loading conditions and accidental damage by wrong handling while transporting to the construction site, which induces damage leading to large permanent deformations. The ultimate capacity of the steel members is reduced due to permanent deformations. In such a case, the residual strength assessment of the member becomes critical before using it in structural applications. This study proposes a methodology by incorporating the Digital Image Correlation (DIC) technology to capture the whole field deformations and deformed shapes and model the 3D damaged shape of the specimen. Initially, the surface details in cartesian coordinates of the deformed specimen are obtained by scanning using a set of two cameras in a 3D DIC setup. The scanned images are stitched to obtain the complete 3D surface coordinates of the specimen. Further, the data is processed by regularising nodes, surface smoothening using 2D Inverse Fourier Transform, and converting the topographical information into the surface point cloud data or nodes, which is used to generate a mesh. The mesh is imported into Finite Element Analysis (FEA) software like Abaqus, and the damaged specimen's residual strength is predicted. The residual strengths of two (one circular and one square) damaged tubes were predicted using the proposed methodology and validated with the experimental results.
Evaluation of Blockchain Based Solutions to Improve Indian Agriculture

Indian agriculture, a critical pillar of the nation's economy, faces multifaceted challenges, including fragmented land holdings, lack of transparency, information asymmetry, and complex supply chains. This abstract delves into the potential of blockchain technology as a catalyst for comprehensive improvements in the Indian agricultural landscape, offering innovative solutions to these longstanding issues. Blockchain technology, with its decentralized, immutable ledger, holds promise in introducing transparency, traceability, and trust throughout the entire agricultural ecosystem. Blockchain's smart contracts can streamline the crop insurance process, automating claims and ensuring quicker and more accurate payouts to affected farmers. The successful implementation of blockchain technology in Indian agriculture holds the potential to revamp the sector, making it more efficient, transparent, and economically rewarding for all stakeholders. However, the realization of this potential depends on addressing infrastructure challenges, promoting digital literacy among key actors, and fostering cooperation between the government, private sector, and farmers. The comprehensive integration of blockchain solutions is poised to be a transformative step in building a more resilient and sustainable Indian agricultural system.
Stress resistance and psychological characteristics, of first-year students during the examinatiojn session

This article is devoted to the problem of stress and stress resistance of students during the session. The relevance of this problem is important for first-year students, since they are the ones who are exposed to the period of adaptation after schooling, besides, it is the first session in their life. Young people, encountering new methods of studying at universities, which are different from school ones, do not always cope with the demands and workload. We also look at stress during exams, when students are most prone to stressful situations. Students' difficulties in overcoming the new system of education, problems with their financial situation are often accompanied by nervous tension, excessive irritability, lethargy, decreased willpower, and anxiety. In addition, it is worth considering that many teenagers lead a rather passive lifestyle due to advanced technologies, which also affects their mental state. In our article, we will consider some methods of combating stress, taken both from scientific sources and from the personal experience of students. And also consider the concepts of "stress resistance" and "stress" for a deeper understanding of this problem. Student life is communication with interesting people, various events, acquaintances, as well as new educational activities, a new format of knowledge, a new teaching staff. For first-year students, this is a time of serious trials, their life situation is changing and they need to adapt to it.
Navigating Web Application Security: A Survey of Vulnerabilities and Detection Solutions

In an increasingly digital world, web-based applications have become the cornerstone of business operations and personal activities. However, this widespread use also exposes these platforms to a variety of security vulnerabilities. This paper aims to shed light on the pressing question: “How secure are our web applications?” Through an exhaustive review of existing literature, we catalog the most frequently mentioned vulnerabilities of web applications and cloud-hosted services, as well as the tools commonly cited for their detection. This survey serves as a critical resource for cybersecurity professionals, policy makers, and anyone invested in web security, emphasizing the need for vigilant cybersecurity measures.
Quantum Fisher information of an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si122.svg" display="inline" id="d1e200"><mml:mi>N</mml:mi></mml:math>-qubit maximal sliced state in decoherence channels and Ising-type interacting model

Various quantum states have been suggested to realize high-precision measurement and quantum Fisher information (QFI) is one of the most important indicators to evaluate the performance. Here, we systematically investigate the QFI of an N-qubit maximal sliced (MS) state in different environments. In the ideal situation, we present the analytical QFI and it decays from N2 to (N−1)2 as the probability amplitude α changed from 0 to 1, which denotes the decreased metrological ability. In the amplitude damping channel, the variation trends of QFI with respect to the damping probability p are similar and it decreases to the number of qubits N at p=1, meaning the vanished entanglement superiority. However, it is different in the phase damping channel where with the increase of the number of qubits a turning point of QFI equal to N is found at p closing to 0.1. In the Ising-type interacting model, with the increasing interaction strength γ, the QFI with respect to α shows a crossover from decreasing to increasing. Particularly, at the critical point γ=1, the QFI of an N-qubit MS state is found analytically equal to N2 and regardless of α. This indicates a great potential in achieving the Heisenberg-limited metrology and may shed some new light on quantum information science.
Target detection algorithm based on super- resolution color remote sensing image reconstruction

An improved generative adversarial network model is adopted to improve the resolution of remote sensing images and the target detection algorithm for color remote sensing images. The main objective is to solve the problem of training super-resolution reconstruction algorithms and missing details in reconstructed images, aiming to achieve high-precision detection of medium and low-resolution color remote sensing targets. First, a lightweight image super-resolution reconstruction algorithm based on an improved generative adversarial network (GAN) is proposed. This algorithm combines the pixel attention mechanism and up-sampling method to restore image details. It further integrates edge-oriented convolution modules into traditional convolution to reduce model parameters and achieve better feature collection. Then, to further enhance the feature collection ability of the model, the YOLOv4 object detection algorithm is also improved. This is achieved by introducing the Focus structure into the backbone feature extraction network and integrating multi-layer separable convolutions to improve the feature extraction ability. The experimental results show that the improved target detection algorithm based on super resolution has a good detection effect on remote sensing image targets. It can effectively improve the detection accuracy of remote sensing images, and have a certain reference significance for the realization of small target detection in remote sensing images.
Digital literacy competence, digital literacy practices and teacher identity among pre-service teachers

This study investigated how different dimensions of digital literacy (DL) competence were related to digital literacy practices in teaching and teacher identity. Survey data were collected from 910 pre-service teachers in China. Structural equation modelling (SEM) was conducted to analyse the data. Findings indicated that among the five dimensions of DL competence, three were significant positive predictors of digital practices in teaching, including information and information literacy, communication and collaboration, and security. In addition, digital literacy practices significantly and positively predicted teacher identity and mediated the effect of DL competence on teacher identity. The study suggests the significance of enhancing DL competence and integrating digital technology as pedagogical tools to help pre-service teachers bridge the gap between their personal use of technology and teacher identity in relation to digital literacy.
Robotic dining delight: Unravelling the key factors driving customer satisfaction in service robot restaurants using PLS-SEM and ML

In the past few years there has been a remarkable surge in demand for robot service restaurants. However, as both the technology and the concept of such restaurants are relatively new, there is a limited understanding of how consumers would react to this new change in the service industry. This study focuses on the key factors influencing customer satisfaction and their intention to repeat the experience by using two staged hybrid PLS-SEM and Machine Learning approaches. The finding confirms that perceived enjoyment, speed, and novelty influence customer satisfaction, whereas perceived usefulness has no influence. Additionally, the study uncovers that customer satisfaction and trust positively mediate the relationship and establish the link with repeat experience. The machine learning models (Artificial Neural Network, Support Vector Machines, Random Forest, K-Nearest Neighbors, Elastic Net) predict the intention to repeat the experience of the service robot with an overall model fit of around 57%. We also discussed several new and useful theoretical and practical implications for enhancing the customer experience during the visit to the restaurants.
Transfer learning for deep neural networks-based classification of breast cancer X-ray images

Nowadays, deep neural networks (DNNs) are helpful tools for mammogram classification in breast cancer screening. But, in Vietnam, there is a relatively small number of mammograms for training DNNs. Therefore, this study aims to apply transfer learning techniques to improve the performance of DNN models. In the first step, 10,418 breast cancer images from the Digital Database for Screening Mammography were used for training the CNN model ResNet 34. In the second step, we fine-tune this model on the Hanoi Medical University (HMU) database with 6,248 Vietnamese mammograms. The optimal model of ResNet 34 among these models achieves a macAUC of 0.766 in classifying breast cancer X-ray images into three Breast Imaging-Reporting and Data System (BI-RADS) categories, BI-RADS 045 (‘incomplete and malignance’), BI-RADS 1 (‘normal’), and BI-RADS 23 (‘benign’), when tested on the test dataset. This result is higher than the result of the ResNet 50 model trained only on the X-ray dataset of 7,912 breast cancer images of the HMU dataset, which achieves a macAUC of 0.754. A comparison of the performance of the proposed model of ResNet 34 applying transfer learning with other works shows that our model’s evaluation results are higher than those of the compared models.
Design Optimization Using Modified Differential Evolution Algorithm

Objectives: The key objective of this article is to suggest a modified differential evolution (MDE) algorithm for design problem optimization particularly reactor network design (RND) problem. Methods: During the few decades differential evolution (DE) algorithm achieved noticeable progress and solved a wide variety of optimization issues. However, the DE suffers from low diversification, poor exploration ability and stagnation. Hence, using concept of the particle swarm optimization mechanism (PSO), suggested MDE employed new mutation operator, to balance exploitation and exploration activities. Also, on the basis of time-varying scheme new mutation operator integrates new control parameter, to avoid stagnation. A group of 6 unconstrained benchmark functions are solved, to investigate the presentation of MDE algorithm. Moreover, its practical superiority is further verified by solving RND problem. Findings: The experiential results show that the suggested MDE performs well in each case of unconstrained benchmark functions with the highest rate of success. Moreover, optimize the RND problem very effectively with the lowest time (2.98s) and fewer number of function evaluations (12729). Furthermore, outcomes suggest that the proposed MDE exhibits a better or at least competitive performance compared to evolutionary algorithms. Novelty: The exploitation and exploration ability of the suggested MDE are balanced efficiently due to use of memory facts (i.e. novel mutation operator) and adapted (i.e. new time-varying) control parameters. Keywords: Evolutionary algorithms, differential evolution, mutation, control parameter, design optimization
The approach of learning content through technology

Digitalization is the dominant form of the existence and the development of today and future society. For education, the term „digital‖ implies digital literacy as an essential life skill, which is not just knowledge of how to use technology, but an understanding of how to use technology for individual and social development. Primary school students have faced significant barriers in the era of online learning. This raised awareness of how to use technology and this knowledge for educational purposes. By designing blended learning activities, learning contexts can be created that involve accessing digital applications and resources, integrating technology into an effective teaching approach.
Design of a nature-inspired sun-tracking device in context of rural India

The efficiency of the photovoltaic system is traditionally being increased with the help of solar concentrators. The concentrators based on biomimetic structures of various flowers have proven to be effective in increasing performance efficiency. The present study proposes the design of a nature-inspired, novel and passive, single-axis solar tracker that operates on the principle of variable coefficients of thermal expansions of different metals in a bimetallic plate. The proposition mimics the heliotropism mechanism of Sunflowers to achieve acceptable results. It involves the use of a solar PV panel mounted on a gear assembly, connected to a bimetallic plate via connecting linkage and a concave mirror to reflect the incident sun rays on the plates during the day. The resultant deflection in the plates rotates the mounted solar PV panel to follow the sun throughout its diurnal course. There were certain conditions that were determined mathematically to ensure correct working of the proposed design. CAD modelling was adopted to better study the functioning of the envisioned contraption as a whole. The proposed design is easy to construct; thereby making it an efficient, promising and economical alternative to other passive and fixed ones.