Reviewerly/exHarmony huggingface dataset:
Always-on authenticity: Challenging the BeReal ideal of “being real”

This paper examines the mobile app BeReal, a popular social media platform, and challenges its claim to fame as a uniquely authentic platform. Through a critical analysis of the app’s user experience and an exploration of popular discourse among social media users regarding its design, I seek to assess this claim. BeReal promotes authenticity, or “being real,” through the act of users posting interesting content on demand and in real time. The app enforces this authenticity by imposing restrictions: users can only post once a day, at a specific time determined by BeReal, and in one take. Violating these rules triggers a judgment system that notifies other users when posts are made late or retaken. Despite the platform’s promise of enabling users to express their true selves through its restrictive functionality, I argue that its version of authenticity instead intensifies the need for external curation due to an “always-on” mentality.
AEFormer: Zoom Camera Enables Remote Sensing Super-Resolution via Aligned and Enhanced Attention

Reference-based super-resolution (RefSR) has achieved remarkable progress and shows promising potential applications in the field of remote sensing. However, previous studies heavily rely on existing and high-resolution reference image (Ref), which is hard to obtain in remote sensing practice. To address this issue, a novel structure based on a zoom camera structure (ZCS) together with a novel RefSR network, namely AEFormer, is proposed. The proposed ZCS provides a more accessible way to obtain valid Ref than traditional fixed-length camera imaging or external datasets. The physics-enabled network, AEFormer, is proposed to super-resolve low-resolution images (LR). With reasonably aligned and enhanced attention, AEFormer alleviates the misalignment problem, which is challenging yet common in RefSR tasks. Herein, it contributes to maximizing the utilization of spatial information across the whole image and better fusion between Ref and LR. Extensive experimental results on benchmark dataset RRSSRD and real-world prototype data both verify the effectiveness of the proposed method. Hopefully, ZCS and AEFormer can enlighten a new model for future remote sensing imagery super-resolution.
Geomorphometric modeling and mapping of the Larsemann Hills Antarctic Oasis

Geomorphometric modeling is widely used in geosciences. However, geomorphometric modeling and mapping of Antarctic oases has not been performed so far. This article presents the first results of geomorphometric modeling and mapping of the Larsemann Hills oasis obtained in the 68th Russian Antarctic Expedition in January–April 2023. As input data, we used a fragment of the Reference Elevation Model of Antarctica (REMA). From the extracted and edited digital elevation model, we derived models and maps of the following 17 morphometric variables: slope, aspect, horizontal curvature, vertical curvature, mean curvature, Gaussian curvature, minimal curvature, maximal curvature, unsphericity curvature, difference curvature, vertical excess curvature, horizontal excess curvature, ring curvature, accumulation curvature, catchment area, topographic index, and stream power index. We conducted a field geomorphometric interpretation to provide with correct physical geographic, geological, and geomorphological interpretations of morphometric maps. In this fieldwork, we carried out 54 foot routes with the total length of about 422 km. During the routes, we collected 150 rock samples for further petrological and mineralogical analyses and three-dimensional modeling of the samples. Morphometric maps can be useful for geological, geomorphological, soil, biological, and hydrological studies. The ultimate goal of the author’s multiyear project is to create a digital geomorphometric atlas of Antarctic oases and other ice-free Antarctic territories.
Enhancing Global Optimization for Sustainable Development Using Modified Differential Evolution

Among popular evolutionary algorithms (EAs), Differential Evolution (DE) is secondhand for comprehensive optimization. However, it has numerous restrictions like slow convergence, stagnation etc. Moreover, in DE choice of its mutation and control factor is also challenging for better optimization. To enhance search capability of DE, a modified differential evolution (mDE) is suggested in this article. It adopted the novel mutation strategy, using concept of PSO (particle swarm optimization) mechanism. On the other hand, to avoid stagnation at local minima, new control operator integrated with the proposed mutation strategy based on the time-varying scheme. By using the memory information and dynamically changed control factors the exploration and exploitation capability of mDE are well balanced. Six unconstrained complex benchmark functions (unimodal and multimodal) are solved to certify the presentation of the mDE method. The simulation result shows that the mDE has good global search ability and stronger proficiency. Its search precision and the convergence speed much better against the other optimization EAs.
Empowering the Visually Impaired: A Sustainable ML-Based Currency Recognition System

This study presents a currency detection system specifically designed for visually impaired individuals, leveraging convolutional neural networks (CNN) implemented in machine learning with the TensorFlow framework. The main objective is to achieve a high accuracy rate of 95 percent in accurately detecting and classifying different denominations of currency notes, thereby promoting financial inclusivity for the visually impaired. The dataset used for training encompasses a wide range of currency images, accounting for various denominations, orientations, and lighting conditions. Preprocessing techniques are employed to enhance image quality and ensure standardized dimensions. The CNN architecture incorporates multiple convolutional and pooling layers, followed by fully connected layers for classification. To prevent overfitting, rectified linear unit (ReLU) activation functions are utilized along with dropout regularization. Through rigorous experimentation and training, the currency detection system achieves an exceptional accuracy rate of 95 percent, effectively identifying and classifying currency notes. The integration of TensorFlow enhances computational efficiency, enabling architectural optimizations and hyperparameter tuning. By incorporating this system into assistive technologies, such as mobile applications or smart devices, real-time currency recognition can be facilitated, empowering visually impaired individuals to engage in independent financial transactions. Future research directions may involve expanding the dataset, incorporating additional security features, and evaluating system performance in real-world scenarios. In summary, this study highlights the effectiveness of CNN and TensorFlow in developing a practical and accurate currency detection system for visually impaired individuals, fostering financial independence and inclusivity for this community.
The Importance of Sustainable Management of the Geologic Substratum for Exploitable High-Quality Mineral Resources: Mineral Waters in the Călimani Mountains and the Adjacent Areas

The present paper analyzes how the mining operations in the Călimani Mountains and the adjacent areas, historically and in the present day, can influence the exploitation of mineral water resources. The data covers five years and results from continuous monitoring of the natural carbogazeous, non-carbogazeous and balneal waters from 10 sites susceptible to pollution. The study revealed a considerable distance between the water sources and the potentially hazardous mining perimeters, a favorable geologic context preventing the dispersion of pollutants, and a reduced size of the mining sites, except for the sulfur quarry Călimani. Nevertheless, despite an insignificant impact, more robust protective measures and careful monitoring are necessary, given the developing agriculture and industry in the area.
Effectiveness of Agile Sustainable Project Management Approach in Software Industries

The primary objective of the research study is to examine and analyze the various challenges and limitations encountered during the implementation of agile project management in small and medium enterprises (SMEs) operating within the Indian market. This study identified an important limitation as there was no management involvement and employees were aware of the company’s policies and culture. These limitations were identified through a literature review, specific case studies, and case study analysis. As the popularity of agile methods continues to grow, more and more software companies are abandoning the traditional approach to agile development because of its flexible and human approach that enables small teams to work together. This article aims to explore the importance, benefits, and limitations of implementing an agile project management approach in SMEs in the Indian market. The study seeks to gain a deeper understanding of the Indian SME marketplace.
Development of Autonomous Battery Charging Station for Campus Surveillance UAV

Unmanned Aerial Vehicle (UAV) plays an essential role in various industries. It has been used in surveillance, photography, reconnaissance, environmental data collection, agriculture, and many other purposes. However, the main problem of UAVs is that, it is time-consuming to manually recharge the battery and natural phenomena like sunray and rain while operating in an open area. Thus, an autonomous battery charging station can solve the problem as it does not require human interaction and can protect the drone from natural phenomena. In this work, an autonomous charging station for UAV is designed, fabricated, and developed. The main focus of the charging station is the door mechanism, platform mechanism, and positioning mechanism. The Hilux bed size is the primary concern for sizing the charging station as it will be used for transporting, and the dimensions for the charging station is as follows length, width, and height (1450 mm × 1400 mm × 1200 mm). The parallel positioning mechanism has been used to control the UAV on the charging station platform. However, a linear actuator has been used to control the door mechanism of the charging station. After fabrication of the charging station, it can be seen that it can operate in any weather while protecting the UAV from rain and sunray and the operating time of the charging station is less than 90 s.
Minimum-time and minimum-energy trajectory planning under dynamic constraints for a quadrotor

This article proposes an approach for optimizing the trajectory planning of quadrotors in structured environments, taking into consideration the trade-off between task execution time and energy consumption. The constraints taken into account are geometrical (boundary conditions in position and orientation, obstacle avoidance), kinematic (boundary conditions in velocity, linear velocity, and acceleration limits of the system), and dynamic (limit capacities in torque of the actuators, constraints related to the under-actuation of the system). The approach extends the Random Trajectory Profile Approach (RPA) to the case of quadrotors. The results of this approach are validated through experimental tests and compared with those in the literature, demonstrating its effectiveness in offline and online scenarios. The proposed approach offers a promising solution for achieving optimal trajectory planning of quadrotors in structured environments.
Determining the ore-forming processes of Dongshengmiao Zn-Pb-Cu deposit: Evidence from the linear discriminant analysis of pyrite geochemistry

The orogenic base metal deposits gain more and more attentions in recent years. The ore-forming fluids associated with these deposits are enriched in reduced sulfur and have low salinity. Since this kind of fluid has low affinity to transport base metals, pre-enrichment of sulfide should take place during the formation of orogenic base metal deposits. The Dongshengmiao Zn-Pb-Cu deposit, which is located in the northern margin of the North China Block, was considered as orogenic base metal deposit, and a two-stage mineralization process was proposed by previous researchers, but other researchers proposed that the Dongshengmiao is a typical sedimentary exhalative (SEDEX) deposit. Even though the tectonic environment, fluid inclusion, geochronology and isotopic geochemistry have been widely investigated in the Dongshengmiao, its genesis is still under debate, and new data or proof should be presented to decipher the genesis model of the Dongshengmiao deposit. In this study, linear discriminant analysis (LDA), one of the commonly used machine learning methods, was employed to discriminate the genesis of pyrite in the Dongshengmiao deposit based on the trace element concentrations analyzed by LA-ICP-MS. The LDA results demonstrate that nearly all the early-stage fine-grained pyrites (Py1) show the same geochemical features as the pyrites from volcanic hosted massive sulfide (VHMS), whereas most of the late-stage recrystallized pyrite (Py2) is of orogenic origin. The Mesoproterozoic submarine volcanic activities resulted in pre-enrichment of sulfide with VHMS geochemical signature in the rocks hosting Dongshengmiao deposit. The pre-enriched sulfides were subsequently remobilized during the subsequent metamorphism in the early Cretaceous, leading to the formation of giant an orogenic base metal deposit.
Involutory permutation automorphisms of binary linear codes

We investigate the properties of binary linear codes of even length whose permutation automorphism group is a cyclic group generated by an involution. Up to dimension or co-dimension 4, we show that there is no quasi-group code whose permutation automorphism group is isomorphic to [Formula: see text]. By generalizing the method we use to prove this result, we obtain results on the structure of putative extremal self-dual [Formula: see text] and [Formula: see text] codes in the presence of an involutory permutation automorphism.
Ensemble Bagging Discriminant and Logistic Regression in Classification Analysis

The main problem that often becomes a challenge in classification analysis is a class imbalance, for example, in bank credit collectability where performing loans (PL) are 5% and non-performing loans (NPL) are 95%. The purpose of this research is to develop a classification model for the imbalance of collectibility data on Bank X mortgage credit. The analysis developed is Ensemble on Discriminant Analysis and Logistic Regression. The ensemble used in this study is Bagging (Bootstrap Aggregating). The data used are secondary data on bank X mortgage credit collectibility with a sample of [Formula: see text] = 100 and simulation data. Generated data with [Formula: see text] = 1000 and consists of two scenarios, namely, data with unbalanced classes (50:950) and data with balanced classes (500:500). Evaluation of the classification model is seen from the accuracy, sensitivity, and specificity. The results of classification analysis with Ensemble using Bagging Discriminant and Logistic Regression Bagging on secondary data and simulations are better than ordinary Discriminant Analysis and Logistic Regression. The sensitivity and specificity of the credit collectibility classification using Bagging Discriminant and Logistic Regression Bagging are also higher. The originality in this study is in the form of an Ensemble model using Bagging Discriminant and Logistic Regression which can improve the performance of classification analysis and has implications for reducing risk both for banks and for Bank X KPR customers.
Refining Sentiment Predictions: Obtaining an Unbiased Business Sentiment Index from Japanese Newspapers

Economic indices, such as the consumer price index, gross domestic product, and purchasing managers’ index, are typically computed based on economic data (e.g., prices of goods and incomes) and surveys. Recently, there has been considerable research to predict these indices using alternative data. One such approach is to utilize textual data, specifically newspaper articles, to predict a business sentiment index (BSI). However, newspapers often exhibit biases, which may impact the resulting index. This potential issue has not received adequate attention. Thus, this work first investigates whether these biases do influence the predicted indices and then proposes an approach to obtaining an unbiased business sentiment index. For this purpose, we analyze multiple Japanese newspapers, including one financial and three general papers with varied political orientations. We then predict a monthly BSI for each newspaper based on its content using a Transformer-based sentiment analysis model. To achieve an unbiased BSI, we employ the dynamic factor model. This model decomposes the time series of BSIs into a common dynamic factor shared across different newspapers and specific dynamic factors unique to individual newspapers. Our experiment indicates that the magnitudes of the predicted BSIs and the specific dynamic factors closely align with the political stances of the newspapers. Furthermore, the common dynamic factor exhibits a stronger and more positive correlation with composite indices and a survey-based BSI, namely the Tankan index.
Competency-Based Approach to Primary Training of Young Digital Workforce in the Digital Economy as a Foundation for Combating Climate Change

The research aims to develop a competency-based approach to the initial training of the young digital workforce in the digital economy as a foundation for combating climate change. The research methodology is based on the scientific provisions of the competency-based approach to training, in accordance with which a map of competencies of digital human resources for their involvement in the active fight against climate change has been developed. Using the method of comparative analysis, the author’s approach is compared with the existing approach in terms of its contribution to combating climate change. The result substantiates the preference of the new competency-based approach to the initial training of the young digital workforce over the existing approach due to the advantages in terms of contribution to the fight against climate change. These advantages include the systemic nature of digital and green competencies, a common set of climate change competencies for all universities, a high degree of state regulation of the training of young digital staff, and higher competitiveness of young digital staff in the labor market and their greater involvement in the fight against climate change. The theoretical significance of the author’s findings is that they have formed a systemic vision of the prospects of combating climate change in the digital economy through the training and employment of a green digital workforce. The practical significance of the obtained results is related to the fact that they make it possible to improve university management practices and human resource management practices in the business of the green digital economy.
Two-step multi-view data classification based on dynamic Graph-ELM

This paper focuses on the classification problem of multi-view data, aiming to improve the classification accuracy of current algorithms on multi-view data. Previous multi-view classification algorithms are usually based on exploiting the complementarity of different views and fusing features from different views. A representative category is the graph-based method, which builds a graph matrix for each view, and then fuses the graph matrices of different views to obtain a unified graph. These methods have the following problems: firstly, the graph matrix is simply based on sample similarity usually; secondly, the learned graph matrix does not change dynamically; thirdly, the weight of the graph representation matrix for a single view cannot be learned in the unified graph matrix. Therefore, this paper designs a Two-step classification algorithm based on Dynamic Graph-ELM, called TSDGELM. In the TSDGELM, the dynamic Graph-ELM is used to obtain the graph representation matrix of each view to save the local neighbor information of the data, and then a joint graph learning algorithm is designed based on the GBS (Graph-Based System) mechanism to fuse the graph matrix of the single-view, and finally the united graph is input into the classifier. To evaluate the effectiveness of the proposed method in this work, we conduct a series of experiments on eight datasets, and the results demonstrate the superiority of the proposed method.
A transformer-based generative adversarial learning to detect sarcasm from Bengali text with correct classification of confusing text

Sarcasm detection research in Bengali is still limited due to a lack of relevant resources. In this context, getting high-quality annotated data is costly and time-consuming. Therefore, in this paper, we present a transformer-based generative adversarial learning for sarcasm detection from Bengali text based on available limited labeled data. Here, we use the Bengali sarcasm dataset 'Ben-Sarc'. Besides, we construct another dataset containing Bengali sarcastic and non-sarcastic comments from YouTube and newspapers to observe the model's performance on the new dataset. On top of that, we utilize another Bengali sarcasm dataset 'BanglaSarc' to further prove our models' robustness. Among all models, the Bangla BERT-based Generative Adversarial Model has achieved the highest accuracy with 77.1% for the 'Ben-Sarc' dataset. Besides, this model has achieved the highest accuracy of 68.2% for the dataset constructed from YouTube and newspaper, and 97.2% for the 'BanglaSarc' dataset.
Formation of Students’ Media Culture in the Conditions of Digitalization of Education in Kazakhstan

Digitalization is a trend followed by countries striving to develop a competitive economy and improve the quality of human capital. One of the main conditions for improving Kazakhstan’s national competitiveness in the global world is the transition of all levels of education to the digital system and improvement of the quality of human capital. Digitalization of the educational process requires the formation of a media culture of future pedagogical specialists. The article analyzes the current state of digitalization of higher education in Kazakhstan, as well as the implementation of state programs in several directions: the creation of a national open educational platform, the integration of ICT into educational programs of universities, the opening of ICT departments of universities on the production base, the introduction of new specialties related to information and communication technologies, an increase in the number of state grants in this field the region, as well as the intensification of work on the development of distance learning. The central place of the research is the analysis of the problem of teachers’ media culture, which is considered as a condition for the development of media education and media competence of students. This is crucial for the formation of a digital educational environment, involving the training of specialists with media competence, who are able to effectively use digital resources in their professional activities. The effectiveness of the introduction of basic or elective training courses aimed at the formation of media education and media culture into the educational program of pedagogical specialties is considered. The results of the experimental work are presented.