Reviewerly/exHarmony huggingface dataset:
Characteristics of the ‘ecological consciousness’ concept: Content and definition within the framework of an interdisciplinary method

The most important factor of taking suitable response measures to combat current environmental challenges is a complete and comprehensive description of the environmental consciousness phenomenon. The identification of links between the internal ecological worldview of a person and social norms and regulations let influencing the environmental behavior of entire states by methods of education and culture. In this regard, it is necessary to explain the content of the ecological consciousness concept, which is possible only if you take an interdisciplinary approach to research. In this work, ecological consciousness is analyzed from the ecological component and the philosophical theory of consciousness points of view, described by the key domestic and foreign positions on the issue of describing the content of this phenomenon. For this purpose, historical and problematic issues of formation of ecological discourse and, in fact, of ecological consciousness itself in domestic science are investigated. At the end of the article, several key characteristics of ecological consciousness, based on the generalization of the studied material, are presented, as well as its experimental definition is proposed, designed to reflect the internal complexity and interdisciplinary nature of the required concept.
Unboring online learning: New Moodle features

The publication presents the experience of online learning at the university in the period of Pandemic-2019. It is proposed to discuss the following questions: how to keep students' attention in an online class? why students can be passive in an online class? how to maintain a good learning rhythm during an online lecture? The aim of the article is to analyze the experience of pedagogical activity during the period of forced isolation to identify the methods of effective (not boring) online learning and to characterize the new features of the Moodle platform. Based on the review of articles by foreign and Russian authors it is established that it is necessary to create a methodology of online learning. Theoretical and methodological basis of the publication are the works of Robert I.V., Polat E.S., Panyukova S.V., Vertekki B., Saba F., Harrison R., Dugiamas M. It is determined that non-boring online classes are effective classes with stable motivation of students, requiring the development of the concept of the training course. It is substantiated that the Moodle system realizes the social constructivist pedagogical concept with the help of special elements: weekly course outline, introductory presentation, Internet forums, essays, Internet surveys.
Subjective learning as a deep postulate of the modern concept of higher (university) education

The phenomenon of subjective learning, discovered by Russian psychology and pedagogy as a higher educational technology of universities, has not, however, been actively developed and introduced into the system of higher professional education. The instinctive (instrumental) acquisition of types of activity experience is similar to the results of animal training. But a person masters all the wealth of socio-historical experience of the activities of previous generations of people, clothed in a word, thought, image. The appropriation of functional and operational mechanisms of a lifetime kind of socio-historical experience of human activity by an individual can be realized only in subjective learning in the form of individual self-study teaching actions and activities in general and cognitive self-assimilation of the conceptual provisions of science. Subjective learning will not take place in full without the student’s self-study in a scientific discipline and the active participation of the teacher as a mentor and supervisor. In the conducted exclusive pedagogical experiment, the search was carried out for the forms of the fundamentally new initiative type of educational practice being discussed — the subject-activity personalized self-learning of students to the conceptual appropriation of knowledge in dialogue with the teacher.
PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19

The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.
Mengukur Kualitas Layanan Perpustakaan Universitas Muslim Nusantara Al-Washliyah Medan Menggunakan Metode Libqual

This research discusses measuring the quality of library services at the Muslim Nusantara Al Washliyah University of Medan using the Libqual method. The aim of the research is to determine the service quality of the Muslim Nusantara Al Washliyah Medan University Library based on three libqual dimensions, namely Affect of Service, Information Control and Library as Place. This type of research is quantitative research with a descriptive approach. The data collection technique in this research was carried out through distributing questionnaires. The sample in this study was 3,159 users of the Nusantara Al Washliyah Medan Muslim University Library from 2021, who were limited using the Slovin formula, so the number of respondents was 97 people. The sampling technique in this research used non-random accidental sampling. The results of this research show that service quality is based on the Affect of Service aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.03 and the quality of library services is considered good but has not met ideal expectations. what the user wants or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.01. Based on the Information Control aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.05 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the tolerance zone with the superiority gap (SG) being negative at a score of -1.07. Based on the Library as Place aspect, users are quite satisfied with the quality of library services or have met the minimum expectations of users with a positive adequancy gap (AG) at a score of 0.16 and the quality of library services is considered good but has not met the ideal expectations desired by users or is in the zone tolerance with superiority gap (SG) is negative at a score of -1.11.
Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks

Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.
Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.
A Robust Method for the Unsupervised Scoring of Immunohistochemical Staining

Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomarkers with specific antibodies. As such, immunohistochemical images are complex and their features are difficult to quantify. Recently, we proposed a novel method, including a first separation stage based on non-negative matrix factorization (NMF), that achieved good results. However, this method was highly dependent on the parameters that control sparseness and non-negativity, as well as on algorithm initialization. Furthermore, the previously proposed method required a reference image as a starting point for the NMF algorithm. In the present work, we propose a new, simpler and more robust method for the automated, unsupervised scoring of immunohistochemical images based on bright field. Our work is focused on images from tumor tissues marked with blue (nuclei) and brown (protein of interest) stains. The new proposed method represents a simpler approach that, on the one hand, avoids the use of NMF in the separation stage and, on the other hand, circumvents the need for a control image. This new approach determines the subspace spanned by the two colors of interest using principal component analysis (PCA) with dimension reduction. This subspace is a two-dimensional space, allowing for color vector determination by considering the point density peaks. A new scoring stage is also developed in our method that, again, avoids reference images, making the procedure more robust and less dependent on parameters. Semi-quantitative image scoring experiments using five categories exhibit promising and consistent results when compared to manual scoring carried out by experts.
Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

Abstract To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.
Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression

<h2>Abstract</h2><h3>Objective</h3> Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence. <h3>Materials and methods</h3> Using de-identified electronic health records of 52,861 post-operative encounters, we applied a data programming paradigm (implemented in the Snorkel software) for the development of a machine learning classifier for opioid-induced respiratory depression. Our approach included subject matter experts creating 14 labeling functions that served as noisy labels for developing a probabilistic Generative model. We used probabilistic labels from the Generative model as outcome labels for training a Discriminative model on the source data. We evaluated performance of the Discriminative model with a hold-out test set of 599 independently-reviewed patient records. <h3>Results</h3> The final Discriminative classification model achieved an accuracy of 0.977, an F1 score of 0.417, a sensitivity of 1.0, and an AUC of 0.988 in the hold-out test set with a prevalence of 0.83% (5/599). <h3>Discussion</h3> All of the confirmed Cases were identified by the classifier. For rare outcomes, this finding is encouraging because it reduces the number of manual reviews needed by excluding visits/patients with low probabilities. <h3>Conclusion</h3> Application of a data programming paradigm with expert-informed labeling functions might have utility for phenotyping clinical phenomena that are not easily ascertainable from highly-structured data.
Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program

Abstract Objectives We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios. Materials and methods The study included 22,621 mammograms of 8825 women within a 10-year biennial two-reader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, and Youden’s index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping. Results The AI software achieved an AUC of 89.6% (86.1–93.2%, 95% CI). The optimal threshold was 30.44, yielding 72.38% sensitivity and 92.86% specificity. Initially, AI identified 57 screening-detected cancers (83.82%), 15 interval cancers (51.72%), and 4 missed cancers (50%). AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier). No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69.5% reduction in workload and a 30.5% increase in accuracy. Conclusion This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70%. Clinical relevance statement The study proposes a more efficient method for screening programs, both in terms of workload and accuracy. Key Points • Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers. • AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage. • AI has the potential to facilitate early diagnosis compared to human reading.
Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.
Polarization-Selective Enhancement of Telecom Wavelength Quantum Dot Transitions in an Elliptical Bullseye Resonator

Semiconductor quantum dots are promising candidates for the generation of nonclassical light. Coupling a quantum dot to a device capable of providing polarization-selective enhancement of optical transitions is highly beneficial for advanced functionalities, such as efficient resonant driving schemes or applications based on optical cyclicity. Here, we demonstrate broadband polarization-selective enhancement by coupling a quantum dot emitting in the telecom O-band to an elliptical bullseye resonator. We report bright single-photon emission with a degree of linear polarization of 96%, Purcell factor of 3.9 ± 0.6, and count rates up to 3 MHz. Furthermore, we present a measurement of two-photon interference without any external polarization filtering. Finally, we demonstrate compatibility with compact Stirling cryocoolers by operating the device at temperatures up to 40 K. These results represent an important step toward practical integration of optimal quantum dot photon sources in deployment-ready setups.
Correction: Guo et al. A Siamese Transformer Network for Zero-Shot Ancient Coin Classification. J. Imaging 2023, 9, 107

Jochen Büttner was not included as an author in the original publication [...].
Mapping words to the world: Adults, but not children, understand how mismatching descriptions refer.

How do children learn to connect expressions (e.g., "that red apple") to the real-world objects they refer to? The dominant view in developmental psychology is that children rely on descriptive information, "red" and "apple." In contrast, linguistic theories of the adult language attribute primacy to grammatical elements: words such as "that" or "another" first establish the status of potential referents within the discourse context (old or new) before descriptions can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explored this novel prediction in adults and children. Over four experiments, we found that (a) adults relied on the articles to identify the referent, even when the description did not fit, consistent with grammar-first accounts; (b) consistent with description-first accounts, and unlike adults, 3- to 5-year-old children prioritized the descriptions provided by nouns and adjectives, despite being sensitive to grammatical information. This suggests that children connect expressions to referents differently from adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment

Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.