Reviewerly/exHarmony huggingface dataset:
Studying the Topic of the Course "The World Around Us": "World Cultural Heritage Sites in Russia and Abroad"

The article characterizes the difficulties that younger schoolchildren face when studying the theme of the course program "The World Around Us", dedicated to familiarizing students with natural and cultural objects of Russia and other countries included in the UNESCO World Heritage List. The article considers the method of consistent and successive acquaintance of students from the first to the fourth grade with the most significant and accessible World Heritage sites, with special attention to the emotional perception of a natural or cultural object, providing awareness of its beauty, uniqueness and significance.
Extending RiC-O to Model Historical Architectural Archives: The ITDT Ontology

Historical architectural archives enjoy attention from diverse audiences, acting as a primary source of information for architects, historians, public authorities, and common citizens alike. In Italy, the interest in architectural archives has grown slowly but steadily for the last 20 years. However, architectural archives do not generally follow the trend common for museums and galleries in publishing digitized materials and providing standard metadata for individual records. The information that is available online usually includes only an archival finding aid, instead of metadata about the individual records, or fully digital versions of the records. While cataloguing standards for archival descriptions of architectural records have existed at least since the 1980s, the rise of Linked Open Data as a framework for publishing cultural heritage data has allowed archivists to enhance these archival descriptions with richer contextual information and links to external knowledge bases. In this paper we present the ITDT ontology, an extension of the Records in Contexts Ontology that facilitates the representation of architectural records and of the context related to architectural projects, its process, and participating entities. We discuss the application of the ontology to the project files of Italian architect and engineer Dino Tamburini (1924–2011), and the creation of a digital archive offering multiple perspectives over the records.
A Lesson in Literary Reading in the Third Grade Based on the Story of K.G. Paustovsky “The Thief Cat “ (1–2 hours)

The article considers a variant of a lesson in literary reading, in which third-grade students study the story of Konstantin Georgievich Paustovsky "The Thief Cat". The teacher uses active teaching methods, offering third-graders at different stages of the lesson to work in pairs of a permanent composition, to conduct a cognitive research search. In the technology of the lesson, the classic methods of working with such a literary genre as a story are combined with some author's methods.
A 3D image-based method to measure soil stiffness in triaxial tests

Abstract This paper describes a new simple and inexpensive method for accurately measuring the three-dimensional deformation of soil specimens during triaxial tests. The method incorporates a cost-effective image capture system that can be applied to any triaxial apparatus as well as an automated post-image analysis approach for processing the images acquired during triaxial tests. Image capture is performed using multiple low-cost Raspberry Pi cameras and accessories placed outside the triaxial chamber. The system employs particle image velocimetry, camera calibration and stereophotogrammetry to determine the spatial variation in axial and radial sample strains as samples are loaded. It is demonstrated that axial and radial displacements can be measured with an accuracy of 1–2 μm, respectively, and it is verified by comparison with data from triaxial tests on sand samples that the full nonlinear stiffness–strain degradation of soil samples from fully elastic conditions can be determined without the need for any additional sensors.
Finite groups in which distinct nonlinear irreducible characters have distinct codegrees

Abstract For an irreducible complex character χ of a finite group G , its codegree is defined as . The main focus of this paper is to provide a classification of finite groups where distinct nonlinear irreducible characters have distinct codegrees.
Key-Frame Extraction for Reducing Human Effort in Object Detection Training for Video Surveillance

This paper presents a supervised learning scheme that employs key-frame extraction to enhance the performance of pre-trained deep learning models for object detection in surveillance videos. Developing supervised deep learning models requires a significant amount of annotated video frames as training data, which demands substantial human effort for preparation. Key frames, which encompass frames containing false negative or false positive objects, can introduce diversity into the training data and contribute to model improvements. Our proposed approach focuses on detecting false negatives by leveraging the motion information within video frames that contain the detected object region. Key-frame extraction significantly reduces the human effort involved in video frame extraction. We employ interactive labeling to annotate false negative video frames with accurate bounding boxes and labels. These annotated frames are then integrated with the existing training data to create a comprehensive training dataset for subsequent training cycles. Repeating the training cycles gradually improves the object detection performance of deep learning models to monitor a new environment. Experiment results demonstrate that the proposed learning approach improves the performance of the object detection model in a new operating environment, increasing the mean average precision (mAP@0.5) from 54% to 98%. Manual annotation of key frames is reduced by 81% through the proposed key-frame extraction method.
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble

In Hyperparameter Optimization (HPO), only the hyperparameter configuration with the best performance is chosen after performing several trials, then, discarding the effort of training all the models with every hyperparameter configuration trial and performing an ensemble of all them. This ensemble consists of simply averaging the model predictions or weighting the models by a certain probability. Recently, other more sophisticated ensemble strategies, such as the Caruana method or the stacking strategy has been proposed. On the one hand, the Caruana method performs well in HPO ensemble, since it is not affected by the effects of multicollinearity, which is prevalent in HPO. It just computes the average over a subset of predictions with replacement. But it does not benefit from the generalization power of a learning process. On the other hand, stacking methods include a learning procedure since a meta-learner is required to perform the ensemble. Yet, one hardly finds advice about which meta-learner is adequate. Besides, some meta-learners may suffer from the effects of multicollinearity or need to be tuned to reduce them. This paper explores meta-learners for stacking ensemble in HPO, free of hyperparameter tuning, able to reduce the effects of multicollinearity and considering the ensemble learning process generalization power. At this respect, the boosting strategy seems promising as a stacking meta-learner. In fact, it completely removes the effects of multicollinearity. This paper also proposes an implicit regularization in the classical boosting method and a novel non-parametric stop criterion suitable only for boosting and specifically designed for HPO. The synergy between these two improvements over boosting exhibits competitive and promising predictive power performance compared to other existing meta-learners and ensemble approaches for HPO other than the stacking ensemble.
Detection of Reflected XSS Vulnerabilities Based on Paths-Attention Method

Cross-site scripting vulnerability (XSS) is one of the most frequently exploited and harmful vulnerabilities among web vulnerabilities. In recent years, many researchers have used different machine learning methods to detect network attacks, but these methods have not achieved high accuracy and recall rates and cannot effectively combat XSS attacks. Designing a model that can achieve high accuracy and truly proactive defense against reflected XSS vulnerabilities has become a top priority for maintaining user network security at this stage. In this paper, we propose a detection model for reflected XSS vulnerabilities based on the paths-attention method (PATS model). Firstly, the model converts vulnerability data into an intermediate representation of abstract syntax trees, then traverses the abstract syntax tree to generate multiple sets of syntactic paths, and then converts them into vector representations through word embedding matrices. The model extracts semantic features using attention mechanisms to improve training effectiveness by assigning appropriate weights to different sets of syntactic paths as it learns with neural networks, which realizes the transformation from passive defense to active defense. Additionally, in the dataset processing section, we point out the shortcomings of current research datasets and construct a reliable dataset composed of 1000 malicious samples from NIST and 10,000 benign samples from GitHub for experimentation purposes. Experimental results show that compared with other machine learning models, the paths-attention method can achieve an accuracy rate of 90.25% and F1-score of 81.62%, while reducing the training time by half to 30 h.
Emotional Appeal in the Tweets: A Study on Indian National Political Parties

The use of emotional appeal in dialogue by political parties to attract voters has become common practice. Social media has made it easier to spread emotional appeal. The research presented is based on the emotional appeal in the tweets of political parties. The main objectives of the present research are to find out the emotional appeal in tweets, the nature of the tweets, and the context of the tweets. The data in the research has been analyzed by the method of Textual Discourse Analysis research. It is clear from the research results that political parties use the most anger and hate emotional appeals in tweets. The highest percentages of tweets from parties are negative. The highest percentages of tweets from political parties have a social context.
FDDS: Feature Disentangling and Domain Shifting for Domain Adaptation

Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model’s suitability in the original scene, we present a method called feature disentangling and domain shifting (FDDS) for domain adaptation. FDDS utilizes sample information from both the source and target domains, employing a non-linear disentangling approach and incorporating learnable weights to dynamically separate content and style features. Additionally, we introduce a lightweight component known as the domain shifter into the network architecture. This component allows for classification performance to be maintained in both the source and target domains while consuming moderate overhead. The domain shifter uses the attention mechanism to enhance the ability to extract network features. Extensive experiments demonstrated that FDDS can effectively disentangle features with clear feature separation boundaries while maintaining the classification ability of the model in the source domain. Under the same conditions, we evaluated FDDS and advanced algorithms on digital and road scene datasets. In the 19 classification tasks for road scenes, FDDS outperformed the competition in 11 categories, particularly showcasing a remarkable 2.7% enhancement in the accuracy of the bicycle label. These comparative results highlight the advantages of FDDS in achieving high accuracy in the target domain.
Actualization. Sexual abuse, imprinting, and Tourette’s syndrome. Protagonist, deuteragonist, tritagonist, and hybris

I propose the examination of imprinting resulting from sexual abuse as a potential factor that may influence various outcomes, including homosexuality, stuttering, and autism in both men and women. It is suggested that imprinting originally triggered by sexual abuse might contribute to the development of these conditions. One possible sequence, among others, could involve the following: Sexual abuse imprinting (leading to imprinting of homosexuality) may be associated with imprinting of stuttering and autism in both men and women. These conditions, observed with an average ratio of four men to one woman, are known to be challenging to modify. These collective observations give rise to the imprinting syndrome of sexual abuse. The purpose of the first part of this update opinion article is to expand both the references and the information that supports it by including Tourette's syndrome as another possible consequence of child sexual abuse. For more information on the background and procedures used, it is suggested to read reference 22. Through bioenergemal communication, we establish interaction with human bioenergemes (BEGs) that arrive at the bioenergemal (BEL) universe after people biocollapse (die). This BEL information does not intend to ratify or rectify the official version of these facts; it only presents the bioinformation that some BEGs shared with us.
An Enhanced Method on Transformer-Based Model for ONE2SEQ Keyphrase Generation

Keyphrase generation is a long-standing task in scientific literature retrieval. The Transformer-based model outperforms other baseline models in this challenge dramatically. In cross-domain keyphrase generation research, topic information plays a guiding role during generation, while in keyphrase generation of individual text, titles can replace topic roles and convey more semantic information. As a result, we proposed an enhanced model architecture named TAtrans. In this research, we investigate the advantages of title attention and sequence code representing phrase order in keyphrase sequence in improving Transformer-based keyphrase generation. We conduct experiments on five widely-used English datasets specifically designed for keyphrase generation. Our method achieves an F1 score in the top five, surpassing the Transformer-based model by 3.2% in KP20k. The results demonstrate that the proposed method outperforms all the previous models on prediction present keyphrases. To evaluate the performance of the proposed model in the Chinese dataset, we construct a new Chinese abstract dataset called CNKIL, which contains a total of 54,546 records. The F1 score of the top five for predicting present keyphrases on the CNKIL dataset exceeds 2.2% compared to the Transformer-based model. However, there is no significant improvement in the model’s performance in predicting absent keyphrases.
Communicating Uncertainty in Weather Forecasts and Warnings: Challenges and Visualization Examples

The mobile weather app nowadays represents the most important channel for MeteoSwiss to communicate its forecasts and warnings to the general public. The forecast presentation on the app has evolved from an initially deterministic format to now also include probabilistic elements. However, these first steps towards including uncertainty information have provoked mixed user feedback, highlighting the challenge of effectively communicating uncertainty information to our stakeholders. If communicated effectively, forecast uncertainty information leads to better decisions thus illustrating the added value of such information (Joslyn and LeClerc, 2013).&amp;#160; To address this challenge, we have started an effort to explore new ways effectively communicating forecasts and their uncertainty and will present examples of new visualizations. We start from the existing and well-established forms of forecast visualizations including time series of forecasts and spatial maps and extend these to convey forecast uncertainty in an intuitive and thus effective manner. To gauge the effectiveness of these new visualizations, we will perform end-users evaluations. Such end-user evaluations are seen as key element but represent a substantial effort: There is a risk of rejecting promising concepts if the prototype visualization is not of sufficient visual quality, as graphical details may decide on acceptance or rejection by end-users. Our ultimate goal is to enable the general public to take well-informed and thus better decisions by improving their understanding of the uncertainty associated with weather forecasts and warnings.&amp;#160;Susan Joslyn and Jared LeClerc, Decisions With Uncertainty: The Glass Half Full, Current Directions in Psychological Science, Vol. 22, 4, 2013, https://doi.org/10.1177/0963721413481473
Learning sparse reparameterization with layer-wise continuous sparsification

Sparse reparameterization in Deep Neural Networks (DNNs) aims to achieve a better tradeoff between the network parameter count and performance. Recently, the lottery ticket hypothesis suppose that excellent sub-networks (“winning tickets”) exist in dense randomly-initialized networks. These sparse sub-networks trained from scratch are able to reach the performance of their dense counterparts. Compared with Iterative Magnitude Pruning that relies on pruning strategies, the Continuous Sparsification algorithm learns the “winning tickets” with gradient-based methods, achieving better performance. In this paper, we propose Layer-wise Continuous Sparsification (LCS) scheme for finding sparse sub-networks, in which the parameterized relaxation of step functions used to remove network parameters in each layer is integrated into the DNN loss as an optimization objective. LCS utilizes a family of sigmoid functions to asynchronously filter important per-layer weights throughout training, yielding sparser and better sub-networks. Experiments show that our method surpasses state-of-the-art methods for sparse reparameterization. Additionally, the proposed method can be utilized as a regularization technique to further improve the accuracy of dense networks1.
StreamAD: A cloud platform metrics-oriented benchmark for unsupervised online anomaly detection

Cloud platforms, serving as fundamental infrastructure, play a significant role in developing modern applications. In recent years, there has been growing interest among researchers in utilizing machine learning algorithms to rapidly detect and diagnose faults within complex cloud platforms, aiming to improve the quality of service and optimize system performance. There is a need for online anomaly detection on cloud platform metrics to provide timely fault alerts. To assist Site Reliability Engineers (SREs) in selecting suitable anomaly detection algorithms based on specific use cases, we introduce a benchmark called StreamAD. This benchmark offers three-fold contributions: (1) it encompasses eleven unsupervised algorithms with open-source code; (2) it abstracts various common operators for online anomaly detection which enhances the efficiency of algorithm development; (3) it provides extensive comparisons of various algorithms using different evaluation methods; With StreamAD, researchers can efficiently conduct comprehensive evaluations for new algorithms, which can further facilitate research in this area. The code of StreamAD is published at https://github.com/Fengrui-Liu/StreamAD.
Impact of the Covid-19 pandemic on teacher tweeting in Spain: needs, interests, and emotional implications

The dissemination of Covid-19 imposed the confinement of a large part of the world's population. For this reason, face-to-face classes in Spain were interrupted and did not resume until September 2021. The situation forced schools to move both teaching and communication between teachers to a digital environment, which favoured greater use of social networks. This paper conducts an exploratory study of 30751 tweets extracted from eight educational hashtags (#eduhora, #claustrovirtual, #SerProfeMola, #otraeducaciónesposible, #claustrotuitero, #profesquemolan, #orgullodocente, and #soymaestro) used by the educational community of teachers in Spain. A semantic content analysis is carried out using a mixed methodology based on public data mining and sentiment analysis. The analysis of the data provided novel information about the needs, interests and concerns, as well as the emotional implications that teachers expressed on the social network Twitter during the transition to virtual teaching. The lock-in situation was associated with increased emotional content in the tweets analysed in the sample, irrespective of the positive, negative or neutral polarity of the tweets. The results also show that teachers in Spain use social networks both for professional development and emotional support and that this trend has increased after Covid-19. The use of Twitter is linked to continuous professional development in times of particular difficulty, also in Spain, as has been the case in other countries. The findings of the study show that the historical Twitter archive is a valid resource for the analysis of teachers' feelings in longitudinal research including the Covid-19 period.
Analysis of the Influence of E-Learning Service Quality Factors on Student Perceived Value and Student Satisfaction, with Implication to Student Loyalty

ABSTRACT The COVID-19 pandemic has changed the learning process, which was previously carried out conventionally with face-to-face interaction, into an online learning activity (e-learning). e-Learning has several benefits, but it also has some challenges that need to be addressed. A preliminary survey has been conducted by asking open-ended questions regarding students’ experience and obstacles during e-learning to determine their perceptions of e-learning, and the results showed that there were problems faced by students in implementing e-learning, especially pertaining to student loyalty. Previous research has stated that e-learning service quality exercises an influence on student satisfaction and student loyalty. This research focusses on the influence of e-learning service quality factors on student perceived value (SPV) and student satisfaction, and ultimately on its role in influencing student loyalty. This research was conducted using a quantitative approach, wherein data were gathered, using judgement sampling, from 309 undergraduate students enrolled across six universities in Tangerang. These data were analysed by partial least squares structural equation modelling (SEM-PLS), and the results of the analysis indicated the presence of a positive and significant relationship between these variables. Resultant to the analysis, there emerged some useful suggestions for educational institutions and lecturers that would enable them to improve the quality of e-learning. The findings of the present study imply that improving the factors of e-learning service quality will inculcate in students a favourable value perception towards the university, as well as create a feeling of satisfaction in them, arising from the fulfilling character of the experience to which they were exposed during the e-learning process; these phenomena are expected in turn to increase students’ loyalty towards the university.