Reviewerly/exHarmony huggingface dataset:
Coming of Age transclasse. Zur Netflix-Serie Outer Banks (2020–)

Coming of Age transclasse. Reflections on Netflix’ teen series Outer Banks (2020–): Scenes involving work are rather rare in coming-of-age series on tv. Instead, the focus is on everyday life at school, family and peer groups or, as in the Netflix series Outer Banks (2020–), on the long summer vacation as an adventure space. In the Outer Banks of North Carolina, an amphibious, hybrid landscape, everyone has the same goal: to find the gold treasure of the sunken Royal Merchant. Everyone shares the belief that wealth can free them from their personal problems, but also from their burdensome heritage. And yet, as the off-screen voice of the teenage narrator and protagonist John B. makes clear right at the beginning, there are two seemingly radically opposed ways of living: “It’s a sort of place where you either have two jobs or two houses.” Society is divided: the Pogues, people who work – mainly in the houses, gardens and yachts of the rich – and the Kooks, wealthy members of the leisure class. The characters all insist on their class identity and celebrate their separation from the other group. But over the course of the first season, it becomes apparent that class in Outer Banks is not necessarily reproduced in the way Bourdieu described it. Many characters are class transgressors (transclasse, cf. Jacquet 2018) and suffer from the constraints of their social roles. By linking the pursuit of quick money with the breaking open of past guilt, the series negotiates the question of reproduction and the possibilities of breaking out of it.
Public Health vs. Personal Privacy During COVID-19 in China

As an essential part of the battle against the COVID-19 pandemic, the Chinese government has taken effective prevention and control measures to contain the spread of COVID-19 on the mainland by tracking the human flow. Everyone is requested to show a health QR code and take temperature before entering public places. The citizen’s temperature data, personal information, location, movement, and other tracing information can be collected. As the information is collected, transmitted and processed, citizens’ privacy is at risk of exploitation. Rising data protection, security and privacy issues have become China’s most imperative ethical concerns. This chapter seeks to critically discuss and analyze the government policies, the rising issues, and the responses from the public and the government in China to explore how China has handled and managed the issue of mobile privacy while leveraging mobile as an effective way to contain the spread of COVID-19.
Designing Smart Voice Command Interface for Geographic Information System

Voice based command activation has gained momentum as a feature of user interface replacing the mouse or pointing device-based menu selection. Among various software driven systems such as internet of things, search engines and robotic systems use the voice command interface as the primary interaction between the user and the system keeping the conventional user interface as secondary. Voice command interface has much pay offs and advantages over conventional menu driven user interface or mouse-driven user interface. This paper presents the research landscape on voice command interface and proposes a design of voice-based interface for a GIS (Geographic Information System). Further we compare the proposed voice command interface with open source and COTS (Commercial Off-The-Shelf) voice command interface models. The proposed voice interface model has significant reduction in the word error rate by 3.49% compared to existing models.
Light Adaptation for Classification of the Upper Gastrointestinal Sites

Plenty of computer-aided blind-spot monitoring systems based on the classification model of the upper gastrointestinal sites are developed to enhance the quality of gastroscopy. However, the performance of the white light (WL) based model drops deeply while changing the light source to the special light (SL), a narrowed-spectrum technology. A naive solution is to collect as much data from SL as from WL, but it is hard and time-consuming. In this work, we propose a novel light adaptive module that is only trained by common labeled WL images and unlabeled SL images. Our proposed structure is a plug-in module including a light classification head and a reconstruction decoder. The light classification head is trained in an adversarial manner, which prevents the backbone network to extract light-related features. The reconstruction decoder facilitates the complete preservation of the extracted structural features. The result showed that the original classification model added with our proposed light adaptive module could significantly improve the classification performance under SL and keep the original accuracy under WL, which may help endoscopists achieve better gastroscopy.
FEATURES OF THE FORMATION OF COGNITIVE UNIVERSAL EDUCATIONAL ACTIONS OF YOUNGER SCHOOLCHILDREN IN EXTRACURRICULAR ACTIVITIES

The formation of cognitive universal educational actions is the basis of educational standards of the new generation. Cognitive DMS are aimed at developing the ability of students to independently assimilate new knowledge, form the necessary skills in particular and develop their own competence in general, as well as successfully apply the acquired knowledge and skills in life. The article talks about various areas of implementation of extracurricular activities in primary school, which allows the teacher to successfully form cognitive universal educational actions of students through purposeful and systematic work with children.
The Role of Ornaments for the Gedhe Kauman Mosque

This study aims to describe the role of the ornaments found in the Gedhe Kauman Mosque by having various forms with different meanings. The Gedhe Kauman Mosque building is a place of worship whose history is still attached to the local community, especially in Yogyakarta. The Gedhe Kauman Mosque was built during the Sultanate of Hamengku Buwono with an architect Ki Wiyukusumo with the aim of forming an Islamic state which at that time, the city of Yogyakarta was still in the process of Islamization and the spread of Islam was still being moved. This research is descriptive qualitative by discussing some of the history of the Gedhe Kauman Mosque building as well as some parts of the ornaments printed on the Gedhe Kauman Mosque building. The research data was obtained by means of literature study, observation, documentation and interviews with sources who know about the Gedhe Kauman Mosque building and its contents. There are several ornaments that will be discussed with each having a different meaning. The conclusion of this study is to add insight into works of art that are applied to buildings of worship with a very important meaning in them.&#x0D; Keywords: Art and Ornament Mosque; Design; Islam; Moslem; Yogyakarta Indonesia
Regularization Approach for Accelerating Neural Architecture Search

Modern Artificial Neural networks utilizes vast topologies to become applicable to the vast majority of the ML problems. Such topologies have an enormous number of parameters nowadays, which make it easier to use, but harder to train and modify. With such a number of parameters, the usage of semi-automatic techniques for constructing or adjusting new models for topical problems is vital for the whole industry. It makes it essential to optimize such methods like Neural Architecture Search (NAS) to efficiently utilize computational resources of the GPU and whole cluster. Since modern NAS tools are commonly used to optimize models in different areas or make a combination of models from the past, they have to use a plenty of computational power to perform certain hyperparameters optimization routines. Many NAS methods are of a highly parallel nature, but still they need a lot of computational power and have an enormous convergence time. The key to increasing performance of many NAS methods is boosting performance of the training of the highly-depth and synthesized model, to evaluate the probe of the model and obtain the score for this epoch. It means that demands for parallel implementations need to be available in different cluster configurations and should utilize as many nodes as possible, showing high scalability. However, straightforward approaches where NAS solving does not consider previous results lead to wasteful utilization of computation power. In this article, we introduce a new method that can improve convergence in NEAT-based NAS method using L1/L2 regularization during the evolution step.
Collaborative Learning During the Pandemic: The Role of Mobile Devices

Collaborative learning encourages students to participate more actively in their learning process. They can discuss, share, assess, and take responsibility for their own and their peers’ learning. It is the context where knowledge convergence happens subsequently. With the COVID-19 pandemic onset and lingering, mobile devices have become more essential in various learning contexts. Mobile learning enables students learning in and outside classrooms with easy, flexible, and unlimited features. Against this backdrop, this study examines the acquisition of practical skills in translation within the framework of collaborative learning through mobile devices. Based on an exploratory study through surveys and observations, we examine how knowledge convergence happens collaboratively while facilitated by mobile devices in and outside classroom settings.
A Matching Algorithm to Assess Web Interfaces

The work presented in this article is part of ongoing research on the automated assessment of simple web applications. The proposed algorithm compares two interfaces by mapping their elements, using properties to identify those with the same role in both interfaces. The algorithm proceeds in three stages: firstly, it selects the relevant elements from both interfaces; secondly, it refines elements’ attributes, excluding some and computing new ones; finally, it matches elements based on attribute similitude. The article includes an experiment to validate the algorithm as an assessment tool. As part of this experiment, a set of experts classified multiple web interfaces. Statistical analysis found a significant correlation between classifications made by the algorithm and those made by experts. The article also discusses the exploitation of the algorithm’s output to access both the layout and functionality of a web interface and produce feedback messages in an automated assessment environment, which is planned as future research.
UApredictor: Urban Anomaly Prediction from Spatial-Temporal Data using Graph Transformer Neural Network

Urban anomalies are abnormal events such as a blocked driveway, illegal parking, noise, crime, crowd gathering, etc. affect people and policy managers drastically if not handled in time. Prediction of these anomalies in the early stages is critical for public safety and mitigation of economic losses. However, predicting urban anomalies has various challenges like complex spatio-temporal relationships, dynamic nature, and data sparsity. This paper proposes a novel end-to-end deep learning based framework, i.e., UApredictor that utilizes stacked spatial-temporal-interaction block to predict urban anomaly from multivariate time-series data. We model the problem using an attribute graph, where we represent city regions as nodes to capture inter region spatial information using a spatial transformer. Further, to capture temporal correlation, we utilize a temporal transformer, and the interaction module retains complex interaction between spatio-temporal dimensions. Besides, the attention layer is added on the top of the spatial-temporal-interaction block that captures important information for predicting urban anomaly. We use real-world NYC-Urban Anomaly, NYC-Taxi, NYC-POI, NYC-Road Network, NYC-Demographic, and NYC-Weather datasets of New York city to evaluate the urban anomaly prediction framework. The results show that our proposed framework predicts better in terms of F-measure, macro-F1, and micro-F1 than baseline and state-of-the-art models.
A Knowledge Graph Approach towards Re-structuring of Scientific Articles

With expedition of huge number of research articles published in each domain, retrieval of relevant articles based on researcher's interest and requirement have become challenging. Further, there are circumstances where a researcher may not get the specific information sought after, regardless of whether it is there in the document collection. This is since the article in database is structured only based on title and citation data while the conceptual information and algorithms described inside an article are disregarded. In this work, we propose a useful methodology for re-structuring documents in the database by considering the document as a whole and representing the keywords and key-phrases extracted from the article data using Knowledge Graphs. Clustering of nodes of graph segregate articles into different domains and sub-domains. Knowledge graph is further explored to identify most important documents using modularity and then the documents are sorted based on relevance. Thus, proper structuring of the documents helps the researchers to recognize applicable content from a large database in short span of time since database associated with the query system is improvised. This technique is beneficial to all the researchers who are trying to resolve a problem by identifying apt documents for information need.
Photovoltaic Power Predictor Module Based on Historical Production and Weather Conditions Data

In recent years the demand for electrical energy has increased significantly. Usually, the electrical grid covers this demand. However, this fuel energy is known for its significant carbon footprint. For that reason, different mechanisms to bring cleaner energies have been explored, like hydraulic, wind, thermal, and one of the most popular solar energy. Although solar energy is abundant and environmentally friendly, the photovoltaic energy that comes from the sun, solar production is subject to different external perturbations, such as environmental conditions. Therefore it has been necessary to develop other methods based on statistics, machine learning, or deep learning to make solar forecasting and predict production and weather conditions. Specifically, this work proposes an evaluation of three different deep learning models to predict irradiance, temperature, and production of a photovoltaic system located in the city of Cartagena, Colombia. Those are irradiance and temperature using the historical data on production and weather conditions. This data has been registered on a web platform for seven months, from January 1, 2022, until June 28, 2022.
Parameterizing the Rank List Model with Optimal Pricing Decision

The rank list model is a general non-parametric choice model that provides an intuitive and flexible framework to capture rational consumers' preferences. The rank list model assumes that each customer has a fixed and consistent preference ranking over all products and chooses the product that ranks highest among the offered set. However, consumers' preferences are likely to be affected when some features of the products change. Thus, we propose an approach to parameterize the rank list model and estimate the parameters, including price sensitivity and noise distribution. The parameterized rank list model can be estimated through either a regularized linear regression or a second-order cone program. With the utility-price relationship explicitly captured, we could reformulate the corresponding multi-product pricing problem as a mixed-integer linear program and efficiently solve it using off-the-shelf solvers. Numerical experiments demonstrate the predictive and prescriptive power of the proposed approach against other common parametric choice models.
Prospective functional role model of the teacher

Problem of the discrepancy between the quality of pedagogical activity and changes in post-industrial society is found all over the world, which requires a new vision of its competence structure. The purpose of the article is to substantiate the need to change perspectives when building models of a teacher, their functional-role content, and to develop a prospective (future-oriented) model of a teacher using the example of continuous pedagogical education of an engineering and technological profile. Based on the content analysis of foresights on education, analysis of works on advanced vocational education, the authors identified categories of functions, roles, competencies and carried out modeling of professional activity based on changes in these variables and their relationship. A three-dimensional prospective model of a teacher of engineering and technological education has been developed on the basis of isolating functions (World CDIO Initiative), competencies (Atlas of future professions), roles (analysis of experience) in the process of content analysis. The authors postulate the possibility of extrapolating the model to other areas of professional pedagogical activity.
CO2 Site Characterization: an Efficient Integrated G&amp;G Workflow

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Analysis of government regulations sentiment regarding the Eid al-Fitr homecoming during Covid-19 pandemic in Indonesia using Twitter and Instagram data

The Covid-19 pandemic has resulted in an often uncertain situation. On this basis, the government has implemented a ban on going home for the second time in 2021 to prevent a potential increase in Covid-19 cases. This rule raises pros and cons in society. Twitter and Instagram as social media platforms then became a means to voice reactions to the regulation, as well as opinions and criticisms The goal of this study is to find out how people feel about the situation the "Prohibition of Homecoming in 2021". The data mining approach is used in this study to classify public sentiments conveyed not only through the Twitter platform but also Instagram. The Naive Bayes and Decision Tree algorithms were used to create the classification model. On Twitter data 87.93% F1 score and 92.63% F1 Score on Instagram data. This study shows, the majority of people have negative sentiments about the "Prohibition of Homecoming in 2021" both on Twitter and Instagram platforms.
MATIC: Memory-Guided Adaptive Transformer for Image Captioning

Transforming the visual knowledge into linguistic description is a demanding and essential task known as image captioning, which necessitates a vision-language-based system to co-relate the multi-modal information. Transformer architecture has recently become cutting-edge in addressing the image captioning-related problems. In this paper, the utility of the transformer is explored by bridging single layer memory-guided encoder and multi-layer adaptive attention decoder framework entitled as Memory-guided Adaptive Transformer for Image Captioning. The memory-based encoder is used to capture the relative spatial information from CNN-based visual features and transfer it to the decoder, which then learns the inter-modality affinity between visual and linguistic features. The decoder with multi-headed adaptive attention reconciles the visual and linguistic information to describe the visual contents in a comprehensible way by balancing visual and semantic patterns through adaptive gate channeling. The performance of the proposed method is evaluated on widely used captioning datasets viz. Flickr8k and Flickr30k. The proposed method performs remarkable and earns a superior caption generation score on both datasets as compared to state-of-the-art.
Supervised Models Enhancement using UnSupervised Transition in Mobile Network

Industrial enterprises daily challenge AI research for better solutions. One of the most difficult challenges is dealing with massive amounts of data that have a high degree of similarity and a dense distribution. Innovative strategies are required to find a valid data analysis model. Feature engineering and domain transformation both played a big role in this domain. In this paper we propose a novel architecture to engineer new hidden data features using unsupervised models. These new features are generated without going through the standard clustering validation process. This architecture improves the overall performance of the supervised model compared to traditional supervised approaches commonly employed in comparable situations.
Embedding Anomaly Detection Autoencoders for Wind Turbines

Machine Learning solutions for anomaly detection can be applied in the industry to extend the lifetime of components by promoting actuation in real-time, like optimising machinery parameters to mitigate faults. The development of these approaches usually takes place on Cloud platforms. Nevertheless, Cloud platforms interacting with the real-time control loop add extraneous and unpredictable delays due to service availability and possible communication issues. Thus, embedding anomaly detection solutions in monitoring and control systems is an alternative to enable the aforementioned interaction. In a previous work, we investigated a Deep Autoencoder solution based on vibration data to detect anomalies in Wind Turbines. The solution is currently in usage in a Cloud platform and achieved promising results, which motivated its embedding. This paper demonstrates the process of embedding the solution while maintaining the detection performance metrics. The proposed embedding process reduced the execution time of the preprocessing steps to 18.09% of the original method while saving up to 54.59% of energy on average. An automatic search for low-cost configurations, while evaluating the impact of preprocessing steps, resulted in adequate configurations, improving the original anomaly detection performance and reducing memory utilisation up to 5.5x.