Reviewerly/exHarmony huggingface dataset:
Digital Learning Transformation in Higher Education: International Cases of University Efforts to Evaluate and Improve Blended Teaching Readiness

The global pandemic of 2019 brought heightened awareness to institutions of higher education of the need to engage in a digital transformation that extends beyond university business operations to the pedagogy of the classroom. This paper is a case study that explores three international cases of universities in Colombia, Brazil, and Mongolia that are at different stages along the path of a digital pedagogical transformation. This article tells each story, including (1) what is driving the local need to engage in digital transformation, (2) what the major challenges and barriers are to achieving a transformation, and (3) what efforts are being made to help each university to move along the path towards adoption and change. It concludes with discussing three major themes that emerged from the case studies: (1) the role of local policy in shaping digital transformation, (2) the importance of developing human capacity with technology, and (3) the potential for digital transformation to bring hope.
Geochemical and Isotopic Fractionation in the Hypogene Ore, Gossan, and Saprolite of the Alvo 118 Deposit: Implications for Copper Exploration in the Regolith of the Carajás Mineral Province

In the Carajás Mineral Province, gossan formation and lateritization have produced numerous supergene orebodies at the expense of IOCG deposits and host rocks. The Alvo 118 deposit comprises massive and disseminated hypogene copper sulfides associated with gossan and mineralized saprolites. The hypogene reserves are 170 Mt, with 1% Cu and 0.3 ppm Au, while the supergenes are 55 Mt, comprised of 30% gossan and 70% saprolite, with 0.92% Cu and 0.03 ppm Au. The gossan includes goethite, malachite, cuprite, and libethenite zones. The saprolite comprises kaolinite, vermiculite, smectite, and relics of chlorite. In the hypogene mineralization, Ag, Te, Pb, Se, Bi, Au, In, Y, Sn, and U are mainly hosted by chalcopyrite and petzite, altaite, galena, uraninite, stannite, and cassiterite. In the gossan, Ag, Te, Pb, Se, and Bi are hosted by Cu minerals, while Au, In, Y, Sn, and U are associated with iron oxyhydroxides, in addition to Zn, As, Be, Ga, Ga, Mo, Ni, and Sc. As supporting information, δ65Cu values indicate that the gossan is immature and, at least partly, not affected by leaching. In the saprolite, Ga, Sc, Sn, V, Mn, Co, and Cr are associated with the iron oxyhydroxides, partially derived from the host rock weathering. The δ56Fe values indicate that hypogene low contribution of the hypogene mineralization to the saprolite iron content. The association of Al2O3, Hf, Zr, Th, TiO2, Ce, La, Ba, and Sr represents the geochemical signature of the host rocks, with dominant contributions from chlorites, while In, Y, Te, Pb, Bi, and Se are the main pathfinders of Cu mineralization.
Comprehensive analysis of aero-engine vibration signals based on wavelet transform method

Abstract A single type of signal processing means that it is difficult to analyze vibration signals comprehensively and effectively. By comprehensively using wavelet analysis techniques, a comprehensive and in-depth study of aero-engine vibration conditions is realized as a way to carry out health management. By introducing various types of wavelet analysis techniques and using Labview2022 programming, corresponding signal processing tools are developed for the analysis of the collected vibration signals. The comprehensive analysis of aero-engine vibration signals based on the wavelet transform method is realized, and the corresponding products are successfully applied in engineering practice.
Real-Time Object Detection and Localization for Autonomous Driving

&amp;nbsp;The term “object detection” refers to a technology that enables humans to recognise specific types of things present in visual media. One of the important applications of the technique is autonomous driving cars. In the application, the activity is to detect the various objects present in the single image frame. Examples of objects belonging to multiple classes are trucks, bikes, persons, cars, dogs, and cats. For this task, we use object localization and classification as we have to locate multiple objects in the image. Various techniques available in the market based on Deep Learning use inbuilt architectures such as VGG-16 and InceptionV3. Using these techniques to solve the problem is a reasonable solution but the response time from these architectures may not be feasible as the autonomous vehicles have to react in less than 0.02 milliseconds in order to avoid collisions of all sorts. So using YOLO, we simply predict the classes and the bounded co-ordinates of the object in a single run of the model and detect multiple objects from the image rather than focusing only on the interested regions of the image as formerly employed by various models. YOLO is fast and accurate with the help of Convolution Neural Networks and is less likely to produce localization errors.
Optimization of Cryptocurrency Algorithmic Trading Strategies Using the Decomposition Approach

A cryptocurrency is a non-centralized form of money that facilitates financial transactions using cryptographic processes. It can be thought of as a virtual currency or a payment mechanism for sending and receiving money online. Cryptocurrencies have gained wide market acceptance and rapid development during the past few years. Due to the volatile nature of the crypto-market, cryptocurrency trading involves a high level of risk. In this paper, a new normalized decomposition-based, multi-objective particle swarm optimization (N-MOPSO/D) algorithm is presented for cryptocurrency algorithmic trading. The aim of this algorithm is to help traders find the best Litecoin trading strategies that improve their outcomes. The proposed algorithm is used to manage the trade-offs among three objectives: the return on investment, the Sortino ratio, and the number of trades. A hybrid weight assignment mechanism has also been proposed. It was compared against the trading rules with their standard parameters, MOPSO/D, using normalized weighted Tchebycheff scalarization, and MOEA/D. The proposed algorithm could outperform the counterpart algorithms for benchmark and real-world problems. Results showed that the proposed algorithm is very promising and stable under different market conditions. It could maintain the best returns and risk during both training and testing with a moderate number of trades.
Empowering leadership and innovative behaviour in the context of the hotel industry: Knowledge sharing as mediator and generational differences as moderator

Establishing employee innovative behaviour (IB) is essential to achieve competitive advantage. Thus, in the context of Indonesia’s hotel industry, this study examines generational differences in the relationship between empowering leadership (EL) and IB, with knowledge sharing (KS) as the mediator. Data were collected by distributing questionnaires to hotel employees in Indonesia through purposive sampling. A total of 186 responses were obtained. Partial least squares structural equation modelling (PLS-SEM) was then employed to test each hypothesis. Results demonstrate that all hypotheses were confirmed. EL positively determines KS and IB. In addition, IB was positively determined using KS. This study also confirmed the importance of KS as a mediator in the relationship between EL and IB. Additionally, the results identified generational differences (Gen X and Gen Y) in these relationships. A significant difference was observed in the effect of EL on KS and IB between Gen X and Gen Y. The difference between Gen X and Gen Y was also confirmed in the relationship between KS and IB, and the mediating role of KS in the relationship between EL and IB.
Dual-context aggregation for universal image matting

Natural image matting aims to estimate the alpha matte of the foreground from a given image. Various approaches have been explored to address this problem, such as interactive matting methods that use guidance such as click or trimap, and automatic matting methods tailored to specific objects. However, existing matting methods are designed for specific objects or guidance, neglecting the common requirement of aggregating global and local contexts in image matting. As a result, these methods often encounter challenges in accurately identifying the foreground and generating precise boundaries, which limits their effectiveness in unforeseen scenarios. In this paper, we propose a simple and universal matting framework, named Dual-Context Aggregation Matting (DCAM), which enables robust image matting with arbitrary guidance or without guidance. Specifically, DCAM first adopts a semantic backbone network to extract low-level features and context features from the input image and guidance. Then, we introduce a dual-context aggregation network that incorporates global object aggregators and local appearance aggregators to iteratively refine the extracted context features. By performing both global contour segmentation and local boundary refinement, DCAM exhibits robustness to diverse types of guidance and objects. Finally, we adopt a matting decoder network to fuse the low-level features and the refined context features for alpha matte estimation. Experimental results on five matting datasets demonstrate that the proposed DCAM outperforms state-of-the-art matting methods in both automatic matting and interactive matting tasks, which highlights the strong universality and high performance of DCAM. The source code is available at \url{https://github.com/Windaway/DCAM}.
Investigating and Identifying Fraudulent Behaviors of Medical Claims Data Using Machine Learning Algorithms

Healthcare is essential in pandemic times, but it is crucial for the well-being of daily life. Many countries allocate substantial funds towards providing high-quality healthcare services. As healthcare expenses escalate, policymakers and funders are increasingly focused on investigating the underlying factors driving the high costs of medical resources. A comprehensive analysis carried the required expenses towards identification, valuation, and measurement of resources utilized for the diagnosis process. The objective of the chapter is to provide how the data analysis is carried which helps to identify fraudulent behaviors. The generated model assists health management organizations in identifying suspicious behaviors toward claims. Healthcare fraud is a severe threat to global health results, and could lead to misuse, scarce resources, and negative impacts on healthcare access, infrastructures, and social determinants of health. Healthcare fraud is associated with increased healthcare costs in most of the leading countries. The proposed research work provides an estimation mechanism for utilizing health resources and their impacts on healthcare costs. This chapter proposes strategic ways of handling healthcare data to prevent future healthcare fraud, decrease healthcare expenditure, and adequately use resources to benefit the population. This chapter works on three primary datasets and a synthetic dataset aggregated from the primary datasets. The data preprocessing is carried out at different levels of the model, which truly enhances the data quality. The model is constructed at three levels; the first level analyzes datasets in which it extracts the primary features and provides constructive decisions and outcomes on the processing of data. Regressive analysis of the hierarchical grouping mechanism helps to know the detailed features that could affect healthcare and prevent resource misuse.
Smart Water Bottle with Smart Technology

Working people have forgotten the importance of water intake. Smart Water Bottle will blow an alarm if it is being used by homemakers or people at home. This device can also be configured to their smartphones, which would send a message or alarm to their smartphones, making the user remember the intake of water by keeping some time interval. If configured to their mobiles, it would send a record of the amount of water taken by the user. The latest emerging technology like IoT, Android and many others will definitely be useful to the user.
Security Threats and Detection Mechanisms in Machine Learning

Machine Learning refers to computer programming or data science that can learn from data. For example, the performance of T is increased with experience E if a computer program is learned from E in some tasks T, and performance is measured as P. The learning of computers is allowed without explicit programming in Machine Learning, which is a field of research. Machine learning can be used in various applications, such as banking, travel and tourism, healthcare, marketing, insurance, and human resources. Machine learning is a powerful tool for implementing security applications.
Performance analysis and sustainability assessment of a building integrated solar PV system

The two key pillars of sustainability are the environment and the economy. This study examines the economic viability, environmental impact, and energy assessment of solar photovoltaic (PV) for the climate of Gwalior, India. This PV system study is based on the system's 30-year lifespan and annual performance. This study aims to calculate the reduction in CO2 emissions caused by the production of electrical energy, embodied energy and energy payback time (EPBT) are also examined. The solar PV system has a payback period of about 5.10 years for the climatic condition of Gwalior Madhya Pradesh, which is extremely low compared to its life. The above study is done for the faculty rooms of Madhav Institute of Technology and Science (MITS), Gwalior, India during working hours from 10:00 am to 6:00 pm (8 hrs). The illumination in the faculty rooms is also calculated at different locations and found better on the table of faculty members. The PV system will reduce 89.095 tons of CO2 throughout its lifespan, demonstrating its viability from a sustainable perspective. This study aims to delve into the ‘Sustainable Development of Buildings’ by incorporating various solar-powered technologies to transform a structure into an environmentally sustainable.
PRODUCTO INNOVADOR EN LA REALIZACIÓN DE ACTIVIDADES DE PROYECTOS ESCOLARES

The article examines one of the leading problems of modern education related to ensuring a personalized approach in the implementation of federal educational standards for basic general education. The results of the development and practical implementation of a student’s personal account are presented as a means of increasing digital competence and the effectiveness of students’ project activities.
Quantum computation of π → π* and <i>n</i> → π* excited states of aromatic heterocycles

The computation of excited electronic states is an important application for quantum computers. In this work, we simulate the excited state spectra of four aromatic heterocycles on IBM superconducting quantum computers, focussing on active spaces of π→π∗ and n→π∗ excitations. We approximate the ground state with the entanglement forging method, a qubit reduction technique that maps a spatial orbital to a single qubit, rather than two qubits. We then determine excited states using the quantum subspace expansion method. We showcase these algorithms on quantum hardware using up to 8 qubits and employing readout and gate error mitigation techniques. Our results demonstrate a successful application of quantum computing in the simulation of active-space electronic wavefunctions of substituted aromatic heterocycles, and outline challenges to be overcome in elucidating the optical properties of organic molecules with hybrid quantum-classical algorithms.
Traditions and the role of communication in the moral and aesthetic development of the child

The existing concept of musical education of teachers is based on methodological and theoretical provisions regarding the humanization of education, the concept of the cultural and historical development of the individual, the provision of the social value of a person, and the socio-environmental determinism of his development in the process of musical specialization, the meaning of people's musical traditions. The musical environment is the human environment, which not only shapes a person's musical interests and needs but also changes itself under the influence of people. The change in the musical environment is also due to the intensive development of the modern quality management system, as well as the properties of music as an art form. We consider the interaction of art and the personality of the future teacher as a means of artistic, moral, and aesthetic development of personality; as a creative process of the formation of aesthetic values and guidance systems; development of artistic perception and interests; harmonization of the human person; reproduction of spirituality, personality, etc.
Nonlinear Scattering Matrix in Quantum Optics

It is well known that the scattering matrix plays an important role in quantum optics. This matrix converts the incoming characteristics of scattered radiation into output characteristics. Currently, only special cases of such a scattering matrix are known, which are determined by a specific problem. In this work, a general form of the scattering matrix is obtained, which can be applied to a wide range of problems. It is shown that previously well-known and widely used scattering matrices can be obtained from the resulting scattering matrix. The results obtained can be used to describe new quantum states, the scattering matrices of which have not yet been studied.
Developing an Inspector-Centric Blockchain-Enabled Conceptual Framework for BIM Management in Mars Buildings

Due to the unique atmospheric conditions on Mars, the management of essential information in Mars buildings is of great importance. Even the smallest error or manipulation of data can create irreparable risks for residents. Martian buildings require a strong security shield to ensure accurate and unaltered data processing. In this article, the factors affecting the buildings of Mars and the lives of the inhabitants of Mars were identified and analyzed, and seven key factors were identified. These factors were then integrated into Mars building information systems using blockchain technology, defining four distinct alert levels for specific building conditions. This research is based on the simulation of Martian buildings, and there has been no laboratory case to test the proposed method until now. The findings showed that the proposed framework for Martian buildings was better than similar studies based on Earth, and there was no similar case to compare the results in Martian buildings. The ground-breaking integration of blockchain and building information modeling (BIM) on Mars opens up new opportunities for extraterrestrial building control methods and marks the beginning of the evolution of this field, but given that there is still no construction in the field of buildings on Mars, organizations and bodies that work in this field can use the results of this research to check the compatibility of the proposed method with Martian buildings.
An investigation of social media labeling decisions preceding the 2020 U.S. election

Since it is difficult to determine whether social media content moderators have assessed particular content, it is hard to evaluate the consistency of their decisions within platforms. We study a dataset of 1,035 posts on Facebook and Twitter to investigate this question. The posts in our sample made 78 misleading claims related to the U.S. 2020 presidential election. These posts were identified by the Election Integrity Partnership, a coalition of civil society groups, and sent to the relevant platforms, where employees confirmed receipt. The platforms labeled some (but not all) of these posts as misleading. For 69% of the misleading claims, Facebook consistently labeled each post that included one of those claims—either always or never adding a label. It inconsistently labeled the remaining 31% of misleading claims. The findings for Twitter are nearly identical: 70% of the claims were labeled consistently, and 30% inconsistently. We investigated these inconsistencies and found that based on publicly available information, most of the platforms’ decisions were arbitrary. However, in about a third of the cases we found plausible reasons that could explain the inconsistent labeling, although these reasons may not be aligned with the platforms’ stated policies. Our strongest finding is that Twitter was more likely to label posts from verified users, and less likely to label identical content from non-verified users. This study demonstrates how academic–industry collaborations can provide insights into typically opaque content moderation practices.
Blockchain and the Law: Setting the Floor

Blockchain and the law has over the past years become a field in which a multitude of developments have taken place, and the tendency is for that progress to continue. However, due to the seemingly never-ending stream of publications and ongoing developments in the blockchain and crypto markets, the essence of the topic—concerning the legal effects and consequences of the growing adoption of blockchain based-technologies and their applications such as crypto and smart contracts—risks becoming a little lost. This chapter discusses the problem of blockchain and the law at its most basic and fundamental levels, and in so doing considers the essential traits of blockchain and the essential traits of law, looking into how both are related, and how new developments should be deployed, in a way that is faithful to the technology’s promise and capacity, and to the law’s function as the main tool for social order.
Implementasi Pembelajaran Bahasa Inggris dengan Media Kartu Gambar di SD Negeri 1 Kadipaten Tasikmalaya Kelas IV

This study aims to determine the ability of students to understand learning English in the application of the 2013 curriculum using picture media at SD Negeri 1 Kadipaten Tasikmalaya class IV. Learning media is an important component in the learning process in order to achieve learning objectives. The learning media used in this study is using media images which are expected to train students' cognitive abilities in memorizing English vocabulary using media images. Where the number of grade IV students at SD Negeri 1 Kadipaten is 48 people, 10 female students and 40 male students. This research is a type of qualitative research and data collection techniques in this study are through observation and documentation. In this study, students were very enthusiastic in participating in the learning process. We know that in the 2013 curriculum learning English was abolished, this became a trigger for students' learning motivation in learning English. Thus this teaching practice research can be said to be successful in improving students' ability to know and understand English vocabulary through the media of pictures.
Exact solutions of a deep linear network <sup>*</sup>

Abstract This work finds the analytical expression for the global minima of a deep linear network with weight decay and stochastic neurons, a fundamental model for understanding the landscape of neural networks. Our result implies that the origin is a special point in the deep neural network loss landscape where highly nonlinear phenomenon emerge. We show that weight decay strongly interacts with the model architecture and can create bad minima at zero in a network with more than one hidden layer, qualitatively different from a network with only one hidden layer. Practically, our result implies that common deep learning initialization methods are generally insufficient to ease the optimization of neural networks.