Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Manajemen Absensi Online dengan Barcode scanner Menggunakan Power Apps

Attendance is a form of attachment or obligation between employees and companies, especially at PT. IDX with work locations for project employees who are spread across many companies and different office locations, namely in Jakarta and Bandung. Based on this study, an online-based employee attendance system using the barcode scanner method is applied, because previously employees were still using manual signatures with insufficient security systems and concerns about data leakage, with this attendance system employees do not need to sign manually, resulting in fraud. attendance can be avoided. The application of QR code technology to the employee attendance system can speed up the employee attendance process. Besides that, it can also help make it easier for the HRD Team in managing employee attendance reports to make it more effective and efficient.Keywords:Attendance Application, Attendance System, Power Apps, One Drive Application.
Effect of an instruction manual using an e-learning system on the improvement of assembly work

Extensive knowledge of products and manufacturing technologies is essential for improving the production process. In addition to knowledge, the ability to optimize the process from basic understanding is required. Taking the productivity improvement in the assembly work as an example, this study investigated the difference between the paper manual and the video manual and its effect on training for the improvement of the production process. As a result, the paper manual was effective in understanding the basics, and the video manual was effective in optimizing the process.
LibGuides: Research Data Management: UCL Research Data Repository

A guide to managing outputs of research projects and handling issues such as copyright and data protection laws
Value Iteration in Continuous Actions, States and Time

Classical value iteration approaches are not applicable to environments with continuous states and actions. For such environments, the states and actions are usually discretized, which leads to an exponential increase in computational complexity. In this paper, we propose continuous fitted value iteration (cFVI). This algorithm enables dynamic programming for continuous states and actions with a known dynamics model. Leveraging the continuous-time formulation, the optimal policy can be derived for non-linear control-affine dynamics. This closed-form solution enables the efficient extension of value iteration to continuous environments. We show in non-linear control experiments that the dynamic programming solution obtains the same quantitative performance as deep reinforcement learning methods in simulation but excels when transferred to the physical system. The policy obtained by cFVI is more robust to changes in the dynamics despite using only a deterministic model and without explicitly incorporating robustness in the optimization. Videos of the physical system are available at \url{https://sites.google.com/view/value-iteration}.
Crystallization via non-classical pathways: Nanoscale imaging of mineral surfaces&amp;#160;

&amp;lt;p&amp;gt;The advancement in analytical imaging techniques, including atomic force microscopy (AFM) and scanning and transmission electron microscopies (SEM and TEM), has allowed us to observe processes occurring at mineral surfaces in situ at a nanoscale in real space and time and hence giving the possibility to elucidate reaction mechanisms. Classical crystal growth theories have been established for well over 100 years and while they can still be applied to explain crystal growth in many growth scenarios, we now know that these models are not always an accurate description of the mechanism of all crystal/mineral growth processes, especially where nanoparticle formation is observed. Consequently there is a current challenge at the forefront of understanding crystal/mineral growth mechanisms. This work describes experimental observations of non-classical crystallization processes at the nanoscale. Using AFM as well as SEM and TEM imaging, we demonstrate that many minerals commonly grow by the attachment of nanoparticles on an existing mineral surface, often resulting from the coupling of dissolution of a parent phase and the precipitation of a new product mineral. Through varied examples of crystal/mineral growth, including calcite and other carbonates, barite, brucite, and apatite, we define the importance of the mineral-fluid interface and the aqueous fluid interfacial (boundary) layer in the control of crystal growth. Whether a crystal will grow by classical monomer attachment resulting in step advancement or by the formation, aggregation and merging of nanoparticles, will be controlled by the aqueous fluid composition at the mineral-fluid interface. The processes described also allow for the development of porosity within the new mineral and hence have important consequences for fluid movement and element mobility within the Earth. Additionally an understanding of natural mineral growth has implications for geomimetic applications for the manufacture of functional engineered materials.&amp;lt;/p&amp;gt;
Humanidades digitales globales para descifrar la ‘ciudadanía pandémica’

This article revolves around the necessary education on the challenges from the digital global humanities perspective. It focuses on aspects related to pandemic citizenship and how they are creating a necessary requirement to interpret global challenges from an interdependent perspective.
Unique Geometry and Texture From Corresponding Image Patches

We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process. We show that four observations are sufficient in general, and we characterize the ambiguous cases. The results are applicable to shape from texture and texture-based structure from motion.
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond.

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is often distributed across different institutions and cannot be shared due to privacy concerns. Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models without sharing sensitive data, but their implementation is time-consuming and requires advanced programming skills. Here, we present the FeatureCloud AI Store for FL as an all-in-one platform for biomedical research and other applications. It removes large parts of this complexity for developers and end-users by providing an extensible AI Store with a collection of ready-to-use apps. We show that the federated apps produce similar results to centralized ML, scale well for a typical number of collaborators and can be combined with Secure Multiparty Computation (SMPC), thereby making FL algorithms safely and easily applicable in biomedical and clinical environments.
Foreign Financial Inflows and Poverty in Nigeria

This paper aims to study the effect of foreign financial inflows on poverty in Nigeria (1981-2015). The impetus for this study stems from the paradoxical observation that, although Nigeria is blessed with a high influx of foreign financial inflows (FFI), the country grieves from severe and lasting poverty. It is expected for every economy that there is existence of foreign financial inflows that the rate of poverty should be at its minimal level. The major objective of this research work is to examine how foreign financial inflows influences poverty in Nigeria. In the process of carrying out this research work, various variables such as inflation, gross domestic product, exchange rate, foreign direct investment, remittance, oil revenue and non-oil revenue were adopted. The data used in this research were secondary data obtained from the Central Bank of Nigeria Statistical Bulletin 2016 and World Development Indicators (WDI). The augmented dickey fuller test (ADF) for unit root test, Correlation analysis, Bound test and Autoregressive Distributed Lag Model (ARDL) were used in the estimation to this paper work. Findings were drawn from the research that foreign direct investment and oil revenue are significant to poverty reduction in Nigeria while the rest of the variables adopted were insignificant. Therefore, recommendation is suggested that the government should create a conducive environment for investors to be willing to invest so as to increase the foreign direct investment and also encourage the export of oil product in order to increase revenue from it.
Gradual Program Analysis for Null Pointers

Static analysis tools typically address the problem of excessive false positives by requiring programmers to explicitly annotate their code. However, when faced with incomplete annotations, many analysis tools are either too conservative, yielding false positives, or too optimistic, resulting in unsound analysis results. In order to flexibly and soundly deal with partially-annotated programs, we propose to build upon and adapt the gradual typing approach to abstract-interpretation-based program analyses. Specifically, we focus on null-pointer analysis and demonstrate that a gradual null-pointer analysis hits a sweet spot, by gracefully applying static analysis where possible and relying on dynamic checks where necessary for soundness. In addition to formalizing a gradual null-pointer analysis for a core imperative language, we build a prototype using the Infer static analysis framework, and present preliminary evidence that the gradual null-pointer analysis reduces false positives compared to two existing null-pointer checkers for Infer. Further, we discuss ways in which the gradualization approach used to derive the gradual analysis from its static counterpart can be extended to support more domains. This work thus provides a basis for future analysis tools that can smoothly navigate the tradeoff between human effort and run-time overhead to reduce the number of reported false positives.
EFFECTIVE LEARNING OF CHEMISTRY AMONG SECONDARY SCHOOL STUDENTS: THE ROLE OF LOCALLY SOURCED INSTRUCTIONAL MATERIALS

The present study aimed to examine the predictive role of locally sourced instructional materials on the practical learning of chemistry among secondary school students. A total of two hundred and eighteen secondary school students participated in the study. A cross-sectional survey design was adopted. The participants responded to a self-report measure on their experience of locally sourced instructional material and the perceived learning outcome. A simple linear regression was conducted on the data, and the result showed that locally sourced instructional material statistically predicted effective learning of chemistry. Finding and conclusions are discussed.
How to Explain Neural Networks: A perspective of data space division.

Interpretability of intelligent algorithms represented by deep learning has been yet an open problem. We discuss the shortcomings of the existing explainable method based on the two attributes of explanation, which are called completeness and explicitness. Furthermore, we point out that a model that completely relies on feed-forward mapping is extremely easy to cause inexplicability because it is hard to quantify the relationship between this mapping and the final model. Based on the perspective of the data space division, the principle of complete local interpretable model-agnostic explanations (CLIMEP) is proposed in this paper. To study the classification problems, we further discussed the equivalence of the CLIMEP and the decision boundary. As a matter of fact, it is also difficult to implementation of CLIMEP. To tackle the challenge, motivated by the fact that a fully-connected neural network (FCNN) with piece-wise linear activation functions (PWLs) can partition the input space into several linear regions, we extend this result to arbitrary FCNNs by the strategy of linearizing the activation functions. Applying this technique to solving classification problems, it is the first time that the complete decision boundary of FCNNs has been able to be obtained. Finally, we propose the DecisionNet (DNet), which divides the input space by the hyper-planes of the decision boundary. Hence, each linear interval of the DNet merely contains samples of the same label. Experiments show that the surprising model compression efficiency of the DNet with an arbitrary controlled precision.
CREATING AND IMPLEMENTING A SMART SPEAKER

We have seen significant advancements in Artificial Intelligence and Machine Learning in the 21st century. It has enabled a new technology where we can have a human-like conversation with the machines. The most significant use of this speech recognition and contextual understanding technology exists in the form of a Smart Speaker. We have a wide variety of Smart Speaker products available to us. This paper aims to decode its creation and explain the technology that makes these Speakers, Smart.
Meta-heuristic innovative algorithm of multi-objectives in tasks timing at cloud computing system

In this article a mathematical model with twin objectives is presented. The objectives are considered as:Minimization of the maximum tardiness of tasks completion time and the total early tasks penalties. Since tasks timing is a tardy and indefinite factor in cloud computing; therefore problem solving model is used as the combined Meta-heuristic innovative algorithm of multi objective swarm of particles based Parto archive has been used. The suggested algorithm with genetic operators as well as the directed and repeated counterpart structures in the format of multi operators are taken to assess the algorithm application. The results will be sorted based on quality, distraction, integrated, the number of non-defeated solutions and the gap from the ideal one is compared with the evolutionary algorithm results titled genetic algorithm. The final results of solved model indicate that firstly, this algorithm is stronger than NSGA-II algorithm but is weaker in timing, norms and scales. In other words, the suggested algorithm, is more capable to discover solutions, accordingly.
Methods of Developing Students Listening Competence in Music Culture Lessons

This paper discusses ways to develop students' listening skills through a variety of demonstrations and in music culture classes, and presents the results of research in this area.
The Effect of Camera Height on The User Experience of Mid-air 360° Videos

Mid-air 360° videos are videos shot by placing the camera on the drone or helicopter. However, how the camera height of mid-air 360° videos affects user experience is unclear. The study explores whether the camera's height affects users' immersion, presence, and realism. Results suggest that when the camera height is higher, immersion decreases for acrophobic people while first drops and then rises for others because of the broad vision and beautiful scenery. Higher camera height brings a higher presence and worse realism, especially in distance details. Our work contributes to better understanding and designing of mid-air 360° video experiences.
Point-rating System as an Effective Way to Assess Students Knowledge in Credit Module System

The article is devoted to the adoption of credit and module system and rating and scoring system into the educational process to the characteristic of such innovative aspects as information, methodology, logistics and personnel support necessary for that to the advantages and disadvantages of implementation of a new form of educational process organization. The main form of assessment of student learning in the framework of the credit-modular system is a point rating assessment (PRS) of the quality of students knowledge. A PRS for monitoring students academic work is introduced with the aim of motivating them to the subject being studied and improving the quality of training.
Towards Synthetic Multivariate Time Series Generation for Flare Forecasting

One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest resulting in an extreme imbalance in the data. There have been many methods introduced in the literature for overcoming this issue; simple data manipulation through undersampling and oversampling, utilizing cost-sensitive learning algorithms, or by generating synthetic data points following the distribution of the existing data. While synthetic data generation has recently received a great deal of attention, there are real challenges involved in doing so for high-dimensional data such as multivariate time series. In this study, we explore the usefulness of the conditional generative adversarial network (CGAN) as a means to perform data-informed oversampling in order to balance a large dataset of multivariate time series. We utilize a flare forecasting benchmark dataset, named SWAN-SF, and design two verification methods to both quantitatively and qualitatively evaluate the similarity between the generated minority and the ground-truth samples. We further assess the quality of the generated samples by training a classical, supervised machine learning algorithm on synthetic data, and testing the trained model on the unseen, real data. The results show that the classifier trained on the data augmented with the synthetic multivariate time series achieves a significant improvement compared with the case where no augmentation is used. The popular flare forecasting evaluation metrics, TSS and HSS, report 20-fold and 5-fold improvements, respectively, indicating the remarkable statistical similarities, and the usefulness of CGAN-based data generation for complicated tasks such as flare forecasting.
Dembowski-Ostrom polynomials and Dickson polynomials

We give a complete classification of Dembowski-Ostrom polynomials from the composition of Dickson polynomials of arbitrary kind and monomials over finite fields. Moreover, by using a variant of the Weil bound for the number of points of affine algebraic curves over finite fields, we discuss the planarity of the obtained Dembowski-Ostrom polynomials. Dembowski-Ostrom polynomials play a crucial role in coding theory and cryptography.
Quantum Error Correction Inapplicable to Really Superpositioned States

QEC (Quantum Error Correction) assumes that qubit states are disturbed by errors primarily through interactions with their local environment states. The problem is that, when quantum states are superpositioned, QEC improperly assumes all the superpositioned terms share same local environment states and resulting errors though they are, in general, different term by term to which QEC with the fixed number of syndrome qubits is, except for some slightly superpositioned states such as two terms ones, not applicable. Further, fundamental difficulty to scale quantum parallelism regardless of implementation and physical details is derived from theory of Shannon.
Cultural heritage monument complex monitoring data analyses using machine learning algorithms

&amp;lt;p&amp;gt;Cultural heritage monuments, that were created by mankind for centuries are scattered throughout the world. Most of them are experiencing impacts coming from nature and humans each year that result in damage and changing their common state. Many of the monuments are facing critical conditions and require diagnostics, study and planning and management of conservation/rehabilitation works. Due to the impact of environmental factors such as temperature, humidity, precipitation, the existence of complex structure of cracks, infiltrated water and runoff water streams, together with active tectonics in the region, Uplistsikhe and Vardzia rock-cut city monuments located in Georgia face problems and permanent destruction.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We have developed continuous monitoring systems that are installed in Vardzia and Uplistsikhe.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;These systems are generating large amounts of data and it is almost impossible to analyze this data using conventional methods. In parallel with technological development, it is now possible to analyze big data using machine learning. We decided to use machine learning to address our problem. This approach gave us some interesting results. We were able to detect correlations between different sensors, see anomalies in data that gave us some clues about hazard zones. Additionally&amp;amp;#160; models and predictions about the monument's condition were made.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our work shows that machine learning could be used to estimate conditions make predictions about monuments state.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNS) [Grant Number fr17_90]&amp;lt;/p&amp;gt;
EDUCAÇÃO 3.0: PRÁTICAS PARA A INTRODUÇÃO DA SOCIEDADE COLABORATIVA À EDUCAÇÃO

This article aims to analyze the relationship of the circular economy with technological resources within the Society 3.0. Humanity and technology develop concurrently, but high innovative levels are achieved without real implementation in basic aspects. By directing it to the educational aspect we come, then, to Education 3.0. With the intention of disseminating this concept, the present project discusses problematizations and opportunities as well as a model to be followed in order to guide, through the management, institutions and educational centers. Through exploratory bibliographic research, significant results were obtained, demonstrating the importance of digital transformation involving collaboration and communication.
Development Of R&amp;D Light Industry Project Implementation Skills In University Students

The article describes the project-based training of students aimed at creating a system of intellectual and professional knowledge and skills, embodied in the final competitive consumer products or services, and developing a creative personality.Each student has to implement one project per year.During the project implementation, students participate in identifying needs of the university, assess available technical capabilities and economic feasibility.The project complexity is determined by knowledge, skills and abilities of the students.Project research develops qualities and skills required to adapt to professional life: the ability to use science-intensive technologies, make rational decisions, search for and process information, accumulate experience in solving problems related to the demand analysis, develop design and manufacturing technologies, calculate the production cost.Students participate in competitions, exhibitions, conferences, thematic discussions and disputes; employers are invited to project defenses.In addition, project activities boost the development of entrepreneurial skills.The implementation of a project-based teaching method makes it possible to increase the intellectual potential, educational and professional levels of future specialists who are able to master and use scientific and technological achievements, apply knowledge in the design, production and sale of their products and services.