Reviewerly/exHarmony huggingface dataset:
One Hour Ahead Solar Irradiation Forecast by Deep Learning Network Using Meteorological Variables

With the exponential growth in electricity demand, a renewable energy resource comes with the best alternate especially solar cells. The penetration of these resources, interconnection with the grid, the accurate and precise forecasting of next hour electricity generation is the most important factor to manage the grid. This paper discusses the comparative study of two deep learning models: long short term memory (LSTM) network and gated recurrent unit (GRU) network for the forecasting of global solar irradiance (GHI). The meteorological parameters: wind direction, dew point, pressure, temperature, solar zenith angle, relative humidity, wind speed and precipitation are considered to train these deep learning networks. The study used the clear sky index (CSI) calculation to stationarize the data. The models are trained using the one year of hourly datasets, while the testing is performed for one hour ahead monthly solar irradiation forecast. The experimental results of GRU and LSTM networks are compared to the naïve model (benchmark model) in terms of root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The result shows that the GRU network outperform LSTM network and benchmark model with annual average RMSE of 69.8117 (w/m2), annual average MAPE of 71.777(w/m2) and R2 of 0.86.
Growing Up with My Brother, Doctor Don: Interview with Charles Lindberg

In a spring 2021 interview, Charlie Lindberg remembered his Brooklyn-based childhood
Off the page and onto your screen: How to set-up a virtual literary festival

Literary festivals are an opportunity for authors and readers to mingle and interact. Libraries that host literary festivals connect book lovers and writers in a very personal way, enabling libraries to bask in the glory of that connection. They allow fans to get close to a beloved writer, hear a reading, and maybe even get a book signed. And like almost everything else during the pandemic, they have had to adapt or go into indefinite hiatus.
An Encryption Method Involving Homomorphic Transform

Data security is an important aspect for datasets of every size and type. Data security refers to provide a secure environment to datasets, web-based datasets, and preventing unauthorized access to the data. The key data security technique is encryption, where the digital data is encrypted. As result, the unauthorized person or hacker gets unreadable data. The computing process generates data that is encrypted by the key and can be decrypted with the correct key only. In this research, an approach is proposed of using transforms to provide a better cryptography process to secure data even during communication. The result of the proposed method indicates that in any condition, the ciphertext will always be the same for each input, but the encryption key will change each time, which makes this encryption complex to break
TÉCNICAS DE PRÉ-PROCESSAMENTO PARA SONS RESPIRATÓRIOS EM CLASSIFICADORES IA

The auscultation of lung sounds is a technique frequently used in medicine for the diagnosis of diseases or disorders on the respiratory system. The conventional stethoscope is an instrument used for auscultation and its proper use requires practice and experience. This article presents the development of an electronic system for recording sounds coming from pulmonary auscultation, which is going to be recorded, analyzed and compared, through two types of auscultation. Signal acquisition and conditioning circuits, analog and digital filters, amplification circuits and signal reconstruction circuitswere implemented with the aim of facilitating the identification of any disorders.The results obtained are presented and discussed, providing a clear scenario of the advantages and disadvantages between the use of electret and PZT sensors.
Design and Operating System of Floating Waste Treatment Facility in Belakang Padang

Abstract The district of Belakang Padang consists of 108 islands. On these islands, 43 islands are inhabited and 65 islands are uninhabited. The source of pollution on this island comes from community activities on land, such as throwing garbage in the sea and so on. In this paper, Floating Waste Treatment Facility will be designed on Self-Propelled Barge (SPB) with principle dimension: Lpp = 13 m; B = 2.48 m; and T = 0.589 m. The barge can accommodate 7 tons of waste consisting of a garbage tank and a sorting process along with a plastic press machine to reduce the volume of plastic waste. In the calculation of the volume requirement for waste weighing 7 tons, that is 16.43 m3. With an estimated 9 hours of sorting work, it can meet the volume of waste needs. The total investment for the construction of this facility is Rp 362,087,076.12 and the annual operating costs are Rp 458,692,802.67. This facility is feasible to build because of the value of several economic metrics as follows: NPV Rp 488,234,793; IRR 26% and PI 2,348.
Lexicographic Portrait of the Italian Approximative Quasi and Its Synonyms

This article analyzes the nominative approximation strategies in the Italian language and provides an integrated semantic description of the approximate functions of the lexeme quasi and its synonyms. The article is divided into four sections. In the first section we introduce the term approximation, as it is a relatively poorly studied semantic category, although scientific interest in this regard is growing. In the second section we present a fundamental, but still little known, publication on approximation based on the study of the French language, being the first comprehensive study on this semantic category. In the third section we present the dictionary use of the word quasi. Finally, in the fourth section, we analyze and describe the morphological, semantic, pragmatic and syntactic properties of lexeme quasi and its synonyms, as well as their combinability and communicative characteristics in order to provide an analysis model for further studies on the semantic category of approximation. The semantic analysis of the approximative quasi and its synonyms demonstrate how the dictionary meaning of the approximatives is fundamental to identify and distinguish their various approximate characters.
Building an R Package

In this chapter and the next, we connect our simple C++ library of statistical functions to R. We do this by creating an R package using Rcpp. We then use this wrapper component to expose the functionality we want. This chapter focuses on both the project setup and the mechanics of building packages with RStudio. The following chapter focuses on the details of using Rcpp as a framework for connecting C++ and R.
METHODOLOGICAL CHALLENGES ARISING IN POLYGONAL GRAPHICS

М.Ю. Рудюк, А.М
A Neural-Network Distortion Estimation-based Adaptive Video Streaming in Wireless Networks

Video streaming over error-prone wireless networks can result in unsatisfactory user experience, which includes two factors: video distortion due to video compression and packet loss and video smoothness due to the mismatch of video bitrate and channel bandwidth, packet retransmission delay. Existing algorithms have addressed the problem of adaptive video bitrate and optimization of transport layer parameters separately, which can lead to non-optimal solution in terms of user experience. In this paper, we propose a cross-layer design, which integrates an optimization framework for selecting optimal video bitrate and redundancy models under varying channel conditions. In this design, to enhance video quality and satisfy delay constraint of streaming applications, different redundancy levels are configured for different types of video frames based on their importance and video bitrate also is configured adaptively according to selected redundancy and channel bandwidth. Our cross-layer design features a neural network model-based distortion estimator to facilitate the decision of redundancy and video bitrate. This distortion estimator takes into account video characteristics, redundancy models, encoder settings, and channel packet error rate to accurately predict the video distortion for each option of video bitrate and redundancy model under varying channel conditions. Simulation shows that our approach can improve the distortion of video streaming with different types of videos in the different wireless network enviroments.
Chain Graph Explanation of Neural Network Based on Feature-Level Class Confusion

Despite increasing interest in developing interpretable machine learning methods, most recent studies have provided explanations only for single instances, require additional datasets, and are sensitive to hyperparameters. This paper proposes a confusion graph that reveals model weaknesses by constructing a confusion dictionary. Unlike other methods, which focus on the performance variation caused by single-neuron suppression, it defines the role of each neuron in two different perspectives: ‘correction’ and ‘violation’. Furthermore, our method can identify the class relationships in similar positions at the feature level, which can suggest improvements to the model. Finally, the proposed graph construction is model-agnostic and does not require additional data or tedious hyperparameter tuning. Experimental results show that the information loss from omitting the channels guided by the proposed graph can result in huge performance degradation, from 91% to 33%, while the proposed graph only retains 1% of total neurons.
Multi-Robot Robust Motion Planning based on Model Predictive Priority Contouring Control with Double-Layer Corridors

Disturbance poses a major challenge for the safety and real-time performance of robust robot motion planning. To address the disturbance while improving the real-time performance of multi-robot robust motion planning, a model predictive priority contouring control method is proposed. First, an improved conflict-based search (ICBS) planner is utilized to plan reference paths. The low-level planner of the conflicted-based search (CBS) planner is replaced by the hybrid A* planner and reference paths are adopted as an initial guess of model predictive priority contouring control. Second, double-layer corridors are proposed to provide safety guarantees, which include static-layer corridors and dynamic-layer corridors. The static-layer corridors are generated based on reference paths and the dynamic-layer corridors are generated based on the relative positions and velocities of robots. The double-layer corridors are applied as safety constraints of model predictive priority contouring control. Third, a prioritization mechanism is devised to improve computational efficiency. Priorities are assigned according to each robot’s task completion percentage. Based on the assigned priority, multiple robots are grouped, and each group executes the model predictive priority contouring control algorithm to acquire trajectories. Finally, our method is compared with the centralized method and the soft constraint-based DMPC. Simulations verify the effectiveness and real-time performance of our approach.
Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data (Preprint)

<sec> <title>BACKGROUND</title> The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. </sec> <sec> <title>OBJECTIVE</title> We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. </sec> <sec> <title>METHODS</title> We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. </sec> <sec> <title>RESULTS</title> In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. </sec> <sec> <title>CONCLUSIONS</title> Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods. </sec> <sec> <title>CLINICALTRIAL</title> <p /> </sec>
PEMODELAN HARGA EMAS DUNIA MENGGUNAKAN METODE NONPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI R

Investing in gold is a flexible choice because it can be sold at any time and used as an emergency fund. Investors should have the knowledge to predict data from time to time to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. The ARIMA model is strict with the assumptions that the data must be stationary, the residuals must be normally distributed, independent, and with constant variance, so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the daily world gold price data will be modeled using a local polynomial nonparametric model as an alternative because the assumptions in the ARIMA are not fulfilled. The data is divided into 2 parts, namely in sample data from January 2, 2020 to November 30, 2020 to form a model and out sample data from December 1, 2020 to December 31, 2020 used for evauation of model performance based on MAPE values. The chosen best model is the local polynomial model with Gaussian kernel function of degree 5, bandwidth of 373, and local point of 1744 with an MSE value of 482.6420. The local polynomial model out sample data MAPE value is 0.61%, indicating that the model has excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.
Prospects of Humanitarian Education: From Subject to System Rationality

The article examines the current issues of reducing the multi-vector nature of modern humanities education while maintaining the key models of educational practice. The article deals with the evolution of Russian humanities education in connection with the internal and external logic of development associated with the transformation of geopolitical conditions. The existing and possible trajectories of changes in humanities education are analyzed. A comparative analysis of non-classical and post-nonclassical humanities education is carried out, and their specific parameters are determined. New approaches to the constitution of basic models of humanitarianism are justified. The research is based on the philosophical and scientific paradigm approach. Situations of autonomous and additional coexistence of educational paradigms are modelled.
Design and implementation of a hybrid IoT system of data collection and visualization

To carry out a scientific study on a given problem (medical, environmental, etc.), it is often necessary to have recourse to a reliable dataset. In Africa, access to data is generally a problem for the scientific community. Most of the existing datasets are detained by some tiers organization who often do not give access to it. To overcome these problems related to access to data, scientists generally use traditional methods (surveys, on-site readings, etc.) for their study. However, these methods are not only expensive, but also require a regular visit to the site for updates. In this article, we propose an autonomous solution for collecting and analyzing data based on the Internet of Things (IoT). The solution can be used to collect data of different kinds. Unlike traditional data collection methods, the proposed solution is much cheaper and allows real time data collection. To demonstrate its proper functioning, a practical case on the environmental data collection will be studied in this paper.
An enhanced algorithm for improving real-time video transmission for tele-training education

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame's total distortion, ensuring better flow rate allocation and enhancing the video frames' reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame's total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.
Edge detail enhancement algorithm for high-dynamic range images

Abstract Existing image enhancement methods have problems of a slow data transmission and poor conversion effect, resulting in a low image-recognition rate and recognition efficiency. To solve these problems and improve the recognition accuracy and recognition efficiency of image features, this study proposes an edge detail enhancement algorithm for a high-dynamic range image. The original image is transformed by Fourier transform, and the low-frequency and high-frequency images are obtained by the frequency-domain Gaussian filtering and inverse Fourier transform. The low-frequency image is processed by the contrast limited adaptive histogram equalization, and the high-frequency image is obtained by the nonsharpening masking and gray transformation. The low-frequency enhanced and the high-frequency enhanced images are weighted and fused to enhance the edge details of the image. Finally, the experimental results show that the proposed high-dynamic range image edge detail enhancement algorithm maintains the image recognition rate of more than 80% during the practical application, and the recognition time is within 1,200 min, which enhances the image effect, improves the recognition accuracy and recognition efficiency of image characteristics, and fully meets the research requirements.
Aplikasi Penyedia Lowongan Kerja Di Wilayah NTB Berbasis Web Menggunakan Framework Laravel Dan Mysql

This research is motivated by employment that tends to be difficult when looking for work in companies. In other words, applicants still use the usual methods to find information about job vacancies. They go to companies to look at job vacancies notice boards, and look in print media such as newspapers, magazines, brochures, or person-to-person information. Companies that want to open job vacancies also do the same thing. This method has the disadvantage that it is not effective and efficient, because it requires time and money. Therefore, this researcher built a web-based job vacancy provider system using the waterfall model with observation data collection techniques and literature study. This will increase effectiveness in finding work according to their respective fields or abilities. The purpose of this system is to provide convenience for job seekers and companies. Job seekers can obtain job search information online, companies can provide job search information through the website and assist companies in finding prospective applicants according to the desired criteria. From this research, it produces information on web-based job vacancies equipped with a search menu for the desired vacancy category
Bringing All the Stakeholders to the Table: A Collaborative Approach to Data Sharing

Objective: This paper examines a unique data set disclosure process at a medium sized, land grant, research university and the campus collaboration that led to its creation. Methods: The authors utilized a single case study methodology, reviewing relevant documents and workflows. As first-hand participants in the collaboration and disclosure process development, their own accounts and experiences also were utilized. Results: A collaborative approach to enhancing research data sharing is essential, considering the wide array of stakeholders involved across the life cycle of research data. A transparent, inclusive data set disclosure process is a viable route to ensuring research data can be appropriately shared. Conclusions: Successful sharing of research data impacts a range of university units and individuals. The establishment of productive working relationships and trust between these stakeholders is critical to expanding the sharing of research data and to establishing shared workflows.