Reviewerly/exHarmony huggingface dataset:
Solar Irradiation Forecasting using Genetic Algorithms

Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. In the current study, Machine Learning techniques such as Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization are used to forecast solar irradiation. The data used for training and validation is recorded from across three different geographical stations in the United States that are part of the SURFRAD network. A Global Horizontal Index (GHI) is predicted for the models built and compared. Genetic Algorithm Optimization is applied to XGB to further improve the accuracy of solar irradiation prediction.
It and information security

Internet has come to stay in all activities of most of the people who carry out transactions such as reservation for journey, payment of bills, bank transactions, medical prescriptions and so on and so forth in their everyday life. Such activities involve handling of storage and retrieval of information. When computers became popular in public use, the devices were of standalone nature. Therefore there was no problem for individuals or organisations in ensuring the security of information stored in the computers. However, with the connectivity of computers with outside world through Internet the information held privately in computer became accessible to unknown persons who could play mischief by hacking, phising, etc. That is, information stored in computers fallen prey to unscrupulous and unauthorised persons for manipulations by them. Sometime ago, strangers entered the house of a owner of private company running the business of employment exchange at mid of night, opened his computer and attempted to steal the data of prospective candidates who had applied for job. On hearing the commotion the owner woke up and the thieves ran away. The number of such cyber crimes have increased recently thus putting at grave risk the business of corporations. Of late, there have been a large number of cases of abuse of computer information. In this modern age of widespread internet use for various business activities of an organization, it has unwittingly exposed the confidential information of these organizations to hackers and antisocial elements. This has endangered the security of information of the organizations to very high level of risks. Classified information at the hands of anti-social elements could jeopardize the security of the country too. Information Security (IS) is the most important aspect of IT and its risk management helps the organizations to identify and manage the potential risks associated with information and information technology in a cost-effective way.
Data efficiency in graph networks through equivariance

We introduce a novel architecture for graph networks which is equivariant to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes. In particular, it is equivariant to the Euclidean and conformal orthogonal groups in $n$-dimensions. Thanks to its equivariance properties, the proposed model is extremely more data efficient with respect to classical graph architectures and also intrinsically equipped with a better inductive bias. We show that, learning on a minimal amount of data, the architecture we propose can perfectly generalise to unseen data in a synthetic problem, while much more training data are required from a standard model to reach comparable performance.
General-Theoreticalissuesrelated to electronic or digital evidence

In this paper, the concepts of electronic or digital evidence are scientifically analyzed. In particular, the legal and technical features of digital evidence, issues related to their collection, storage, verification, transportation and evaluation were studied. At the same time, the problems and gaps in national legislation and law enforcement practice in dealing with digital evidence have been critically examined. Scientifically based ways and criteria for their elimination have been developed. The article is based on scientific and practical research in the field of working with digital evidence, the views of theoretical scientists and practitioners, as well as technical research. As a result of the research, the methodological rules of working with digital evidence were analyzed. National legislation, investigative and judicial practice, best international practices and practices are studied, their achievements and shortcomings are substantiated on the basis of the author's conclusions. Based on this, the most appropriate criteria have been developed for law enforcement entities to collect, verify and evaluate digital evidence. The article analyzes the problems in the field from a systematic, legal, scientific and methodological point of view, on which the author's conclusions are formed. At the same time, evaluation of expert opinions are also covered. Scientifically based proposals and recommendations have been developed for the legislature and its subjects.
Single RGB-D Camera Teleoperation for General Robotic Manipulation

We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hammering and 3mm clearance peg in hole. We propose the use of non-Cartesian oblique coordinate frame, dynamic motion scaling and reposition of operator frames to increase the flexibility of our teleoperation system. We hypothesize that lowering the barrier of entry to teleoperation will allow for wider deployment of supervised autonomy system, which will in turn generates realistic datasets that unlock the potential of machine learning for robotic manipulation. Demo of our systems are available online https://sites.google.com/view/manipulation-teleop-with-rgbd
DEEP LEARNING: TECHNIQUES AND APPLICATIONS

Nowadays deep learning is the present field of machine learning. The learning approach of deep learning is very supervised effective, time and cost effective time method in machine learning. This approach is not restricted but it accepts various procedures and topographies which can be applied to an immense number of difficult problems .In a very stratified way this technique learns the illustrative and differential features.Deep learning methods have made a important breakthrough with appreciable performance in a wide variety of applications with useful security tools.This technique considered to be the best choice for finding complex architecture in high-dimensional data by adding back propagation algorithm .This paper approaches to various technique and application of deep learning.
Towards Verifiable Mutability for Blockchains

Due to their immutable log of information, blockchains can be considered as a transparency-enhancing technology. The immutability, however, also introduces threats and challenges with respect to privacy laws and illegal content. Introducing a certain degree of mutability, which enables the possibility to store and remove information, can therefore increase the opportunities for blockchains. In this paper, we present a concept for a mutable blockchain structure. Our approach enables the removal of certain blocks, while maintaining the blockchain's verifiability property. Since our concept is agnostic to any consensus algorithms, it can be implemented with permissioned and permissionless blockchains.
Supplemental Material: The impact of a tear in the subducted Indian plate on the Miocene geology of the Himalayan-Tibetan orogen

Published ages and geochemical compositions of Cenozoic igneous rocks from the Gangdese belt and Himalayas.
HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter value is computationally intensive and unintuitive due to the stochastic nature of these methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. HyperNP can be trained on a fraction of the total data instances and hyperparameter configurations and can compute projections for new data and hyperparameters at interactive speeds. HyperNP is compact in size and fast to compute, thus allowing it to be embedded in lightweight visualization systems such as web browsers. We evaluate the performance of the HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP is accurate, scalable, interactive, and appropriate for use in real-world settings.
Detection and Classification of Transmission Line Insulator Defects with Res-SSD Optimization Method

Aiming at the problem of insulator state detection in intelligent power grid inspection tasks, an insulator defect detection method based on combining advanced deep learning models and constructing independent modules is proposed. Here, the Visual Geometric Group Network (VGG) is used to extract features, and lightweight advantages of Single-hot Multi-frame Detector (SSD) is applied to build a skeleton network. And then, based on the target prior feature, a Fuzzy Clustering Algorithm (FCM) is used to generate target non-object prior, and the output results of adjacent layers are combined by hopping to achieve feature sharing in this method. The experimental results show that the detection accuracy of the optimized network based on Res-SSD reaches 91.3%. The proposed method greatly improves the detection accuracy of insulators with different line-of-sight distances, effectively suppresses negative samples in complex backgrounds, reduces the search space of algorithm, and enriches semantic information.
On Living Dam Planning for Earthquake Prevention and Disaster Reduction

Abstract Based on the comprehensive research on the technology of “geographical name code”, “modern nonmetallic structure”, “high temperature fermentation biogas digester waste disposal power generation”, “Internet plus earthquake prevention and disaster mitigation APP” and “new theory of securities public investment”, this paper puts forward a new concept of “earthquake prevention and disaster reduction” in dam, which will obviously alleviate the shortage of farmland, cross river traffic, small hydro-power development in mountainous areas, and environmental protection waste. One belt, one road area, and the county level place code are proposed to lay the foundation for the new intelligent platform for earthquake emergency international earthquake preparedness and disaster reduction. The basic theory of securities public investment and its application in the PPP project for earthquake prevention and disaster mitigation are put forward, and the specific planning plan and budget for the construction of the dam in the poverty-stricken mountainous area are put forward. We will demonstrate the one to one belt, one road and one country. This paper is originated from practice and has a new idea, which will obviously promote the new development of human earthquake prevention and mitigation.
Reflective Gem to Improve Receptive Communication Skills for Students with Hearing Impairment

The inability to receive excitatory voice experienced by the student with hearing impairments presents diverse impacts. The impacts include difficulties in understanding the information that others convey, lengthy understanding sentences, and holding verbal communication. This research aimed to develop a reflective maternal method integrated with the Android system to improve vocabulary mastery to support students with hearing impairments to perform receptive communication. There were twelve students with hearing impairments within the 15-23 age range as the samples. This research belongs to research and development adapted from the multimedia development model for learning. Product feasibility test was performed through the alpha and beta tests. The average expert validation score was 4.25, and the average score of the material expert was 4.50. Both expert validation results were in an excellent category. Also, the results of the media experts' validation were 3.58 in the good category. Meanwhile, the average score of beta test was 3.58 in the good category. It can be concluded that Android apps can be operated via smartphones or computer devices to improve the receptive communicative ability of students with hearing impairments. This application is called Reflective Game. This research recommends an Android application program that can be used by Guidance and Counseling teacher accompanied by special school teachers to improve students' vocabulary understanding for the student with hearing impairment. Besides, it provides skills to understand proverbs, opposites (anonymous), and synonyms.
Supporting the activities of curators who do not have a basic pedagogical education at S. Seifullin Agro Technical University

At present, the priority goal of the higher education system is the formation of a personality not only professionally competent, but also tolerant, capable of creativity, able to predict his role in a dynamic world, responsible and passionate. Actually, all these characteristics are set by the labor market for the training of qualified and competitive personnel. However, as scientific research and teaching practice show, not all students can independently achieve the necessary results due to their lack of adaptation to higher education. Therefore, we need specialists who can help them in this and show them the right direction. In this regard, there is a growing interest in the study of curatorial activity, its role and place in the higher education system. The article, within the framework of the "School of Curators" created at S. Seifulin Agro Technical University, analyzes the results of diagnostics in order to identify the pedagogical competence of curators, focuses on a number of reasons that impede their activities, as well as a number of tested areas of activity that contribute to their elimination, offers a number of measures aimed to improve the pedagogical culture of curators of student groups who do not have a basic pedagogical education.
Multi-Granularity Network with Modal Attention for Dense Affective Understanding

Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation. In the recent EEV challenge, a dense affective understanding task is proposed and requires frame-level affective prediction. In this paper, we propose a multi-granularity network with modal attention (MGN-MA), which employs multi-granularity features for better description of the target frame. Specifically, the multi-granularity features could be divided into frame-level, clips-level and video-level features, which corresponds to visual-salient content, semantic-context and video theme information. Then the modal attention fusion module is designed to fuse the multi-granularity features and emphasize more affection-relevant modals. Finally, the fused feature is fed into a Mixtures Of Experts (MOE) classifier to predict the expressions. Further employing model-ensemble post-processing, the proposed method achieves the correlation score of 0.02292 in the EEV challenge.
An Exhaustive Scrutinization & Deployment of Cloud Computing

Cloud Computing is an innovation idea that helps in reducing the computing cost. Cloud Computing offers better Computing through improved utilization and reduced administration and infrastructure costs. Cloud computing is the long-held dream of computing as a utility. Cloud Computing is the combination of Software as a Service and Utility Computing. This paper is the first systematic review of peer-reviewed academic research published in this field, and aims to provide an overview of the swiftly developing advances in the technical foundations of cloud computing and their research efforts. Structured along the technical aspects on the cloud agenda, we discuss lessons from related technologies; advances in the introduction of protocols, interfaces, and standards; techniques for modelling and building clouds; and new use-cases arising through cloud computing. Cloud computing are managed by Cloud Management tools, loaded and tested by various other software testing tools. Cloud computing modelling and simulation is done by Cloud Sim or SPECI or Ground Sim or Dc Sim on the basis of testing benchmark.
COVID-19 Authorized Entry with Face Mask Detection using Raspberry Pi

There are no effective face mask detection applications in the current COVID-19 scenario, which is in great demand for transportation, densely populated places, residential districts, large-scale manufacturers, and other organizations to ensure safety. In addition, the lack of big datasets of photographs with mask has made this task more difficult. With the use of Python programming, the Open CV library, Keras, and tensor flow, this project presents a way for recognizing persons without wearing a face mask using the facial recognition methodology. This is a self-contained embedded device that was created with the Raspberry Pi Electronic Development Board and runs on battery power. We make use of a wireless internet connection using USB modem. In comparison to other existing systems, our proposed method is more effective, reliable, and consumes significantly less data and electricity
How to vEGU: display uploads and commenting

&amp;lt;p&amp;gt;In 2020 as an emergency response to the COVID-19 outbreak, we at EGU pivoted to an online-only conference in a very short period of time. Although this meant that lots of things that we normally enjoy about our General Assembly were lost, some things were also gained, as we were able to experiment with digital solutions to the challenges of digitally presenting science. One of these solutions, the online pre-uploads of display materials and longer-term commenting on them, was very popular, so we are bringing it back again for vEGU21&amp;amp;#8230;. with a few adjustments. So how do you upload your displays and comment on materials this year? It&amp;amp;#8217;s as easy as 1, 2, 3!&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;1: Add display materials!&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;From 31 March to 31 May, all accepted abstracts will have a space provided to upload display materials. These materials can be whatever you want &amp;amp;#8211; a quick slide show, a poster-style image, or a long-form version of your presentation slides &amp;amp;#8211; much like you can have in a regular PICO session. You can also, in addition to your data materials, upload a video that can even be a full-length recording of your talk! Your only limit is in the file size: you have 50 MB per abstract for *.pdf, *.ppt/pptx, *.pps/ppsx, *.png, and *.jpg files, and 200 MB for *.mp4 video files.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;2: Create a vPICO summary slide!&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;During the first part of each vPICO session, each author has a scheduled time slot to give a 2-minute summary of their research. This 2 minute summary will help your audience to decide what ideas they want to follow up with speakers when discussing the work in more detail during the subsequent chat session. To facilitate your live 2-minute presentation, you need to upload a single summary slide as a *.png or *.jpg with a resolution of 150 dpi and 16:9 dimensions (in addition to your longer display materials).&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;3: Commenting is cool!&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Like last year, commenting will be open on all uploaded display materials between 31 March and 31 May. The main difference for 2021 is that during this time, only registered conference attendees will be able to comment, unless the abstract authors have opted out of the commenting feature (in which case comments will be turned off). Authors will be notified of any new comments by email.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;We are very excited to see all your wonderful and diverse display materials uploaded before 19 April 2021, though you&amp;amp;#8217;ll be able to make adjustments at any time during the two-month commenting period. The 2-minute summary slide has to be uploaded at least 24 hours ahead of your scheduled presentation time, so we are ready to help you present your work for 2021.&amp;lt;/p&amp;gt;
Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote

We present a new second-order oracle bound for the expected risk of a weighted majority vote. The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a.k.a. one-sided Chebyshev's), which is amenable to efficient minimization. The new form resolves the optimization challenge faced by prior oracle bounds based on the Chebyshev-Cantelli inequality, the C-bounds [Germain et al., 2015], and, at the same time, it improves on the oracle bound based on second order Markov's inequality introduced by Masegosa et al. [2020]. We also derive a new concentration of measure inequality, which we name PAC-Bayes-Bennett, since it combines PAC-Bayesian bounding with Bennett's inequality. We use it for empirical estimation of the oracle bound. The PAC-Bayes-Bennett inequality improves on the PAC-Bayes-Bernstein inequality of Seldin et al. [2012]. We provide an empirical evaluation demonstrating that the new bounds can improve on the work of Masegosa et al. [2020]. Both the parametric form of the Chebyshev-Cantelli inequality and the PAC-Bayes-Bennett inequality may be of independent interest for the study of concentration of measure in other domains.
Friending with a teacher as a way of forming meaningful life strategies of a future educator

The article contains an analysis of the concept of a semantic sphere as a structure-forming component of a person’s meaningful life strategies. The necessity of using friending as a way to form life-sense orientations of a future teacher, as well as the importance of self-actualization and self-development for successful professional activity are substantiated. The article analyzes structural components of the meaningful concept of the personality of a future teacher. The study presents diagnostic tools, the results of the experiment, as well as a program for the formation of meaningful life strategies for a future teacher.