Reviewerly/exHarmony huggingface dataset:
Downscaling global, beam and diffuse horizontal irradiance based on hour resolution global horizontal irradiance using Markov mixture distribution modeling

This study introduces a downscaling method for generating beam, diffuse and global horizontal irradiance based on hour resolution global horizontal irradiance. The study adapts and evaluates the two time-series Markov-chain Mixture distribution model for generating correlated solar irradiance components for downscaling from hour to minute resolution. The model is tested on radiometer data from two climatic regions: Oahu, Hawaii, USA and Norrköping, Sweden. Results show that the downscaled normalized global, beam and diffuse solar irradiance components are statistically similar to the test data for both locations. As a test of universality, the model trained on SMHI data is tested on NREL data and vice versa. It can be concluded that the output time-series are still realistic, but not neccessarily statistically applicable to the specific climatic region. The downscaling method is available at Github JoakimMunkhammar/N2.
Research and Development of Three-Dimensional Isolation System for SFR

The development of a SFR(:Sodium-cooled Fast Reactor) is carried out by Japan Atomic Energy Agency and others. Not only the decrease of horizontal seismic loads, but also the decrease of vertical seismic loads is important in SFR which has thin wall structures. Accordingly, the three-dimensional isolation system is developed for the seismic design of the components. This isolation device consists of a thick rubber bearing, four coned disc spring units, four vertical oil dampers, eight sliding functions and a supporting function. Furthermore, horizontal oil dampers are installed for damping force. This isolation device has few critical development elements and no additional function such as the rocking suppression device. In this isolation system, the natural frequency in the horizontal direction is 0.29Hz, the natural frequency in the vertical direction is 3.0 Hz. This isolation device is premeditatedly developed from 2017. This project has carried out load tests with full and half scale coned disc springs. The design method of coned disc spring units has been confirmed. Furthermore, this project has developed the horizontal oil damper of high allowable velocity and the vertical oil damper. This project is carrying out load tests of the half scale three-dimensional seismic isolation device, which consist of the thick rubber bearing, the coned disc spring units, the supporting function and so on. The development of this isolation device will be continued sequentially. The fundamental design method of this isolation device is confirmed in this project.
Light field image super-resolution using selective kernel convolution

Over the years, many methods have emerged to solve the super-resolution problem of light field images, and among them, those methods based on deep learning are noted quite attractive recently. Although the features extracted from epipolar domain for the super-resolution of light field images are actively investigated due to their potential capability of well capturing the relationship between spatial and angular domains, we note that spatial features are still the most important foundation in feature extraction. In this paper, we design a network, named as LFSelectSR, employing multiple convolutional kernels to fully extract spatial features and introduce a dynamic selection mechanism that can extract the most valuable spatial features. By training and testing the network using well-known datasets, we demonstrate its excellent performance of achieving the level of state-of-the-arts under certain conditions.
Research Trends and Implications for Domestic and International Digital Literacy Using Keyword Network Analysis before and after the COVID-19 Pandemic

In addition to the rapidly c hanging digital environment, the need for digital literacy is increasing for all members of society today due to the COVID-19 pandemic. Earlier, digital literacy consisted of a level of simple understanding and utilization of computer-based digital texts, but recently it has developed into the ability to diagnose and solve problems based on ethical awareness. In particular, COVID-19 is considered a major inflection point in digital literacy research in that it has resulted in the transfer of most of its activities from offline to online. Therefore, in this study, changes in domestic and international digital literacy research starting in 2020, when COVID-19 started and spread, and the implications thereof were analyzed. Network analysis was conducted by dividing 797 papers on the subject of domestic and international digital literacy into two periods based on their publication date, and quantitative changes in research, keyword frequency, keyword connection centrality, and cluster maps were identified. Research related to digital literacy has steadily increased in all periods in Korea and (except for the first period) abroad, but especially in Korea, which showed an increase of 180% in the second period. In addition, an examination of the keywords appearing in the study confirmed that the average number increased during the second period more than 2.5 times as much as during the first period in both domestic and international research, and the clusters also became more diversified. In addition, it was confirmed that “education” and direct and indirect keywords appeared as key research areas of digital literacy throughout the entire period, and from the first to the second period, digital literacy expanded from the ability to use simple digital tools to diagnosing and solving problems.
A Detailed Study on Preventing the Malicious URLs from Cyber Attacks

Today, there are more malicious URLs and cyber-attacks than ever. Cybercrimes are developing along with the software system’s progress. If we are able to stop one attack, then another one will follow in a different direction. These URLs were given via an unidentified website or email. The URLs were supplied in order to collect your personal information or to leak passwords and data content. You and your system are substantially impacted when you click on an unfamiliar URL. The difficulty is that browsers can only block well-known spam websites. At the moment, software incorporates several types of security measures, such as spam messages and call notifications, and our web browser implements these by employing blacklisting to protect from unknown URLs. As a result, not all cyberattacks could be alerted to. Our study is on identifying malicious URLs and phishing assaults from various websites, and we may apply cyber security by utilizing machine learning and deep learning. Many publishers have published on various attacks. These strategies allow us to stop URLs from spammers or hackers as well as cyberattacks like phishing. When we send an email or URL that appears to have come from a reliable source, the attacker can obtain our personal information if we click on it or access it. Therefore, we can protect our software utilizing machine learning and deep learning approaches from this kind of URLs. In order to prohibit or report assaults and cybercrimes, machine learning and deep learning algorithms are employed in cyber security to identify malicious and phishing URLs submitted by unidentified individuals.
A Survey on Evolution of Video Summarization Techniques in the Era of Deep Learning

By choosing the most instructive segments of the video content, video summarization systems seek to produce a succinct and comprehensive summarization. The most advanced techniques are those that use contemporary Deep Neural Network (DNN) architectures. Several strategies were developed in the past. The most current developments in the field are highlighted while providing a full analysis of current deep-learning-based approaches for broad video summarization. This study reviews the video summary technologies and an examination of key elements of deep-learning-based algorithms. An analysis of deep learning-based algorithms is provided to analyze the development of deep learning-based algorithms for video summarization and to offer suggestions for the pace of future research. The results of these comparisons, as well as some previously discussed challenges regarding the datasets and the applicability of evaluation procedures, will leverage future directions.
A verified VCGen based on dynamic logic: An exercise in meta-verification with Why3

With the incresasing importance of program verification, an issue that has been receiving more attention is the certification of verification tools, addressing the vernacular question: “Who verifies the verifier?”. In this paper we approach this meta-verification problem by focusing on a fundamental component of program verifiers: the “Verification Conditions Generator” (VCGen), responsible for producing a set of proof obligations from a program and a specification. The semantic foundations of VCGens lie in program logics, such as Hoare logic, Dynamic logic, or Separation logic, and related predicate transformers. Dynamic logic is the basis of the KeY system, one of the foremost deductive verifiers, whose logic makes use of the notion of update, which is quite intricate to formalize. In this paper we derive systematically, based on a KeY-style dynamic logic, a correct-by-construction VCGen for a toy programming language. Our workflow covers the entire process, from the logic to the VCGen. It is implemented in the Why3 tool, which is itself a program verifier. We prove the soundness and (an appropriate notion of) completeness of the logic, then define a VCGen for our language and establish its soundness. Dynamic logic is one of a variety of research topics that our dear friend and colleague Luís Soares Barbosa has, over the years, initiated and promoted at the University of Minho. It is a pleasure for us to dedicate this work to him on the occasion of his 60th birthday.
An explicit exploration strategy for evolutionary algorithms

Bio-inspired algorithms are a trending topic in the field of optimization. In general, evolutionary algorithms start their search process by generating a random population. Although there are some works in the literature that propose a mechanism to generate the initial population of a particular algorithm, there is no general approach to improve exploration in evolutionary algorithms. In this work, a versatile strategy is proposed for improving the exploration of any evolutionary algorithm. An exhaustive set of experiments was designed by applying 4 state-of-the-art algorithms to a well-known benchmark of continuous functions. A comparison based on statistical tests was made to demonstrate the performance of the proposed policy. The results indicate that the explicit exploration strategy is effective.
Communication, Computing, and Caching Trade-Off in VR Networks

As technology continues to advance, virtual reality (VR) video services are able to provide an increasingly realistic video experience. VR applications are limited, since the creation of an immersive experience requires processing and delivery of incredibly huge amounts of data. A potential technique to decrease the operation time for VR, as well as its energy use, is mobile edge computing (MEC). In this study, we develop a VR network in which several MEC servers can supply field-of-view (FOV) files to a VR device in order to satisfy the transmission requirements of VR video service and improve the quality of the experience. In this way, the projection process from 2D FOV to 3D FOV and the cached data is possible on an MEC server or a VR device. A cooperative computational offloading and caching strategy is developed as a decision matrix to reduce transmission requirements based on the service time constraint requirement. The VR video service mechanism is examined through the decision matrix. The trade-off between communication, caching, and computation (3C trade-off) is further implemented by means of a closed equation for the decision matrix. Results from simulations show that the suggested technique can perform close to optimally compared to alternative opposing methods.
Research on the Beijing Academic Library and Information System

Beijing Academic Library & Information System (BALIS) is the largest regional university library alliance (RULA) in Beijing, which has 100 member libraries, covering almost all university libraries (UL) of Beijing.BALIS was founded in 2007, through more than fifteen years of development, which has formed a certain business scale.Its services mainly cover six aspects: document delivery (DD), interlibrary loan (ILL), group procurement, joint consulting, business training, and technical support.BALIS is the representative of Chinese RULAS.The fifteen years of rapid development of BALIS are also the fifteen years of rapid development of RULAS in China.Through researching it, the construction ideals (CI), organizational structure (OS), and operation methods (OM) of the Chinese RULAS can be well revealed.Therefore, this article adopts the method of case analysis to grasp the development context and law of RULAS in China.
Analysing observed categorical data in SPSS AMOS: a Bayesian approach

This study has a didactic purpose to help applied investigators and practitioners to understand the roles of observed categorical data (OCD) in structural equation modelling (SEM) and the appropriate ways of analysing such data under SPSS AMOS. To that end, the study reviews types of OCD (nominal, ordinal, dichotomous and polytomous) and their incorporation into SEM under AMOS to play different roles. The study presents two applications from the health and retirement study where Bayesian statistical inference is used to analyse one set of OCD variables serving as endogenous variables with/without groups created by another OCD variable. Besides, the study demonstrates the typical ways of summarising, reporting and interpreting the results from Bayesian statistics, and compares AMOS with several other SEM programmes (Mplus, R lavaan, Stata and SAS PROC CALIS) on handling OCD. The study concludes with summaries of the findings for its intended audience.
Environmental Care Attitude Analysis of Prospective Biology Teachers

Current environmental problems are still seriously elevating.One way to reduce the threat of environmental problems is to change students' perspective and behavior to be more concerned toward the environment.This research aims to describe the environmental care attitude of student or biology teacher candidates in University of Papua.The sample in this study amounted to 75 respondents to biology teacher candidates.The data was collected by disseminating questionnaires through Google Form due to the pandemic conditions of Covid-19.The environmental care attitude questionnaire was organized based on 5 indicators, namely limits to growth attitude, anti-anthropocentrism, balance of nature, anti-exemptionalism eco-crisis.The results of the analysis showed that the highest attitude was on the eco-crisis indicator with an average value of 4.00, followed by the balance of nature attitude indicator with an average of 3.99, the limits to growth attitude indicator with an average of 3.81, the anti-anthropocentrism attitude indicator averaging 3.61 and the anti-exemptionalism attitude indicator averaging 3.48.The environmental attitude category was dominated by the good category of 86.7%, the excellent category by 8.0% and the low category by 5.30% and there was no apathy category / no matter the environment.Thus, in general, the students' attitude of caring toward the environment of Biology Study Program in the University of Papua is within the category of Good.
Novel Deep CNN Model based Breast Cancer Classification

Breast cancer is one of the terrible diseases among women worldwide. Better classification of breast cancer saves more human life and gives more confidence to extent their survivability. Deep Learning plays a vital role to solve real world complex problems in almost all domains including medical image processing. In this study, a novel DeepCNN model is proposed to classify Breast Cancer with better accuracy. Further, hyper-parameter optimization using Random Search is implemented to optimize the number of epochs, learning rate, and a dropout rate of the proposed DeepCNN model. Finally, the results are compared with various pre-trained models such as VGG19, Resnet50, Resnet101V2, InceptionResNetV2, InceptionV3, and Xception. Among all, the novel custom DeepCNN model produces higher accuracy of 99.18% using Random Search optimizer.
Humanizing geoscience education research through participant-driven visual representation

AbstractAs a response to the increased need for humanization and representation in STEM, a set of open-source, customizable icons was developed using stackable vector graphics to allow participants to design their own research icons. These icons have the potential to both grant research participant’s greater agency in their representation and attempt to minimize bias introduced by the researcher. Additionally, the representation provided by the developed icons help to illuminate those that are participating in geoscience. The visual representation of research participants provides an opportunity for these individuals to see themselves represented in geoscience spaces and aids in visualizing the geoscience community. Examples of implementation in both quantitative and qualitative research are provided and the authors encourage their use by the broader research community.Keywords: Representationiconsdiversityparticipant agency
A machine learning approach for Urdu text sentiment analysis

Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work.
A Novel Generative Image Inpainting Model with Dense Gated Convolutional Network

Damaged image inpainting is one of the hottest research fields in computer image processing. The development of deep learning, especially Convolutional Neural Network (CNN), has significantly enhanced the effect of image inpainting. However, the direct connection between convolution layers may increase the risk of gradient disappearance or overfitting during training process. In addition, pixel artifacts or visual inconsistencies may occur if the damaged area is inpainted directly. To solve the above problems, we propose a novel Dense Gated Convolutional Network (DGCN) for generative image inpainting by modifying the gated convolutional network structure in this paper. Firstly, Holistically-nested edge detector (HED) is utilized to predict the edge information of the missing areas to assist the subsequent inpainting task to reduce the generation of artifacts. Then, dense connections are added to the generative network to reduce the network parameters while reducing the risk of instability in the training process. Finally, the experimental results on CelebA and Places2 datasets show that the proposed model achieves better inpainting results in terms of PSNR, SSIM and visual effects compared with other classical image inpainting models. DGCN has the common advantages of gated convolution and dense connection, which can reduce network parameters and improve the inpainting effect of the network.
Collapse of Dark States in Tavis–Cummings Model

The singlet state of a system of two two-level atoms changes smoothly, remaining dark, as the Hamiltonian TC is slowly deformed, despite the inapplicability of the adiabatic theorem to this case. In this case, there is a small probability of emission of free photons, which does not depend on the smoothness of the deformation of the Hamiltonian. The effect of spontaneous emission is enhanced by the addition of one more pair of atoms in the singlet state due to the exchange of virtual photons in the cavity. A similar effect was also established for the case when atoms can move between two cavities, but here, on the contrary, with an increase in the number of atoms, the emission decreases. This purely quantum effect must be taken into account in practical manipulations with atomic singlets; however, its weakness testifies, rather, to the stability of dark states and the prospects for their use in information exchange (quantum cryptographic protocols) and as an energy accumulator for nono-devices.
Isomorphism of nonlocal sets of orthogonal product states in bipartite quantum systems

To characterize nonlocal sets of orthogonal product states (OPSs) in a given bipartite quantum system, we propose two new concepts, i.e., orthogonal relation matrix of a set of OPSs and isomorphism of different sets of OPSs. In ℂ3⊗ℂ3 quantum system, we find that two sets of OPSs, which are isomorphic to a nonlocal set of OPSs, are locally indistinguishable. In ℂ5⊗ℂ5 quantum system, we construct a new nonlocal set of OPSs according to the orthogonal relation matrix of a set of OPSs that cannot be perfectly distinguished by local operations and classical communication (LOCC). These results show a phenomenon that a set of OPSs is locally indistinguishable if its orthogonal relation matrix is equivalent to the orthogonal relation matrix of another set of OPSs that cannot be perfectly distinguished by LOCC in a given quantum system, and the orthogonal relation matrix of a nonlocal set of OPSs can be used to construct a new nonlocal set of OPSs. On the other hand, we give the geometric interpretation of nonlocal sets with isomorphism relation by orthogonal graphs (OGs). Our results, which can be used to classify nonlocal sets of bipartite OPSs, improve the theory of local distinguishability and deepen people’s understanding of the structures of nonlocal sets.