Reviewerly/exHarmony huggingface dataset:
Web Personalization with Usage-Based Clustering

Information on World Wide Web has been filling in a remarkable way. This raises a serious worry on data over-burden difficulties for the clients. Recovering the most significant data from the web according to the client prerequisite has become hard on account of the enormous assortment of heterogeneous archives. One way to deal with beat this is to customize the data accessible on the Web as indicated by client necessities. This is called Web Personalization process that changes data/administrations conveyed by a Web to the necessities of every client or gathering of clients, taking their standards of conduct. Successive Sequential Patterns (FSPs) that are separated from Web Usage Data (WUD) are vital for dissecting and understanding clients' way of behaving to work on the nature of administrations presented by the World Wide Web (WWW). Client standards of conduct are expected to fabricate profiles of every client, it is made to utilize which Personalization of site.
Characterizing and correcting Heliosat Surface Solar Radiation bias on intra-day time scales with deep neural networks

&amp;lt;p&amp;gt;Accurate intraday forecasts of surface solar radiation (SSR) are essential for utility companies and electricity grid operators. However, satellite SSR estimates can suffer from surprisingly low accuracies on short observation time scales and from significant spatial and temporal biases when compared to ground-based SSR measurements.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present a bias assessment of two high-resolution satellite SSR products for intra-day application time scales. The satellite SSRs are retrieved with the Heliosat SARAH-2 and the HelioMont algorithms (M&amp;amp;#252;ller et al., 2015; St&amp;amp;#246;ckli, 2013). We investigate intra-day and intra-hour estimates for altitudes from 200 to 3570 m a.s.l. We make use of 133 ground stations of the high-precision monitoring network SwissMetNet for the bias analysis. For solar zenith angle (SZA) lower than 90 degrees, we find that Heliosat SARAH-2 underestimates SSR at high altitudes (over 1000m) with an instantaneous root mean squared deviation (RMSD) of 179 W/m2and a mean bias deviation (MBD) of -68 W/m2 in the winter half year. The bias magnitude is approximately the double w.r.t. low altitude stations, due to difficulties in distinguishing snow-covered surfaces from clouds.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We also present an intra-hour bias correction approach: a deep neural network exploiting time-encoding features to model the bias in the time dimension. Our model achieves up to 30% RMSE reduction in the case of Heliosat SARAH-2, especially in mountainous regions. Moreover, we highlight the importance of the clear-sky index for bias correcting the Heliosat SSR estimates. Including a clear-sky index as a regressor in the bias correction improves the bias correction on average from 15.5% to 21.9% RMSE reduction. We also discuss the relevance of bias correcting satellite-derived SSR maps for short-term forecasting applications of SSR.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;References&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;M&amp;amp;#252;ller, R., U. Pfeifroth, C. Tr&amp;amp;#228;ger-Chatterjee, J. Trentmann, and R. Cremer (2015), Digging the METEOSAT Treasure-3 Decades of Solar Surface Radiation, Remote Sensing, 7(6), 8067-8101, doi:10.3390/rs70608067.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;St&amp;amp;#246;ckli (2013). The HelioMont Surface Solar Radiation Processing. Scientific Report 93, MeteoSwiss, 122 pp.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;
Challenges on the Digitalisation of the Universities in the European Higher Education Area

AbstractIn digital transformation, universities take a pivotal role in relation to the scientific basis of new digital technologies and the provision of digital education in an academic context. A university system, properly equipped with digital-related human and real capital is essential for driving a country’s digitalisation agenda. This chapter evidences the challenges faced by Germany in the development of societal digital potential and digital universities. German universities find the development of a digital university difficult as the process of digitalisation requires collective cross-discipline working and cultural change. Research activities need to be reconceived in the context of digitalisation, in terms of better and new data and methods. Respective digital solutions need to be communicated to secure engagement. Through digitalisation, teaching approaches will become less didactic and more personalised. Here again, universities need to pursue their ways of reform. Furthermore, the university administration will face a cultural change from pure budgetary administration to sophisticated strategic management. Several logics are evolving, the research logic and the educational logic, the communication logic in research and teaching, and the administrative and control logic. Higher education systems around the world have gone through large reforms during the past decades – although with different speeds and to varying degrees of success. However, it seems clear that Nordic and Baltic university systems are much further developed and more successful than the German one. The case of Germany is one where the need for improvement, for learning from others, and for a positive stance toward self-reflection and criticism is quite obvious. The COVID-19 crisis and the uncovering of many digital weaknesses in the German university system have proven to be powerful push factors. A number of improvements are already visible, such as in online teaching formats, digital exams, and video-conferencing. Nevertheless, policy makers and university managers are still obliged to take further, more purposeful steps.
Self-Adaptation Feature Attention Network with Multi-Step Fusion for Single Image Dehazing

Although single image dehazing has been widely studied as a common low-level computer vision task, it still faces serious challenges such as limited ability to dehaze real foggy pictures. We propose an efficient end-to-end self-adaptation feature attention (SAFA) network with multi-step fusion for this purpose. The proposed SAFA module can adaptively expand the receptive field to obtain the key structure information in space and extract more comprehensive and accurate features. In addition, considering the lack of connection between features acquired at low and high levels in the network, we also implement a multi-step fusion module, which makes the features of different layers in the network complementary effectively in the process of image recovery. The network structure is simplified, and the required computing resources are significantly reduced by decreasing network parameters. For multiple datasets and photographs with real haze, our method demonstrates better efficiency and availability, both quantitatively and qualitatively.
Neural network-based methods for generating synthetic satellite images in the solar spectral range

&amp;lt;p&amp;gt;Solar satellite channels of instruments onboard geostationary or polar orbiting satellites provide high resolution information on clouds and aerosols that is valuable for numerical weather prediction. The solar channels are sensitive to the microphysical properties of cloud and aerosol particles and contain better information on water content than the thermal channels. The direct assimilation of solar satellite images or their application for the evaluation of numerical weather prediction (NWP) models requires sufficiently fast and accurate forward operators, which solve radiative transfer (RT) problems to compute synthetic images from the NWP model output. As multiple scattering complicates the solution of radiative transfer problems in the solar spectral range, standard RT methods are too slow for this purpose. Faster methods have been developed for cloud-affected visible channels, but are limited to non-absorbing channels and do not take aerosols into account. Machine learning methods provide a promising way to accelerate the complex radiative transfer computations in satellite forward operators and to overcome the limitations of previous approaches. Here we report on experiments based on deep feed forward neural network. It is demonstrated that using neural networks the amount of training data that has to be computed with standard radiative transfer methods can be reduced by several orders of magnitude, compared to previous approaches, while increasing the speed by an order of magnitude and improving accuracy. Moreover, tangent linear and adjoint versions required for variational data assimilation can easily be implemented and do not have to be adapted when network structure or training data are changed. We discuss optimizations to reduce the computational effort and provide examples for applications that require more input parameters than cloud-affected visible channels and have only become feasible with the new approach.&amp;lt;/p&amp;gt;
Understanding Earthquakes Triggered by Wastewater Injection

A deep dive into a 2015 Oklahoma earthquake reveals new insights into the dynamics of quakes induced by wastewater injection, and could help inform future earthquake hazard modeling.
Digital Transformation in Higher Education Learning and Teaching

AbstractThis chapter attempts to provide answers to the question of why Digital Transformation (DT) in higher education institutions has not been wholly successful. It also looks at how this situation can be overcome through the application of an integrative concept of quality literacy in DT composed of competencies in quality strategies, practice and culture. A SWOT analysis of organisational and individual competencies required for DT in higher education learning and teaching is presented along with preliminary results of an Erasmus+ project on fostering digital competencies of educators and students. These studies help identify organisational, pedagogical and ethical weaknesses, as well as opportunities and challenges in DT. The chapter provides recommendations on how universities can make DT a successful reality and identify DT competencies required by stakeholders.
Quality Increases as the Error Rate Decreases

In this paper we propose an approach to the design of processes and software that aims at decreasing human and software errors, that so frequently happen, making affected people using and wasting a lot of time for the need of fixing the errors. We base our statements on the natural relationship between quality and error rate, increasing the latter as the error rate decreases. We try to classify errors into several types and address techniques to reduce the likelihood of making mistakes, depending on the type of error. We focus on this approach related to organization, management and software design that will allow to be more effective and efficient in this period where mankind has been affected by a severe pandemic and where we need to be more efficient and effective in all processes, aiming at an industrial renaissance which we know to be not too far and easily reachable once the path to follow has been characterized, also in the light of the experience.
DETEKSI KEPITING MOLTING MENGGUNAKAN TEKNIK KLASIFIKASI MACHINE LEARNING

Soft crab is an export product where foreign demand is much higher than production. In the production of soft crabs, it is done by keeping the crabs individually in a crab box which is placed in the pond until they molt. Molting is a natural process of molting, i.e. removing the old tough skin for growth purposes. Shortly after molting, the new crab shells are still very soft and will harden again after water absorption occurs. Therefore it is important to monitor molting crabs to help farmers in the cultivation of soft shell crabs. The number of crab datasets is 1060 which consists of 1000 training data and 60 testing data. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest Classifier (RFC). KNN, SVM, and RFC are classification algorithms from Machine Learning. This study aims to compare the performance of the three algorithms so that the performance of the three algorithms is known. Several parameters are used to configure the KNN, SVM, and RFC algorithms. From the results of the trials conducted, KNN has the best performance with 98.33% accuracy, 98.33% precision, 98.38% recall, and 98.38% F1 Score.
A study on the perception of Chinese teachers on the implementation of the IB DP curriculum

The purpose of this study was to find out the characteristics that appear based on the teachers' actual experience in implementing the IB DP curriculum in a situation where the number of schools introducing the IB DP in China is rapidly increasing. By examining the difficulties they are experiencing, intending to explore the direction of activation for effective implementation. In order to achieve the purpose of the study, 5 Chinese teachers with more than 3 years of experience in IB DP subjects were interviewed. The main research results are as follows. Teachers highly evaluated the comprehensive and balanced course composition and rational evaluation structure. But they complained about the difficulties of increased workload and complicated classroom situations in the class.&#x0D; And teachers pointed out the ambiguity of the IBO evaluation criteria and the inconsistency of the evaluation methods of each school. Through the results of the study, it was confirmed that the teachers were actively participating in the active growth and promotion of IB DP education in China, and it was found that teachers were experiencing difficulties in the process of implementing IB DP in the class. Based on this, it emphasized the need for long-term cooperation between educational administrative agencies related to international education in China, schools operating IB DP, and teachers who implement IB DP for effective implementation of the IB DP curriculum.
Hand-Gesture Detection Using Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sign language is a non-verbal language that Deaf persons exclusively count on to connect with their social environment.The problem that occurs in two-way communication using sign language is a misunderstanding when learning new terms that need to be taught to deaf and mute people. To minimize these misunderstandings, a system is needed that can assist in correcting hand gestures so that there is no misinterpretation in teaching new terms. Several optimality properties of PCA have been identified namely: variance of extracted features is maximized; the extracted features are uncorrelated; finds best linear approximation in the mean-square sense and maximizes information contained in the extracted feature. The classification uses the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. From the results of experiments with different image size variables, the largest accuracy was obtained with an image size of 449x449 of 76.20%. While the lowest accuracy of 52.38% is obtained through scenarios with image sizes of 57x57 and 45x45. Therefore, differences in the use of image sizes have an influence on the accuracy of hand signal prediction. The smaller the size given, the smaller the accuracy obtained. This is indicated by the decreasing accuracy value when given a smaller size in the four scenarios that have been studied.
Loop Holes in Web Based Security

Loopholes in web-based security is very important topic to learn and understand during creating and running an web based application, as there are many different was to breach an application some of them are since the 1st ever site was made, such as SQL Injection, Broken Authentication &amp; Session Management, XSS, Security Misconfiguration, CSRF and many more, where there is many different ways to manage those type of vulnerability like sanitization of data before accepting it, proper validation of data, proper handling and configuring of web-based application.
Violence Detection using Deep Learning

Violence detection occasion in surveillance gadget is performed a crucial sizable position in enforcement of regulation and metropolis safe. The Violence efficacy of the violence occasion detector calculate with aid of using the reaction and the accuracy and usually over extraordinary types of cognizance both velocity or accuracy or both. But now no longer thinking of generality over extraordinary kinds of the video source. In this paper, offer a real-time violence detection primarily based on totally deep getting to know method. This proposed version include CNN as a function extraction and LSTM because the temporal relation getting to know method, which cognizance on 3 component this is average generality, accuracy and rapid reaction time. The recommended version attaining 97% accuracy with a velocity 130 frames/sec. Differentiating of accuracy and velocity of the presented an version with a remaining paintings instance that the proposed remaining paintings within side the subject of the violence detection.
Towards Performance of NLP Transformers on URL-Based Phishing Detection for Mobile Devices

ackers are increasingly launching phishing attacks via SMS and social media. Games and dating apps introduce yet another attack vector. However, current deep learning-based phishing detection applications do not apply to mobile devices due to the computational burden. We propose a lightweight phishing detection algorithm that distinguishes phishing from legitimate websites solely from URLs to be used in mobile devices. As a baseline performance, we apply Artificial Neural Networks (ANNs) to URL-based and HTML-based website features. A model search results in 15 ANN models with accuracies &gt;96%, comparable to state-of-the-art approaches. Next, we test the performance of deep ANNs on URLbased features only; however, all models perform poorly with the highest accuracy of 86.2%, indicating that URL-based features alone are not adequate to detect phishing websites even with deep ANNs. Since language transformers learn to represent context-dependent text sequences, we hypothesize that they will be able to learn directly from the text in URLs to distinguish between legitimate and malicious websites. We apply three state-of-the-art deep transformers (BERT, ELECTRA, and RoBERTa) for phishing detection. Testing custom and standard vocabularies, we find that pre-trained transformers available for immediate use (with fine-tuning) outperform the model trained with the custom URL-based vocabulary. In addition, we test a thinner BERT transformer which is suitable for lightweight devices like mobiles, called MobileBERT. Our results emphasize that evaluation metrics of this model are competitive to other models in this study, yet the testing time is significantly less, making this model a choice for embedding phishing detection algorithms in mobile phones. Using pre-trained transformers to predict phishing websites from only URLs has five advantages: 1) requires little training time (230 to 320 s), 2) is more easily updatable than feature-based approaches because no pre-processing of URLs is required, 3) is safer to use because phishing websites can be predicted without physically visiting the malicious sites, 4) is easily deployable for real-time detection and is applicable to run on mobile devices, and 5) using a mobile specific transformer yields comparable performance and predicts 3 times faster than the other transformer models in this study.
Multicloud Based Backup and Disaster Recovery Service

In the recently years organizations have started to generate huge amounts of information of different formats. Availability of this huge amount of data collected is required for continuity of business during disasters .It is one of the main necessities of the time. Currently these backup and recovery services run with disadvantages of being very expensive with high restore and backup time. Solution to the above issues is cloud based platforms which will be used for these services. Cloud based platforms can be used for any amount of resources limited or boundless. Scheduling strategy COST-RTO tradeoffs scheduling algorithm is used to find the right server location to back up the replicated data. This algorithm mainly reduces the cost and data recovery time during a disaster. Here 3 backups are made and storage of these replicas of the data is done by this replica scheduler. Methods like incremental backup is being used to reduce backup time, metadata of the backed up data will be generated. This generated meta-data will help to retrieve data faster while restoring .Delivering these solutions as cloud services for faster and lowered cost accessing is the main objective which is fulfilled by the application..
DESKRIPSI KEBUTUHAN PEMBELAJARAN DARING MENGGUNAKAN PLATFORM GATHER TOWN UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA SISWA

The purpose of this study was to describe the need for online learning using theplatform Gather Town to improve student mathematics learning outcomes. The type of research used is descriptive qualitative. The population in this study was class VIII SMP Negeri 3 Cisauk, while the sample was taken by purposive sampling technique, namely class VIII.4 and VIII.5 as many as 88 students. Data collection techniques in this study used interview techniques, questionnaires, and the results of the Semester Results Assessment (PAS). First, all students' mathematics learning outcomes are still above the KKM, but 43% of students fall into the category of low student learning outcomes. Second, the learning media used did not stimulate students to play an active role in learning mathematics. Third, 83% of students are interested in online learning using the gather town platform. This study concluded that online learning media using the gather town platform needs to be applied to improve students' mathematics learning outcomes.
Traffic Analysis using Image Processing to Alert Traffic Control

In this paper, we present a scheme for traffic analysis using Image Processing to alert traffic control. In this, the vehicles are not being detected by sensors as we are detecting by images with the use of python language we are going to implement it in our project. Once image is captured from digital media, it is fed into image processing after that it detects vehicles from image using open cv libraries, after that at the end vehicles are detected on basis on vehicle count, and time will be set as per so reduce the road traffic congestion. This system contains the solution to three problems of traffic system. First one being the pre-defined set of timings set for each traffic signal despite the density circumstances. For this we have changed the signal timings. The working would be as follows, in a traffic junction of four lanes the density is measured on each lane at distance of 50 meters through the Image Processing. After that count the vehicle and turn on green light for time period deepening on vehicle count ratio. this is done so that the lane having highest density is allowed to clear the traffic first, the other lanes will be given green signal after this in a circular pattern. If in cases where the density is greater, the signal timing is increased seconds