Reviewerly/exHarmony huggingface dataset:
Availability Level of Digital Citizenship in the Computer and Information Technology Book for the Third Grade of the Intermediate Stage in the Kingdom of Saudi Arabia: مستوى توافر المواطنة الرقمية في كتاب الحاسب وتقنية المعلومات للصف الثالث المتوسط بالمملكة العربية السعودية

This study aimed to know the availability level of the elements of digital citizenship in the content of computer and information technology book for the third grade of the intermediate stage in Kingdom of Saudi Arabia. The study followed the descriptive methodology represented by the content analysis method, and the sample of the study was the computer and information technology book for the third stage in intermediate school and a form of content analysis was used for collecting data after ascertaining its validity and reliability. The study found the availability of (15) indicators out of (63) indicators within the elements of digital citizenship and a total of (358) frequencies, which indicates to the poor availability of the elements of digital citizenship in the content of the book, as the digital culture element had the highest percentage of availability among the nine elements (83.79%), and the rest of the elements of digital citizenship received very low percentages of availability and distributed varyingly which did not exceed (9%), while the element of digital health and safety, and the element of digital rights and responsibilities were not available entirely. The study recommended that the elements of digital citizenship should be included in the content of computer and information technology book for the third intermediate stage for the intermediate stage to be used towards the benefits of digital technologies and avoid their danger and educating computer teachers about it.
Use an Efficient Neural Network to Improve the Arabic Handwriting Recognition

Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.
PENGARUH PENGGUNAAN LEMBAR KERJA PESERTA DIDIK BERBANTU MODEL COOPERATIVE TYPE SCRAMBLE TERHADAP HASIL BELAJAR MATA PELAJARAN EKONOMI KELAS X SEMESTER GANJIL SMA NEGERI 1 BANDAR SRIBHAWONO TAHUN PELAJARAN 2019/2020

Student Worksheet (LKPD) is a teaching material used by teachers to support the learning process. Containing material sheets and assignments that must be done, LKPD is also used to optimize student activities in learning. The scramble type of cooperative model is learning to practice the accuracy and speed of answering questions. Students must combine the right brain and the left brain. Not only that, students also have to guess quickly the answers to the questions that are available but are still in random conditions.&#x0D; Based on the results of a survey conducted by researcher at SMA &#x0D; Negeri 1 Bandar Sribhawono as well as information from teachers in the field of economic studies, it was found that there are still many students in Grade X social studies whose learning outcomes are not yet complete. Based on these problems, the formulation of the problem in this study is whether there is an influence on the use of LKPD Assisted by the Scramble Type of Cooperative Model on the Learning Outcomes of Economic Subjects in Class X Odd Semester of SMA S 1 Bandar Sribhawono Academic Year 2019/2020? " This is to determine the effect of the use of Scramble Type of Cooperative Model Assisted LKPD on Learning Outcomes of Lesson Semester Class Economics in SMA 1 Bandar Sribhawono 2019/2020 Academic Year. This study uses an experimental method (Quasi Experimental) with the experimental design type Nonequivalent Control Group Design.&#x0D; Based on the analysis of research data using a simple linear regression formula obtained a = 34.1767 b = 0.7530 so that Y = a + bX, is Y = 34.1767 + 0.7530X. The results of the study stated that the hypothesis was accepted, with tcount&gt; ttable, and it was seen that at a significant level of 5% ie 14.51&gt; 1.70 which can be seen in the G table of statistical tables, students with economic learning outcomes included in the complete category were 27 students or by 75%, while students with economic learning outcomes included in the unfinished category are 9 students or by 25%. Thus there is a positive influence on the use of LKPD Assisted Scramble Learning Model Towards the Results of Economic Learning Grade X of Odd Semester of SMA Negeri 1 Bandar Sribhawono Academic Year 2019/2020.&#x0D;
A Data-driven Exploratory Service Composition Tool for Data Scientists

In the era of big data, data scientists gain insights from data and make decisions. However, when constructing a data analysis pipeline, data scientists are often required to be proficient in multiple algorithm models and theoretical foundations, and they have to select and combine multiple algorithm models, and repeatedly adjust algorithms and parameters to construct a high-performance data analysis pipeline. In response to the above problems, this paper proposes an exploratory service composition tool, which can perform real-time service recommendations according to users’ needs and data features, and assists users in constructing data analysis pipelines in an exploratory manner. This method can reduce the difficulty of data analysis, effectively save labor costs, and improve the quality and efficiency of data analysis.
Arguments for Tweakable Block Cipher-Based Cryptography

As discussed in Sect. 1.3 , a TBC is a variant of a BC that accepts a public tweak to change its behavior. TBC-based AEAD designs have a lot of nice performance and security features that we will discuss in this chapter. First, we give an early history of TBC designs in Sect. 4.1. Then, we discuss the impact of the TWEAKEY framework in Sect. 4.2.
A Novel Gamma Filter for Positive Parameter Estimation

This work develops a new recursive Bayesian estimation algorithm for gamma-distributed random variables. The time prediction and measurement update steps are developed, and both are shown to have analytic closed formed solutions under certain conditions. Furthermore, the update is shown to be unbiased, this is theoretically guaranteed by the choice of estimator and demonstrated numerically through Monte Carlo methods with a simple example.
A hybrid approach for feature selection based on global and local optimization for email spam detection

These days, Due to the universal availability of internet connections, email correspondence has become one of the most cost-effective and efficient approaches for official and business users. Millions of spam emails are being shared every day. To protect individuals or organizational data, Spam detection is needed. In Spam detection, handling high dimensional datasets in machine learning is high in time complexity and space complexity. To decrease time complexity and space complexity, feature selection is required to remove the irrelevant features. In this paper, the proposed idea is to optimize the feature selection method to decrease time complexity, space complexity and increase accuracy as well. The proposed feature selection method is a hybrid method based upon two optimization techniques; the first one is global optimization using the chi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> selectkbest method, The second one is local optimization using the Tree-based feature selection method. Experiments are carried out to compare four different classifiers. The proposed idea performs well on precision, memory, and accuracy efficiency tests, according to the findings.
Chapter 11. Generic analysis of mobile application reviews in English and Spanish

In this paper, the Systemic Functional Linguistics (SFL) approach to genre is applied to English and Spanish mobile application reviews from Google Play Store. These reviews are shown to be divisible into two optional stages, namely Evaluation and Description, the first part being more subjective and based on the author’s opinion and the second one more objective and focused on describing issues. The frequency of use of the Attitude axis from Appraisal Theory provides support for the distinction of those stages. The combination of the polarity of the contents of each stage together with the number of stars given to the item reviewed creates six patterns for the reviews. However, no noticeable differences between English and Spanish reviews were observed.
Phonological properties as predictors of text success

In this study, the relation of phoneme structure and success rate of Czech texts is investigated. The study is based upon two phenomena: (1) the beauty-in-averageness effect and (2) the euphony principle. The main objective is to examine whether one or the other prevails in the perception of written text, whether they interact in some way, or whether the phoneme structure has no major effect on the text’s appeal to the readers. Several phoneme groups, both vowel and consonant, are focused on.
Chapter 6. Swedish FrameNet++ and comparative linguistics

In this chapter we describe a multilingual extension of Swedish FrameNet++, intended to address research questions of a broad comparative nature, in genealogical, areal and typological linguistics, focusing on the integration into Swedish FrameNet++ of so-called core vocabularies, used in several linguistic subfields in order to conduct massive comparative studies involving large numbers of languages. Specifically, we describe the inclusion of two such lexical databases covering several hundred South Asian languages, with the aim of investigating areal and genealogical connections among these languages.
Sub-Pixel Depth Perception Based on Flexible and Separated Structured Light

We develop the method of machine learning for sub-pixel depth perception based on flexible and separated structured light, and describe novel framework for getting the extrinsic parameters of the system without complicated calibration in advance. We combined the extrinsic parameter calculation process into the structured light decoding process. PSO-C4.5 Decesion Tree algorithm is used to classify and match sub-pixel based on the Label. According to the matching result, the disparity map at the sub-pixel level is obtained, and the extrinsic parameters of the measurement system are obtained according to the fundamental matrix and initial pose information. In contrast to previous work, we formulate the problem of correspondence matching as a classification problem, which allows us to correctly take into account effects caused by scene movement. All optimization problems are solved with machine learning techniques. We test our method on a number of real-world examples as well as our new benchmark dataset for depth map estimation, and compare results to the state-of-the-art methods. The proposed method is both faster as well as more accurate.
SINS/GPS Tightly-Coupled Integration Navigation Method Based on Quadrature Point Updating Cubature Kalman Filter

SINS/GPS tightly-coupled integration navigation is based on nonlinear Kalman filtering algorithm. Aiming at solving the problems of non-Gaussian and high-order information loss and low filtering accuracy in it, a new SINS/GPS tightly-coupled integration navigation method based on quadrature point updating cubature Kalman filter is proposed. Pseudo-range information are used to participate in filtering in the nonlinear measurement model. Taking the correction effect of measurement quadrature points on the state quadrature point into account, the state update equation is established to directly update the state quadrature point, so as to build the whole SINS/GPS tightly-coupled integration navigation process. Taking the surface ship as the simulation object, the method we put forward is applied to the ship simulation model. The navigation results clearly indicate that in the SINS/GPS tightly-coupled integration navigation problem under the non-linear measurement model, the filtering accuracy of this method we put forward is brilliant compared to that of the traditional non-linear filtering algorithm including the extended Kalman filter and the cubature Kalman filter, especially, it has better estimation accuracy for velocity parameters and north position parameters. The feasibility and validity of the method are verified.
Big data feature selection using fish and frog optimization

Abstract Big data denotes a large amount of data which includes a wide range of such methodologies like big data collection, storage, analysis, and managing the data. Every data collected in this process (homogeneous or heterogeneous considered as data), we called as big data. In this article, fish colony and their social behavior are used recently for developing an algorithm, we called as novel represented as fish swarm optimization algorithm (FSOA), which is based on the fish swarm and its behavior while search for food. The shuffled frog leaping algorithm (SFLA) is one which we introduced recently for finding near optimal solutions. The technique of Hybrid FSO‐SFLA is used here for evaluating performance in big data queries.
LFM/PRBC Modulation Signal Parameter Estimation

Abstract Since pseudo-code phase modulation and carrier frequency modulation composite reconnaissance signals have both the good anti-interference capability and the low probability of interception, they have been widely used in electronic reconnaissance systems. This paper proposes a multi-parameter estimation method for pseudo-random binary phase code (PRBC) and linear frequency modulation (LFM) composite modulation signal in a complex electromagnetic environment. Firstly, this paper uses the square operation to remove the code phase information; secondly, we use the delay correlation and two times frequency estimation to estimate the starting frequency and the frequency modulation rate; finally, the wavelet transform algorithm is utilized to estimate the code rate parameter. Through the simulation analysis and full comparison with other methods, we can prove the performance superiority of this method under low signal-to-noise ratios (SNRs).
A partial EM algorithm for model‐based clustering with highly diverse missing data patterns

The expectation‐maximization (EM) algorithm for incomplete data with highly diverse missing data patterns can be computationally expensive. A partial expectation‐maximization (PEM) algorithm is developed to ease this computational burden. This PEM algorithm circumvents the need for a traditional E‐step by performing a partial E‐step that reduces the Kullback‐Leibler divergence between the conditional distribution of the missing data and the distribution of the missing data given the observed data. The PEM and EM algorithms are compared in terms of computation time and convergence on simulated data. The PEM algorithm is illustrated using a latent Gaussian mixture model to cluster a white bread sensory analysis dataset.
A study of multi-sensory senses in museum virtual-visits

Abstract In the forced situation of living virtual during the recent pandemic, also human spatial perception needs to develop through experience and senses during virtual activities. People can go to new places using virtual media such as pictures, 360 panoramas, movies, Google Street View, and virtual tours while being physically separated and socially isolated. This also applies to museum visits when visitors can simply observe. This article offers data from a mixed-methods empirical study that examines how three Indonesian museums, Museum Pendidikan Surabaya, Museum Tsunami Aceh and Museum Bank Indonesia Jakarta, altered their perceptions in establishing a feeling of virtual space. The study has undertaken an identification of place descriptors related to senses multi-sensory systems. The respondents are young people in their twenties who have no prior museum experience. It demonstrates that in a virtual spatial experience, the respondents' perceptions are mostly influenced by the sensory system, which gets diverse information from the media, rather than social signals, which are frequently cited as the most important aspects in perceiving locations in real life. The component of familiarity (recalling memory) is also essential in detecting and identifying the sensory descriptors in this study. In a virtual spatial experience, all sensory systems perform differently; in this study, the visual and auditory sensory systems are the two strongest, while the chemical sensory system is the weakest. Virtual visits, although on a lesser scale than physical visits, can benefit from the multi-sensory system, which is crucial in museums.
AI Driven Identification of Fake News Propagation in Twitter Social Media with Geo-Spatial Analysis

With an explosion of online users over social media around the globe, we are no longer strangers to anything and anybody. With increased availability and exchange of information, the propagation of fake news and posts has also increased. Fake news refers to falsified versions of facts that get circulated among the general public to deliberately deceive people rapidly in the network. With the dawn of social networking, the dissemination of fake news has increased a lot due to share-ability, speed and lack of accountability. To address such problems on social media, we have presented an innovative geo-spatial detection mechanism for identifying fake news on Twitter. Our artificial intelligence based proposed strategy is implemented based on machine learning to improve accuracy for ensuring appropriate classification of news being posted on the social media platform so that online users may remain aware of getting duped by fake content.
Research on Shandong Province Teaching Resource Database System Based on Clustering Algorithm

In the autumn of 2004, Shandong Province, as one of the first pilot provinces to carry out the new curriculum reform of ordinary high schools, began a new round of curriculum reform. But at the same time, there is also a lack of curriculum resources and teachers in Shandong Province, which makes some advanced ideas in curriculum reform difficult to achieve the expected effect. With the development of information technology and modern educational technology, the teaching resource database system is widely used in various colleges and universities. This paper designs and implements the teaching resource database system based on clustering algorithm, which can effectively improve the retrieval efficiency of teaching resources and improve the overall performance of the teaching resource database management system. Teaching resource library is an important part of modern education. Its construction can provide students with a good learning environment and meet their learning needs. The construction of teaching resources is the foundation of educational informationization, and it is a systematic project that needs long-term construction and maintenance, so it can promote the development of this educational infrastructure work continuously and steadily.
A Vision-Based Guiding Method for Autonomous Route Planning of Micro Quadrotor UAV

In this paper, a vision-based guiding method was proposed to address the issue of autonomous route planning for micro Quadrotor Unmanned Aerial Vehicle (quad-UAV). In the autonomous flight stage, the skeleton line, which represents the main extension of roads, is extracted to get the potential forward direction (FD). Then the current FD is defined as the minimum value of all angles between potential FD with the last FD. In the stage of autonomous landing, the parking apron is detected based on the roundness of a closed region with an appropriate threshold. To tradeoff the stability and speed, the forward destination location is calculated according to the amplitude of change of the FD. Additionally, the PID controller is further applied in the whole route planning. The proposed algorithm is verified on the parrot micro quad-UAV platform. And the results illustrate that the proposed scheme can autonomously control the UAV fly along the road and land on the parking apron, and exhibit good performance.
Low light image enhancement method based on guided filtering

Low light image enhancement methods based on classic Retinex model attempts to estimate the illumination component and to project it back to the corresponding reflectance. Therefore, the accuracy of illumination component estimated determines the performance of enhancement results. Based on Retinex model, this paper proposed an illumination component estimating method by guided filtering. In the proposed method, bright channel is used for obtaining a rough illumination map. Then, the gray image converted by the input low light image is employed as the guidance image and we refine the rough illumination map for obtaining a structural-awared illumination map by guided filtering. Lastly, the enhanced image can be achieved by a simple computation. Experimental results demonstrated that the performance of the proposed method significantly overpasses conventional low light image enhancement methods.
Hybrid Classification Approach for Software Defect Prediction with Feature Reduction and Clustering

Software product refers to the software which is developed for a specific requirement. Simultaneously, engineering deals with the development of product using explicit technical fundamentals and methods. The software defect can be predicted in diverse stages in which data is utilized as input and pre-processed, attributes are extracted, and classification is performed. This research work makes the implementation of several classifiers in order to predict the software defect. These classifiers are GNB (gaussian naive bayes), Bernoulli NB, RF (random forest) and MLP (multilayer perceptron) which are employed with the objective of forecasting the software defect. The performance of the software defect is enhanced by developing an ensemble classifier. In the introduced ensemble classifier, the PCA (Principal Component Analysis) algorithm is integrated with class balancing. Python is executed to implement the introduced model. Diverse metrics are considered to analyze the results concerning accuracy, precision and recall.
Research on risk prediction methods of informationized water conservancy projects

The construction and cost calculation of water conservancy projects are related to the construction of national infrastructure, and it is also a systematic and comprehensive project scale. The cost of each stage of a water conservancy project will involve the cost of the entire project. However, in actual work, more attention is paid to the cost of the project in the construction phase, and there is no reasonable forecast of the cost of the project in the various tasks in the early stage of construction. This is also very unfavorable to the overall cost of water conservancy projects. A detailed analysis of the methods of reasonable forecasting of the project cost in the various work of the early stage of water conservancy project construction is carried out. The article mainly studies the methods of reasonably predicting the project cost in the early stage of water conservancy project construction, and puts forward reasonable ideas and opinions.