Reviewerly/exHarmony huggingface dataset:
An Efficient Novel Approach for Detection of Recolored Image Using Deep Learning for Identifying the Original Images in Public Surveillance

Background: Pitie et al. used the adjustment in themes of a picture to align the desired classification of another pixel by a common activity in image editing. Whereby, they are difficult to find the right mapping because it needs multiple rigorous methods such as matching, combining the channels, or evolving the contrast. Image recoloring is a process of changing the color of the image and is one of the most significant purposes of manipulation. Recoloring is a process of adjusting the colors of the image by changing one color to another or by adjusting color intensity with another color, adjusting the brightness or contrast, and converting colors to shades of gray. Purposes: Image coloring identification technique is derived from the deep learning techniques that includes CNN architecture design by using different layers. This also includes the actual image and two channels relying on inter-channel correlation and illumination mapping to find out the recolor pictures. Based on the usage of the layers, the accuracy and sensing of time may vary. A new architecture based on the stated layers and the use of a filter is designed to increase accuracy, decrease sensing time, and also reduce misclassification when compared. This architecture is different from the existing architecture for the detection of recolored images. Methods: This study is designed with a new architecture shown in Fig. 1 to improve the accuracy and reduce the time taken while training the network. To solve the problem, Convolutional Neural Networks is one of the techniques used, and Deep Neural Networks classes that rely on Multi-Layer Perception and Back Propagation which is mainly used for image processing, classification, segmentation, and also for other auto-related data and differs from conventional. Results: The output for an image is shown in table whether it is a recolored image or a normal image only after training the network model. Based on the training of the network, the accuracy is considered as mini-batch accuracy where 100% accuracy is acquired. Conclusions: Both inter-channel and illumination consistency are employed to help feature extraction. Datasets are applied and used different filters size and the different number of filters. A major effect on classification accuracy is predicted by designing a structure of filters. Basic recommendations for future work are using more intricate and deeper models for unpredictable problems. The model is more effective by the integration of DNN with the theory of enhanced learning.
The System Design of Avionics Coordinated Test Based on Dual-arm Cooperative Robot

The introduction of collaborative robots in aircraft cockpit for autonomous maintenance of avionics can improve labor efficiency and reduce the probability of human error and other quality problems. This paper designs a set of avionics joint test system for aircraft cockpit based on dual-arm cooperative robot. The system includes physical simulation of aircraft cockpit, installation and positioning of dual-arm robot, end-effector, joint test perception, image processing and human-computer interaction system. The robot can automatically calibrate the position of the robot and avionics equipment through machine vision. The robot can carry out automatic detection of avionics equipment according to the pre-set detection program, and can compare the detection with the display information of the corresponding instrument in real time to confirm its correctness. The system can save the result data of all detection in the server database, and carry out data analysis and mining to optimize the detection operation method.
Analysis of ways to increase stability of cryptographic algorithms on algebraic lattices against time attacks

The aim of this work is to study the algorithms, the stability of which is based on the search for a short lattice vector, as well as to obtain time-resistant parameters of these algorithms. Existing methods for generating keys and choosing parameters for cryptographic transformations on algebraic lattices resistant to time attacks are considered. It is shown that the uniform distribution of coefficients for generating the NTRU algorithm keys has certain shortages, namely, a limited number of parameters suitable for use in cryptographical transformations. This is due to the vulnerability of this algorithm to time attacks. The possibility of using a discrete normal (Gaussian) distribution to form a key pair, which will prevent the sensitivity of the algorithm to time attacks, is considered. This method of generation requires checking the obtained sample for compliance with the properties of the normal distribution. The usage of SAGA tests has been proposed. They make it possible to check the Gaussian samples obtained using the discrete normal distribution. The verification result shows whether or not the sample has properties that are inherent in the normal distribution. The application of the SAGA statistical tests to the NTRU cryptographic transformation polynomials allowed us to conclude that the discrete Gaussian sample makes it possible to generate time-resistant parameters using the norm or the length of the short basis (vector) of the lattice as the mean-square deviation.
Estimating Water Content and Grain Size of Intertidal Flat Sediments Using Visible to Shortwave-Infrared Reflectance and Sentinel 2A Data: A Case Study of the Red River Delta, Vietnam

Sediment properties such as water content (WC) and grain size (GS) are essential to characterize the environmental conditions of tidal flats.This article aimed to develop appropriate models to estimate the WC and GS of surface sediments for an intertidal flat on the Red river delta (Vietnam) using Sentinel 2A (S2A) images.The spectral reflectance, WC, and GS of 96 subsamples from 12 sediment samples collected on December 17, 2017 were measured to clarify their relationships.The WC was highly correlated with the reflectance ratio of two shortwave-infrared bands, R(2190)/R(1610) (R 2 = 0.93).The median GS (D 50 ) at 0%, 15%, and 20% of WC was significantly correlated with the reflectance ratio of the near-infrared band (842 nm) versus the visible-green band (560 nm) (R 2 > 0.78).Next, D 50 was estimated from a multivariate regression model using this band ratio, the visible-red band (665 nm), and WC.The accuracy of the models was verified by comparisons with WC and D 50 from 20 samples collected on March 12 th 2019 (RMSE of both WC and D 50 < 15%).Then, the WC and sediment type distributions were mapped by applying these models to two S2A scenes.The maps showed high WC (>30%) in very fine sediments (silts), which is consistent with other intertidal flats with similar sediment types.This article was limited to fine sediment samples.Therefore, our next step is to incorporate coarse sediments into the models to provide more universal mapping of WC and sediment types.
Self-Supervised Speaker Recognition Training using Human-Machine Dialogues

Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication. Learning speaker representations, in the context of supervised learning, heavily depends on both clean and sufficient labeled data, which is always difficult to acquire. Noisy unlabeled data, on the other hand, also provides valuable information that can be exploited using self-supervised training methods. In this work, we investigate how to pretrain speaker recognition models by leveraging dialogues between customers and smart-speaker devices. However, the supervisory information in such dialogues is inherently noisy, as multiple speakers may speak to a device in the course of the same dialogue. To address this issue, we propose an effective rejection mechanism that selectively learns from dialogues based on their acoustic homogeneity. Both reconstruction-based and contrastive-learning-based self-supervised methods are compared. Experiments demonstrate that the proposed method provides significant performance improvements, superior to earlier work. Dialogue pretraining when combined with the rejection mechanism yields 27.10% equal error rate (EER) reduction in speaker recognition, compared to a model without self-supervised pretraining.
Pengaruh Metode Drill Menggunakan Quizizz Terhadap Peningkatan Kemampuan Menghafal Huruf Hiragana Siswa Kelas X SMA Negeri 1 Batu

This study aims to discover the effect, the learning process carried out by the teacher, and students' perceptions in the application of the drill method using quizizz to improve students' ability in memorizing hiragana letters. The researcher used the theory of drill method and discuss the steps while observing the learning process of hiragana. This research is pre-experimental research uses a quantitative approach method with a pre-experimental one-group pretest-posttest design. The data was collected through tests, observations, closed-ended questionnaires, and documentation. In processing and analyzing the data, the researcher used the assistance of the Statistical Package for the Social Sciences (SPSS) version 21 program to drawing a conclusion using descriptive statistics. The results show that the average score for the pretest was 58.8 and 83.6 for the posttest score. From the test results using Paired Sample t-test, t count is greater than t table (t count = 7,698 &gt; t table = 2,026) with a significance value of 0.000. The conclusion is the drill method using Quizziz could significantly improve students' ability to memorize hiragana letters. How well the teacher applied the method also affects the results. In addition, based on the results of the questionnaire, the students show a good perception toward the use of drill method through Quizizz to improve their ability in memorizing hiragana letters.&#x0D; Keywords: Drill Method, Memorization Skills, Hiragana
Face Recognition Attendance Management System Based on Data Analytics

Face recognition systems are utilised in a variety of situations. In this digital age, every industry is affected. One of the most well-known Face recognition is one of the most widely used biometrics. It can be used for a variety of things. Among other things, security, authentication, and identification are all important. Considering its limited accuracy when compared to retina and thumbprint recognition, and because it is a popular method of identification, it is widely used. Recognition of people's faces for Attendance tracking systems can also be utilised in Educational Institutions, Organizations. Because the Current manual is outdated, It takes a long time to set up and maintain an attendance system. Fundamentally, this approach aims to establish a class attendance system Face recognition technology is used in this system. There's also the option of having a proxy attend. As a result, As a result, demand for this system is increasing. Database development, face detection, face recognition, and attendance updating are the four steps of this system.The photos of the kids in class are used to generate the database.
Students' Interest in Reading English Texts

The purpose of this study was to examine students’ reading habits and the factors that influence their desire to read English texts. A total of thirty-four EFL students were chosen. A qualitative descriptive approach was used. A questionnaire and interviews were used to gather information. The study’s findings revealed that the students had a strong desire to read. Several factors influenced students’ interest, including their effort in reading, their motivation and assumptions about reading, all of which were categorized as at ‘high’ levels. The motivation of students had a significant impact on their desire to read. The desire to learn new things about interesting topics became the primary motivator for students to read. Given the importance of reading, it is critical to prepare an engaging reading text as well as engaging teaching methods to pique students’ interest in reading.&#x0D; Keywords: Students’ Interest, Factor, Reading English Texts
COLLEGE ENQUIRY CHATBOT

College Enquiry Chat bot is a type of web application, the main moto of this project is to provide information regarding college. It gives the information regarding college only. The information may be like “how many branches in college?”,” what are the courses offered by the college?”,” how many students per each branch?”. By adding some more features and doing some improvements we can embedded this project into working site of college. The chatbot created here is a web-based application which used Natural Language Processing Libraries and Artificial Intelligence Language to have conversations with humans. It is a simple bot which answers the queries regarding the college. Here the chatbot is interaction between human and machine. The machine may be like computer, mobile, tab etc. We can access the information from anywhere and using any device. In order to make a machine easy to understand human language we are using “Natural Language Processing”. The interaction between computer and human will be easy when we use “Natural Language Processing
Research on risk assessment method of energy system based on data mining

In order to overcome the problem of data index confusion and index weight ambiguity in the traditional energy system risk assessment process, this paper proposes an energy system risk assessment method based on data mining.This method USES data mining technology and quantitative index processing method to select risk assessment index of energy system, construct risk assessment index system of energy system, determine the weight of risk assessment index of energy system, and build risk assessment model of energy system on this basis.The experimental results show that the weighing accuracy and evaluation accuracy of the proposed method are above 90%, and the skewness coefficient is always close to 0. The method has a high degree of rationality in energy risk index selection, high precision in index weight and high accuracy in evaluation results, which can effectively guarantee the safety of urban energy system.
Impact of Financial Development on Inclusive Growth in Nigeria

This study examined the impact of financial development on inclusive growth in Nigeria using a time series data obtained from secondary sources between 1999 and 2019.Financial development was measured using broad money supply and domestic credit to private sector, while inclusive growth was measured from income perspective using per capita GDP and from expenditure perspective using household consumption expenditure.The data were mainly obtained from World Development Indicators data base for various years.The data were analysed using Autoregressive Distributed Lag Bound test approach.The results of the ARDL revealed that financial development proxy with broad money supply exert significant positive impact on per capita income and household consumption expenditure in both short and long run.On the contrary, domestic credit to private sector has significant negative impact on per capita income in short and long run while the impact on household consumption expenditure was not significant in both short and long run.The study therefore recommends that the government can use broad money supply as one of the financial development instruments to promote inclusive growth in Nigeria. in addition, attention should be paid to the allocation of funds to private sector and the efficiency of such fund in order to reverse unproductive impact of fund allocated to private sector on inclusive growth in Nigeria.
Image denoising algorithm of social network based on multifeature fusion

Abstract A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient convolution neural structure of multifeature fusion is constructed for image denoising. The gray features of social network image are collected, and the gray values are denoising and cleaning. Based on the image features, multiple denoising is carried out to ensure the accuracy of social network image denoising algorithm and improve the accuracy of image processing. Experiments show that the average noise of the image processed by the algorithm designed in this study is reduced by 8.6905 dB, which is much larger than that of other methods, and the signal-to-noise ratio of the output image is high, which is maintained at about 30 dB, which has a high effect in the process of practical application.
Modeling a Local Dissimilarity Map With Weibull Distribution–Application to 2-Class and Multi-Class Image Classification

Due to the considerable increase of images in everyday life, many applications require a study on their similarity.The main challenge is to find a simple and efficient method to compare and classify image pairs into similar and dissimilar classes.This study presents a new method to image pairs comparison and classification based on the modeling of the Local Dissimilarity Map (LDM).The LDM is a tool for locally measuring the dissimilarity between two binary or grayscale images.It is a measure of dissimilarities based on a modified version of the Hausdorff distance, which allows quantifying locally the dissimilarities between images.This measure is completely without parameters and generic.The image pairs classification (2-class classification) method is structured as follows.First, a statistical model for the LDM is proposed.The model parameters, used as descriptors, are relevant to discriminate similar and dissimilar image pairs.Second, classifiers are applied to compute the classification scores (2-class classification problem).In addition, this approach is robust with respect to geometric transformations such as translation compared to the state-ofthe-art similarity measures.Although the main objective of this paper is to apply our approach to image pairs classification, it is also performed on a classification with more than two classes (multi-class classification).Experiments on the well-known image data sets *NIST and on old print data set prove that the proposed method produces comparable, even better results than the state-of-the-art methods in terms of accuracy and F 1 score.
Reading Culture from the Psychological and Educational Perspectives

The article discusses reading culture and reading as a value in Slovenia, with theoretical considerations supported by empirical results of foreign and domestic psychological and educational studies on reading culture. In this paper we try to answer the questions of what reading culture is and what its elements are, when reading can become a value and what the obstacles are in the development of reading culture from the psychological perspective. We answer these questions from the perspective of the family and school environment and suggest some ways of dealing with these obstacles, especially from the perspective of the role of the family and the school or teachers, as pointed out by the National Strategy for the Development of Reading Literacy.
Augmented reality (AR) technology on the android operating system in human respiratory system: From organ to cell

Understanding the object of learning material that is abstract is currently still an obstacle faced by students. The use of technology in learning is expected to reduce the obstacles faced by these students. Augmented reality (AR) is a technology that is currently being widely applied in the learning process because it is considered capable of helping students understand abstract learning materials with 3D object features. This study aims to design and develop AR, especially in the anatomical structure of organs in the human respiratory system, which is then integrated into biological practice in the laboratory. The AR application has been designed, created, validated, and published under AR Synapse (Augmented Reality Respiratory System). The AR application developed is based on the android system and has gone through validation tests by experts. Five expert validators who work as lecturers and high school teachers. Expert validation results with an average score of 3.6 from a maximum score of 4.0, which states that the AR Sinaps application is 91.2%, has functioned well, supports learning, provides objects relevant to teaching materials, and is considered to increase students' learning motivation. Furthermore, it has good prospects for continuous development. Students' satisfaction survey and user evaluation also gave a good response with an average rating percentage of 85.1%.
CHANGES IN THE TRENDS IN THE USE OF INFORMATION AND COMMUNICATION TOOLS FOR FUTURE PROFESSIONAL AND TECHNOLOGICAL EDUCATION SPECIALISTS

Formulation of the problem. The study aims to substantiate and determine the dynamics of changes in the use of information and communication tools in the process of training future specialists in vocational and technological education. Materials and methods. The study used theoretical analysis, surveys (questionnaires, interviews), observations, systematization, analysis of data, comparisons, and generalizations. Results. The analysis of a survey of future specialists in vocational and technological education conducted in 2016-2021 at TNPU named V. Hnatyuk is presented. Despite the high and medium levels of grades in the school computer science course, before the COVID-19 pandemic, some of the students rated their readiness to use computer technology as low, which was explained by gaps in practical training, software updates, and occasional use of digital technology in the classroom. The locations of students' work with computer equipment in the 2019/2020 academic year (at home, in offices and laboratories, with friends, at the place of work of parents, in Internet clubs, at the place of employment, etc.) were studied. The COVID-19 pandemic has led to the prompted introduction of distance learning, the use of computer technology and software in all classes, which has helped to increase the IT skills of participants in the educational process. Conclusions. Analyzing the research data, it can be argued that the COVID-19 Pandemic has led to the rapid development of ICT in education. The dominant means of ensuring the educational process were laptops (99.2%) and mobile devices (98.4%). Most used software - Microsoft Office, Google cloud services; online platforms - Zoom, Google Meet, digital board Padlet; social networks - Viber, Telegram, WhatsApp; LMS systems (Google Class, Moodle). Further research is needed on the optimization of tasks for students, compliance with sanitary and hygienic standards of work with information and communication tools.
Smart Three-Dimensional (3D) Hologram as an Innovative Teaching Tool in Virtual Learning Environment during Exigent Circumstances

It cannot be denied that almost all aspects of human life are in gadgets. Gadgets are equipped with different types of applications that can make human activities more effective. Smart 3D hologram is one of the applications that can be downloaded and installed on a smartphone. Smart 3D Hologram is considered as the most advanced technology that gives several advantages in education such as helping teachers and students to view devices that cannot be seen in traditional teaching-learning, offering virtual teachers, and making teachers and students still connected. It also shows us the real-size object. This article intends to describe Smart 3D hologram, an innovative teaching tool for future education that can be used offline in students-parents’ smartphones in a virtual learning environment. It is hoped that smart 3D hologram can become a smart solution in facilitating students in learning during exigent circumstances.