Reviewerly/exHarmony huggingface dataset:
COMPARISON OF SATELLITE AND TERRESTRIAL DATA OF OBSERVATIONS OF SUNSHINE DURATION ON THE EXAMPLE OF THE TERRITORY OF UKRAINE

In this article, a comparison of satellite and terrestrial data on the sunshine duration is determined on the example of the territory of Ukraine for the period of the calendar summer of 2011-2019. In particular, it was established that the value of the correlation coefficient of satellite and terrestrial ancient of sunshine duration is within 0.50 – 0.80, which corresponds to a moderate and significant correlation, and therefore a sufficiently large dependence between these types of obtaining meteorological and actinometric information of the studied parameter. Attention is also focused on the possibilities of improving the accuracy of ground data using universal multi-purpose solar radiation sensors, for example BF5 type sensors; and high accuracy of satellite data is confirmed. The average total error of the sunshine duration was determined based on satellite data and data from terrestrial weather stations. The obtained results indicate that ground weather stations have several times lower accuracy of measurements, in contrast to satellite data. After all, the average maximum total error of the sunshine duration of terrestrial data is four times higher than the average maximum total error of satellite data of the studied quantity. This, in turn, allows us to use satellite data for a qualitative assessment of the sunshine duration on the territory of Ukraine and allows us to assert that, in addition to ground data, it is possible to use satellite data of the sunshine duration as a studied quantity, as well as satellite data of any quantity in general. In addition, the influence of weather and climate anomalies on the obtained comparison results was investigated. As a result of the research, it was noted that mostly the months with the best correlation correspond to a more homogeneous anomaly of air temperature and precipitation, and the months with the minimum correlation are merked by significant heterogeneity in the space of anomalies of the fields of these meteorological quantities. On a territorial scale, when analyzing data on the sunshine duration, it is necessary to detail meteorological values that depend on the studied parameter by region or individual areas.
A Deep Learning Approach to Terracotta Warriors’ Facial Recovery and Quadratic

Facial restoration and animation of degraded images of Terracotta Warriors are important for enhancing the vitality and promotion of cultural relics. In this paper, based on a deep learning theoretical framework, blind facial restoration is performed on degraded images of terracotta warriors using rich and diverse facial priors in pre-trained generative adversarial networks. The facial prior is involved in the image generation process through the spatial feature transformation layer to achieve a good balance of realism and fidelity in the terracotta warrior restoration process. The anime style migration algorithm is used to realize the animalization of the terracotta warrior restoration image by learning the style features of the anime reference image and fitting the style migration process. The experimental results show that the method in this paper achieves facial recovery of the terracotta warriors with high fidelity; the generated secondary terracotta heads have obvious anime style and the content information remains highly consistent with the original images.
Using the Strongest Adversarial Example to Alleviate Robust Overfitting

Overfitting is considered to be one of the dominant phenomena in machine learning. A recent study suggests that, just like standard training, adversarial training(AT) also suffers from the phenomenon of overfitting, which is named robust overfitting. It also points out that, among all the remedies for overfitting, early stopping seems to be the most effective way to alleviate it. In this paper, we explore the role of data augmentation in reducing robust overfitting. Inspired by MaxUp, we apply data augmentation to AT in a new way. The idea is to generate a set of augmented data and create adversarial examples(AEs) based on them. Then the strongest AE is applied to perform adversarial training. Combined with modern data augmentation techniques, we can simultaneously address the robust overfitting problem and improve the robust accuracy. Compared with previous research, our experiments show promising results on CIFAR-10 and CIFAR-100 datasets with PreactResnet18 model. Under the same condition, for $$\boldsymbol{l_\infty }$$ attack we boost the best robust accuracy by $$\boldsymbol{1.57\%}$$ – $$\boldsymbol{2.89\%}$$ and the final robust accuracy by $$\boldsymbol{7.51\%}$$ – $$\boldsymbol{9.42\%}$$ , for $$\boldsymbol{l_2}$$ attack we improve the best robust accuracy by $$\boldsymbol{1.64\%}$$ – $$\boldsymbol{1.74\%}$$ and the final robust accuracy by $$\boldsymbol{3.80\%}$$ – $$\boldsymbol{5.99\%}$$ , respectively. Compared to other state-of-the-art models, our model also shows better results under the same experimental conditions. All codes for reproducing the experiments are available at https://github.com/xcfxr/adversarial_training .
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition

Conversational speech normally is embodied with loose syntactic structures at the utterance level but simultaneously exhibits topical coherence relations across consecutive utterances. Prior work has shown that capturing longer context information with a recurrent neural network or long short-term memory language model (LM) may suffer from the recent bias while excluding the long-range context. In order to capture the long-term semantic interactions among words and across utterances, we put forward disparate conversation history fusion methods for language modeling in automatic speech recognition (ASR) of conversational speech. Furthermore, a novel audio-fusion mechanism is introduced, which manages to fuse and utilize the acoustic embeddings of a current utterance and the semantic content of its corresponding conversation history in a cooperative way. To flesh out our ideas, we frame the ASR N-best hypothesis rescoring task as a prediction problem, leveraging BERT, an iconic pre-trained LM, as the ingredient vehicle to facilitate selection of the oracle hypothesis from a given N-best hypothesis list. Empirical experiments conducted on the AMI benchmark dataset seem to demonstrate the feasibility and efficacy of our methods in relation to some current top-of-line methods. The proposed methods not only achieve significant inference time reduction but also improve the ASR performance for conversational speech.
Character and Digit Recognition Using ANN Back Propagation Algorithm and Image Segmentation

In the field of pattern recognition, one of the demanding areas of study is character and digit recognition. It has broad use, including car license plate identification, bank account numbers, postal codes, invoice numbers, etc. In this work, an effort is made to recognize letters and numbers from images for English letters and numbers with an image segmentation process using a multilayer feedforward neural network. The data used for this work contains 36 classes which 10 belong to numbers and 26 for letters. 80% of the data is used for training the neural network model and 20% is used for testing the algorithm. In this work, the images are resized for training into 28x28 pixels, each of which has 784 pixels that will be used as features for neural network algorithm training. The final outcome shows that there are a strong recognition rate and 95% of accuracy for this work.
Implementation of Student Worksheets with Android-Based on Natural Science Subject

This study was conducted cause the student's learning outcomes were less successful. Based on the Midterm Assessment (PTS) evaluation at SDN 2 Mojo, 27 students (25%) were incomplete. Based on the data, the lowest value is 40, and the highest value is 80. The research adopts the ADDIE model with Mollena and Januszewski's theories: analyze, design, development, implementation, and evaluation. Research gets the following results: 1) Student Worksheet with the android system for Natural Science learning activities with style material created using the Android Studio application, operating through the lollipop type android system; 2) Student Worksheets with developed android systems included in the criteria were very feasible; Assessment was carried out by material and media experts with an assessment of the presentation component 90%, while the feasibility of content with a value of 92%; 3) The student worksheet was effective as a learning facility in Natural Science learning. The average pre-test value was 55.57143, and the post-test with a value of 80.88571. The t-test results showed that tcount ≤ - ttable with tcount calculation of -14,463 less than the ttable, which was -1.995469, then Ho was rejected. N-Gain earned amounted to 0.569775. As a result, there was a difference in the value of Natural Science style material after using Student Worksheets or LKS android-based. Keywords: Student Worksheets, Natural Science Subject, Worksheets Andorid Based
An innovative multidisciplinary approach to the teaching computer networks and cybersecurity

This contribution deals with the proposal of a new approach to the teaching of computer networks-oriented subjects. The main focus is to implement a multidisciplinary approach, with which we want to increase the quality of teaching, reform the study content, and motivate students of networking subjects to study programming and cybersecurity. This created article summarizes the current state of teaching, identifies existing deficiencies and, in the form of recommendations, proposes a new methodology for solving deficiencies and updating the content. Part of the proposal is the identification of important areas of informatics (computer networks, cybersecurity, programming, database systems, machine learning and development of android applications), technologies, and the variety of designed sample tasks to demonstrate the importance of connecting students' knowledge from several areas of informatics. The practical outputs of the work are as follows: created study materials in the form of PDF documents, annotated source codes of sample solutions and a website with educational content. The created materials were tested in university and high school teaching by a total of 198 participants, of which 4 were teachers and 194 were students aged 15 to 24. More than 86% of these students evaluated the created materials positively and would be interested in expanding the current educational content. Based on the received feedback, it is possible to claim that the issue should be further addressed, and an effort should be made to integrate it into the teaching process.
An Adaptive SAR and Optical Images Registration Approach Based on SOI-SIFT

SAR and optical images registration is a key step for remote sensing image processing, match navigation and information fusion. Although there are many methods for SAR images registration, their performance will decrease between SAR and optical images. Moreover, these algorithms suffer from lack of matching pairs of the feature points and uneven distribution between SAR and optical images. Therefore, they cannot accurately achieve the registration between optical and SAR images. To solve the above deficiencies, we propose an efficient image registration approach based on SAR and optical image-scale invariant feature transform (SOI-SIFT). Firstly, a linear edge enhancement based on gray feature and histogram equalization is introduced. In this stage, we enhance the edge features of the image so that the number of image feature points can be greatly increased. Then, for feature points purification, we use fast sample consensus algorithm to filter duplicate and wrong matching feature points. SOI-SIFT can be more adapted to the heterogeneous image matching. Experimental results have shown the superiorities of the proposed method.
On a Preliminary Theory of Communication in Distributed Software Development

Communication is one of the leading challenges faced by teams working in a distributed setting; yet, little has been theorized about how communication occurs in such context. Our long-term research goal is to construct a Communication Theory in Distributed Software Development, aiming to propose a theoretical foundation for future academic studies on the topic of communication and a reference for industry practitioners. To achieve this goal, we are using Grounded Theory, including an Exploratory Literature Review before the theory construction, to confirm the research gap. In this paper, we present a further preliminary version of the Communication Theory comprising six theoretical categories and 31 subcategories. The theory brings, up to know, a consolidated body of knowledge and points out the main concepts that define what communication is in distributed software teams.
Development of Rotor-distributed Aerial Manipulators to Perch on Ceilings using Thrust and Flight/Perching and End-effector Position Control

In this paper, we develop a rotor-distributed aerial manipulator and propose a flight, perching, and end-effector position control. The robot that consists of a foot and rotor-distributed arm module can fly and perch on ceilings using the rotor thrust. For deformation during flight, we improve the linear quadratic intergral (LQI) control using gimbal control. For stable perching, we propose a quadratic programming (QP) based controller to calculate the desired contact wrench, considering the static friction and zero moment point (ZMP) conditions on the footplate. Furthermore, we propose an end-effector control based on the inverse kinematics (IK), considering rotor thrust and joint velocity/torque limitations for stable manipulation during perching. Finally, we verified the end-effector stability and conducted a drill manipulation. The experiments show that the end effector of the multilink aerial robot during perching becomes more stable than those during flight.
Impact of Code Switching on ESL Learners during Pandemic at The Islamia University of Bahawalpur, Rahim Yar Khan Campus

This study investigated the impact of code-switching on ESL learners specifically during pandemic situations. The population was the language teachers of The Islamia University of Bahawalpur, Rahim Yar Khan. The basic focus of the study was to find out the advantages that teachers achieved through code-switching in ESL classrooms during pandemics. It tried to find out how code-switching is efficient to increase the learning proficiency among ESL learners. Actually, COVID-19 added to the sufferings of the world and caused the immeasurable loss. After health, the greatest loss was observed in the field of education. This pandemic slowed down the pace of learning by creating a lot of hurdles in the way of gaining knowledge. Physical classes were extinct and the online system was new for the learners and instructors both. In the present era, a good number of educational institutions in Pakistan have declared that the language of instruction in the classrooms should be English, which is the chief second language in the country. When a language user jumps from one language to another language, this process is called code-switching. This technique is widely used in Pakistani classrooms. It is believed to be beneficial to switch from one code to another in an ESL class. Sociolinguistics measures code-switching not only as a constituent of communal social life but surrounds the societal strata of speakers’ linguistic differences, societal variables, and societal settings. Seemingly language teachers deliberately choose code-switching for the promotion, comprehension, and improvement of second language (L2) learning. This paper puts light on the advantages of code-switching on the track of language learning, investigates a few strands of code-switching and then its efficacy in developing L2 knowledge amongst the students at The Islamia University of Bahawalpur, Rahim Yar Khan Campus. The findings of the study make clear that code-switching causes learning sessions to connect and it facilitates the learners to switch from L2 to L1. It can be implied that code-switching has a strong impact on language teaching.
Factors Influencing Mass Media Credibility

The Functions of the Executive, written by the famous American management scientist Chester Barnard and published in 1938, inaugurated the school of social system in management theory.
Lightweight Cryptography Approach for Multifactor Authentication in Internet of Things

IoT is developing into a vast web of heterogeneous networks that are expected to interconnect over 41 billion gadgets by 2025, producing about 79 zettabytes of data. The heterogeneous network will enable a wide range of digital services that use cloud and communication technologies to power applications for smart cities. It is crucial to safeguard their communications as consumers access these services remotely in a pervasive environment across open channels. The people who have access to the useful data collected by IoT devices are known as IoT users. The Internet of Things (IoT) users control smart gadgets with worldwide internet connectivity to access global data. Identity, passwords, and biometrics are the main considerations for authenticating IoT users. Biometrics is one of the important components in this case because it is unique to each IoT user and enables proper user authentication. This authentication is done using the SHA3 with Chaotic Behavior method. Multi-factor OTP (Biometric data, Random Number), timestamp, contest, and password are the foundations for authenticating. This system dramatically reduced the compute, communication, and authentication costs while mitigating the aforementioned vulnerabilities. Using the commonly used Burrows-AbadiNeedham (BAN) logic, we have demonstrated the validity of the method and examined its efficiency using a simulator.
Can We Build a Quantum Computer?

This paper presented and evaluated obstacles and solutions for these problems in building quantum computers.From the perspective of hardware, problems such as processing and controlling qubits, the loss of qubits, the noisy environment, and precision are discussed.From the perspective of algorithms, this paper evaluates quantum algorithms for P&NP problems, Simon's problems, and noisy environments (this problem is discussed in both the aspect of hardware and the aspect of the algorithm).Finally, some future applications of quantum computers and their limitations are appraised.Even though many challenges hinder the construction of quantum computers, it's still able to build quantum computers which have great potential.
Developed conjugate gradient method in training neural networks to predict verholst model results

The method of conjugate gradient has been suggested to be used in training of Artificial Neural Networks with a technique of feedforward. It has been explained that the suggested method of CG is precise in predicting the numbers of the Iraq population. The numerical results are very near to the exact solution of Verholst Model.
Image Colorization : GAN and Autoencoder Models’ Performance Comparison

Two basic models, the GANand Autoencoder models, are designed to color black-and-white pictures. The purpose is to compare the color data and methods of black-and-white pictures obtained by the two models through continuous training. The advantages and disadvantages of the two models are found through comparison, such as overfitting. Judging from the advantages and disadvantages, which model is more suitable for black-and-white picture coloring can train better data. The results show that from the image, it can be seen that the valuable loss of Autoencoder is better than the training loss, which is the last. When the time data is 15 ~ 35, there is an error. In contrast, the GAN model has serious over-fitting problems at any time node, which can not summarize and color all the possible black-and-white photo data. It is concluded that from all the advantages and disadvantages, the error of the Autoencoder model is small in all aspects. Gan model has serious errors, which may lead to incomplete data and training. As shown in the figure, this point is located below the valuable loss of the GAN model, which means that all valuable losses cannot train a reasonable and color picture through this point. Similarly, many black-and-white photo data similar to this point cannot obtain the color picture after coloring through this model. This situation is called overfitting, which is also the biggest problem of the Gan model. It shows that the Autoencoder model is more suitable for black-and-white picture coloring.
Pengembangan Media Power Point Interactive (PPT) pada Pembelajaran Intermediate Speaking di IKIP Budi Utomo Malang

This research is research development. The resulting product is a PowerPoint Interactive media for learning speaking skills, especially in Intermediate Speaking. Issues discussed in this study is how to develop PowerPoint interactive media for communicating in Intermediate Speaking in Ikip Budi utomo Malang. The stage of development consists of 5 stages, namely a) needs analysis b) design phase, c) creation of the media, d) validation from the expert , e) the final product. These learning media has already validated by the expert that have more than 10 years experience in teaching . This research use the question form to evaluate the quality of media response, lecturer and student response. The development of PowerPoint Interactive media for Intermediate Speaking skills are developed through need analysis from learners.The results of this study indicate that the media is created using the Microsoft Office PowerPoint-based virtual media worthy of being used as a medium of learning conversation skills. The production quality is determined by the feasibility of media aspect, learner’s needs and the interactive design . From the results of a test run of the product stated that media PowerPoint in learning intermediate speaking skills worth used in class, in addition to that, this media could be used for learners. The quality of final product Power Point media is known by conducting a test run of the product. Tests performed with two stages, namely 1) the validation from the expert media and design, (2) field trials to students of Ikip Budi Utomo in Intermediate speaking.
Additive Pattern Databases for Decoupled Search

Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. Despite their success for explicit-state search, though, abstraction heuristics are not available for decoupled state-space search, an orthogonal reduction technique that can lead to exponential savings by decomposing planning tasks. In this paper, we show how to compute pattern database (PDB) heuristics for decoupled states. The main challenge lies in how to additively employ multiple patterns, which is crucial for strong search guidance of the heuristics. We show that in the general case, for arbitrary collections of PDBs, computing the heuristic for a decoupled state is exponential in the number of leaf components of decoupled search. We derive several variants of decoupled PDB heuristics that allow to additively combine PDBs avoiding this blow-up and evaluate them empirically.
Cyber Crime: Gone phishing

It may surprise some, but even childcare companies can be prey to online criminals. Annette Rawstrone reports