Reviewerly/exHarmony huggingface dataset:
Generalization Capability of Deep Learning

One of the main reasons for the enormous success of deep neural networks is their amazing ability to generalize, which seems mysterious from the perspective of classic machine learning. In particular, the number of trainable parameters in deep neural networks is often greater than the training data set, this situation being notorious for overfitting from the point of view of classical statistical learning theory. However, empirical results have shown that a deep neural network generalizes well at the test phase, resulting in high performance for the unseen data.
Modified Particle Swarm Optimization with Chaotic Initialization Scheme for Unconstrained Optimization Problems

Different variants of particle swarm optimization (PSO) algorithms were introduced in recent years with various improvements to tackle different types of optimization problems more robustly. However, the conventional initialization scheme tends to generate an initial population with relatively inferior solution due to the random guess mechanism. In this paper, a PSO variant known as modified PSO with chaotic initialization scheme is introduced to solve unconstrained global optimization problems more effectively, by generating a more promising initial population. Experimental studies are conducted to assess and compare the optimization performance of the proposed algorithm with four existing well-establised PSO variants using seven test functions. The proposed algorithm is observed to outperform its competitors in solving the selected test problems.
Anamoly Analyzing and Exploring for Wireless Sensor Networks

Observing individual areas with a possibly untrusted server presents protection dangers to the checked people. To this end, we propose a security protecting area observing framework for remote sensor systems. In our framework, we plan two in system area anonymization calculations, to be specific, asset and quality-mindful calculations, that intend to empower the framework to give top notch area checking administrations for framework clients, while protecting individual area security Both calculations depend on the settled k-obscurity security idea, that is, an individual is undefined among k people, to empower believed sensor hubs to give the total area data of observed people for our framework. The asset mindful calculation plans to limit correspondence and computational expense, while the quality-mindful calculation expects to augment the precision of the total areas by limiting their checked territories. To use the total area data to give area observing administrations, we utilize a spatial histogram approach that gauges the dissemination of the checked people dependent on the assembled total area data. The usage procedure, proposed frameworks and different perspectives are plainly talked about in proposed frameworks
YouTube self-management education for hypertensive patients in the COVID-19 pandemic era: Is this non-face-to-face program satisfactory in a community?

초록·키워드 목차 오류제보하기 Objectives: This study explored YouTube"s educational program developed for hypertensive patients in response to the COVID-19 pandemic at a community Hypertension and Diabetes Registration and Education Center and evaluated participant satisfaction. Methods: We conducted content analysis of hypertension-related videos uploaded on the YouTube channel “I Want to Know Blood Vessels!” and descriptive analysis of a cross-sectional study that evaluated the satisfaction of 213 hypertensive patients who watched these videos. Results: From April 2020 to November 2021, the center delivered a total of 25 videos related to hypertension. Most individual educational materials were viewed for seven to nine minutes, whereas the total screening time reached over 3 hours. Readers viewed lifestyle habit videos on exercise (up to 6,530 times) and nutrition (up to 1,400 times) more often than specialized videos on hypertension. Of the participants, 100% were satisfied with non-face-to-face self-management education, whereas 99.5% felt they received help from the contents. Conclusions: The improved accessibility of information and communication through YouTube’s educational strategy can enhance health education and promotion programs, while improving hypertensive patients’ health behavior. However, further research is needed to devise a more appropriate method for developing educational materials based on patient video inquiry patterns and preferences. #COVID-19 pandemic #YouTube #self-management education #hypertension #non-face-to-face program #satisfaction Ⅰ. 서론Ⅱ. 연구방법Ⅲ. 연구결과Ⅳ. 논의Ⅴ. 결론References
Response Analysis of SMA Regency Bengkulu Selatan Students and Teachers' Needs for the Development of E-Module of Alter Current Electricity Materials

The purpose of this study was to analyze the response to the need for e-module learning media in Bengkulu Selatan Regency for physics teachers and students of SMA N 1, SMA N 6 and SMA N 7. The research data were taken from a population of physics teachers and students from SMA N 1, SMA N 6 and SMA N 7 in South Bengkulu Regency. The data collection technique in this study was using a questionnaire filled out via google form. The data analysis technique in this study is a quantitative analysis technique and concluded qualitatively by using validity and reliability tests to determine valid and reliable questionnaires. The results of this study indicate that the instrument used is valid and reliable. This can be seen from the results of the validity test there is no data excluded (Excluded). Based on the results of the study, it was stated that students and teachers strongly agreed if an android-based e-module was developed on alternating current electrical material.
RHETORICAL ANALYSIS AT THE SERVICE OF BUSINESS COMMUNICATION: GENDER-BASED DIFFERENCES IN STUDENTS' PERCEPTION OF THE SPEAKER

The paper points out the benefits of rhetorical analysis and rhetorical criticism in developing business communication skills. At Zagreb School of Economics and Management, both the American and the European approach to business communication have been combined, with LSP courses taught in the first year and business communication and rhetoric courses taught in the second year. An experiment was conducted on a sample of 99 students, including 57 female and 42 male participants, who were asked to assess the teenage activist Greta Thunberg and her speech at the UN Climate Action Summit in 2019. The research focused on the role of the gender in perception of quality, attractiveness of the speech, the use of ethos, pathos and logos, persuasiveness and the influence potential. The results showed that male students gave lower grades to Greta and her speech, unlike the female students, who would also be more willing to change their behavior as the result of listening to Greta’s speech. Nevertheless, these differences were statistically significant only for a limited number of questions. The potential gender bias to speakers should be addressed in the rhetoric and business communication course design. Exposing students to a diverse set of speakers increases their critical thinking skills, ensuring higher objectivity and bias-free assessment of speakers including their peers.
GrabCut Algorithm Fusion of Extreme Point Features

Image segmentation is an important part of computer vision. Among them, interactive image segmentation only provides useful prior knowledge to the computer. GrabCut interactive image segmentation is a classic algorithm for the use of graffiti, line drawing, and square in the user interaction process. The box interaction method brings inconvenience to the user's operation. Aiming at the above problems, a GrabCut interactive image segmentation algorithm fused with extreme point features is proposed. First, the user marks the top, bottom, left, and right extreme points of the target area in the image, and uses the algorithm to solve the extreme value box with the extreme point as the apex; then select the extreme value box as the foreground image, the image outside the extreme value box is the background image. Then the initial segmentation foreground and Gaussian mixture model parameters of Grabcut are determined, the foreground image is segmented using the minimum cut algorithm, and the energy function is minimized through several iterations to complete the target segmentation. Introduction.
METHOD OF IDENTIFICATION OF OBJECT STATES ACCORDING TO THE RESULTS OF FUZZY MEASUREMENTS OF CONTROLLED PARAMETERS

The subject of consideration is the task of identifying the states of an object based on the results of fuzzy measurements of a set of controlled parameters. The fuzziness of the initial data of the task further complicates it due to the resulting inequality of the controlled parameters. The aim of the study is to develop a method of identifying the states of a fuzzy object using a fuzzy mechanism of logical output taking into account possible differences in the level of information content of its controlled parameters. The method of obtaining the desired result is based on the modification of the known mathematical apparatus for building an expert system of artificial intelligence by solving two subtasks. The first is the development of a method for assessing the informativity of controlled parameters. The second is the development of a method for constructing a mechanism for logical inference of the relative state of an object based on the results of measuring controlled parameters, which provides identification. In the first problem, a method is proposed for estimating the informativity of parameters, free from the known disadvantages of the traditional Kulbak informativity measure. In implementing the method, it is assumed that the range of possible values for each parameter is divided into subbands in accordance with possible states of the object. For each of these states, the function of belonging to the fuzzy values of the corresponding parameter is defined. At the same time, the correct problem of estimating the informativity of a parameter is solved for cases when this parameter is measured accurately or determined fuzzily by its belonging function. The fundamental difference between the proposed logical output mechanism and the traditional one is the refusal to use the production rule base, which ensures the practical independence of the computational procedure from the dimension of the task. To solve the main problem of identifying states, a non-productive approach is proposed, the computational complexity of which practically does not depend on the dimension of the problem (the product of the number of possible states Results.per the number of controlled parameters). The logic output mechanism generates a probability distribution of the system states. In this case, a set of functions of belonging of each parameter to the range of its possible values for each of the states of the object is used, as well as a set of functions of belonging to fuzzy measurement results of each parameter. Conclusions. Thus, a method of identifying the state of fuzzy objects with a fuzzy non-productive output mechanism is proposed, the complexity of which does not depend on the dimension of the task.
Petition extraction and department detection from citizens’ petitions for e-government’s benefit

Today governments improve the process of involvement of their citizens with the aid of information and communication technology. Despite the fact that almost all of government regulations are now available online. However, analyzing these unstructured data is hard due to their size and complexity. On the other hand, the great developments in text mining allowed using it to deal with complex problems rather than just a simple keyword search operation. In this paper, a proposed system for the retrieval of textual information and relationships using text mining approaches is examined. This, therefore, would help policy makers to discover if an association is existing between citizens’ opinions and policies using textual data gathered from blogs and other electronic public forums. Furthermore, an approach for e-governance decision support is proposed, which simulates the Iraqi scenario. Petitions extraction and department detection from citizen’s petitions have achieved using the proposed system. Experimental results using more than 5000 petitions from Baghdad governorate, show that 97% ratio in department detection and 94% ratio in petition extraction.
Research on Trajectory Tracking Algorithm Based on LSTM-UKF

Aiming at the problems of excessive error and inability to track the traditional target tracking algorithm in the absence of observations, a trajectory tracking model combined with Long Short-Term Memory (LSTM) is designed. Combining the LSTM network model with the Unscented Kalman Filter (UKF), using the autonomous learning and memory characteristics of the LSTM network, provide the UKF algorithm with the predicted value of the observations, and optimize the UKF algorithm for the target object in the absence of the observations. Tracking effect. Finally, the verification and analysis are carried out for three different sports conditions. The simulation results show that the LSTM-UKF algorithm model still has a good tracking effect even in the absence of observations.
A Meta-Analytic Structural Equation Modelling on the Unified Theory of Acceptance and Use of Technology in Higher Education

Researchers in educational technology have researched for factors to explain problems that hinder acceptance of e-learning management systems in institutions of higher learning.Based on unified theory of acceptance and use of technology (UTAUT), the study examined the influence of performance expectancy, effort expectancy, social influence and facilitating condition on acceptance of E-learning Management System (EMS) in institutions of higher learning in Kenya.Descriptive research design and in particular cross-sectional design were employed in order to empirically investigate the extent to which problems influence adoption of E-learning Management System (EMS).A self-administered questionnaire, face-to-face interviews and observation were administered to a sample size of 600 that consisted of lecturers, students, administrators, and technical staff from at least five Universities within Nairobi Metropolitan.Analysis was done using descriptive as well as inferential statistics in order to draw inferences between the variables.The study found that use of EMS was a new technology as most of the respondents had an experience of less than 3 years.Also the study found that expected performance, enabling infrastructures, institutional policies, training support and leadership and ease of effort use influenced the adoption of EMS in institutions of higher learning.
Image Keypoint Matching Using Graph Neural Networks

Image matching is a key component of many tasks in computer vision and its main objective is to find correspondences between features extracted from different natural images. When images are represented as graphs, image matching boils down to the problem of graph matching which has been studied intensively in the past. In recent years, graph neural networks have shown great potential in the graph matching task, and have also been applied to image matching. In this paper, we propose a graph neural network for the problem of image matching. The proposed method first generates initial soft correspondences between keypoints using localized node embeddings and then iteratively refines the initial correspondences using a series of graph neural network layers. We evaluate our method on natural image datasets with keypoint annotations and show that, in comparison to a state-of-the-art model, our method speeds up inference times without sacrificing prediction accuracy.
Measuring Interactions in Categorical Datasets Using Multivariate Symmetrical Uncertainty

Interaction between variables is often found in statistical models, and it is usually expressed in the model as an additional term when the variables are numeric. However, when the variables are categorical (also known as nominal or qualitative) or mixed numerical-categorical, defining, detecting, and measuring interactions is not a simple task. In this work, based on an entropy-based correlation measure for n nominal variables (named as Multivariate Symmetrical Uncertainty (MSU)), we propose a formal and broader definition for the interaction of the variables. Two series of experiments are presented. In the first series, we observe that datasets where some record types or combinations of categories are absent, forming patterns of records, which often display interactions among their attributes. In the second series, the interaction/non-interaction behavior of a regression model (entirely built on continuous variables) gets successfully replicated under a discretized version of the dataset. It is shown that there is an interaction-wise correspondence between the continuous and the discretized versions of the dataset. Hence, we demonstrate that the proposed definition of interaction enabled by the MSU is a valuable tool for detecting and measuring interactions within linear and non-linear models.
PyRef: Refactoring Detection in Python Projects

Welcome to the 21st IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM 2021)! We hope that you enjoy the conference and that it will inspire you in your future research. Should you require a certificate of participation please contact Alexander Serebrenik, the general chair of SCAM, at a.serebrenik@tue.nl. For questions related to the Morressier virtual platform please contact support@morressier.com.
Construction of Ubiquitous Multimodal Mobile Education Model for College English Based on Cloud Computing

A historic revolution will occur in the field of English education based on “cloud” computing and ubiquitous learning theory. Traditional language teaching classroom, which takes classes and courses as unit, will be confronted with severe challenge. Under the background of Internet Plus, the multimoding teaching methods, which is an all-in-one method made of text, pictures, audios and videos, etc. have gradually taken the place of the traditional single “ text-paraphrasing plus PPT” learning methods. The author explores the multimoding English teaching methods under the background of Internet Plus, which are based on the theories of the multimodal discourse analysis and mobile teaching. Therefore, this paper puts forward the study of English ubiquitous language modality and creates an innovative ecological study environment system for English teaching, providing a new perspective for research.
Visualizing Multiplayer Game Spaces

In this article, we compare four different “game spaces” in terms of their usefulness in characterizing multiplayer tabletop games, with a particular interest in any underlying change to a game’s characteristics as the number of players changes. In each case, we take a 16-D feature space and reduce it to a 2-D visualizable landscape. We find that a space obtained from optimization of parameters in Monte Carlo tree search is most directly interpretable to characterize our set of games in terms of the relative importance of imperfect information, adversarial opponents, and reward sparsity. These results do not correlate with a space defined using attributes of the game tree. This dimensionality reduction does not show any general effect as the number of players changes. Therefore, we consider the question using the original features to classify the games into two sets: 1) those for which the characteristics of the game change significantly as the number of players changes and 2) those for which there is no such effect.
ANALYSIS OF OPERABILITY OF THE SPECIAL VEHICLE TRANSMISSION

The work is devoted to the analysis of the transmission of a special vehicle (SV) for moving on various surfaces, in particular on stepped ones, designed for people with disabilities. Based on the analysis of various vehicles for the movement of people with disabilities, a model of a SV was created. The main difference of proposed SV is the possibility of using the movement of the passenger՚s center of mass relative to the chassis of a SV for stabilization of the movement. A kinematic scheme of the SV transmission which includes the planetary reducer was created. The reducer provides movement of SV at various modes. Also SV transmission was modeled in the Solidworks software. The analysis of the performance of this transmission when moving a SV on various surfaces was made on base of created 3D-model. Based on the analysis, the efficiency of the transmission of a SV was proved both when moving on a straight surface, and in walking mode when overcoming steps.
Experimental resources needed to implement photon number splitting attack in quantum cryptography

Abstract The analysis of the security of quantum key distribution systems with respect to an attack with nondemolishing measurement of the number of photons (photon number splitting—PNS attack) is carried out under the assumption that in the communication channel in each parcel there is a pure Fock state with a different number of photons, and the distribution of states by number of photons has Poisson statistics. In reality, in the communication channel in each parcel there are not individual Fock states, but a pure coherent state with a random phase—a superposition of Fock states with different numbers of photons. The paper analyzes the necessary experimental resources necessary to prepare individual Fock states with a certain number of photons from the superposition of Fock states for a PNS attack. Optical schemes for implementing such an attack are given, and estimates of experimental parameters at which a PNS attack is possible are made.
Biometrics vendors point way to Covid-19 ‘immunity passports’

Biometric technology is driving global efforts to develop Covid-19 ‘immunity passports’ that could enable people to prove they have recovered from the virus, escape lockdown and so help national economies restart after the pandemic.
Identifikasi Website Phishing Menggunakan Algoritma Classification And Regression Trees (CART)

With the increase in internet users and the development of technology, the threats to its security are increasingly diverse. One of them is phishing which is the most important issue in cyberspace. Phishing is a threatening and trapping activity someone by luring the target to indirectly provide information to the trapper. The number of phishing crimes, this has the potential to cause several losses, one of which is namely about the loss of privacy of a person or company. This study aims to identify phishing websites. The Classification And Regression Trees (CART) algorithm is one of the classification algorithms, and the dataset in this research taken from the UCI Repository Learning obtained from the University of Huddersfield. The method used in this research is problem identification, data collection, pre-processing stage, use of the CART algorithm, validation and evaluation and withdrawal conclusion. Based on the test results obtained the value of accuracy of 95.28%. Thus the value of the accuracy obtained using the CART algorithm of 95.28% categorized very good classification.
Phishing URL Detection and Vulnerability Assessment of Web Applications Using IVS Attributes with XAI

Sudhakar, Vivek John Mahalingam, Srimathi Venkatesh, Vaman Vetriselvi, V.The number of cyber attacks which are carried out are increasing day by day and pose a serious threat worldwide. The most common methods of launching these attacks include delivering phishing URLs and exploiting built in vulnerabilities present in web applications due to faulty and error prone code. Most of these vulnerabilities occur due to the absence of input user data validation before processing. This paper presents solutions to identify phishing URLs and input validation and sanitization based vulnerabilities, i.e., SQL injection, Cross Site Scripting, and File Inclusion in PHP code using machine learning models, after the extraction of certain IVS attributes.