Reviewerly/exHarmony huggingface dataset:
National and regional experience in the implementation of environmental education for sustainable development and preservation of regional natural and cultural heritage

Aim. The article is to identify effective conditions for the development of ecological culture in Dagestan schools in the interests of sustainable regional development. Material and Methods . To achieve this goal, adequate methods were used, taking into account the specifics of each stage of the work: analysis of scientific literature (psychological-pedagogical, methodological and environmental), regulatory and programme-methodical documents about the problem; observation, pedagogical experiment, testing, study of pedagogical experience and methods of mathematical processing of experimental results (%2-criterion). Results. The development of the global environmental crisis has set before society the task of survival and ensuring the existence of mankind, in the context of the concept of "sustainable development of mankind", considered as development that meets the needs of the present and does not jeopardize the ability of future generations to meet their needs. This allows us to state the relevance of introducing effective models of environmental education for sustainable development (one-subject, multisubject and mixed) into school curricula. The results of the study show the effectiveness of various types of ecologisation (subject, inter-subject and meta-subject) and the "mixed model". A textbook on regional ecology (Ecology of Dagestan) was developed, taking into account the specifics of the laws of functioning of the ecosystems of the planet and the region. With the inclusion of regional ecology in the "mixed model", school environmental education for sustainable development has been completed. Conclusions. Environmental education in the interests of sustainable development and preservation of the natural and cultural heritage of the region is effective in the greening of school disciplines and the use of a "mixed model", and the inclusion of a national and regional component in their content.
Exploratory and correlation analysis on the impact of accessibility testing on PC software development projects

The work consists of a theoretical introduction that conceptualizes software engineering, software development, testing, escaped defects, user experience, usability and accessibility. In order to conduct a research in the area of tests focused on accessibility, an analysis of real projects was proposed. Interviews were conducted with managers of software projects that are already in the market, extraction of data from the same projects, exploratory analysis of the data, correlation tests and structuring of the results found. At the end of the research, it was observed the relevance of some data and the significance of the relationship between them. From the results obtained, it is concluded the relevance of the construction of test cases for accessibility, as well as the factors that contribute to the teams to give due attention to accessibility tests, such as team composition and external collaborations.
EDUTOKING: Improving the English-Speaking Skills of Grade 9 Learners using Tiktok-Based Activities

The popularity of TikTok led the researchers to determine if its use improves the English-speaking skills of the Grade 9 students in a Science High School in Olongapo City, Philippines. The study used a descriptive-comparative research design where the researchers developed a self-assessment tool and a teacher observation checklist validated by experts in order to determine the students’ perceived English-speaking skill level. One hundred thirteen students participated in the research, and the study analyzed the data using descriptive and inferential statistics with the help of the open-source software, Jamovi. The study found no significant difference between the pretest and posttest scores of the respondents before and after integrating TikTok-based activities in their lessons using a paired sample t-test in the Jamovi software. However, for teacher-observers, the intervention is perceived as beneficial to improve the English-speaking skills of the students. The study argues that despite the popularity of TikTok amongst teenage students, it is something other than a language learning tool for students. However, the teachers observed that using the intervention can improve the English-speaking skills of the students. Overall, there is an evident disparity between the perceptions of the students and teachers regarding whether or not TikTok can have the ability to improve the English-speaking skills of the students. The researchers recommend that teachers may consider developing contextualized TikTok-based activities to make the speaking tasks more relevant and meaningful.
Effect of Foreign Students Adaptation to College Life according to Situational Factors: Focusing on Interpersonal Skills, Korean Level, and Part-time Jobs

This study seeks to understand the situation to help foreigners adapt to college life, and to determine the effect of international students’ situational factors such as Korean language level, part-time job time, part-time job income, and interpersonal skills. A survey was conducted on 198 foreign students attending University K located in Metropolitan City D. One-way analysis of variance (ANOVA) and t-test were conducted to investigate college life adaptation and interpersonal skills according to the factors of foreign students. This study identified Pearson correlation coefficients between Korean language level, part-time job time, part-time job income, interpersonal skills, and college life adaptation. In addition, multiple regression analysis established the effect of the aforementioned factors. College life adaptation and interpersonal skills showed an intermediate level, and the relationship between the Korean language level, interpersonal skills, and college life adaptation exhibited a positive correlation. Part-time job hours displayed a positive correlation. Korean language level and interpersonal skills had a positive effect on college life adaptation, but part-time job hours and income did not affect college life adaptation. This study suggests that a program that can increase the level of Korean and interpersonal skills is needed.
Adaptive Laguerre-Gauss channels for detection and search of irregularly shaped lesions

Model observers are one of the most common tools to evaluate signal detectability on medical images. There is a large literature of model observers applied to simple signals such as disks or Gaussian blobs. However, these signals do not represent realistic scenarios where lesion shapes are irregular and lesion location is unknown. For instance, in breast imaging, masses can be elongated, spiculated or bumpy. We study how different model observers perform on irregularly shaped signals including the Non-Pre-Whitening (NPW) and two variations of the Channelized Hotelling Observer (CHO), one with symmetrical Laguerre-Gauss channels and one with adaptive Laguerre-Gauss channels. The novel adaptive Laguerre-Gauss channels are built taking into account the shape of the lesion by modifying the distance matrix used to build channels that adopt the shape of the signal. We embedded four different signal shapes in 2D realistic backgrounds in a digital mammography image generated using the VICTRE in silico breast imaging system and studied the models’ performance for detection and search. Our results show that the adaptive channels perform better than symmetrical channels with the most irregular signal (a star and an elongated mass).
Uncertainty analysis of virtualized systems with live migration

Benefiting from the advantages of virtualization technology, virtualized systems have been widely studied and adopted in enterprise systems, aimed at reducing resource usage and enhancing system reliability. An important feature of virtualized systems is the virtual machine (VM) live migration, which brings convenience to transfer running VMs from one physical server to another without service interruption. To achieve a high available/reliable virtualized system, it is crucial to evaluate its system availability/reliability. The model-based approach is commonly used and proved effective. However, it is well-known that the model parameters typically involve estimation error due to the limitation of data samples or expert&apos;s knowledge, so-called uncertainty. In this paper, we analyze the uncertainty propagation in the availability model of a virtualized system with live migration through a momentbased approximation method. A numerical experiment is conducted to quantify the impact of the parameter uncertainty on the system availability.
FaST-Net: Face Style Self-Transfer Network for masked face inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods.
A hybrid fruit fly optimization algorithm based on three dimensional space

The original fruit fly optimization algorithm (FOA) for the optimal solution in two dimensional space has been proposed to address complex optimization problems in a myriad of domains, such as production scheduling, path planning, financial investment, and software engineering. However, FOA is confronted with two defects unsolved. One is that the convergence speed is relatively low and the other is that the convergence accuracy is relatively low. In this paper, to solve the abovementioned two problems and improve the original FOA, we firstly propose a novel hybrid fruit fly optimization algorithm after due diligence, known as HFOA-3D based on three dimensional space, which takes advantage of crossover operator and mutation operator of genetic algorithm. We also conduct an experiment including five classic test functions to evaluate the performance of HFOA-3D. Finally, the experiment result shows that HFOA-3D outperforms the original FOA in terms of convergence speed and convergence accuracy.
Breast Cancer Detection Mammogram Imagesusing Convolution Neural Network

One in eight women globally develop breast cancer. By identifying the cancer of the breast tissue cells, it is diagnosed.Utilizing various algorithms and methodologies, modern medical image processing systems examine histopathology images that have been recorded by a microscope.Medical imaging and pathology tools are being processed using machine learning techniques.Computer-aided methods are used to achieve better outcomes than manual pathological detection systems since manually identifying a cancer cell is a laborious operation and entails human mistake. Transfer learning and fine-tuning can also be used to get the most out of a CNN that has already been trained. The first is to develop simple models or adapt existing ones to reduce the time investment and the number of training instances.In deep learning, this is typically accomplished by first extracting features with the assistance of a convolutional neural network (CNN), and then categorizing data with the assistance of a fully connected network. The field of medical imaging makes extensive use of the technique of deep learning because it does not necessitate prior knowledge in a field that is related to it. Within the scope of this investigation, we trained a convolutional neural network to generate forecasts that had an accuracy of up to 88.86%.
Python Programming for Analytics

This chapter introduces Python. We discuss the fundamentals of Python that are required to perform business analytics, data mining, and machine learning. This chapter provides enough basics to start using Python programming for data analysis. We will explore the Pandas and Numpy tools using Jupyter Notebook so you can perform basic data analytics to create models. We will be discussing the Pandas DataFrame, Numpy arrays, data manipulation, data types, missing values, data slicing and dicing, as well as data visualization.
Relationship Data Mining

The association rule, also called market basket analysis, is the most popular model to find the relationship among the transactional data. In this chapter, we will discuss the association rule and modeling technique to find the buying relationship patterns.
Neural Networks

Though neural networks have been around for many years, because of technological advancement and computational power, they have gained popularity recently and now perform better than other machine learning algorithms today. In this chapter, we will discuss using neural networks and associated deep neural network algorithms to solve classification problems.
Cluster Analysis

Clustering is an unsupervised learning technique to categorize data in the absence of defined categories in the sample data set. In this chapter, we will explore different techniques and how to perform clustering analysis.
R for Analytics

This chapter introduces the R tool. We’ll discuss the fundamentals of R required to perform business analytics, data mining, and machine learning. This chapter provides enough basics to start R programming for data analysis. This chapter also introduces the data types, variables, and data manipulations in R and explores various packages of R and how they can be used for data analytics.
Evaluating Analytics Model Performance

There are several ways to measure the performance of different models. This chapter discusses the various measures to test models’ performance, including regression, classification, and clustering methods.
Research and Application of Intelligent Generation Technology of Device Labels for Power Internet of Things

In order to achieve accurate description of devices in the application environment of the Internet of Things, to meet the needs of business applications for device labels, and promote tag based device data sharing and value mining, this paper studies and applies the tag intelligent generation technology. The standardized management of the whole life cycle of equipment label definition is realized, and the Power Internet of Things equipment label system is established through the design of label production process for power assets and equipment. This paper studies two tag generation technologies. One is to use rule engine to effectively integrate, understanding and applying expert experience, the other is to adopt the automatic machine learning technology scheme to realize the automatic machine learning model construction with high performance and high efficiency, so as to obtain the best model effect and apply it to diversified business data and complex scenarios. The intelligent labeling of labels is realized, and a unified, universal and comprehensive power equipment label library is established through these two technologies.
Prediction of Carrying Capacity of Digital Twin Power Information Communication Network Based on CNN-GRU Neural Network

With the acceleration of major national strategic deployments such as "Digital China" and "Network Power", the digital transformation of state-owned enterprises has become the general trend. In accordance with the development goals of the "14th Five-Year Plan" power communication network, the power grid company has put forward specific requirements for the digital transformation of power communication. At present, there are problems such as untimely fault response, large fault warning error and low accuracy in the power information communication network. This paper proposes a research on the carrying capacity prediction of the digital twin power information communication network based on the CNN-GRU neural network. The digital twin model collects historical and real-time carrying capacity data, predicts the carrying capacity data in different time periods through the CNN-GRU neural network, finds nodes with abnormal carrying capacity, and handles faults in time. Accurately predict the carrying capacity, so as to realize early warning of power information and communication network failures
Fake Account Detection in Social Networks with Supervised Machine Learning

Online Social Networks (OSNs) are the fastest growing and integral part of Internet technology. It is a viably powerful tool for communication that interconnects people and enforces substantial positive influence on education, healthcare, entertainment, and other aspects of society. However, some people misuse these platforms for cybercrime or to spread fake content, which damages social integrity and harmony. They create fake accounts by feeding wrong credentials or bypassing constraints. Hence, maintaining social networks’ integrity is essential by identifying fake accounts that mislead or confuse social media users. In this paper, we have considered fake accounts as anomalies in OSNs. We have proposed a supervised machine learning-based fake account detection model to deal with such peril, which can be a handy tool to control the overflow of online social crime. We have collected our own Twitter dataset and used another ICC benchmark dataset to validate the model’s effectiveness. We achieved over 95% accuracy of the proposed model. Results show that the proposed model can effectively detect fake accounts.
A Survey For Medical Image Encryption Techniques in Tele-medicine and E-healthcare

In this pandemic situation, radiological Images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyze and filter themselves. Due to overflowof patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with Integrity and availability. Due to varied features of image data, different algorithms are used to protect data from unauthorized access. This paper basically center on the distinctive cryptographic techniques used for the image encryption and decryption of the medical image data within the field of information security. In addition, a comparative analysis is carried out among the parameters used to measure security of the existing literatures.
An Ontology-based Sentiment Analysis Approach To Discovering Hidden Affected Objects

Nowadays, the topic of sentiment analysis has attracted a wide range of artificial intelligence technologies suchas Natural Language Processing (NLP), neural network. Long Short-Term Memory (LSTM), a deep neural network, has proven the effiency in the task of sentiment classification. However, there is a shortage of semantic knowledge from such classification results. This paper introduces a sentiment analysis approach based on a knowledge model to discover hidden affected objects, which combines ONtology and SentiMent technology, named ONSEM. In order to confirm the efficiency of the ONSEM framework, an experiment on a sentiment analysis dataset was developed and then yielded promising experimental results.
HotCloudPerf'23 Workshop Chairs' Welcome

It is our great pleasure to welcome you to the 2023 edition of the Workshop on Hot Topics in Cloud Computing Performance - HotCloudPerf 2023.
A secure and efficient authentication and multimedia data sharing approach in IoT-healthcare

Currently, healthcare services are encountering challenges, particularly in developing countries wherein remote areas encounter a lack of highly developed hospitals and doctors. IoT devices produce enormous security-sensitive data; therefore, device security is considered an important concept. The main aim of this work is to formulate a secure key generation process in the data-sharing approach by exploiting the Rider Horse Herd Optimization Algorithm (RHHO). Here, eight phases, like the initialization phase, registration phase, key generation phase, login phase, data protection phase, authentication phase, verification phase, and data decryption phase are exploited for secure and efficient authentication and multimedia data sharing. The proposed RHHO model is the integration of the Rider Optimization Algorithm (ROA) and Horse herd Optimization Algorithm (HOA). The proposed RHHO model achieved enhanced performance with a computation cost of 0.235, an accuracy of 0.935and memory usage of 2.425 MB.
Estimation for the number of MDS Matrices, Recursive MDS Matrices and Symmetric Recursive MDS Matrices from the Reed-Solomon Codes

The diffusion layer of the SPN block ciphers is usually built on the basis of the MDS (Maximum Distance Separable) matrices which is the matrix of the maximum distance separable code (MDS code). MDS codes have long been studied in error correcting code theory and have applications not only in coding theory but also in the design of block ciphers and hash functions. Thanks to that important role, there have been many studies on methods of building MDS matrices. In particular, the recursive MDS matrices and the symmetric recursive MDS matrices have particularly important applications because they are very efficient for execution. In this paper, we will give an estimate of the number of MDS matrices, recursive MDS matrices and symmetric recursive MDS matrices built from Reed-Solomon codes. This result is meaningful in determining the efficiency from this method of building matrices based on the Reed-Solomon codes. From there, this method can be applied to find out many MDS matrices, secure and efficient symmetric recursive MDS matrices for execution to apply in current block ciphers. Furthermore, recursive MDS matrices can be efficiently implemented using Linear Feedback Shift Registers (LFSR), making them well suited for lightweight cryptographic algorithms, so suitable for limited resources application.
Prediction When Factors are Weak

In macroeconomic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of macro predictors. Nevertheless, the theoretical justification of this approach often relies on a convenient and critical assumption that factors are pervasive. To incorporate information from weaker factors, we propose a new prediction procedure based on supervised PCA, which iterates over selection, PCA, and projection. The selection step finds a subset of predictors most correlated with the prediction target, whereas the projection step permits multiple weak factors of distinct strength. We justify our procedure in an asymptotic scheme where both the sample size and the cross-sectional dimension increase at potentially different rates. Our empirical analysis high- lights the role of weak factors in predicting inflation, industrial production growth, and changes in unemployment.