Reviewerly/exHarmony huggingface dataset:
Provably correct aspect-oriented modeling with UPPAAL timed automata

This chapter presents a method for constructing formal aspect-oriented models whose correctness is assured by construction. The chapter revisits the principles of aspect-oriented modeling (AOM) and defines a method for constructive development of aspect-oriented UPPAAL timed automata models. At first, we introduce new types of join points and define model weavers for them to weave advice models with base model. As second contribution, we propose conditions to be satisfied by aspect models for correct weaving and guarantee noninterference between aspects. Elaborating the concepts of AOM and verification, while addressing the problems of complexity and traceability, forms the theoretical basis for correct-by-construction AOM. The theoretical results are exemplified using a practical case study for a home rehabilitation system. The quantitative characterization of the advantages of the proposed AOM method is presented in the form of analytical estimates of the modeling effort and model-checking time complexity of verifying the correctness of weaving.
Mathematical models of control processes and stability in problems of mechanics

The development of scientific directions of mechanics as a result of research in analytical mechanics, space dynamics and applied mathematics is considered. The basic theorems of analytical dynamics were extended to mechanical systems of variable composition. The abstract concept of a point of variable mass is introduced in the form of a small area of space, endowed with mass, associated with the main body. The subject of consideration is a system of particles with constant masses, the composition of which varies. This allows one to take into account the change in mass and the internal movement of particles, leading to the creation of reactive forces.
Research on the Development and Operation Effect of the Innovation and entrepreneurship education network teaching platform in the Internet + Era

The innovation and entrepreneurship education network teaching platform is a comprehensive network education platform constructed by using computer network technology that integrates teaching, teaching resource library management, and teaching management. This article first analyzes its construction background and necessity, and then explores the development and design of the innovation and entrepreneurship education network teaching platform on the basis of practice, and finally uses the analytic hierarchy process and fuzzy comprehensive evaluation method to improve the satisfaction of W University's innovation and entrepreneurship education network teaching platform operation An evaluation study was carried out, and the study showed that the use of the online teaching platform not only greatly improved student satisfaction, but also promoted the improvement of innovation and entrepreneurship teaching quality.
How Single Neuron Processes Information

This paper proposes the hypothesis of how a single neuron processes information, that is, a specific Frame of Stimuli can only produce a specific Frame of Responses, or multi-value operation, that is, a specific combination of multiple input values generates a specific combination of multiple output values, and then discusses the possibility of such hypothesis.
DBSCAN-Based Automatic De-Duplication for Software Quality Inspection Data

Software quality inspection will generate too much data, and removing duplicate data can improve the efficiency of software quality inspection.This paper studies the automatic de-duplication method of software quality inspection data based on density-based spatial clustering of applications with noise (DBSCAN) clustering.Intelligent optimization algorithm is used to generate software quality inspection data by initializing individuals, calculating fitness function value, improving individuals and splitting individuals that meet the conditions.Local linear embedding algorithm is selected to extract software quality inspection data features by searching neighborhood points, calculating reconstruction weight and projection vector.The extracted features are used to select DBSCAN multi-density clustering algorithm of regional division, and the automatic de-duplication of software quality inspection data is realized by grid division, data bin dividing and grid merging.The experimental results show that the precision and recall of this method are higher than 99%, and the resource consumption rate is low, which can effectively improve the efficiency of software quality inspection.
Online Boycott through A Sentiment Analysis: A Pilot Study

research-article Share on Online Boycott through A Sentiment Analysis: A Pilot Study Authors: Chih-Chin Liang Department of Business Administration, National Formosa University, Taiwan Department of Business Administration, National Formosa University, TaiwanView Profile , Bo wei Liao Department of Business Administration, National Formosa University, Taiwan Department of Business Administration, National Formosa University, TaiwanView Profile Authors Info & Claims MISNC '21: Proceedings of the 8th Multidisciplinary International Social Networks ConferenceNovember 2021 Pages 91–94https://doi.org/10.1145/3504006.3504026Published:17 March 2022Publication History 0citation38DownloadsMetricsTotal Citations0Total Downloads38Last 12 Months38Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives.People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles.Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities.Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms.This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates.In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks.To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN).The used ML models are trained and tested by using different combinations of features selection techniques.To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as 'Suitability') has been used which combines the accuracy of the model with its execution time.The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.
Assessment of the quality of educational processes in an educational organization based on the analytic hierarchy process

The article is devoted to the use of the Saaty's analytic hierarchy process in assessing the quality of management of the work of the teaching staff of the university. It is proposed to use a hierarchical model with the assessment of criteria, subcriteria, the formation of alternatives using matrices of pairwise comparisons. The ability to objectively assess the work of the teaching staff by using the mathematical method of hierarchical structures is especially valuable. A hierarchical model is shown, an algorithm for making managerial decisions and predicting possible results is described. A hierarchical structure for assessing the quality of the work of teachers has been built, taking into account the current progress and attendance of students. Hierarchies of evaluation criteria and subcriteria have been formed, a scale of priorities (weights) of criteria has been established and, in accordance with them, the alternatives have been evaluated using the method of linear convolution. As a result of this approach, the relative importance of the given alternatives is determined for all criteria in the hierarchy. An example of assessing the quality of a teacher's work using a hierarchical model is given. Matrices of paired comparisons were constructed at each level in relation to each criterion of the higher level. It is concluded that this approach can be proposed for an objective assessment of the quality of the teacher's daily work. The work was carried out in the field of system analysis, management and information processing, algorithms for constructing hierarchical structures in the assessment of management processes were used.
College English Teaching Based on the Network Mode

With the advancement and development of computer network technology, many aspects of people's lives are being greatly affected by the Internet era, especially in education. Traditional English teaching is facing the challenge of the computer and network environment to reform its teaching mode, and it also indicates an opportunity to update its teaching methods. This paper explores the college English teaching based on the network mode, guides the effective integration of resources between the network and the curriculum, gives play to the superiority of the information age, makes the network learning mode more in line with the requirements of modern education, and develops into an efficient teaching method. Based on the survey of college students' networked learning situation and teachers' networked teaching situation, it is concluded that under the network mode, 10.5% of students use the Internet to learn English for more than 4 hours, and 43% of teachers use the network platform to teach. The main purpose is to stimulate students' interest in learning.
MEASURES TO REGULATE CRYPTOCURRENCY: FROM AFRICAN COUNTRIES SPECIFICITY TO CHINESE TOTAL CONTROL

The article is devoted to the path of the formation of cryptocurrency from electronic money and the formation of the system as a whole. The history of the issue and the stages of the formation of virtual money turnover are analyzed in order to understand the cause-and-effect relationships occurring in the cryptocurrency market today using the example of the countries of Africa and China as a totalitarian regulator. The article examines the international and national level of cryptocurrencies regulation. The article studies the mechanisms that affect cryptocurrencies as a progressive phenomenon in itself, but however, receiving a negative coloring due to the "duality of human nature" and the presence in it a tendency to achieve financial goals not always by legal means. The serious issue of the reason for a strict system of restrictions on cryptocurrencies is being raised. The severe dangers associated with financial terrorism and other activities of criminal elements trying to circumvent sanctions of the authorities are analyzed. As a disastrous result – the criminals are beyond the reach of law enforcement and other competent authorities. Cryptocurrencies are a new mechanism for movement and storage of funds for criminal communities. The importance of the criminal aspect in the system of restrictions and prohibitions formed in relation to cryptocurrencies is emphasized. The article makes an introduction about the need to understand the measures that are taken by the countries in relation to cryptocurrencies based on their general policy, form of government and development trends.
Rainfall prediction through TRMM dataset using machine learning model

In our world, rainfall forecasting is extremely important. Agriculture is seen as the primary source of revenue for the economy. A proper estimate of rainfall is needed to make proper agricultural investments. Rainfall forecasting is needed for people living in coastal areas, in addition to agriculture. People living by the coast are at a higher risk of heavy rain and flooding, so they should be aware of the weather forecast well in advance so that they can schedule their stay accordingly. We use a machine-learning algorithm to predict rainfall for this reason. . Machine Learning algorithm used is Linear Regression. In linear regression, to predict the dependent variable (rainfall) using an independent variable (soil moisture). A comparison of two machine learning algorithms reveals which is more effective.
Long-term Characterization of Political Communications on Social Media

Social media play an important role in shaping political discourse, creating a public sphere that enables discussions, debates, and deliberations. Therefore, several studies analyzed and characterized the communication of politicians in social media through political and non-political short text messages. However, classifying short messages as political is not a trivial task, especially considering that political topics change regularly over time. While some politicians only share professional communications about their political agenda and activities, others prefer a more personal and informal approach, sharing communications about the most varied subjects, such as religion, sports, and their families. Thus, in this work, we propose a methodology to characterize the communication of Brazilian politicians over the years in terms of the amount of political and non-political messages they post. For this task, we collected more than 3M tweets from 914 Brazilian deputies from October 2013 to October 2019 (6 years span). We characterize their communications through a machine learning classifier that labels all tweets as political and non-political and is robust to concept drifts. We found that the main concept drifts occurred during important events of Brazilian politics, such as the impeachment of former president Dilma Rousseff in 2016 and presidential and municipal elections. Moreover, we characterize the explosive rise of the right seen just before the 2018 elections. Although left-wing politicians post more on social media, the participation of right-wing politicians is broader and more evenly distributed. Finally, we reveal the increase of public engagement over time, especially with the left and right political tweets.
GMDH-Type Neural Networks for Predicting Financial Time Series: A Study of Informational Efficiency of Stock Markets

A theory of Efficient Market Hypothesis (EMH) has been introduced by Fama to analyse financial markets. In particular the EMH theory has been proven in real cases under different conditions, including financial crises and frauds. The EMH assumes to examine the prediction accuracy of models designed on retrospective data. Such prediction models could be designed in different ways that motivated us to explore Machine Learning (ML) methods known for building models providing a high prediction performance. In this study we propose a "deep" learning method for building high-performance prediction models. The proposed method is based on the Group Method of Data Handling (GMDH) that is the deep learning paradigm capable of building multilayer neural-network models of a near-optimal complexity on given data. We show that the developed GMDH-type neural network has outperformed the models built by the conventional ML methods on the Warsaw Stock Exchange data. It is important that the complexity of the designed GMDH-type neural-networks is defined by the number of layers and connections between neurons. The performances of models were compared in terms of the prediction errors. We report a significantly smaller prediction error of the proposed method than that of the conventional autoregressive and "shallow" neural-network models. This finally allows us to conclude that traders will be advantaged by the proposed method.
Secured Cloud Storage System Using Auto Passkey Generation

The cloud storage system is an improved online drive storage system with storage on cloud. It can handle all details about a file uploads with categories, preceded by file download whenever required. The file gets uploaded with some encryption strategy and stored safely on the cloud, later on while downloading the file, the decryption is done using a security key provided through the registered mail ID. The details include file name, type of file (Private/Public) and auto generated passkey. In case of system, they need a lot of time for searching the file which is not a secured way. So the accuracy will be maintained in this application. This system is managed by the application in cloud server. It is the work of the cloud application which is headed in place to observe the complete procedure.
Robust Face Mask Detection Using Local Binary Pattern and Deep Learning

The ongoing COVID-19 has caused a great amount of serious troubles for people around the world. Even though vaccination has been proven to be safe and highly effective against the COVID-19, it is far away to prevent thoroughly the spread of the disease and truly halt the pandemic. Therefore, we need to apply additional methods aside from vaccine injection, such as keeping the distance between people and always using the face masks during the ordinary conversations, in efforts to further reduce the COVID-19 contagion rate. To implement such methods, this research aims to investigate an efficient approach to detect and warn people that they should wear mask whenever they go to public places. Our proposed system studies the benefits of Local Binary Pattern (LBP) and deep learning model to provide accurate face mask detection and classification system. After comprehensive testing, we found that our system provided the detection rate up to 90% with the Kaggle, Face-Mask-Net, and our own datasets.
The Optimized Path for Innovation Performance of Chinese Virtual Reality Industry

We analyze the influencing factors of enterprise innovation performance and their structural relationship in the context of Chinese virtual reality industry. We also provide an optimized path for the development of Chinese virtual reality industry by explaining how each factor affects innovation performance. In order to better carry out the field research, python is used to collect and select VR enterprises with better innovation n performance on the official website of the China State Intellectual Property Office. Then, we conducted seminars, designed and distributed questionnaires to collect data on the basis of the general characteristics of virtual reality enterprises. We construct and verify a moderated double-mediating model and analyze the micro-effect mechanism of innovation performance of VR enterprises. The result shows that in the innovation process of Chinese VR enterprises, inward open innovation, outward open innovation and innovation ability all have mediating effect in the path of innovation environment affecting innovation performance, and knowledge sharing has a positive moderating effect in the relationship between innovation ability and innovation performance.
Augmented Reality: A tool for Interactive Learning Environment

AR provides students with the opportunity to practice their knowledge and skills by seamlessly combining digital information with the real-world environment. In addition to practicing real-world scenarios, AR can also provide interactive learning environments through interactive activities. AR has the potential to save time and money in the case of high-cost educational needs. AR systems, which can be used to increase collaborative learning experiences, enable the teaching of lessons innovatively and interactively by presenting information in 3D format, thereby facilitating students’ skill acquisition. Besides, AR systems positively affect students’ motivation and cognitive learning. They help to develop their spatial and psychomotor-cognitive skills. AR can provide hints and feedback visually, auditorily, or seasonally to improve students’ experiences. Through these features, AR systems can be integrated into teachers’ lecture notes. Thus, the abstract information to be taught can be conveyed to the students in a concrete way. Using AR in the classroom can improve learning by helping educators create interactive classrooms that increase student engagement.The purpose of this research is to measure and understand the impact of an augmented reality mobile application on the learning motivation of students. The main novelty of this research is to motivate the use of augmented reality to improve the communication and collaboration skills between children, especially autistic children, and the game-based evaluation of pupils in various teaching areas, allowing for a stress-free testing environment.
Course Analysis and Management System Design Based on Big Data Technology

Currently, in the process of accelerating the construction of digital campuses, big data technology is being applied to the analysis of college teaching courses. Through in-depth analysis and processing of grade data, not only can teachers help teachers understand the current learning situation of students, but also based on students’ knowledge. Teach students in accordance with their aptitude, organize teaching more flexibly, and improve teaching efficiency. The purpose of this article is to study the design of curriculum analysis and management system based on big data technology. This article first analyzes the background of teaching and curriculum reform, the current situation of curriculum management, and introduces the data mining technology involved in the system. Afterwards, the course evaluation model was studied in depth, a course evaluation analysis decision-making system model was proposed, the system performance and operation requirements were analyzed, the system was designed and implemented, and the decision-making schemes and courses beneficial to teaching were outlined. The experimental results show that there are differences in the teaching behavior of teachers with different professional titles. Among them, the proportion of changes in the teaching behavior of lecturers and teachers accounts for about 63%, and the distribution of teaching activities of professors and associate professors is more reasonable.
Utilization of Banana Peel and Carton Waste in Manufacturing of Thermal Insulation Board

As the temperature continues to rise due to global warming, the used of polyurethane thermal insulation foam board has become prevalent over the recent years. Despite its usefulness, it has a negative impact on our environment because the production of a pound of polyurethane foam board emits 3.7 pounds of carbon dioxide. Several studies have found that banana peel and carton waste can be used as a potential substitute in the production of polyurethane thermal insulation foam board due to its thermal resistivity. This study employed four different tests which includes flammability test, bend test, screw test and thermal capability test to determine the effectiveness of the product. The tests result showed that the best proportion to produce an affordable, lightweight and environment friendly insulation board is 31.33% banana peel, 31.33% carton waste, 23.50% banana resin, 7.8% polyvinyl acetate and 6% chlorinated rubber paint. The computed payback period is 4 years and 266 days, indicating that the business is feasible.