Reviewerly/exHarmony huggingface dataset:
Correlation between Degree Modifiers and their Effect on Meaning: Iraqi EFL learners' Perception

Abstract &#x0D; &#x0D; The semantics of degree modification and its effect on mutating the meaning of structures they appear in have held considerable appeal for specialists in areas ranging from lexical semantics to language pedagogy. Although the general concept of degree modifiers received a lot of attention in the twentieth century studies in linguistics, it has been treated only theoretically. The pedagogical side of degree modifiers seems to be a neglected topic. This paper is an attempt at addressing this gap in the literature. The main aim of this research is to examine students' knowledge in the functions of the degree modifiers and their semantic effect. The paper investigates whether the effect of degree modifiers on meaning is distinguished to the same extent by fourth year students majoring in English. To this end, the researcher designed a multiple choice question test to determine whether students are able to find the correlation between the type of degree modifier used and the meaning of the given sentences. Data analysis was performed using a number of descriptive measures in addition to a T-test and a Chi-square test. The results show that the Iraqi English major students face severe difficulty in distinguishing differences in meaning resulting from using degree modifiers in different contexts. The results also show a gradation in difficulty across the different types of modifiers.
Development of a model for detecting network traffic anomalies in distributed wireless ad hoc networks

Mobile ad hoc networks are one of the promising directions of the edge computing technology and they are used in various applications, in particular, in the development of intelligent transport systems.A feature of mobile ad hoc networks lies in the constantly changing dynamic network topology, as a result of which it is necessary to use reactive routing protocols when transmitting packets between nodes.Mobile ad hoc networks are vulnerable to cyber-attacks, so there is a need to develop measures to identify network threats and develop rules for responding to them based on machine learning models.The subject of this study is the development of a dynamic model for detecting network traffic anomalies in wireless distributed ad hoc networks.Within the framework of this study, methods and algorithms of data mining and machine learning were applied.The proposed approach to traffic monitoring in wireless distributed ad hoc networks consists in the implementation of two stages: initial traffic analysis to identify anomalous events and subsequent in-depth study of cybersecurity incidents to classify the type of attack.Within the framework of this approach, the corresponding models are constructed based on ensemble methods of machine learning.A comparative analysis and selection of the most efficient machine learning algorithms and their optimal hyperparameters has been carried out.In this paper, a formalization of the traffic anomaly detection model in distributed wireless ad hoc networks is carried out, the main quantitative metrics of network performance are identified, a generalized algorithm for detecting traffic anomalies in mobile ad hoc networks is presented, and an experimental study of the network segment simulation is carried out from the point of view of performance degradation during the implementation of various network attack scenarios.Network distributed denial of service attacks and cooperative blackhole attacks have the greatest negative impact on the performance of the mobile ad hoc network segment.In addition, the network simulation results were used to build a machine learning model to detect anomalies and classify types of attacks.The results of a comparative analysis of machine learning algorithms showed that the use of the LightGBM method is the most effective for detecting network traffic anomalies with an accuracy of 91 %, and for determining directly the type of attack being carried out with an accuracy of 90 %.The proposed approach for network anomalies detection through the use of trained traffic analysis models makes it possible to identify the considered types of attacks in due time.The future development direction of this research is the consideration of new scenarios for the emergence of network attacks and online additional training of the constructed identification models.The developed software tool for detecting network traffic anomalies in distributed mobile ad hoc networks can be used for any type of wireless ad hoc networks.
Preface

Due to the continuing effect of Covid-19 epidemic, 2022 International Conference on Computer Modeling and Big Data Analytics (CMBDA 2022) has been successfully held on 1st July 2022 as an online conference. Organized by Hubei Zhongke Institute of Geology and Environment Technology, CMBDA 2022 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations. CMBDA 2022 proceeding is a collection of outstanding submissions in the field of Computer modeling and simulation, Artificial intelligence, Machine learning, Data mining, Big data analytics, Algorithms, etc. 35 articles were selected from 84 submissions after peer-review, and authors were participated in CMBDA 2022 with oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including Beijing Institute of Space Mechanics &amp; Electricity, Guangdong University of Technology, Pearl River Water Resources Commission of Ministry of Water Resources, National Digital Switching System Engineering &amp; Technological Research Center, Huazhong University of Science and Technology, Yantai University, etc. List of Technical Committee, Co-Chair, Conference Program Chair, International Committee Members are available in this pdf.
Light weight recommendation system for social networking analysis using a hybrid BERT-SVM classifier algorithm

Social media platforms, such as Twitter, Instagram, and Facebook, have facilitated mass communication and connection.Due to the development as well as the advancement of social platforms, the spreading of fake news has increased.Many studies have been performed for detecting fake news with machine learning algorithms; but these existing methods had several difficulties, such as rapid propagation, access method and insignificant selection of features, and low accuracy of the text classification.Therefore, to overcome these issues, this paper proposed a hybrid Bidirectional Encoder Representations from Transformers -Support Vector Machine (BERT-SVM) model with a recommendation system that used to predict whether the information is fake or real.The proposed model consists of three phases: preprocessing, feature selection and classification.The dataset is gathered from Twitter social media related to COVID-19 real-time data.Preprocessing stage comprises Splitting, Stop word removal, Lemmatization and Spell correction.Term Frequency Inverse Document Frequency (TF-IDF) converter is utilized to extract the features and convert text to binary vectors.A hybrid BERT-SVM classification model is used to predict the data.Finally, the predicted data is compared with the preprocessed data.The proposed model is implemented in MATLAB software with several performance metrics carried out, and these parameters attained better performance: accuracy is 98 %, the error is 2 %, precision is 99 %, specificity is 99 %, and sensitivity is 98 %.Therefore the better effectiveness of the proposed model than existing approaches is shown.The proposed social networking analysis model provides effective fake news prediction that can be used to identify the Twitter comments, either real or fake.
Connecting Latent ReLationships over Heterogeneous Attributed Network for Recommendation

Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence between neighbors and provide two different strategies with the attention mechanism to aggregate neighbors' information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-art methods.
Formal Modelling and Security Analysis of Inter-Operable Systems

Emerging technologies utilised in building modern systems make them inter-operable but potentially exposed to security threats. Thus, engineers need to consider the system structure and behaviour at the design level. This paper addresses the security risk assessment of inter-operable IoT systems designed in BIP (Behaviour-Interaction-Priority). For this purpose, we model different attacks scenarios from Microsoft STRIDE threats catalogue and identify the threats entry points in the component-port-connector architecture. Using standards communication styles such as message passing, we compose architectural components to model data flow between communication entities. We use BIP statistical model checking to assess the architecture conformance regarding security properties expressed in temporal logic.
A Generalized Inverted Dirichlet Predictive Model for Activity Recognition Using Small Training Data

In this paper, we develop the predictive distribution of the generalized inverted Dirichlet (GID) mixture model using local variational inference. The main goal is to be able to tackle classification problems involving small training data sets. The two main ingredients of the proposed predictive model are the GID distribution which provides flexibility for the modeling of semi-bounded data that are naturally generated by different sensors outputs and the efficient of variational inference as a deterministic approximation to fully Bayesian approaches. The merits of the proposed model are shown via synthetic data and a real application that concerns activities recognition.
Big-Data Analysis of Characteristics of Chinese Translation of Emily Dickinson Poems: Case Study of Student Translators' Translation Practice

A Big-data analysis is carried out by building up relevant language corpus and applying Antconc 3.4.4, etc., to examine the characteristics of 1000 Chinese translations of 10 Emily Dickinson poems translated by 100 undergraduate students of South China University of Technology. Statistics and analysis reveal that the translation of Dickinson's poems by student translators is not faithful to the original poems, neither in form nor in content. Instead, without following the literal translation principle, student translators' translation bears distinct evidence of subjective initiative and arbitrary conduct, including altering stanza numbers and line numbers, omitting punctuation, adding modifiers to simple nouns(images), and cutting off content that is difficult to understand and translate, etc.; thus students' translation practice is more like self-fulfillment of their individual needs than a serious academic event.
Computation and analysis of anisotropic solar radiation model for India

Abstract The solar radiation data are a very important parameter to forecast generation from any solar PV (Photo Voltaic) plant to be installed at a suitable location, though it is not readily available for many locations because of the costly measurement instruments, and their maintenance, and calibration issues. The solar irradiation data available from NASA and Meteonorm has the prediction topology of consolidating 20–25 years old irradiation data which has the question of reliability. Furthermore, at various locations in India, the irradiance value of Meteonorm is overestimated than actual. In this research study an attempt has been made to develop an algorithm for the anisotropic solar radiation model by coalescing Gopinathan empirical parameter, Klucher anisotropic model, and Collares-Pereira &amp; Rabl (GKCR) empirical parameter which gives the best results of forecasted daily and monthly solar radiation at almost every location in India. The computed results are compared with Meteonorm and the NASA database for three distinct locations (New Delhi, Chennai, and Kolkata) at different azimuths and tilt angles. The results obtained i.e., solar tilted radiation and tilt gain were very close to that simulated using PVsyst software using Meteonorm and NASA database. The computed results were compared using various statistical tools with measured irradiance values for four distinctly located solar PV plants. The computed result shows the least error compared to actual data for two locations. Employing the GKCR empirical algorithm mentioned in the research article, hourly irradiance data for the next day, for any specific tilt and azimuth angle can be estimated using available weather forecast data (sunshine duration) and can circumvent the penalty levied by the Load dispatch Centre. Furthermore, by using monthly data anticipated by the algorithm, the question of data reliability of chargeable Meteo sources working on coalescing data of antiquated years like NASA and Meteonorm can be solved.
Interpretation The Meaning Of Kanji in Basic II, Intermediate I and II Textbooks Which Have The Root Kanji Shitagokoro

&lt;p&gt;&lt;em&gt;Kanji are letter comes from China which are also used as a writing system in Japanese. Kanji are read in two ways, Onyomi and Kunyomi. The basic root of Kanji character is called Bushu. Bushu consists of many types of which one of them is called Shitagokoro. Bushu Shitagokoro is a root that has (kokoro) written at the bottom of a Kanji and symbolizes nature and feelings. Based on the theory of meaning of Denotation and Connotation by Gorys Keraf and the theory of the formation of Kanji or Rikusho, this study aims to determine the interpretation of the meaning of Kanji which has the basic character of Bushu Shitagokoro. This research examines using library research in the data collection stage and descriptive methods in the data analysis stage. The results obtained in this study are 12 Kanji in the form of Keisei Moji and Kaii Moji which have the basic character of Shitagokoro which are included in Jouyou Kanji or Kanji that are used in general.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords : Rikusho, Kanji Root, Shitagokoro, Denotation, Connotation&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
GIFT RECOMMENDATION SYSTEM

Nowdays, many people are moving on the online shopping. One of the main viewpoint ofshopping is give the gift to the someone on the special occasion. People always take decision on every day which gift is suitable and what should I gift to a someone like Father, Mother, Friend on their birthday, anniversary. When people are search the gift online there are many choices and many gift, if a person search the gift for wedding Anniversary then many gift are shown that’s why people are confused. So ourproject is based on building a system that will generategift recommendations for a user on his specific events,relation. Suppose, user search the gift for father on his birthday, only father’s birthday related gift are shown. Key Words- Gift Recommendation, Recommendation System, Viewpoint
Realization of Book Collaborative Filtering Personalized Recommendation System Based on Linear Regression Equation

Abstract In the context of information construction, faced with a large number of network information data, in order to obtain more valuable information in an effective time, researchers put forward a recommendation algorithm for information explosion. Because the recommendation algorithm has been successfully applied in many fields such as business, the relevant algorithm system has also begun to be fully implemented in the field of books. There are many kinds of recommendation algorithms, among which collaborative filtering recommendation technology is the most widely used. Therefore, based on the humanized library service concept, this paper mainly studies the influencing factors of reader satisfaction, and on the basis of constructing multiple linear regression model, clarifies the practical significance of constructing collaborative filtering personalized recommendation system for libraries. Then, an improved clustering algorithm is proposed to reduce the dimensionality of the original matrix, and an empirical analysis is made on the book collaborative filtering personalized recommendation system based on linear regression equation by using the operation idea of calculating the score according to the borrowing time. The final results show that, according to the multiple linear regression model between reader satisfaction and its influencing factors, in order to improve library service quality and build a good learning and reading environment, collaborative filtering algorithm should be combined to build a personalized book recommendation system, and this system is feasible in practical application..
Cost-Aware Type Theory

Although computational complexity is a fundamental aspect of program behavior, it is often at odds with common type theoretic principles such as function extensionality, which identifies all functions with the same $\textit{input-output}$ behavior. We present a computational type theory called $\mathbf{CATT}$ that has a primitive notion of cost (the number of evaluation steps). We introduce a new dependent function type "funtime" whose semantics can be viewed as a cost-aware version of function extensionality. We prove a collection of lemmas for $\mathbf{CATT}$, including a novel introduction rule for the new funtime type. $\mathbf{CATT}$ can be simultaneously viewed as a framework for analyzing computational complexity of programs and as the beginnings of a semantic foundation for characterizing feasible mathematical proofs.
Digital Technologies In The Educational Process Of The University

The article is devoted to the problem of introducing digital technologies into the educational process of the university. Today the digitalization of education is becoming one of the priority areas for the development of the Russian system of all levels of education, which is explained by a number of reasons: universal informatization requires the training of a specialist who meets the requirements of modern society, where digital education becomes a means of obtaining knowledge, skills; wide opportunities for using digital education in the practice of organizing the educational process; unhindered access to various information educational resources and platforms, etc. All of them enhance the possibility of training a specialist who can work with information, which is one of the requirements of the Federal State Educational Standard. Thus this technology requires special knowledge and information culture from both the teacher and the students. Digital education has given a big impetus to the development of the system of continuing professional education in general, to the development of distance learning technologies widely used in the practice of training specialists, as well as to increasing the share of student independent work. So the use of various elements of digital education makes it possible to expand the opportunities for students to master their future profession and become a highly demanded specialist in the labor market.
Development Scenarios And Modeling For System Of Engineering Education At Universities

Educational institutions training engineers and technical experts operate in a dynamic environment to which they are bound to respond by developing new organizational forms for the adaptation to new conditions. The rapid development of information technology, economic situation, and hardly-predictable events like the waves of the COVID-19 pandemic and the restrictions of people's mobility pose new problems for the directors of educational institutions. The pursuit for a flexible management structure that would satisfy the current requirements brings about the necessity for a formal description of universities and their environments based on the systemic approach. The purpose of this research was to test a model based on a fuzzy cognitive map and assess the possibility of change scenario development in the education system. The authors demonstrate that this approach can conveniently describe the elements of the education system and its environment, as well as the communications and links between them. The authors obtained and analyzed the modeling results showing the possibility of using event scenarios for the facilitation of strategic decisions concerning the management of engineering education at a technical university.
Temperature Dependent Resistors in RLC-series Networks

In previous works by the authors, temperature dependent resistors (TDRs) have been modeled as resistors whose resistance changes with temperature as temperature increments are produced by the current through them.In such works, resistive networks containing TDRs have been thoroughly studied. Both cases of networks excited by DC and AC voltage supplies have been considered. However, so far, the studied networks were composed of resistors only. In the present work, the study is extended to RLC-series networks whose resistive components are TDRs. Single and multiple AC voltage sources are considered.This extended study required the solution of systems of nonlinear ODEs. Results are shown as plots of voltages, currents, temperatures, and resistances. Some interesting conclusions may be inferred from the network responses to low and high frequency sources, when signal periods are compared to TDR time constants.
Predictive Modeling For Improving The Quality Of Educational And Methodological Documentation

The preparation of educational and methodological documentation in educational institutions is an important element of the educational process. The purpose of the scientific article is to develop quality criteria for the educational and methodological complex of the discipline. We pay attention on checking the compliance of the document content. The paper presents the criteria for the effectiveness of the developing the educational-methodical complex of disciplines. We propose the method for improving the quality of educational and methodical documentation based on the work of a neural network. The method allows automating the process of checking educational and methodical documentation for the content. Natural language processing (Bert tokenizer) and machine learning methods (regression methods) are used as a tool. The problem of the quality of educational and methodological documentation can be solved by introducing specialized automated systems and their modules, which allow not only to generate appropriate templates for educational and methodological documentation but also to check the internal content of documents.