Reviewerly/exHarmony huggingface dataset:
Mixup Regularized Adversarial Networks for Multi-Domain Text Classification

Using the shared-private paradigm and adversarial training has significantly improved the performances of multi-domain text classification (MDTC) models. However, there are two issues for the existing methods. First, instances from the multiple domains are not sufficient for domain-invariant feature extraction. Second, aligning on the marginal distributions may lead to fatal mismatching. In this paper, we propose a mixup regularized adversarial network (MRAN) to address these two issues. More specifically, the domain and category mixup regularizations are introduced to enrich the intrinsic features in the shared latent space and enforce consistent predictions in-between training instances such that the learned features can be more domain-invariant and discriminative. We conduct experiments on two benchmarks: The Amazon review dataset and the FDU-MTL dataset. Our approach on these two datasets yields average accuracies of 87.64\% and 89.0\% respectively, outperforming all relevant baselines.
A Study of English Listening and Speaking Teaching Mode for College Students: Using Multimedia Network Technology

The rapid development of globalization has increased the amount of communication taking place worldwide. Hence, for a college student in thiscentury, a good command of English, especially in terms of oral communication skills, has become essential. This paper presents an overview of twoteaching modes: the traditional approach to classroom teaching, and the multimedia network-based mode. In order to compare the effectiveness ofeach mode, two hundred non-English major freshmen from the University of Science and Technology Liaoning were chosen as subjects. They weredivided equally into two groups comprising control classes and experimental classes. All classes undertook a basic college English course, althoughthe control classes were taught using the traditional teaching method, and the experimental classes were taught using the new teaching approach.Subsequently, students in the experimental classes were surveyed to determine their opinions about and attitudes to the new mode of teaching. Theresults showed that the students in the experimental classes had significantly improved English listening and speaking performance compared to thestudents in the control classes. Moreover, most of the students in the experimental classes considered that the new teaching mode was more interestingand better stimulated their interest in learning.Keywords: College English, multimedia network, listening and speaking teaching, teaching modeCite AsM. Tian, A Study of English Listening and Speaking Teaching Mode for College Students: Using Multimedia Network Technology, Engineering Intelligent Systems, vol. 29 no. 1, pp. 5-10, 2021.
Use of Pedagogical Technologies in Conducting

In this state is presented information about the science of conducting, the use of new pedagogical technologies in training and the formation of skills for the preparation of students for innovative activities.
Scaling Unsupervised Domain Adaptation through Optimal Collaborator Selection and Lazy Discriminator Synchronization

Breakthroughs in unsupervised domain adaptation (uDA) have opened up the possibility of adapting models from a label-rich source domain to unlabeled target domains. Prior uDA works have primarily focused on improving adaptation accuracy between the given source and target domains, and considerably less attention has been paid to the challenges that arise when uDA is deployed in practical settings. This paper puts forth a novel and complementary perspective, and investigates the algorithmic challenges that arise when uDA is deployed in a distributed ML system with multiple target domains. We propose two algorithms: i) a Collaborator Selection algorithm which selects an optimal collaborator for each target domain, and makes uDA systems more accurate and flexible; ii) a distributed training strategy that allows adversarial uDA algorithms to train in a privacy-preserving manner. We provide theoretical justifications and empirical results to show that our solution significantly boosts the performance of uDA in practical settings.
Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration

Saliency detection based on the complementary information from RGB images and depth maps has recently gained great popularity. In this paper, we propose Complementary Attention and Adaptive Integration Network (CAAI-Net), a novel RGB-D saliency detection model that integrates complementary attention based feature concentration and adaptive cross-modal feature fusion into a unified framework for accurate saliency detection. Specifically, we propose a context-aware complementary attention (CCA) module, which consists of a feature interaction component, a complementary attention component, and a global-context component. The CCA module first utilizes the feature interaction component to extract rich local context features. The resulting features are then fed into the complementary attention component, which employs the complementary attention generated from adjacent levels to guide the attention at the current layer so that the mutual background disturbances are suppressed and the network focuses more on the areas with salient objects. Finally, we utilize a specially-designed adaptive feature integration (AFI) module, which sufficiently considers the low-quality issue of depth maps, to aggregate the RGB and depth features in an adaptive manner. Extensive experiments on six challenging benchmark datasets demonstrate that CAAI-Net is an effective saliency detection model and outperforms nine state-of-the-art models in terms of four widely-used metrics. In addition, extensive ablation studies confirm the effectiveness of the proposed CCA and AFI modules.
Application of Attention Model Hybrid Guiding based on Artificial Intelligence in the Course of Intelligent Architecture History

Application of the attention model hybrid building based on the artificial intelligence in the course of the intelligent architecture history is studied in this article. A Hadoop distributed architecture using big data processing technology which combines basic building information with the building energy consumption data for the data mining research methods, and conduct a preliminary design of a Hadoop-based public building energy consumption data mining system. The principles of the proposed model were summarized. At first, the intelligent firewall processes the decision data faster, when the harmful information invades. The intelligent firewall can monitor and also intercept the harmful information in a timelier manner. Secondly, develop a problem data processing plan, delete and identify different types of problem data, and supplement the deleted problem data according to the rules obtained by data mining. The experimental results have reflected the efficiency of the proposed model.
Discovery Engine for Finding Hidden Connections in Prose Comprehension from References

Reading is one of the essential practices of modern human learning.Comprehending prose text simply from the available text is particularly challenging as in general the comprehension of prose requires the use of external knowledge or references.Although the processes of reading comprehension have been widely studied in the field of psychology, no algorithm level models for comprehension have yet to be developed.This paper has proposed a comprehension engine consisting of knowledge induction which connects the knowledge space by augmenting associations within it.The connections are achieved through the automatic incremental reading of external references and the capturing of high familiarity knowledge associations between prose concepts.The Ontology Engine is used to find lexical knowledge associations amongst concept pairs, with the objective being to obtain a knowledge space graph with a single giant component to establish a base model for prose comprehension.The comprehension engine is evaluated through experiments with various selected prose texts.Akin to human readers, it could mine reference texts from modern knowledge corpuses such as Wikipedia and WordNet.The results demonstrate the potential efficiency of using the comprehension engine that enhances the quality of reading comprehension in addition to reducing reading time.This comprehension engine is considered the first algorithm level model for comprehension compared with existing works.
Deep Reinforcement Learning-Based Product Recommender for Online Advertising

In online advertising, recommender systems try to propose items from a list of products to potential customers according to their interests. Such systems have been increasingly deployed in E-commerce due to the rapid growth of information technology and availability of large datasets. The ever-increasing progress in the field of artificial intelligence has provided powerful tools for dealing with such real-life problems. Deep reinforcement learning (RL) that deploys deep neural networks as universal function approximators can be viewed as a valid approach for design and implementation of recommender systems. This paper provides a comparative study between value-based and policy-based deep RL algorithms for designing recommender systems for online advertising. The RecoGym environment is adopted for training these RL-based recommender systems, where the long short term memory (LSTM) is deployed to build value and policy networks in these two approaches, respectively. LSTM is used to take account of the key role that order plays in the sequence of item observations by users. The designed recommender systems aim at maximising the click-through rate (CTR) for the recommended items. Finally, guidelines are provided for choosing proper RL algorithms for different scenarios that the recommender system is expected to handle.
PEDAGOGIC IDEAS FOR STUDENTS OF INDEPENDENT WORK WITH THE BOOK

The article analyzed the works of scientists (Avtomonova P. P., Boyko Y. V., and Zaichenko I. V., Zenkin A. S., Karandasheva V. N., Kirdyaeva V. M., Kuzmenko N. M., Lasch A P., Lvov I. P., Pylhaeva F. P,. Pogrebna Ya. A., Czerniawska A. P.), who developed methods of optimal study book, textbook and other paper media. Types of independent work with the coursebook, additional materials are necessary to have when studying or reading books and other manuals. Selected types of readers (based on lectures “Students' independent work with the book” by I. P. Lvov), the preferences of readers on the nature of the literature and recommendations for its choice. It is substantiated that, according to I. P. Lvov, the book plays an important role in the development of thinking and speech culture of students. The Ukrainian teacher singled out four groups of students. Representatives of the first group are only interested in the reading process. To the second group of readers I. P. Lvov included people who like to read adventure literature and detective stories. The third group consisted of readers who are interested in serious, scientific literature, new books of modern bookstores. To the fourth type of readers, the scientist referred those who seek knowledge and therefore makes serious demands on the book. It is proved that the Ukrainian teacher emphasized that the effectiveness of reading books is possible if the reader has a system of knowledge in a particular field, has the skills of good orientation in the text with the selection of basic principles and ideas. The result of mental work should be clearly designed in the form of writing or speaking, and the effectiveness of the process of working with the book depends not only on speed but also on the method of reading.
On Computation Complexity of True Proof Number Search

We point out that the computation of true \emph{proof} and \emph{disproof} numbers for proof number search in arbitrary directed acyclic graphs is NP-hard, an important theoretical result for proof number search. The proof requires a reduction from SAT, which demonstrates that finding true proof/disproof number for arbitrary DAG is at least as hard as deciding if arbitrary SAT instance is satisfiable, thus NP-hard.
COMPARATOR IDENTIFICATION IN THE CONDITIONS OF BIFUZZY INITIAL DATA

When solving a large number of problems in the study of complex systems, it becomes necessary to establish a relationship between a variable that sets the level of efficiency of the system's functioning and a set of other variables that determine the state of the system or the conditions of its operation. To solve this problem, the methods of regression analysis are traditionally used, the application of which in many real situations turns out to be impossible due to the lack of the possibility of direct measurement of the explained variable. However, if the totality of the results of the experiments performed can be ranked, for example, in descending order, thus forming a system of inequalities, the problem can be presented in such a way as to determine the coefficients of the regression equation in accordance with the following requirement. It is necessary that the results of calculating the explained variable using the resulting regression equation satisfy the formed system of inequalities. This task is called the comparator identification task.&#x0D; The paper proposes a method for solving the problem of comparator identification in conditions of fuzzy initial data. A mathematical model is introduced to describe the membership functions of fuzzy parameters of the problem based on functions (L–R) – type. The problem is reduced to a system of linear algebraic equations with fuzzy variables.&#x0D; The analytical relationships required for the formation of a quality criterion for solving the problem of comparator identification in conditions of fuzzy initial data are obtained. As a result, a criterion for the effectiveness of the solution is proposed, based on the calculation of membership functions of the results of experiments, and the transformation of the problem to a standard problem of linear programming is shown. The desired result is achieved by solving a quadratic mathematical programming problem with a linear constraint. The proposed method is generalized to the case when the fuzzy initial data are given bifuzzy
IMPROVING STUDENTS’ VOCABULARY BY USING MIMICRY MEMORIZATION METHOD

The research is about improving the students’ vocabulary by using Mimicry Memorization Method of eighth grade at SMP Berea Tondano. The purpose of this research is to find out the students’ vocabulary improvement after teach by Mimicry Memorization Method. This research is quantitative research uses pre-experimental design with one group pre-test and pos-test. This study involves 18 students. The result of this study shows that the mean score of post-test (63,61) is higher than score of pre-test (55). The result of this research shows Mimicry Memorization Method is suitable to use in help the students to improve their vocabulary. English teacher suggests to apply Mimicry Memorization Method in order to help students to improve their vocabulary. The teacher should prepare the material well to get maximum result in teaching learning process. Teacher should create the condition of the class to be fun and livelier.Key Words: Improving, vocabulary, mimicry memorization method.
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium

In this paper we deal with robust cooperative multi-agent reinforcement learning (CMARL). While CMARL has many potential applications, only a trained policy that is robust enough can be confidently deployed in real world. Existing works on robust MARL mainly apply vanilla adversarial training in centralized training and decentralized execution paradigm. We, however, find that if a CMARL environment contains an adversarial agent, the performance of decentralized equilibrium might perform significantly poor for achieving such adversarial robustness. To tackle this issue, we suggest that when execution the non-adversarial agents must jointly make the decision to improve the robustness, therefore solving correlated equilibrium instead. We theoretically demonstrate the superiority of correlated equilibrium over the decentralized one in adversarial MARL settings. Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution. The global variables with mutual information are proposed to help agents learn robust policies with MARL algorithms. The experimental results show that our method can dramatically boost performance on the SMAC environments.
Peculiarities of students’ assessment of psychological safety of educational space

The purpose of the article. The aim of this study is identification of differences in assessments of the psychological safety of the educational space of cadets studying in special conditions and students of civilian institutions of higher education. Methodology. The basis of this study is the theoretical and methodological provisions of I. Baeva on the psychological security of the educational space and its structure. Results. In the course of the study, the following features of the study of the structural components of the psychological security of the educational space were established: – for students, relationships with teachers are more important than with other students, while for cadets, relationships with other cadets are more important than relationships with teachers; – the opportunity to seek help is more important for students than cadets, while maintaining personal dignity and the opportunity to take the initiative are more important for cadets than for students; – there was a difference in the assessments of cadets and students of satisfaction with the most important characteristics of the educational environment: cadets are slightly less than students who are satisfied with relationships with other cadets (while this aspect is most important for them) and attention to their requests and proposals; – when assessing the respondents’ own security, it was found that students feel equally protected from all threats. Cadets feel more protected from ridicule, threats, contempt and hostility, public humiliation, disregard by teachers than by classmates; – the analysis of reference indicators for the respondents of their free economic zones allows us to state that for students their free economic resources are very significant, while the cadets are mostly neutral towards their free economic resources. Practical implications. The identified features of the cadets’ assessment of the psychological safety of the educational space can be the basis for the elimination of those factors that are perceived as psychologically dangerous, and thus the creation of optimal conditions for the implementation of the educational process. Value (originality). The peculiarities of cadets’ assessments of such parameters of psychological security of educational space as satisfaction, security and referentiality are revealed. Key words: educational space, psychological security, psychological security of educational space, cadet, departmental institutions of higher education.
Exploring the effective use of mobile devices by previously disadvantaged English language student educators the Constructivist way

The use of smartphones and tablet computers is at the leading edge of technology and studying. The majority of students who study English in previously disadvantaged institutions (PDIs) possess handheld mobile devices, and this augurs well for the effective use of these devices. The use of handheld mobile devices can revolutionise the learning and teaching of English language among the students, who usually queue to use desktop computers. The use of handheld mobile devices could also foster collaborative and independent learning. The aim of this paper is to discuss the responses of English language student educators to a questionnaire on the use of handheld mobile devices at a PDI in South Africa.
Sikap Peserta Diklat PIM Tingkat IV terhadap Model Pembelajaran Blended Learning

ATTITUDES OF LEVEL IV PIM TRAINING PARTICIPANTS TOWARDS BLENDED LEARNING LEARNING MODEL. The development of Information Technology is one of the driving forces behind e-government adoption. Nevertheless, the learning process of Government officials capability creation is already conceivably undertaken electronically. This study aims to discover perceptions of government officials towards mixed learning in year 2018 group of 40 DIKLAT PIM IV training participants. This research takes a quantitative approach using descriptive statistics and Analysis of Variance as analysis tool. The T-test tested the first testing hypotheses. A one-way ANOVA was conducted to test second hypotheses. The outcome indicates a constructive attitude towards blended learning among DIKLAT PIM IV 2018 training participants. Gender-based attitudes about blended learning are substantially different.
Elucidation of Fixation Time on the Number of Answers of Kraepelin Test

Calculation tasks consist of reading, calculating, and writing. The purpose of this study is to clarify the students with a great number of response mechanisms, based on the amount of work, using the Kraepelin test as a model. For this purpose, it has been proposed to create bubble diagrams by classifying them into “writing” and “non-writing” (reading and calculating, pre-reading) tasks by combining eye movements and images. Based on the results obtained by this method, it was clarified that the students with great numbers of answers had less variance in fixation time of “non-writing” (especially in calculating), and “while writing” pre-reading was performed at the timing of writing beginning. Finally, for the students responding to the Kraepelin Metronome rhythm test or repeating the same problem, the eye movements were examined. It was clarified that the pre-reading occurs due to timing and calculating including memory.
Enhancing Accessibility of Messages Using Clustering and Labeling In Micro blogging’s

In recent years social media micro blogs has gaining popularity due to increasing availability, immediacy and new way of communication medium.The increasing quantity of micro blogging messages creates many challenges for its proper adoption.One such problem of difficulty in accessibility of interested micro blog messages is addressed in our proposed System using NLP technique.The traditional method of message accessing is slow and doesn't establish any semantic and structural relationship between the words in a sentence.The proposed system overcomes this using clustering and labeling of messages having similar semantic and verbal association.The result shows 50% improvement in the message accessibility compared to manual method of searching the particular message in a large micro blogs of 100 pages.
Decoding of (Interleaved) Generalized Goppa Codes

Generalized Goppa codes are defined by a code locator set $\mathcal{L}$ of polynomials and a Goppa polynomial $G(x)$. When the degree of all code locator polynomials in $\mathcal{L}$ is one, generalized Goppa codes are classical Goppa codes. In this work, binary generalized Goppa codes are investigated. First, a parity-check matrix for these codes with code locators of any degree is derived. A careful selection of the code locators leads to a lower bound on the minimum Hamming distance of generalized Goppa codes which improves upon previously known bounds. A quadratic-time decoding algorithm is presented which can decode errors up to half of the minimum distance. Interleaved generalized Goppa codes are introduced and a joint decoding algorithm is presented which can decode errors beyond half the minimum distance with high probability. Finally, some code parameters and how they apply to the Classic McEliece post-quantum cryptosystem are shown.