Reviewerly/exHarmony huggingface dataset:
SENSORY DEVELOPMENT OF SENIOR PRESCHOOLS WITH DEPRIVATION OF MENTAL DEVELOPMENT BY IMMONTESSORI-MATERIAL

The article analyzes the results of a study of the formation of standard ideas (color, size, shape) in preschoolers with mental retardation (MDR). The authors emphasize the levels of development of sensory standards in children with mental retardation, determine the main areas of work on the development of sensory standards in older preschool children with CPD using Montessori materials. Following the principles of classical humanism, M. Montessori created a concept based on the child's personality, a concept based on confidence in the abilities of each child, his ability to succeed despite special needs. Classes on this method are held in a specially prepared environment. Since Montessori's approach assumes that the game room should be divided into several parts, the following areas are considered standard: natural, practical life, sensory, linguistic, mathematical, to which it is advisable to add creative, playful and sports. The child can do where h likes and how much he likes. The task of the teacher is to "adapt" to the interests of the child, for whom it is important to create space for action. Under such conditions, children will learn easily and with pleasure. The use of Montessori materials is aimed at the development of fine motor skills, senses (sight, hearing, taste, smell, touch). According to M. Montessori, careful actions with small objects form intelligence, develop speech, attention and memory, so in her system there are many activities related to the operation of objects, guessing them by touch, sorting. We consider it expedient to continue working in the outlined direction in order to prove the effectiveness of this method with its further implementation in the educational process of children with CPD, as well as to adapt this method for other categories of children. Studies have shown that Montessori materials are an effective means of sensory development of older preschoolers with mental retardation. As a result of the application of this technique, most children improve tactile sensitivity, general and fine motor skills, visual-motor coordination, attention and memory.
Research on the impact and strategy of integrating artificial intelligence technology into the development of university education

In recent years, artificial intelligence has not only had a greater impact on people's lives, but has also revolutionized our education system and provided a direction of teaching reform for traditional teaching.The integration of advanced artificial intelligence technology into college teaching has become the mainstream of the times.This paper firstly outlines the current situation and development trend of AI college education research at home and abroad, cites the application of AI in college education, and makes a case study of Shanghai Jiaotong University classroom, and finally puts forward the current problems and corresponding countermeasures.We hope to be able to relevant research to promote the reform of college teaching with advanced national artificial intelligence technology, and the contribution to the development of college education in China.
Research on the Design of Online Teaching Platform of College Dance Course based on IGA Algorithm

As a comprehensive art form, dance plays an integral role in developing the overall quality of students.However, with the increasing progress of IT technology, the traditional classroom-based teaching mode of dance course can no longer adapt to the current educational environment.This research designs an e-teaching platform system for college dance lessons based on the IGA algorithm (Improved Genetic Algorithm, IGA).First, it establishes a mathematical model of artificial intelligence questions and then proposes the functional design of an online teaching platform for college dance course based on the IGA algorithm.The feasibility of the proposed online teaching platform system of college dance course based on the IGA algorithm is validated via a number of testes.When the iteration count is 500, the success rate of the IGA algorithm reaches 99%; when the iteration count is 100, the average fitness is 0.929; when the iteration count is 100 times, the moderate calculation value is 0.936.While for the traditional Genetic algorithm (GA), the results are 88.6%, 0.73, and 0.752, respectively.By comparing with the traditional teaching mode based on GA algorithm, the proposed method based on IGA algorithm is obviously superior in many aspects.
Attack and Defense Methods for Graph Vertical Federation Learning

To further protect citizens' privacy and national data security, graph federation learning has been widely used and rapidly developed. However, with the deployment and landing of graph federation learning tasks, the security issues involved are gradually exposed. To deeply study the application security issues of graph federation learning, this paper proposes an attack method and privacy protection defense method for graph data in the framework of vertical federation learning. The research revolves around the attack method. First, noise is randomly generated, combined with the attacker's embedding features, and fed into the server model, and the calculated results are compared with the real values to obtain the loss values. Then the attacker's attack model is updated to generate a new inference of the attacked embedding. The experiments conducted on two real-world datasets both obtained MSE metrics below 1, which fully illustrates the effectiveness of the attack method. Further research is conducted around the defense method, which uses a computed differential noise added to the uploaded embedding information to achieve the defense against privacy theft. In the experiments, the related attack metrics are significantly reduced with almost no impact on the main task performance.
A Brief Analysis on the Psychological Advantages of Natural English Teaching Approach for Children

To Chinese primary school students, English belongs to the second language of learning.In the actual learning process, subject to numerous factors such as the huge difference between native language and foreign language, it is impossible to completely eliminate various cultural barriers and regional barriers.However, by applying the Natural Approach where teaching activities are carried out in a natural teaching mode, English classroom teaching can be closely linked with students' real life.Through setting up a natural and real classroom environment and using flexible and diverse teaching approaches, students' learning enthusiasm can be better stimulated, who can be guided to acquire certain language skills in an active way, thus improving the classroom teaching effect.
Data augmentation methods for semantic segmentation-based mobile robot perception system

Data augmentation has become a standard technique for increasing deep learning models? accuracy and robustness. Different pixel intensity modifications, image transformations, and noise additions represent the most utilized data augmentation methods. In this paper, a comprehensive evaluation of data augmentation techniques for mobile robot perception system is performed. The perception system based on a deep learning model for semantic segmentation is augmented by 17 techniques to obtain better generalization characteristics during the training process. The deep learning model is trained and tested on a custom dataset and utilized in real-time scenarios. The experimental results show the increment of 6.2 in mIoU (mean Intersection over Union) for the best combination of data augmentation strategies.
On Feuerbach's View of History

Feuerbach, as a humanist, set up the essence of human beings with the concept of category, and believed that it was the object religious consciousness that first emerged from human beings and its transformation that determined the trend of different historical stages.The Christianity that was subordinate to God, the political religion that belonged to human beings, and the religion that belonged to "love" that determined the social content of human beings in different periods.In the historical perceptual activities, it is reason, will and love, as the essence of human beings, as the highest source and absolute rights, that provide the awareness and cognitive power of the existence of objects.In human history, the development of history can not go beyond the principle of the essence of category.Therefore, the infinite category consciousness determines the development inevitability, inheritance and conditionality of human history.
Research on the application of computer big data technology in the auxiliary system for teaching after-school exercises

College English teaching after-school problem assistance system is a new stage of deep integration of English teaching and modern information technology, and it is also a hot spot in current education informatization research. The college English teaching after-school problem assistance system is guided by the English classroom teaching theory in colleges and universities. This system is a quality-effective dynamic intelligent classroom that is applied in all stages and processes before, during and after class. Based on the review of the current situation of college English smart teaching in the era of big data, this paper proposes that the design of college English classroom teaching under the background of big data should be guided by the concept of smart teaching, and the online teaching of college English should be fully combined with offline teaching. As for the teaching strategy of this key learning link after class.
Sentence Forming Errors Based on the Valence of German Verbs in Student Writing in the Aufsatz II Course

This study aims to describe the form of sentence errors based on the verb valency in the student's essay in the Aufsatz II course and identify the factors that influence the students to make these kind of mistakes. This research used qualitative descriptive method. The data source of this research were the students of the German Literature Department, Universitas Negeri Malang class of 2017 Offering C. The data used in this research are sentence errors based on the verb valency found on the student’s essays and interview results. The results of this research indicate that: (1) Students made mistakes in writing sentences based on the verb valency, with the form of complements errors, such as of Nominativergänzung, Akkusativergänzung, Dativergänzung, Präpositivergänzung, Situativergänzung, Direktivergänzung, sentence errors with reflexive Verben, errors in adding sentence constituents, verb selection errors, and sentence without verb errors, (2) factors that cause those errors were difficulty in learning the German grammar, difficulty in determining the complements required by the verb, Indonesian interference, and rarely practice writing in German.
Empirical investigation of the influence of continuous integration bad practices on software quality

Continuous Integration (CI) is a powerful tool to leverage software development in a safe, reliable, and efficient way. However, in day-to-day of software development, bad habits can arise in working with CI, which can make CI not reach its full potential in projects. These bad habits we call CI bad practices. This paper presented an exploratory study on closed-source projects to investigate how CI bad practices can affect software quality. We observe (1) the impact on the quality internal attributes after the implantation of CI, (2) the evolution of software quality indicators over time, and (3) the bad practices considered most harmful to the software quality by the development teams. Our results mean that projects affected by CI bad practices will not necessarily have their quality impaired. However, this does not mean that quality will be maintained or improved over time. Our analysis of the quality indicators has allowed us to observe that, generally, they have remained stable, and the number of quality issues reported has not decreased considerably either. Finally, the quality indicator most affected by the CI bad practices was maintainability, followed by reliability and security. In addition, the vast majority of the bad practices analyzed were classified as having a medium level of effort for resolution. In this sense, we also classify bad practices according to the level of effort/impact on the quality that can help prioritize their resolution.
Comparative Analysis on EEG Controlled Wheelchair using Raspberry Pi and ATMEGA-328 Microcontroller

Aim: The aim of this research work is to analyze the accuracy of the ATMEGA-328 Microcontroller, used in Electroencephalogram(EEG) controlled wheelchairs, and to compare the accuracy rate between ATMEGA-328 and Raspberry Pi microcontroller.Materials and Methods: Data collection containing innovative wheelchair control movement output from the hardware designed was used in this research.sampleswere considered as (N=20) for ATMEGA-328 microcontroller , and (N=20) for Raspberry Pi microcontroller in accordance to total sample size calculated using clinical.comby keeping the alpha error threshold by 0.05, enrollment ratio as 0:1,95% confidence interval , power at 80%.Result: Comparison of accuracy rate is done by independent sample test using IBM-SPSS software.There is a statistical indifference between the output obtained from ATMEGA-328 (65.4%) showed higher results in comparison to Raspberry Pi (58.4%) with (p=0.080;p>0.05).Conclusion: EPOC headset appears to give better sensitivity than Neurosky mindwave portable headset in processing the EEG signals in an EEG controlled wheelchair.
Improving Dynamic Hand Gesture Recognition based IR-UWB using Offline Data Augmentation and Deep Learning

This paper presents a dynamic hand gesture recognition method that relies on combining a deep learning model with a traditional classifier, with the aim of eliminating the manual feature extraction phase. To this end, we proposed to use a multi-stream CNN-LSTM for automatic feature extraction in conjunction with a multi-class Support Vector Machine (SVM) for classification. The proposed model consists of an efficient and lightweight architecture based on depth-separable convolution layers, which effectively reduces the computational cost and learning time while maintaining high recognition performance. The model is trained on the public UWB Gestures dataset and can automatically learn range-time information to identify different types of hand gestures. In order to achieve optimal results and avoid overfitting, we adopted offline data augmentation strategies to expand the training sample size. The results obtained showed that our proposed model achieves high performance on several measures, including precision, recall, F1-score and accuracy.
A Note on the Maximum Size of a Prefix Code

In the presented paper, we investigate the problem of finding the maximum possible cardinality of a dictionary of a prefix code for a string of a given length. Namely, we present a sharp proof of the cardinality of such a dictionary using results from the number theory. What is more, the presented formula is for the general case of a string over any, not just binary, alphabet. Furthermore, we give conditions on the existence of the so-called canonical dictionary for such a string, where the codewords of the dictionary have at most two different lengths, differing by one. Our approach is based on reformulating the problem of finding the maximum possible cardinality of a dictionary for a string of a given length as the problem of finding the maximum possible number of summands in the Kraft-Szillard partition of the number representing the length of the string, by solving a Diophantine equation related to the canonical partition of the number. One of the areas of applications of presented results is the security-estimate of ciphers based on prefix codes.
Experimental Study and Intelligent Prediction on Pressure Fluctuation of Accumulator Under Ocean Conditions

Abstract Liquid sloshing will occur in liquid storage tanks such as accumulator of floating nuclear power plant (FNPP) subjected to additional inertial forces under motion conditions. The study carried out measurement experiments based on the 6-DOF platform to study the sloshing characteristics and pressure variation rule of the accumulator. The results show that surging will induce many kinds of nonlinear free surface sloshing forms, it can be seen that the law of pressure variation is mainly dominated by natural frequency and excitation frequency based on time and frequency domain analysis. Then the study combines the automatic encoder and extreme learning machine to build the deep extreme learning machine (DELM) network to predict the pressure in time series. Based on the phase space reconstruction of the time sequence, the pressure results of the next time are output after the last 15 pressure data are input. The prediction results show that the DELM model has fast speed and high precision and the predicted value is in good agreement with the experimental data. So this study can provide a reference for the pressure monitoring and the artificial intelligence application of FNPP.
Research on the promotion mechanism of college teachers' core competence in information-based teaching

Building a team of high-quality teachers is the basic work of university construction. This paper specifically analyzes the meaning of College Teachers' information-based teaching core ability, and comprehensively improves our teachers' information-based teaching ability by building a platform for improving teachers' information-based teaching ability, designing teachers' information-based teaching ability improvement activities and constructing information-based teaching ability training resources.
MCQL: A Multi-node Consortium Blockchain Query Method Based on Node Dynamic Adjustment

Blockchain plays an important role in the secure storage and efficient query of application data, and blockchain query performance is also a challenging research task. The system may send a large number of query requests to a node with strong responsiveness, which will cause the node to be paralyzed and cause the system to crash. Due to the high time complexity of consensus reached by single-layer nodes in the consortium blockchain system, it affects the generation of new blocks, which indirectly affects the system query performance and reduces query efficiency. And for a multi-node system, using the same access priority for nodes with different corresponding rates will undoubtedly affect the query efficiency of the system. This paper proposes a query method in the case of multiple nodes in a consortium blockchain network. This method divides nodes into different levels according to their response capabilities, gives incentive values to nodes that successfully respond to query requests, and dynamically adjusts the level of nodes according to the incentive values. The higher the level, the higher the access priority, which fully considers the security and query performance. The experimental results show that the performance of the method proposed in this paper is better than the existing multi-node query methods of the consortium blockchain, which can improve the query performance of the consortium blockchain system and ensure the security of the system.
Compound Critiquing for Improving Query Refinement on Conversational Recommender System

A conversational recommender system (CRS) is a form of a recommender system to recommend a product through conversational dialogue. We have developed a CRS based on product functional requirements in previous research. This CRS has good interaction capabilities between the system and the user because it can accommodate users unfamiliar with technical features (novice users). However, when user needs are still general, the system will ask questions again to narrow down the user needs (query refinement). A good query refinement process is when one iteration of query refinement can significantly reduce the number of products that match the query. Thus, the interaction process can be more efficient. The problem with functional requirements-based CRS is that interaction based on functional requirements causes the query refinement process to be slower. Based on this problem, we combine query refinement based on functional requirements and technical features to speed up the query refinement process so that user-system interaction is expected to be more efficient. In this study, we focus on applying compound critiquing for technical features-based query refinement involving compound critiquing. We propose the FP-Growth algorithm for generating frequent itemset because it has the fastest execution time than other algorithms such as Apriori and ECLAT.
SePass: Semantic Password Guessing Using k-nn Similarity Search in Word Embeddings

Password guessing describes the process of finding a password for a secured system. Use cases include password recovery, IT forensics and measuring password strength. Commonly used tools for password guessing work with passwords leaks and use these lists for candidate generation based on handcrafted or inferred rules. These methods are often limited in their capability of producing entirely novel passwords, based on vocabulary not included in the given password lists. However, there are often semantic similarities between words and phrases of the given lists that are highly relevant for guessing the actual used passwords. In this paper, we propose SePass, a novel method that utilizes word embeddings to discover and exploit these semantic similarities. We compare SePass to a number of competitors and illustrate that our method not only is on par with these competitors, but also generates a significant higher amount of entirely novel password candidates. Using SePass in combination with existing methods, such as PCFG, improves the number of correctly guessed passwords considerably.
Who Is Missing? Characterizing the Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus

A conversation corpus is essential to build interactive AI applications. However, the demographic information of the participants in such corpora is largely underexplored mainly due to the lack of individual data in many corpora. In this work, we analyze a Korean nationwide daily conversation corpus constructed by the National Institute of Korean Language (NIKL) to characterize the participation of different demographic (age and sex) groups in the corpus.
Lithological Patterns Based on Arc Fitting

Map of geological cross section is the most intuitive expression of strata in the vertical direction.Realizing automatic filling of lithologic patterns and reflecting the changes of stratum curvature are important contents of map of geological cross section drawing.The traditional lithologic pattern filling method can not show the change of rock stratum dip angle.To solve this problem, an algorithm of lithologic pattern drawing based on arc fitting is proposed.By analyzing the mathematical relationship between the dip angle of the rock stratum and the boundary line of the rock stratum, the data needed for drawing can be increased, and then the curve and lithologic pattern can be drawn according to the dip angle of the rock stratum.The application results show that the lithologic pattern compiled by the algorithm changes with the change of rock dip, and the first derivative of a single curve in the lithologic pattern is continuous and the segments are circular arcs.