Reviewerly/exHarmony huggingface dataset:
The mediating effect of grit on the relationship etween the self-esteem and motivation for choosing teaching profession of pre-service early childhood teachers

본 연구는 예비유아교사의 자아존중감과 교직선택동기 간의 관계에서 그릿의 역할을 살펴보고, 예비유아교사의 긍정적인 교직선택동기를 강화하기 위한 방안을 모색하고자 수행되었다. 이를 위하여 예비유아교사 215명을 대상 으로 설문조사를 바탕으로 자료를 분석하였다. 그 결과에 따르면 첫째, 예비유아교사의 자아존중감, 능동적 교직선 택동기, 그릿은 보통 이상 수준을 보였으나, 수동적 교직선택동기는 보통보다 낮은 인식수준을 보였다. 둘째, 예비유 아교사의 자아존중감, 교직선택동기, 그릿 간의 상관관계에서 자아존중감과 능동적 교직선택동기, 자아존중감과 그릿, 능동적 교직선택동기와 그릿은 정적 상관이 있었으나, 수동적 교직선택동기는 자아존중감과 그릿과의 관계에서 부적인 상관을 나타냈다. 셋째, 예비유아교사의 자아존중감과 교직선택동기 중 능동적 동기와의 관계에서 그릿은 부분매개효과가 있었다. 이는 예비유아교사의 능동적 교직선택동기를 강화하기 위해서는 자아존중감과 그릿을 강화하기 위한 노력이 필요함을 시사한다.The purpose of this study was to investigate the role of grit in the relationship between self-esteem and motivation for choosing teaching profession(MCTP). For this purpose, 215 pre-service early childhood teachers were surveyed and analyzed. The results of analysis are as follows: Frist, the self-esteem of the pre-service early childhood teacher, the active MCTP, and grit were higher than average, but passive MCTP was lower than average. Second, there were a positive correlation between self-esteem and active MCTP, self-esteem and grit, active MCTP, but passive MCTP were negatively correlated with self-esteem and grit. Third, Grit had a partial mediation effect on the relationship between self-esteem and motivation for choosing teaching profession of pre-service early childhood teachers. This suggests that pre-service early childhood teachers need more effort to strengthen their self-esteem and grit in order to increase motivation for choosing teaching profession.
Cognitive Ecology and Cognitive Agriculture - New Interdisciplinary Scientific Areas

Cognitive ecology and cognitive agriculture are new scientific areas that have arisen at the interface of neurobiology (neurogenetics and neurophysiology), physiology, ecology and agriculture.Over the past decades of human development, individually, various scientific fields have achieved great success.However, at this stage of development, great attention should be paid to the development of interdisciplinary research (theoretical and practical).Cognitive ecology is based on recent advances in neurogenetics (nano-model theory of genome functioning) [5], neurophysiology (modern understanding of the formation of higher nervous activity) [9] and human physiology (presence of the biocommunication system in humans) [10].Cognitive ecology is of strategic importance for environmental protection.Cognitive agriculture is mainly based on the presence of the biocommunication system in humans, animals and plants, which creates a common biological field of our ecosystem.This biofield has a completely molecular structure and consists of free-living microorganisms and mobile genetic elements.Cognitive agriculture is aimed at ensuring the food security of mankind.Before we present the practical significance and benefit of these scientific areas, let us give a scientific definition of cognitive science, ecology, cognitive ecology and cognitive agriculture.
A Product Similarity Method Based on Deep Confidence Network

To improve product recommendation network, this paper mainly proposes a product similarity calculation algorithm based on deep confidence network.A high-dimensional product is firstly constructed and then input into the DBN model to obtain low-dimensional product feature data.Founded on the low-dimensional product feature data, the similarity between products can be calculated by the cosine formula.Through the data experiment, it is found that as the output dimension of the low-dimensional product feature matrix decreases, the similarity of the product similarity matrix also decreases, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing, which means that the information extracted from the original input matrix is refined, and the effective information of a product is increasing.
Are Sampling Heuristics Necessary in Object Detectors

In training deep object detectors, the problem of foreground-background imbalance has been addressed by several heuristic methods, such as online hard example mining (OHEM), Focal Loss, and gradient harmonizing mechanism (GHM). These methods either re-sample the training examples (i.e. hard sampling), or re-weight them discriminatively (i.e. soft sampling). In this paper, we challenge the necessity of such hard/soft sampling heuristics in training deep object detectors. First, without hard/soft sampling, we reveal that the scale and the stability of the classification loss greatly influence the final accuracy. Thus, we propose a guided loss scaling technique to control the classification loss during training, without using any hyper-parameter. We also propose to optimally initialize the model to ensure the stability of the classification loss. Moreover, we propose an adaptive thresholding technique to refine predictions during inference. These three ingredients constitute our Sampling-Free mechanism, which is fully data diagnostic and avoids the laborious hyper-parameter search for hard/soft sampling. We verify the effectiveness of our Sampling-Free mechanism in training one-stage, two-stage, multi-stage, and anchor-free object detectors, where our method always achieves higher accuracy on COCO and PASCAL VOC datasets. We also use the Sampling-Free mechanism for instance segmentation to demonstrate its generalization ability. Code is released at: this https URL.
Merging Clusters in Summary Structures for Data Stream Mining based on Fuzzy Similarity Measures

Fuzzy Clustering is one of the mining techniques that have been used to extract information from Data Streams.The d-FuzzStream algorithm is a fuzzy version of the Online-Offline Framework, which consists of two steps: an online step, where a summary structure formed by fuzzy microclusters is built and an offline step, where the micro-clusters are clustered in batch mode.The quality of the data summary depends on the criteria used to decide whether an example starts a new micro-cluster or is absorbed by the existing ones; and whether two microclusters became similar enough to be merged.In d-FuzzStream algorithm such decisions are based on concepts of fuzzy dispersion and a distance-based fuzzy clusters similarity.In this paper we investigate the behavior of different fuzzy similarity measures on the decision of merging two fuzzy micro-clusters during the online step.Experiments were run using five synthetic data sets and four fuzzy similarity measures.The results obtained are analyzed and discussed through informative and purity measures.
VLSI Architecture for Systolic-Like Modular Multipliers over GF (2m) Build on Irreducible All-One Polynomials

By using irreducible AOP, an effective recursive formulation is proposed implies systolic implementation of these finite field multiplications over GF (2 m ). Here, a recursive algorithm derived for the multiplication and used it in designing a systematic and localized bit-linear dependence graph for computing systolic multiplication. This dependence graph is altered to a fine-grained dependence graph (DG) by using node splitting method. This parallel systolic architecture is mapped from finegrained DG. Compared to other structures, it doesn't include any global communication for reducing the modules. The proposed architecture has same time compared to other existent bit-parallel systolic structure and includes registers to a lesser extent. This proposed structure has an ascendable latency of l+ [log 2 s] +1 cycles which is minimum compared with existing designs. This structure is proposed specifically for hardware complexity in the structure and throughput scalability to meet the area-time tradeoff by maintaining the overall latency in resource-constrained application.
Speech by the chairman of the far eastern branch of the RAS, RAS academician V.I. Sergienko

Speech by the chairman of the far eastern branch of the RAS, RAS academician V.I. Sergienko
Analysis and Assessment of Physical Chemistry Examination Proposition with Educational Measurement

This paper introduces the specific method of educational measurement by analyzing and evaluating final examination proposition of polymer chemistry. Long-term teaching practice shows that this method could guide and improve the teaching effects and should be widely applied and mastered among teachers.
Weighted Context-Free Grammars Over Bimonoids

We introduce and investigate weighted context-free grammars over an arbitrary bimonoid K. Thus, we do not assume that the operations of K are commutative or idempotent or they distribute over each other.We prove a Chomsky-Schützenberger type theorem for the series generated by our grammars.Moreover, we show that the class of series generated by weighted right-linear grammars over a linearly ordered alphabet Σ and K coincides with that of recognizable series over Σ and K.
Vessel Detection in Space-Borne Photography using CNN

Information extracted from aerial photographs has found applications in different areas including agriculture, meteorology, oceanography, defence and climate modelling. In many cases information extraction is still performed by human experts, making the process slow costly, and error prone. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. Monitoring human activity and analysing the patterns of sea vessels or ships is a topic of major and increasing interest. From fishing, exploration, supply analysis, drilling, transportation, tourism all these are monitored by government using the satellite imagery. Maritime Domain Awareness (MDA) is the main governing body which looks over the security, safety and economy by defining areas and boundaries. The aim of this project is to address the difficult task of detecting the location of large ships in satellite images. We would like to do this automated process by using Convolution Neural Networks. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.We obtained 4000 images of various ports from state California.1000 images contain ships in them and the remaining 3000 images doesn't have any ships in them.
Steganography Performance over AWGN Channel

Steganography can be performed using frequency domain or spatial domain.In spatial domain method, the least significant bits (LSB) is the mostly used method where the least significant bits of the image's pixels binary representation are used to carry the confidential data bits.On the other hand, secret data bits in the frequency domain technique are hidden using coefficients of the image frequency representation such as discrete cosine transform (DCT).Robustness against image attacks or channel's noise is a key requirement in steganography.In this paper, we study the performance of the steganography methods over a channel with Added White Gaussian Noise (AWGN).We use the bit error rate to evaluate the performance of each method over a channel with different noise levels.Simulation results show that the frequency domain technique is more robust and achieves better bit error rate in a noisy channel than the spatial domain method.Moreover, we enhanced the steganography system robustness by using convolution encoder and Viterbi decoder.The effect of the encoder's parameters, such as rate and constraint length is evaluated.
Cloud Management System A Case Study of Bundelkhand University

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Analysis of Speech Function and Mood System of WhatsApp’s Terms of Service

This study is aimed to find out the kinds of speech function realizations in Whatsapp’s terms of service and to find out mood types in each clause in WhatsApp’s terms of service. It is analyzed through identification of clauses Mood structures then being classified into four kinds of speech function; Statement, Question, Command and Offer.&#x0D; There are two kinds of clauses. They are 186 clauses of Terms of Service and 150 clauses of Privacy Notices. The mood types found in the text are 139 declarative clauses and 23 imperative clauses on text of Terms of Service. There are 123 clauses of declarative and 18 imperative clauses on the text of Privacy Notices. There are 262 declarative clauses in text, 41 imperative clauses. It means that declarative clauses are the most dominant in the text. The speech functions found are 262 (86, 46%) statements {divided in 139 in Terms of service and 123 in privacy notices}, command is 41 (13, 53%) {divided in 23 in the Terms of service and 18 privacy notices.&#x0D; It can be concluded that the Terms of service and privacy notices are giving information and ask the reader to do something to the users of WhatApps’s applications. WhatApp’s Terms of Service were dominated by declarative as mood types and all of them used statements as speech functions. Privacy Notices were dominated by declarative as mood types and statements as speech functions. It means that WhatsApp’s Terms of Service mostly give information than demand service and privacy notices mostly give the information and contains of demand of users.&#x0D; &#x0D; Keywords: Speech functions, mood types.
Forced Decoding Based Rule Extraction for Phrased-Based Translation

Statistical machine translation models typically learn phrase or syntactic translation rules extracted from parallel data with word alignments, but the approach suffers from a problem where some desirable rules are blocked due to word alignment mistakes. Many researchers focus on improving the translation rule extraction by correcting word alignment mistakes, while this paper presents a phrase-based forced decoding method to address this issue, and a rule-augmentation approach is proposed to improve the quality of translation based on the translation rules extracted by forced decoding method. Experiment on Chinese-English translation task demonstrates significant improvements over the baseline system.
The Proving of Artificial Intelligence Generated Content’s Works of Authorship Characteristic and the Authorship of Artificial Intelligence

In the field of intellectual property law there are many different views on the attributes of artificial intelligence generated content and the identity of artificial intelligence. Judging whether a thing is a work of authorship or not should follow objective criteria. The artificial intelligence generated content satisfies the criteria of "originality" and "intellectual achievement". Therefore, we should recognize and protect it as a work of authorship under the copyright law. Besides, from the perspectives of the influence on human enthusiasm for creation, the impact upon the legal system and the history of the development of intellectual property law, it is reasonable to recognize artificial intelligence as authorship.
Revisiting Foreground-Background Imbalance in Object Detectors

In training deep object detectors, the problem of foreground-background imbalance has been addressed by several heuristic methods, such as online hard example mining (OHEM), Focal Loss, and gradient harmonizing mechanism (GHM). These methods either re-sample the training examples (i.e. hard sampling), or re-weight them discriminatively (i.e. soft sampling). In this paper, we challenge the necessity of such hard/soft sampling heuristics in training deep object detectors. First, without hard/soft sampling, we reveal that the scale and the stability of the classification loss greatly influence the final accuracy. Thus, we propose a guided loss scaling technique to control the classification loss during training, without using any hyper-parameter. We also propose to optimally initialize the model to ensure the stability of the classification loss. Moreover, we propose an adaptive thresholding technique to refine predictions during inference. These three ingredients constitute our Sampling-Free mechanism, which is fully data diagnostic and avoids the laborious hyper-parameter search for hard/soft sampling. We verify the effectiveness of our Sampling-Free mechanism in training one-stage, two-stage, multi-stage, and anchor-free object detectors, where our method always achieves higher accuracy on COCO and PASCAL VOC datasets. We also use the Sampling-Free mechanism for instance segmentation to demonstrate its generalization ability. Code is released at: this https URL.
C# parser for extracting cryptographic protocols structure from source code

Cryptographic protocols are the core of any secure system. With the help of them, data is transmitted securely and protected from third parties' negative impact. As a rule, a cryptographic protocol is developed, analyzed using the means of formal verification and, if it is safe, gets its implementation in the programming language on which the system is developed. However, in the practical implementation of a cryptographic protocol, errors may occur due to the human factor, the assumptions that are necessary for the possibility of implementing the protocol, which entail undermining its security. Thus, it turns out that the protocol itself was initially considered to be safe, but its implementation is in fact not safe. In addition, formal verification uses rather abstract concepts and does not allow to fully analyze the protocol. This paper presents an algorithm for analyzing the source code of the C# programming language to extract the structure of cryptographic protocols. The features of the implementation of protocols in practice are described. The algorithm is based on the searching of important code sections that contain cryptographic protocol-specific constructions and finding of a variable chain transformations from the state of sending or receiving messages to their initial initialization, taking into account possible cryptographic transformations, to compose a tree, from which a simplified structure of a cryptographic protocol will be extracted. The algorithm is implemented in the C# programming language using the Roslyn parser. As an example, a cryptographic protocol is presented that contains the basic operations and functions, namely, asymmetric and symmetric encryption, hashing, signature, random number generation, data concatenation. The analyzer work is shown using this protocol as an example. The future work is described.
Layanan Informasi Karier Berbasis Field Trip untuk Meningkatkan Pemahaman Karier Siswa

CAREER INFORMATION SERVICES BASED ON FIELD TRIP TO IMPROVE STUDENTS UNDERSTANDING. The aims of this study is to: (1) find out the implementation of career information services in Magelang Regency; (2) produce career information service models with field trip techniques to improve career understanding of vocational students; (3) determine the level of career understanding of students in Muhammadiyah 1 Mertoyudan Vocational School; and (4) knowing the effectiveness of career information services with field trip techniques to improve career understanding of students at Mertoyudan 1 Muhammadiyah Vocational School. The population in this study were students of class XII of Muhammadiyah 1 Mertoyudan Vocational School with 29 students who were used as samples. The study design used pre-experimental: one group pretest-posttest design with saturated sampling. The data analysis technique used is Paired sample t-Test with consideration in this study using one independent variable (Independent Variable). The result of the effectiveness test of this study is that there is an increase in students' career understanding through career information services with field trip techniques. The level of career understanding of students has increased after participating in career information services with field trip techniques. This is seen from the increase in the results of the student's career understanding scale before and after being given career information services with a field trip technique of 28% and strengthened through the effectiveness test using a t-test that shows the -t count &lt;-t-table, which is -9,583 &lt;-2,045 or data Sig (0,000) &lt; 0.05.
A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.
Development of Mathematics Learning Books for Primary Teacher Education Students

This study aims to compile a book for elementary mathematics learning media for students of the faculty of education, Medan State University.This elementary school mathematics learning book is compiled based on observations on lecturing time.It is seen that the learning resources used by the lecturers are dictates that have been prepared for a long time without being revised to upgrade the material contained in the dictates so that they are in accordance with the current curriculum.The specific objectives of this research are: developing a book of elementary mathematics learning media which will be used by students of the faculty of education in the lecturing process replacing the dictates they have been using.This research uses Developmental Research.The subject of this research is students of the faculty of education in Medan State University.In the early stages, observations were made during the lecture process and continued by conducting interviews with students.From the results of the observations and interviews, an initial draft of the elementary school mathematics learning book was compiled.Furthermore, the draft of the elementary mathematics learning media book was validated by a team that aims to assess the quality of the elementary mathematics learning books so that an effective learning media book will be compiled.The results targeted in this study are effective elementary mathematics learning books that will later be used by students of the Faculty of Education of Medan State University, replacing the long-used dictates.