Reviewerly/exHarmony huggingface dataset:
A Study on Pre-Service Teachers’ Perceptions and Experiences about Online Teaching Practicum

This study aims to explore pre-service teachers’ perceptions and experiences about online teaching practicum. To this end, a questionnaire survey was conducted for 40 fourth-year pre-service teachers who had teaching practicum in the spring semester of 2020 and 24 third-year pre-service teachers who had teaching practicum in the fall semester of 2020. The analysis of the survey results shows the perceptions of pre-service teachers online teaching practicum was divided into four major categories and ten small categories. Pre-service teachers mentioned the effect of the on-line teaching practicum, which allowed them to learn as much as face-to-face teaching practicum, and responded that they could prepare for future education, especially by learning the media for online classes. Third and fourth year pre-service teachers showed some differences in their perceptions and experiences about online teaching practicum. Fourth-year pre-service teachers initially started with negative thoughts on online teaching practicum, but changed to positive perceptions throughout the practicum. Starting with the burden and concern of the first teaching practicum rather than worrying about the online teaching practice itself, third-year pre-service teachers were able to have positive experiences of online teaching practicum. Through the help of teachers and several real-time and video class experiences, they could learn about their shortcomings in planning and teaching lessons. Almost all of the pre-service teachers were most disappointed in technical aspects such as network speed, sound problems, and lack of interaction with students. Pre-service teachers also mentioned the need for more practical pre-training, which not only informs how to use the programs such as MS Teams but also informs how to overcome the various problems that arise in real-time classes. In the case of the English class, they wanted more guidance on the class situation where there was less interaction with students, the activities to replace group activities, and the problems of solving the level differences among students.
Towards Generalized Artificial Intelligence by Assessment Aggregation with Applications to Standard and Extreme Classifications

The paper studies the impacts of an aggregative Convolutional Neural Network (CNN) for the derivation of generalized artificial intelligence when starting from a specialized set of CNNs. The aggregation proposed relies on machine learning from specific examples being the categorical probabilities fed by the downstream specialized CNNs under consideration. Aggregation proof of concepts are provided in terms of multimodel, multimodal and distributed schemes. The multimodel framework is such that different CNN models operating on the same modality cooperate for decision purpose. The multimodal framework implies specializations of CNNs with respect to different input modalities. The distributed framework proposed is associated with assessment exchanges: it such that the aggregation aims at determining relevant joint assessments for mapping a given input to a single or a multiple output category. Performance of these aggregation frameworks are shown to be outstanding for both standard and extreme classification issues.
The research and development of parallel high speed electronic data destruction platform

With the rapid development of information technology, the working data is stored in variety of storage media, and data destruction becomes the frontier and hot issue in recent years. Data destruction business has become a developing direction of State Grid information work. But data destruction time is always long, and storage media transportation always occupies a lot of resources. In order to solve this question, this paper thinks of development of parallel high speed electronic data destruction-platform, and shows the situation of the equipment research, development in detail. The application of data destruction platform is widely and electively and the solution can improve the efficiency of data destruction, reduce working cost.
A Practical ROI and Object Detection Method for Vision Robot

For visual robots, accurate recognition of objects and extraction of useful features are the basis for the next action. In order to exclude the influence of non-critical regions and improve the processing speed, this paper proposes a target detection method based on regions of interest (ROI). Firstly, the preprocessing steps such as filtering, color space conversion, and contour extraction are carried out for the acquired image frames. Then, the contour mask is used to obtain the irregular ROI. After that, color, shape, area and other information are adopted to identify the target and extract features such as angle and distance. This method achieves excellent performance on monocular robots.
Research on Image Classification Method Based on Improved Xception Model

Image classification is an important basic problem in computer vision research, and it is also the basis of other high-level vision tasks such as image segmentation, object tracking, and behavior analysis. Since the features extracted by the neural network are not necessarily all useful features, this paper is optimized on the basis of Xception, and a Convolutional Block Attention Module (CBAM) is introduced to learn channel attention and spatial attention information separately to enhance the discrimination of features. Experiments were conducted on the monkey breed classification dataset. The results show that the improved Xception model based on the CBAM module proposed in this paper can classify these images with an accuracy of 90.05%. Compared with related algorithms, the training accuracy of this model increased by 0.6–1.2%, which further proves that the CBAM module can improve the accuracy of Xception, so as to improve the reliability and stability of image classification.
Class incremental learning via Multi-hinge distillation

Class incremental learning has been proposed and developed widely to solve the problem in which training data is continuously used to extend the model's knowledge. It has been applied in many fields, such as pattern recognition and computer vision. However, traditional incremental learning methods facing the challenge of catastrophic forgetting. To this end, we use a generative adversarial network to assist the model's class incremental learning. Apart from that, some methods employ old classifier output soft targets for generated pseudo-data to help classifier keep knowledge of old classes, but generated samples with soft target and new class samples with the explicit training labels will lead to asymmetric information problem between old and new classes during incremental learning. This paper presents a new knowledge distillation method by using multi-hinge cGAN, we use multi-hinge distillation transfer class-correlation information among all classes from discriminator to the classifier, and help classifier overcomes catastrophic forgetting. Experiments on MNIST and CIFAR10 show that our method is comparable to many state-of-art incremental learning methods.
Elimination Et Choice Translating Reality (ELECTRE)

Elimination Et Choix Traduisant He realite (ELECTRE) is a decision-making method that was proposed by Bernard Roy and his team.
Optical Image Encryption Vulnerability Detection by Using Generative Adversarial Networks

Although the rapid development of science and technology makes information transmission more convenient, it also brings about information leakage. Therefore, information needs to be encrypted. Among many encryption methods, optical encryption based on computer generated hologram (CGH) is very popular for its parallelism and low cost. Generally, unauthorized receivers are unable to realize decoding without the encryption rule, security key or decryption instrument. However, the vulnerability and security of CGH based optical encryption has not been verified effectively. In this paper, a new type of Deep Learning (DL) framework based on Generative Adversarial Networks (GAN) is proposed to restore unknown plaintexts. Unauthorized users can extract information from encrypted images by the learning model without a direct retrieval of the encryption rule or security key. Meanwhile, a classification network is added into GAN to improve its classification accuracy. Experimental results show that the convolutional neural network (CNN) is robust to the encryption rule. The applicability of this network for different optical encryption algorithms is also verified. The proposed method is demonstrated to be effective and feasible to the cryptoanalysis of CGH based optical encryption systems, which makes an effective contribution to the the improvement of optical encryption technology.
A Multi Exposure Synthetic HDR Image Algorithm based on Cascaded Network

The following topics are dealt with: learning (artificial intelligence); feature extraction; deep learning (artificial intelligence); convolutional neural nets; medical image processing; image classification; pattern classification; diseases; neural nets; optimisation.
Semi-supervised Feature Selection Based on Cost-Sensitive and Structural Information

Feature selection is an important process of high-dimensional data analysis in data mining and machine learning. In the feature selection stage, the cost of misclassification and the structural information of paired samples on each feature dimension are often ignored. To overcome this, we propose semi-supervised feature selection based on cost-sensitive and structural information. First, cost-sensitive learning is incorporated into the semi-supervised framework. Second, the structural information between a pair of samples in each feature dimension is encapsulated into the feature graph. Finally, the correlation between the candidate feature and the target feature is added, which avoids the misunderstanding of the feature with low correlation as the salient feature. Furthermore, the proposed method also considers the redundancy between feature pairs, which can improve the accuracy of feature selection. The proposed method is more interpretable and practical than previous semi-supervised feature selection algorithms, because it considers the misclassification cost, structural relationship and the correlations between features and target features. Experimental results show that the promising performance of the proposed method outperforms the state-of-the-arts on eight data sets.
Sentiment analysis of the song "mojito"

With the development of the Internet, people share views and opinions on things anytime and anywhere. While receiving information, people also produce various information. Based on the evaluation of Jay Chou's new song mojito by different users on Douban, this paper uses Python's JSON tool to calculate the positive and negative probability value of each comment by setting the probability value of positive tendency greater than 0.5 as positive evaluation, otherwise as negative. In order to understand the reasons for user ratings directly, a word cloud map is drawn based on comment data.
A New Trusted Inter-Domain Authentication Scheme for Wireless Networks

Based on identity-based signature scheme and direct anonymous attestation of trusted computing, a new trusted inter-domain authentication scheme for wireless networks was proposed. It realizes identity authentication by using identity-based signature scheme to construct authentication proof, and uses direct anonymous attestation to verify the trustiness of platform. It is showed that the proposed scheme not only could achieve mutual authentication, but also has a secure anonymity, and could realize verifying the trustiness of platform. Compared with available schemes, the proposed scheme has superiority in security.
Text-Based Captcha Using Text-Adhesion and Visual Compensation

CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) is a test for distinguishing between computers and humans, and has a very wide range of applications on the Internet. Most websites require users to submit CAPTCHAs when registering to log in or submit some form data to improve the security of the site, thus avoiding malicious attacks by automated robots and spammers. In this paper, a text-based CAPTCHA Using text-adhesion and visual compensation is introduced. This CAPTCHA is designed to use character cascading technology and partial defect technology to effectively prevent the machine from using the character cutting and machine learning techniques to verify the CAPTCHA, but human vision can easily separate the characters that are layered together and can fill in some of the missing characters to easily identify the CAPTCHA. In order to test the ability of the CAPTCHA to resist automatic machine identification, four kinds of specialized OCR recognition software and two online OCR recognition websites were used to identify and verify 1000 CAPTCHAs. The test results show that none of the six OCR recognition tools can correctly identify one completely CAPTCHA(correctly recognized), and the probability of not being recognized and misidentified is over 99.5%, which proves that the CAPTCHA is a very high security technology.
Low-resolution image matching based on stacked sparse auto-encoder network and non-linear coupled metric

To solve the problem of multi-resolution image matching, a novel deep metric learning method combining deep network and metric learning is proposed. In the optimal deep feature extraction, the stacked sparse auto-encoder network is constructed to extract the key representative features of images. In the feature coupled metric, we use the non-linear supervised coupled mapping function to extract the discriminative features, and the reliable matching of different resolution images is implemented in the coupled space. The experiment results based on Yale-B and ORL face databases show the effectiveness of the proposed method.
THE FORMATION OF THE METHODOLOGICAL COMPETENCE OF FUTURE TEACHERS BY MEANS OF PEDAGOGICAL PRACTICE

The article reveals pedagogical practice as an important component of the formation of the methodological competence of future teachers. Methodological competence as an important tool for the teacher training is examined in the relationship of its structural components. It has been shown that among all key competencies for future teachers training the mastering the methodical competence means of pedagogical practice is absolutely important since it reflects the ability of future teachers to effectively engage in educational activities, to creatively implement the acquired theoretical and practical experience involving effective technology training and development. The article reveals the results of an empirical study aimed at revealing the dynamics of the formation of key competencies in the context of early professionalization. The lack of methodological competence as a new integrative personality issue of future teachers is assumed to become a barrier in the situation of the fulfilling professional tasks. The following methodologies were used in the empirical study: B.Dodonov's methodology "The Formula of Motivation"; the methodology of T. Dubovitska "The Level of Professional orientation". In order to study the personality-reflexive criterion as one of the components of the methodological competence of future teachers, the methodology of A. Lazukin and N. Kalin "Creativity Manifestation" has been adapted and, moreover, the author's questionnaire has been used. The sample consisted of 111 teachers and 362 students, 186 students of which belonged to the experimental group (EG), and 176 – to the control one (CG).
Research on Open Entity Semantic Relation Extraction in Big Data Environment

Aiming at the problem that traditional entity relationship extraction needs to define a comprehensive entity relationship type system, poor portability, and the inability to process massive Internet texts, an open Chinese entity relationship extraction method based on CRFs(Conditional random fields) is proposed. At the same time, due to the limited computing resources of a single machine, an open entity relationship extraction method based on the Hadoop distributed computing framework is proposed. Experiments show that the CRFs model with syntactic parse tree has improved accuracy and recall in the entity relationship extraction task; this paper also designed an experiment to compare the entity relationship extraction based on the Hadoop distributed computing framework with the stand-alone version. The experiment shows that the former extraction effect is better.
A High-Performance Wide FPGA Adder Based on Carry Chains

Nowadays, FPGA has become an important platform to implement digital circuits and systems. In order to meet the requirements for applications of large bit-width addition such as encryption, a new structure of FPGA adder is proposed in this paper. The structure is characterized by its carry generation circuit which combines the advantages of both carry-selection structure and compact carry-compression structure. The circuit can quickly generate a carry-out bit with a small quantity of hardware resources. And all carry-transmission directions in the proposed structure are from bottom to top, which is consistent with the carry-transmission direction of the carry chain in the FPGA. The proposed wide FPGA adder's structure reduces the large delay of the long wire to connect adjacent carry chains in FPGAs. Experimental results show that the proposed FPGA adder possesses superior overall performance to other excellent ones, when the bit width of the adders is larger than 1000 bits.
Incremental Information Disclosure in Qualitative Financial Reporting: Differences Between Fraudulent and Non-fraudulent Companies

This research investigates how fraudulent companies differ in their narrative disclosure strategies across multiple reporting venues. Fraudulent companies must avoid discussing fraud-relevant items, or they must present a fabricated version of their story. To reduce involuntary information disclosure and attempt to convince their audience that what they say is true, fraudulent firms may have greater language similarity than non-fraudulent firms between their quarterly earnings calls and the subsequent Management’s Discussion & Analysis section of the quarterly financial statement. I measure language similarity between a company’s earnings calls and their corresponding financial statements. I find that the Chief Financial Officers of fraudulent companies have significantly higher language similarity between disclosure venues than their non-fraudulent peers. I also test the predictive ability of these measures using several classification schemes, achieving fraud-detection accuracy levels between 53% and 71%. The paper concludes with a discussion of the findings and avenues for future research.
An Efficient Framework for Cloud Infrastructure Security

Cloud Computing has developed as another worldview in which users can use on-request services, as per their requirements. Be that as it may, security concerns are essential hindrances to a more extensive appropriation of clouds. Recently conceived ideas that clouds presented, for example, multitenancy, resource sharing and redistributing, make new difficulties for the security research. DDoS (Distributed Denial of service) attack is the greatest danger to the cloud since it influences the accessibility of services. To identify the attack on every worker, an indicator is sent that utilizes an entropy-based methodology for detecting DDoS. A critical deviation in entropy speaks to the DDoS attack. Inorder to shield the cloud climate from malicious users, we proposed a novel trust based admittance control model. This model approves the client dependent on client trust esteem, before getting to the cloud resources. The client must be trusted before getting to the resources and the resources must be trusted before offering the support to the client. On the off chance that the trust estimation of both client and cloud resource are more than their trust limit esteem, at that point they are viewed as trusted. Simulation results show that proposed model performs in a way that is better than comparative models as far as rightness of result.
Improve the Scale Invariance of the Convolutional Network for Crowd Counting

The main challenges of crowd counting are considerable variations in complex scenes/backgrounds. This paper first reveals that the Convolution Neural Networks (CNNs) are incapable of addressing these problems. To solve this problem, we propose a novel attention mechanism to improve the scale invariance of convolutional networks. Our method can not only automatically exploit spatial awareness to optimize the convolutional features but also imitate the human attention mechanism to remove the noise of the background. It is worth noting that it can easily plug-and-play into the vanilla convolution/pooling layer with relatively little computation cost. We have integrated our method into several state-of-the-art methods. Extensive experiments on five popular benchmarks demonstrate that our approach significantly outperforms other state-of-the-art methods and beats entire convolution/pooling layer in all cases.
Impact of Statistical Language Model on Example Based Machine Translation System between Kazakh and Turkish Languages

In this paper a hybrid example based machine translation system between Kazakh and Turkish languages is presented. The system mainly based on example based machine translation method which is supported by a statistical language model for the target language. Translation templates are learned at morphological level from a bilingual parallel corpus of Turkish and Kazakh languages. Translations can be performed at both directions using these learned translation templates. Our main aim with this hybrid example based machine translation system is to obtain more accurate translation results by pre-gained knowledge from target language resource. One of the reasons that we propose this hybrid approach is that monolingual language resources are more widely available than bilingual language resources.
Automatic Generation of Substation Power Network Diagram based on the Existing Diagrams Feature Library

This paper proposes a method for automatic generation of the power network diagram of plants and substations for grid dispatching system. Establish a local graphic feature library in layers, extract the feature quantity of the existing plant diagrams based on graph-model-data-database integration. Match the features of new diagram and the existing diagrams layer by layer. Then Generate the new diagram automatically with combination, replacement, addition and subtraction of the existing diagrams containing the characteristics of the target diagram.
SlipSwap: Reduce the slippage that is incurred during the swap of tokens using Algorithmic analysis

These days, crypto-currency is getting popular in society. Many people around the globe are using these coins for transactions. This world of crypto-currency works on the principle of exchange between different cryptocurrencies. It is observed that while exchanging currencies, we often get less amount of money compared to what we hope for, as exchange rates are volatile, and they increase if a considerable amount is exchanged. This phenomenon of the exchange rate dropping, when the exchange amount increases is called slippage. To solve this problem, we propose a novel application/API - Slipswap. In this work, we propose a web application “SlipSwap” that takes the input value of one currency and gives an optimal way to exchange so that the user loses the least amount of money. There is no such tool available, which helps users save a significant amount of money. Further, the proposed tool can be used as an API and can be integrated with different platforms.