Reviewerly/exHarmony huggingface dataset:
Problems and Suggestions on the Curriculum of Integration between Vocational and General Education under the Background of "Double Reduction"

Taking the curriculum of integration between vocational and general education as the key point to explore the development of education under the background of "Double Reduction" and analyzes the historical process of its difficult start, exploration and development, deepening of reform, and moving towards the period of strategic opportunity under the background of "Double Reduction". Preliminary proven the curriculum of integration between vocational and general education under the background of "Double reduction" there is a social coordination imbalance and the contradiction between education demand, curriculum as pilot reform of the utilitarian intentions obvious at the grassroots level and the deepening of the contradictions of the curriculum of integration between vocational and general education. From the perspective of the core of this phenomenon, combined with the theory and reality based on practice, using the case analysis and comparative analysis to explore the positive development path of the curriculum of integration between vocational and general education. First, to achieve a benign balance of the ideological cognition of general education and vocational education with family, school, and social comprehensive practice education. Second, to establish a diversified education evaluation mechanism. Third, to return to school education with the construction of various forms of cooperative fusion mechanisms in order to improve the quality of the curriculum of integration between vocational and general education.
Graph Generation for Quantum States Using Qiskit and Its Application for Quantum Neural Networks

This paper describes a practical approach to the quantum theory using the simulation and processing technology available today. The proposed project allows us to create an exploration graph so that for an initial starting configuration of the qubits, all possible states are created given a set of gates selected by the user. For each node in the graph, we can obtain various types of information such as the applied gates from the initial state (the transition route), necessary cost, representation of the quantum circuit, as well as the amplitudes of each state. The project is designed not as an end goal, but rather as a processing platform that allows users to visualize and explore diverse solutions for different quantum problems in a much easier manner. We then describe some potential applications of this project in other research fields, illustrating the way in which the states from the graph can be used as nodes in a new interpretation of a quantum neural network; the steps of a hybrid processing chain are presented for the problem of finding one or more states that verify certain conditions. These concepts can also be used in academia, with their implementation being possible with the help of the Python programming language, the NumPy library, and Qiskit—the open-source quantum framework developed by IBM.
Analysis of the current situation of Heze City Youth Basketball Club

At present, basketball in China has been rapidly popularized. Heze city is a prefecture-level city, an economic and cultural center, and has many basketball clubs, but some problems have been gradually exposed in its rapid development process. Therefore, the research and analysis of Heze Youth Basketball Club is the original intention of this paper. This paper investigates more than 10 youth basketball clubs in Heze city, and draws the following conclusions and countermeasures: most of Heze youth basketball clubs are founded by individuals, and there are great differences in the number of students, the size of the company and the ratio of men and women.Most of the Heze youth clubs in the urban areas and the suburbs are relatively few, more rented university or school space is mainly indoor.The youth basketball club should appropriately increase the contact with the school, hold more regular basketball games, strengthen the publicity of the club, attract more social forces, pay attention to the basic skills practice of the students, and improve the management mechanism.
Inter-State Disparity in Education loans in India: An Intertemporal Analysis

The fiscal constraints of the government and the increasing demand for education from the masses have led to finding out some alternative sources for funding education in general and higher education in particular. Education loans has become one of the alternatives for financing higher education all over the world. In India, since the announcement of the Model Education loans Scheme in 2011, commercial banks have come forward to finance higher education. Now, education loans has seen increasing relevance and hence the flow of education loans is increasing year by year. However, a look at the disbursal of education loans in India shows its skewed distribution among various states and regions. The South Indian States are found to be the major beneficiaries of the scheme, while bigger states like UP and Bihar are lagging behind. So, this uneven distribution of education loans vitiates the very purpose of this scheme. In this context, this article examines the growth of higher education and the growth of education loans in India
Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification

Deep neural networks (DNNs) have been known to be vulnerable to adversarial attacks. Adversarial training (AT) is, so far, the only method that can guarantee the robustness of DNNs to adversarial attacks. However, the robustness generalization accuracy gain of AT is still far lower than the standard generalization accuracy of an undefended model, and there is known to be a trade-off between the standard generalization accuracy and the robustness generalization accuracy of an adversarially trained model. In order to improve the robustness generalization and the standard generalization performance trade-off of AT, we propose a novel defense algorithm called Between-Class Adversarial Training (BCAT) that combines Between-Class learning (BC-learning) with standard AT. Specifically, BCAT mixes two adversarial examples from different classes and uses the mixed between-class adversarial examples to train a model instead of original adversarial examples during AT. We further propose BCAT+ which adopts a more powerful mixing method. BCAT and BCAT+ impose effective regularization on the feature distribution of adversarial examples to enlarge between-class distance, thus improving the robustness generalization and the standard generalization performance of AT. The proposed algorithms do not introduce any hyperparameters into standard AT; therefore, the process of hyperparameters searching can be avoided. We evaluate the proposed algorithms under both white-box attacks and black-box attacks using a spectrum of perturbation values on CIFAR-10, CIFAR-100, and SVHN datasets. The research findings indicate that our algorithms achieve better global robustness generalization performance than the state-of-the-art adversarial defense methods.
Web Site Fingerprint Attack Generation Technology Combined with Genetic Algorithm

An anonymous network can be used to protect privacy and conceal the identities of both communication parties. A website fingerprinting attack identifies the target website for the data access by matching the pattern of the monitored data traffic, rendering the anonymous network ineffective. To defend against fingerprint attacks on anonymous networks, we propose a novel adversarial sample generation method based on genetic algorithms. We can generate effective adversarial samples with minimal cost by constructing an appropriate fitness function to select samples, allowing us to defend against several mainstream attack methods. The technique reduces the accuracy of a cutting-edge attack hardened with adversarial training from 90% to 20–30%. It also outperforms other defense methods of the same type in terms of information leakage rate.
Cochlear CT image segmentation based on u-net neural network

Before cochlear implantation, accurately identifying the cochlea's morphology is necessary. This study proposes an improved network model based on U-Net, which can realize automatic segmentation of human cochlear anatomy in computed tomography (CT) images. The CT scan data of 100 patients requiring cochlear implantation diagnosed in our hospital were randomly collected. It was divided into a training set (n = 75) and a test set (n = 25). All data were manually segmented by two clinicians. At the same time, U-Net was used for deep learning of the above data. The cochlea in the test set was compared with the dice similarity coefficient (DSC) and 95% Hausdorff surface distance (HD95%) by manual and automatic segmentation. The DSC and HD95% of manual cochlear image segmentation were 0.761 and 4.343, respectively. The DSC and HD95% were 0.742 and 4.217, respectively, for automatic segmentation of cochlear structure using the U-Net network structure. The difference of DSC and HD95% between the two segmentation methods was not statistically significant (P > 0.05). The cochlea can be thoroughly segmented automatically based on the U-Net neural network, and the precision is close to manual segmentation.
THE INFLUENCE OF INTELLECTUAL ACTIVITY ON THE DEVELOPMENT OF REFLECTIVENESS IN A PERSON

The article is devoted to the issues of reflexivity and social intelligence in a person. According to him, the relationship between the characteristics of reflexivity in a person and social intelligence is substantiated. Also, a psychological study was conducted to investigate the relationship between reflexivity and social intelligence, and the results were analyzed.
Deviations from the Language Norm: Linguo-Ecological Aspect (by the Material of the German-Language Microblog)

The aim of the study is an analytical review of deviations from the language norm in German-language Internet communication based on the texts of the Twitter microblog. The rapid exchange of information with the simultaneous minimization of speech efforts, as well as the self-expression of communicants through the author’s word creation often entail deviations from the language norm. In the course of the study, the author identifies the main types of deviations at various language levels: phonographic, morphophonemic, orthographic, grammatical-syntactic and lexical. The article analyses the causes of deviant phenomena, as well as the features of their functioning. The scientific novelty of the work lies in the fact that for the first time it uses a linguo-ecological approach to the study of language deviations in Internet communication, which allows us to assess the nature and extent of their impact on language development. The obtained results showed that the German microblog is dominated by a tendency to simplify the language, save speech efforts, intentional and unintentional violation of the language norm, which indicates the analytical vector of the German language development at the present stage.
Text Model of Massive Open Online Courses in German

The study aims to describe a text model of massive open online courses (MOOCs) of educational nature as a means of scientific knowledge transfer. The study is novel in that it describes the typical structure of MOOC texts and identifies the main functions of their elements. As a result of the study, it has been found that clear structuring is characteristic of all elements of MOOC texts. The introduction is a meta-text, which contains a detailed description of the main part of the MOOC, while the conclusion is an intertext indicating the connection of the main part of the MOOC with other texts of scientific and academic discourses. The text-forming strategies of MOOCs include the information strategy peculiar to all the structural elements of the text, the strategy of enhancing cognitive activity implemented in the main text, as well as the conventional strategy used primarily in the introduction and the conclusion. MOOC texts are characterised by the democratic nature of utterance expressed by various lexical and grammatical means; explicit intertextuality consisting in an abundance of citations and references to sources and related courses; coherence; presence of markers of internationality of knowledge; redundancy of information; presence of concrete vocabulary with the prevalence of abstract vocabulary.
Online Parameter Estimation for Student Evaluation of Teaching

Student evaluation of teaching (SET) assesses students' experiences in a class to evaluate teachers' performance in class. SET essentially comprises three facets: teaching proficiency, student rating harshness, and item properties. The computerized adaptive testing form of SET with an established item pool has been used in educational environments. However, conventional scoring methods ignore the harshness of students toward teachers and, therefore, are unable to provide a valid assessment. In addition, simultaneously estimating teachers' teaching proficiency and students' harshness remains an unaddressed issue in the context of online SET. In the current study, we develop and compare three novel methods-marginal, iterative once, and hybrid approaches-to improve the precision of parameter estimations. A simulation study is conducted to demonstrate that the hybrid method is a promising technique that can substantially outperform traditional methods.
PENGEMBANGAN NEURAL NETWORK UNTUK PREDIKSI KUALITAS AIR

Research on artificial intelligence to determine water quality has been widely developed as a human endeavor toimprove the quality of life. This study employs an artificial neural network (ANN) to determine the optimalclassification model for determining the safety of water. This study uses existing Kaggle generic datasets. Numerouspreprocesses were performed on the dataset starting from cleaning the data from missing values and outliers toequalizing the weights of each parameter with the min-max scaler. This study compares the accuracy of ANN modelin various scenarios constructed with 10, 15, 20, and 30 neurons. Scaled Conjugate Gradient is implemented as thelearning algorithm for developing the prediction model. The obtained results of the experiments vary betweenscenarios. Overall accuracy increases when the number of neurons is between 10 and 20, and decreases when thenumber of neurons is between 20 and 30.
GPS-free synchronized pseudo-random number generators for internet-of-things

Introduction Securing wireless communications in internet-of-things (IoT) requires both generation and synchronization of random numbers in real-time. However, resource constraints on an IoT device limit the use of computationally intensive random number generators and the use of global positioning systems (GPS) for synchronization. In this paper, we propose a synchronized pseudo-random number generator (SPRNG) that uses a combination of a fast, low-complexity linear-feedback-shift-register (LFSR) based PRNG and a slow but secure, synchronized seed generator based on self-powered timers. Methods A prototype synchronized self-powered timer (SSPT) array was fabricated in a standard silicon process and was used to generate dynamic random seeds for the LFSR. The SSPTs use quantum-mechanical tunneling of electrons to operate without any external power and are practically secure against tampering, snooping, and side-channel attacks (both power and electromagnetic). Results In this work, we explore protocols to periodically and securely generate random bits using the self-powered timers for seeding the LFSR. We also show that the time-varying random seeds extend and break the LFSR periodic cycles, thus making it difficult for an attacker to predict the random output or the random seed. Using the National Institute of Standards and Technology (NIST) test suite we verify the randomness of the measured seeds from the fabricated ensemble of SSPTs together with the random bit sequences generated by a software-seeded LFSR. Discussions In this modality, the proposed SPRNG could be used as a trusted platform module (TPM) on IoTs and used for verifying and authenticating secure transactions (e.g., software upgrades). Since the SPRNG system does not require access to GPS for synchronization, therefore it could be used in many resource-constrained and adversarial environments.
DeepQ Residue Analysis of Computer Vision Dataset using Support Vector Machine

A vision-based human computer interface is used to automatically recognize human mood.Image processing techniques used include a web camera for eye detection.Appearance tracking method (ABT) is measured face identification and K means Nearest Neighbor (K-NN) is used for eye detection.DWT -Discrete Wavelet Transform and DCT -Discrete Cosine Transform are suitable to extract features of eye and SVM is used to classify eye expressions.Classification of eye expressions includes anger, fear, happiness, disgust, neutral and sad.Experimental results confirm that the proposed method recognized facial expressions with higher accuracy.
An evolutionary algorithmic framework cloud based evidence collection architecture

Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the Backtracking Search Optimization Algorithm search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version - 3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.
Lasso-based variable selection methods in text regression: the case of short texts

Abstract Communication through websites is often characterised by short texts, made of few words, such as image captions or tweets. This paper explores the class of supervised learning methods for the analysis of short texts, as an alternative to unsupervised methods, widely employed to infer topics from structured texts. The aim is to assess the effectiveness of text data in social sciences, when they are used as explanatory variables in regression models. To this purpose, we compare different variable selection procedures when text regression models are fitted to real, short, text data. We discuss the results obtained by several variants of lasso, screening-based methods and randomisation-based models, such as sure independence screening and stability selection, in terms of number and importance of selected variables, assessed through goodness-of-fit measures, inclusion frequency and model class reliance. Latent Dirichlet allocation results are also considered as a term of comparison. Our perspective is primarily empirical and our starting point is the analysis of two real case studies, though bootstrap replications of each dataset are considered. The first case study aims at explaining price variations based on the information contained in the description of items on sale on e-commerce platforms. The second regards open questions in surveys on satisfaction ratings. The case studies are different in nature and representative of different kinds of short texts, as, in one case, a concise descriptive text is considered, whereas, in the other case, the text expresses an opinion.
On the scalability of data augmentation techniques for low-resource machine translation between Chinese and Vietnamese

Neural Machine Translation (NMT) has constantly been shown to be a standard choice to build a translation system, in both academia and industry. For low-resource language pairs, data augmentation techniques have been widely used to tackle the data shortage problem in NMT. In this paper, we investigate the scaling behaviour of transformer-based NMT model to the increasing amount of synthetic data. Through the experiments, conducted in the Chinese-to-Vietnamese translation task, we aim to provide a guideline to the application of several methods such as back-translation, tagged back-translation, self-training and sentence concatenation in a low-resource, less-related language pair. Our results suggest that choosing the appropriate amount of synthetic data is a crucial task when building NMT systems. In addition, when combining methods, it is recommended to tag the data sources before training.
PERFORMANCE ANALYSIS BETWEEN INTERPRETED LANGUAGE-BASED (LARAVEL) AND COMPILED LANGUAGE-BASED (GIN) WEB FRAMEWORKS

Web frameworks are essential for web developers, allowing them to build web applications more efficiently and effectively. This research paper compared two web frameworks with different programming language implementations: interpreted programming language and compiled programming language. PHP/Laravel was chosen as the representative interpreted programming language, and Go/Gin was chosen as the representative compiled programming language, because they were the most popular based on their star counts on GitHub. Based on a research paper by the University of Minho, Go was nearly 10 times faster than PHP in terms of execution time. Despite this, PHP is still the most commonly used web technology among the top 1 million websites. The results of a series of tests comparing the two showed that Gin was over 86 times faster than Laravel in terms of requests per second and time per request. The choice of which web framework to use will depend on the specific needs and goals of the project. However, if performance is the only criterion, then Gin is a better choice than Laravel.