Reviewerly/exHarmony huggingface dataset:
Methodical Competence Deficits of Vocational Training Teachers

The labor market today, more than ever, needs effective and highly qualified personnel ready to work in conditions of turbulence and uncertainty, and therefore teachers who are able to provide appropriate training for such personnel. However, the professional competences level of vocational training teachers does not always ensure the required quality. Therefore, it becomes important to organize a continuous monitoring system of the educational sphere and the labor market, which makes it possible to timely diagnose professional deficits of teaching staff and organize educational activities to eliminate them. The purpose of the article is to determine the reasons for the emergence of teachers’ professional deficits in organizations of the secondary vocational education system, to propose ways to overcome these deficiencies based on the identified educational needs of teachers. The study of professional deficiencies of vocational training teachers is implemented based on the competence approach, the concept of vocational education and the theory of professional deficits. The main method for collecting primary data was a questionnaire survey (n = 589). The groups of professional deficiencies of vocational training teachers in the field of methodical competence were identified (the use of modern forms and methods of organizing vocational and pedagogical training; the use of modern pedagogical, including digital technologies; creating conditions for the personal and professional students’ development; control and assessment). The research materials are an illustration of the experience of organizing monitoring of the educational sphere. The revealed level of vocational training teachers’ professional competence and specific deficiencies in methodical literacy will be useful for state bodies and other organizations of the vocational education system when developing a set of educational measures aimed at improving the professional competences of pedagogical staff for the vocational pedagogical education system.
An Exploratory Study of Python's Role in the Advancement of Cryptocurrency and Blockchain Ecosystems

Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice.
A hybrid architecture for secure Big-Data integration and sharing in Smart Manufacturing

Big-Data integration and sharing is a key aspect of the manufacturing industry, as it enables real-time monitoring and control of production processes. However, integrating and sharing Big-Data also raises concerns about performance, privacy, and security. This study presents a hybrid architecture for integrating and sharing Big-Data in the manufacturing industry, with a focus on addressing performance, privacy, and security concerns. The proposed architecture combines Apache Spark, IPFS, and Blockchain technology, and is based on a sharding approach that encrypts and distributes data to enable faster and more efficient data transfer. The Blockchain network is used to provide a secure and tamper-proof record of the data transfer, ensuring that the data is only accessible to authorized parties and that any changes are visible to all parties on the network. While the proposed architecture includes a verification method to meet quality requirements, only the uploading and downloading processes were validated through specification.
Interactive Game Based Self-study Package on First Aid Procedure Training

There are numerous dangerous illnesses nowadays that, with prompt and effective first aid, can be treated to avoid death, such as stroke.The accurate understanding of first aid, however, has not received much attention.It is challenging to comprehend because of how complicated the steps of first aid procedure are.Therefore, our project team developed a first aid game.It is important to comprehend and support stroke victims.The purpose of this study was to develop a stroke first aid game and provide advice on stroke first aid to the public.This game was developed as 3D interactive games by designing games that can simulate real situations, so that players can enjoy the game and experience its reality.To collect information about the players' satisfaction with the game, questionnaires were used gathering information from 30 individuals with the ages range of 15 to 25 years old and they were tested before and after playing the game.The findings indicated that players have much more knowledge and satisfaction from playing games.In the opinion of some players, the game could be improved to give players more challenge and excitement..
From cadCAD to casCAD2: A Mechanism Validation and Verification System for Decentralized Autonomous Organizations Based on Parallel Intelligence

The governance technology of decentralized autonomous organizations (DAOs) provides an effective solution for solving existing organizational management issues. Governance mechanisms of DAOs are usually encoded in smart contracts in the form of rule sets and executed automatically. However, the mechanism’s logical flaws and code errors expose DAOs to unpredictable risks. Complex adaptive dynamics computer-aided design (CadCAD) can test the effectiveness of the mechanisms through simulation. Nonetheless, as DAOs are typical complex systems with social and engineering complexity, managing, controlling, and supervising their operation through traditional methods are difficult. The parallel intelligence theory based on artificial societies, computational experiments, and parallel execution (ACP) method provides an effective research framework and practical method for solving DAOs’ governance issues. Therefore, in this article, we propose a parallel mechanism verification method and execution system, namely, complex adaptive systems for computer-aided dynamic design (casCAD2) as an extension of cadCAD. Leveraging parallel intelligence and cyber–physical–social systems (CPSS), casCAD2 is capable of probing into the laws that govern system evolution within a simulated environment. It serves as a robust tool for verifying the efficacy of DAOs’ mechanisms and predicting their potential risks. We also build a parallel market-based anchoring mechanism (MAM) system to demonstrate how it can be used for DAOs’ mechanism verification. This study can provide a new research method and application system for DAOs’ effective governance.
Parity-detection-based Mach-Zehnder interferometry with coherent and non-Gaussian squeezed vacuum states as inputs

We theoretically explore the advantages rendered by non-Gaussian operations in phase estimation using a parity-detection-based Mach-Zehnder interferometer, with one input being a coherent state and the other being a non-Gaussian squeezed vacuum state (SVS). We consider a realistic model to perform three different non-Gaussian operations, namely, photon subtraction, photon addition, and photon catalysis on a single-mode SVS. We start by deriving the Wigner function of the non-Gaussian SVSs, which is then utilized to derive the expression for the phase sensitivity. The analysis of the phase sensitivity reveals that all three different non-Gaussian operations can enhance the phase sensitivity under suitable choices of parameters. We also consider the probabilistic nature of these non-Gaussian operations, the results of which reveal the single-photon addition to be the optimal operation. Further, our analysis also enables us to identify the optimal squeezing of the SVS and the transmissivity of the beam splitter involved in the implementation of the non-Gaussian operations.
Brain Storm Optimization Integrated with Cooperative Coevolution for Large-Scale Constrained Optimization

Large-scale constrained optimization problems (LSCOPs) are challenging to solve because of the high dimensionality and constraint limitations. Although cooperative coevolution (CC) has been applied to LSCOPs, more efficient optimizers that could be adapted to CC are still required. In this paper, we propose ConBSO, a variant of the brain storm optimization (BSO) designed for constrained optimization. Then, ConBSO is integrated into constraint-objective cooperative coevolution (COCC), denoted as COCC-ConBSO. To evaluate the performance of COCC-ConBSO, we test it on the benchmark suite with 12 LSCOPs and compared it to several algorithms, including two algorithms based on the COCC framework and three state-of-the-art large-scale constrained optimization algorithms. Experimental results demonstrate the adaptability of ConBSO to COCC and highlight the competitiveness of COCC-ConBSO in solving LSCOPs.
TRANSFORMATIVE IMPLICATIONS AND EVIDENCE-BASED PRINCIPLES FOR CREATING CONTENT FOR MATHEMATICS LEARNING IN THE METAVERSE

TRANSFORMATIVE IMPLICATIONS AND EVIDENCE-BASED PRINCIPLES FOR CREATING CONTENT FOR MATHEMATICS LEARNING IN THE METAVERSE
Quantum speed limit from a quantum-state-diffusion method

Characterizing the most efficient evolution, the quantum speed limit (QSL) plays a significant role in quantum technology. How to generalize the well-established QSL from closed systems to open systems has attracted much attention. In contrast to the previous schemes to derive the QSL from the reduced dynamics of open systems, we propose a QSL bound from the point of view of the total system consisting of the open system and its environment using a quantum-state-diffusion method. The application of our scheme to a two-level system reveals that the system possesses an infinite speedup capacity in the noiseless case, which is destroyed by the environment under the Born-Markovian approximation. It is interesting to find that the capacity in the noiseless case is recovered in the non-Markovian dynamics as long as a bound state is formed in the energy spectrum of the total system. Enriching the characterization schemes of the QSL, our result provides an efficient way to control the QSL of open systems.
A Study on the Security of Online Judge System Applied Sandbox Technology

The majority of programming courses currently employ online judge systems as lesson materials. Online judge systems are becoming more common as the number of courses and persons studying computer science and information engineering grows. At the same time, there is an increase in the number of attacks against online judge systems; for example, Denial-Of-Service attacks, whose goal is to disrupt the target system by exhausting resources and blocking ordinary users from using the service normally. As a result, preventing attacks on online judge systems is becoming increasingly crucial. This research investigates and organizes these attack techniques, as well as develops a threat model for the online judge system by the STRIDE threat model approach, which provides a way to classify attacks into six categories. This research also designs code analysis rules and implements a code analysis tool. This tool can assist developers in analyzing the existing online judge system to determine whether the judge system is vulnerable to attack and dealing with vulnerability as soon as feasible to improve the judge system’s security. After enhancing the security of online judge systems, the system can foster trust and reliability among users as benefits.
Imbalanced Learning in Heart Disease Categorization: Improving Minority Class Prediction Accuracy Using the SMOTE Algorithm

Mediana Aryuni, Suko Adiarto, Eka Miranda, Evaristus Didik Madyatmadja, Albert Verasius Dian Sano, and Elvin Sestomi. International Journal of Fuzzy Logic and Intelligent Systems 2023;23:140-51. https://doi.org/10.5391/IJFIS.2023.23.2.140
Improving the Competitiveness of Education in Russia at the International Level

This article provides an analysis of the factors affecting the competitiveness of domestic higher education: the positioning of Russian universities in international rankings, the degree of digitalization of education and science in the Russian Federation, as well as the export potential of Russian education. The first part presents the data of the analysis of higher educational institutions of the Russian Federation by the most popular international rating agencies, shows the methods and principles for calculating key indicators for each of the ratings. The higher places in international rankings show popularity and prestige of not only a particular university, but also the market of educational services in the country in general. The export of education is an important factor contributing to the increase of global competitiveness. The second part of the work describes government programs that help increase the export of domestic education by promoting Russian universities among foreign applicants, and also presents an action plan to increase the export of higher education in Russia. The degree of digitalization of higher education is shown as a determining factor, the importance of the digital transformation of domestic education, as well as the benefits of distance learning, is established. The federal program "Strategy for the Digital Transformation of Science and Higher Education", developed in 2021 by the Ministry of Science and Higher Education of the Russian Federation with the aim of achieving "digital maturity" in the field of education, is analyzed. A consistent review of projects developed in the field of education, as well as data from rating agencies, made it possible to determine the main directions for increasing the competitiveness of domestic higher education, to describe strategies and activities that contribute to the prompt achievement of all goals.
Replications in Reversible Concurrent Calculi

Process calculi such as CCSor the $$\pi $$ -calculus provide specification languages for the study and correctness of communication protocols. They also served in detailing subtle differences between formalisms to represent infinite behaviors, notably in expressiveness [7, 16]. To our knowledge, such results were never investigated from a reversible perspective. One question we would like to answer is how recursion, replication and iteration compare in the reversible setting. Of course, comparing them requires to define them first, and this short note highlights some of the difficulties in defining replication for reversible concurrent calculi.
Gas Cost Analysis of Fractional NFT on the Ethereum Blockchain

With the rise of NFTs, which serve as proof of ownership for assets, security tokens that enable transactions without intermediaries have become a popular topic. Tokenization eliminates the need for centralized markets and allows for fast trades. In addition to tokenization, there are attempts to increase asset liquidity by fractionalizing them, a concept known as fractional ownership. Despite the existence of some platforms that use tokenization and fractional ownership, there is still limited research on fractional NFTs, and institutional support is lacking. In this paper, we propose possible implementations of fractional NFTs and evaluate their gas costs, which are crucial for providing fractional NFT-related services. As most NFTs are minted based on the Ethereum blockchain, we implement fractional NFTs using ERC standards. Our evaluation shows that ERC-721 or ERC-1155 NFTs fractionalized into ERC-20 FTs have the lowest long-term gas costs.
Efficient measurement schemes for bosonic systems

Abstract Boson is one of the most basic types of particles and preserves the commutation relation. An efficient way to measure a bosonic system is important not only for simulating complex physics phenomena of bosons (such as nuclei) on a qubit based quantum computer, but for extracting classical information from a quantum simulator/computer that itself is built with bosons (such as a continuous variable quantum computer). Extending the recently proposed measurement schemes for qubits, such as shadow tomography and other local measurement schemes, here we study efficient measurement approaches for bosonic systems. We consider truncated qudit and continuous variable systems, corresponding to simulated bosons on a discrete quantum computer and an inherent boson system, respectively, and propose different measurement schemes with theoretical analyses of the variances for these two cases. We numerically test the schemes for measuring nuclei vibrations simulated using a discrete quantum computer and a continuous variable Gaussian state, and the simulation results show great improvement of the performance of the proposed method compared to conventional ones.
Adaptive Search-based Repair of Deep Neural Networks

Deep Neural Networks (DNNs) are finding a place at the heart of more and more critical systems, and it is necessary to ensure they perform in as correct a way as possible. Search-based repair methods, that search for new values for target neuron weights in the network to better process fault-inducing inputs, have shown promising results. These methods rely on fault localisation to determine what weights the search should target. However, as the search progresses and the network evolves, the weights responsible for the faults in the system will change, and the search will lose in effectiveness. In this work, we propose an adaptive search method for DNN repair that adaptively updates the target weights during the search by performing fault localisation on the current state of the model. We propose and implement two methods to decide when to update the target weights, based on the progress of the search's fitness value or on the evolution of fault localisation results. We apply our technique to two image classification DNN architectures against a dataset of autonomous driving images, and compare it with a state-of-the art search-based DNN repair approach.
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and energy. To fill this significant gap, we propose an early-rejection mechanism to turn down low-return prompts by foretelling the code completion qualities without sending them to the code completion system. Furthermore, we propose a lightweight Transformer-based es-timator to demonstrate the feasibility of the mechanism. The experimental results show that the proposed estimator helps save 23.3% of computational cost measured in floating-point operations for the code completion systems, and 80.2% of rejected prompts lead to unhelpful completion.
Detecting Condition-Related Bugs with Control Flow Graph Neural Network

Automated bug detection is essential for high-quality software development and has attracted much attention over the years. Among the various bugs, previous studies show that the condition expressions are quite error-prone and the condition-related bugs are commonly found in practice. Traditional approaches to automated bug detection are usually limited to compilable code and require tedious manual effort. Recent deep learning-based work tends to learn general syntactic features based on Abstract Syntax Tree (AST) or apply the existing Graph Neural Networks over program graphs. However, AST-based neural models may miss important control flow information of source code, and existing Graph Neural Networks for bug detection tend to learn local neighbourhood structure information. Generally, the condition-related bugs are highly influenced by control flow knowledge, therefore we propose a novel CFG-based Graph Neural Network (CFGNN) to automatically detect condition-related bugs, which includes a graph-structured LSTM unit to efficiently learn the control flow knowledge and long-distance context information. We also adopt the API-usage attention mechanism to leverage the API knowledge. To evaluate the proposed approach, we collect real-world bugs in popular GitHub repositories and build a large-scale condition-related bug dataset. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods for detecting condition-related bugs.
Active fault-tolerant anti-input saturation control of a cross-domain robot based on a human decision search algorithm and RBFNN

This article presents a cross-domain robot (CDR) that experiences drive efficiency degradation when operating on water surfaces, similar to drive faults. Moreover, the CDR mathematical model has uncertain parameters and non-negligible water resistance. To solve these problems, a radial basis function neural network (RBFNN)-based active fault-tolerant control (AFTC) algorithm is proposed for the robot both on land and water surfaces. The proposed algorithm consists of a fast non-singular terminal sliding mode controller (NTSMC) and an RBFNN. The RBFNN is used to estimate the impact of drive faults, water resistance, and model parameter uncertainty on the robot and the output value compensates the controller. Additionally, an anti-input saturation control algorithm is designed to prevent driver saturation. To optimize the controller parameters, a human decision search algorithm (HDSA) is proposed, which mimics the decision-making process of a crowd. Simulation results demonstrate the effectiveness of the proposed control methods.
Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques.