Reviewerly/exHarmony huggingface dataset:
Central State Archive of Scientific and Technical Documentation of St. Petersburg, One of the Largest Specialized Centers for Storage and Usage of Documents on National History of Science and Technology: 1972–2022

The article deals with preservation of documentary sources on history of technology, research, and development of technical projects, thus addressing copyright compliance and thereto pertaining wide availability of scientific achievement and also identifying such materials. The process of creating repositories of such materials in the USSR and their functioning in the Russian Federation has been long. As Leningrad/St Petersburg with its hundreds of research institutes, design bureaus, etc. was one of the centers of science, technology, and engineering (and remains so), the archive’s creation was preceded by a lengthy process of the so called “passportization,” that is, data sheet production for scientific and technical documentation, deposited in the course of city organizations’ work. To accelerate this process, in 1967, a special group was created for scientific and technical documentation under the Archival Department of the Executive Committee of the Leningrad City Council. The “passportization” identified over 300 development organizations, storing about 27 million items of scientific and technical documentation. In 1967 proposal about creation of the State Archive of Scientific and Technical Documentation in Leningrad was made to the Executive Committee of the Leningrad City Council and the Chief Archival Directorate under the Council of Ministers of the USSR, but nearly five years had passed before the Leningrad State Archive of Scientific and Technical Documentation was created by order of the Leningrad City Executive Committee no. 75-r of January 19, 1972 to preserve the most valuable scientific and technical documentation of Leningrad project and design organizations and research institutes. During the 1970s, a real team of like-minded archivists was put together and the Archive’s structure was formed, remaining to this day with some minor modifications. The archive staff gained experience of processing scientific and technical documentation and its accounting during internships in departmental technical archives; supervised by chief engineers, chief designers, architects, and leading specialists of developer organizations, they studied the organizations’ structure and main activities: projects, project composition, scientific reports, theses, design and marking of technical documentation, accounting system, etc. At present, the archive holds 463,871 items in 480 fonds of organizations, fonds of personal provenance, and other archival collections.
ESTIMASI PARAMETER MODEL REGRESI LINIER DENGAN PENDEKATAN METODE BAYESIAN

The method used to estimate the regression parameters in this study is the Bayes method. The steps in estimating the parameters of the linear regression model using the Bayes method are to determine the Likelihood function from the normal distribution density function, determine the Prior Non-Informative distribution from a normal distribution, then look for the Posterior distribution by switching the Prior distribution with the Likelihood function.Writing this thesis aims to determine the estimation of the parameters of the linear regression model with the Bayesian method approach. From the regression model generated by the OLS method, it was identified that there was one Outlier data. Outlier is a factor that influences parameter estimation in linear regression model. A model will be shown in an example data case by comparing the results of the Ordinary Least Square (OLS) method using R and the results of the Bayesian method using WinBUGS. It can be seen that the MSE value obtained from the Bayesian Method estimation is smaller than the MSE value obtained from the OLS Method estimate. The Mean Square Error (MSE) value obtained from the estimation of the OLS Method is 0.8469 while the Mean Square Error (MSE) value obtained from the estimation of the Bayesian MCMC Method is 0.3723. This shows that the Bayesian MCMC method is much better than the OLS method.&#x0D; &#x0D;
Optimal Input Scale Transformation Search for Deep Classification Neural Networks

The paper deals with problem of optimal input scale search for deep classification neural networks. It is shown that state-of-the-art deep neural networks are not stable to input image scale, leading to quality degradation. The paper demonstrates relevance of the topic on classical image classification DL-pipeline. Unlike previous researchers, who aim to build entire complex invariant neural nets, we claim that computing optimal input transformations (e.g. scale) is a more perspective way for successful neural networks real-life applications. Thus, a new scale search algorithm for DL image classification is proposed in the paper, based on empirical hierarchical analysis of activation values.
Organ classification Using Quantum Convolution Network

Automated body part recognition is an essential part of medical image analysis. It is one of the core aspects of any medical image identification or segmentation. It reduces search time with increased accuracy in detecting an organ of interest. Here, we present a quantum machine learning based organ classification model for X-RAY data sets of hand, ankle, knee, and chest. We have used deep quanvolutional neural network. Quanvolutional neural network is a hybrid neural network where input data are encoded into quantum encoding, and then it is processed with a classical neural network. It is also called HQCNN (Hybrid Quantum Convolutional Neural Net-work) [3]. Our results show that HQCNN based implementation gives 92.5% validation accuracy with fast convergence, while classical CNN gives 93% validation accuracy.
TopSelect

Building robust industrial machine learning (ML) models requires incorporating domain knowledge in feature selection. This ensures building meaningful ML models that fit the context of the industrial process that consists of complex networks of thousands of elements interconnected by flows of material, energy, and information. Despite the various automatic feature selection methods, they are still outperformed by the manual feature selection that embeds the industrial domain knowledge. This paper proposes an industrial feature selection method that (1) automatically captures domain knowledge from topology models holding information on the industrial plant and (2) identifies the relevant process signals (i.e., features) to a specified process element (i.e., to which an ML model is being built). We performed an empirical case study on an industrial use case to evaluate the effectiveness and efficiency of the proposed method in comparison to existing ones from literature.
POSSIBILITIES OF USING VIRTUAL REALITY TECHNOLOGIES IN EDUCATION

An important trend in today’s conditions (Russia’s military aggression, the COVID-19 pandemic) is the use of information and communication technologies in the modern educational process, including virtual reality technologies. Scientists of the National Academy of Pedagogical Sciences of Ukraine pay considerable attention to the use of virtual reality technologies in education in Ukraine. Their works reflect the accumulated domestic and foreign experience in working with virtual reality technologies, the introduction of virtual reality platforms in distance learning. In their research, researchers note that virtual reality technologies, as an effective tool of the educational process, have great potential for solving problems of the educational process. In their work, scientists note that the prospect of further research is the mass introduction into the educational process of modern educational institutions of virtual reality technologies, as well as the development of methodological and didactic materials for their effective use.In their work, scientists and practitioners of educational institutions pay considerable attention to one of the promising areas of application of virtual information technology, in particular the development of virtual laboratories. These software environments can serve to provide a better level of learning material and with which you can make it more saturated, visual, bright and accessible. A virtual learning environment allows you to model the behavior of real-world objects in a computer environment and promotes the acquisition of new knowledge and skills on a more conscious and deeper level. The issue of virtual laboratories is widely covered in the foreign press. First of all, there are the problems of implementing a virtual laboratory for medical students, such as nurses and exercise specialists, and the work of a virtual educational research laboratory in the nuclear field, including the development of 3D models of the TRIGA reactor using virtual reality models.
Improving Gender Classification based on Skin Color using CNN Transfer Learning

Gender is one of the important features in soft biometrics. Gender classification has its own importance that can further improve facial recognition performance. However, there is an issue related to inaccurate gender classification from facial images especially for dark-skinned women. In this paper, a gender classification study was conducted to investigate face image classification based on skin color. Transfer learning method is used to improve the accuracy of gender classification. The MobileNet model was selected in this research. Two types of transfer learning namely transfer learning through feature extraction and transfer learning through fine tuning have been implemented. Performance is measured by considering the accuracy of bright and dark skin faces. The experimental results show that, with proper training techniques, gender classification can achieve competitive classification accuracy by using transfer learning methods.
Multilayer Perceptron Optimization on Imbalanced Data Using SVM-SMOTE and One-Hot Encoding for Credit Card Default Prediction

Credit risk assessment analysis by classifying potential users is an important process to reduce the occurrence of default users. The problems faced from the classification process using real-world datasets are imbalanced data that causes bias-to-majority in model training outcomes. These problems cause the algorithm to only focus on the majority class and ignore the minority class, even though both classes have the same important role. To overcome this problem, a combination of One-hot encoding (OHE) and SVM-Synthetic minority oversampling technique (SVM-SMOTE) techniques are used for the optimization process of the MLP classification algorithm. OHE is used to encode values categorical nominal and SVM-SMOTE for the oversampling. The results of the measurement of the ability of the model generated from the optimized MLP are then compared with the baseline using the AUC score. The data used is the default of credit card client dataset from Taiwan which has 30000 instances. The result of the highest AUC score of the MLP that has gone through optimization is 0.7184, an increase of 0.2179 compared to the baseline.
Image Watermarking Technique for Digital Al-Quran based on Fibonacci Transform via DWT

Advent growth in information and communication technologies has accelerated the needs to secure digital media. Demands for digital media when on the move urged Quran readers to install the Quran mobile apps on their device. However, there is no secure mechanism to legalize its content credential. On the other hand, mobile developer finds difficulty in getting access to readily authentic Quran database sources. Malaysia government has introduced Smart Quran mobile apps without any counterfeit protection. Correspondingly, the contents are vulnerable to hacking, falsification which consequently endangering Islamic institutional prestige. There is also a lack of credible authorization institutions to verify authenticity of the digital Quran that could possibly deviate the Muslim faith by reciting falsified content and meaning. To fill the void, this paper explores the utilization of an image watermarking to preserve the Quran digital image. The study presents new blind RGB image watermarking using Fibonacci Transform via Discrete Wavelet Transform (DWT) and manipulates the R component of the Al-Quran image. The second level of LL decomposition of the cover image will then be substituted by the Fibonacci-transformed watermarked version. DWT embedding is achieved through alpha blending technique. Final watermarked image is obtained once RGB conversion is performed. The efficiency of the method is assessed towards 10 samples. Verification is conducted via the extraction of the endorsed license image. The results demonstrate enhanced robustness and imperceptibility with average PSNR and SSIM of 92.13 dB and 0.99 respectively. Forgery cases of digital Quran applications are therefore tackled.
Augmenting Histological Images with Adversarial Attacks

Neural networks have shown to be vulnerable against adversarial attacks - images with carefully crafted adversarial noise that is imperceptible to the human eye. In medical imaging tasks this can be a major threat for making predictions based on deep neural network solutions. In this paper we propose a pipeline for augmenting a small histological image dataset using State-of-the-Art data generation methods and demonstrate an increase in accuracy of a neural classifier trained on the augmented dataset when faced with adversarial images. When trained on the non-augmented dataset, the neural network achieves an accuracy of 55.24 on the test set with added adversarial noise, and an accuracy of 97.40 on the same test set when trained on the augmented dataset.
Fuzzy Image Enhancement Method based on a New Intensifier Operator

In recent years, fuzzy image enhancement methods have been widely applied in image enhancement, which generally consists of three steps: fuzzification, modify membership(using intensifier (INT) operator), and defuzzification.This paper proposed a new INT operator used in fuzzy image enhancement.The INT operator is adjustable for different test images.The image enhancement method is as follows, firstly, calculate the image threshold (T ) using the OTSU method.Secondly, calculate pivotal point p corresponding to T , and find the corresponding INT operator function.Finally, use the INT operator in fuzzy Image Enhancement.The INT operator is used multiple times in the image processing process to obtain multiple result images.Comparative experiments show that the proposed new INT operator has better image enhancement effect when INT operator is applied at the same number of times.On the other hand, more intermediate process result images can also be obtained through the proposed new INT operator.More result images can provide material resources for the subsequent image processing.
A Blockchain-based Model for Securing IoT Transactions in a Healthcare Environment

A blockchain is a data structure that is implemented as a distrusted database or digital ledger.The transactions are saved to a block of transactions that is attached in turn to the blockchain after the verification process, in which each block in the chain contains a hash signature of the previous block in addition to the hash signature of the block itself.The blocks on the blockchain are chained as an immutable list using the proof-of-work procedure, where there is no way to alter or delete an attached block due to the strict security policy used for structuring the chain of blocks.Each node holds a copy of the blockchain in which the miners take the responsibility of verifying and attaching blocks to the blockchain.The Ethereum blockchain introduced the smart contract which holds logic to be processed once the contract is established.These smart contracts are developed via the Solidity programming language.This proposed paper exploits the Ethereum blockchain along with smart contracts as the base technology for implementing the proposed blockchain-based model.The paper aims to develop a multilayered blockchain-based model, in which the blockchain model is set up on a private blockchain Ethereum network where the nodes share the electronic medical records (EMR) among the P2P (peer-to-peer) network that will be used to secure the IoT medical transactions.Solidity smart contract, introduced by Ethereum, is deployed to handle the EMR "open-query-transfer" operations on the private network, whereas the miners are responsible to validate the transactions.Finally, the research conducts a performance analysis of the Ethereum network using the Ethereum Caliper, considering several performance factors, which are: Maximum Latency, Minimum Latency, Average Latency, and Throughput.
Design Guidelines for Developing Systems for Dialogue System Competitions

Because dialogue system development involves a variety of factors and requires multifaceted consideration, design guidelines for such development would be helpful. Although a neural-based approach can be used, it requires a vast amount of dialogue data, which would take too much effort to collect in the case of a system for a specific, fixed-length dialogue. Furthermore, the system design should explicitly consider errors in automatic speech recognition and language understanding, because they degrade the user impression and are inevitable when the system talks with general users. Accordingly, we propose design guidelines for developing such dialogue systems. Systems developed with the aid of these guidelines took first place in two dialogue system competitions: the situation track of the second Dialogue System Live Competition and a pre-preliminary contest of the Dialogue Robot Competition. Our proposed design guidelines are to: (1) make the system take initiative, (2) prevent dialogue flows from relying too much on user utterances, and (3) include in utterances that the system understands what the user said. We describe details and examples for the systems designed for each of the two competitions.
K-NCT: Korean Neural Grammatical Error Correction Gold-Standard Test Set Using Novel Error Type Classification Criteria

Recently, active research has been conducted on Korean grammatical error correction on machine translation (MT) and automatic noise generation. However, there is no gold-standard test set for objective and official comparative analysis. A significant limitation is measuring the ill-defined performance because the experimental error types in the train set are also included in the test set. Moreover, error types in the training set are also included in the test set. Additionally, the types of errors for qualitative analysis are defined differently with no explicit guidelines. This study proposes a gold-standard test set called the Korean Neural Grammatical Correction Test set (K-NCT) for Korean grammatical error correction using a new error type classification guideline. To ensure the factuality and reliability of the proposal, we conduct a quantitative analysis using a commercialization system and human evaluation. Experimental results demonstrate that the proposed grammatical error correction test set has a well-balanced, diverse, and precise guideline. Our dataset is available at https://github.com/seonminkoo/K-NCT.
A toolchain for testing OPC UA interfaces

Open Platform Communications Unified Architecture (OPC UA) is an increasingly important standard for industrial communication, which brings up the need for testing. Still, functional or formal verification of the implementation relies on conformance testing or custom solutions. Model-based testing has been studied and applied for decades, targeting many different technologies. OPC UA already provides a defined serialization format for its interfaces, representing a simple model of the application that could be utilized for testing.In this paper, we present our work on applying model-based testing to OPC UA server applications using the serialized interface description as the model. We introduce a possible toolchain for that approach and, based on that, discuss the possibilities and shortcomings of the interface description as a model for testing.
Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia

Grab Indonesia is one of the leading online motorcycle taxi companies in Indonesia and has a large number of customers in Indonesia. The level of customer satisfaction varies with the services provided, so there must be suggestions and complaints from customers. Sentiment analysis can be used as a solution to determine the level of service satisfaction in order to improve the system and service. This study aims to determine the level of satisfaction of Grab Indonesia users through the Grab application in the Playstore. One of the approaches that can be used is LSTM. LSTM is an RNN algorithm development to solve the vanishing gradient problem. LSTM has the disadvantage of only running can only capture information from one direction. Bidirectional LSTM (BiLSTM) is an LSTM method that has been developed, where BiLSTM can capture information from two directions. In this BiLSTM method, the more data, the better the algorithm's performance. The test results show that BiLSTM is more reliable than LSTM in the case of sentiment analysis on the Indonesian Grab service. BiLSTM produces the best accuracy of 91% and training loss of 28%. Suggestions for future research can produce more and varied word representations by considering the word embedding combinations.
A Smart Contract Approach in Pakistan Using Blockchain for Land Management

Management of land records includes actions such as registration and transfer of property ownership. For many nations, land ownership and management are important sources of income. Corrupted spans from small-scale payments to large-scale cause an abuse for government. In the literature, a number of concerns have been raised about Land Record Management. There are several problems with Land Record Management in developing nations, such as tampering with land records and no methods of retrieving a full property ownership record, operating multiple linked Land Record Management Systems independently, etc. Traditional land record management solutions do not solve these challenges. We propose a Blockchain-based Land Record Management system for Pakistan to solve these concerns. It has been decided to use the suggested system, and the specifics of its implementation are described in this thesis.
An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed.