Reviewerly/exHarmony huggingface dataset:
Non-Face-to-Face P2P (Peer-to-Peer) Real-Time Token Payment Blockchain System

With the increase in intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals’ personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas-charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smartphones or application platforms.
Bayesian modeling via discrete nonparametric priors

Abstract The availability of complex-structured data has sparked new research directions in statistics and machine learning. Bayesian nonparametrics is at the forefront of this trend thanks to two crucial features: its coherent probabilistic framework, which naturally leads to principled prediction and uncertainty quantification, and its infinite-dimensionality, which exempts from parametric restrictions and ensures full modeling flexibility. In this paper, we provide a concise overview of Bayesian nonparametrics starting from its foundations and the Dirichlet process, the most popular nonparametric prior. We describe the use of the Dirichlet process in species discovery, density estimation, and clustering problems. Among the many generalizations of the Dirichlet process proposed in the literature, we single out the Pitman–Yor process, and compare it to the Dirichlet process. Their different features are showcased with real-data illustrations. Finally, we consider more complex data structures, which require dependent versions of these models. One of the most effective strategies to achieve this goal is represented by hierarchical constructions. We highlight the role of the dependence structure in the borrowing of information and illustrate its effectiveness on unbalanced datasets.
Forging measurement using passive stereo vision

Dimensional inspection during manufacturing of high-temperature forging workpieces is still an unsolved research topic. In this work, an approach to reconstruct the hot forging surface using passive stereovision was proposed. The challenges are the irregular thermal radiation of the forging in the visible spectrum, which is affected by scales, heat haze, and an unstable environment. Unlike the approaches presented in the literature, we use the radiated light, without an external light source. High dynamic resolution images were used to image the forging surface with highly varying brightness. The effect of optical filtering on the contrast of the fine surface structure was investigated and evaluated by spectral analysis of the images, with the best results obtained in the green spectrum. A comparison of feature detectors was performed for the specific surface structure of the hot forging. The best results in terms of density of reconstructed points were obtained using the KAZE method. The final verification of the achievable accuracy was performed in a measurement volume of approx. 500×400×350 mm. An 877 &deg;C hot steel pipe sample was measured with a single point standard deviation error of 0.28 mm, which is an order of magnitude higher than under ideal conditions, but acceptable in comparison with manufacturing tolerances. Further developments could be related to the application of dense image matching, further investigation of interfering effects, and application of the proposed methods in the industrial environment.
Investigating the spillover effects of Bitcoin's financial fluctuations on other digital currencies

The purpose of this study is to investigate the effects of volatility spillover from Bitcoin as the largest digital currency on selected digital currencies. Volatility spillovers are a warning for risk management among cryptocurrencies and are especially instructive during periods of crisis. We investigate the effects of volatility spillover between digital currencies through the conditional covariance matrix. The findings show that Bitcoin had the highest volatility spillover on Dogecoin, Dash, and Ripple among digital currencies, respectively, and had the lowest volatility spillover on Ethereum. Bitcoin is used more as an asset than a currency and the Bitcoin market is more volatile than other currencies and prone to potential price bubbles. Based on the results, the bubbles in the digital currency market show that the market is irrational and due to the speculative behaviour of investors and the excitement of the Bitcoin market, it is causing economic instability.
Missa: a regional approach to maintain validity

Blockchain is a new technique developed to eliminate central management of information by dividing maintenance between validators interested in participating for an expected reward. Unlike previous work, this invested in the practical impossibility of dealing with a large number of maintainers who may be business owners expecting a reward or aiming to foster the ecosystem. Inspired by the proverb "solidarity is not an act of charity, but mutual aid between forces fighting for the same goal." This work introduces Missa, which is a way to foster trust between different maintainers and the platform by exchanging valuable data structures to provide a new approach to maintaining ledger validity in a fast, reliable, and secure manner. The solution was evaluated in terms of security discussion, environmental modelling, formal study of important components, and conceptual comparison. Finally, an actor model implementation, network simulation and unit tests were demonstrated.
New wafer shape measurement technique for 300mm blank vertically held silicon wafers

The shrinking depth of focus of high numerical aperture immersion microlithography optics requires a tight wafer flatness budget. Bare wafer surface topography variation is a significant part of the focus budget for microlithography. Thus, as the wafer surface quality becomes increasingly important, the metrology to control the surface quality is increasingly challenged1. Advanced lithographic patterning processes require a detailed map of the free, non-gravitational, wafer shape, to avoid overlay errors caused by depth-of-focus issues2. The semiconductor industry has been using interferometry-based techniques for measuring the free form wafer shape of blank silicon wafers for several years1. In this paper we introduce a new measurement technique, Wave Front Phase Imaging (WFPI), that can measure the free form wafer shape of a silicon wafer by acquiring only the intensity of the reflected light. In the WFPI system, the wafer is held vertically to avoid the effects of gravity during measurements. The wave front phase is then measured by acquiring the 2-dimensional intensity distribution of the reflected, non-coherent, light at two or more distances along the optical path. This method allows for very high data acquisition speed and a high number of data points equal to the number of pixels available in the CMOS imaging sensor used. In the measurements presented in this paper, we acquired 16.3 million data points on the full 300mm blank silicon wafer, generating a lateral resolution of about 65μm per pixel. Blank silicon wafer manufacturers need to acquire such metrics as bow, warp, and flatness among other parameters, to provide with the silicon wafer when sent to the device maker fabs. These metrics are easily derived by generating a free form wafer shape map of both the front and the back surfaces.
MANIPULATION IN THE MULTICULTURAL INTERNET SPACE: SUBSTITUTION OF THE SEMANTIC COMPONENT OF A LANGUAGE SIGN

Aim. To identify the features of the manipulation, which is based on the substitution of the semantic component of a linguistic sign. Methodology. The main content of the study is the analysis of the structure of manipulation observed in conflict communicative situations. In the course of the research, methods of lexical and semantic and propositional analysis, as well as methods of formal logic, were used. Results. The analysis showed that the basis of manipulative communication in a multicultural environment is the substitution of semantic components of the concepts that have a similar linguistic expression. The relations of outness and intersection between the volumes of these concepts are revealed, the semantics of language representatives is determined. Research implications. A number of directions for further research is identified: the use of methods of logical analysis of the language units representing manipulative communication, the influence of Internet resources on the development of conflict speech situations.
Educating young teachers in sense of mental view as an example of teaching foreign languages

In this article it is analyzed the creation of the systematic drills based totally on psychological and didactic standards in language educating for creating language abilities for learners. Moreover, since teachers are extremely pragmatic individuals, it turns out that relevance drives are their motivation. They want training focused on what issues they and their students are having with their teaching and learning so they may make their own decisions about those issues. We should have understood that teachers need to set their own learning goals, just like all adult learners do.
Shape measurement of phase objects using fringe projection technique

Due to the advancements in the field of optical metrology, it has found its applications in various areas such as biomedical, automotive, semiconductors, aerospace, etc. The popularity of optical techniques for metrology has increased by multiple folds owing to its non-invasive nature with ease of setup, fast data acquisition, and remote sensing ability. Optical techniques include hologram interferometry, speckle photography, speckle interferometry, Moire interferometry, photoelasticity, fringe projection technique, etc. The holographic interferometry technique works by quantifying the optical phase of the object by measuring the change in the interference fringes due to the shape of an object. This technique has a large number of advantages, but a steep object leads to a large number of fringes in the field of view, which are not resolvable as they fail to satisfy the Nyquist criterion. In this work, the fringe projection technique, which is a non-interferometric, non-invasive technique for generating 3D surface information is employed to measure the shape of a phase object like a wedge. Fringe projection is presented as a robust and compact technique for shape measurement of phase objects as it utilizes lesser components and has less complexity compared to the holographic technique.
Social presence as a training resource: comparing VR and traditional training simulations

From immersive simulations to interactive tutorials, Virtual Reality (VR) is transforming the way we learn and practise new skills. Especially for social skills training, a growing number of simulations have been designed in which trainees learn to master difficult communicative situations. One of the factors to which the effectiveness of VR as a learning technology is attributed to is the users’ feeling of social presence during the simulated interaction. This paper presents the evaluation of (1) a role play training, (2) a learning app and (3) a VR training application in a workshop series. Social presence was perceived as equally convincing and engaging for the prototypical VR scene as for the traditional form of role play, although the course of the interaction in VR was highly determined compared to the interaction dynamics of a human role play. In our interpretation, this confirms social presence as a valuable resource for training social interaction, which spans across various learning settings and methods in increasingly blended or hybrid learning and working contexts.
HGAT: smart contract vulnerability detection method based on hierarchical graph attention network

Abstract With the widespread use of blockchain, more and more smart contracts are being deployed, and their internal logic is getting more and more sophisticated. Due to the large false positive rate and low detection accuracy of most current detection methods, which heavily rely on already established detection criteria, certain smart contracts additionally call for human secondary detection, resulting in low detection efficiency. In this study, we propose HGAT, a hierarchical graph attention network-based detection model, in order to address the aforementioned issues as well as the shortcomings of current smart contract vulnerability detection approaches. First, using Abstract Syntax Tree (AST) and Control Flow Graph, the functions in the smart contract are abstracted into code graphs (CFG). Then abstract each node in the code subgraph, extract the node features, utilize the graph attention mechanism GAT, splice the obtained vectors to form the features of each line of statements and use these features to detect smart contracts. To create test data and assess HGAT, we leverage the open-source smart contract vulnerability sample dataset. The findings of the experiment indicate that this method can identify smart contract vulnerabilities more quickly and precisely than other detection techniques.
Application of Machine Learning for Prediction and Monitoring of Manganese Concentration in Soil and Surface Water

This study explored the application of machine learning, specifically artificial neural network (ANN), to create prediction models for manganese (Mn) concentration in soil and surface water (SW) on the island province with two open mine pits overflowing to two major rivers that experienced mining disasters. The two ANN models were created to predict Mn concentrations in soil and SW from 12 and 14 input parameters for soil and SW, respectively. These input parameters were extracted from extensive field data collected at the site during sampling program in 2019, 2021, 2022, and initially processed with spatial analysis via geographic information system (GIS). All datasets were then divided for model training and validation, using 85% and 15% ratio, respectively. Performance evaluation of each model with mean absolute percentage error (MAPE) and root mean squared error (RMSE) confirmed the accuracy of both models. The soil Mn model achieved MAPE and RMSE values of 2.01% and 23.98, respectively. The SW Mn model was split into two models based on SW Mn values within the 0–1 mg/L range and &gt;1 mg/L range. The SW Mn model for &gt;1 mg/L performed better with MAPE and RMSE of 4.61% and 0.17, respectively. Feature reduction was also conducted to identify how the models will perform if some input parameters were excluded. Result showed sufficient accuracy can still be obtained with the removal of 4–5 input parameters. This study and these models highlight the benefit of ANN to the scientific community and government units, for predicting Mn concentration, of similar environmental conditions.
Research on Chinese Semantic Named Entity Recognition in Marine Engine Room Systems Based on BERT

With the development of intelligentization in maritime vessels, the pursuit of an organized and scalable knowledge storage approach for marine engine room systems has become one of the current research hotspots. This study addressed the foundational named entity recognition (NER) task in constructing a knowledge graph for marine engine rooms. It proposed an entity recognition algorithm for Chinese semantics in marine engine rooms that integrates language models. Firstly, the bidirectional encoder representation from transformers (BERT) language model is used to extract text features and obtain word-level granularity vector matrices. Secondly, the trained word embeddings are fed into a bidirectional long short-term memory network (BiLSTM) to extract contextual information. It considers the surrounding words and their sequential relationships, enabling a better understanding of the context. Additionally, the conditional random field (CRF) model was used to extract the globally optimal sequence of named entities in the marine engine room semantic. The CRF model considered the dependencies between adjacent entities that ensured a coherent and consistent final result for entity recognition in marine engine room semantics. The experiment results demonstrate that the proposed algorithm achieves superior F1 scores for all three entity types. Compared with BERT, the overall precision, recall, and F1 score of the entity recognition are improved by 1.36%, 1.41%, and 1.38%, respectively. Future research will be carried out on named entity recognition of a small sample set to provide basic support for more efficient entity relationship extraction and construction of a marine engine room knowledge graph.
Robustness of NMF Algorithms Under Different Noises

In machine learning, datasets are often disturbed by different noises. The Nonnegative Matrix Factorization (NMF) algorithm provides a robust method to deal with noise, which will signiﬁcantly improve the efﬁciency of machine learning. In this investigation, the standard NMF algorithm and L2,1-Norm Based NMF algorithm are studied by designing experiments on different noise types, noise levels, and datasets. Furthermore, Relative Reconstruction Errors (RRE), accuracy, and Normalized Mutual Information (NMI) are used to evaluate the robustness of the two algorithms. In this experiment, there is no signiﬁcant difference in performance between the two algorithms, while L2,1-Norm Based NMF algorithm shows relatively small advantages.
Context-Based Support to Enhance Developers’ Learning of Software Security

Software security is an ongoing problem, largely due to a lack of security knowledge among software developers from diverse backgrounds. To counter this, security experts are attempting to offer a broad range of knowledge resources to enlighten developers about increasing cybersecurity threats. Unfortunately, the abundance of knowledge resources does not seem to have much of an impact on reducing the issue of software security. The ineffective teaching and learning approaches for software security have created difficulties for developers in learning security knowledge. This research employs a four-cycle of Design Science Research Methodology (DSRM) to integrate necessary elements in the development of a context-based learning system for security education and learning. The final artifact is an ontology-based web application that facilitates a contextualized learning process by providing security knowledge through contextual software cases. Through evaluation in pedagogical and software development environments, it is proven to contribute a viable solution to the problem domain. While these results are positive, the innovative context-based artifact benefits not only the domain of software engineering but also other educational fields, such as information security and computer security.
Domain adapted broiler density map estimation using negative-patch data augmentation

To overcome the lack of labelled data in real-world situations such as agriculture, transfer learning has recently been in the spotlight. Domain adaptation is a transfer learning method that can migrate well learned knowledge from a source domain to a target domain. However, it is difficult to ensure the generalisation to adequate feature space when source and target domain are heterogeneous due to their excessive domain shift. In this study, a negative-patch (NP) data augmentation was proposed to improve the domain adaptation performance between heterogeneous domains for density map estimation. The proposed NP was applied to Domain-Adversarial Training of Neural Networks (DANN) for broiler flock density estimation. A people crowd dataset and a broiler dataset were used as source and target domain datasets, respectively. Experiments were conducted by the model configuration, and the broiler counting performances were compared. The results showed that the DANN with NP could reduce the error by up to 54% for DANN without NP and that the NP has demonstrated its generalized performance for multiple source domain datasets.
Evaluation of Computer Technologies for Calculation of Exact Approximations of Statistics Probability Distributions

This paper is devoted to the evaluation of the hardware resources of computer systems for solving a computationally expensive problem such as the calculation of the probability distributions of statistics by the second multiplicity method based on Δ-exact approximations of samples with a size of 320–1280 characters and an alphabet power of 128–256 characters. The accuracy is Δ=10−5 and the total solution time should not exceed 30 days or 2.592×106 seconds for 24/7 computing. Owing to the use of the properties of the second multiplicity method, the computational complexity of the calculations can be brought within the range of 9.68×1022−1.60×1052 operations with the number of tested vectors at 6.50×1023−1.39×1050. The solution of this problem for the specified parameters of samples during the given time requires hardware resources which cannot be provided by modern computer technology such as processors, graphics accelerators and programmable logic integrated circuits. Therefore, in this paper, we analyze the possibilities of promising quantum and photon technologies for solving the problem with the given parameters. The main advantage of quantum computer systems is the high speed of calculations for all possible parameter values. However, quantum acceleration will not be achieved to calculate the probability distributions of statistics due to the need to check all the obtained solutions. Here, the number of obtained solutions corresponds to the dimension of the problem. In addition, due to the current development level of quantum hardware components, it is impossible to create and use 120 qubit quantum computers for the solution of the considered problem. Photon computers can provide high computation speeds at low power consumptions and require the smallest number of nodes to solve the considered problem. However, unsolved problems with the physical implementation of efficient memory elements and the lack of available hardware components make the use of photon computer technologies impossible for calculating the probability distributions of statistics in the near future (5–7 years). Therefore, it is most reasonable to use hybrid computer systems containing nodes of different architectures. To solve the problem on various hardware platforms (general purpose processors, GPUs and FPGAs) and configurations of hybrid computer systems, we suggest to use the architecture-independent high-level programming language SET@L. The language combines the representation of calculations as sets and collections (based on the alternative set theory of P. Vopenka), the absolutely parallel form of the problem represented as an information graph and the paradigm of aspect-oriented programming.