Reviewerly/exHarmony huggingface dataset:
Review of Dunn (2022): Natural Language Processing for Corpus Linguistics

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Distance Learning in the Dual System of Engineering Education: Peculiarities and Capabilities

Introduction . Today, there is a heated discussion about the improvements that can be made in engineering education. The education process can be directly affected by new distance learning technologies that are gaining attention since the limitations of the COVID-19 pandemic. In this context, while implementing distance technologies, one must consider special aspects of educational process organization conditioned by the need of practical training of future engineers. Purpose. In our study, we will discover the peculiarities and capabilities of distance technologies in their implementation to the learning process in the context of engineering education dual system. Materials and Methods. We used key provisions of the social learning theory, the diffusion of innovation theory, and the technology acceptance model. The questionnaire method and the Mann–Whitney U test were applied for the assessment of students and teachers of the joint department vision on distance technologies in education. The empirical base was formed on the survey results of senior students and teachers of the joint department, Don State Technical University. Results . We considered the essence of the dual approach in engineering education formed on the direct participation of companies in a learning process. The special aspects of the application of distance technologies in engineering training are described. The specifics of the distance technologies implementation in engineers' training of the joint department during and after the pandemic are reflected. We described the joint department experience in implementing distance learning in the educational process. The special aspects of the implementation of Internet platforms and how they are perceived by students and teachers from the joint department are described. Discussion . In general, engineering students do not have serious problems with the online learning mode or the lack of digital skills. However, they do not want to study only with distance technologies. Therefore, they see the perspective in blended learning.
Blockchain and Business Process Management (BPM) Synergy: A Comparative Analysis of Modeling Approaches

Blockchain technology has become a powerful disruptive force that upends established ideas in several industries. A fascinating point of convergence is that of blockchain technology and Business Process Management (BPM), where the distributed and immutable characteristics of blockchain promise to completely transform the modeling, implementation, and oversight of business processes. This symbiosis offers a singular chance to develop corporate processes that are more efficient, safe, and transparent. Nevertheless, to guarantee that blockchain-specific components are accurately represented in these processes, modeling techniques need to be critically examined as part of integrating blockchain into BPM. This literature review examines blockchain-BPM integration using different modeling methodologies. Though well-established, traditional BPM approaches may need help with blockchain-specific aspects. Blockchain-oriented modeling includes smart contracts and decentralized consensus. Hybrid models with blockchain and traditional elements are popular. Adaptability, model clarity, and blockchain integration are evaluated in the analysis. This literature review aims to improve corporate processes’ efficiency, security, and transparency by investigating how to model the integration of blockchain and BPM better.
Analysis of Sustainable Drainage System in SMA 4 Area, Jayapura City, Papua

Abstract Flooding one of the problems that arise during the rainy season. This is very common in areas with tropical climates, where during the dry season it can be very dry and lack of water, but during the rainy season it is abundant with water and at worst a disaster can occur. Runoff and inundation a usually problem in Jayapura City, with hill topography that tends the large, resulting flood and inundation in flat and upstream areas. One the public areas is often affected by runoff or flood and innudation at the SMA 4 area of Jayapura City, where in this area is most important community activity because it is related to educational activities so attention need paid overcome the problems that occur mainly as a result of runoff. Sustainable drainage system application that prioritizes water quality and runoff water quantity management is very important to overcome the problems runoff and innudation. The result study is find a solution to runoff and iinudation. Analysis method useing Hec-Ras application for calculation discharge and channel dimensions using Sustainable Urban Drainage method which is popularly applied in various countries for water quality and runoff quantity management. The researched result are to obtain the maximum rainfall discharge value and runoff design, drainage channel dimensions, and the appropriate sustainable drainage system in SMA 4 area of Jayapura City[1].
Preliminary Result of Swarm Activities in Toba Region Using Dense Temporary Network

Abstract For the first time, an earthquake swarm occurred in 2021 in Lake Toba (Indonesia), one of the largest calderas in the world. Although the earthquakes were located in the volcanic environment, the swarm activities could be related to the tectonic activities from the Sumatra fault system. The swarm activities occurred at a very shallow depth and were felt with the intensity of II–IV MMI by local people. The objective of this research was to investigate the characteristics of the earthquake swarm in the Toba Caldera from the waveforms recorded by temporary stations in March – June 2021. A total of 26 seismic stations were deployed for two months in May and June 2021. Automatic detection of seismic waves is important because it can be used for earthquake early warning systems and speeds up analysis of origin time, P-, and S arrival times, and earthquake locations. We found characteristics of spectrum waveform from low to high frequency of a non-swarm earthquake, while high frequency seismograms were generated by the swarm earthquake. Furthermore, we successfully captured the hypocentre that was distributed in the SW – NE direction and may figure out the unknown geological system that is responsible for the swarm activities.
Multiview human action recognition via deep discriminant analysis network

Homogeneous data fusion plays a crucial role in multiview human action recognition (MvHAR). However, most existing methods tend to employ the concatenate strategy, ignoring the underlying correlation among views and degrading the recognition performance. To this end, we propose a practical MvHAR framework based on a deep discriminant analysis network that excavates multiview video features to obtain a more discrimination representation, from which the view correlation can be explored. Specifically, the spatial–temporal features of multiview data are extracted by the convolution network, and then a deep multiview feature fusion network is proposed to project these features into an advanced subspace for efficient fusion. Traditional methods can lead to class overlap problems, but, to avoid this problem, we improve the separation between two classes by using pairwise between-class scatter. Experiments on five benchmark datasets indicate the efficiency of our framework compared with the advanced algorithms under four metrics.
Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.
Integrating blockchain into global finance: a case study of SWIFT's strategic collaboration with Chainlink

This study scrutinizes the strategic blueprint implemented by SWIFT in 2023, focusing on the integration of blockchain technology into the financial communication infrastructure. By conducting a systematic analysis, juxtaposed with current academic dialogues, the research reveals SWIFT's response to the emerging demands for secure, efficient, and collaborative financial transactions. The collaboration with Chainlink is highlighted as a practical move towards addressing the exigencies of enhanced cyber resilience and governance in the face of evolving technological landscapes. The study's findings illuminate the potential of SWIFT's approach to streamline the transition towards blockchain technology, minimizing the adaptation requirements for financial institutions, and suggesting a replicable model for widespread adoption. The relevance of this research extends beyond academic circles, offering insights into the adoption of blockchain technology at a critical juncture in financial history, with implications for improved transactional security and efficiency that benefit a broad spectrum of stakeholders. Regulatory bodies, financial entities, and consumers stand to gain from the advancements detailed in this study, which outlines a path for the integration of technology that promises a more cohesive, secure, and efficient global financial system. This research contributes to the literature by providing a nuanced understanding of blockchain's practical applications and sets the stage for future innovations in financial operations.
A Pad$ \acute{e} $ approximation and intelligent population shrinkage chicken swarm optimization algorithm for solving global optimization and engineering problems

&lt;abstract&gt;&lt;p&gt;Bio-inspired optimization algorithms are competitive solutions for engineering design problems. Chicken swarm optimization (CSO) combines the advantages of differential evolution and particle swarm optimization, drawing inspiration from the foraging behavior of chickens. However, the CSO algorithm may perform poorly in the face of complex optimization problems because it has a high risk of falling into a local optimum. To address these challenges, a new CSO called chicken swarm optimization combining Pad$ \acute{e} $ approximate, random learning and population reduction techniques (PRPCSO) was proposed in this work. First, a Pad$ \acute{e} $ approximate strategy was combined to help agents converge to the approximate real solution area quickly. Pad$ \acute{e} $ approximate was grounded in a rational function aligning with the power series expansion of the approximated function within a defined number of terms. The fitting function used in this strategy employs the above rational function and the extreme points are calculated mathematically, which can significantly improve the accuracy of the solution. Second, the random learning mechanism encouraged agents to learn from other good agents, resulting in better local exploitation capability compared to traditional CSO. This mechanism has a special idea that when it comes to selecting random individuals, it selects from the same type of high-performing agents, rather than selecting them completely at random. Third, a new intelligent population size shrinking strategy was designed to dynamically adjust the population size to prevent premature convergence. It considers fitness function calls and variations in recent optimal solutions creatively. To validate the algorithm's efficacy, PRPCSO was rigorously tested across 23 standard test functions and six kinds of practical engineering problems. We then compared PRPCSO with several mainstream algorithms, and the results unequivocally established PRPCSO's superior performance in most instances, highlighting its substantial practical utility in real engineering applications.&lt;/p&gt;&lt;/abstract&gt;
A control strategy for improving the accuracy of lateral tracking of autonomous vehicles

Lateral tracking is a fundamental technology for autonomous vehicles, particularly in narrow and complex scenes such as parking lots and alleys, where precise control is critical. To enhance the efficacy and precision of the traditional Pure Pursuit algorithm (PP) in the lateral tracking of low-speed autonomous vehicles, this study proposes a continuous adaptive piecewise fitting method based on the cubic Bézier curve to smooth the reference path, followed by a search for the look-ahead point based on the geometric relationship between the vehicle and the reference path. An improved search algorithm based on vehicle speed and road curvature is used to identify the look-ahead point. In case of significant changes in the reference path curvature, the initially obtained look-ahead point is modified. The proposed Improve Pure Pursuit Method (IPP) is then used to calculate the steering angle of the front wheel. To validate the proposed method, the Carsim/Simulink co-simulation model and the autonomous vehicle platform are established. The simulation and real-vehicle tests demonstrate that compared with the pure pursuit algorithm based on Vehicle speed control (VPP) and Fuzzy Logic control pure Pursuit (FPP), IPP performs better in complex low-speed lateral tracking scenes. It reduces the issue of cutting corners when the vehicle enters a corner and enhances lateral tracking accuracy.
Special Knowledge and Forensic Examination as a Procedural Safeguard of Intellectual Property Rights Protection

Forensic examination is an important procedural safeguard for the protection of infringed intellectual rights in cases where the use of special knowledge is necessary to resolve a dispute, and at the same time it should not be considered as a procedural safeguard and a means of protection in cases where special knowledge is not required to establish the fact of the use of an object of intellectual rights. At the legislative level, there is no legal regulation of the issue of the need for special knowledge to establish the fact of the use of an object of intellectual rights in disputes about their infringement, it is not complete at the level of clarifications of the Supreme Arbitration Court of the Russian Federation and the Supreme Court of the Russian Federation, in some aspects it differs from the long-established Rospatent approaches and, therefore, it needs clarification. Due to the similar legal nature of industrial designs, trademarks (service marks) and copyright objects of a visual nature, the paper proposes to clarify and apply uniformly an approach by virtue of which special knowledge is not required but the perception of a narrow circle of buyers/users of the relevant goods/services may be used to assess their identity or similarity, general visual impression and derivative. In such cases, both the conclusion of a forensic examination and non-judicial expert opinions should not be treated as admissible evidence. In other cases, when special knowledge is necessary, it is proposed to consider only those conclusions of forensic examination and extra-judicial examinations that were carried out by persons qualified not only in the field of protection, examination and evaluation of intellectual property rights, but also in the field to which the object under study belongs. Such examinations can also be of a complex nature.
Algorithm of Development and Methodology of Using Software for Information Conversion in the Educational Process

The article is devoted to the results of substantiation and creation of the algorithm for the development of a software tool for information transformation, which is advisable to be used in the process of digital transformation of education in higher educational institutions. In the course of substantiating this algorithm, the role of studying issues related to the information transformation in the formation of professional competencies of future specialists in the field of information technology is determined; the advantages of using the C language in comparison with other programming languages for software development are determined; the possibilities of working with memory using pointers in the C programming language are considered. The key code fragments of a software tool developed in accordance with the proposed algorithm related to the transformation of textual information into numeric data and numeric data into textual information are presented. Examples of the operation of this soft-ware tool are demonstrated and the ways for its use is presented when conducting laboratory or practical work in disciplines whose topics are related to information technology, with students of the 1st and 2nd years of study. The ways of improving the proposed algorithm aimed at increasing the number of academic disciplines, within which it is advisable to use the software developed in accordance with it, are determined.
ON THE COMPARISONS OF LOGICS IN TERMS OF EXPRESSIVE POWER

This paper investigates the question “when is a logic more expressive than another?” In order to approach it, “logic” is understood in the model-theoretic sense and, contrary to other proposals in the literature, it is argued that relative expressiveness between logics is best framed with respect to the notion of expressing properties of models, a notion that can be captured precisely in various ways. It is shown that each precise rendering can give rise to a formal condition for relative expressiveness that has appeared in the literature. Five such conditions are exposed, tested for some properties and compared to each other. As the formal conditions for relative expressiveness have various levels of stringency, some results on lifting some conditions to stricter ones are explored. Finally, a discussion on the properties of these formal conditions is presented. Special attention is given to notion of meaning equivalence, and how one may consider that it holds or not, depending on the weight attributed to logical and non-logical constants in expressiveness comparisons.
Multivariate contaminated normal mixture regression modeling of longitudinal data based on joint <scp>mean‐covariance</scp> model

Abstract Outliers are common in longitudinal data analysis, and the multivariate contaminated normal (MCN) distribution in model‐based clustering is often used to detect outliers and provide robust parameter estimates in each subgroup. In this paper, we propose a method, the mixture of MCN (MCNM), based on the joint mean‐covariance model, specifically designed to analyze longitudinal data characterized by mild outliers. Our model can automatically detect outliers in longitudinal data and provide robust parameter estimates in each subgroup. We use iteratively expectation‐conditional maximization (ECM) algorithm and Aitken acceleration to estimate the model parameters, achieving both algorithm acceleration and stable convergence. Our proposed method simultaneously clusters the population, identifies progression patterns of the mean and covariance structures for different subgroups over time, and detects outliers. To demonstrate the effectiveness of our method, we conduct simulation studies under various cases involving different proportions and degrees of contamination. Additionally, we apply our method to real data on the number of people infected with AIDS in 49 countries or regions from 2001 to 2021. Results show that our proposed method effectively clusters the data based on various mean progression trajectories. In summary, our proposed MCNM based on the joint mean‐covariance model and MCD of covariance matrices provides a robust method for clustering longitudinal data with mild outliers. It effectively detects outliers and identifies progression patterns in different groups over time, making it valuable for various applications in longitudinal data analysis.
Using learners’ problem-solving processes in computer-based assessments for enhanced learner modeling: A deep learning approach

Abstract Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve assessment questions. Unfortunately, how to make the best use of both product and process data to sequentially model learning behaviors is still under investigation. This study proposes a novel deep learning-based approach for enhanced learner modeling that can sequentially predict learners’ future learning performance (i.e., item responses) based on modeling their history learning behaviors. The evaluation results show that the proposed model outperforms another popular deep learning-based learner model, and process data learning of the model contributes to improved prediction performance. In addition, the model can be used to discover the mapping of items to skills from scratch without prior expert knowledge. Our study showcases how product and process data can be modelled under the same framework for enhanced learner modeling. It offers a novel approach for learning evaluation in the context of computer-based assessments.