Reviewerly/exHarmony huggingface dataset:
Immersive Learning Methods and Their Impact on the Perception of Learning Material

The contribution discusses immersive teaching methods used in the engineering education, such as VR technologies, the video game mechanics for learning purposes (Minecraft, etc.), interactive online whiteboards (Miro, etc.). This study has the theoretical significance due to the exploring the effectiveness and significance of immersive teaching methods in modern engineering education based on existing data and materials, as well as ways to adapt effective methods to Russian reality. The use of VR technologies, video game learning mechanics and interactive online whiteboards in the framework of engineering education is not sufficiently covered in the scientific field as we see it. This study determines the features of the immersive teaching methods in education and their impact on the perception of educational material that applies to its practical significance. Also, the study concerns the answer of how to apply them in the framework of training engineers. Some universities are beginning to introduce these methods into their educational programs, but stumble upon the certain difficulties within this implication. This study will allow universities to introduce best practices into their curricula and improve the immersive methods in the educational process. This will give universities the opportunity to remain competitive in the development of new technologies. The research methodology was based on a meta-analysis of the main semantic and statistical studies on the selected topic. The secondary studies’ results were combined by quantitative assessment methods. Published (secondary) research results on the problem of using immersive methods in engineering education are summarized.
Distinguishing Good from Bad: Distributed-Collaborative-Representation-Based Data Fraud Detection in Federated Learning

Breaking down data silos and promoting data circulation and cooperation is an important topic in the digital age. As data security and privacy protection have received widespread attention, the traditional cooperation model based on data centralization has been challenged. Federated learning provides technical solutions to solve this problem, but the characteristics of multi-party cooperation and data invisibility make it face the risk of data fraud. Malicious participants can manipulate data individually or in collusion to illegally obtain data or influence federated learning model. This paper proposes a novel data fraud detection method based on distributed collaborative representation and realizes the effective detection of federated learning data fraud through collaborative clustering, adaptive representation and dynamic weighting. The method proposed in this paper overcomes weakness in the existing methods that detect data fraud mechanically and statically, which cannot be organically combined with the training objectives and process. It realizes the dynamically continuous anti-collusion soft constraint detection while ensuring fraud detection and contribution evaluation are relatively independent. Our research is of great significance for federated learning to deal with the risk of data fraud and better apply to real-world scenarios.
The Cognitive Approach to the System of Engineering Education Under Digitalization

The relevance of engineering education as a factor ensuring the sustainability of Russian industrial production is due to the level of the system development which depends on the understanding and updating of the strategy for pursuing a balanced industrial policy. The contribution deals with the problems that can be solved by combining the factors of engineering education based on the cognitive approach. The authors considered the system evolution of engineering education in Russia and strategic guidelines to accumulate knowledge by engineers. Education is one of the prerequisites for achieving the sustainable development, and it is also a tool for effective management. As a methodological basis for the study, the authors used such general scientific methods as system analysis, synthesis, abstraction and analogy, generalization and classification. As a result, the authors concluded that to ensure the socio-economic sustainable development of the Russian Federation, we need transformations and changes in approaches when training engineers. In the work, the authors identified the key areas that can set a new vector for the educational development: the revival and development of the integrated learning system that ensures partnerships between universities and business community; advantages and disadvantages of the cognitive approach in engineering education; a cognitive mind map of the student-engineer’s under digitalization.
Analysis of Public Space in Historic Districts Based on Community Governance and Neural Networks

Based on the perspective of community governance, this paper explores the research path of public space renewal in historic city districts. Taking the historical public space of Lhasa city as an example, this paper analyzes the causes of the characteristics of public space, analyzes the logical relationship between public space and urban renewal and community governance, applies the neural network evaluation model. Firstly employed cell rasterization, indicator classification, and population density estimation to calculate the score for each indicator. And then the principal component extracted by principal component analysis is used as the input node of the neural networkit determines the weight of each indicator by adopting the entropy method, based on which it built a multi-dimensional indicator system to evaluate public space satisfaction of historic District of Lhasa and finally, optimizes the pattern of historical public space of Lhasa city by improving the spatial structure. The aim is to create intelligent living blocks and optimize the life mode of historical blocks. Regarding significance, first, from the human-oriented perspective, the exploration of public space renewal in historic districts is in essence a process in which people develop continuously and their needs are continuously satisfied. This approach can meet the multilevel needs of residents at different stages. Second, from a social perspective, the innovative pattern of community governance can help achieve self-satisfaction within the district, radiate to the surrounding industries, and relieve the pressure of social employment. Third, from the perspective of urban renewal implementation, research on the public space renewal of historic districts can help promote the balance of the social-ecological environment, improve regional governance and promote the high-quality development of cities. The future outlook is presented.
Augmented Pre-Segmentation Method for Medical Image Dataset Based on Machine Vision

At present, the preparation of data is a costly and time-intensive process in in the deep learning tasks of medical images. At the same time, there is more noise in labeling, and the time cost of labeling is relatively high. We propose a method based on machine vision to reproduce multiple valid samples from original samples. In the process, the morphological feature information of the image is first identified and exacted from the medical image. Secondly, a priori features are added to divide the pictures while improving the sample availability rate. Third, the Roberts quality evaluation score is calculated to exclude low-quality samples. The example presented in the experiment shows that the sample dataset was increased up to 50-100 times the original through image processing on laparoscopic vascular images. The samples reproduced by our method can also be marked with the thick label of the original image.
The Relationship Between Work-Induced Sexual Abstinence and Psychological Health of Chinese Male Seafarers: The Positive Influence of Job Satisfaction

The specific nature of the seafaring occupation keeps seafarers away from partners and shore life for long periods at sea. Therefore, seafarers suffer many challenges in the workplace including frustration of their sexual needs. This study aimed to investigate the relationship between work-induced sexual abstinence, psychological health and job satisfaction among seafarers. A mixed-method approach focusing on cross-sectional survey-based quantitative research was used. The sample included 617 Chinese active seafarers who experienced sexual abstinence on board. Hierarchical cluster analysis was conducted to divide the respondents into different groups corresponding to 14 psychological problems induced by sexual abstinence and experienced in the workplace. Multiple linear regression analysis was applied to identify the relationship between tolerance level of seafarers to sexual abstinence and their job satisfaction. Four distinct groups of seafarers, influenced differently by sexual abstinence, were identified, namely "Saint," "Adapter," "Average people," and "Sufferer" clusters. Multiple linear regression analysis indicated that tolerance level of seafarers to sexual abstinence was positively influenced by overall job satisfaction, perceived working environment, ability utilization for other jobs, own choice for working as a seafarer and perceived friendliness of co-workers, while it was negatively influenced by perceived job responsibility. The study innovates by examining the relationship between sexual abstinence and the psychological health of seafarers. It also extends the function of job satisfaction, which can contribute positively to the tolerance level to sexual abstinence. Management recommendations to address sexual abstinence issues are proposed to better protect the health and psychological well-being of seafarers and enhance operational safety on board.
Research on stress curve clustering algorithm of Fiber Bragg grating sensor

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors.
Number of cycles of small length in a graph

Let G be a simple undirected graph. In this article, we obtain an explicit formula for the number of 8-cycles in G in terms of the entries of its adjacency matrix. We provide new formulae to find the number of cycles of length 4, 5 and 6 in G. When the girth of G is 10 (resp. 12), an explicit formula for the number of cycles of length 10 (resp. 12) is given. New formulae to find the number of paths of length 3, 4 and 5 in G are also obtained.
Square-glyphs: assessing the readability of multidimensional spatial data visualized as square-glyphs

Glyphs have long been used to approach the challenge of visualising multidimensional data with geospatial reference. Depending on the glyph design, data-dense visualizations of several concurrent data dimensions can be created. The square-glyph is a compound glyph to represent up to four data dimensions, e.g. walkability indices, with reference to a gridded geographic space (Bleisch and Hollenstein 2018 [Exploring multivariate representations of indices along linear geographic features. Proceedings of the 2017 International Cartographic Conference, Washington D.C. (pp. 1–5)]). In this paper, we present a user study to evaluate the readability and interpretability of the square-glyphs. We compare user performance with square-glyph plots containing two and four simultaneously mapped data dimensions under different value compositions. Our results show that the user performance with square-glyphs does not decrease as the number of data dimensions represented increases from two to four. The study results indicate no significant differences in efficiency and effectiveness between the four-dimensional square-glyphs and the two-dimensional square-glyphs. The average values of five adjacent glyphs can be estimated with a mean error of eight percentage points. The results suggest that equal value distances between the displayed dimensions are more accurately perceived in a lower-value composition than in higher-value arrangements.
Retrace error calibration for interferometric measurements using an unknown optical system

In classical interferometric null test measurements, the measurement and reference beam path should be the same. A difference in the beam paths results in the so called retrace error. One very common approach to avoid retrace errors is to adapt the measurement wavefront to the reference wavefront with a computer generated hologram (CGH), which is costly and time consuming. A much more flexible approach is to do non nulltest measurement in combination with mathematical treatment of retrace errors. Most of such methods are based on iterative optimization or calibration of the nominal optical design of the interferometer. While this may be a convenient solution in the context of research, the more common use may be limited due to the need of the optical design of all interferometer components. In many cases, the optical designs of standard off the shelf optical assemblies are not available or disclosed by the manufacturer. This is especially true for transmission spheres of interferometers. We introduce the so called Black Box Model (BBM), used in the well known Tilted Wave Interferometry (TWI), as a mathematical model to account for retrace errors in interferometry. The Black Box Model is based on point characteristic functions which are adapted to the result and behavior of a real interferometer by calibration. With an extended calibration method, the need of a specific optical design of the interferometer is no longer necessary. Thus the method is attractive for a wide field of use in interferometry with standard off the shelf components.
A study on Diem and Aptos distributed ledger technology

In the paper, Diem Distributed Ledger Technology (DLT) and Aptos blockchain data are examined. First, the paper presents a general overview of the Diem project from a technical point of view. Second, it presents a study that aims to collect and analyze data from the Diem and Aptos blockchain, in order to verify some properties declared in the technical paper. For instance, a relevant property of the Diem and Aptos blockchains is their transactions' throughput, i.e. the rate at which valid transactions are committed into a block by the Diem blockchain in a one-second interval of time (transactions per second, TPS) and the interval of time for a transaction to be confirmed. In the case of the Diem blockchain, the data were collected over three months (January 1–March 31, 2022) while in the case of the Aptos blockchain, the data were collected over a month (December 1 - December 31, 2022). These data can be found on a GitHub repository that is publicly accessible. The results of the data analysis show that the average transactions' throughput is about 60 TPS and the waiting time is on average 1 minute and 40 seconds. Moreover, the paper sheds light on some Diem features that are unique when compared to similar blockchains, such as Ethereum. Some of these unique features are the consensus mechanism based on the BFT consensus protocols (Byzantine Fault Tolerance, 2017), its accounting system based on a hierarchical model and its programming language, Move, used to code smart contracts. Through this study, the capabilities of both Diem and Aptos blockchains will be explored in more depth.
A generative adversarial network-based approach for mitigating inference attacks in emerging wireless networks

The proliferation of smart, connected, always-listening devices has introduced significant privacy risks to users in wireless networks comprising dense massive devices. Beyond the notable risk of eavesdropping, intruders can adopt machine learning techniques to infer sensitive information from audio recordings on these devices, resulting in a new dimension of privacy concerns and attack variables for wireless network users. Techniques such as sound masking and microphone jamming have effectively prevented eavesdroppers from listening to private conversations. In this study, we explore the problem of adversaries spying on wireless network users to infer sensitive information with machine learning techniques. We then analyze the role of randomness in the effectiveness of sound masking for mitigating sensitive information leakage. We propose a generative adversarial network (GAN)-based approach for privacy preservation in the network, which generates random noise to distort the unwanted machine learning-based inference. Our experimental results demonstrate that GANs can be used to generate more effective sound masking noise signals which exhibit more randomness and effectively mitigate deep learning-based inference attacks while preserving the semantics of the audio samples in wireless networks. The GANs would find useful applications in addressing the proliferating privacy and security concerns in 5G and the envisioned 6G wireless networks.
Argumentation mining of Chinese online reviews with pre-training and pointer networks

The Internet has generated a large amount of unstructured online texts that contain a large amount of argumentative content. How to automatically extract the argumentative contents from these online texts is an important issue. First, a corpus of argumentation about Chinese online current affairs reviews is built. The existing argumentation annotation scheme is also modified by adding two additional types of elements, "central conclusion" and "intermediate conclusion", and their corresponding argumentation relations to reflect the more complex structure of the long review texts. The pre-trained large models are used to identify and classify the argumentative elements in these texts. Then, the penultimate hidden state of the best model is used as the input of the pointer network for argumentative elements relationship identification. The experimental results display that the "pre-trained large model + pointer network" model improves the performance of the existing models.
Argument Schemes and a Dialogue System for Explainable Planning

Artificial Intelligence (AI) is being increasingly deployed in practical applications. However, there is a major concern whether AI systems will be trusted by humans. To establish trust in AI systems, there is a need for users to understand the reasoning behind their solutions. Therefore, systems should be able to explain and justify their output. Explainable AI Planning is a field that involves explaining the outputs, i.e., solution plans produced by AI planning systems to a user. The main goal of a plan explanation is to help humans understand reasoning behind the plans that are produced by the planners. In this article, we propose an argument scheme-based approach to provide explanations in the domain of AI planning. We present novel argument schemes to create arguments that explain a plan and its key elements and a set of critical questions that allow interaction between the arguments and enable the user to obtain further information regarding the key elements of the plan. Furthermore, we present a novel dialogue system using the argument schemes and critical questions for providing interactive dialectical explanations.
Evaluating the practical effectiveness of college counselors’ ideological and political education using big data video streaming

With the changing trends in ideological and political teaching methodologies in colleges and higher institutes, the previous political and ideological teaching mode of blackboard and power point-enabled face-to-face instruction is in danger of being phased out. As a result, college counselors’ work in political and ideological education is an essential component of college-level instruction. The reform’s focal points, i.e., integral platforms, are wireless communication and big data video streaming. These platforms provide a sense of situation, immersion, and involvement that traditional ideological and political education lacks. Using big data concepts for wireless-enabled video streaming, this paper assesses the effectiveness of college counselors’ ideological and moral instructions. We provide data-driven insights to improve the efficacy of education by analyzing a large quantity of video data, which include teaching content, student behavior, and interaction status. We present a big data-enabled evaluation approach that blends ideological and moral education aspects, employing least squares fitting analysis for indicator distribution control, fuzzy support vector machine and fuzzy clustering models for assessment. The study conducts quantitative analysis, dynamic monitoring, and feature extraction of efficacy indicators by assessing college counselors’ advice based on parameters and benefits. The experiment shows that the proposed method has a high degree of confidence in evaluating the effectiveness of college counselors’ ideological and moral education and can dynamically adjust their approach to improve data interaction ability and analysis.
Construction of Mizo: English Parallel Corpus for Machine Translation

Parallel corpus is a key component of statistical and Neural Machine Translation (NMT). While most research focuses on machine translation, corpus creation studies are limited for many languages, and no research paper on a Mizo–English corpus exists yet. A high-quality parallel corpus is required for Natural Language Processing activities including machine translation, Chatbots, Transliteration, and Cross-Language Information Retrieval. This work aims to investigate parallel corpus creation techniques and apply them to the Mizo–English language pair. Another goal is to test machine translation on the newly constructed corpus. We contributed to LF Aligner tool to support Mizo language for Mizo sentence alignment in corpus development. Our effort created the first large-scale Mizo–English parallel corpus with over 529K sentences. The pre-processed corpus was used for Mizo-to-English NMT. It was evaluated using BLEU, Character F1 Score (ChrF), and Translation Edit Rate (TER) scores. Our system achieved BLEU 45.08, ChrF 65.36, and TER 41.16, setting a new benchmark for Mizo-to-English translation.