Reviewerly/exHarmony huggingface dataset:
Ecosystem of the University as a Subject of Educational Analytics

Introduction . New trends in the philosophical analysis of higher education development are associated with the prospects for an interdisciplinary approach to organizing coordinated processes in the multidisciplinary university, taking into account the peculiarities of information and intellectual technology of digital culture. The University with the unity of its functional connections presents a complex multilevel ecosystem, which state is determined by the intersection of the interests of various subjects included into the vocational training system. This article examines the convergence processes in the university ecosystem associated with educational strategy planning, in particular with the coordination process between the university goals and expectations of employers and graduates. Methodology and sources. The research is based on the system methodology and principles of the communicative action theory. In the university ecosystem, the key role in the functional connections organization is played by the processes of convergence in relation to the intentions of the participants of interaction in the educational space, the identification of attitudes that motivate cooperation and mutual reinforcement of optimal behavioral strategies. The empirical basis is the statistical data from a number of sociological surveys conducted at Peter the Great St. Petersburg Polytechnic University, as well as research data on the target attitudes of companies in their interaction with universities. Results and discussion . Comparison of the level of consistency of companies' requests in finding employees and the educational goals of the university showed a common desire to form a high potential of future employees, which requires pedagogical technologies to motivate and develop project skills and constructive activities. In the university development, an important factor is the formation of a common field of goals and expectations realized in the graduate model, with account of the industry knowledge and new technologies development, as well as formation of skills that characterize the potential of self-development in the future profession. Conclusion. The development of an ecosystem approach in the analysis of modern university activities has revealed prospects for combining university knowledge with practical skills in planning various forms of partnership with business and other representatives of the professional community. Convergence processes in the university ecosystem ensure the interpenetration of pedagogical technologies and agent strategies in achieving high-quality professional training.
Symmetric 4-adic complexity of quaternary sequences with low autocorrelation and period $ pq $

In this paper, we investigate some quaternary sequences with low autocorrelation and period pq where p and q are two odd distinct primes.These sequences are constructed by interleaving the cyclotomic sequences of period p according to the quadratic residues with respect to q.We estimate the symmetric 4-adic complexity of considered sequences and show that they have a sufficiently large 4-adic complexity to effectively resist the attack of the rational approximation algorithm.
The Improving Student’s Speaking Achievement By Using YouTube Media on New Normal Era

This study aimed to demonstrate an improvement in speaking achievement among class X-1 ITP Surabaya students for the academic year 2022–2023. Their inadequate verbal communication abilities are the issue. As a result, the researcher used YouTube media during engaging class activities to teach speaking. Classroom action research is the approach used in this study. actions that are repeated twice. Both qualitative and quantitative analyses of the study's findings were performed. According to the findings, using YouTube media helped students' oral communication achievement, which was demonstrated by their exam scores. In cycle 1, 36% of students passed the exam; in cycle 2, that number rose to 84%. Student involvement increases and their activities take center stage during the learning process, according to observational findings. Additionally, the survey reveals that students strongly agree that using YouTube media during class activities makes them happy. This demonstrates how students' speaking achievement improved when using YouTube media for learning
Perception of Peruvian Students Studying in Biological Sciences about the Advantages of Virtual Classes during the COVID-19 Pandemic

There is significant educational research interest regarding the assessment of the benefits of virtual education implemented during the COVID-19 pandemic for university programs that were essentially face-to-face. The objective of the study was to determine the effect of the management of online resources that the teacher had on the valuation of the advantages of online classes in biological sciences, mediated by the students’ perception of virtual practices as well as the accessibility and use of online resources. A total of 332 Peruvian students studying in biological sciences from a public university, enrolled in five undergraduate academic years, of which 184 were women and 148 were men, participated. A non-experimental predictive design of causal relationships was used with the methodology of structural equation modeling. According to the SEM model (CFI and TLI &gt; 0.95, RMSEA and SRMR &lt; 0.05), the valuation of the advantages of virtual classes during the pandemic was significantly predicted by the valuation of virtual practices (positively) as well as by the accessibility and management of online resources by students (negatively); likewise, the use and mastery of digital and online resources by teachers had an indirect effect on the valuation of virtual classes, but direct effects on virtual practices and accessibility to digital resources by students. Also, virtual practice was the most crucial variable in predicting the valuation of online classes (β = 0.48, p &lt; 0.001). In conclusion, the student’s perception of the teachers’ handling of online resources during the COVID-19 pandemic was determinant as a favorable valuation of the advantages offered by online classes, a relationship that is mediated by virtual practices and accessibility to online resources.
Design of Interactive Learning Materials with Concept of Sustainability Integrated into Macau Drunken Dragon Dance

The Feast of Drunken Dragon is a special intangible cultural heritage of Macau, which is also known as the Drunken Dragon and Lion Dance Gala. Every year, on the eighth day of the fourth month of the lunar calendar, members of the Macau Fish Traders Association hold a drunken dragon and lion dance activity at the Macau Sanjie Guild Hall. Festivals play a part in cultural designing. They not only attract a large number of tourists but also promote local culture. The Drunken Dragon Dance is currently encountering many problems such as the inability to introduce new performance methods as technicians are getting older, and young people are unwilling to inherit it. This cultural heritage tends to gradually decline. The Cultural Affairs Bureau of the Macao Government has paid attention to the problem. However, the publicity methods are still mainly based on videos, simple online graphics, and cultural and creative products, and the impact of the COVID-19 epidemic harmed the promotion. With the rapid development of technology, mobile devices provide digital reading and augmented reality (AR) to assist learning. Thus, learning is not limited in space and time, and individuals continue to use it without hindrance. Using the concept of experiential learning, we design interactive teaching materials with dynamic images to advertise the historical stories of the Feast of Drunken Dragon in Macau, folk rituals, and traditional dragon performances through the AR interactive experience of the drunken dragon festival. The concept of learning by doing is an experiential way to understand relevant intangible cultural heritage knowledge. Using digital content, teaching material design is provided based on user experience to achieve sustainable education.
Efficient Meta-Learning through Task-Specific Pseudo Labelling

Meta-learning is attracting attention as a crucial tool for few-show learning tasks. Meta-learning involves the establishment and acquisition of “meta-knowledge”, enabling the ability to adapt to a novel field using only limited data. Transductive meta-learning has garnered increasing attention as a solution to the sample bias problem arising from meta-learning’s reliance on a limited support set for adaptation. This approach surpasses the traditional inductive learning perspective, aiming to address this issue effectively. Transductive meta-learning infers the class of each instance in time by considering the relation of instances in the test set. In order to enhance the effectiveness of transductive meta-learning, this paper introduces a novel technique called task-specific pseudo labelling. The main idea is to produce synthetic labels for unannotated query sets by propagating labels from annotated support sets. This approach allows the utilization of the supervised setting as is, while incorporating the unannotated query set into the adjustment procedure. Consequently, our approach enables handling a larger number of examples during adaptation compared to inductive approaches, leading to improved classification performance of the model. Notably, this approach represents the first instance of employing task adaptation within the context of pseudo labelling. Based on the experimental outcomes in the evaluation configurations of few-shot learning, specifically in the 5-way 1-shot setup, the proposed method demonstrates noteworthy enhancements over two existing meta-learning algorithms, with improvements of 6.75% and 5.03%, respectively. Consequently, the proposed method establishes a new state-of-the-art performance in the realm of transductive meta-learning.
A Study on Classification Method of Mine Vibration Based on Microseismic Monitoring Cloud Service Platform

With the gradual deepening of mining depth, sudden ground pressure disasters such as rock burst and collapse caused by deep high stress and high rock pressure are major hidden dangers affecting mine safety production. Microseismic monitoring technology has been widely applied in the field of mine ground pressure disaster warning. The existing microseismic monitoring system has some problems, such as inaccurate automatic recognition and classification of waveform signals, low quality and low efficiency of manual processing. In this paper, the automatic classification of vibration signals in the process of mining is studied, and the automatic classification results are uploaded to the cloud service platform in real time, which solves the technical bottleneck of the existing microseismic monitoring system. Meanwhile, the real-time monitoring of mine ground pressure safety is guaranteed based on the cloud service platform of microseismic monitoring.
Security aspects related Intelligent e-Health Systems

Current health situations and crises require physicians and researchers to convert traditional healthcare services to a new generation of digital healthcare known as e-health smart systems that operate close to the end user with the objective to provide medical assistance using advanced technologies including Internet of Medical Things technology, edge computing, and cloud computing paradigms. However, this system architecture reveals limitations in terms of latency, bandwidth, power consumption as well as security and privacy of collected data. Through this paper, a presentation of different healthcare monitoring system layers has been defined, in addition to listed challenges faced by the deployment of this technology, while providing an introduction of security risks that threaten the user data safety alongside the e-health layers. Moreover, a summarized discussion of Blockchain technology came over to provide a secure decentralized architecture in sharing data across allowed entities.
Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding real-time anomalies. The method employs rough set theory, a dynamic k-means clustering algorithm, and an interval superimposition approach for finding periodic, partially periodic, and fuzzy periodic clusters in the subspace of the dataset. The data instances are thought to be anomalous if they either belong to sparse clusters or do not belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms on a synthetic and a real-life dataset. It can be found experimentally that our method outperforms others and runs in cubic time.
A Communication-Efficient Federated Text Classification Method Based on Parameter Pruning

Text classification is an important application of machine learning. This paper proposes a communication-efficient federated text classification method based on parameter pruning. In the federated learning architecture, the data distribution of different participants is not independent and identically distributed; a federated word embedding model FedW2V is proposed. Then the TextCNN model is extended to the federated architecture. To reduce the communication cost of the federated TextCNN model, a parameter pruning algorithm called FedInitPrune is proposed, which reduces the amount of communication data both in the uplink and downlink during the parameter transmission phase. The algorithms are tested on real-world datasets. The experimental results show that when the text classification model accuracy reduces by less than 2%, the amount of federated learning communication parameters can be reduced by 74.26%.
Russian Neo-Kantianism Through Eyes of American Researcher. Book Review of Thomas Nemeth’s “Russian Neo-Kantianism: Emergence, Dissemination, Dissolution”

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YOLOv5-FF: Detecting Floating Objects on the Surface of Fresh Water Environments

Real-time detection and timely treatment of floating objects on rivers, lakes and reservoirs is very essential to protect water environment and maintain the safety of navigation and water projects. YOLOv5, as a one-stage object detection solution, is very suitable for real-time floating object detection. However, it suffers from the problem of the false detection and missed detection of floating objects especially of small floating objects. In this paper, we conducts a series of improvements on YOLOv5 to alleviate the problem. Concretely, we propose a hybrid attention mechanism supporting the interaction among channels over a long distance while preserving the direct correspondence between channels and their weights. Base on the attention mechanism, we propose an adaptive feature extraction module to capture the feature information of objects in the case of the feature loss caused by downsampling operations. Based on the attention mechanism and dilated encoder, we construct a feature expression enhancement module to cover large objects while not losing small objects in the same certain scale range. We also add a detection layer for small objects to improve the performance in detecting small floating objects. The experiments on the data set verify the usefulness and effectiveness of our work.
Human-centric and semantics-based explainable event detection: a survey

Abstract In recent years, there has been a surge of interest in Artificial Intelligence (AI) systems that can provide human-centric explanations for decisions or predictions. No matter how good and efficient an AI model is, users or practitioners find it difficult to trust it if they cannot understand the AI model or its behaviours. Incorporating explainability that is human-centric in event detection systems is significant for building a decision-making process that is more trustworthy and sustainable. Human-centric and semantics-based explainable event detection will achieve trustworthiness, explainability, and reliability, which are currently lacking in AI systems. This paper provides a survey on human-centric explainable AI, explainable event detection, and semantics-based explainable event detection by answering some research questions that bother on the characteristics of human-centric explanations, the state of explainable AI, methods for human-centric explanations, the essence of human-centricity in explainable event detection, research efforts in explainable event solutions, and the benefits of integrating semantics into explainable event detection. The findings from the survey show the current state of human-centric explainability, the potential of integrating semantics into explainable AI, the open problems, and the future directions which can guide researchers in the explainable AI domain.
Digital portfolio as means of student’s creative activity when organizing independent-project work

The article examines some of the features of the transition to the digital economy and the digitalization of education, which require new approaches, and means in the system of training specialists. The article discusses some aspects of using tools and portals to create a digital portfolio, which is included as components of the digital educational environment of a university. Project work must rank as one of the most exciting teaching methodologies a lecturer can use.
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing

Abstract Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks. However, learning complex structures with S2S models remains challenging as external neural modules and additional lexicons are often supplemented to predict non-textual outputs. We present a systematic study of S2S modeling using contained decoding on four core tasks: part-of-speech tagging, named entity recognition, constituency, and dependency parsing, to develop efficient exploitation methods costing zero extra parameters. In particular, 3 lexically diverse linearization schemas and corresponding constrained decoding methods are designed and evaluated. Experiments show that although more lexicalized schemas yield longer output sequences that require heavier training, their sequences being closer to natural language makes them easier to learn. Moreover, S2S models using our constrained decoding outperform other S2S approaches using external resources. Our best models perform better than or comparably to the state-of-the-art for all 4 tasks, lighting a promise for S2S models to generate non-sequential structures.
Future Design Phenomenon: Effect of Metronome

The article is dedicated to the phenomenon of designing future in the paradigm of objective reality. Based on the I. Prigozhin’s conclusions on the process of coherence of separate parts into a whole through the establishment of a common development pace of subsystems and the idea of constructivism that a person comprehends social reality through construction, one of the ways of the process of designing the future via its construction by the “extended self” is described. The concept of “the image of the thing” is analyzed as a tool with which the future is constructed in the space of actual objectivity with elusive meanings. It is shown that in everyday reality we deal with “things in motion” - the moment when a thing exists simultaneously in offline and online environments as a kind of impersonal object that does not have an owner. The mechanism of occurrence of the “metronome effect” in the processes of designing the future is revealed. It is proved that the peculiarity of modern reality lies in the endless variability of the images of the future in the space of impersonal objectivity, when the images of things “without an owner” become a tool for constructing the future. For the first time the article reflects on the essence of digital extensions of a person (extended self) in interaction through images of things as “things-in-motion”, which defines a new sociality and suggests a different view of the processes of communication in social networks. It is revealed that the two conditions in the processes of online communications: “being in touch” and “objectivity of consumption” allow us to explain the essence of the processes of designing the future with the key role of the “extended self”. It is concluded that leveling the boundaries between “public” and “personal” in online communications is an innovation in the processes of constructing images of the future, which today has become part of its awareness processes and thus triggers the emergence of the phenomenon of designing the future.
Application of hybrid encryption algorithm in physical layer software control

In order to address the risk of information leakage in physical layer software, the author proposes the application of hybrid encryption algorithms in physical layer software control. First of all, on the basis of the successful construction of the hardware platform, select the baseband chip of the encryption terminal, calculate the block cipher in the symmetric encryption algorithm, and establish a RAS cryptosystem to encrypt the transmission system in a mixed way, so as to realize the loss prevention transmission of user information data. The experimental results showed that the accuracy of the first transmission experiment reached 98.5%. After multiple experiments, it was concluded that the accuracy of network transmission systems using hybrid encryption algorithms is relatively higher than traditional methods in the process of information and data transmission. In the previous experiment, the accuracy could still reach 98%, which proves that the accuracy of information transmission has significantly improved compared to traditional methods. It has been proven that compared with traditional network transmission systems, the transmission accuracy of the designed transmission system is significantly improved, effectively avoiding the loss of important data information for users.
Cryptocurrency pricing determining factors

Cryptocurrencies have emerged as a popular investment option in recent years. This paper aims to identify and analyse the factors that determine the pricing of cryptocurrencies. The existing problem is the lack of a comprehensive framework for understanding cryptocurrency pricing. The study is necessary to help investors make informed decisions about investing in cryptocurrencies. To examine determinants of cryptocurrency prices, the study used five cryptocurrencies and employs the GMM techniques. The study used multiple variables (coin prices; coins issued per day; difficulty and market capitalisation) to test how they can determine cryptocurrency prices. Findings showed that coins that uses higher hash rate, which has higher difficulty, higher market capitalisation and has lower number of coins that are mined on daily basis, is likely to have its pricing improved over short to medium terms. Overall, this research work provides valuable insights into the factors that determine the pricing of cryptocurrencies.