Reviewerly/exHarmony huggingface dataset:
Enhanced crustal and intermediate seismicity in the Hindu Kush-Pamir region revealed by attentive deep learning model

The Hindu Kush-Pamir region (HKPR) is characterized by complex ongoing deformation, unique slab geometry, and intermediate seismic activity. The availability of extensive seismological data in recent decades has prompted the use of deep learning algorithms to extract valuable insights. In this study, we present a fully automated approach for augmenting earthquake catalogue within the HKPR. Our method leverages an attention mechanism-based deep learning architecture to simultaneously detect events, perform phase picking, and estimate magnitudes. We applied this model to a ten-month dataset (January 2013–October 2013) from 83 stations in the region. Utilizing a robust criterion to evaluate the model's probabilities, we associated phases at different stations and pinpointed earthquake locations in the region. Our results demonstrate a significant enhancement, revealing nearly four and a half times more earthquakes than previously documented in the International Seismological Center (ISC) catalogue. A notable portion of these newly detected events falls within the category of very low-magnitude earthquakes (<3), which were absent in the ISC catalogue. Notably, our spatiotemporal analysis reveals a concentration of crustal seismicity along poorly mapped neotectonic north and northeast-oriented faults in the western Pamir, as well as the Vakhsh Thrust System and the Darvaz Karakul Fault. These findings underscore potential sources of future seismic hazards. Furthermore, our expanded earthquake catalogue facilitates a deeper understanding of the interplay between crustal and intermediate seismic activity in the HKPR, shedding light on the deformation and active faulting resulting from Eurasian-Indian plate interactions.
Using and facilitating social videogames as warm-ups for embodied design

Warm-ups, as preliminary activities to physical training, are an inherent part of any physical exercise and sports practice. Rooted in the concept of embodied cognition, embodied design is a paradigm that shifts the focus of interaction design from external artefacts and devices to the human body (Svanæs &amp; Barkhuus, 2020). A body-centred design emphasises the importance of having the human body at the centre of the entire design cycle, conveying movement, physical expressivity, feelings, and aesthetics, in a design process that uses the body as both a resource and target. Designing with the body is, therefore, a physically demanding activity that requires a specific set of warm-ups to educate and prepare designers mentally, socially, and physically for the act of embodied design. We propose social videogames as a resourceful framework for creating such a set of warm-up exercises. This paper presents a methodology comprising off-the-shelf commercial videogames whose rules have been adapted for embodied design. Three studies have been executed to validate the game's capacity as warm-up activity and icebreaker for embodied creativity and to study the conditions for an optimal method facilitation to external instructors in preparation for an applied session. The method applied is qualitative and quantitative feedback data gathered from the three studies using questionnaires, tests, observation, and open interviews. The results of the series of studies showed the potential of the proposed methodology as warm-ups for teaching, training, and practising embodied design, as well as giving insights on how to facilitate it. Overall, the game-based warm-ups for embodied design preparation using off-the-shelf movement games have a social and playful nature. The proposed twisted gameplays make them suitable to exert body moves and get ready to think and design with their bodies. The study on facilitation shows the need for a preparation session supported by an experienced person. However, one introductory session is enough for the toolbox to become an easily configurable resource that adapts to the facilitator's needs and goals. We suggest including sample implementation cases along with instruction cards of the embodied games. Further, the warm-up games are customisable using the toolbox's modifier cards.
Computer Games as a Pedagogical Tool for Creating Cyber Security Awareness

Cyberattacks are increasing world-wide, both on corporate networks and public institutions network. During 2022 hackers were widening their aim to target business collaboration tools such as Slack, Teams, OneDrive, and Google Drive with phishing exploits, making for a rich harvest of sensitive data given that most organizations’ employees continued to work remotely. In the later years we have also seen academic institutions becoming more frequent targets for both cybercriminals and state sponsored hackers. In 2022 the education/research sector was most attacked industry globally, seeing a 43% increase compared to 2021, with an average of 2,314 attacks per organisation every week. Schools and universities have the unique challenge of dealing with children or young adults, many of which use their own devices, work from shared locations, and often connect to public Wi-Fi without thinking of the security concerns. Additionally, Russia’s war on Ukraine has led to an increase in spear-phishing activity targeting educational institutions in NATO countries. This situation calls attention to the need for awareness raising about personal cyber security. In Norway, Nord University is one of a few universities with specific focus on security and preparedness, including cyber security awareness. The university has two information security courses: one belonging to furthering education and one within the MBA-study program in Security and Preparedness Management. This paper examines the question whether serious games and online quizzes can create heighten cyber security awareness and motivate individual students to change their cyber security behaviour. The paper reports on the findings based on written reflection notes from students belonging to both courses. The technique of examination and interpretation applied to the data was qualitative thematic content analysis supported by computer-assisted qualitative data analysis software NVivo. Theoretical analysis was guided by social constructionist perspective on knowledge creation. While many advantages of the use of online quizzes and computer games were specified by students, challenges were identified as well. However, the findings show that the use of serious games and online quizzes can be an efficient approach to raising security awareness among participating students.
Prime Control-IQD: a Platform for Software Quality Analytics

During the software development process, a huge amount of data about what is happening is produced and stored. Such a fact is even amplified when distributed software development and several working teams are taken into account. From the operational to the strategic level, it is difficult to reason about the process and make informed and evidence-based decisions about the development process. This paper introduces Prime Control-Intelligent Quality Dashboard (IQD), a platform that supports software quality analytics; its main goal is to provide means for stakeholders to understand the status of software projects from a quality perspective and to support decision making processes. So far, we deployed Prime Control-IQD in ten clients, extracting data and presenting visually pieces of relevant information for several professionals and managers. It has replaced manual in-house initiatives and there has been a constant demand for new features and evolution.
Implementing Exploratory Testing in an Agile Context: A Study Based on Design Science Research

Exploratory Testing (ET) is a testing technique that has been spreading in the agile environment, as it allows professionals to learn quickly, adjust their tests and, in the process, find software problems that often need to be foreseen in test plans. However, the ET approaches evidenced by the literature are not applicable in the practical context of agile teams. In this sense, this paper proposes a method that facilitates the implementation of ET, considering the agile development scenario, the frequent change in requirements, and the generation of simple and robust artifacts. We use a problem-oriented research method called Design Science Research (DSR). Our main findings are that a method with well-defined activities facilitates the understanding and use of ET in the agile environment.
An Approach to Regression Testing Selection based on Code Changes and Smells

Regression testing is a software engineering maintenance activity that involves re-executing test cases on a modified software system to check whether code changes introduce new faults. However, it can be time-consuming and resource-intensive, especially for large systems. Regression testing selection techniques can help address this issue by selecting a subset of test cases to run. The change-based technique selects a subset of test cases based on the modified software classes, reducing the test suite size. Thereby, it will cover a smaller number of classes, decreasing the efficiency of the test suite to reveal design flaws. From this perspective, code smells are known to identify poor design and threaten the quality of software systems. In this study, we propose an approach to combine code change and smell to select regression tests and present two new techniques: code smell based and code change and smell. Additionally, we developed the Regression Testing Selection Tool (RTST) to automate the selection process. We empirically evaluated the approach in Defects4J projects by comparing the new techniques' effectiveness with the change-based as a baseline. The results show that the change-based technique achieves the highest reduction rate in the test suite size but with less class coverage. On the other hand, test cases selected using code smells and changed classes combined can potentially find more bugs. The code smell-based technique provides a comparable class coverage to the code change and smell approach. Our findings highlight the benefits of incorporating code smells in regression testing selection and suggest opportunities for improving the efficiency and effectiveness of regression testing.
Visualizing Feasible Regions for Optimization Problems on High-Dimensional Permutations using Dimensionality Reduction Methods

In this paper, we undertake an exploration into the application of contemporary dimensionality reduction techniques to address traditional combinatorial optimization problems. Our research introduces the utilization of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method to visualize the admissible solution regions within high-dimensional permutations. The main objective is to mitigate the challenges posed by combinatorial explosion. Through our investigation, we discover that the proposed approach yields promising outcomes, offering significant insights and enhancing the comprehension of the solution space for high-dimensional permutations. This research contributes to the advancement of understanding and analysis within the field of combinatorial optimization, highlighting the potential benefits of employing modern dimensionality reduction methods in this context.
Analysis of Features-Brightness Bias in Computer Vision

This paper explores the impact of mask brightness on various computer vision techniques. We conducted experiments on multiple applications, including reconstruction, generation, object recognition, and vectorization. Darker features relative to the background resulted in improved performance for autoencoders, Potrace vectorization, and GAN training stability. However, this relationship did not hold for Mask R-CNN and YOLO. Nonetheless, the brightness difference between masks and backgrounds influenced object recognition confidence. Our findings highlight the importance of considering brightness variations to avoid biases and provide insights for further research
The Method of Identifying Irrelevant Information in Service-Oriented Corporate Systems

The article considers an important scientific and applied task of developing a method for detecting irrelevant information, which is an important area of development and implementation of web-based information systems. The analysis of modern methods and means of evaluation of irrelevant and unreliable information in service-oriented corporate systems is carried out and the main problem areas that arise in the process of their functioning are identified. A method of filtering data based on metrics to assess the relevance of information has been developed. An example of the application of metrics for evaluating the results of using various services for the analysis of soil and ground-water quality. The main results of research presented in the article are: metrics for assessing the relevance of information obtained using services in corporate information systems; method of data filtering based on the metrics of assessing the relevance of information within the studied subject area. The peculiarity of the developed method is that it can be implemented as a software add-on to service-oriented infor-mation systems. The use of the proposed intelligent methods of data processing, which are obtainedwith the use of services, will increase the efficiency of analysis of irrelevant information and reduce the time to identify irrelevant sources of its provision.
The Use of Immersive Technologies to Implement a Multimodal Approach in the Educational Process

The article provides a theoretical analysis of the problem of using immersive technologies to ensure a multimodal approach in the educational process. The basic principles of the multimodal approach have been determined, which will contribute to ensuring the effectiveness of the educational activities of students with different initial levels of training and intellectual abilities. To implement a multimodal approach in the educational process, it is proposed to use the VARK learning style (combination of 4 learning modes: visual, aural, read/write, and kinesthetic). It has been established that the use of immersive technologies opens up new opportunities and gives the educational process the properties of visibility, concentration, dynamism, safety and effectiveness. A model for the implementation of a multimodal approach in the process of training future specialists in professional education with the use of immersive technologies has been developed. To determine the readiness of participants in the educational process to introduce immersive technologies into the educational process, a questionnaire has been conducted. The analysis of the results of the experiment confirms the need to use immersive technologies to ensure a high-quality educational process based on the implementation of a multimodal approach and the interest of students and teachers in this.
The Impact of AI on Business: Opportunities, Risks, and Challenges

Artificial intelligence (AI) has the potential to rev-olutionize the business landscape, offering numerous opportuni-ties for businesses to automate processes, personalize customer experiences, and explore new revenue streams. However, it also presents significant risks and challenges, including potential bias in algorithms, job displacement, and cybersecurity vulnerabili-ties. This paper explores the opportunities, risks, and challenges of implementing AI in business by examining successful AI implementation case studies and the challenges and lessons learned from AI projects. The paper also explores emerging trends and technologies in AI and the ethical considerations and governance of AI in business. Ultimately, the paper highlightsthe importance of carefully considering the implications of AI adoption for businesses and society.
Social Recommendation Based on Hierarchical Attention Network

Social recommendation is based on the traditional collaborative filtering, mining users’ potential preferences in the recommendation system by integrating social information, alleviating the data sparsity problem of traditional recommendation, and improving the accuracy of recommendation. Based on the excellent performance of deep learning in feature extraction, many recommendation systems use the network framework of deep learning for feature extraction, and combine attention network and deep learning to model social recommendation. From the perspective of social information, this paper selects the hierarchical attention network model to learn users’ static behavior preferences, uses the multi-head structure in the self-attention mechanism to capture the different effects of different social friends on users’ behavior preferences, and optimizes the modeling effect of attention network on users’ social influence. The simple weight division of the influence of ordinary attention networks on social friends in the early stage of modeling can not meet the calculation of the influence value of friends in the current complex social relationships. The model based on hierarchical attention networks can more accurately learn the social influence of users’ friends on their social networks. A number of experiments have been carried out on the proposed model on the data sets Epinions and Gowalla. First, the proposed model is compared with the existing classical cutting-edge recommendation methods. The experimental results verify that the proposed model is superior to the comparison experiment in the evaluation indicators of different data sets.
The System Architecture of the Software for Modeling Harmful Emissions in Soil

The article considers a software architecture designed to model the intricate processes of soil pollution resulting from the dispersion of nitrogen dioxide in both soil and groundwater. The proposed system encompasses essential modules that enable users to input initial conditions, carry out calculations, visualize outcomes, and make necessary adjustments to the model, as and when required.
Parallel Computations in the Problem of Identification of Interval Discrete Models based on Swarm Intelligence of a Bee Colony

The article discusses the problem of parametric identification of interval discrete models of complex objects. A new scheme of parallel computations for solving the problem of parametric identification based on the behavioral model of an artificial bee colony is proposed and justified. The effectiveness of the proposed parallel computing scheme is considered using the example of parametric identification of a mathematical model of the anaerobic microbiological fermentation process in biogas plants. A nonlinear second-order discrete model describes the dynamics of residual organic waste mass in the biogas reactor during fermentation. According to the results of the experiments, a 2-fold acceleration of the algorithm’s computational time was achieved.
Ontology as a Software Superstructure to the System for Mathematical Modeling based on Interval Data

The article explores a significant scientific challenge related to the development of techniques and tools for constructing discrete models of complex objects using interval difference equations. This approach combines ontological principles with the analysis of interval data to broaden the range of applications for these models. It serves as a catalyst for advancing applied research in fields such as national defense, environmental protection, medicine, and other domains where mathematical models of objects with distributed parameters play a crucial role in decision support systems. The fundamental aspect of this mathematical modeling approach, based on interval analysis, involves multiple parameter estimations for input-output models. These models are constructed using experimental data, where output variables are expressed in interval form. The key research findings outlined in the article include: An explanation of how the ontological approach to mathematical modeling, using interval data, can be applied for software development. This application aims to expand the range of scenarios in which models can be used while maintaining their predictive capabilities. A step-by-step framework is proposed for creating an ontology-driven software system for mathematical modeling based on interval analysis. This framework outlines the process of developing, utilizing, and updating the ontological model of the subject area of mathematical modeling with interval data. One distinctive feature of the approaches discussed in this article is their adaptability as software extensions to applied systems of mathematical modeling that utilize interval analysis. By combining interval analysis and ontological representation of the subject area, these approaches enhance the efficiency of computational procedures for identifying models of complex objects. They also allow for the adaptive utilization of different models in various subject areas within decision support systems.
High-Performance GMDH Algorithm of Neural Network Type for Solving Big-Data Modelling Problems

This paper describes in detail the fastest iterative GMDH algorithm -Generalized Relaxational Iterative Algorithm (GRIA). This algorithm was specifically designed to solve modeling problems described by high-dimension input matrices. Its fast speed is based on the independence of the most compute-intensive part of GMDH algorithms from the number of observations and use of recurrent formulas for coefficient estimations and selection criterion calculation when building each possible solution. The tests and experiments conducted in this paper prove the GRIA’s ability to solve modelling problems from big data sets.
Internal and External Threats in Cyber Security and Methods for Their Prevention

The article considers internal and external approaches to cyber security in web systems. The object of research is cyber security approaches and techniques. Methods of research are based on analyzing statistical data and comparing existing approaches in the literature. There were reviewed the annual “Internet crime report” by the Federal Bureau of Investigation for 2020 year, “Results and Analysis” report by Verizon, and the report “Ransomware attackers down shift to ’Mid-Game’ hunting in Q3 2021” by Coveware, highlighted fields for research. Also, there were formulated internal rules for cyber security in companies with high-level sensitive information and defined main types of external cyber threats and ways for them to be avoided in the web-applications context.
The Method of Modeling the Characteristics of a Swarm of UAVs as an Object with Distributed Parameters Based on the Analysis of Interval Data

-A number of scientific studies using UAV swarms are considered. The expediency of developing mathematical models of UAV swarm characteristics as an object with distributed parameters for problems of interval data analysis is shown. A computational scheme for building model of the distribution of harmful emissions of vehicle exhaust gases is proposed. The verification of the built model was carried out on test data obtained for the specified observation area, which confirmed the sufficient prognostic properties of the model.