Reviewerly/exHarmony huggingface dataset:
Development of Personal and Professional Culture as the Basis for Engineer’s Effective Activities

The article deals with the specific character of modern engineering education, its meaning seen by the authors as the development of a competent specialist, who is well aware of complex socio-cultural circumstances, responsible and able to act professionally in the rapidly changing environment of science and industry. Such specialist is the bearer of personal and professional culture, being a special type of mental activity arrangement, which manifests itself and finds realization in the professional functioning, which embodies various apprehended, adopted, reproduced and newly elaborated patterns of culture. The paper aims at specifying the concept of “personal and professional culture of an engineer” from the viewpoint of a qualitative characteristic of consciousness and vital activity and behavior. In this study, the authors for the first time structurally characterize this concept by its reasonable analysis in the unity of all such components as general culture, communicative culture, information-methodological culture, culture of personal improvement, professional competence, culture of thinking and creative qualities. The discussion substantiates the technology of effective social and natural interaction, contributing to the development of a cultural-like techno sphere, which encourages self-realization of an engineer in his activity as a special area of culture (to carry out his assignment), and therefore can be the equivalent of his social dignity. Personal and professional culture is studied as a special qualitative characteristic of human consciousness and life, reflecting the attained level and character of the development of professionally significant personal qualities of the specialist, representing the system of his values and emotional attitudes, which affect the efficiency of his engineering activities. The authors suggest its integrative model with invariant indicators.
An Autonomous Humanoid Robot Designed to Assist a Human with a Gesture Recognition System

This paper presents the design of an autonomous humanoid robot designed to optimize and enrich customer service in showrooms, e.g., electronic equipment, mobile network operators, and generally in stores with various articles. The proposed humanoid robot design is distinguished by two key components: a sensor-equipped mobile platform with drives and a body featuring a head outfitted with a touch tablet and an RGBD camera. The control system enables autonomous navigation in both known and uncharted environments, with a special focus on diverse, crowded, and cluttered spaces. To enhance its adaptability, this robot is not only fitted with LIDAR sensors but also cliff and ultrasonic sensors. While the interactive ability with humans is an expected functionality, this paper brings forth certain distinct innovations in humanoid robot design for customer service. One of these unique aspects includes the robot’s ability to physically alter its configuration, such as rotating its head and adjusting the height of its torso to maintain line-of-sight with the customer. This capability signifies a novel degree of spatial responsiveness that exceeds static interaction. Moreover, the proposed robot is equipped with a user-friendly gesture recognition system, uniquely designed to detect and recognize simple human hand gestures. This attribute paves the way for understanding simple commands such as requests for assistance. Upon recognizing a request, the robot tailors its services by following the person around the showroom, effectively assisting and answering customer queries or displaying requisite information on its screen. This active assistance model, specifically tailored for human interaction, showcases the robot’s unique capability to respond proactively and dynamically to human inputs.
Education, Online Presence and Cybersecurity Implications: A Study of Information Security Practices of Computing Students in Saudi Arabia

Information technology is considered as a key enabler to achieve “education for all” as a sustainable development goal; however, involvement in the education sector has introduced security risks along with benefits. Students’ exposure to the internet has increased the probability of cybersecurity attacks. To foster a more sustainable use of technology, it is crucial that students are made aware of information security risks and can keep themselves protected in the online sphere. In this paper, we present the results of a cross-sectional study that explores information-security awareness among students in Saudi Arabia. Empirical data were collected using an online questionnaire and a factor analysis was conducted using partial least-squares structured equation modelling. Based on the existing literature, we focused on four key constructs: password management, infrastructure management, email management, and the perception of security. The results of this study have highlighted that email management and infrastructure management were seen as relevant factors, whereas password management and the perception of security were not considered relevant factors by the respondents. We have also chalked out recommendations to improve cybersecurity awareness among students. The findings of this study will potentially help educational institutions and parents to prepare students in adopting security practices while they are online.
Learning Spatial Graph Structure for Multivariate KPI Anomaly Detection in Large-Scale Cyber-Physical Systems

Anomaly detection on multivariate Key Performance Indicators (KPIs) is a key procedure for the quality and reliability of large-scale Cyber-Physical Systems (CPSs). Although extensive efforts have been paid in learning normal data distributions, the spatial dependence of different dimensional KPIs is barely explored to reasonably represent the complexity and time-varying nature of systems. In this paper, we propose to model the spatial dependence of multivariate KPI by combining a more reasonable graph learning method with a graph attention mechanism to obtain the complex spatial dependence in an unsupervised manner. First, we transform the multivariate KPI into graph structures with a specially designed KPI graph learning module. Second, the Graph Attention mechanism extracts the spatial dependence in the KPI graphs. Finally, our method jointly trains forecasting-based model and reconstruction-based model to detect anomalies. Through a large number of related experiments on four real-world datasets, we demonstrate the feasibility of our method and the F1-Score improves by 9% over the baseline model. Further analysis shows that the graph learning method in this paper can more reasonably describe the dependence between multivariate KPI, and the graph attention mechanism can more accurately capture the correlation between them, which is helpful for fault diagnosis.
Hierarchical Decision-Making Framework for Multiple UCAVs Autonomous Confrontation

Autonomous decision-making for air confrontation between unmanned combat aerial vehicles remains hard to be designed due to dynamic situations and complex interactions. Rule-based decision-making methods provide a powerful solution with better interpretability. However, various hand-crafted rules may result in conflicts and poor scalability issues. To overcome this problem, this work proposes a hierarchical decision-making framework called State-Event-Condition-Action (SECA), which integrates the finite state machine and event-condition-action frameworks. This framework provides three products for system design: the SECA model–an abstract model of rules; the SECA state chart–a graphical visualization of rules; and the SECA rule description–a machine-readable format for practical deployment. The SECA framework offers several advantages, including convenient deployment, high efficiency, better logicality, and scalability. Simulation results demonstrate that the SECA framework enables autonomous decision-making in air confrontation scenarios and outperforms the event-condition-action framework in terms of computational time and cost-effectiveness. Furthermore, the generalization test in robot navigation tasks verifies its potential applicability to other domains with different background knowledge.
Principles Behind Semantic Relation between Common Abbreviations and their Expansions on Instagram

The phenomenon of abbreviation used on Instagram is an interesting thing since the users modify the abbreviation form of expansion words making deviation from the original meaning. The principle of meaning relation between abbreviations and their expansions is used to make a change to the purposes of Instagram users. This research was aimed to describe the principle of abbreviation used in Instagram. An exploitation method was used by the technique of making screenshots and recording the abbreviation data. The results of research indicate that the principle of abbreviation consists of three forms namely inclusion, contact, and complementary. The results of this research can be very effective in understanding as well as how to use abbreviations, especially on Instagram. The Novelty of the study is in investigating the abbreviation used in Instagram.
Shaped-Charge Learning Architecture for the Human–Machine Teams

In spite of great progress in recent years, deep learning (DNN) and transformers have strong limitations for supporting human-machine teams due to a lack of explainability, information on what exactly was generalized, and machinery to be integrated with various reasoning techniques, and weak defense against possible adversarial attacks of opponent team members. Due to these shortcomings, stand-alone DNNs have limited support for human-machine teams. We propose a Meta-learning/DNN → kNN architecture that overcomes these limitations by integrating deep learning with explainable nearest neighbor learning (kNN) to form the object level, having a deductive reasoning-based meta-level control learning process, and performing validation and correction of predictions in a way that is more interpretable by peer team members. We address our proposal from structural and maximum entropy production perspectives.
Privacy-Preserving Bloom Filter-Based Keyword Search Over Large Encrypted Cloud Data

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basic <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rivacy-preserving <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> loom filter-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</u> eyword <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> earch scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a new index structure T-CSCBF utilizing the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> win Bloom Filter (TBF) technology. Based on this, we propose an improved scheme PBKS+, which assigns a unique inclusion identifier to each position in each BF with privacy protection. Formal security analysis proves that our schemes are secure against Indistinguishability under Selective Chosen-Plaintext Attack (IND-SCPA), and extensive experiments using real-world datasets demonstrate that our schemes are feasible in practice.
HunEmBERT: A Fine-Tuned BERT-Model for Classifying Sentiment and Emotion in Political Communication

The growing number of digitally accessible text corpora and the accelerating development of NLP tools and methods (particularly the emergence of powerful large-scale language models) have allowed their widespread use in various classification tasks, including the vast field of sentiment analysis. However, these models must often be fine-tuned to perform this task efficiently. Therefore, we aimed to create a transformer-based fine-tuned model for the emotion and sentiment analysis of Hungarian political texts. The training data for the model were the manually annotated parliamentary speech texts from 2014 to 2018, which have the advantage of being rich in various emotions. The compiled corpus can be freely used for research purposes. In our work, we describe in detail the process of fine-tuning the Hungarian BERT model for sentiment and emotion classification, the performance achieved, and the typical classification errors, mainly due to a lack of recognition of pragmatic and other language use features by the fine-tuned models.
Health-5G: A Mixed Reality-Based System for Remote Medical Assistance in Emergency Situations

Mixed reality is the combination of virtual and augmented reality to interactively and believably merge physical and computer-generated environments.This paper discusses the design of Health-5G, a scalable mixed reality-based system that facilitates and supports emergency response by medical emergency teams.Health-5G is supported by a distributed architecture divided into four interrelated applications responsible for advanced computer-human interaction, effective real-time videoconference, medical device integration, and communication infrastructure, respectively.The mixed reality layer is provided by the headset Microsoft Hololens 2™.Health-5G is based on scenarios in which emergency personnel wear mixed reality glasses that can transmit audio, video, and data streams bidirectionally over a 5G network to medical specialists stationed in a hospital at any distance.Thanks to Health-5G, the specialist will be able to access the emergency team's point of view at any time and provide verbal and visual instructions, including gestures and positioning of graphical markers in 3D space.In this way, emergency personnel can provide the best possible care to the patient without having to wait for them to arrive at the hospital, saving a lot of time in scenarios where every second can make a difference.Health-5G also addresses the integration of medical devices and the collection of the patient's medical data in a scalable way through optical character recognition.A case study is discussed where Health-5G is used to attend a patient in the street suffering from syncope due to third-degree atrioventricular block.Latency and performance tests over a 5G network are also discussed.To the best of our knowledge, there is no comprehensive solution in the literature that provides all the capabilities offered by Health-5G in terms of functionality and advanced interaction mechanisms within the context of remote, immersive support in emergency situations.
Estimation of drone intention using trajectory frequency defined in radar's measurement phase planes

Abstract This paper presents a radar‐based algorithm for autonomous estimation of drone intention. The algorithm is based on radar's kinematic measurements, providing fast and robust intention estimation for multiple targets. The core idea of the proposed algorithm is to build intention‐specific features for each intention in advance and use them in actual drone surveillance situations. To effectively depict the trajectory characteristics of various intentions, the trajectory frequency is computed on multiple phase planes through Monte Carlo Simulations. Finally, a naive Bayes classifier is applied to integrate the trajectory likelihood in different phases and ultimately compute the intention likelihood of all intentions concerned. Numerical simulations for the three candidate intentions of image acquisition, smuggling, and kamikaze attack demonstrated the performance of the presented method. The simulation results show that one can estimate the true intention of a specific drone by comparing the value of each intention likelihood.
Cascaded Compositional Residual Learning for Complex Interactive Behaviors

Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation. However, such complex behaviors are difficult to learn because they involve both high-level planning and low-level motor control. We present a novel framework, Cascaded Compositional Residual Learning (CCRL), which learns composite skills by recursively leveraging a library of previously learned control policies. Our framework combines multiple levels of pre-learned skills by using multiplicative skill composition and residual action learning. We also introduce a goal synthesis network and an observation selector to support combination of heterogeneous skills, each with its unique goals and observation space. Finally, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">residual regularization</i> for learning policies that solve a new task, while preserving the style of the motion enforced by the skill library. We show that our framework learns joint-level control policies for a diverse set of motor skills ranging from basic locomotion to complex interactive navigation, including navigating around obstacles, pushing objects, crawling under a table, pushing a door open with its leg, and holding it open while walking through it. The proposed CCRL framework leads to policies with consistent styles and lower joint torques, and successfully transfer to a real Unitree A1 robot without any additional fine-tuning.
Research of Energy Detection Method in Spectrum Sensing in Cognitive Radio Networks

This paper presents the threshold value determination model, its implementation algorithm, and the characteristics of the threshold value depending on the probability of false signal detection, developed for the purpose of researching the method of energy determination in spectrum sensing in cognitive radio networks. Also, the main methods of single-band spectrum sensing in cognitive radio networks were initially studied in the article, and the main spectrum detection algorithms were comparatively analyzed.
Learning CS Subjects of Professional Software Development and Team Projects

Professional Software Development (PSD) course is about the emerging profession of software engineering which involves developing, deploying, testing, and maintaining software. Currently, the Bachelor of Science with Honours in Computing Science (CS) joint degree programme of University of Glasgow (UofG) and Singapore Institute of Technology (SIT) carries out both PSD and Team Projects (TP) courses concurrently, where students are expected to apply the theory learnt from PSD to real-world projects in TP. TP is a practical project continuation from knowledge learnt in the PSD course. Both PSD and TP last two trimesters in parallel. This paper analyses the advantages and disadvantages of the current learning methods in PSD and TP. It is possible that there is still room for improvement in the current system. Tо further analyse the current learning system, a comparison of how the Software Engineering course is taught in other universities is also performed, from where ideas and methodology are proposed. The proposed methodology is analyzed and discussed from the suggestions or feedback of the CS students who have gone through both PSD and TP courses. The purposes are to improve the learning effectiveness of both PSD and TP courses.
A Means to what End? Evaluating the Explainability of Software Systems using Goal-Oriented Heuristics

Explainability is an emerging quality aspect of software systems. Explanations offer a solution approach for achieving a variety of quality goals, such as transparency and user satisfaction. Therefore, explainability should be considered a means to an end. The evaluation of quality aspects is essential for successful software development. Evaluating explainability allows an assessment of the quality of explanations and enables the comparison of different explanation variants. As the evaluation depends on what quality goals the explanations are supposed to achieve, evaluating explainability is non-trivial. To address this problem, we combine the already well-established method of expert evaluation with goal-oriented heuristics. Goal-oriented heuristics are heuristics that are grouped with respect to the goals that the explanations are meant to achieve. By establishing appropriate goal-oriented heuristics, software engineers are enabled to evaluate explanations and identify problems with affordable resources. To show that this way of evaluating explainability is suitable, we conducted an interactive user study, using a high-fidelity software prototype. The results suggest that the alignment of heuristics with specific goals can enable an effective assessment of explainability.
Prioritization of DevOps Maturity models using Fuzzy TOPSIS

DevOps has become an increasingly popular approach to software development and operations. DevOps has evolved rapidly in recent years, with numerous maturity models being proposed to help organizations assess their level of adoption and identify improvement areas. However, there is no consensus on which model is the most effective, as different models may be more suited to different organizational contexts. This paper compares six popular DevOps maturity models using the Fuzzy TOPSIS Multiple Criteria Decision Making (MCDM) methods. Fuzzy TOPSIS is a popular MCDM technique that can handle imprecise and uncertain information. We analyze each model based on seven criteria: Culture, Automation, Continuous Integration/Continuous Delivery, Monitoring and Feedback, Security, Metrics and Measurement, and Continuous Learning and Improvement, and determine which model is the most suitable according to industry standards. The analysis revealed that Radstaak's DevOps maturity model outperforms the others when evaluated through seven standard criteria for assessing maturity models.
Software Architecture Design of a Serverless System

Context: Serverless computing allows developers to create and deploy applications without the need to manage any underlying infrastructure, making it a more efficient and effective way to bring products to market. Serverless technology is gaining widespread adoption among many companies, becoming increasingly popular. However, the adoption of serverless technology brings with it several new challenges. Objective: To this end, we plan to gain a deep understanding of challenges and strategies, architectural issues and their causes, architectural patterns, antipatterns, migration towards serverless architecture, and state-of-the-art practices for vendor lock-in problems. Methodology: The research objective will be met through the use of an industrial empirical approach, including interviews, a case study, and a questionnaire survey. Possible outcomes: The expected outcomes would be (i) a multivocal literature review on design areas of serverless architecture (ii) an evidence-based framework for synthesizing serverless architectural challenges/solutions (iii) a decision-making process for migrating to serverless architecture (iv) a decision-making framework for selecting vendor platform.