Reviewerly/exHarmony huggingface dataset:
Ensuring the reliability of remote online monitoring systems

the issue of ensuring the reliability of the functioning of global systems of remote online monitoring of the condition of patients is considered. Remote monitoring is in demand when supervision the health status of disabled people with cardiovascular diseases, pregnant women and children. It is especially in demand in the context of a pandemic and the accompanying restrictions on movement. Monitoring is carried out “transparently” for the patient, without involving him in the subtleties of the process of interaction with a medical institution, ensuring the preservation of his mobility due to the proposed concept of implementing a wearable online monitor device. The procedures of the online monitor initialization and distribution of functions in it are considered. Threats to the normal functioning of distributed systems with online monitors are presented, including existing problems of a general nature related mainly to global communications. Solutions are offered to ensure reliable operation of wearable devices as part of online monitors.
Wireless sensor networks communication protocols

Modern technological advances allow remote monitoring of all social and production-related processes. It is important to ensure network resources and choose a network protocol based on the requirements during the monitoring process. The focus is on wireless communication protocols. When conducting a comparative analysis of wireless communication protocols, it is necessary to study based on several indicators. The network size, transmission time, power and transmission distance, power consumption, including the power consumption of microcircuits and coding efficiency of these protocols are important parameters in choosing a protocol. In this paper, the communication protocols used in the process of remote monitoring are studied on the example of the healthcare system.
Simultaneous Planning and Execution for Quadrotors Flying Through a Narrow Gap Under Disturbance

The dynamics of a quadrotor exhibit the challenges of underactuation, strong coupling, and external disturbances. In order to handle these difficulties and enable quadrotors to align the six-degree-of-freedom (6-DOF) pose of the tilted narrow gap during the traversing maneuver, this article proposes a strategy based on simultaneous planning and execution (SPAE). In this strategy, the unknown disturbance is considered in both the planning and execution of the trajectory simultaneously. Specifically, the disturbance is estimated online during system execution and then combined with the attitude angle of the narrow gap and maximum thrust to constraint acceleration of trajectory. Given the state constraints and physical limitations in thrust, velocity, and jerk, a minimum time trajectory planning algorithm is developed based on a piecewise cubic function to realize aggressive flight. In this algorithm, the coupled maximum thrust is decoupled into three independent axes. Each axis can then generate time-efficient third-order trajectories that satisfy arbitrary state constraints and dynamic feasibility. The average computing time of the algorithm is less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$150 ~\mu \text{s}$ </tex-math></inline-formula> on the STM32 embedded platform, which is suitable for quadrotors with limited onboard computing capability. Finally, simulations and outdoor experiments were conducted to verify the effectiveness of the proposed system strategy. The outdoor results demonstrate that the proposed strategy can enable the quadrotor to carry out aggressive flying through a narrow gap in a natural disturbance environment.
EI-HCR: An Efficient End-to-End Hybrid Consistency Regularization Algorithm for Semisupervised Remote Sensing Image Segmentation

Recently, remote sensing image (RSI) semantic segmentation technology has advanced greatly, with the fully supervised process achieving particularly strong performance. However, the technology depends heavily on dataset labels, leading to high annotation costs. To alleviate this problem, we propose a novel efficient end-to-end hybrid consistency regularization algorithm (EI-HCR) for the semi-supervised semantic segmentation of RSI, wherein only a few labeled images and a large number of unlabeled images are effectively used. First, we devise data perturbation (DP) consistency regularization (CR), which includes a data mix-up method to combine unlabeled and labeled images. Then, we employ teacher and student networks to conduct model perturbation (MP) CR. Both segmentation results are regarded as pseudo-labels for each other. In the end, the semi-supervised loss is composed of DP and MP consistency loss, and supervises network training along with the fully supervised loss. More importantly, we first combine the characteristics of knowledge distillation to make the student network more lightweight, efficiently reducing the model inference time. Experimental results demonstrate the effectiveness of EI-HCR on the ISPRS Vaihingen and Massachusetts Buildings datasets. With only 5% of the labeled images, EI-HCR can achieve the same accuracy as the fully supervised training with 50% of the labeled images, and the number of student model parameters is only 9.64 M, indicating the method’s great advantages over other algorithms.
Quality management of the educational process

The article discusses issues related to the quality of the learning process for students and management of the educational process based on digitization of knowledge acquired by students. The article presents an algorithm for adjusting reserves to improve the quality of knowledge of students. A detailed assessment of the quantitative relationship between parameters of the educational process is provided. Special attention is paid to determining strategies for managing the quality of education. Hypotheses put forward in the article are confirmed by experimental data and mathematical calculations. Quality management of the educational process is analyzed in detail using an example. In the example, students are divided into 4 groups based on their risk of losing the quality of the educational process. Based on the analysis of identified risk areas, conclusions are drawn and recommendations are given for choosing management strategies to improve the quality of the learning process for each group of students. With correctly chosen management interventions in the educational process, its efficiency is significantly increased, and accordingly, the quality and level of knowledge of students are substantially improved. Thus, the article presents a methodology for managing the educational process that defines the goals, tasks, and stages of management, provides algorithms for adjusting learning reserves, and choosing strategies for managing the quality of the educational process. The application of this approach in practice allows for a significant improvement in the efficiency of student learning.
The Role of Cybersecurity Knowledge and Awareness in Cybersecurity Intention and Behavior in the United States

Despite the rapid escalation of cybercrimes in modern society, few viable cybersecurity and cybercrime prevention programs exist. Consequently, this research considers cybersecurity as a key element for mitigating the potential risk and incidents of cybercrime. The study aims to understand the role of individuals’ cybersecurity knowledge and awareness in shaping their cybersecurity intention and behavior in the United States. The results reveal that information and communications technology variables, education, income, and gender are contributing predictors of a level of cybersecurity knowledge. The effects of information and communications technology variables varied across gender for both correct and uncertain responses. The findings highlight the importance of a comprehensive understanding of both technical and human/individual factors for cybersecurity knowledge and education.
Implementing Digital Transformations in Higher Education Following COVID-19: A Norwegian Case Study

Drawing on the theory and conceptualizations of organizational readiness for change and taking a more critical approach to organizational readiness that takes seriously actors’ different and sometimes conflicting understandings of change and digital transformation processes, this chapter explores the implementation of digital tools and systems in Norwegian higher education (HE) prior to and following the outbreak of the COVID-19 global health pandemic. The analysis focuses on the challenges and bottlenecks associated with digital implementation in a complex environment by focusing on contextual and situational factors. The chapter investigates ongoing developments in Norwegian HE in the context of dynamics across the Nordic region, illuminating the micro-level practices, experiences, and responses to digital transformation of university actors at multiple levels in a Norwegian university selected as the main case study.
Research on Effective Advertising Types in Virtual Environment

Virtual reality (VR) applies various types of advertisements (ads) to promote brands while collaborating with companies. This study aims to present effective advertisement types by verifying user responses in a VR environment. First, by analyzing the cases of advertisements with immersive content, the types of advertisements in VR were defined as avatar costumes, products, and wall posters. The user response was measured in two categories: gaze response measured by the eye-tracking VR advertisement monitoring system (EVAMS) and the advertisement effect analyzed through surveys. As a result of analyzing the user responses, the avatar costumes among the advertisement types caused the highest visual attention and advertisement effect. In addition, by analyzing the correlation between visual attention and the advertisement effect, it was observed that there was a positive relationship between the number of fixations and advertisement attention, fixation time, and advertisement recall. Thus, it was confirmed that the higher the number of fixations and the longer the fixation time, the more positively an advertisement was affected. In addition, it is expected that the results of this study can be used as a reference for effective advertisement directing in VR content development and advertisement directing and processing.
High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images

The rapid development of digital multimedia has resulted in a massive storm of large-scale data. The data compression method reduces data size and lowers transmission costs. As a result, data-hiding research in the compression domain poses significant challenges to researchers. This work proposes a highly imperceptible data-hiding scheme for a compressed absolute moment block truncation coding (AMBTC) image. The proposed method addresses the shortcomings of the current block-based AMBTC data-hiding scheme, with an incredibly complex block as non-embedding. This is because embedding in complex blocks causes substantial distortions in the image, resulting in low imperceptibility performance. Combination theory was implemented to ensure the high imperceptibility of the modified compressed images. The experimental findings proved that the proposed method achieved high imperceptibility and high hiding capacity by modifying one pixel in a block to embed confidential bits.
Parameters and Structure of Neural Network Databases for Assessment of Learning Outcomes

The purpose of this study is to determine the methodology, develop a theory of construction, put into practice algorithmization and implement the functionality of a hybrid intelligent system for assessment of educational outcomes of trainees on the basis of the identified keyword parameters and structure of the artificial neural network using expert systems and fuzzy simulation; to develop a methodology for the construction of structural-logic, hierarchical, functional and fractal schemes for structuring databases of the didactic field of learning elements; to determine the content, structure of parameters and database components, selection criteria and the content of complexes of educational standards. The methodology of introducing intelligent systems into mathematical education is on the basis of the Hegelian triad: thesis (implementation of the coherence principle) – antithesis (implementation of principles of the fractality and historiogenesis) – synthesis (implementation of the principles of self-organization and reflection of the complex system inversion integrity). Requirements for the organization and construction of the artificial neural network for assessment of personal achievements on the basis of fuzzy simulation have been developed. In the direction of using elements of fractal geometry, the technological structures of clusters that constitute the basis of generalized structures have been developed. In particular, it is revealed that the didactic field of learning elements is equipped with a system of multi-level hierarchical databases of exercises, motivational-applied, research, practice-oriented tasks using expert systems and integration of mathematical, information, natural-science and humanities knowledge and procedures.
MuTr: Multi-Stage Transformer for Hand Pose Estimation from Full-Scene Depth Image

This work presents a novel transformer-based method for hand pose estimation-DePOTR. We test the DePOTR method on four benchmark datasets, where DePOTR outperforms other transformer-based methods while achieving results on par with other state-of-the-art methods. To further demonstrate the strength of DePOTR, we propose a novel multi-stage approach from full-scene depth image-MuTr. MuTr removes the necessity of having two different models in the hand pose estimation pipeline-one for hand localization and one for pose estimation-while maintaining promising results. To the best of our knowledge, this is the first successful attempt to use the same model architecture in standard and simultaneously in full-scene image setup while achieving competitive results in both of them. On the NYU dataset, DePOTR and MuTr reach precision equal to 7.85 mm and 8.71 mm, respectively.
Compact-Fusion Feature Framework for Ethnicity Classification

In computer vision, ethnicity classification tasks utilize images containing human faces to extract ethnicity labels. Ethnicity is one of the soft biometric feature categories useful in data analysis for commercial, public, and health sectors. Ethnicity classification begins with face detection as a preprocessing process to determine a human’s presence; then, the feature representation is extracted from the isolated facial image to predict the ethnicity class. This study utilized four handcrafted features (multi-local binary pattern (MLBP), histogram of gradient (HOG), color histogram, and speeded-up-robust-features-based (SURF-based)) as the basis for the generation of a compact-fusion feature. The compact-fusion framework involves optimal feature selection, compact feature extraction, and compact-fusion feature representation. The final feature representation was trained and tested with the SVM One Versus All classifier for ethnicity classification. When it was evaluated in two large datasets, UTKFace and Fair Face, the proposed framework achieved accuracy levels of 89.14%, 82.19%, and 73.87%, respectively, for the UTKFace dataset with four or five classes and the Fair Face dataset with four classes. Furthermore, the compact-fusion feature with a small number of features at 4790, constructed based on conventional handcrafted features, achieved competitive results compared with state-of-the-art methods using a deep-learning-based approach.
Named Entity Identification in the Power Dispatch Domain Based on RoBERTa-Attention-FL Model

Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa’s middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa’s last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model’s performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%.
Educating for Professional Digital Competence? Exploring Teacher Education in a New Learning Space

Undervisningsverkstedet, henceforth abbreviated as UV, constitutes a newer initiative within campus-based teacher education at a Norwegian university. It includes a location and resources for varied student-active teaching and aims to foster professional digital competence. The location has flexible furnishings and includes various digital and analogue resources. In an international context, this type of classroom is known as a ‘future classroom lab’ and originates from an initiative by the European Schoolnet. Over the years, these types of classrooms have spread across Europe, yet they mostly target schools and libraries. However, in 2022, several higher education institutions in the Nordics that offer teacher education have been establishing this type of learning environment. Taking a qualitative approach with interviews, we study teacher educators’ perceptions on how to promote professional digital competence for their students in this new type of learning space. Findings indicate that UV as a distinct learning space has the potential to transform and enhance the development of professional digital competence for teachers and students. Findings also demonstrate the value of campus-based learning and of new learning spaces. This is an important observation after the years of remote online teaching and learning caused by the pandemic. Moreover, our study demonstrates that the digital transformation of teacher education affects the role of the teacher, which again may also cause epistemic changes.
Comparative Analysis of Image Augmentation and Data Deduplication Techniques

In today's scenario, data is growing very quickly; each connected device generates and transfers a significant amount of data online. Devices can't process or store this massive data because of their low processing and storage capabilities. Through the use of a cloud storage system, the researchers have suggested solutions. For latency-sensitive applications, it is crucial to investigate alternate storage optimization approaches due to the latency and bandwidth difficulties of cloud storage systems. In order to increase efficiency, storage optimization techniques like dimensionality reduction and deduplication may be deployed close to the system's edge.
Efficient Bounding Strategy for CEC 2020 Winner Algorithms in Solving Production Planning Problems

The performance of the recently proposed metaheuristic techniques is mainly analyzed by solving the bound-constrained real parameter benchmark functions of the competitions conducted as part of the prestigious conference, Congress on Evolutionary Computation (CEC). The bounding strategies are an integral part of the algorithms as it aids in determining the optimal solution in a feasible search space; however, they are not discussed as part of the algorithmic descriptions. The bounding strategy utilized for solving the benchmark functions is not necessarily beneficial in solving real-life optimization problems. This work studies the impact of switching from the innate bounding strategy to corner bounding strategy on results obtained from the two algorithms, namely the Improved Multi-Operator Differential Evolutionary (IMODE) algorithm and Adaptive Gaining-Sharing Knowledge (AGSK) algorithm, the winners of the CEC 2020 competition on solving bound-constrained real parameter benchmark functions. The performance of these two techniques is analyzed in solving a real-life production planning with their innate bounding strategy, and the obtained results are not satisfactory. The default bounding strategies of the two algorithms are replaced with a strategy, and the performance of the resulting modified IMODE (M-IMODE) and modified AGSK (M-AGSK) on the production planning is improved significantly. The results for best fitness obtained from M-IMODE have enhanced in the range of 77.84–99.09% for Cases 1–4 and 19.99–50.96% for Cases 5–8. AGSK is unable to determine feasible solutions, while the values obtained by M-AGSK are more improved in the range of 36.77–67.5% than M-IMODE and in the range of 0.03–15.29% than sTLBO.
CFG Analysis for Detecting Vulnerabilities in Smart Contracts

Smart contracts are a leading light moment in blockchain technology 2.0 and are universally used in various applications. A smart contract is a digital agreement for financial transactions. A logical or syntactical error in the smart contract will breach the system’s security. However, smart contracts are not always secure, and numerous flaws exist and cannot be modified due to the immutability feature of smart contracts. The most critical problems for smart contracts are vulnerabilities, resulting in massive financial losses. As a result, extensive research should be conducted before installing a smart contract. Despite the fact that numerous methods for detecting vulnerabilities have been proposed in previous research, all of them have a high false positive and false negatives. To address these issues, Ethersolve, a method based on symbolic execution at the EVM bytecode level, was used to analyze the strategies. A static analysis method based on the symbolic implementation of the Ethereum operand stack to settle Ethereum bytecode jumps and establish an accurate control-flow graph (CFG) of the accumulated smart contracts. Here we analyze the CFG of smart contracts using Ethersolve tool. And then weigh the benefits and drawbacks of Ethersolve and present our findings.
Abstractive Text Summarization for Tamil Language Using m-T5

Summarization is the act of reducing a long form of text to a shorter version by lowering the length of the original text while retaining and keeping the summary’s core and the informational parts which conveys the content’s meaning. Summarizing lengthy papers by hand is difficult and prone to inaccuracy. The exponential rise of information and data has made automatic text summarization crucial. It leads to a summary generated by a summarizing system that enables users to understand the document’s contents. Abstractive and extractive summarization are the two categories into which summarization falls. We can see that there are not many automatic text summarizers available for Indian languages. Our main goal in this direction is to create an automatic abstractive text summarizer for Tamil using the m-T5 transformer model. We have also used manual dataset of Tamil Kid’s stories for training and testing.