Reviewerly/exHarmony huggingface dataset:
Challenges and Limitations in Human Action Recognition on Unmanned Aerial Vehicles: A Comprehensive Survey

An Unmanned Aerial Vehicle (UAV), commonly called a drone, is an aircraft without a human pilot aboard. Making UAVs that can accurately discover individuals on the ground is very important for various applications, such as people searches, and surveillance. UAV integration in smart cities is challenging, however, because of problems and concerns such as privacy, safety, and ethical/legal use. Human action recognition-based UAVs can utilize modern technologies. Thus, it is essential for future development of the aforementioned applications. UAV-based human activity recognition is the procedure of classifying photo sequences with action labels. This paper offers a comprehensive study of UAV-based human action recognition techniques. Furthermore, we conduct empirical research studies to assess several factors that might influence the efficiency of human detection and action recognition techniques in UAVs. Benchmark datasets commonly utilized for UAV-based human action recognition are briefly explained. Our findings reveal that the existing human action recognition innovations can identify human actions on UAVs with some limitations in range, altitudes, long-distance, and a large angle of depression.
Using Learning Analytics to Explore Responses from Student Conversations with Chatbot for Education

Chatbot simulates humans' conversations through computer programs using natural language. They are developed for various reasons and purposes, such as virtual characters and entertainers or as an interactive game component. Nowadays, Chatbots for education are using widely to enable students to engage with learning content constantly. In this study, students use Chatbot to query the Web Programming learning contents such as code description, coding and problem-solving. However, the successful responses of Chatbot to student queries is unknown. The lecturers also do not know the desired learning content of students who use the Chatbot. Thus, the purposes of this study are to explore the probability of a student getting successful responses in each conversation and to identify the students desired learning content. The learning analytics method is used to analyse the learning data of Chat-bot. The data analysis performed is descriptive analysis and binomial probability testing. The finding of the studies showed that the value of successful responses of Chatbot is high. The most desired learning content by the students is related to three categories of Web Programming contents, which is the hypertext Preprocessor (PHP), database &amp; structured query language (SQL) and hypertext markup language (HTML). The Chatbot is updated based on proposed actions to provide more successful responses.
Nanotilus: Generator of Immersive Guided-Tours in Crowded 3D Environments

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This article proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nanotilus</i> —a new visibility and guidance approach for very dense environments that generates an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">endoscopic</i> inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multi-scale, multi-shell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models – SARS-CoV-2 and HIV. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.
College English Online Teaching Model Based on Deep Learning

In today’s era, online teaching plays an important part in the college English teaching. Deep learning, famous for its ability of imitating the learning process of human brains and obtaining the internal essential features or rules of voice, videos, images, and other data, can be applied to assist and improve the college English online teaching which involves a wide use of those data. Based on the combination of the multilayer neural network model and the k-means clustering algorithm, this paper designs a kind of deep learning method that can be used to assist and improve the college English online teaching. Experiments were designed to test the reliability of this deep learning method. The results show that the optimization algorithm designed in this paper, which can adjust the learning rate, will improve the common probability gradient descent algorithm. Besides, it is proved that the deep learning’s efficiency of the CNN model is significantly higher than that of the MLP model. With the help of this deep learning method, it becomes feasible to apply the technologies related to the artificial intelligence to help teachers deeply analyze and diagnose students’ English learning behavior, replace the teachers in part to answer students’ questions in time, and automatically grade assignments in the process of the college English online teaching. Surveys and exams were then conducted to evaluate the effect of the application of the college English online teaching model based on deep learning on the students’ learning cognition and their academic performance. The results show that the college English online teaching model based on deep learning can stimulate students’ learning motivation and improve their academic performance.
A Novel Approach for COVID-19 Patient Condition Tracking: From Instant Prediction to Regular Monitoring

Purpose: The aim of this research is to develop an accurate and interpretable aggregated score not only for hospitalization outcome prediction (death/discharge) but also for the daily assessment of the COVID-19 patient's condition. Patients and Methods: In this single-center cohort study, real-world data collected within the first two waves of the COVID-19 pandemic was used (27.04.2020–03.08.2020 and 01.11.2020–19.01.2021, respectively). The first wave data (1,349 cases) was used as a training set for the score development, while the second wave data (1,453 cases) was used as a validation set. No overlapping cases were presented in the study. For all the available patients' features, we tested their association with an outcome. Significant features were taken for further analysis, and their partial sensitivity, specificity, and promptness were estimated. Sensitivity and specificity were further combined into a feature informativeness index. The developed score was derived as a weighted sum of nine features that showed the best trade-off between informativeness and promptness. Results: Based on the training cohort (median age ± median absolute deviation 58 ± 13.3, females 55.7%), the following resulting score was derived: APTT (4 points), CRP (3 points), D-dimer (4 points), glucose (4 points), hemoglobin (3 points), lymphocytes (3 points), total protein (6 points), urea (5 points), and WBC (4 points). Internal and temporal validation based on the second wave cohort (age 60 ± 14.8, females 51.8%) showed that a sensitivity and a specificity over 90% may be achieved with an expected prediction range of more than 7 days. Moreover, we demonstrated high robustness of the score to the varying peculiarities of the pandemic. Conclusions: An extensive application of the score during the pandemic showed its potential for optimization of patient management as well as improvement of medical staff attentiveness in a high workload stress. The transparent structure of the score, as well as tractable cutoff bounds, simplified its implementation into clinical practice. High cumulative informativeness of the nine score components suggests that these are the indicators that need to be monitored regularly during the follow-up of a patient with COVID-19.
New Technologies in Educational Solutions in the Field of STEM: The Use of Online Communication Services to Manage Teamwork in Project-Based Learning Activities

STEM education as an innovative paradigm of education is being rapidly introduced into the curricula of higher educational institutions. The research is devoted to the study of the capabilities of online communication services for managing teamwork aimed at the development and implementation of innovative investment projects in the STEM education environment. An innovative concept of a distance learning course – Designing investment projects in the field of health care and recreation – is suggested based on organizational principles. The online communication services such as Mirapolis Virtual Room, Moodle and Microsoft Teams implemented into the educational interaction program made it possible to create a high-quality real-time e-learning environment. The online course was tested by 200 students. The results of the experiment confirmed the effectiveness of the use of online communication services and platforms in the development of professional practical experience within the educational concept of STEM. Students appreciated the high level of the innovative component in team interaction programs (98%), as well as, in the approaches to cooperation with business entities (95%); 91% of students noted the high level of quality of the educational content, pedagogical (84%), technological (92%) and conceptual (78%) innovative value of the educational product. Taking a course that is based on the STEM model had a dramatic impact on the development of the key competencies of the 21st century. The study can be of interest to teachers who are looking for effective pedagogical models that can be easily adapted to the needs of the business environment and the generation of students of the new digital era.
Modernisation of urban governance: An approach of ‘Blockchain + Big Data’

Abstract Mathematics is a prerequisite for the development of blockchain technology. The deeply penetrated mathematical ideas support the establishment of the trust mechanism of the whole blockchain system, which makes the blockchain technology autonomous, decentralised, not so easy to tamper, open, anonymous and also possesses other characteristics. Due to these characteristics, the introduction of blockchain will greatly solve a series of problems faced by the quality and acquisition of big data in cities, and release more data vitality. Based on the perspective of chain blocks and big data fusion, this paper puts forward that data are the foundation of modern urban governance. Data management has become the key to modern urban governance. It puts forward that the building of a big data management system based on blockchain will strengthen the construction of the intelligent city and modernisation of urban governance capabilities.
Generalized Galois-Fibonacci Matrix Generators Pseudo-Random Sequences

The article discusses various options for constructing binary generators of pseudo-random numbers (PRN) based on the so-called generalized Galois and Fibonacci matrices. The terms "Galois matrix" and "Fibonacci matrix" are borrowed from the theory of cryptography, in which the linear feedback shift registers (LFSR) generators of the PRN according to the Galois and Fibonacci schemes are widely used. The matrix generators generate identical PRN sequences as the LFSR generators. The transition from classical to generalized matrix PRN generators (PRNG) is accompanied by expanding the variety of generators, leading to a significant increase in their cryptographic resistance. This effect is achieved both due to the rise in the number of elements forming matrices and because generalized matrices are synthesized based on primitive generating polynomials and polynomials that are not necessarily primitive. Classical LFSR generators of PRN (and their matrix equivalents) have a significant drawback: they are susceptible to Berlekamp-Messi (BM) attacks. Generalized matrix PRNG is free from BM attack. The last property is a consequence of such a feature of the BM algorithm. This algorithm for cracking classical LFSR generators of PRN solves the problem of calculating the only unknown – a primitive polynomial generating the generator. For variants of generalized matrix PRNG, it becomes necessary to determine two unknown parameters: both an irreducible polynomial and a forming element that produces a generalized matrix. This problem turns out to be unsolvable for the BM algorithm since it is designed to calculate only one unknown parameter. The research results are generalized for solving PRNG problems over a Galois field of odd characteristics.
Photostable fluorescent molecules on layered hexagonal boron nitride: Ideal single-photon sources at room temperature

Photoblinking and photobleaching are commonly encountered problems for single-photon sources. Numerous methods have been devised to suppress these two impediments; however, either the preparation procedures or the operating conditions are relatively harsh, making them difficult to apply to practical applications. Here, we reported giant suppression of both photoblinking and photobleaching of a single fluorescent molecule, terrylene, via the utilization of hexagonal boron nitride (h-BN) flakes as substrates. Experimentally, a much-prolonged survival time of terrylene has been determined, which can have a photostable emission over 2 h at room temperature under ambient atmospheres. Compared with single molecules on a SiO2/Si substrate or glass coverslip, a more than 100-fold increase in the total number of photons collected from each terrylene on h-BN flakes has been demonstrated. We also proved that the photostability of terrylene molecules can be well maintained for more than 6 months even under ambient conditions without any further protection. Our results demonstrate that the utilization of h-BN flakes to suppress photoblinking and photobleaching of fluorescent molecules has promising applications in the production of high-quality single-photon sources at room temperature.
Digital competences in Nordic teacher education

We investigate how digital competences are being integrated into teacher education (TE) across the Nordic countries - Denmark, Sweden, Norway, and Finland in this article. We make the case that there has been an expansion of the agenda for digital competences in education. Digital competences have developed from an information and communication technology perspective to also include a critical, social, and creative understanding of digital technologies and computing competences. Methodologically, we make use of doc-ument analyses, qualitative questionnaires, and interviews with participants in the field. With an emphasis on Danish TE, we explore how TE in the Nordic countries has responded to this agenda on policy and institutional levels. We suggest that the Danish approach to the expanded agenda can augment tendencies and challenges in Nordic responses to digitalisation in TE. A key finding is that Nordic countries respond to the expanded agenda in different ways regarding policy regulation, content areas, and how digital com-petences are organised and distributed on a local level. Tendencies and challenges identified across Nordic countries are valuable to ensure the continual development of teachers’ digital competences.
Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease

This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.
Students’ experiences and acceptance of emergency online learning due to COVID-19

At the outbreak of the COVID-19 pandemic, higher education institutions around the world were compelled to bring forth suitable alternatives to secure continuity in the process of education. To gather data that would indicate the quality and efficiency of online teaching in higher education in south and south-eastern Serbia initiated at the outbreak of the pandemic and the state of emergency declared in March 2020, we conducted a research study to provide the necessary information from students attending undergraduate academic studies. The research was conducted in two phases. The first phase consisted of quantitative research conducted on a sample of 211 students. The data collected were processed by resorting to factor analysis to determine specific factors as a starting point for designing questions for the second research phase. The second phase was implemented by conducting semi-structured interviews on a sample of 61 students. The main intention was to determine key characteristics of the teaching process realised in the context of an emergency transition to the form of teaching which had not been prepared or resorted to beforehand. We identified certain specifics in this regard, as a starting point for improving existing practice. Implications for practice or policy: It is necessary to secure a suitable level of institutional support, to maintain the quality of higher education in unforeseen social circumstances. It is necessary to adequately prepare for and support teachers and students in online teaching at both institutional and national level. To avoid adverse effects on individual students in the circumstances of online teaching, it is necessary to meet students´ educational, social and emotional needs.
Differential Anomaly Detection for Facial Images

Due to their convenience and high accuracy, face recognition systems are widely employed in governmental and personal security applications to automatically recognise individuals. Despite recent advances, face recognition systems have shown to be particularly vulnerable to identity attacks (i.e., digital manipulations and attack presentations). Identity attacks pose a big security threat as they can be used to gain unauthorised access and spread misinformation. In this context, most algorithms for detecting identity attacks generalise poorly to attack types that are unknown at training time. To tackle this problem, we introduce a differential anomaly detection framework in which deep face embeddings are first extracted from pairs of images (i.e., reference and probe) and then combined for identity attack detection. The experimental evaluation conducted over several databases shows a high generalisation capability of the proposed method for detecting unknown attacks in both the digital and physical domains.
Improving the Model for Person Detection in Aerial Image Sequences Using the Displacement Vector: A Search and Rescue Scenario

Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this paper, the continuation of our previous research is presented. The main goal is to further improve detection results, especially in terms of reducing the number of false positive detections and consequently increasing the precision value. We present a new approach that, as input to the multimodel neural network architecture, uses sequences of consecutive images instead of only one static image. Since successive images overlap, the same object of interest needs to be detected in more than one image. The correlation between successive images was calculated, and detected regions in one image were translated to other images based on the displacement vector. The assumption is that an object detected in more than one image has a higher probability of being a true positive detection because it is unlikely that the detection model will find the same false positive detections in multiple images. Based on this information, three different algorithms for rejecting detections and adding detections from one image to other images in the sequence are proposed. All of them achieved precision value about 80% which is increased by almost 20% compared to the current state-of-the-art methods.
Efficient Hardware Implementation of Large Field-Size Elliptic Curve Cryptographic Processor

Due to rapid development in secured technological devices, the efficient implementation of a large field-size elliptic curve cryptosystem (ECC) is becoming demanding in many critical applications. Therefore, this paper presents a new Montgomery point multiplication (PM) algorithm to optimize and balance the signal flow and resource utilization efficiency. Thereafter, we have presented an efficient ECC processor architecture over <inline-formula> <tex-math notation="LaTeX">$GF(2^{m})$ </tex-math></inline-formula> with m &#x003D; 409 and 571 for the proposed Montgomery PM algorithm. Finally, we have given a detailed comparison and performance analysis (in terms of area-delay product) to show that the proposed cryptographic processor has superior performance as compared to the competing designs. The implementation results after place &#x0026; route on Xilinx Virtex 7 and Kintex Ultrascale&#x002B; are provided. The achieved results reveal that the proposed large field-size ECC processor (and the proposed design strategy) can be extended and applied in many security-demanding applications.