Reviewerly/exHarmony huggingface dataset:
Virtual Classroom in the Formation of Educational Quality Standards

The research involved conducting a diagnostic survey of 552 teachers and administrators, which revealed a low level of knowledge about the current legal regulations on educational quality standards. Both managers and teachers recognized the need for further training in this area. To address this gap, the studio implemented a virtual training program consisting of two modules: one focused on educational quality and the other on educational quality standards. At the end of the virtual training course, a satisfaction survey was administered to 22 participants (teachers and managers) who had completed the entire program. The survey indicated a significant improvement in participants’ knowledge of educational quality standards, because of their participation in the virtual course entitled “Leafing through and eyeing educational quality standards”. The first module addressed Educational Quality, focused on objective four of the 2030 Agenda, highlighting Ministerial Agreement 091/2017. The second module delved into the Educational Quality Standards, emphasis is placed on the manual for the implementation and evaluation of the Educational Standards. It is important to note that although ignorance of educational quality standards is a central issue addressed in the work, the research emphasizes the research methods used, the implementation of a virtual classroom for training and the positive impact observed in the knowledge of the participants through the virtual course.
A Semantically Informed Benchmark Dataset for Computer Vision in Aviation Systems

Deep learning and convolutional neural networks have greatly improved the accuracy of computer vision technologies. As a result, computer vision for aviation systems is a rapidly expanding field, with systems being developed to detect aircraft, improve aircraft maintenance, and even assist in navigation tasks as Unmanned Aerial Vehicles (UAVs) move towards autonomous flight. Despite the rapid expansion of technology in terms of accuracy and novel applications, there is a lack of trust and adequate testing in computer vision research, making it difficult to implement these models within the highly regulated aviation industry. Verification techniques, specifically testing metrics traditionally applicable to software systems are no longer applicable to the non-deterministic and black-box nature of deep learning models. In alignment with current developments in testing techniques for deep learning, this work proposes a system for creating and managing a novel benchmark dataset of images to test all computer vision models. The images in this dataset are embedded with semantic knowledge from a general knowledge graph and a Bidirectional Encoder Representations from Transformers model to represent the semantic relationships between objects within an image. As a result, the system can validate the performance of computer vision models, even when the objects being detected are unlabeled in the original testing set. This novel benchmark dataset demonstrates an accurate testing system that works with UAV-based detection systems currently in development. This work can be incredibly beneficial to developing the concept of trust surrounding computer vision, allowing the expanded use of computer vision in aviation operations.
Enhancing Counter Drone Operations Through Human-AI Collaboration: A Hierarchical Decision-Making Framework

In recent years, drones have become popular for various applications, including surveillance and delivery. Detecting and classifying drones is crucial for security and regulatory purposes, as well as for improving the efficiency of drone operations. In this work, we propose a human-AI collaboration framework for drone detection, tracking, and classification using multiple sensors, such as ground radar and ground pan-tilt-zoom (PTZ) camera. Our approach combines traditional signal processing and machine learning to classify drones using radar tracks, and a deep learning-based model for detecting and classifying drones from video data. However, due to the limitations of supervised learning, there are always scenarios where these models fail, such as unseen/untrained backgrounds, new aircraft types or signatures. To address this issue, we developed a human-like model and proposed a methodology that involves object detection and a referral policy to trigger a simulated human review of the event.We demonstrate the effectiveness of our human-AI collaboration framework through extensive simulations utilizing data collected from ground radar, and comparing it with standalone AI systems. We also evaluate the effectiveness of various referral policies for different human decision models. In conclusion, the proposed human-AI collaboration framework for drone detection, tracking, and classification has the potential to improve the efficiency and accuracy of drone operations, while ensuring regulatory compliance and enhancing security. Multiple sensors and a referral policy is applied in the proposed framework to trigger human review of events. This is an important step towards addressing the limitations of supervised learning in scenarios where traditional models fail, and towards achieving the full potential of drones in various applications.
Google Sites: An Effective and Accessible Solution to Improve the Teaching of Mathematics in Virtual Environments

Education aims to generate learning in students, however, there is a discrepancy between what is learned in class and its practicality in everyday life. A vision of schooling of learning continues to be maintained that seems to marginalize the knowledge that young people have acquired during the transition to the new academic normality, through the media and digital platforms. Google Sites is a platform that allows you to congregate a variety of functions such as: videos, calendars, presentations, text files, spreadsheets, among others. For this reason, the objective is to design a web page for the teaching of mathematics in children of sixth grade of Basic General Education. This research used a mixed-approach methodology by applying a survey to 35 students from the public basic education school of the Metropolitan District of Quito. 16 teachers from the same school and an interview with 2 specialists. Information was collected, qualitative and quantitative results were analyzed and interpreted for the measurement of variables. The findings reflected that Google Sites being a multifunctional platform allows to enable the process of teaching mathematics due to the use of videos, spreadsheets, gamification, online documents, dynamic tables.
Enabling Linux in Aerospace Applications

The Linux kernel is one of the most successful open-source projects to emerge in the last thirty years. With contribution rates at nine commits per hour, it continues to improve, adding new features and showing no sign of losing momentum. This makes it a useful platform to leverage new technology, as the features are likely already implemented and tested well ahead of proprietary software. However, this rate of change has a downside when rigorous analysis is required to determine if a system can be deployed for a specific application with safety considerations. In addition, development by thousands of independent developers makes it difficult to document a cohesive set of requirements and architecture – necessary elements for flight certified avionics software. This paper will overview Linux kernel evolution, outline the benefits of using Linux in aerospace, and introduce the "Enabling Linux in Safety-Critical Applications" (ELISA)[ep] project working to bring Linux into safety-critical industries. Despite challenges, Linux provides important benefits, making it attractive for avionics. As one of the premier open-source projects in the world, the Linux kernel attracts innovative contributions that represent best practice. Rather than depending on security by obscurity, the open nature of the code provides a crowd-sourced rapid response to security vulnerabilities. The licensing of the kernel facilitates rapid and efficient deployment to a wide range of developers, testers, and customers. ELISA, a Linux Foundation project, brings together industry practitioners from specific domains to enable the practical use of Linux in safety-critical environments along with processes for effective safety analysis. The paper ends describing the overall goals of ELISA, the products, and working groups – with a focus on the recently formed Aerospace working group.
A performance characterization of AI algorithms on energy-efficient hardware with applications to robust autonomous landing

Artificial Intelligence (AI) is penetrating industries on many levels, and a European wide project consisting of the main industrial leaders are working on new chip architectures and algorithms to enable very low power frameworks. Boeing Aerospace Spain (BAS) has created a novel use case in the Transport and Smart Mobility sector together with other national partners dedicated to Robust Autonomous Landing (RAL), in which AI algorithms considered vital for Unmanned Aerial Vehicle (UAV) are deployed to energy-efficient hardware. We have implemented diverse Deep Neural Network (DNN) to address RAL by means of tasks related to runway and obstacles detection and communication parameter processing. Once implemented, the models have been deployed on Field-programmable gate array (FPGA)-based accelerators to assess the reduction in power consumption, compared to traditional Graphics Processing Unit (GPU) hardware. The reductions in power consumption presented in this work outline a great potential to explore these technologies as enablers for real-time autonomy applications, generally in problems where the use of DNNs in low power scenarios is required.
Generation of RNP Approach Flight Procedures with an RRT* Path-Planning Algorithm

We present a framework capable of generating required navigation performance authorization required approach (RNP AR APCH) procedures by using a combination of the optimal version of the path-planning rapidly-exploring random tree (RRT*) algorithm and Dubins paths. Procedures are generated by taking into account design constraints defined by the international civil aviation organization (ICAO) procedures for air navigation services - aircraft operations (PANS-OPS). The framework is used to compute several approach procedures for two airports in Japan, Kumamoto and Kitakyushu airports. Several feasible procedures are successfully obtained in a low amount of computational time, many of them resembling the actual procedures published in the selected airports. The output of our framework represents a valuable input for procedure designers, who could later refine the obtained results with specific flight-procedure-design software.
A Hybrid Framework for Object Distance Estimation using a Monocular Camera

Object distance estimation using the monocular camera is a challenging problem in computer vision with many practical applications. Various algorithms are developed for distance estimation using a monocular camera; some involve traditional techniques, while others are based on Deep Learning (DL). Both methods have limitations, such as requiring camera calibration parameters, limited distance estimation range, or the object of interest should be relatively large to get accurate distance estimation. Due to these drawbacks, such algorithms cannot be easily generalized for many practical applications. In this paper, we propose a hybrid monocular distance estimation framework that consists of You Look Only Once version 7 (YOLOv7) algorithm for visual object detection and linear regression model for distance estimation. For our use case, this framework is trained on our field-captured Unmanned Aerial Vehicle (UAV) dataset to detect and estimate distance of UAVs. The dataset includes videos of UAVs obtained from different Point of View (POV) using a Pan-Tilt-Zoom (PTZ) camera that captures and tracks UAVs in the large field of view. Video frames are synchronized with the distance range data obtained from Radio Detection and Ranging (RADAR) sensor which will act as ground truth for regression model. The regression model is trained on input features such as bounding box coordinates, the average number of red, blue, and yellow pixels within the bounding box, and embedded features of detected objects obtained from YOLOv7 and output were RADAR range measurements. Trained UAV detection network has mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</inf> of 0.854, mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.5:.95</inf> of 0.595 and distance estimation regressor has Mean Squared Error (MSE) of 0.06375 on independent test set. We validated this framework on our field dataset and demonstrated that our approach could detect and estimate distance efficiently and accurately. This framework can be extended for any real-world monocular distance estimation use case just by retraining the YOLOv7 model for desired object detection class and regression model for object-specific distance estimation.
CerDES - A Certificateless DTLS-Based Encryption Solution for IEEE 802.15.4 Drone Communications

Today more and more applications including drones use the communication standard IEEE 802.15.4 to transmit manifold data over the network. This data is often of sensitive matter like navigation data or measured data triggering actions. To provide secure communication within these networks, it is required to have security solutions that can run efficiently on devices that have, due to the nature of their hardware, limited resources. Symmetric-key systems can provide confidentiality by using a shared secret, but run into the key distribution problem, as these secrets need to be agreed upon and exchanged securely. Asymmetric-key systems allow for entities that have never met before to exchange key material over an unsecure channel, and use it to agree upon a shared secret. Nevertheless, the necessity of authenticating the public key to avoid impersonation attacks is of utmost importance, and is usually done with certificates. CerDES an authenticated certificateless key agreement scheme is integrated into the DTLS protocol, forgoing the need for certificates to authenticate the exchanged key material. CerDES allows end-to-end secure communication between the wireless network devices, like drones, and a central server. The evaluation proved that sniffing tools are unable to analyse the encrypted payload as long as no access exists to the central server hosting the keys. Thus, it can be stated separation of server activities is an essential key for security in wireless environments.
Continuous Time Neural Network

The architecture and learning process of a multilayer perceptron are considered. Presented a transformation of Res-Net into CTNN. The problem of silhouette recognition from white noise by these networks is defined and implemented using the Python programming language.
Density-Based Outlier Detection: Supervised Approach Based on Virtual Points

- Outlier detection plays an important role in a wide variety of domains and has many applications. The examples are fraud detection, anomaly detection in industrial systems, healthcare, cybersecurity, environmental monitoring and marketing. The aim of outlier detection is to identify and analyze data points or observations that differ significantly from the rest of the data that may indicate potential fraud, disease outbreaks, or equipment failures. The unsupervised approach to outlier detection is often preferred because it does not require labeled data, meaning that the algorithm can identify outliers in a dataset without having to know beforehand which observations are anomalous. This is particularly useful when there is limited prior knowledge about the data, or when the anomalous observations are rare or unknown. We propose a novel approach to outlier detection which doesn't require labeled data but is based on the supervised learning technique similar to decision tree construction. Our method overcomes many of the limitations of traditional outlier detection techniques by partitioning the space into cluster and empty regions using a decision tree. We accomplish this by introducing virtual data points and modifying the decision tree algorithm accordingly. The results of our experiments on synthetic and real-world datasets show that the method is both highly efficient and scalable.
Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Hybrid Monte Carlo Tree Search Approach

We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem is motivated by Strategic deconfliction under the FAA’s "Unmanned Aircraft Systems Traffic Management" Concept of Operations. MRWPDP falls under a class of vehicle routing and scheduling problems, and as such is NP-Hard. In MRWPDP, a graph network is given which consists of depots, drop-off sites, and multiple routes connecting the two. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is to optimally schedule the departure time and assign routes to a known set of vehicles at the depot. We propose a heuristic solution to the problem by borrowing techniques from Mixed Integer Linear Programming (MILP), Constraint Programming, and Monte Carlo Tree Search (MCTS). The resulting hybrid framework is MCTS with Bound-and-Prune (BP) and rapid simulated updates (U), or MCTS-BP-U. This approach is able to quickly provide a feasible solution for MRWPDP, even for large problem instances up to 1000 vehicles. We provide a MILP formulation of MRWPDP and compare its performance against MCTS-BP-U in terms of solution quality. An agent-based model simulation is conducted as a final step to validate the efficacy of our approach.
A Decentralized Paper Dissemination System Employing Blockchain Technology, Peer Review and Expert Badges

Peer review represents the status-quo when it comes to evaluating research articles that are submitted to conferences and journals. The significance of a computer science article is given by the prestige of the publication and is correlated with the inclusion in the ISI Web of Science index.This paper discusses the issues of the current paper publication paradigm and proposes a decentralized approach to the paper dissemination and the peer review processes. On the one hand, decentralization and transparency are obtained by employing smart contracts, through blockchain technology. On the other hand, an optimization of the paper rating system is obtained by employing a system of expert badges, based on NFTs, which ensure that the peer review process is just and that only specialists in the fields associated to the contributed paper offer proficient feedback. Other proposed facets include the remuneration of reviewers, a method of allowing the proposed system to expand based on the community’s input, and a solution for allowing the organization of conferences.
Vestibular Apparatus Training in Czech Air Force Analysis and Opportunities

Initial flight training is a new type of space movement for a future pilot. The pilot adapts to the new movement during the first flights, but this process may be accompanied by phenomena such as fatigue, performance degradation, or nausea. In the Czech Air Force environment, future pilots are prepared at the University of Defence, where they undergo flight training during their second year. Prior to this, they use a range of equipment for physical training on which it is possible to derive angular and linear accelerations similar to those experienced by a pilot in flight. The aim of this paper is to analyze and compare selected devices available at the University of Defence, Brno, based on a questionnaire survey on the manifestations of Motion Sickness. The Motion Sickness Assessment Questionnaire (MSAQ) was used for this investigation. 11 students performed three exercises in the first part: gymnastics obstacle course, human gyroscope, and 360° swing. The students self-assessed their manifestations in the form of the MSAQ. In the second part, the instructors assessed the manifestations of motion sickness during flight training in relation to the pilot’s performance decline. Questionnaire data were analyzed using Friedman’s test, and post-hoc multicompare analysis was performed in case of differences between exercises. The results showed that the significant differences between the three types of exercises lie mainly in their influence on factors such as sweating, feeling dizzy, fatigue, feeling of rotation, restlessness, blurred vision, and feeling weak. No significant differences were found in the other 11 items. Each of the tested exercises had its specifics; when appropriately combined, they have the potential to prepare future pilots also due to their availability before the start of flight training.
An approach for defining faults in MIL-STD-1553 bus in an Avionics Architecture

This paper presents a detailed and realistic simulation environment for the very widely used MIL-STD-1553 communication protocol. We then propose a machine-learning approach for detecting malicious traffic and preventing faults caused by cyber-attacks. Our approach utilizes clustering according to the features of a given dataset. We demonstrate our approach with the presented simulation environment in this paper. To this end, we construct a testbed to implement various scenarios such as spoofing and DoS attacks. We generate message traffic with unauthorized RT messaging. We apply our anomaly detection approach to our traffic and present an analysis of the results.
Mission Planning for a Multiple-UAV Patrol System in an Obstructed Airport Environment

This paper investigates using multiple unmanned aerial vehicles (UAVs) to carry out routine patrolling at an airport to enhance its perimeter security. It specifically focuses on mission planning of the system to facilitate efficient patrolling with consideration of local buildings and restricted airspace. The proposed methodology includes three aspects: 1) a vision-based set cover algorithm to construct the patrolling network, 2) an obstructed partitioning-based clustering algorithm for recharging station placement, and 3) a mixture integer quadratic programming (MIQP) algorithm to plan routes for UAVs minimizing the maximum idle time through out all patrolling waypoints. The main contribution of this work is that it provides a comprehensive mission planning solution for UAVs persistently patrolling in a complex environment characterized by blocked vision and restricted airspace. The proposed methodology is evaluated through intensive simulations in the context of the Cranfield Airport scenario.