Reviewerly/exHarmony huggingface dataset:
AVIBRIEF: Automated Vocal Information Briefings Dataset

Automated briefings are an essential information source for pilots as they provide a comprehensive and up-to-date picture of meteorological and aeronautical conditions, ensuring safe and efficient flight operations. These briefings come in a variety of formats, a notable few being Automated Terminal Information Service (ATIS), Automated Surface/Weather Observation System (ASOS/AWOS). These systems provide an audio broadcast of the current weather, runway surface conditions, and other pertinent information to pilots without them having to reference the air traffic control tower or ground observers. While most aircraft are equipped with VHF radios for receiving information through audio transmissions, it poses challenges as pilots must accurately interpret the audio and manually execute necessary actions. Though digital data transmissions are the ideal solution to improve accuracy and allow automation, most airplanes lack the necessary equipment for this. As a result, general aviation (GA) will continue to rely on audio transmissions for the foreseeable future. However, recent advances in speech recognition and natural language processing can bridge this gap by converting analog audio information into digital data, thus enabling automation. These advanced technologies rely on vast amounts of data, particularly human-verified transcriptions, to be effective in aviation applications. To facilitate research in this area, we propose and publicly release an automated informational briefings voice dataset (AVIBRIEF) for Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) research purposes.
Adversarial Attacks and Defense on an Aircraft Classification Model Using a Generative Adversarial Network

As artificial intelligence becomes increasingly popular, so too does the frequency it is attacked. One area where artificial intelligence models are being embraced heavily is avionics, where it is used in a variety of areas including intrusion detection, surveillance, radar, navigation, and more. These networks are vulnerable to a number of attacks, one of which being the adversarial attack. Adversarial attacks aim to perturb the input to the image classification model, which can lead to output misclassifications, lowering the overall model accuracy. This paper will demonstrate how such an attack can lower the accuracy of a convolutional neural network (CNN) trained on an aircraft image dataset. The perturbed inputs will be generated by a generative adversarial network (GAN) trained on the same dataset as the CNN. GANs consist of two neural networks, a generator and a discriminator. The generator will continually create new images based off of the dataset, while the discriminator decides whether or not the image is generated or from the reference dataset. This forms a feedback loop, where the generator continues to improve based on the output of the discriminator. Once trained, the outputs of the GAN will be used to misclassify the trained CNN in an attempt to lower the model’s accuracy. Following this, adversarial defenses such as feature squeezing and random smoothing will be discussed. These defenses attempt to mitigate the damage done by an attack, usually by adapting the model, modifying the input, or modifying the data the model is trained on. This can cause the accuracy of the model to slightly fall from its original accuracy, but with the benefit of being more resilient to adversarial attacks. We analyze this tradeoff and highlight the advantages of adversarial training in learning-enabled avionics systems.
Learning the Dynamic Environment of an Original Game Using Hierarchical Reinforcement Learning Methods

This paper compares the performance of two reinforcement learning algorithms, Q-Learning and MAXQ-0, in learning to play an original game. An extension of MAXQ-0 algorithm, MAXQ-P is introduced, which enhances the variety of the tree nodes with simple, ordered and repetitive nodes. The hierarchical approach provided by MAXQ-P finds the optimal solution faster than the flat Q-Learning approach but converges more slowly. Furthermore, the performance of the MAXQ-P algorithm decreases after a certain number of episodes due to representation error in the weights of the model. To address this issue, the model is periodically tested with an exploration value of 0, and if the model successfully finds the solution, it is stored for future use. This study provides insights into the benefits and drawbacks of using hierarchical reinforcement learning algorithms for complex tasks and highlights the importance of carefully designing and training such algorithms for optimal performance.
Minimal Communication between Drones Using Guidance Vector Fields in Dense Airspace

In response to escalating drone traffic, this paper investigates the interplay between drone density, communication frequency, and collision avoidance. We utilize artificial potential fields as a guidance mechanism for drones navigating in dense airspace. Through numerical simulations involving two to nine drones, we analyze how communication frequency correlates with collision rates as drone density increases. Our findings reveal the nuanced relationship between these variables, showing that communication frequency requirements increase with drone density for collision-free navigation. Furthermore, we present a theoretical framework predicting the relationship between drone density and the minimal communication frequency required for safe operations. We also demonstrate a simple experiment in the flying arena to highlight the influence of the rate of positional information sharing on the paths taken by drones.
A Study on the Perception of Digitalization and Use of Digital Technologies in Higher-Education Institutions

Correlating the education and training system with the evolution of today’s technologies is a complex but necessary process for training, improvement, and lifelong learning. Digital technologies are present today in all fields, and have become increasingly necessary in higher education institutions, not only to attract students, to bring them into a familiar environment, but also to initiate and improve their skills in what is an evolution in science. The digitalization of education services is necessary to have a more accessible, interactive, and personalized educational process and this also involves the use of digital technologies, elements that contribute to people’s self-education, and education outside of schools’ walls. In this paper we present aspects related to the digitalization of education, both from the perspective of educators and learners. It is a study carried out in two Romanian universities, the University of Craiova and the “Constantin Brâncuși” University of Târgu Jiu. The purpose of this work is to emphasize the necessity of digitalization, its importance, but also to emphasize the stage in which the two universities are now, in relation to what is meant by digitalization, who should be involved in the digitalization of the institution, what activities should be digitalized, knowledge of the digital signature concept or whether respondents use machine learning tools such as ChatGPT, or others. The results of this study reflect the opinion of the teachers and students of the two universities.
Comparative Study of ABC and GWO Implementations on Raspberry Pi 3

The paper provides a comparative analysis of two popular swarm meta-heuristic algorithms deployed on the Raspberry Pi 3 microcomputer. Both Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO) were implemented from scratch in pure C in order to study their performance on relatively low-power device. Due to the limited computing power of Raspberry Pi 3, it is important to evaluate its productiveness in the execution of ABC and GWO to figure out the corresponding computational capabilities. For this task, a bunch of complicated benchmark functions have been used. The numerical experiments have shown both the advantages and disadvantages of these methods running on RPi3, as well as the peculiarities of the configurations that affect the time of complete execution of the tasks. It has been quantitatively discovered, that the increase of problems complexity leads to the significant increase of execution time. The obtained results signify the necessity of boosting the performance by using the concurrent computations which is simplified by the availability of four physical CPU cores and suitability of the considered algorithms for parallelization.
Nvidia Jetson NanoPlatform Using for Accelerating Image Recognition

- This article analyzes modern methods of image recognition using CUDA and TensorRT hardware and software technologies. Method accelerating the execution of ready-made mathematical models of neural networks to image recognition implemented on the Nvidia Jetson Nano Platform. Proprietary mathematical neural network fast image recognition on the YOLOv5 model was created. It's transformed and optimized by CUDA and TensorRT tools. A performance-implemented neural network optimized and non-optimized versions comparison make.
A Blockchain-Based Secure Mutual Authentication System for E-Government Services

Throughout the last decade, the online e-governance platform's services have promoted engagement between individuals and institutions by completing different e-government transactions. To utilise the government platform's services, the user must first be granted access to the e-government, which is accomplished by two-factor authentication based on log-in and password through SMS or even three-factor authentication by adding smart cards. Due of the use of electronic government by the conventional techniques of the central server to transmit data, these services suffer from the issue of centralisation, which is represented by the danger of exposure to an attack that leads to a malfunction that may result in service disruption and closure. Moreover, these vulnerabilities exacerbated consumers' worries about their data's privacy and security. Blockchain technology protects and decentralises log-in information in our electronic authentication system. The evaluation demonstrated that blockchain-based authentication is feasible and manageable. By utilising blockchain technology, the proposed decentralised authentication model aims to create a secure architecture that fully protects the electronic governance platforms through the authorised access process of users, overcomes the problem of centralisation, and protects user log-in and credential information and data rights through the use of digital signatures, encryption, and hashing technologies. The results showed that the total time for the user account creation process took “0.8” seconds to generate the user's public and private keys, and less than “0.1” seconds to create the user certificate, and less than “0.1” second to create the user's transaction, while the total time for the user's access to e-government services took “0.1” second.
Quadratic Programming Optimization Applied to Robotics Systems

Reactive kinematic control in velocity space is closely related to the Jacobian presented in the system. However, if the Jacobian is rank-deficient, certain task-space velocities become unachievable, leading to controller degradation. This work presents an approach to controlling robotic systems based on a quadratic programming formulation that yields solutions in just a few milliseconds. The proposed framework is tested in simulation using a two-link planar robotic arm and a mobile robot. Simulations are implemented in MuJoCo and Webots, respectively, to demonstrate the efficiency of the formulation.
Performance Comparison of Deep Learning Networks for Runway Recognition in Small Edge Computing Environment

The purpose of this work is to investigate and compare three popular segmentation networks for runway recognition in a small edge computing environment that has the potential to become a part of avionics in the near future. For the networks, we examine YolactEdge, STDC-seg, and DDRNet, and for the small edge computing environment, we choose NVidia Jetson Orin AGX and Xavier NX. In order to verify their real-time performance as an on-board system, we first tested them on three different edge computers. We also conducted multiple flight tests with an actual aircraft and constructed a runway image dataset that was used for the training and evaluation of the networks. According to our result, YolactEdge, which is an instance segmentation model, showed a good performance, but it was difficult to run in real-time on our small computing environment. STDC-seg and DDRNet, which are semantic segmentation models, presented over 0.95 mIoU with approximately 0.1 seconds of inference time. In particular, DDRNet-23-slim, which is the smallest model of DDRNet, showed the highest mIoU and the shortest inference time among the networks we tested in this work.
Modification of Combined Unsupervised-Supervised Cascade Scheme for Small Biomedical Data Classification

The task of improving classification accuracy in the case of analysis of short datasets is an important problem, in particular, for express diagnostics in biomedicine. Hybrid methods, including ensemble methods, are gaining more and more popularity for their solution. This paper proposes a modification of the unsupervised-supervised cascade scheme, the purpose of which is to reduce the dimensionality of the extended dataset intended for analysis. The proposed modification includes the use of the clustering method for data pre-processing, as well as the parallel application of a linear classifier at the first level of the cascade. As a result of this approach, the initial inputs of the task are replaced by only one attribute, the output signal of the linear classifier, which, together with the markers belonging to each data cluster, forms a new dataset for training the final classifier on the second cascade level. The simulation was performed on a real dataset using six different linear classifiers. Through comparison, it was established both a reduction in the time resources required for training the method and an increase in the accuracy during the analysis of small sets of biomedical data.
The Influence of Sampling Parameters on the Learning Error of a Multilayer Neural Network

The influence of sampling parameters on the learning process for a multi-layer neural network (MLNN) when recognizing printed numbers was studied. MLNN had three hidden layers of 28 neurons in each. Adam, AdamMax, and AMSGrad optimization learning methods were used. Testing the learning error of this neural network was carried out by constructing maps of the dynamic modes of the error function in alpha – beta2 coordinates. Alpha is the stationary value of the learning step, beta2 is the optimization parameter of the error function, which determines the contribution of the square of the gradient of the error function. It was established that the maps of dynamic regimes testify to the existence of a greater number of observed periodicities for input arrays with smaller dimensions. An increase in the set of the number itself and the variants of their distortions leads to a decrease in the learning error. The heterogeneity of the input array contributes to the process of the appearance of local minima, and therefore to more complex behavior of the error function from the learning parameters. The obtained results prove that the heterogeneity of the input array is a catalyst for the retraining process of the neural network.
Experience of the Disruption of Educational Technologies Based on the UTAUT Model

The use of active methodologies such as platforms, videos, gamification allow to generate meaningful learning with the objective of supporting and improving the teaching and learning process. The adoption of digital technologies in the classroom in an online learning environment became necessary in the context of the COVID-19 pandemic as educators around the world faced difficult challenges. This research study is based on the experience of factors related to the acceptance of technologies in an educational setting experience developed in the context of the Physics subject in an educational institution, exploring how students evidence the acceptance of technological resources in a disruptive way during a pre- and post-employment experimental analysis using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A quantitative empirical-analytical research methodology with a quasi-experimental approach was applied. A literature review is carried out in a first phase, and a disruptive experience of the use of technologies based on the UTAUT model in an educational institution. When students carry out activities with the use of technologies for development, 88.76% of students obtain grades higher or equal to 7 out of 10, while 11.23% fail to improve their performance. There are several models that allow measuring the degree of acceptance of the use of digital technologies in the educational environment, but the strongest model due to the flexibility of its environment is UTAUT because it relates and integrates essential elements of other models and theories applied in different contexts where digital technologies are used, emphasizing the educational environment. It is concluded that the increase of student-centered activities using technologies contributes positively to the knowledge consolidation process of students.
The Influence of the Input Array on the Learning Error of a Multilayer Neural Network

For a multilayer neural network (MLNN) with three hidden layers, the influence of the input array size on the learning process was studied when recognizing printed digits. A study was conducted both with and without the use of optimization training methods such as AMSGrad, and AdaDelta. The learning error of this neural network was analyzed using the Fourier spectra of the error function and constructing branching diagrams when analyzing the logistic function that describes the doubling of existing local minima. The Fourier spectra were found to indicate a more significant number of harmonics in the training process for arrays with smaller dimensions. Increasing the input array size for representing the digits <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{3}\times \mathbf{5},\ \mathbf{4}\times \mathbf{7},\ \mathbf{28}\times \mathbf{28}$</tex> pixels) leads to more homogeneous learning process. It is demonstrated that the existence of error function harmonics throughout the range of learning parameter variations, given a fixed learning rate, is associated with the heterogeneity of the input array. In other words, the heterogeneity of the input array provokes the emergence of local minima and, consequently, the more complex behavior of the error function for the learning parameters. Therefore, the heterogeneity of the input array, under the condition of a fixed learning rate, acts as a catalyst for the neural network retraining process.
A BERT-based Intent Recognition and Slot Filling Joint Model for Air Traffic Control Instruction Understanding

Air Traffic Control (ATC) relies on vocal communication between controllers and pilots to ensure safety and improve control efficiency. However, as the volume of flights grows and the frequency of vocal communication becomes higher, the work load on controllers becomes heavier, which may lead to unsafe accidents. It is crucial to extract information from control speech and understand it. But the highly specialized terminology and the varied presentation of ATC instructions make it challenging to develop a comprehensive semantic parsing model for extracting key information Intents and Specific Parameters (ISPs) of ATC instructions. To address this issue, this paper proposes a BERT-based Intent Recognition and Slot Filling joint model (BERT-IRSF) for ATC instruction understanding. The proposed model employs an end-to-end approach to extract sentence representations with context-dependent relationships. Firstly, the text-rich embedding vectors of ATC instructions are fused. Secondly, the semantic extraction of control commands is performed using a transformer encoder, in which a federated model is also integrated that explicitly establishes the connection between intents and slots, processing both tasks simultaneously by sharing semantic features. Finally, the labels are predicted using softmax and the Conditional Random Field (CRF) layer to get the intent and slot results. The proposed model is evaluated on a real operational ATC semantic understanding dataset, and experimental results show significant improvement in multiple metrics compared to other state-of-the-art deep learning methods, which demonstrates the potential of the proposed method to providing decision support for ATC automation system.
Digital Transformation in Higher Education: Proposed Initiatives from the Salesian Polytechnic University Assessment

Digital Transformation is widely addressed in the literature, however, studies focused on strategies for its implementation in Higher Education are little addressed. The objective of this research is to explore the Digital Transformation initiatives of an Ecuadorian Higher Education Institution to generate useful knowledge for higher education managers and leaders. Through a case study, this research explores the Digital Transformation initiatives of the Salesian Polytechnic University (UPS) of Ecuador. Techniques such as a documentary study, interviews, and observations were applied for the definition and discussion of the initiatives according to three research questions: (PI1) What technologies, methodologies, or models may support the definition of Digital Transformation initiatives? (PI2) What Digital Transformation initiatives may be considered in the context of Ecuadorian Higher Education? (PI3) What business concepts may be considered in the definition of Digital Transformation initiatives? The main results provide a vision of digitalization needs in Ecuadorian higher education and how they may be solved. The results of this research indicate that technological support to expand the virtual offer, as well as the inclusion of technologies for data analysis are essential for an adequate Digital Transformation.
A Comparative Study of Car Tracking Algorithms for Road Traffic Surveillance Applications

In this paper, we approach the important topic of video surveillance systems which automatically identify and track moving vehicles in different environments. A comparative evaluation has been performed on four algorithms which have been adapted for counting vehicles. Among these algorithms, two of them utilize contour detection techniques to accomplish this task, while the remaining two rely on feature detection techniques to count vehicles passing through a designated region of interest using both contour detection and feature detection techniques. The proposed algorithms have been implemented and tested on a set of video sequences and an accuracy measure was computed to determine the most effective algorithm. The results show that the YOLO-based algorithm has the best average accuracy, while the Algorithm 1, which is based on background detection and tracking, has the most consistent performance.