Reviewerly/exHarmony huggingface dataset:
Bi-Objective Task Scheduling Based on Heuristic Initialization of the Jellyfish Search Algorithm in Cloud Computing

In the computing cloud environment, task scheduling is the most important feature affecting system performance, and its improvement can contribute to achieving user satisfaction. There are many strategies for handling task scheduling; the available algorithms focus on reducing execution time while ignoring other quality of service (QoS). This paper presents a bi-objective hybrid optimisation model based on integrating the Min-Min task scheduling algorithm and the Jellyfish Search Algorithm (JSA) for independent task scheduling. First, the Min-Min task scheduling algorithm is used to initiate the initial solutions, and then JSA is used to schedule cloudlets to the appropriate virtual resources. The CloudSim toolkit is used for analysing the results of the presented algorithm. A comparison with previously presented methods is performed, namely MCT-PSO, CETSA, and ACOBF. Performance is evaluated based on time, energy consumption, and throughput metrics. The results reveal that the proposed method performs better than the existing methods.
Explainability of Deep Reinforcement Learning Method with Drones

Recent advances in artificial intelligence (AI) technology demonstrated that AI algorithms are very powerful as AI models become more complex.As a result, the users and also the engineers who developed the AI algorithms have a hard time explaining how the AI model gives the specific result.This phenomenon is known as "black box" and affects end-users' confidence in these AI systems.In this research, explainability of deep reinforcement learning is investigated for counter-drone systems.To counter a drone, a deep reinforcement learning method such as double deep Q-network with dueling architecture and prioritized experience replay is proposed.In counter-drone systems, catching the target as soon as possible is expected.Otherwise, the target can be gone in a short time.To understand how the agent performs more quickly and accurately, figures representing rewards, drone locations, crash positions, and the distribution of actions are analyzed and compared.For example, the positions of the drones in a successful episode during training can be analyzed by the actions the agent performed and the rewards in this episode.In addition, the actions agent took in episodes are compared with action frequencies during training and it is seen that at the end of the training, the agent selects the dominant actions throughout the training.However, at the beginning of the training, the distribution of actions is not correlated with the actions selected at the end.The results showed that the agent uses different flight paths by using different actions to catch the target drone in different episodes and different models.Finally, the generation of a saliency map is investigated to identify the critical regions in an input image which influences the predictions made by the DQN agent by evaluating the gradients of the model's output with respect to both the image and scalar inputs.
Open Challenge for Intrusion Detection on Air-Ground Communication: From Data Analysis to Simulation

Military and civil Unmanned Aerial Systems (UASs) have already started widely populating the airspace. UAS will share the same airspace routes with other aircraft and carry out distinct missions. The operation of such systems relies on secure communication between UASs and Ground Control Stations (GCS). Recent publications have shown that UAS communication protocols like MAVLink are vulnerable to attacks compromising their operations. In this paper, we evaluate an intrusion detection mechanism on an air-ground communication system using Machine Learning (ML) methods. To validate our approach, we evaluated the performance of our algorithm through a Software-In-The-Loop (SITL) testbed. We captured normal and abnormal traffic operations in the testbed, with Man-In-The-Middle (MITM) attacks representing the abnormal traffic operations. Next, we derived generic aggregation features (statistics) over MAVLink messages and evaluated supervised and unsupervised methods to detect the attack. The supervised decision tree method presented the best performance (F1-score 0.99) during this analysis. In contrast to the sound performance during data analysis, the detection algorithm did not perform as expected when integrated into the SITL testbed environment. This behavior raises some open challenges for network-based intrusion detection on UAS operation, and this paper aims at setting the stage for further discussions and research initiatives.
A Hybrid and Scalable Sentiment Analysis Framework: Case of Russo-Ukrainian War

Research on the sentiment analysis of large datasets using traditional engines has become challenging and may result in computational bottlenecks. Therefore, a distributed big-data processing environment should be considered to manage these large datasets. Owing to the ambiguity and subjectivity of human labelling in the context of text analysis, the possibility of replacing it with automated labelling is becoming necessary to make the process easier and more effective. Another important issue is that some lexicon methods are ineffective in classifying opinions based on texts not included within the lexicon corpus's terms. Hence, we proposed a hybrid framework that integrates lexicon ensemble and Apache Spark ML pipeline with multiple feature combinations scenarios to improve performance. As a case study to test its effectiveness, we explored the sentiments towards the outbreak of the Russo-Ukrainian war when people around the world started flooding the Twitter social media platform with their tweets. Analyzing these tweets can provide valuable insight into people's opinions and international community reactions to the ongoing crisis. Various comparative experiments were conducted on the combined features using four Spark ML classifiers: Naive Bayes (NB), Logistic Regression (LR), Support Vector Classifier (SVC), and Multilayer perceptron (MLP). The various hybrid method experiment results showed that the Multilayer Perceptron (MLP) classifier had achieved a maximum accuracy of 89.49% compared with the other models. The lexicon ensemble of Max voting results indicates that people generally hold more negative sentiments than positive or neutral.
Cross-Project Defect Prediction using Supervised and Unsupervised Learning: a Replication Study

Successful software projects are now an important challenge, the main focus of the engineering community being to predict software failures based on the history of buggy classes. However, software defect prediction techniques are effective as long as there is enough data to train the prediction model. To mitigate this problem, cross-project defect prediction is used. The purpose of this investigation is two-fold. First, replicate the experiments in the original articles and second, investigate other settings regarding defect prediction with the aim of bringing new insights and results on the best approach. In this study, three supervised (Random Forest -RF, Logistic Model Tree -LMT, Naive Bayes -NB) and three unsupervised (Expectation Maximization -EM, DBSCAN, KMeans) approaches are investigated. The experiments used use preprocessed methods (normalization and feature selection). Two sets of experiments are performed considering all available features and using preselected features by Principal Component Analysis, each set of experiments being employed on both supervised and unsupervised methods. The results of the replicated experiments confirm the original findings: when using supervised methods considering all features the best method is NB, followed by RF and LMT and similar to better results when considering fewer features (with PCA); when using unsupervised methods the results are not better than the original; however, when considering fewer features the results obtained with the newly considered methods (EM, DBSCAN, KMeans) are better than in the original paper.
A User-Centered Extended Reality Authoring Tool Development Framework for General Aviation Weather Training

This paper describes a comprehensive framework for the development of an extended reality (XR) authoring toolkit for general aviation weather training. Conventional weather training methods often rely on static, text-based materials, which might result in limited engagement and failure to provide an immersive experience tailored to the unique learning needs of students. Conversely, XR-enhanced lessons offer promising alternatives to enhance weather-related knowledge and decision-making. However, existing XR toolkits typically require advanced programming skills, posing challenges for non-technologist instructors. To address this gap, a content-centered approach that shifts the focus from the technicalities of developing augmented reality visualizations to enabling non-technologist instructors to readily interact with weather objects and scenario elements is proposed. The development framework, rooted in the user-centered design approach, encompasses understanding the user's context, specifying requirements, and developing a prototype. This study provides a description of the framework, accompanied by a case study specifically tailored to developing an XR authoring toolkit for general aviation weather training. Emphasis is placed on the significance of conducting comprehensive user studies and adhering to evidence-based software design guidance throughout the development process. The proposed framework is expected to contribute to the development of user-friendly 3D authoring environments, empowering instructors to create engaging and interactive aviation weather lessons. By enabling non-technologist instructors to create XR-enhanced lessons without requiring extensive computer skills, the framework aims to broaden access to XR-based training, catering to a wider audience.
Experimental Validation of Fuzzy-Optimal Control for a Two-Wheeled Inverted Pendulum

This paper presents an optimal controller with integral action based on Takagi-Sugeno (TS) fuzzy modeling of a multivariable nonlinear system to control a two-wheeled inverted pendulum. The T-S fuzzy model was obtained by generating input/output data using Monte Carlo simulations. A Kalman Filter enhances trajectory tracking when estimating the orientation angle, which is problematic due to electromagnetic interference. Furthermore, the Kalman filter is also used to remove the drift effect of the gyro sensor to improve the estimation of the tilt angle. The proposed controller is robust against modeling uncertainties through mass change; it also presents a steady-state error very close to zero, which brings the system to the desired final states. The designed controller was implemented on the Lego Mindstorms NXT 2.0 educational platform using the RobotC programming language.
Reducing Reversible Circuit Line Count Using Boolean Techniques

Reversible circuits are essential for quantum computing and low-power digital design. However, designing reversible circuits with a low line count and minimal gate cost is challenging due to the inherent constraints of reversible logic gates. The two factors are known to be trade-offs, such that minimizing one of them is compromised with extended complexity in the other. The hierarchical synthesis approach of reversible circuits is scalable and usually results in circuits with relatively low gate costs but high line counts. In this paper, we propose a simple systematic hierarchical approach to reduce the line count of reversible circuits by utilizing Boolean techniques and properties of reversible logic gates. Our approach is designed to maintain the gate count with minimal increase while reducing the number of lines required for circuit realization. This reduction in line count offers significant improvements in circuit complexity. Therefore, our approach provides an efficient and cost-effective method for realizing reversible circuits.
Comparative Analysis of Convolutional and Capsule Networks on Decreasing Dataset Sizes: Insights for Real-World Applications

This paper presents a comparative analysis of the performance of convolutional and capsule neural networks on a dataset with decreasing sizes of training data. The study evaluates both normal and pre-trained models on a dataset ranging from 10 to 7500 training samples to investigate the efficacy of capsule networks in handling smaller datasets, a common challenge in real-world applications. Results indicate that convolutional networks outperform capsule networks on larger datasets, but as the dataset size decreases, capsule networks show improved performance and eventually surpass convolutional networks. But with very small sizes, capsule networks show problems with their accuracy. Pre-training surprisingly does not improve the performance of CNN, especially for smaller datasets, however, pretrained CapsNet showed some improvements for smaller datasets. These findings suggest that capsule networks are more effective than convolutional networks for datasets with a specific size, and pre-training further improves their performance for smaller sizes. The study offers insights into the practical applicability of capsule networks in real-world scenarios with limited dataset sizes, aiding in the selection of appropriate network architectures for diverse datasets and applications.
Auto-Derivation of Functional Flow Block Diagrams from System Architecture using the eSAM Method

The Electronic System Architecture Modeling (eSAM) method is a new model-based systems engineering (MBSE) approach for modeling system architectures. eSAM was created to effectively model Integrated Modular Avionics (IMA) architectures but is not limited to IMA applications. eSAM leverages a distributed modeling approach, where hosted systems are independently modeled by system owners and allocated to the IMA platform by the IMA system integrator.This paper details a novel method for auto-deriving and generating a bottom-up Functional Flow Block Diagram (FFBD). eSAM provides a continuously updated model of functional dependencies at the functional data exchange level. This model is automatically derived from the eSAM hybrid logical/physical system architecture model and associated functional allocations. This addresses a key challenge of maintaining functional analyses since functional dependencies evolve during the development cycles, requiring the FFBDs to be updated periodically. Due to lagging diagram maintenance, FFBDs typically do not accurately represent the detailed system design until the end of system development when the design stabilizes. In contrast, eSAM provides auto-drawn views of FFBDs throughout development, minimizing the overhead of manually synchronizing model views and providing confidence that the FFBD represents the current, detailed component-level system model. eSAM has been implemented using the MathWorks System Composer™ tool, and a custom eSAM toolbox was generated to provide this FFBD capability.
Real-time Classification, Localization and Tracking System (Based on Rhythmic Gymnastics)

Rhythmic gymnastics, which includes both fast movements and turns of various body parts, as well as a large set of complex non-standard poses and their combinations, was chosen to research the problems of developing an effective real- time classification, localization and tracking system. The choice of rhythmic gymnastics as a model task is dictated by the fact that researchers can create a controlled environment for conducting experiments without restriction of movement and the possibility of rapid repetition of poses, which significantly increases the effectiveness of research and accelerates its implementation.
Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot

This paper presents a study on the mapless navigation of autonomous mobile robot using Deep Reinforcement Learning in an intralogistics setting. The task is to make an autonomous mobile robot learn to navigate to a goal without prior knowledge of the environment. In this paper, a controller using the Soft Actor-Critic algorithm is designed, trained, and applied for navigating the robot equipped with $360^{\mathrm{o}}$ LiDAR and front camera sensors. The controller is successfully validated in an almost fully observable environment under extensive simulations. Furthermore, we investigate the performance of the proposed controller in a partially observable environment and possible limitations. We use a 3D Temporal Convolution Network for processing the time series image data from visual observations. Besides Partial Observability, we also address the problem of sparse positive rewards in training the Deep Reinforcement Learning algorithm with a combined approach of Automatic Curriculum Learning and Dual Prioritized Experience Replay.
Verifiable Computing in Avionics for Assuring Computer-Integrity without Replication

Safety-critical digital systems such as Fly-by-wire control have demanding integrity and availability requirements which significantly exceed the occurrence rates of random hardware faults observed in digital computers. As a result, system designers need to employ reliable fault detection and mitigation techniques. Until now, the only method to achieve sufficiently reliable fault detection for systems that can cause hazardous or catastrophic events, is to replicate computer lanes and detect faults by comparing outputs. However, this comes with a large overhead in development cost, computing resources and additional requirements towards the application. We propose to apply a novel cryptographic technique to reliably detect faults and thereby assure integrity of avionics computers: Succinct Non-Interactive Arguments of Knowledge allow components to quickly verify computations without repeating the computation. We present a novel concept for building high-integrity avionics systems and set up a laboratory demonstrator for a simplified pitch control system. Our major results include the successful demonstration of the first self-proving and self-verifying cyber-physical system in a laboratory environment.
Goal-Driving Control as a Base Model of the Feeling Artificial Intelligence

When creating unmanned systems (US), the main attention is paid to the problem of autonomy. The use of artificial intelligence (AI) is one of the ways to increase the level of US autonomy in a disordered environment. Today, a new generation of Feeling AI (FAI) is aimed to support the process of control of the implementation of action plan of Autonomous Intelligence US (AIUS). The peculiarity of the AIUS control task is that making decision in real time about current action use the state of the plan's implementation, the current situation and the ability to implement the remaining part of the action plan to achieve the goal. The article examines sequence control and rules-based control methods. The structure of a multi-layer distributed fuzzy logic system (FLS) combined with production rules system is given. A modified fuzzy inference engine which thanks to the introduction of a fuzzy certainty factor as a complex number is able to uniformly process both linguistic variables of FLS and facts from production rules system, was considered. An example of AIUS control task and computer experiments with a wheeled robot are given.
Noise-immune Transfer of Decimal Data with Protection Based on Permutations

The article proposes a method that solves the problem of hiding decimal numbers from unauthorized access while simultaneously detecting error packets in them and correcting single errors. The method is based on the encoding of decimal numbers by binary-coded permutations. With this encoding, each digit of a decimal number is first converted into a binary-coded permutation using a special encryption table and, after further mixing with other permutations, is transmitted to the receiver. On the receiving side, it is checked for errors and, after correcting them with the help of a key, it is converted into decimal digits. Since each digit of a decimal number contains 1 of 10 numbers, their encoding requires 10 permutations and, accordingly, at least 4 permutation elements: 0, 1, 2, 3. These elements form set of 24 permutations, which consists of 10 used and 14 redundant permutations. This redundancy, as well as the natural redundancy of binary-coded permutations, allows them to detect packets of errors and correct single errors.
Strategic path planning for high density operations in unstructured and partially structured urban airspace

This work uses a multi-objective path planner as backbone to solve trajectory planning for a set of UAVs requesting access to the urban airspace. The planner accounts for path length, ground risk, contingency landing sites and energy consumption for defining the optimal UAV trajectories. A modular planning framework has been conceived which adapts to different airspace structures. Specifically, this paper deals with completely unstructured and partially structured (with geovectoring constraints) airspace. Performance of the planner in these two cases is assessed in a simulated scenario taken from real world environments, as a function of the number of UAVs, the selected optimization rules, and the size of the scenario. Results suggest using geovectoring approach is beneficial in terms of conflict resolution, until the geovectoring zone capability saturates.
The Impact of Parameters on the Efficiency of Keypoints Detection and Description

This paper comprehensively investigates the efficiency and performance of the keypoints detection and description methods in computer vision and image processing. Four widely used methods - SIFT, SURF, ORB, and BRISK are compared and analyzed. The study aims to assess the influence of different parameters on detecting and describing keypoints, specifically focusing on the limitation of the number of keypoints and Lowe's ratio values. The effectiveness of each method is evaluated by analyzing the similarity coefficient for pairs of images with varying degrees of similarity. It has been found that the similarity coefficient is also rising, with Lowe's ratio rising. At the same time, both Lowe's ratio and detected keypoints number limitation significantly impact the achieved methods' performance. The results obtained from the experiments provide valuable insights into the pros and cons of each keypoints method. Furthermore, we suggest how to optimize parameter settings to achieve optimal performance. The findings of this research can benefit researchers and developers working in computer vision and image processing.
TensorFlow vs. PyTorch in Classifying Medical Images – preliminary results

The paper presents a first series of results regarding the comparison of two deep neural networks designed, trained, and validated with the help of two deep learning frameworks, PyTorch and TensorFlow, in the classification of a large set of breast histopathology images. The original dataset consisted of 162 whole mount slide images of breast cancer specimens scanned at $40\times$. From that, 277,524 patches of size $50 \times 50$ were extracted (198,738 invasive ductal carcinoma negative and 78,786 positive). Invasive Ductal Carcinoma is the most common subtype of all breast cancers. The metrics compared in the training and validation phases were loss, accuracy, precision, recall and specificity. The architecture used for convolutional neural network has 8 layers: 3 convolution layers, 3 max-pooling layers, 1 linear layer and a sigmoid function. The number of epochs is considered a hyperparameter. It defines the number of times the entire data set must be worked through the learning algorithm. In our study we used 5 epochs. For the 8-layer network, PyTorch performed better in accuracy, precision, recall, and loss in both training and validation. In contrast, specificity is lower. Recall or sensitivity is around 73%, and specificity around 93%. Our estimates can be considered and continued since a diagnostic study must have both sensitivity and specificity of at least 70%. The results so far are promising, and we propose to continue the experiments in several directions.