Miracle-dz/UniFAR-FTUs huggingface dataset:
From Components to Caring: The Development Trajectory of a Socially Therapeutic Assistive Robot (STAR) Named Therabot™

Designing a socially therapeutic assistive robot (STAR) for use in mental healthcare is an emerging interdisciplinary process, for which there is a lack of comprehensive guidance and standards. This paper provides insight into the design and development process of the socially therapeutic assistive robotic dog, Therabot™. By outlining the project’s eleven-year history, exploring a snapshot of its current state, and discussing its trajectories we aim to share a useful glimpse into the processes that guide the creation of a STAR.
Real-Time UAV and Payload Detection and Classification System Using Radar and Camera Sensor Fusion

Unmanned Aerial Vehicles (UAVs) have experienced remarkable progress and widespread utilization, highlighting the need for robust detection and classification systems to ensure safety and security. This paper presents a comprehensive study on UAV and payload detection and classification, emphasizing the fusion of multiple data sources to enhance accuracy and reliability. A fusion system integrating radar and Pan-Tilt-Zoom (PTZ) camera data is developed and evaluated. Experimental results demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of > 94% for UAV detection and > 90% for payload classification. The findings underscore the system's potential for real-world applications in UAV and payload detection and classification scenarios, addressing the growing demands in this field.
Building Ensemble of Deep Networks: Convolutional Networks and Transformers

This paper presents a study on an automated system for image classification, which is based on the fusion of various deep learning methods. The study explores how to create an ensemble of different Convolutional Neural Network (CNN) models and transformer topologies that are fine-tuned on several datasets to leverage their diversity. The research question addressed in this work is whether different optimization algorithms can help in developing robust and efficient machine learning systems to be used in different domains for classification purposes. To do that, we introduce novel Adam variants. We employed these new approaches, coupled with several CNN topologies, for building an ensemble of classifiers that outperforms both other Adam-based methods and stochastic gradient descent. Additionally, the study combines the ensemble of CNNs with an ensemble of transformers based on different topologies, such as Deit, Vit, Swin, and Coat. To the best of our knowledge, this is the first work in which an in-depth study of a set of transformers and convolutional neural networks in a large set of small/medium-sized images is carried out. The experiments performed on several datasets demonstrate that the combination of such different models results in a substantial performance improvement in all tested problems. All resources are available at https://github.com/LorisNanni.
Research on the Construction of a Digital Twin Model for a 3D Measurement System of Gears

As an advanced metrology technology, three-dimensional (3D) gear measurement using linear laser techniques is susceptible to the influence of multiple coupling factors. Consequently, there exists an urgent need for new principles to effectively decouple the effects of each factor. Digital twins realize the connection between physical and virtual spaces. By fusing multi-source data from the existing system, a high-fidelity digital model is established to realize the simulation of the gear measurement process in this paper. Moreover, simulation of the light reflections of tooth flanks and the dynamic error coupling process can effectively clarify the coupling mechanism of measurement errors. This paper presents a method of constructing a digital twin model of a 3D measurement system for gears and realizes the expression of the actual measurement processing. It provides the foundation for subsequently studying the reflection characteristics of the tooth flank at different incident angles and searching for the optimum incident attitude of the line structured light sensor.
Deep CNN-Based-Flower Species Recognition System

There are numerous species of flowers worldwide, and these species hold outstanding economic, cultural, and ecological significance in human life. Flower species recognition benefits applications like searching for flowers in digital libraries and floriculture. Recently, with the high advancement in computer vision and deep learning technologies, automated flower species recognition systems have attracted many scientific researchers. Exploiting deep learning to recognize flower species from images accurately represents an existing problem that should be resolved. Therefore, this paper uses various deep convolutional neural network (CNN) approaches like (VGG-19, ResNet-152V2, MobileNet-V2, InceptionResNet-V2, DenseNet-201, and Xception) are carried out to recognize various flower species. The flower images dataset acquired from Kaggle includes five categories and is utilized in the experiments. Among other implemented approaches, DenseNet201 attained the highest results with an accuracy of 94.12%.
Design space exploration for safe and optimal mapping of avionics functionality on IMA platforms

Future avionic systems will be increasingly automated. The size and complexity of the avionics functions in these systems will increase likewise. The degree of attainable automation directly depends on the avionics system's computing power and the efficiency of available tools that map the overall functionality onto the target heterogeneous platform architecture. In safety-critical scenarios, these automation tools must also provide safety guarantees that aid or drive the certification processes.In line with this automation goal, We propose a novel design space exploration technique for the mapping functionality on IMA platforms. The design space exploration technique returns mappings of the functionality onto the platform that are safe and increasingly resource-efficient. A safe mapping is one where the functional and extra-functional requirements are met. A resource-efficient mapping is one where fewer processing elements are used to achieve a safe mapping. More importantly, the proposed technique can return computational proof that no safe mapping is likely possible. This proof is key for safety-critical contexts.To demonstrate the suitability of our technique for avionics systems design scenarios, we investigate its use with an industrial avionics case based on the ones from the PANORAMA ITEA3 project. The case study includes two avionics functionalities, one control functionality, and one streaming-like functionality. The platform is hierarchical and heterogeneous, with elements oriented for higher safety and elements oriented for higher performance. The avionics case-study evaluation shows that our novel design space exploration technique's abstractions and assumptions adequately represent avionics design scenarios directly or through a systematic overestimation.The technique is openly available within the design space exploration tool IDeSyDe. Therefore, designers can immediately benefit from the optimality and safety guarantees given by our novel design space exploration technique in their avionics design process.
Rectangular Patch Antenna Optimization using Metaheuristic Algorithms of Swarm Intelligence: A Comparative Study

This article presents a comparative study of six nature-inspired metaheuristic algorithms based on swarm intelligence for solving a specific optimization problem in the design of a rectangular patch antenna for resonance generation at a frequency of 2.4GHz. The algorithms used were: Bat Algorithm, Firefly Algorithm, Harris Hawks Optimization, Marine Predators Algorithm, Particle Swarm Optimization, and Slime Mould Algorithm. Results show that all algorithms can be useful for solving the optimization problem, however, there are considerable differences in terms of error in the fitness function, obtained gain, and convergence time. The selection of the best algorithm will depend on the priorities when formulating the optimization problem.
Study of the Morphology of Macroporous Silicon Obtained by Metal-Assisted Etching Using Chromium

The article examines the morphology of porous silicon doped with phosphorus and boron obtained by metal-stimulated etching using Cr. Selective Sirtles etchant with the composition of HF - 100 cm3, CrO3 - 50 g, H2O - 120 cm3 was chosen. The application of the Cr film was carried out by thermal sputtering in a vacuum. The thickness of the film was 10–40 nm. After the etching process, a macroporous structure with pyramid-shaped pores was revealed on the surface of the plates. It was established that with an increase in the thickness of the metal catalyst film and with an increase in the duration of etching, the size of the pores and etching pits increases. A uniform macroporous structure can be obtained by etching silicon with a chromium film 10–15 nm thick. When using a film with a thickness of 20–30 nm, the size of etching pits increases sharply. When using a chromium film with a thickness of more than 30 nm, uneven etching of the film is possible, which leads to uneven pore formation. The size of the pores on the surface of silicon doped with boron is much smaller than on the surface of silicon doped with phosphorus, which is caused by a decrease in the concentration of the doping impurity.
Improved ANFIS Model Based on Confinement Optimized PSO to Predict the Air Quality of Delhi, India

This paper proposes an improved version of the adaptive neuro-fuzzy inference system (ANFIS) to forecast the air quality index in New Delhi, India. We have used confinement optimized particle swarm optimizer (COPSO) to improve the performance of ANFIS, where COPSO is an improved version of particle swarm optimizer (PSO). This proposed model has been trained with previous nine years air quality index time series data of Delhi city to forecast the air pollutants such as particulate matter (PM10), sulfur dioxide (SO2) and carbon monoxide (CO) as these three air pollutants are found to have severe impact on the air quality of Delhi. We compare the performance of the proposed model (COPSO-ANFIS) with the performance of PSO-ANFIS, polynomial regression and decision tree-based models, where the result shows a significant improvement compared to other approaches.
Representation of Coherent Structures from Volume Data Using Quality-oriented Features and Genetic optimization

Representing relevant information from volume data sets is a problem often faced in visualization. Generating meaningful images from highly-complex volume data sets is a challenging, tedious task requiring specialized knowledge of the distribution and properties of the data. Traditionally, this task has been carried out manually via specialized user interfaces. We propose a volume visualization pipeline which facilitates the automatic generation of high-quality images from volume data sets. Our method involves a direct volume renderer which generates images from volume data based on visual mappings provided by a transfer function. Central to our approach is a quality-focused descriptor which exploits the properties of the distribution of gradient orientations of an alpha-bounded surface within the volume. This feature is useful for determining transfer functions that result in the rendering of corresponding images depicting various details from the volume. We show that by using this feature as an optimization objective, the generation of high quality images can be automated. Using simple genetic algorithms, we can automatically generate sets of images illustrating coherent, easily-distinguishable and high-quality surfaces of relevant structures from volume data.
A Proposal Method for Identifying Integrating Systems with Inverse Response and Dead Time

Integrating processes with an inverse response and dead time is a complex dynamic system that presents an interesting challenge for control engineering. Several control approaches have based their design principles on the concept of the internal model. In response, identification works have emerged using complex analytical calculations and high computational processing. Our method aims to determine a systematic and understandable procedure based on First-Order Plus Dead-Time modeling and graphical inspection. The current study is tested in a high-order integrating system with inverse response and dead time; metrics acquired from the resulting model are: ($R^{2}$=0.9992, RMSE =0.0096, and MAE =0.0068).
Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management

Advanced Air Mobility (AAM) is the next generation of air transportation that includes new entrants such as electric vertical takeoff and landing (eVTOL) aircraft, increasingly autonomous flight operations, and small UAS package delivery. With these new vehicles and operational concepts comes a desire to increase densities far beyond what occurs today in and around urban areas, to utilize new battery technology, and to move toward more autonomously-piloted aircraft. To achieve these goals, it becomes essential to introduce new safety management system capabilities that can rapidly assess risk as it evolves across a span of complex hazards and, if necessary, mitigate risk by executing appropriate contingencies via supervised or automated decision-making during flights. Recently, reinforcement learning has shown promise for real-time decision making across a wide variety of applications including contingency management. In this work, we formulate the contingency management problem as a Markov Decision Process (MDP) and integrate the contingency management MDP into the AAM-Gym simulation framework. This enables rapid prototyping of reinforcement learning algorithms and evaluation of existing systems, thus providing a community benchmark for future algorithm development. We report baseline statistical information for the environment and provide example performance metrics.
Safety Control for the Quadrotor Unmanned Aerial Vehicle Despite Complete Lose of One Rotor

This paper focus on the fault tolerance control associated with complete loss of controller one rotor for the quadrotor unmanned aerial vehicle(UAV). As the quadrotor UAV is widely used in many industrial filed, it is critical to ensure the safety flight even when losing one rotor completely. The quadrotor UAV is a typical compact under-actuated mechanical system, which indicating that there is no more redundant generated thrust when one of the four rotors is complete loss. Thus, in this paper, the dynamic model of the quadrotor is degraded, and the yaw control is sacrificed for stabilizing the attitude and the altitude control. Then, the reduced attitude controller is proposed for maintaining the flight property when losing one rotor completely, and the disturbance observer is combined to compensate the modelling uncertainties and the unknown external disturbances. Furthermore, the integral geometric position controller is also developed to guarantee the flight performance under the effects of unknown external disturbances. The stability of the control system is ensured via the Lyapunov based stability analysis. At last, numerical simulation results are provided to verify the effectiveness of the proposed control strategy. The good position and attitude tracking performance can be achieved despite complete loss of one rotor.
Towards a Meaningful Experience on the Use of Digital Educational Resources and Media created at Ardora

Nowadays, in different areas of education, more and more innovative educational strategies are being considered in the classroom, moving away from traditional methods. The objective of this study is directed to the learning of mathematics in high school students through the application of the flipped classroom methodology with the support of gamified interactive elements developed with the Ardora tool. Research is developed with an empirical-analytical methodology of quantitative quasi-experimental cut. Two phases are elaborated for the study, the first phase is a bibliometric review for an analysis of the relevant existing works. In the second phase, an evaluation activity was carried out in four subjects of the second year of high school, Calculus, Statistics, Algebra and Equations. In a total population of 40 students, the flipped classroom methodology is combined with the use of interactive tools with the Ardora program. It is evident that the results of the students’ evaluations are satisfactory. The students faced situations of technology management but with excellent results, on the other hand, the teachers require additional preparation to overcome difficulties when using the flipped classroom approach.
Mixed Reality Framework for Eye-in-Hand Collaborative Robot-Human Interaction

In recent years, the manufacturing sector has undergone a significant transition from a paradigm of robotic autonomy to human-robot collaboration. This approach allows operators from different domains to rapidly adapt to a new way of working and reduces redundant learning costs, while also enabling intuitive robot control. To further enhance this paradigm, integrating Mixed Reality (MR) technology has emerged as a promising approach to creating and working in a virtual workspace while having a safe environment for training. This work develops a framework, based on digital twin representation, to integrate the MR with real-life equipment and humans. This allows for the control of a collaborative robot in an environment with objects or humans perceived by the eye-in-hand visual sensor. The efficiency of the proposed framework is emphasized by a comparison of Unity simulations versus the real-time behavior of the collaborative robot.
Validation of a Scale of Labor Skills in Crisis Situations

All organizations can have a crisis time, in recent years, some factors such as the pandemic, political and economic problems have shown that workers need to develop skills to manage new dynamics and create employment strategies. “The organization must have a good performance that is oriented towards the vision, mission, goals and objectives of the organization. For this we need qualified human resources, because good training and other supportive social spaces are needed to improve the quality of human resources” [1]. One of the key challenges for government organizations today is to deliver performance effectively and efficiently. Therefore, the purpose of this study is to validate the work skills scale in times of crisis, which can be used to find out the knowledge, skills, abilities and attitudes of employees to manage staff training in the future. Currently, only a few instruments allow measuring work skills during a crisis, this instrument measures orientation to results in different situations, building relationships, vision and foresight, decision-making, creativity, resilience, emotional intelligence, social networks in each question. This study validates the Competencies for Working in Crisis as a new psychometric tool. The scale was validated after collecting data from 401 SME (small and medium-sized enterprises) employees from different provinces in the Ecuador. It is a descriptive study, non-experimental and longitudinal. Factor structure, reliability and validity measures were also examined using SPSS-21 statistical procedure. Finally, for the analysis of internal consistency, Cronbach’s Alpha coefficients were calculated with values (.78), which is considered acceptable [2].
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.
Attribute Selection, Outliers Impact Study and Visualization within Breast Cancer Detection

Classification of mammography data into two types of breast tumors, benign or malignant, is an effective screening tool and the primary way of diagnosis and decision-making. This report aims to opt for the most relevant attributes of the well-known Wisconsin Breast Cancer Diagnostic Data Set to reduce its size at first. The reduction was performed initially by ranking attributes and finally by “decision tree” analysis. The clipped data set had only six attributes, against 31 in the initial one. The five most relevant attributes were the following: “perimeter_worst,” “area_worst,” “concave points_worst,” “texture_mean,” and “concave points_mean. ” If possible, It should be done without losing classification potency. Over and above, our extra goal was to find classifiers that provide acceptable performance while allowing visualization of the results in a way accessible to clinicians. Here, we mean various visualization tools in the Machine learning framework: “decision trees,” association rules, attribute ranking, and so forth, to improve breast cancer diagnosis.
Positioning of Autonomous Underwater Vehicles using Machine Learning Techniques

Autonomous underwater vehicles have many applications in surveillance, search and rescue, and cargo delivery. However, these vehicles lack an effective positioning mechanism such as GPS in outdoor environments. Although several positioning alternatives have been proposed previously, the use of acoustic signals in conjunction with trilateration techniques is one of the simplest and cheapest options, but there are still problems related to multi-path propagation, signal fading, and reverberant environments. On the other hand, machine learning is revolutionizing the field of data-driven models for making predictions and/or pattern recognition. In fact, machine learning regression is an efficient technique for establishing the relationship between a set of independent features and a dependent variable that takes continuous values. Therefore, this work proposes the use of machine learning as an alternative for the positioning of underwater drones depending on the sound received by an array of hydrophones. An experimental testbed was set up to obtain the training and validation datasets that include the sound generated by an underwater drone while moving in a pool with 4 hydrophones. The results show that the precision of this proposal is better than those previously obtained in other works, reaching an average accuracy of 27 cm in a 10 × 5 meter pool.