Reviewerly/exHarmony huggingface dataset:
A Software Tool for Teaching the Homotopy-Based Path Planning Method for Mobile Robot Applications

In this work, a software tool is presented, developed for easy implementation in academic projects and mobile robotics teaching using the collision-free path planner based on continuation homotopy (HPPM). A concise explanation of the HPPM formulation is offered, along with a discussion on the key parameters essential for its implementation. A primary objective of this study is to ensure that the HPPM is accessible to students and researchers, thereby encouraging its adoption in academic and research contexts. An entire section is devoted to introducing the precompiled planning software tool, detailing how it can be integrated as a module in Python scripts. Following this, three case studies are showcased, where the tool is employed to solve real environment maps containing circular obstacles. In the final case study, the practical application of the planner, resulting in collision-free paths for a Parallax S2 robot, is demonstrated. The study concludes by addressing several crucial considerations for implementation, followed by concluding remarks and directions for future research.
Verticality Online Monitoring (VENOM) for Transmission Tower Assessment Based on Arduino

Transmission line reliability is very important in an electrical energy distribution system. One of the most important components in the distribution of electrical energy is the Transmission Tower. Distributed locations and various geographical conditions require that each tower stand in solid condition with a strong foundation structure. The condition of the tower can experience deformation either because the soil conditions around the tower are moving, the foundation is experiencing cracks, or due to the influence of the pull from the transmission line which is experiencing anomalies, causing it pull the tower structure. Currently, the assessment method involves measuring the tilt of the tower using a Theodolite tool, which is carried out by visiting the tower and then processing the data using software to obtain the tilt of the tower. This method requires significant effort because each data collection requires measurements at spread-out tower locations and often difficult-to-reach geographical conditions. Additionally, the data obtained is only relevant to the time of measurement, so to get trending tilt data, it is necessary to visit the location tower whenever data is needed. In this paper a transmission tower tilt monitoring tool is designed using Arduino called VENOM. By using VENOM, trending tower tilt data can be obtained online through a website. This tool saves time, cost, and effort, and also provides an early warning system if there is a tower tilt that exceeds the set slope value limit. With this system, the follow-up to anticipate tower collapse can be carried out immediately, ensuring the reliability of the transmission line, especially regarding tower constraints. As a result, the electricity energy channel becomes more reliable
Deep Reinforcement Learning Applied to IEEE’s Very Small Size Soccer in Cooperative Attack Strategy

In this work, we use a virtual learning framework for the IEEE’s Very Small Size Soccer (VSSS) competition based on Deep Reinforcement Learning (DRL) to train and improve the behavior of agents within the game strategy. The agent was trained on a scenario where a pair of robots must cooperate to score a goal in the adversary field. To achieve that, the concept of Self-Play was used, where the agent learns to cooperate with a copy of itself. Furthermore, the Proximal Policy optimization (PPO) technique was used during the training process. Finally, the trained agent’s performance was evaluated and compared with agents following handmade heuristic behaviors in two scenarios: (1) empty goal (in which the DRL agent was trained on) and (2) against goalie (not seen by the agent during train). As a result, the DRL agent’s outperformed the heuristic behavior agents in both scenarios when comparing success rate: (1) 99.18% against 81.50% and (2) 32.94% against 28.60%. The contribution of this work is the usage of cooperative Self-Play in an offensive move. To the best of our knowledge, this is the first work to succeed in combining Self-Play and the PPO technique in IEEE’s VSSS.
Building an Extensive Database for Training Predictive Models in Image Classification of Mammography Views and Projections using Support Vector Machines

Breast cancer remains a significant cause of mortality among women worldwide. One of the early symptoms of breast cancer is morphological asymmetry. Therefore, prevention systems based on Machine Learning models for classifying breast asymmetry in mammograms can serve as a solution for early detection of breast cancer. This paper presents a method for classifying mammograms based on views and projections, with the aim of training machine learning models to detect asymmetries using Support Vector Machines. The database consisted of 54,668 mammograms, with 48% in Craniocaudal and 52% in Mediolateral Oblique projection, from women with an average age of 58 years ± 10, labeled with BIRADS 0, 1, and 2. Geometric features were extracted by generating masks for the mammograms. The features used for classifying the projection pair included Hu moments, asymmetry, pixel position, circularity, and ellipticity. For the orientation classification, the angle between the image center and the breast centroid was computed. This set of features served as the training information for the SVM model, which successfully classified the projection and orientation. Due to the nature of the features, the model was trained using a linear kernel with a regularization parameter C = 1. The classification performance was evaluated using accuracy metrics, achieving a result of 99% ± 1% and an F1 score = 0.99. The automatic classification approach enables the creation of a labeled database based on views and projections, enhancing the training of machine learning models for future research in this field.
Adaptation of the Super Resolution SOTA for Art Restoration in Camera Capture Images

Preserving cultural heritage is of paramount importance. In the domain of art restoration, developing a computer vision model capable of effectively restoring deteriorated images of art pieces was difficult but now we have good computer vision state-of-art. Traditional restoration methods are often time-consuming and require extensive expertise. The aim of this work is to design an automated solution based on computer vision models that can enhance and reconstruct degraded artworks, improving their visual quality while preserving their original characteristics and artifacts. The model should handle a diverse range of deterioration types, including but not limited to noise, blur, scratches, fading, and other common forms of degradation. We adapt the current state-of-art for the image super resolution based on Diffusion Model (DM) and fine-tune it for Image art restoration. Our result show that the instead of fine-tunning multiple different model for different kind of degradation, fine- tunning one super-resolution. We train it on the multiple datasets to make it robust. code link.
Node-Decoupled Extended Kalman Filter versus Adam Optimizer in Approximation of Functions with Multilayer Neural Networks

The training algorithm is a determining factor in the success of artificial neural networks. Until now, first-order optimizers based on gradient descent are the most used in neural network applications. However, strategies that involve second-order information have been investigated with important results. In this article, a training algorithm based on the Node Decoupled Extended Kalman Filter (NDEKF) is reviewed and compared with one of the most widely used algorithms, Adam optimizer. Two experiments with multilayer neural networks are carried out in the task of approximation of nonlinear functions. In the performance tests of the algorithms, both the speed of convergence and the precision of the approximation are compared, for the same number of epochs in the training process. Results are presented, in which the NDEKF stands out.
Comparison of Encoder-Decoder Networks for Soccer Field Segmentation

Convolutional neural networks consist of state-of the-art models used for the solution of computer vision problems. This paper contributes by evaluating the efficiency of several encoder-decoder neural networks, trained to perform the segmentation of the soccer field in Humanoid KidSize Robot Soccer competitions. To compare the efficiency of several encoders, a total of fourteen neural network models, based on the U-Net and SegNet architectures, were tested and compared in terms of accuracy, cost function value, IoU, and average inference time. Based on that, the networks based on U-Net that utilized the MobileNetv3Small or the ResNet18 for the encoding process were found to be the optimal solution among the considered alternatives to segment the soccer field.
VRI-GazeNet: 3D Gaze Estimation for Real-time Applications

Recognizing where humans are looking is a relevant and challenging task. Modern available AI can compute gaze direction with less than 10° of average angular error, however, these models have an inference time which are too long to be used in real-time applications such as social robots and driving monitoring systems that need to calculate gaze direction quickly. This paper introduces VRI-GazeNet, a gaze estimation network that, when compared to the state-of-the-art models, presents similar accuracy with a speedup of 2.5. Our model is available at https://github.com/VRI-UFPR/GazeNet.
Q-Learning based Local Path Planning for UAVs with Different Priorities

Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.
Design of Cost-Aware Open-Loop Controller For Non-Linearity Compensation Without Embedded Sensors Using Computer Vision

This paper presents a methodology of identification to dynamic modeling for designing an open-loop controller that linearizes the relationship between linear and angular velocities, in the stationary state, of a differential robot with respect to motor speed commands. The proposed approach is particularly valuable to make a nonlinearity compensation for low-cost robots that lack onboard sensing capabilities. By utilizing visual measurements obtained from cameras, i.e. third-person, the sensorless identification process enables the design of an open-loop controller without the need for embedded sensors. This cost-effective solution facilitates the application of linear dynamic controllers in robotic systems where precise control and linear dynamics are required, even in scenarios with limited resources. The proposed methodology offers a practical and accessible solution for enhancing the capabilities of low-cost robots and expanding their range of applications.
Secure Cloud Storage System for Digital Evidence

The paper presents a secure cloud storage system for handling and storing digital evidence. The proposed system allows users to use two-factor authentication based on smart cards to increase access control security. The system enables signing and verifying files with a multi-party computation method in order to distribute trust among more parties. Files representing digital evidence are securely stored in particular case folders with authenticated access control based on different roles. Moreover, the system supports a long-term archivation and backup repository with authenticity and integrity protection based on post-quantum digital signatures. The pilot implementation and results prove that the system is practical and can handle large files. Finally, the paper presents a demonstration use case in order to show how the system could work in practice.
Autonomous Mobile Pharmacy (AMPharmy): Identification of hospital objects and their respective positions

Robots are an increasing force in the daily lives of most people in the world, able to be used for the most different kind of purposes. In this project, the authors propose a medical robot, capable of delivering medicine to patients autonomously through an hospital, and simulate its performance through the CoppeliaSim software, by using computer vision and YOLOv8, state-of-art in object detection, to find specific landmarks through the simulated ambient. The aim of this project is for the robot to be able to navigate the hospital environment autonomously, identifying points of interest and interacting with them. For this, it is first necessary to identify these objectives and the robot’s distance. This work focuses on identifying hospital objects and capturing the distance between objects and the robot.
Robotic actuator control through eye-tracking camera to assist people with physical limitations

Technological advancements have been allowing the development of increasingly advanced assistive methods. These assistive technologies have the potential to increase comfort for people with physical disabilities. Within this context, developing methods capable of identifying user commands and controlling robotic actuators is important to the field. This work presents a methodology to track eye-movements for identifying user pick-and-place commands, using a simple webcam and a robotic arm. Simulation results are also presented using the proposed methodology. From these results, it is possible to verify that the method is viable for further experimentation in a real environment.
An Empirical Study of On-Policy and Off-Policy Actor-Critic Algorithms in the Context of Exploration-Exploitation Dilemma

The Exploration-Exploitation dilemma in Reinforcement Learning (RL) algorithms is about deciding whether to select a sub-optimal path to the outcome and acquire a more varied learning of the environment or to select the greedy path and seek to maximize rewards. It is a fundamental challenge in RL algorithms which influences their learning efficiency. The on-policy and off-policy design of RL algorithms has an influence on their ability to explore non-greedy actions thereby affecting their learning ability. The paper presents the results of experiments conducted to analyze the effect of on-policy and off-policy design and the use of entropy in exploration in actor-critic algorithms and investigate the root causes of effective learning in algorithms. An empirical comparison of Soft Actor Critic (SAC) which is off-policy and Proximal Policy Optimization (PPO), an on-policy algorithm, are performed through these experiments in several continuous OpenAI Gym environments and the effect of exploration strategies like entropy, off-policy target update, and Generalized Advantage Estimate (GAE) factor on the bias-variance balance of these algorithms are analyzed.
Knowledge Discovery of Thyroid Diseases: Association Rule Mining Approach

Thyroid diseases are becoming a common, high-incidence condition across the globe. Hence, understanding the influencing variables is essential for accurate disease diagnosis. Many applications have been proposed by researchers to extract knowledge from the thyroid diseases dataset available in the UCI repository. Their objective was to improve the process by which medical professionals make diagnoses. This study applies Association Rule Mining (ARM) to investigate the hidden patterns in cases diagnosed with hyperthyroid and hypothyroid. Two rule generation algorithms (Apriori and FP-Growth) were used to explore the factors which contribute to thyroid conditions. The analysis showed that females have a high risk to contract hypothyroidism. The results also show that the condition is more likely to affect those over the age of 64 both males and females. On this basis, it is recommended to involve age and sex factors in the diagnosis guideline for medical experts to enhance the thyroid diagnosis process.
Comparison of Feature Extraction and Matching Techniques Applied to Monocular UAV Images With Low-quality and Land Cover Variations

Extracting features from images is a recurring task in applications highly dependent on cameras, mainly on UAV visual navigation. By using sequential images, on different land covers and low-quality images, variable performance is expected based on both modification characteristics. This study applies the techniques GFTT, SIFT, MSER, FAST, BRISK, ORB, AKAZE, and SuperGlue on the dataset KDD-BR 2022, which contains multiple UAV images of various land covers and low-quality images. The performance of each technique on feature extracting and matching is presented while verifying the methods that better generalize this process.
Blockchain-Based Device Authentication Method in Industrial Internet of Things

One of the critical security issues for the Industrial Internet of Things is authentication. Most classical authentication and security mechanisms are centralized and often require another trusted party or large associated costs, such as time and computing power. This study describes a method for authenticating IoT devices in an industrial network based on blockchain. By ensuring full decentralization, the developed method allows to provide a high degree of protection against unauthorized access and forgery of identification data, while maintaining high performance and reliability of the network.
Performance Optimization for Transformer Models on Text Classification Tasks

- The emergence of social media has led to a new set of celebrities, known as influencers. Influencers are individuals who have built a following on social media platforms such as Instagram, YouTube, and Twitter. These individuals have the power to influence the opinions and purchasing decisions of their followers. As a result, brands have sought out partnerships with influencers to promote their products or services. However, with the rise of sponsored content, it has become increasingly difficult to discern whether a post is genuinely endorsed by an influencer or is paid for by the brand. This paper explores how machine learning models can be used to classify influencer tweets as sponsored or not sponsored. Through the use of transformer language models such as BERT, GPT 2 and GPT Neo, we can identify patterns and relations between an influencer and a brand. By analyzing the content and context of influencer tweets, machine learning models can help identify whether a tweet is a sponsored post or not. This paper also takes into consideration the performance metrics such as training time and draws comparison between the models. The use of machine learning models to classify influencer tweets as sponsored or not sponsored will aid in the development of new metrics that accurately measure the effectiveness of influencer marketing. This will, in turn, lead to a more transparent and mutually beneficial influencer marketing industry.
MLGAN: Addressing Imbalance in Multilabel Learning Using Generative Adversarial Networks

A common problem while training supervised deep learning models is the lack of labeled training data. Often real-life datasets such as multilabel datasets suffer from class Imbalance problem, which is inescapable. The limited minority data may not be sufficient for efficient learning and often can cause the networks to overfit. This paper considers the potential appli-cation of Generative Adversarial Networks to restore balance in imbalanced multilabel datasets. We will generate new data for the minority labels and use multilabel learning algorithms to handle multilabel data. We compare our model with MLSMOTE with local label imbalance to validate the effectiveness of our model. Experiments over six real datasets using five different multilabel learning algorithms and five evaluation measures show that our strategy of resampling the multilabel data constantly outperforms MLSMOTE with local label imbalance. Results indicate that imbalance in multilabel datasets is reduced in a classifier-independent way; that is, the classifier should have a deplorable influence on the effectiveness of the resampling strategy. While generating new samples from GAN architecture, we use representative class samples to represent each class distribution which further reduces the training time.
Parasite Detection in Copro Images with a modified Faster R-CNN

Object detection is an important area in computer vision, which has had a great impact in various areas such as medicine. One of the applications in medicine is the detection of parasites since early detection makes it possible to prevent a possible disease, control a disease, or prevent infections from occurring. In this paper, two models of multiclass detection of parasites are presented: one with the Faster R-CNN network, M1, and another M2 with the Faster R-CNN network modifying its cost functions, termed Faster R-CNNM, due to class imbalance. M1 presented a maximum mean Average Precision, mAP, mAP@0.5:0.95 of 0.8154 at epoch 82, and M2 of 0.8256 at epoch 84 in validation. The two models were analyzed using the precision, P, recall, R, and F1-measure, F, metrics for location and the balanced accuracy metric, BAM, for classification. The analysis consisted of making inferences with an intersection over union, IoU, threshold of 0.5. All images in the data set were used in the analysis. M1 obtained P = 0.814, R = 0.99, F = 0.8935 in localization and BAM = 0.815 in classification. The results of M2 in the localization were P = 0.8417, R = 0.992, F = 0.91, and in the BAM classification = 0.872. These results show that M2 performs better than M1 in location and outperforms M1 in the classification because it is better to deal with the class imbalance situation.
Evaluation on IMU and odometry sensor fusion for a Turtlebot robot using AMCL on ROS framework.

When a robot’s coordinate system and the environment map have a correspondence with localization algorithms, problems arise related to the accurate and direct measurement of the robot’s position, which is directly linked to sensor measurements that often contain errors, noise, and influencing factors. In this article, an experiment will be conducted to demonstrate error propagation and the attempt at correction using the mentioned algorithms. This study will involve comparing simulation and odometry results from a real robot through two different trajectories. Correction techniques were used through the ROS framework to assess the robot’s position using the Extended Kalman Filter (EKF), which performs a fusion of data from Odometry sensors, Inertial Measurement Unit (IMU), and the Adaptive Monte Carlo Localization (AMCL) algorithm on a Turtlebot3 in an indoor environment. Additionally, two navigation tests were conducted, one using the map generated by simultaneous localization and mapping (SLAM) and another using a computer-aided design (CAD) map of the environment where the robot is navigating. In this sense, the tests provide us with a comparison of sensor localization and localization algorithms in CAD and SLAM maps.