Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a New Computational Affective System for Personal Assistive Robots

The need of social interaction between human and robot is extensively highlighted in recent studies involving social robots. Language, emotions, postures, and gestures are commonly used to increase the quality of human-computer interaction. In this study, we focus on the design of a cognitive architecture to model the emotions and the dynamics of them to implement artificial empathy during human-computer interaction. Human-like empathy is considered as an emergent behavior based on social interaction with humans, gut feelings, mirroring system, and association between external stimuli and emotions in the developmental robotics theory. Our study uses developmental robotics theory and it presents a simulation of the internal emotional states of an agent/robot. Furthermore, our study demonstrates a model of the changes of the affective state of the robot from one emotion to another, in synchronization with the emotions expressed by its human partner. The robot can adjust its inner state and mood in harmony to the emotional state of the human partner after training. The simulations are performed and the proposed computational affective system is evaluated by the human participants subjectively.
Forecasting Electricity Consumption Using Deep Learning Methods with Hyperparameter Tuning

—In this study, it is tried to estimate one-day electricity consumption by using deep learning methods with a dataset which includes the change in time-dependent electricity consumption. After explaining the time series components and machine learning concepts, general information about previous studies on electricity consumption estimation is given. Since the dataset used is a time series, all the features are emphasized in detail and necessary operations like resample and differencing are performed before proceeding to the modeling. Tuning was applied on hyperparameters which significantly affect the performance of the algorithms used in the modeling stage and the most suitable parameters were searched for each method. Then the best results were compared with each other and the method with the lowest error rate was determined.
Gun Detection with Faster R-CNN in X-Ray Images

-In this article, Faster R-CNN object detection algorithm is used for automatic weapon detection in X-Ray bag images obtained from security points. In the proposed method, Faster R-CNN model was fed by creating 2 different 3 band images. In the first one, the 3 bands of the image are equalized to the high energy values obtained from the X-Ray device. In the other one, 1st band is equalized to high values, 2nd band is equalized to low values and the 3rd band is equalized to the difference between high energy and low energy values. Performance values of the two different image generation techniques that fed the deep learning model were compared. In the study, X-Ray dataset provided by HTR Company was used. When the results are evaluated, it is seen that Faster R-CNN method can be used in weapon detection with very high performance as a deep learning model and the method of creating images that feed the model has an effect on object detection performance.
A Latent Variables Augmentation Method Based on Adversarial Training for Image Categorization with Insufficient Training Samples

In the past few years, people have made great progress in image categorization based on convolutional neural networks (CNN) with the large-scale training set. However, in real-world applications, we may only have insufficient training samples for training CNN. To solve this problem, in this paper, we propose a latent variable augmentation method based on adversarial training (Lagat) to increase the categorization accuracy of CNN with insufficient training samples. In the proposed Lagat model, we propose a uniform loss function, where we take the following two tasks into account. Firstly, we consider the task that we augment latent variables (LVs) from a set of class-specific adaptive distributions and we propose constraints to adjust these LVs distributions. Secondly, we use these sampled LVs to train a predictive image classifier. Moreover, we present an alternative two-play minimization game to optimize this uniform loss function. In addition, the experiment results also demonstrate that the proposed Lagat method delivers higher accuracy than the existing state-of-the-art methods. The source code of the Lagat algorithm is available in Github11https://github.com/itapty-ily/Lagat.
Recognize Moving Objects Around an Autonomous Vehicle Considering a Deep-learning Detector Model and Dynamic Bayesian Occupancy

Perception systems on autonomous vehicles have the challenge of understanding the traffic scene in different situations. The fusion of redundant information obtained from different sources has been shown considerable progress under different methodologies to achieve this objective. However, new opportunities are available to obtain better fusion results with the advance of deep-learning models and computing hardware. In this paper, we aim to recognize moving objects in traffic scenes through the fusion of semantic information with occupancy-grid estimations. Our approach considers a deep-learning model with inference times between 22 to 55 milliseconds. Moreover, we use a Bayesian occupancy framework with a Highly-parallelized design to obtain the occupancy-grid estimations. We validate our approach using experimental results with real-world data on urban scenery.
A Passive Vision System for Weld Bead Inspection System Textures in Underwater Environments

Since approximately 1960, remote operating vehicles (ROVs) have been in use, but they have performed a limited number of tasks. Over the years and with technological advances, the ROVs have been gaining new tools and possibilities that have provided a new range of applications, including the inspection of structures in underwater environments. In the oil and gas sector, for example, it is essential that there is efficiency, care and safety with workers, as well as with production places and instruments. In this way, visual inspection techniques and underwater non-destructive tests are important in determining the quality of the operation. Oil platforms and ships are built with welded steel plates. With the action of time and sea water they start to suffer degradations. Visual inspection of steel welds in an underwater environment is essential, because the existence of discontinuities, geometric imperfections or faults can impair the safety of the structures. Thus, the main contribution of this work is to present a method capable of identifying irregularities in the texture of the weld bead of sheet metal used for heavy steel structures, in underwater environments.
Gain-Scheduled Robust Recursive Lateral Control for Autonomous Ground Vehicles Subject to Polytopic Uncertainties

This paper addresses the lateral control problem for autonomous ground vehicles. In order to better capture the behavior of lateral dynamics, the model is subject to polytopic uncertainties based on different road and load conditions. These uncertainties can potentially degrade the performance of the system and, as a consequence, endanger passengers, pedestrians and surrounding vehicles. It is crucial, then, that the controller handles these parametric deviations to preserve safety and stability. To this end, a robust recursive control algorithm computes a set of feedback gains that minimizes the trajectory tracking error state. Meanwhile, a gain-scheduling strategy interchanges among these recursive gains at each instant. A numerical simulation is also provided to illustrate the effectiveness of the proposed method.
Realistic Single-Shot and Long-Term Collision Risk for a Human-Style Safer Driving

Navigation in congested environments is a challenge for autonomous vehicles and they should consider collision risk metric into their driving behavior. In this paper, we propose a novel two-fold indicator: On the one hand, single-shot risk works in space domain, considering geometries, locations and the velocities of the obstacles in the current scene. On the other hand, long-term risk considers the evolution of the current scene and provides risk values in time domain. The map information and different prediction models (e.g. reachable sets, probabilistic) are considered in the long-term risk, which can then be used in trajectory planning or decision making approaches. Our method can be applied to scenarios with arbitrary road topologies (intersections, roundabouts, highway, etc.) and it is suitable regardless of the scene prediction method. We formulate the single-shot (or short-term) risk with one single function fitted using Monte Carlo (MC) Simulations. The results are evaluated in real scenarios using HighD dataset and compared with other risk indicators such as THW and TTC. In addition, it is applied to a simple trajectory planner in order to demonstrate that the proposed approach imitates human driving style.
ASD-SLAM: A Novel Adaptive-Scale Descriptor Learning for Visual SLAM

Visual Odometry and Simultaneous Localization and Mapping (SLAM) are widely used in autonomous driving. In the traditional keypoint-based visual SLAM systems, the feature matching accuracy of the front end plays a decisive role and becomes the bottleneck restricting the positioning accuracy, especially in challenging scenarios like viewpoint variation and highly repetitive scenes. Thus, increasing the discriminability and matchability of feature descriptor is of importance to improve the positioning accuracy of visual SLAM. In this paper, we proposed a novel adaptive-scale triplet loss function and apply it to triplet network to generate adaptive-scale descriptor (ASD). Based on ASD, we designed our monocular SLAM system (ASD-SLAM) which is an deep-learning enhanced system based on the state of art ORB-SLAM system. The experimental results show that ASD achieves better performance on the UBC benchmark dataset, at the same time, the ASD-SLAM system also outperforms the current popular visual SLAM frameworks on the KITTI Odometry Dataset.
Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds

Traditionally, point cloud-based 3D object detectors are trained on annotated, non-sequential samples taken from driving sequences (e.g. the KITTI dataset). However, by doing this, the developed algorithms renounce to exploit any dynamic information from the driving sequences. It is reasonable to think that this information, which is available at test time when deploying the models in the experimental vehicles, could have significant predictive potential for the object detection task. To study the advantages that this kind of information could provide, we construct a dataset of dynamic occupancy grid maps from the raw KITTI dataset and find the correspondence to each of the KITTI 3D object detection dataset samples. By training a Lidar-based state-of-the-art 3D object detector with and without the dynamic information we get insights into the predictive value of the dynamics. Our results show that having access to the environment dynamics improves by 27% the ability of the detection algorithm to predict the orientation of smaller obstacles such as pedestrians. Furthermore, the 3D and bird's eye view bounding box predictions for pedestrians in challenging cases also see a 7% improvement. Qualitatively speaking, the dynamics help with the detection of partially occluded and far-away obstacles. We illustrate this fact with numerous qualitative prediction results.
Design and Analysis of RUL Prediction Algorithm Based on CABLSTM for CNC Machine Tools

In the cutting manufacturing process, cutting tools are one of the factors closely related to the efficient production and quality of products. order to extend the life of the cutting tool, it is not possible to efficiently set the cutting conditions, resulting in reduced product productivity. Conversely, it is very important to predict the remaining wear time of the cutting tool. If the cutting conditions are set to increase product productivity, the cutting tool wears quickly, and if damage cannot be sustained, the corresponding line stops, so it is very important to predict the remaining wear time of the cutting tool. However, it is difficult to predict the tool’s RUL (Remaining Useful Life) because various are linked together due to the cutting conditions. In this paper, algorithms such as CNN, BILSTM, etc. are used to predict the RUL of a CNC machine tool using the BiLSTM-based CABLSTM algorithm and advanced Taylor’s tool life equations for factors affecting complex RUL.
A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision. In this paper, we propose a neural network model based on trajectories information for driving behavior recognition. Unlike existing trajectory-based methods that recognize the driving behavior using the handcrafted features or directly encoding the trajectory, our model involves a Multi-Scale Convolutional Neural Network (MSCNN) module to automatically extract the high-level features which are supposed to encode the rich spatial and temporal information. Given a trajectory sequence of an agent as the input, firstly, the Bi-directional Long Short Term Memory (Bi-LSTM) module and the MSCNN module respectively process the input, generating two features, and then the two features are fused to classify the behavior of the agent. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance.
6D Object Pose Estimation With Color/Geometry Attention Fusion

The 6D object pose is widely applied in robotic grasping, virtual reality and visual navigation. However, heavy occlusion, changing light conditions and cluttered scenes make this problem challenging. To address these issues, we propose a novel approach that effectively extracts color and depth features from RGB-D images considering the local and global geometric relationships. After that, we apply a graph attention mechanism to fully exploit representations between these features and then fuse them together to predict the 6D pose of a given object. The evaluation results indicate that our method significantly improves the accuracy of the estimated 6D pose and achieves the state-of-the-art performance on LineMOD, YCB-Video, and a new dataset. Ablation studies demonstrate the effect of our network modules.
Prediction and Application of Slot Attributes In Contract Display Advertisement

In this paper, we propose a method that implicitly contains multiple features to describe the relationship between two variables, which was referred to as trend prediction. This method takes into account the relationship between the slot page view and multiple features. The data of different advertising slots are constructed by predicting methods such as tree model and neural network with category feature densification by auto-encoder model. To reduce the smooth time in the prediction curve, we take the logarithm function as a priori to regress the predicted curve. Finally, compared with our previous prediction model, the trend prediction model effectively improves the overall page view under the unconstrained mixed-integer programming model.
Power and rate adaptation in the presence of Bernoulli-Gaussian modeled impulsive interference

In this paper, we analyze power and rate adaptation in a point-to-point link with Rayleigh fading and impulsive interference. We model the impulsive interference as a Bernoulli-Gaussian random process. Adaptation is used to maximize the average spectral efficiency by changing power and rate of the transmission subject to an average power and instantaneous probability of error constraints. Without impulsive interference, it is well known that water-filling is optimal for block fading. We provide two simple schemes that show that the conventional water-filling algorithm is not optimal in an impulsive interference channel.
Online Fiyat Tahmin Modeli Online Price Forecasting Model using Artificial Intelligence for Cryptocurrencies as Bitcoin, Ethereum and Ripple

In this study, a web model which provides price estimation in terms of Turkish Lira for Bitcoin, Ethereum and Ripple which are popular cryptocurrencies, is developed. Using the relevant model, the price estimation of these three crypto currencies between 21.09. 2019 and 20.11. 2019 is carried out on the web with dynamic data. Artificial intelligence methods such as adaptive neural fuzzy inference system, artificial neural networks, polynomial curve fitting and long short-term memory are used for price estimation. The aim of this study is to provide periodic forecasts to individuals or institutions interested in cryptocurrencies and to test the success of the exemplary model of the use of artificial intelligence in finance. When the forecastings that haven’t yet been realized at the relevant dates and the actual values are compared, the successful results show that the model is well established.
Remote Monitoring of Surface Temperature and Moisture Content in Urban Greenhouse

Internet of things (IoT) is a system of interrelated computing devices with unique identifiers that has the ability to transfer data over a network without requiring human interaction. In this paper, an IoT based system is proposed for monitoring surface temperature (humidity and temperature) and moisture content inside a low-cost urban greenhouse. The proposed system consists of the integration of hardware and software parts, in which the hardware part includes a microcontroller and multiple sensors. The performance of three various soil moisture sensors when detecting moisture content of various growing media inside a low-cost urban greenhouse is presented as well. The network setup and web page development using the user-friendly Hostinger platform are discussed as well. The proposed system will enable reliable remote monitoring of the surface temperature and moisture content of growing media inside a low-cost urban greenhouse as well as eliminating the need for human intervention. The data collected by sensors can be used for predictive analysis to track the state of the system in general as well as equipment efficiency.
Support Vector Regression based Direction of Arrival Estimation of an Acoustic Source

The direction-of-arrival (DOA) estimation of an acoustic is instrumental in many applications such as surveillance, robotics, defense, etc. This paper proposes the DOA estimation technique using a support vector regression (SVR) machine-learning model trained on the signals acquired from the uniform linear array (ULA) of microphones. The SVR machine-learning model has been trained using the correlation coefficients of signals at different microphones as the features of the model. The root-mean-square angular error (RMSAE) parameter has been used for performance comparison of SVR with that of Delay-and-Sum (DAS) beamforming, multivariate linear regression (MLR), and multivariate-curvilinear regression (MCR). From the results, it has been observed that the SVR model outperforms the DAS beamforming method as well as other regression models viz. MLR and MCR.
Robust Adaptive Fault-Tolerant Coordinated Control for Biaxial Gantry System with Friction Compensation

In this paper, a novel robust adaptive fault-tolerant coordinated control is proposed for the contour tracking problem of unknown biaxial linear-motor-driven gantry system subject to time-varying actuator faults. Firstly, the dynamic model of the contour error is established by coordinate transformation. The time-varying actuator faults including loss of effectiveness and bias faults with unknown failure time are considered in controller design. The robust adaption law with smooth projection modification is developed to handle for the effect of parametric uncertainties on the system. The proposed control scheme can guarantee that all the signals in the closed-loop system are ultimately bounded even if time-varying actuator faults of the gantry system occur. Finally, two numerical simulations are conducted to show the effectiveness of the proposed control method.
Statistical and physiologically analysis of using a Duffing-van der Pol oscillator to modeled ictal signals

Electroencephalography reflects the averaged electrical activity of neurons associated with physiological and neural disease processes, i.e., epilepsy. In this study, EEG signals recorded during a complex partial seizure are modeled using a coupled Duffing-van der Pol oscillator. The purpose of the paper is the statistical analysis of parameter variability during three phases of seizure: pre-, ictal, and post-ictal. In order to determine the impact of intra -variability on parameter values during seizure spread, in the study, signals from 10 patients are analyzed. Our data are reliable in terms of each patient's sex, age, and medical history. For the first time, the results of the optimal values model parameter are explained based on physiological phenomena. Non-linear models' parameters may be useful for pre-and post-ictal sequences differentiation.
Neural Network to Control a Multiple Angles Kicker in RoboCup Small Size League

The Small Size League (SSL) is a robot soccer league of the Robot World Cup (RoboCup). A recent trend in hardware design in SSL involves multiple angles kicking mechanisms. Combining a multiple angles kicker with a dribbler, which consists of a spinning bar to manipulate the ball through friction, a robot can execute angled and curved shoots and passes. However, due to the complex physical interactions involved, determining how to control the kicking and dribbling devices in order to obtain the desired ball trajectory is not simple. The ball motion may be mathematically modelled by a nonlinear ordinary differential equation (ODE). The parameters needed to perform a desired curved kick are the solution of the inverse problem of the nonlinear ODE. Since this is hard to compute in real-time, this work proposes a neural network to solve this inverse problem. The network is trained using many simulated ball trajectories. Finally, we show simulation results to validate the proposed method.
Application of a ROS / CoppeliaSim Integration in a Practical “OBR” Competition Scenario

This work presents an integration of the Cop-peliaSim robotics simulator together with ROS in a scene inspired by a stage in the practical modality of the Brazilian Robotics Olympics (OBR). A differential-type robot with 5 proximity sensors and 6 luminance sensors was used in the project. The dimensional characteristics of the robot and the position of the sensors, in addition to navigation control and obstacle avoidance strategies are presented throughout the work. The main objective is to allow students to understand the characteristics of the OBR challenges, and to study and interact with a robot in a simulated environment. The robot behavior is implemented using a PD (Proportional Derivative) controller for line-following, and a simple fuzzy controller for obstacle avoidance.
Multigrid Based Sparse Recovery Method for Multidimensional Harmonic Retrieval

In this paper the application of a multigrid based sparse recovery method for multidimensional harmonic retrieval is investigated. Multidimensional modal estimation problem is formulated as a simultaneous sparse recovery problem for each dimension and then the signal parameters are estimated using a multigrid dictionary refinement scheme. Performance of the proposed method is compared against 2-D ESPRIT and Cramer-Rao lower bounds in numerical simulations.