Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology-based Semantic Model for Health Data Interpretation

New opportunities for improved personalized healthcare have emerged due to the recent advances in the development of modern methods which reinforce personalized early risk prediction, prevention and intervention. Using semantic techniques for data integration has become pivotal as it can deliver different ways to represent data, automating the process of data integration, and providing the ability to query semantically. In this paper, we propose a new semantic data model in which health information derived from Parkinson’s, Multiple Sclerosis, and Stroke (PMSS) patients is systematically analyzed to generate and improve knowledge that will be transferred to patient care in order to design and develop innovative health risk prediction and intervention tools. Furthermore, this project focuses on providing new opportunities for improved personalized healthcare and prevention that have been created by new designs and developments of innovative health risk prediction and intervention tools. A core ontology is currently being designed within the ALAMEDA project to deal with the semantic interoperability across heterogeneous datasets along with a semantic framework to concrete the generated heterogeneous data through a shared ontology. The ontology model development and the requirement elicitation will be done based on the components’ capabilities and use case requirements. The heterogeneous and dynamic data will be subjected to annotation through the development of semantic models for data sharing and usage apart from being interpretable.
Recurrent Neural Network Techniques: Emphasis on Use in Neural Machine Translation

Natural Language Processing (NLP) is the processing and the representation of human language in a way that accommodate its use in modern computer technology. Several techniques including deep learning, graph-based, rule-based and word embedding can be used in variety of NLP application such as text summarization, question and answering and sentiment analysis. In this paper, machine translation techniques based on using recurrent neural networks are analyzed and discussed. The techniques are divided into three categories including recurrent neural network, recurrent neural network with phrasebased models and recurrent neural techniques with graph-based models. Several experiments are performed in several datasets to make translation between different languages. In addition, in most of techniques, BLEU is used in evaluating the performance of different translation models.
Accounting for the Effect of Inter-Task Similarity in Continual Learning Models*

Catastrophic forgetting has long been a major obstacle to continual learning. In this paper, we explore the effect of certain task characteristics, in particular inter-task similarity, on the extent of forgetting. Moreover, we experimentally study the effect of these characteristics on the effectiveness of recent state-of-the-art continual learning approaches. We show that the performance of some methods, for example, the recently proposed Learning without Forgetting (LwF) and elastic weight consolidation (EWC) models, is significantly dependent on these characteristics. We propose a rehearsal-based extension to continual learning models to address this vulnerability. We develop this extension first in the context of LwF and later demonstrate its effectiveness with other models such as EWC. We show that a memory budget of 1% of training data is sufficient to significantly improve on performance in cases of low inter-task similarity.
Solving Time-Dependent Traveling Salesman Problem with Time Windows with Deep Reinforcement Learning

Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem. Recently, many researchers have used deep reinforcement learning to solve it. However, traffic factors are rarely considered in their works, in which the traveling time between customer locations is assumed to be constant over the planning horizon. For many practical scenarios, the traffic conditions between customer locations may change over time due to the impact of traffic patterns. Thus, this paper considers a Time-Dependent Traveling Salesman Problem with Time Windows (TDTSPTW), where the time dependency is obtained by fitting the collected traffic data into real-time traffic function with the interpolation method. We propose a deep reinforcement learning framework to solve TDTSPTW. Extensive experiments on TDTSPTW instances indicate that the proposed method can capture the real-time traffic changes and yield high-quality solutions within a very short time, compared with other typical baseline algorithms.
Inter-Subject Transfer Learning Using Euclidean Alignment and Transfer Component Analysis for Motor Imagery-Based BCI

Brain-computer interface (BCI) requires calibration phase to learn user-specific decoder that translates brain signals into desired commands. Calibration phase can be lengthy and exhausting for motor imagery-based (MI) BCI, and can potentially reduce the effectiveness of BCI system. Transfer learning (TL) approaches have been implemented in the field of BCI to tackle this problem. Transfer learning based on domain adaptation reduces domain discrepancy between two domains. Transfer component analysis (TCA) is one domain adaptation technique that maps features of both domains into a new space while simultaneously reducing their domain discrepancy. Recently, Euclidean alignment (EA) is used as transfer learning technique in BCI preprocessing step to align electroencephalography (EEG) trials between subjects. This paper proposed a combination of both EA and TCA (EA-TCA) as a TL approach for inter-subject transfer learning. This paper also proposed TCA method that can reuse existing projection matrix to transform new target data (TCA-W). The efficacy of EA to this method is also observed (EA-TCA-W). Results indicate that EA-TCA and EA-TCA-W outperform classification accuracy of those without EA by 3.32% and 6.50%, respectively. This concludes that EA can improve performance of both conventional TCA and proposed TCA-W.
What Does The Eye Best Tell? An Investigation of Eye Activity for Emotion, Cognitive load and Task Transition Recognition

Eye activity has previously been found to be relevant to emotion, cognitive load and task transition, but studies have mainly focused on one of them and used a single type of task. This motivates an investigation of eye activity for emotion, cognitive load and task transition recognition in a single study with the goal of understanding the capability of eye activity. We recorded 15 participants’ eye data while they completed a sequence of free-viewing emotional image tasks and a sequence of arithmetic tasks. Eye activity features within fixed analysis windows were extracted to classify (i) between four levels of arousal, (ii) three levels of valence, (iii) between three levels of cognitive load, (iv) between affective and cognitive tasks, and (v) between task transition and non-transition. The results suggest that eye activity can best be used for task transition recognition, with an accuracy of 77%, followed by cognitive load level recognition, then affective and cognitive task recognition. Implications of this finding include automatically segmenting long and continuous signals into task units with eye activity before analyzing human behavior.
A BMI-AR Framework for Hands-Free Instruction

While researchers have found benefits in the use of Augmented Reality (AR) for training in maintenance tasks, many existing applications are limited to the use of hand-held controllers for interaction with the virtual environment. One novel alternative to traditional controls is the use of Brain-Machine Interface (BMI) systems and Motor Imagery (MI), using data from the user’s brain in the form of electroencephalography (EEG) waves. While some research has explored the use of BMI with virtual environments, the use of MI in an AR system has not been explored for use in training how to accomplish a procedural task. Therefore, this paper presents a BMI system combined with AR, to create a framework that allows for hands-free traversal of a predefined instruction-set for training using user brain activity. The created prototype allows for control of an AR environment using three modalities: brain activity, voice commands, and hand gestures. The created BMI-AR prototype allows for control of navigation through a predetermined instruction set using a user’s brain waves or voice commands, as well as allow users to see their passive EEG readings of affective mental state in real time. The initial prototype resulted in promising initial machine learning (ML) accuracy scores for motor imagery (MI) tasks.
The Effects of Different Body Positions on Human Physiological Responses Using Universal Motion Simulator

People perform most of their activities while being in an upright position. Nonetheless, there are some circumstances where they are required to adapt to different positions other than the upright position as in air manoeuvres and sport gymnastics. In these unexpected scenarios, the physiological signals are likely to change which can affect their performance. While some studies investigated the correlation between physiological signals and different body positions, to our best knowledge, these studies were limited to a rotating chair (1 or 2 degree of freedoms). Here, we investigated and evaluated human physiological responses (such as pupil diameter, skin temperature, heart rate, and breathing rate) to different seated positions including seated supine, seated side, seated inverted, and seated upright using Universal Motion Simulator (UMS), a 6 degree of freedom simulator with the most realistic acceleration and motion sensation. Open loop acrobatic flight motion sensation for the 11 participants were created and accompanied with a series of pre- and post-questionnaires to subjectively assess the physical wellbeing of each participant. The results of the study based on the objective assessment of collected physiological data showed that the mean heart rate decreases during an inverted position (82 beats per minutes bpm) and increased by an average of 7 beats per minute in an upright position 89.5 bpm. Moreover, the mean breathing rate in an upright position was 18.3 respirations per minutes (rpm) which is higher than mean breathing rate in the side position 19.8 rpm. Furthermore, it was found that the mean pupil diameter (PD) in an upright position was 4.33 mm which is higher compared to other positions. Independent from the motion scenarios and body positions, the Skin Temperature kept increasing which might be because of excitement and other emotional factors.
Bipedal Locomotion Optimization by Exploitation of the Full Dynamics in DCM Trajectory Planning

Walking motion planning based on Divergent Component of Motion (DCM) and Linear Inverted Pendulum Model (LIPM) is one of the alternatives that could be implemented to generate online humanoid robot gait trajectories. This algorithm requires different parameters to be adjusted. Herein, we developed a framework to attain optimal parameters to achieve a stable and energy-efficient trajectory for real robot’s gait. To find the optimal trajectory, four cost functions representing energy consumption, the sum of joints velocity and applied torque at each lower limb joint of the robot, and a cost function based on the Zero Moment Point (ZMP) stability criterion were considered. Genetic algorithm was employed in the framework to optimize each of these cost functions. Although the trajectory planning was done with the help of the simplified model, the values of each cost function were obtained by considering the full dynamics model and foot-ground contact model in Bullet physics engine simulator. The results of this optimization yield that walking with the most stability and walking in the most efficient way are in contrast with each other. Therefore, in another attempt, multi-objective optimization for ZMP and energy cost functions at three different speeds was performed. Finally, we compared the designed trajectory, which was generated using optimal parameters, with the simulation results in Choreonoid simulator.
Impact of Missing Information and Strategy on Decision Making Performance

Decision makers frequently encounter environments without perfect information, in which factors such as the distribution of missing information and estimates of missing information significantly impact decision accuracy and speed. This work presents an experiment which modifies an environment with missing information (total information, option imbalance, cue balance) and examines user estimates of the missing information to understand how accuracy and decision speed respond under time pressure. Results indicate that regardless of the way missing information is estimated, certain distributions of missing information reduce decision accuracy. Results from this work also indicate that beyond information distribution and estimation strategy, differences in decision strategy adopted may explain significant differences in decision performance. High performers tend to ignore a greater percentage of information instead of attempting to estimate it, thereby adopting a strategy more heuristic in nature.
Multirate Adaptive Inverse Dynamics Control of 5 DOF Industrial Gryphon Robot

A multirate adaptive inverse dynamic controller is presented in this study to provide the stability and reliable trajectory for robotic manipulators. The proposed controller has been implemented on the experimental Gryphon which is a five degrees of freedom industrial robot and has emulated the movement of human arm in take and place missions. The multirate structure leads to proper choosing of sampling periods and adequate refresh rates for actuators which apparently improve the tracking performance of the manipulator. By applying the proposed controller, the end-effector motion tracking error asymptotically converges to zero besides preserving the stability. Experimental results are performed to illustrate the performance of the proposed controller.
A Multimodal Biomedical Image Registration Method Based on an Improved Genetic Algorithm Inspired by Hybrid Breeding

Image Registration(IR) has been widely applied in biomedical image processing. It is the process of finding an optimal geometric transformation to align two images, which could be defined as a parameter optimization issue. Genetic Algorithm(GA) is one of the most efficient methods for solving complex optimization problems and it has been applied to the real-coding IR problem. However, the classical GA suffers from premature convergence and is easy to fall into local optimum. Inspired by heterosis, which is a common phenomenon in biology, this study proposes an improved GA. By artificially simulating the breeding process of Chinese three-line hybrid rice, known as a successful application of heterosis, the original crossover and mutation mechanisms of GA are improved, and a dynamic diversity controller is designed. This study conducts several multimodal biomedical IR experiments to compare the improved GA with state-of-the-art IR methods, the results show that the proposed method outperforms the others in most scenarios with faster convergence speed and greater robustness.
Untethered Cable-driven Soft Actuators for Quadruped Robots

Various soft robots have appealing advantages in human-machine interaction and industrial applications due to their intrinsic softness. However, the highly nonlinear response of soft material deformation makes the soft robotic system difficult to model and control. Recent attempts of kinematic modeling and control of soft robots mostly rely on simplified kinematic analysis or machine learning which are not accurate to guild their design and control. This work presents a cable driven untethered soft pneumatic actuator with its chambers inflated and sealed at all time. Its motion, including bending and stretching, can be well controlled by adjusting the cable length, and as a result, repeatable motions could be achieved and an accurate kinematic model can be also established. Based on our design, two untethered soft quadruped robots, one crawling and the other walking, are demonstrated for well-controlled forward and steering motions, which is indispensable toward soft robot control, cooperative operation and intelligent movement.
An Intelligent Robust Control Strategy for an Uncertain Quadrotor

This paper presents an intelligent robust controller design scheme for a quadrotor where system dynamics of quadrotor are not known and quadrotor actuators may suffer from partial loss of effectiveness (PLOE) along with the actuator saturation. Robust controller design employs an integral fast terminal sliding mode controller (IFTSMC) which provides faster response and singularity free control action along with the finite-time convergence. Actuator saturation is accommodated by employing an auxiliary variable. To cope with the unknown quadrotor dynamics, a radial basis function neural network (RBFN) along with the robust compensator has been introduced to directly approximate control law instead of approximating system dynamics separately. The letter requires more computational effort and that is greatly reduced by the proposed methodology. Weights of RBFN are updated in real-time where update laws of these weights are obtained using the principle of Lyapunov stability and overall stability of the system is guaranteed using the same. To show the effectiveness of the proposed approach, simulation results have been provided for ∞-shaped trajectory tracking.
A Real-time Double Emulsion Droplets Detection System using Hough Circle Transform and Color Detection

The object detection based on computer vision algorithms has a wide range of applications in many domains. This paper introduces a real-time detection methodology of 3D printed double emulsion droplets. The first step is to directly segment and extract the color of the inner droplets from the current field of view using thresholding operations of inRange functions of OpenCV in YUV space, so that the droplets color and background color can be obtained separately. Secondly, a binary masking has been employed to locate the centre of the mass of droplets and also distinguish the droplets from surroundings. Next, the Hough circle transform is used to fit a circle to extract the true edge when identifying the contour of the external droplet. The droplet size, generated frequency, and determine whether the droplet is successfully formed can also be obtained using Hough circle transform. A series of experiments have been conducted and indicated that our proposed detection system works well on different double emulsion microfluidic devices with different background and droplet colors.
Rasterized Storage Environments Automatically Designing and Planning Based on Monocular Camera

Storage environments designing and planning is the prerequisite of Automated Guided Vehicles (AGVs) route planning in Automated Storage and Retrieval Systems (AS/RSs), and could reduce much labor force and the energy cost with an automatic way. Although image processing has made great progress, few publications focused on establishing rasterized storage environments. In this paper, we proposed a novel method for automatically designing and planning storage environments based on a monocular camera. First, we take a picture of the vacant warehouse ground with a monocular camera and then obtain the rectangular image using the perspective transformation. Second, we draw the approximate boundary of the storage area and compute the external enclosing rectangle as the exact boundary. Third, the computer divides the environment into several grids based on the size of workstations. Finally, the computer labels the obstacle workstations according to the planning results. The proposed approach is validated with several simulations.
A Fast and Reliable Approach for Driving Style Customization in Autonomous Vehicles

The usage of autonomous vehicles in the transportation sector can achieve the objective of a safe environment. To increase riding comfort in an autonomous vehicle, one main challenge is to implement motion scenarios according to the passenger’s driving behaviours. This leads to customization of the driving style of an autonomous vehicle according to the preference of its passenger. The main disadvantage of the current autonomous vehicles is the regeneration of driving motion signals without taking into consideration the comfort/discomfort of the passengers according to their driving behaviours and preferred driving styles such as acceleration/deceleration rate and steering styles. In this paper, a nonlinear autoregressive network model is developed and trained based on the generated motion scenarios of the passenger and the position of the autonomous vehicle, in order to predict and replicate the motion signals based on the passenger’s driving behaviours. The MATLAB toolbox is used to train the network and forecast the motion signals. The results show the usefulness of the proposed method in terms of a higher shape similarity level and a lower mean square error rate between the actual and forecasted motion signals. These regenerated motion signals can increase the riding comfort of autonomous vehicle’s passengers as it is able to imitate the behaviour of the passengers.
Kernel-VIO : An Optimization-based Tightly Coupled Indirect Visual-Inertial Odometry

This paper proposes an optimization-based tightly coupled Indirect Visual-Inertial Odometry (Kernel-VIO), which weights outliers with hybrid kernel functions. The novelty is that the hybrid weighting method based on kernel functions is firstly extended to the indirect-based VIO, which reduces the weight of outliers by dynamically weighting reprojection residual items, thereby the method suppresses adverse effects of outliers on the state estimator. Specifically, the proposed system processes the visual measurements and the IMU pre-integration in a tightly coupled way, next aligns the two information to complete the system initialization. Then, a nonlinear optimization framework is established by jointly minimizing the IMU pre-integration residuals, the reprojection residuals and prior information of sliding window marginalization to estimate the state of drones. The comparative experiments on the public dataset show that the accuracy and robustness of Kernel-VIO is better than the VINS-Mono without loop closure, and simulations demonstrate the effectiveness of the proposed Kernel-VIO.
Automatic Preprocessing and Ensemble Learning for Cell Segmentation with Low Quality

We propose an automatic preprocessing and ensemble learning for segmentation of cell images with low quality. It is difficult to capture cells with strong light. Therefore, the microscopic images of cells tend to have low image quality but these images are not good for semantic segmentation. Here we propose a method to translate an input image to the images that are easy to recognize by deep learning. The proposed method consists of two deep neural networks. The first network is the usual training for semantic segmentation, and penultimate feature maps of the first network are used as filters to translate an input image to the images that emphasize each class. This is the automatic preprocessing and translated cell images are easily classified. The input cell image with low quality is translated by the feature maps in the first network, and the translated images are fed into the second network for semantic segmentation. Since the outputs of the second network are multiple segmentation results, we conduct the weighted ensemble of those segmentation images. Two networks are trained by end-to-end manner, and we do not need to prepare images with high quality for the translation. We confirmed that our proposed method can translate cell images with low quality to the images that are easy to segment, and segmentation accuracy has improved using the weighted ensemble learning.