Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Programming Learning on High School Students through Educative Apps

Teaching programming to High School students is not an easy task. Usually, they have not yet decided what they would like to major in, as in a Computer Science degree, therefore their lack of motivation may need higher doses of creativity from their teachers trying to attract them. Recently, on Education there is a tendency to implement active learning paradigms where students are the focus of the educational process. An instantiation of these learning methods are gaming environments. This article presents FunJava, a smartphone App for learning programming skills through a virtual teacher that presents the leveled content of an introduction to programming course in Java and offers games to test themselves as they play with other classmates. The system has been evaluated during the academic course 2016-17 by 144 High School students of K-11 and K-10 (16 and 15 years old) and our results indicate that using FunJava had a most positive influence in the students learning. Moreover, the opinion of students reflects that they really enjoyed working with the system and increased the engagement with the course.
Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure

Visual reaching and grasping is a fundamental problem in robotics research. This paper proposes a novel approach based on deep learning a control Lyapunov function and its derivatives by encouraging a differential constraint in addition to vanilla regression that directly regresses independent joint control inputs. A key advantage of the proposed approach is that an estimate of the value of the control Lyapunov function is available in real-time that can be used to monitor the system performance and provide a level of assurance concerning progress towards the goal. The results we obtain demonstrate that the proposed approach is more robust and more reliable than vanilla regression.
Providing navigation assistance through ForceHand: a wearable force-feedback glove

Conveying information through force-feedback has multiple advantages as compared to auditory or visual information transfer. Kinesthetic senses, in general, have shown enormous importance in human communication and to the very experience of being human. Kinesthetic learners tend to learn complex tasks through force-feedback and tactile sensations, in much lesser time as compared to auditory and visual learners. Navigation cues provided through force-feedback could eliminate the need for intensively engaging oneself to mobile devices. This type of feedback cue could also eliminate the need for auditory and visual engagement of the user, which can help sustained awareness of the proximity. This paper attempts the realization of a navigation assistant using a force-feedback glove enabled with low-pressure pneumatic artificial muscles (PAMs). This glove enables the user to receive navigation cues through force-feedback on the forearm. We used a gaming environment to evaluate the feasibility of using the glove as a navigation assistant. The gaming experiment also provided vast subjective feedback. 89.5% of the participants chose force-feedback as their most preferred type of cue. In addition, an experiment was carried out to identify the optimum resolution of input air pressure that could be successfully differentiated by the users. The average % accuracy in identifying the correct larger force from pairs of forces was found to be 97.6%, 89.2%, 80.8%, and 75.6%, respectively, for resolutions of 20, 15, 10, and 5kPa.
Combining Aronson's Puzzle and Flipped Classroom in the Faculty Master's Degree

This article presents an evaluation carried out in the subject Complements for disciplinary training II: Computing, corresponding to the Master's degree teacher training in secondary education, baccalaureate, vocational training and languages taught by the Universidad Rey Juan Carlos. The students of the subject had to learn how to prepare simple web pages, using HTML, CSS and JavaScript languages. To this end, the flipped classroom technique was used to make the necessary contents known, combined with the adaptation of Aronson's cooperative learning puzzle technique, used to carry out a group practice that reflected the acquired knowledge. At the end of the experience, the students expressed their satisfaction with the techniques used and the learning acquired. The qualifications of the practices were also high.
Near-contact grasping strategies from awkward poses: When simply closing your fingers is not enough*

Grasping a simple object from the side is easy — unless the object is almost as big as the hand or space constraints require positioning the robot hand awkwardly with respect to the object. We show that humans — when faced with this challenge — adopt coordinated finger movements which enable them to successfully grasp objects even from these awkward poses. We also show that it is relatively straight forward to implement these strategies autonomously. Our human-studies approach asks participants to perform grasping task by either “puppetteering” a robotic manipulator that is identical (geometrically and kinematically) to a popular underactuated robotic manipulator (the Barrett hand), or using sliders to control the original Barrett hand. Unlike previous studies, this enables us to directly capture and compare human manipulation strategies with robotic ones. Our observation is that, while humans employ underactuation, how they use it is fundamentally different (and more effective) than that found in existing hardware.
Generic System Frequency Response Model for Power Grids With Different Generations

With the increasing integration of renewable generation, many power grids have gradually formed AC–DC hybrid systems. Abnormal operations, such as DC blocking faults and generation trips, have led to several incidents of large frequency deviations. However, current simulation methods result in large errors when estimating the frequency regulation capacity of the system. This paper proposes a generic system frequency-response (SFR) model that can be used to estimate the dynamic frequency behavior of modern large-scale power systems. The limitations of the classical SFR model is first analyzed. Second, a generic SFR model with a more reasonable structure is presented, and the parameter-determination strategy is proposed using both the dynamic and steady-state data. Then, the generic SFR model is built and verified by a simulation case. Finally, a generic SFR model with satisfactory accuracy is established for the power grid in East China based on the measured disturbance data. The results show that the proposed model is promising for broad potential applications.
A Robust Position and Posture Measurement System Using Visual Markers and an Inertia Measurement Unit

Automatic control of mobile robots and robot arms requires techniques for estimating the position and orientation of objects with high accuracy and robustness. Although methods using markers or machine learning have been developed, general-purpose and highly accurate estimations have not been realized. Our team developed a high-accuracy visual marker “LentiMark” for high-accuracy estimations of position and posture, but data are lost when the camera cannot obtain marker images. We therefore developed the Marker-IMU system for integrating visual markers with an inertia measurement unit (IMU). When cameras cannot acquire the image of a visual marker, any missing data are restored from IMU data. However, when calculating positions from acceleration sensor values, sensor error increases estimation error. Therefore, we developed a method for error correction using measurements from before and after the missing data. Evaluation experiments confirm that missing data can be estimated using the proposed method.
Hyperbolic metamaterial platform for realization of efficient wideband directive antennas

Impedance matching and gain are among the most challenging antenna parameters to achieve in case of a broadband operation. Hyperbolic metamaterials, initially introduced for this purposes by optical community, can contribute to this endeavour. Here we propose and numerically analyse a new broadband directive antenna design, based on hyperbolic metamaterial slab with a patterned layer on top. Energy from a subwavelength coaxial cable port is shown to be efficiently harvested into bulk modes of the metamaterial over a broad frequency range. Highly localized propagating waves within the medium have a well-resolved spatial-temporal separation owing to the hyperbolic type of effective permeability tensor. This strong interplay between chromatic and modal dispersions enables routing different frequencies into different spatial locations within compact subwavelength geometry. An array of non-overlapping resonant elements is placed on the metamaterial layer and provides a superior matching of localized electromagnetic energy to the free space radiation
Humanoid Robot Next Best View Planning Under Occlusions Using Body Movement Primitives

This work presents an approach for humanoid Next Best View (NBV) planning that exploits full body motions to observe objects occluded by obstacles. The task is to explore a given region of interest in an initially unknown environment. The robot is equipped with a depth sensor, and it can perform both 2D and 3D mapping. As main contribution with respect to previous work, the proposed method does not rely on simple motions of the head and it was evaluated in real environments. The robot is guided by two behaviors: a target behavior that aims at observing the region of interest by exploiting body movements primitives, and an exploration behavior that aims at observing other unknown areas. Experiments show that the humanoid is able to peer around obstacles to reach a favourable point of view. Moreover, the proposed approach results in a more complete reconstruction of objects than a conventional algorithm that only changes the orientation of the head.
IoT Testbed Security: Smart Socket and Smart Thermostat

Internet of Things (IoT) technology is changing the shape of our lives, however they are raising many security issues. Attackers can exploit the security vulnerabilities of IoT devices for malicious ends. Assessing the security of IoT devices is important before they are distributed to reduce the attack surface. However, the security assessment could be difficult due to the wide variety of IoT devices and their functionalities. Moreover, replicating the testing scenarios and environments can be challenging without a fully documented test scenario. The aim of this research is to design a structure for an IoT security testbed that can analyze the vulnerabilities of IoT devices. The paper provides a road map for an easy-to-setup IoT Security Testbed and a general methodology for constructing the testbed. The structure includes the hardware and software setup as well as a testcase template that standardizes test cases and ensures test replication. Moreover, a simple GUI was developed for managing the test cases, launching them and reporting their results. To showcase the feasibility of the testbed structure proposed, three test cases have been launched on a smart socket and smart thermostat followed by a discussion on assessment results.
On Enhancing Ground Surface Detection from Sparse Lidar Point Cloud

Ground surface detection in point cloud is widely used as a key module in autonomous driving systems. Different from previous approaches which are mostly developed for lidars with high beam resolution, e.g. Velodyne HDL-64, this paper proposes ground detection techniques applicable to much sparser point cloud captured by lidars with low beam resolution, e.g. Velodyne VLP-16. The approach is based on the RANSAC scheme of plane fitting. Inlier verification for plane hypotheses is enhanced by exploiting the point-wise tangent, which is a local feature available to compute regardless of the density of lidar beams. Ground surface which is not perfectly planar is fitted by multiple (specifically 4 in our implementation) disjoint plane regions. By assuming these plane regions to be rectanglar and exploiting the integral image technique, our approach approximately finds the optimal region partition and plane hypotheses under the RANSAC scheme with real-time computational complexity.
Line-based Absolute and Relative Camera Pose Estimation in Structured Environments

3D lines in structured environments encode particular regularity like parallelism and orthogonality. We leverage this structural regularity to estimate the absolute and relative camera poses. We decouple the rotation and translation, and propose a novel rotation estimation method. We decompose the absolute and relative rotations and reformulate the problem as computing the rotation from the Manhattan frame to the camera frame. To compute this rotation, we propose an accurate and efficient two-step method. We first estimate its two degrees of freedom (DOF) by two image lines, and then estimate its third DOF by another image line. For these lines, we assume their associated 3D lines are mutually orthogonal, or two 3D lines are parallel to each other and orthogonal to the third. Thanks to our two-step DOF estimation, our absolute and relative pose estimation methods are accurate and efficient. Moreover, our relative pose estimation method relies on weaker assumptions or less correspondences than existing approaches. We also propose a novel strategy to reject outliers and identify dominant directions of the scene. We integrate it into our pose estimation methods, and show that it is more robust than RANSAC. Experiments on synthetic and real-world datasets demonstrated that our methods outperform state-of-the-art approaches.
A Biologically Inspired Contour Detection Model Based on Multiple Visual Channels and Multi-Hierarchical Visual Information

The information processing mechanism of a visual system captures the real contours of an object through multi-hierarchical processing and integration of the basic features (color, grayscale, etc.) of the image. In more complex situations, a reasonable and effective framework is often required to solve the high-precision problem of contour detection. In this paper, we propose a contour detection model inspired by the biological characteristics of multiple visual channels and multi-hierarchical visual information. In our model, we mainly include information processing channels for color and luminance. The color channel response is obtained by color-sensitive neurons of the primary visual cortex (V1) processing the single-opponent response of the retinal/lateral geniculate nucleus (LGN) layer, and the luminance channel response is selected by the linear X cell response and the nonlinear Y cell response, based on the luminance contrast. The texture suppression of the color and luminance channel responses is enhanced by introducing a sparse coding mechanism. Finally, the fusion weights of the color and luminance channel response energies are calculated using the optimal direction to obtain better detection performance. The experimental results show that our model performs well in complex situations in comparison to current advanced biologically inspired model and some machine learning-based models, further improving the robustness of the non-learning contour detection model.
Early Detection of Preeclampsia based on a Machine Learning Approach

Preeclampsia is one of the death causes of pregnant women and their unborn children. Preventive measures should be taken for detecting the illness as early as possible. The current paper determines the presence of preeclampsia using a non-invasive approach. The blood pressure measurements are generated using a smart bracelet which is part of the i-bracelet Eurostars project, "Intelligent bracelet for blood pressure monitoring and detection of preeclampsia". The considered input data are the age, weight and blood pressure measurements of every pregnant woman. The prediction model comprised a series of hidden states. The Viterbi algorithm was used to determine the presence of a certain hidden state, namely preeclampsia. The accuracy, sensitivity and specificity values of the tested solution prove that it is useful in remote and hospital settings.
Towards Autonomous Industrial-Scale Bathymetric Surveying

Both higher efficiency and cost reduction can be gained from automating bathymetric surveying for offshore applications such as pipeline, telecommunication or power cables installation and inspection on the seabed. We present a SLAM system that optimizes the geo-referencing of bathymetry surveys by fusing the dead-reckoning sensor data from the surveying vehicle with constraints from the maximization of the geometric consistency of overlapping regions of the survey. The framework has been extensively tested on bathymetric maps from both simulation and several actual industrial surveys and has proved robustness over different types of terrain. We demonstrate that our system is able to maximize the consistency of the final map even when there are large sections of the survey with reduced topographic variation. The framework has been made publicly available together with the simulation environment used to test it and some of the datasets.
A Reputation-aware Mobile Crowd Sensing Scheme for Emergency Detection

The unforeseen proliferation of smart devices has set in motion research efforts aimed at building Smart Cities (SCs) that improve the well-being of their citizens. One of the key technologies to achieve a SC is Mobile Crowd Sensing (MCS). In MCS, data is collected from the environment surrounding the smart device owners and utilized in the provision of a wide array of SC services. A prevalent class of services which is attracting increasing attention is smart emergency services, where MCS is leveraged to facilitate the detection and mitigation operations of crises. In this paper, we study the problem of an emergency situation detection based on MCS-provided data from heterogeneous participants. Specifically, we formulate our problem based on Detection Theory and underline its computational complexity. We present a greedy algorithm that aims to balance the trade-off between the decision time and the quality of the final decision. We perform extensive simulation experiments that show how our scheme improves the correct detection rate compared to a naive reputation-unaware baseline.
Learning Object Models For Non-prehensile Manipulation

Using models to represent information about the world is a well known paradigm for successful robot control in the real world. Numerous methods exist today that can leverage these models to make robots perform tasks, either by directly exploiting the model structure or by accessing the model via a simulation. In this work, we explore how robots can use demonstrations to quickly build descriptive models of objects for manipulation tasks. Our framework uses demonstrations to incrementally build task-relevant geometric and physics-based object models that can be used to build simulations of the world that the robot is interacting with. We present experiments that involve estimating geometric features of an object when demonstration data from a user interacting with the object is available. We also demonstrate our method on the task of toppling a box with a 7-DoF manipulator equipped with a palm at its end. Using our approach, the robot is able to complete the task using only a few demonstrations.
Bi-Modal Hemispherical Sensor: A Unifying Solution for Three Axis Force and Contact Angle Measurement

In robotic tasks that require physical interactions such as manipulation and legged locomotion, it is important to simultaneously measure contact forces and contact angles. This paper presents a unified solution for simultaneously measuring three axis contact forces and contact angles for legged locomotion or manipulation. Unlike most tactile sensors, the presented design utilizes the stress field method by sampling pressures over multiple locations within an elastomer, enabling inherently robust operation against impact and abrasive interactions. The presented sensor is designed for point-feet quadrupedal robots and can be easily scaled down for other applications such as grasping. The sampled stress distribution is mapped to output forces $f_{x}, f_{y}$, and $f_{\mathrm{z}}$ and two contact angles, $\theta$ and $\psi$ on the hemispherical sensor surface via Gaussian process regression. The prototype sensor is able track normal and shear forces accurately, achieving a normalized root mean (RMS) squared error of only 1.00%–1.36% for $f_{\mathrm{z}}$ across multiple tests with up to 180N normal force, and a normalized RMS error of 1.71%–4.67% and 1.82%–6.68% for fx and fy, respectively, with up to 80N shear force. Additionally, the footpad is able to estimate the contact location coordinates $\theta$ and $\psi$ with a normalized RMS error of 2.69%−7.51% over a range of $0-40^{\mathrm{o}}$ and 2.79%–9.62% over a range of $0-30^{\mathrm{o}}$, respectively. The footpad can estimate contact location over a maximum range of $\theta=\pm 45^{0}$ and $\psi=\pm 45^{\mathrm{o}}$, and can withstand over 450N of normal force at location $\theta =\psi =0^{\mathrm{o}}$ without reaching saturation. This prototype demonstrates the ability to simultaneously measure force in three axes and contact angles using Gaussian process regression, with the potential to explore other regression methods for embedded computing and miniaturization of the design for finger tip scale sensors.
NGD Synthesizer with Feedback Hybrid Coupler

This paper introduces a hybrid coupler based negative group delay (NGD) topology. The NGD circuit is built with a hybrid coupler with one of output and isolated access ways connected in feedback with a lossy transmission line (TL). It is modelled by S-parameter approach. The NGD topology characteristics are formulated. The synthesis relations enabling to determine the NGD circuit parameters in function of the desired NGD value are extracted. Parametric analyses in function of the TL attenuation and time-delay are discussed.
Enhancing Check-Reinforce Introduction With a Class Response System: The C2RI Method - A Five-Year Study

Some lecturers start their sessions by reviewing or summarizing the main contents covered in the previous session. In general, this review involves the teacher exposing the main concepts and, in some cases, asking about them. This approach, which could be called Check-Reinforce Introduction (CRI), might be seen as having one main drawback; namely, the restricted feedback that lecturers may receive from students due to shyness. Bearing in mind this limitation, we have created what we have called the Classroom Response System CRI (C2RI), which takes advantage of a smartphone-based Classroom Response System (CRS) to obtain more feedback from students during the CRI. We conducted a five-year study on teaching related to technological issues in order to obtain empirical data on whether students consider the use of CRI useful. This is, to our knowledge, the first study involving empirical quantitative data. For this purpose, during the study, we applied the new method (C2RI) to assess whether students prefer C2RI or CRI and whether students’ level of attention, motivation, and performance improved or not. Our findings show that the majority of students consider both methods useful, but the scores are higher in C2RI and they perceive higher level of attention with this method. We have also discovered that their motivation to study between lectures decreased using C2RI, which correlates with a slight decrease in student performance on exams, concluding that this method has to be designed in a way that does not create a false sense of confidence in the students.