Miracle-dz/UniFAR-FTUs huggingface dataset:
Explanations for Non-validation in SHACL

The Shape Constraint Language (SHACL) is a recently standardized language for expressing constraints on RDF graphs. It is the result of industrial and academic efforts to provide solutions for checking the quality of RDF graphs and for declaratively describing (parts of) their structure. We recommend [9] for an introduction to SHACL and its close relative ShEx. Among other, the SHACL standard provides a syntax for writing down constraints, as well as describes the way RDF graphs should be validated w.r. t. to a given set of SHACL constraints. However, some aspects of validation were not completely specified in the standard, like the semantics of validation for constraints with cyclic dependencies. To address these shortcomings, several formalizations of SHACL grounded on logic-based languages with clear semantics have recently emerged [7,2,11]. In SHACL, the basic computational problem is to check whether a given RDF graph G validates a SHACL document (C, T ), where C is a specification of validation rules (constraints) and T is a specification of nodes to which the validation rules should apply (targets). In order to make SHACL truly useful and widely accepted, we need automated tools that implement not only validation, which results in “yes” or “no” answers, but also support the users in their efforts to understand the reasons why a given graph validates or not against a given document. The SHACL specification stresses the importance of explaining validation outcomes and introduces the notion of validation reports for this purpose. If a graph validates a document, the standard has clear guidance how the validation reports should look like. However, the situation is different when the graph does not validate. The principles of validation reports in case of non-validation are left largely open in the standard, which specifies little beyond requiring that the node and constraint violated are indicated. It is not hard to see that, in general, there may be a very large number of possible reasons for a specific validation target to fail, and it is far from obvious what should be presented to the user in validation reports. This is precisely the topic of our study.
VisionKG: Towards A Unified Vision Knowledge Graph

Computer Vision (CV) has recently achieved signiﬁcant improvements, thanks to the evolution of deep learning. Along with advanced architectures and optimisations of deep neural networks, CV data for (cross-datasets) training, validating, and testing contributes greatly to the performance of CV models. Many CV datasets have been created for diﬀerent tasks, but they are available in heterogeneous data formats and semantic representations. Therefore, it is challenging when one needs to combine diﬀerent datasets either for training or testing purposes. This paper proposes a uniﬁed framework using the Semantic Web technology that provides a novel way to interlink and integrate labelled data across diﬀerent data sources. We demonstrate its advantages via various scenarios with the system framework accessible both online and via APIs. 4
Image Representation of a City and Its Taxi Fleet for End-To-End Learning of Rebalancing Policies

In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achieve good customer service and minimize cost while trying to keep the impact on the environment as low as possible. A promising approach is to coordinate the control of the entire fleet, which is foreseen to become even easier with the possibility of autonomous vehicles in mind. Anticipating future demand requires a good understanding of the spatiotemporal distributions of request origins and destinations, and the resulting imbalance between vehicle demand and availability. This results from a multitude of topological, demographic, and social effects, which are almost impossible to sufficiently capture in a handcrafted model of reasonable complexity. This can be circumvented by leveraging machine learning approaches. In this paper, an image-like representation of the city and its fleet's state is introduced. It is comprehensive and intuitive to use as input to convolutional neural networks, which in the past have already been proven to capture spatial relationships very well. This allows operating on realistic, full-sized traffic networks without greatly increasing the number of parameters the neural network has to learn and, hence, keeps the training effort low. Additionally, this state is combined with a similarly constructed repositioning action, reflecting a 2D distribution of a well-performing operational policy. This approach allows replacement of complex, handcrafted mathematical models by a single, compact, auto-encoder-like neural network.
Improving Grasp Classification through Spatial Metrics Available from Sensors

We present a method for classifying the quality of near-contact grasps using spatial metrics that are recoverable from sensor data. Current methods often rely on calculating precise contact points, which are difficult to calculate in real life, or on tactile sensors or image data, which may be unavailable for some applications. Our method, in contrast, uses a mix of spatial metrics that do not depend on the fingers being in contact with the object, such as the object’s approximate size and location. The grasp quality can be calculated before the fingers actually contact the object, enabling near-grasp quality prediction. Using a random forest classifier, the resulting system is able to predict grasp quality with 96% accuracy using spatial metrics based on the locations of the robot palm, fingers and object. Furthermore, it can maintain an accuracy of 90% when exposed to 10% noise across all its inputs.
Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots

Object slip perception is essential for mobile manipulation robots to perform manipulation tasks reliably in the dynamic real-world. Traditional approaches to robot arms’ slip perception use tactile or vision sensors. However, mobile robots still have to deal with noise in their sensor signals caused by the robot’s movement in a changing environment. To solve this problem, we present an anomaly detection method that utilizes multisensory data based on a deep autoencoder model. The proposed framework integrates heterogeneous data streams collected from various robot sensors, including RGB and depth cameras, a microphone, and a force-torque sensor. The integrated data is used to train a deep autoencoder to construct latent representations of the multisensory data that indicate the normal status. Anomalies can then be identified by error scores measured by the difference between the trained encoder’s latent values and the latent values of reconstructed input data. In order to evaluate the proposed framework, we conducted an experiment that mimics an object slip by a mobile service robot operating in a real-world environment with diverse household objects and different moving patterns. The experimental results verified that the proposed framework reliably detects anomalies in object slip situations despite various object types and robot behaviors, and visual and auditory noise in the environment.
Motion Coupling Analysis for the Decoupled Design of a Two-segment Notched Continuum Robot

Multi-segment continuum robots, that offer inherent compliance and distal dexterity, are suitable for deployment in minimally invasive surgical procedures. Cable-driven mechanism is commonly used in continuum surgical robots but could lead to inter-segment motion coupling in a multi-segment robot. In this paper, we present a coupled mechanics model for a two-segment notched continuum robot to analyze the coupled deflection in the proximal segment due to the distal cable force. The model has been developed for two different conditions in which the proximal segment is initially bent (general condition) and initially straight (special condition). It allows us to introduce a decoupled design methodology that systematically determines a stiffness parameter in each of the segments, based on the desired coupled bending angle and other design requirements. Using the method, we fabricated a decoupled notched continuum robot and evaluated the model accuracy compared with experimental data with mean errors of 0.57° and 0.61°, respectively for general and special conditions throughout the 90° distal segment bending angle. It was also shown in a demonstration in a maxillary sinus phantom that the distal segment was capable of independently perform omnidirectional steering without the proximal segment getting in contact with its surrounding nasal wall.
Effects of Sensor Resolution and Localization Rate on the Performance of a Myokinetic Control Interface

Magnetic tracking systems have been widely investigated in biomedical engineering due to the transparency of the human body to static magnetic fields. We recently proposed a novel human-machine interface for prosthetic application, namely the myokinetic interface. This controls multi-articulated prostheses by tracking magnets implanted in the residual muscles of individuals with amputation. Previous studies in this area focused solely on the choice and tuning of the localization algorithm. Here, we addressed the role of the intrinsic properties of the sensors, by analysing their effects on the tracking accuracy and on the computation time of the localization algorithm, through experimentally-verified computer simulations. We observed that the tracking accuracy is primarily affected by the localization rate, which is directly related to the sampling frequency of the sensors, and less significantly affected by the sensor resolution. The computation time, instead, proved positively correlated to the number of MMs, and negatively correlated with the localization rate. Our results may contribute to the development of novel human-machine interfaces for prosthetic limbs and could be extended to a broad range of applications involving magnetic tracking.
A Stable Control Strategy for Industrial Robots with External Feedback Loop

The inner/low-level control loop of most industrial robotic manipulators is protected from any modification by a closed control architecture. The only way to specify joint inputs to them is through position or velocity commands. Furthermore, the inner controller is unknown/uncertain, as it is not revealed to the user. This makes it very difficult to determine the configuration of the inner controller, including the structure and values of the control gains. As a result, integrating external sensory feedback systems into a closed architecture system becomes a difficult task because the interaction of the external feedback loop with the inner control loop can affect the stability of the overall system. In this paper, a stable control strategy is proposed to generate the joint velocity commands for industrial robotic manipulators with uncertain closed control architecture. Based on the proposed method, external feedback controllers can be added regardless of the inner control loop configuration. The proposed external (or outer) control loop approach provides a high degree of design flexibility by enabling smooth implementation of various modern control applications on industrial robots. Unlike previous studies, the proposed control method is not limited to a particular configuration of the controller in the inner loop, nor to the structure of its control gains. The proposed controller design is validated on the UR 5e industrial manipulator.
Complexity-Reduced Super Resolution for Foveation-based Driving Head Mounted Displays

In this paper, we propose a foveation-based super resolution (SR) algorithm to create high resolution images from low resolution inputs for virtual reality head mounted displays. Because the proposed SR algorithm is integrated in the previous foveation-based driving technology to cover the small area around the foveation point that requires high rendering quality, the overall computational complexity is substantially reduced, compared to the whole area SR. The target display has 4 times as high resolution as the input image, therefore, the proposed SR algorithm generates 4× as well as 2× SR images at the same time. To support two SR output images, small area SR, and small number of weights, we employ cropping as well as progressive and recursive framework used in the previous MS-LapSRN. We reduce the number of neurons by placing the deconvolutional layer after convolutional layers, compared to the MS-LapSRN. PSNR and SSIM performances of the proposed SR for the 4× scale are estimated as 31.152 dB and 0.935 for Set5, 26.656 dB and 0.858 for Set14, 27.138 dB and 0.830 for BSD100, and 25.078 dB and 0.836 for Urban100. For the target 8K display of 7,680×4,320, the proposed FovSR-integrated driving technology achieves the substantial reductions by 76.7 % and 99.02 % on the number of lines from 7,680 to 1,788 and the number of neurons from 24,518,246,400 to 239,541,184, respectively.
Dynamic U-Net Using Residual Network for Iris Segmentation

Biometric applications have taken tremendous attention these days due to technological advancements and the high demand for safety and security systems. Regardless of the existing biometric traits such as fingerprints, palm, face, retina, voice, and gait, the iris is known as the most consistent and precise trait. Iris segmentation is the most significant and essential stage in the iris recognition process. The segmentation method is precisely related to the performance accuracy of iris recognition. In this study, we proposed a Dynamic U-Net using ResNet-34 to improve the segmentation results based on the F1 score. The proposed method would produce a better accuracy on the condition of applying post-processing. However, based on the comparative analysis with other methods in the literature, our proposed method has produced a higher F1 score. The segmentation results were compared with the Unified IrisParseNet. Our proposed method has produced 93.66% accuracy, which higher than Unified IrisParseNet at 93.05%, respectively. The computational time is also high, which can be further improved in future work.
Highly Efficient Line Segment Tracking with an IMU-KLT Prediction and a Convex Geometric Distance Minimization

Line segment features become popular in SLAM community. Usually, line-based SLAM systems utilize local appearance descriptors for line segment tracking. However, traditional descriptor-based line segment tracking algorithms suffer from the problem that accuracy and speed cannot be possessed simultaneously, which affects the performance of line-based SLAM systems negatively. We propose a novel line segment tracking method with an IMU-KLT line segment prediction and a convex geometric distance minimization to boost line segment tracking performance in both accuracy and speed. Particularly, the proposed convex geometric distance minimization uses a ℓ1-norm model to minimize geometric constraints between predicted line segments and extracted line segments efficiently. Furthermore, the line segment tracking is embedded into a VIO system and we adapt it to obtain more reliable point tracking. Experimental results on public datasets show that the proposed line segment tracking method achieves much higher accuracy and much less time cost than state-of-the-art level, where not only the number of correct matches increases but also the inlier ratios are increased by at least 35.1% along with a 3 times faster speed. Besides, the VIO system combining the proposed line segment tracking is improved in terms of accuracy.
3-D Semi-Analytical Model of a Superconducting Axial Flux Modulation Machine

This article presents a 3-D semi-analytical model of a partially superconducting axial flux modulation machine for the computation of the flux density distribution, the torque, and the eddy-current losses in the armature. The modulation of the superconducting coil magnetic field by the superconducting bulks is calculated by solving the Maxwell’s equations in scalar potential with the variable separation method. Using Fourier series analysis and the convolution theorem, the bulk region is modeled by a domain with a spatially variable magnetic permeability. This variable permeability enables considering a diamagnetic bulk as well as a more complex magnetic behavior using the Bean model. The results of the proposed model are compared with 3-D finite-element models and good agreement is obtained for a significantly reduced computation time. The proposed model appears to be a suitable tool for optimization and sizing processes.
Assistive supernumerary grasping with the back of the hand

The Dorsal Grasper, an assistive wearable grasping device, incorporates supernumerary fingers and an artificial palm with the forearm and back of the hand, respectively. It enables power wrap grasping and adduction pinching with its V-shaped soft fingers. Designed with C6/C7 spinal cord injury in mind, it takes advantage of active wrist extension that remains in this population after injury. We propose that allowing the operator to actively participate in applying grasp forces on the object, using the back of the hand, enables intuitive, fast and reliable grasping relevant for the execution of activities of daily living. Functional grasping is tested in three normative subjects and a person with C6 SCI using the Grasp and Release Test. Results indicate that this device provides promising performance on a subset of objects that complements the existing compensatory strategies used by people with C6/C7 SCI. We find that the addition of the artificial palm is important for increasing maximum grip strength, by increasing contact friction and protecting the opisthenar.
Temperature-Dependent Properties of Terahertz Window Materials

The dielectric properties of common terahertz window materials (quartz and Teflon) at different temperatures were measured by terahertz time-domain spectroscopy (TDS). The applicability of these materials at temperature up to 230 °C was discussed, and the changes of refractive index and absorption coefficient were analyzed combining with Raman spectroscopy. The optical properties of 6H-SiC crystal in the frequency range of 0.2-2 THz were characterized, indicating that it can be used for terahertz optical devices such as lenses. The temperature- dependent measurements show that all the three materials have good temperature stability.
Contact Forces Preintegration for Estimation in Legged Robotics using Factor Graphs

State estimation, in particular estimation of the base position, orientation and velocity, plays a big role in the efficiency of legged robot stabilization. The estimation of the base state is particularly important because of its strong correlation with the underactuated dynamics, i.e. the evolution of center of mass and angular momentum. Yet this estimation is typically done in two phases, first estimating the base state, then reconstructing the center of mass from the robot model. The underactuated dynamics is indeed not properly observed, and any bias in the model would not be corrected from the sensors. While it has already been observed that force measurements make such a bias observable, these are often only used for a binary estimation of the contact state. In this paper, we propose to simultaneously estimate the base and the underactuation state by exploiting all measurements simultaneously. To this end, we propose several contributions to implement a complete state estimator using factor graphs. Contact forces altering the underactuated dynamics are pre-integrated using a novel adaptation of the IMU pre-integration method, which constitutes the principal contribution. IMU pre-integration is also used to estimate the positional motion of the base. Encoder measurements then participate to the estimation in two ways: by providing leg odometry displacements which contributes to the observability of IMU biases; and by relating the positional and centroidal states, thus connecting the whole graph and producing a tightly-coupled whole-body estimator. The validity of the approach is demonstrated on real data captured by the Solo12 quadruped robot.
Design and Development of a Robotic Bioreactor for In Vitro Tissue Engineering

In this study, a novel robotic bioreactor is presented with capabilities of closed-loop control of force and displacement applied to a tissue scaffold and tissue scaffold stiffness calculation. These characteristics bring the potential of a robotic bioreactor that can optimize the mechanical properties of tissue constructs in order for them to match those of native tissues. Custom position and force control signals are designed to maintain a steady tensioning of the tissue scaffold while the latter one’s mechanical properties evolve in time. We propose a simple model to support the hypothesis that the stiffness of a cell-seeded scaffold increases over time, and thus force control signals need to be adjusted accordingly. The robotic bioreactor is able to measure the stiffness of a scaffold sample relatively accurately, with an average standard deviation of 0.2N/mm. The combination of accurate stiffness measurements and a closed-loop control system equips the robotic bioreactor with the fundamental requirements to achieve stiffness based force control in future in vitro experiments, and thus to a tissue-scaffold responsive technology for advanced tissue engineering.
Robust Adaptive Synchronization of Interconnected Heterogeneous Quadrotors Transporting a Cable-Suspended Load

We tackle the problem of multiple quadrotors transporting a cable-suspended point-mass load. The quadrotors are treated as a virtual leader-follower algorithm, where a multi-layer graph encapsulates the communication and physical interaction. On the one hand, the communication stands for the approach of following the reference trajectory of a virtual leader. On the other hand, the load exerts a distributed tension force on each cable which is modeled as the well-known spring-damping system to each quadrotor establishing an interconnected dynamic. We assume cables are stretchable and have neglectable mass. Both objectives are accomplished through a Model Reference Adaptive Control approach with a robust modification that treats uncertainties and perturbations given by error in parameters, noise in the signal, and the wind drag forces. We prove stability based on Lyapunov approach and the results are shown through simulation.
In-Process Workpiece Geometry Estimation for Robotic Arc Welding based on Supervised Learning for Multi-Sensor Inputs

Due to manufacturing tolerances, the geometry parameters of workpieces are not constant in industrial welding applications. Today, this problem is addressed by either accepting fluctuating part quality or by measuring the geometry and adjusting the configuration of the robot and process controller for each individual part. However, measuring the geometry requires additional manufacturing time or reduces the accessibility of the welding tool. This paper presents an approach for estimating the workpiece geometry based on process data during the welding process, which eliminates the need of measuring the geometry in advance, yet provides the same accessibility of the welding tool. For this approach, a data set is generated by conducting welding experiments with varying part geometries. Welding current, welding voltage and acoustic emissions are recorded. Three supervised learning techniques, which are Support Vector Machines, Random Forest Decision Trees and k-Nearest-Neighbors, are used to train models for estimating the geometry. The results show, that the trained models successfully estimate the part geometry with over 90% accuracy.