Miracle-dz/UniFAR-FTUs huggingface dataset:
Frequency Domain Heart Period and QT Interval Variability Markers Are Linked to Arrhythmic Risk in Long QT Syndrome Type 2

Long QT syndrome type 2 (LQT2) is an inherited disease leading to arrhythmias and sudden death often in response to an abrupt sympathetic stimulus. Heart period (HP) and QT interval variability analyses were carried out over asymptomatic (ASYMP, n=10, age 39.0±11.0 yrs, 9 males) and symptomatic (SYMP, n=7, age 41.7±12.8 yrs, 4 males) LQT2 patients. Beat-to-beat series were derived from 24h Holter ECG recordings during daytime and nighttime. In addition to HP and QT mean and variance, we calculated HP power in high frequency (HF, from 0.15 to 0.5 Hz) band as a marker of vagal modulation directed to sinus node and QT power in low frequency (LF, from 0.04 to 0.15 Hz) band as a marker of sympathetic modulation directed to ventricles. HP and QT means exhibited significant day-night changes in both groups. ASYMP showed a shorter corrected QT and a more reactive vagal modulation leading to significant day-night variations of respiratory sinus arrhythmia. By contrast, SYMP had no significant day-night variations of the vagal control. Conversely, SYMP showed a higher sympathetic control during day compared to ASYMP, having a larger QT variance. These findings suggest that frequency domain HP and QT variability markers can account for the different arrhythmic risk of LQT2 ASYMP and SYMP patients.
Optimal Pose Estimation Method for a Multi-Segment, Programmable Bevel-Tip Steerable Needle

Needle pose tracking is fundamental to achieve a precise and safe insertion in minimally-invasive percutaneous interventions. In this work, a method for estimating the full pose of steerable needles is presented, considering a four-segment Programmable Bevel-Tip Needle (PBN) as a case study. The method estimates also the torsion of the needle that can arise during the insertion because of the interaction forces exerted between the needle and the insertion medium. A novel 3D kinematic model of the PBN is developed and used to predict the full needle pose during the insertion through an Extended Kalman Filter. The filter uses the position measurements provided by electromagnetic sensors located at the tip of the PBN segments as measurement data. The feasibility of the proposed solution is verified through ingelatin experiments, demonstrating remarkable performance with small errors in position (RMSE<1 mm) and orientation (RMSE< 3°) estimation, as well as good accuracy compared to a bespoke geometric pose reconstruction method.
Policy Evaluation and Dynamic Management Based on Matching Tree for XACML

As a widely recognized policy language of access control, the eXtensible Access Control Markup Language (XACML) is widely used with its fine-grained and easy-to-read. With the application of XACML, researchers find that the XACML based policy evaluation and policy management methods can no longer meet the current large-scale requests for efficient access and dynamic management requirements. To improve the performance of policy evaluation based on XACML, we propose a policy evaluation method based on the matching tree to search policy efficiently and avoid the extra consumption of invalid policy participation. Furthermore, we propose a policy dynamic management method based on the matching tree to reduce the scale of the policy to be disabled for management, by adding locks in the tree node and the information mapping table. Through theoretical derivation and the factors that may affect its evaluation performance, we verify the improvement of evaluation efficiency. The simulation also shows the improvement of the evaluation engine based on the matching tree compared with OuenAz.
Kinematic Multibody Model Generation of Deformable Linear Objects from Point Clouds

Control and localization of deformable linear objects (DLOs) require models to handle their deformation. This paper proposes an approach to automatically generate a model from available visual sensor information. Based on point cloud data obtained from a 3D stereo camera, the kinematics of a multibody model formulation are derived. The approach aims to balance the tradeoff between computational complexity and model accuracy. This is achieved with a geometric error criterion that reduces the introduced degrees of freedom (DOF) of the model to a necessary minimum, representing the continuous shape with as few bodies as possible. The approach is evaluated analytically and validated with an experimental scenario of DLO manipulation.
Quantifying Location Privacy Risks Under Heterogeneous Correlations

Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e.g., spatial cloaking, dummy location generation, query caching, and perturbation. However, these LPPMs are vulnerable to inference attacks because of the incompleteness of the captured privacy risks caused by heterogeneous correlations in location data, e.g., semantical, temporal, and social correlations. Consequently, they cannot provide sufficient privacy guarantees due to the absence of embedded heterogeneous correlations in the design process of LPPM. To address these issues, we present QUAD, a framework for quantifying location privacy risks under heterogeneous correlations. QUAD has three features: 1) it enables the modeling and seamless fusion of multiple kinds of correlations that are available to adversaries; 2) it provides a probabilistic representation of the privacy risks faced under heterogeneous correlations; and 3) it achieves the quantification of privacy risks for multiple kinds of LPPMs that are widely used in the literature. To mitigate privacy threats, we propose a defense mechanism embedded with the quantified privacy risks. Extensive experiments on two real-world datasets confirm that QUAD can capture more privacy risks than competitors, and the risks can be dramatically reduced by our defense mechanism.
Robust Dynamic State Estimation for Lateral Control of an Industrial Tractor Towing Multiple Passive Trailers

In this paper, we propose a dynamic state estimation framework for lateral control of a heavy tractor-trailers system using only mass-produced low-cost sensors. This issue is challenging since the lateral velocity of the lead tractor is difficult to measure directly. The performance of existing dynamic model-based estimation methods will also be degraded, as different trailers and payloads cause the tractor model parameters to change. We address this issue by incorporating a kinematic estimator into a dynamic model-based estimation scheme. Accurate and reliable tire cornering stiffness and dynamics-informed lateral velocity of the lead tractor can be output in real-time by using our method. The stability and robustness of the proposed method are theoretically proved. The feasibility of our method is verified by full-scale experiments. It is also verified that the estimated model parameters and lateral states do improve the control performance by integrating the estimator into a lateral control system.
MLTracer: Malicious Logins Detection System via Graph Neural Network

Malicious login, especially lateral movement, has been a primary and costly threat for enterprises. However, there exist two critical challenges in the existing methods. Specifically, they heavily rely on a limited number of predefined rules and features. When the attack patterns change, security experts must manually design new ones. Besides, they cannot explore the attributes' mutual effect specific to login operations. We propose MLTracer, a graph neural network (GNN) based system for detecting such attacks. It has two core components to tackle the previous challenges. First, MLTracer adopts a novel method to differentiate crucial attributes of login operations from the rest without experts' designated features. Second, MLTracer leverages a GNN model to detect malicious logins. The model involves a convolutional neural network (CNN) to explore attributes of login operations, and a co-attention mechanism to mutually improve the representations (vectors) of login attributes through learning their login-specific relation. We implement an evaluation of such an approach. The results demonstrate that MLTracer significantly outperforms state-of-the-art methods. Moreover, MLTracer effectively detects various attack scenarios with a remarkably low false positive rate (FPR).
Model predictive control-based path tracking control for automatic guided vehicles

In this study, a path tracking control method based on model predictive control (MPC) is presented to improve the results of path tracking. A simulation model of an automatic guided vehicle (AGV) is employed in a robot operating system (ROS). A collision free path is established with the ROS under the scene of continuous turning as a reference path. A linear MPC controller is used to control the AGV in the ROS, and a simulation experiment is performed with the combined simulation of ROS and MATLAB. Experimental results verify that the proposed MPC controller has a good tracking performance under complex road conditions.
Camera-Based Indoor Navigation in Known Environments with ORB for People with Visual Impairment

Indoor navigation is a hard problem for those with visual impairment or blindness. This paper presents a real-time camera-based indoor navigation application for the blind and visually impaired in known environments. Contrary to other systems for similar purposes, our system executes all computation locally on mobile devices and does not need any infrastructure-based sensor and signal support. The use of the system is divided into two stages: an offline stage, in which a sighted person helps with landmark identification and route planning, and an online stage, in which a visually impaired person can navigate with voice prompts from the app. Predefined landmark images and navigation information are stored in a database in the offline stage. Navigation information consists of the landmark position, the turning angle, the turning direction, and the distance to the next landmark. In the online stage, navigation information is retrieved from the current landmark image by successfully matching video frames in real time. A similarity score is calculated by ORB using Hamming distances. Voice feedback is provided for users with a text-to-speech function. Our system minimizes the error caused by users’ manipulation and improves stability and accuracy by updating similarity scores continuously.
Collaborative Deep Forest Learning for Recommender Systems

Collaborative filtering (CF) is one of the most practical approaches on recommendation systems by predicting users’ preferences for items based on the user-item interaction information. Besides the connections between users and items, social networks among users can provide auxiliary information to improve the performance of recommender systems. Here, we propose an end-to-end deep learning framework by learning latent social features to embed in a CF approach. First, representation learning is employed on the rating matrix to extract the latent social features. Then, a novel deep learning approach based on cascade tree forest is used in the recommendation process. Experiments on real-world datasets from different domains demonstrate that the proposed Collaborative Deep Forest Learning (CDFL) outperforms the state-of-the-art CF recommendation methods.
Adaptive Nonlinear Control For Perching of a Bioinspired Ornithopter

This work presents a model-free nonlinear controller for an ornithopter prototype with bioinspired wings and tail. The size and power requirements have been thought to allocate a customized autopilot on board. To assess the functionality and performance of the full mechatronic design, a controller has been designed and implemented to execute a prescribed perching 2D trajectory. Although functional, its ’handmade’ nature forces many imperfections that cause uncertainty that hinder its control. Therefore, the controller is based on adaptive backstepping and does not require any knowledge of the aerodynamics. The controller is able to follow a given reference in flight path angle by actuating only on the tail deflection. A novel space-dependent nonlinear guidance law is also provided to prescribe the perching trajectory. Mechatronics, guidance and control system performance is validated by conducting indoor flight tests.
A three-dimensional hexagonal fluorine-doped tin oxide nanocone array: a superior light harvesting electrode for high performance photoelectrochemical water splitting

Photonic nanostructures hold great promise in promoting light harvesting. Here we report the first design and construction of a three-dimensional (3D) hexagonal nanocone array of fluorine-doped tin oxide (FTO) on glass as an excellent electrode for photoelectrochemical (PEC) water splitting. The PEC current density with suitably deposited Ti-doped hematite at 1.23 V vs. the reversible hydrogen electrode (RHE) was increased by 86% to 2.24 ± 0.02 mA cm−2 compared to that with the planar counterpart, mainly ascribable to the special light harvesting effect and the electrode surface area provided by 3D FTO. Upon the embedment of a gold layer to concentrate the incident light onto the hematite layer and the deposition of the Co–Pi catalyst with a modified procedure, the photocurrent experienced a large cathodic shift of onset potential by 360 mV and soared to a high value of 3.39 ± 0.01 mA cm−2 (at 1.23 V), yielding a power conversion efficiency of 0.70% at a potential as low as 0.88 V vs. RHE.
Examination of Screen-Indicated Methods of Gait Training System with Real-time Audiovisual Feedback Function of Ground Reaction Force*

In gait training for walking rehabilitation of patients with stroke hemiplegia or bone joint conditions such as fractures, it is important to recognize the load of the affected lower limbs for improving gait ability and avoiding risks such as re-fractures. A weight scale is used at the actual rehabilitation site to recognize the load. However, in this situation, the trainee must look down to verify whether the scale and their walking posture are correct. In addition, the trainee generally cannot read the load value accurately. Therefore, we have developed a system that can show the load in real time on an eye-level display. By using this system, we expect the patients to be able to perform gait training smoothly while recognizing the state of walking. In this paper, we have reported the results of a clinical trial held at a rehabilitation hospital and an examination of the screen-indicated methods.
Tumbling and Hopping Locomotion Control for a Minor Body Exploration Robot

This paper presents the modeling and analysis of a novel moving mechanism "tumbling" for asteroid exploration. The system actuation is provided by an internal motor and torque wheel; elastic spring-mounted spikes are attached to the perimeter of a circular-shaped robot, protruding normal to the surface and distributed uniformly. Compared with the conventional motion mechanisms, this simple layout enhances the capability of the robot to traverse a diverse microgravity environment. Technical challenges involved in conventional moving mechanisms, such as uncertainty of moving direction and inability to traverse uneven asteroid surfaces, can now be solved. A tumbling locomotion approach demonstrates two beneficial characteristics in this environment. First, tumbling locomotion maintains contact between the rover spikes and the ground. This enables the robot to continually apply control adjustments to realize precise and controlled motion. Second, owing to the nature of the mechanical interaction of the spikes and potential uneven surface protrusions, the robot can traverse uneven surfaces. In this paper, we present the dynamics modeling of the robot and analyze the motion of the robot experimentally and via numerical simulations. The results of this study help establish a moving strategy to approach the desired locations on asteroid surfaces.
Semi-Supervised Partial Multi-Label Learning

Partial multi-label learning (PML) deals with problems where each instance is associated with a candidate label set, which contains multiple relevant labels and some noisy labels. In many real-world scenarios, it is impractical to annotate all examples for a huge-size dataset. Instead, a more common case is that only a small set of the data are annotated with partial labels, while most data are unlabeled. In this paper, we formalize such problems as a new learning framework called Semi-Supervised Partial Multi-label Learning (SSPML). To solve the SSPML problem, a latent label variable is introduced for each example as the low-dimensional embedding of the feature space. On one hand, label variables are recovered by encouraging consistent similarity measurement between the feature space and the label space; on the other hand, the similarities are adaptively updated based on the feedback from the label space. Meanwhile, the multi-label classifier is jointly trained under the supervision of label variables. Extensive experiments on multiple datasets from various real-world tasks validate the effectiveness of the proposed approach.
A Review of Modulation and Control Techniques for Multilevel Inverters in Traction Applications

Traction inverter has been the subject of many studies due to its essential role in the proper performance of the drive system. With the recent trend in increasing the input voltage in battery-powered electric vehicles, multilevel inverters have been proposed in the literature as a promising substitute for conventional two-level traction inverters. A critical aspect of utilizing multilevel structures is employing proper control and modulation techniques. The control system structure must be capable of handling a number of key issues, like capacitor voltage balancing and equal power loss sharing, which arise in multilevel topologies. This paper presents a review of the present-day traction drive systems in the industry, control and modulation techniques for multilevel structures in the inverters, as well as the principal challenges that need to be addressed in the control stage of the multilevel traction inverter. A comparison has been made between different methods based on the most important criteria and requirements of the traction drive system. Finally, future trends in this application are presented and some suggestions have been made for the next generation of traction drives.
π-Map: A Decision-Based Sensor Fusion with Global Optimization for Indoor Mapping

In this paper, we propose π-map, a tightly coupled fusion mechanism that dynamically consumes LiDAR and sonar data to generate reliable and scalable indoor maps for autonomous robot navigation. The key novelty of π-map over previous attempts is the utilization of a fusion mechanism that works in three stages: the first LiDAR scan matching stage efficiently generates initial key localization poses; the second optimization stage is used to eliminate errors accumulated from the previous stage and guarantees that accurate large-scale maps can be generated; then the final revisit scan fusion stage effectively fuses the LiDAR map and the sonar map to generate a highly accurate representation of the indoor environment. We evaluate π-map on both large and small environments and verify its superiority over existing fusion methods.
Active DNN IP Protection: A Novel User Fingerprint Management and DNN Authorization Control Technique

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and −0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.
Dissimilarity Random Forest Clustering

In this paper we present DisRFC (Dissimilarity Random Forest Clustering), a novel Random Forest Clustering approach which, contrarily to current methods which require in input a vectorial representation, works only with dissimilarities, thus being applicable also to all those problems where a vectorial representation is not available but a descriptive dissimilarity measure can be computed. In the DisRFC approach objects to be clustered are first modelled with a novel RF variant called Unsupervised Dissimilarity Random Forest (UD-RF), which functioning mechanisms are both unsupervised and based on dissimilarities. The trained UD-RF is then used to project objects in a binary vectorial space, where effective Kmeans procedures can be used to obtain the final clustering. In the paper we present different variants of DisRFC, thoroughly and positively evaluated using 10 different problems.