Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting

Given the growing installed capacity, wind energy will exert a profound impact on the flexibility of modern energy systems. Wind power forecasting is a practical solution for dealing with the attributed variations and uncertainties, balancing supply and demand, and improving the reliability of the system. To achieve more accurate and generalizable forecast models, comprehensive data sets, supplied by multiple wind farms owing to their spatio-temporal dependencies, are required. In addition, data aggregation/collaboration across many wind farms scattered around a country is difficult, if not impossible, due to complex administrative processes, industry competition, and data privacy and security concerns. This article offers federated learning-based wind energy forecasting as a novel decentralized collaborative modeling method capable of training a single model on data from many wind farms without jeopardizing the privacy or security of data. To this end, rather than sending private data across sites, local model parameters are securely transmitted. A comparison between the proposed private distributed model and non-private centralized and fully private localized models indicates the high performance of the proposed federated learning-based wind power forecasting with 87.96% accuracy. Enjoying the smoothing effect, the higher generalizability of the proposed model with 83.63% accuracy is also substantiated in comparison to localized and centralized approaches while the privacy of the underlying data is preserved.
In-ConcReTeS: Interactive Consistency meets Distributed Real-Time Systems, Again!

The problem of replica coordination is fundamental to building Byzantine fault-tolerant (BFT) distributed systems. Seminal BFT architectures for safety-critical real-time systems from the eighties and nineties relied on custom processors and networks, and are hence not readily usable today. Modern-day deployments on cloud platforms do not “scale down” to embedded platforms and are not designed around timeliness. Recent work on real-time BFT protocols focuses on simulations and reliability analyses. In short, there exist no easily programmable BFT libraries that can be conveniently retrofitted onto real-time applications with deadlines and that perform well on embedded platforms. We propose In-ConcReTeS, a BFT key-value store designed for building highly reliable control applications on commodity embedded platforms. At its core, In-ConcReTeS is a real-time friendly redesign and an efficient implementation of a BFT protocol used by seminal fault-tolerant architectures. We evaluated In-ConcReTeS using an inverted pendulum simulation and an automotive benchmark on a cluster of four Raspberry Pis connected over Ethernet. Our results show that, unlike Redis and etcd, In-ConcReTeS can repeatedly synchronize hundreds of key-value pairs, while tolerating faults, every tens of milliseconds.
Sampling-Based View Planning for MAVs in Active Visual-inertial State Estimation

Micro aerial vehicles usually have strap-down sensors on the vehicle body, leading to the severe coupling effect between perception and trajectory planning. As a result, visual-inertial simultaneous localization and mapping (VI-SLAM) technologies implemented on MAVs suffer from tracking failure problems, especially in featureless environments. To overcome these challenges, based on MAVs with movable camera mechanisms (e.g., gimbal stabilizer, pan-tilt, or bionic neck-eye system), we proposed two sampling-based algorithms for known and unknown environments respectively. The first active perception planning algorithm based on a scene richness model is developed with a built feature map for the environment. Differ from the first algorithm, the second one is modified for active localization in unknown 3D space. It is basically a time-based sampling-based approach that uses the same scene richness model. In addition, it also achieved a balance between exploitation and exploration. With the above solutions, the robustness of visual perception is improved while avoiding over-exploitation of known information. Simulation and real-world experiments are performed to verify the feasibility of our algorithms.
Vision-Assisted Localization and Terrain Reconstruction with Quadruped Robots

Legged robots, specifically quadruped robots, have good locomotion performance in complex and rugged terrain and are becoming widely used in field exploration and rescue missions. To achieve full autonomy in such scenarios, robots need not only accurate localization but also an accurate understanding of the surrounding terrain, which will be used for robots path planning and foothold planning. However, due to the kinetic characteristic and limitation of size, quadruped robots have the disadvantages of high-frequency jitter and limited field of sensors, which lead to some challenges in environmental perception. In this paper, we propose a vision-assisted rugged terrain environment reconstruction and localization method for quadruped robots. We use a depth camera to assist in the generation of high-precision localization and terrain reconstruction results, which can help achieve the autonomous mobility of quadruped robots in this environment. We test our method on a quadruped robot platform. Our experimental results show less error and lower drift in different stairs terrain types than the commonly used lidar-based localization method.
H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry

In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measurements for each visual feature, our method first densifies a LiDAR scan with a deep depth completion network and then fuses it with visual deep depth map estimation in a Bayesian framework. This method reduces pose estimation drift by improving feature-to-feature and point-to-feature matching, as well as scale recovery. The evaluations on the public KITTI odometry benchmark show that our technique achieves better or at least comparable estimates than the state-of-the-art visual-LiDAR and monocular visual odometry approaches.
An experimental study and modelling of roughness effects on laminar flow in microchannels

Three different approaches were used in the present study to predict the influence of roughness on laminar flow in microchannels. Experimental investigations were conducted with rough microchannels 100 to 300μm in height (H). The pressure drop was measured in test-sections prepared with well-controlled wall roughness (periodically distributed blocks, relative roughness k* =k/0.5H≈0.15) and in test-sections with randomly distributed particles anchored on the channel walls (k* ≈0.04–0.13). Three-dimensional numerical simulations were conducted with the same geometry as in the test-section with periodical roughness (wavelength L). A one-dimensional model (RLM model) was also developed on the basis of a discrete-element approach and the volume-averaging technique. The numerical simulations, the rough layer model and the experiments agree to show that the Poiseuille number Po increases with the relative roughness and is independent of Re in the laminar regime (Re<2000). The increase in Po observed during the experiments is predicted well both by the three-dimensional simulations and the rough layer model. The RLM model shows that the roughness effect may be interpreted by using an effective roughness height keff. keff/k depends on two dimensionless local parameters: the porosity at the bottom wall; and the roughness height normalized with the distance between the rough elements. The RLM model shows that keff/k is independent of the relative roughness k* at given k/L and may be simply approximated by the law: keff/k = 1 − (c(ϵ)/2π)(L/k) for keff/k>0.2, where c decreases with the porosity ϵ.
Development and validation of a teacher awareness questionnaire about dyslexia

Background: Dyslexia is a learning disability that affects children of school-going age and exists in all cultures and backgrounds. Dyslexic children are deficient in phonological awareness, which makes the children to fail to attain the skills of reading, writing and spelling commensurate with their intellectual abilities. Inadequate knowledge about the nature of dyslexia by primary school teachers results in poor categorisation of at-risk children.Aim: This study sought to achieve two objectives. The first was to develop and validate the Teachers Awareness Questionnaire (TAQ), while the second goal was to use the validated TAQ to assess primary school teachers’ level of awareness about dyslexia.Setting: Primary school teachers in Nsukka and nearby rural communities filled the TAQ and the Scale of Knowledge and belief about Developmental Dyslexia. Nsukka is called the university town and it is a slow-paced environment that is devoid of the usual hustle and bustle of most major towns in Nigeria.Methods: The cross-sectional design was employed to gather data for the measurement process. Data were analysed using bivariate correlations and descriptive statistics.Results: The TAQ has good internal consistency (r = 0.77) and an adequate convergent validity (r = 0.74, p 0.01). Results also reveal that primary school teachers possessed low level of awareness about dyslexia.Conclusion: The study findings show that primary school teachers in Nigeria lack the requisite knowledge about dyslexia. The study suggests psycho-education for primary school teachers to empower them with information about the condition.Contribution: This study measured primary school teachers’ level of awareness about dyslexia. The results showed that primary school teachers’ awareness about dyslexia is poor, and this suggests that the use of labels (e.g., dullard) by some primary school teachers to describe their pupils may be incorrect. The study recommend to School Management Boards to take action to improve the teachers level of awareness about dyslexia, which would promote early identification of dyslexic pupils and possible intervention.
Learning from Demonstration using a Curvature Regularized Variational Auto-Encoder (CurvVAE)

Learning intricate manipulation skills from human demonstrations requires good sample efficiency. We introduce a novel learning algorithm, the Curvature-regularized Variational Auto-Encoder (CurvVAE), to achieve this goal. The CurvVAE is able to model the natural variations in human-demonstrated trajectory data without overfitting. It does so by regularizing the curvature of the learned manifold. To showcase our algorithm, our robot learns an interpretable model of the variation in how humans acquire soft, slippery banana slices with a fork. We evaluate our learned trajectories on a physical robot system, resulting in banana slice acquisition performance better than current state-of-the-art.
Robotic Actuation and Control of a Catheter for Structural Intervention Cardiology

Structural intervention cardiology (SIC) interventions are crucial procedures for correcting heart valves, walls, and muscle form defects. However, the possibility of embolization or perforation, as well as the lack of transparent vision and autonomous surgical equipment, make it difficult for the clinician. This paper proposes a robot-assisted tendon-driven catheter and machine learning-based path planner to overcome these challenges. Firstly, an analytical inverse kinematic model is constructed to convert the tip location in the Cartesian space to the tendons' displacement. Then inverse reinforcement learning algorithm is employed to calculate the optimal path to avoid possible collisions between the catheter tip and the atrial wall. Moreover, a closed-loop feedback controller is adopted to improve positioning accuracy in a direct distal position measurement manner. Simulation and experiments are designed and conducted to demonstrate the feasibility and performance of the proposed system.
Public Perception on Safety of Autonomous Ferry in the Norwegian Context

This study explores perceived safety of autonomous ferries by public in the Vestfold and Telemark region of Norway. There is a greater focus on automation of ships in this region, largely due the developed of the Yara Birkeland project in the region. The prevalence of existing ferry services in the region makes it natural to knot the technological development together and explore prospects of autonomous ferries. Using structured web-surveys, we found that respondents prefer lower degree of automation, mainly ships with Decision Support System (DSS) and semi-autonomous ships controlled from a Remote Control Center (RCC) with reduced crew on abord. Respondents are skeptical towards fully RCC controlled as well as fully autonomous ferries. Among the respondents, male sample, those very good at swimming as well as technology, and those using ferry once every month are relatively optimistic about safety of autonomous ferries. On the other hand, older people perceive autonomous ferry more risky than younger ones.
Power and Area Efficient Charge-Pump Design for PLL Application

As the design of charge-pump (CP) circuits has progressed, some notable non-idealities, such as current mismatch, phase noise, and reference spur, have been noticed. One of the most important aspects of an effective CP design is lock-in time, yet achieving it always presents a challenge for usage in high-speed mobile communication. This work describes a straightforward drain switching-based charge-pump that uses minimal electricity. There is just an estimated 42.60 µm2 of layout area. The suggested circuit is modelled using the CADENCE VIRTUOSO platform with 90nm CMOS technology working at 1.25GHz at 1.2V supply voltage. In comparison to the preconfigured circuits, the phase noise is stated to be -99.43 dBc/Hz @10MHz, and the current-mismatch is estimated to be 0.73% with an output range of 0-0.85V and lock-in time of 26ns. This qualifies it for PLL applications. To verify the effectiveness of a novel charge-pump circuit, Monte Carlo and process corner analysis are used.
Detecting Fake News in Benchmark English News Dataset Using Machine Learning Classifiers

Misguided news that is presented as false news or that intentionally confounds the truth and the false for personal gain harms society and occasionally individual people. The spread of fake news on social media and other platforms is a serious worry because it has the potential to have a negative influence on society and the country. Smartphone users prefer to read the news on social media due to social media's ease of access. But we cannot be sure how reliable the source is. Traditional fact checking has become impractical due to changes in publishing because of the wave of content producers and other factors in the matter of online news or news on social media. These kinds of unusual circumstances might occasionally have an impact on how people feel and what they believe. The need to identify bogus news led to the creation of this study. In this work, fake news identification is accomplished using ML algorithms Logistic Regression (LR) Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Multinomial Naive Bayes (MNB) and K Nearest Neighbors (KNN). We have determined the precision, recall, F-measure, accuracy for each of the classifiers. To be more specific, we employed a total of 44898 distinct news pieces from a dataset of authentic and fake news to train a ML model using Count vectorizer and TF-IDF as feature extraction approaches, with the highest performing model LR achieving an accuracy of 93.86%.
The effect of decreasing human activity from COVID‐19 on the foraging of fallen fruit by omnivores

Abstract In 2020, a lockdown was implemented in many cities around the world to contain the COVID‐19 pandemic, resulting in a significant cessation of human activity which have had a variety of impacts on wildlife. But in many cases, due to limited pre‐lockdown information, and there are limited studies of how lockdowns have specifically affected behaviors. Foraging behavior is inherently linked to fitness and survival, is particularly affected by changes in temporal activity, and the influence of human disturbance on foraging behavior can be assessed quantitatively based on foraging duration and quantity. The purpose of this study was to determine whether and how the fruit‐foraging behaviors of two omnivores, the Japanese badger (Meles anakuma) and the raccoon dog (Nyctereutes procyonoides), were influenced by the decrease of human activity associated with lockdowns. Specifically, by comparing to a previous study in 2019–2020, we attempted to determine (1) whether foraging behavior increases during the daytime? (2) whether the duration of foraging per visit increases? and (3) what factors animals select for in fruiting trees? The results of the initial investigation showed that the foraging behavior of both species in 2019 was almost exclusively restricted to the nighttime. But as opportunities for foraging behavior without human interference increased in 2020 due to the lockdown, both species (but especially raccoon dogs) showed substantial changes in their activity patterns to be more diurnal. The duration of foraging per visit also increased in 2020 for both species, and the selection during foraging for both species shifted from selecting trees that provided greater cover in 2019 to trees with high fruit production in 2020. Our results show how human activity directly affects the foraging behavior of wildlife in an urban landscape.
ULSM: Underground Localization and Semantic Mapping with Salient Region Loop Closure under Perceptually-Degraded Environment

Simultaneous Localization and Mapping (SLAM) has greatly assisted in exploring perceptually-degraded underground environments, such as human-made tunnels, mine tunnels, and caves. However, the recurring sensor failures and spurious loop closures in these scenes bring significant challenges to applying SLAM. This paper proposes an architecture for underground localization and semantic mapping (ULSM) that promotes the robustness of odometry estimation and map-building. In this architecture, a two-stage robust motion compensation method is proposed to adapt to sensor-failure situations. The proposed salient region loop closure detection contributes to avoiding spurious loop closures. Meanwhile, the 2D pose as the initial value for point cloud registration is estimated without additional input. We also design a multi-robot cooperative mapping scheme based on descriptors of the salient region. Extensive experiments are conducted on datasets collected in the Tunnel Circuit of DARPA Subterranean Challenge.
Geometric MPC Techniques for Reduced Attitude Control on Quadrotors with Bidirectional Thrust

We present two novel nonlinear MPC formulations for reduced attitude tracking on quadrotors with bidirectional thrust capabilities. Reduced attitude tracking is relevant to recovery from partial thrust loss, which can occur due to the failure of one or more motors. The first formulation builds on a linearization of the quadrotor attitude dynamics on $S(2)$ to achieve simultaneous tracking of reduced attitude and total thrust targets. The second formulation, meanwhile, accomplishes the same goal using a linearization of the dynamics on the Lie algebra of $SO(3)$ and a proposed method for projecting Lie algebra errors onto reduced attitude errors. Both methods achieve global tracking on $S(2)$ without requiring the use of computationally expensive sequential quadratic program solvers. Through simulations, we show that the second approach generally tracks aggressive attitude references better, while the first controller offers more reliable regulation.
Image Denoise by Fourth-Order PDE Based on the Changes of Laplacian

Fourth-order partial differential equation (PDE) proposed by You and Kaveh (You-Kaveh fourth-order PDE), which replaces the gradient operator in classical second-order nonlinear diffusion methods with a Laplacian operator, is able to avoid blocky effects often caused by second-order nonlinear PDEs. However, the equation brought forward by You and Kaveh tends to leave the processed images with isolated black and white speckles. Although You and Kaveh use median filters to filter these speckles, median filters can blur the processed images to some extent, which weakens the result of You-Kaveh fourth-order PDE. In this paper, the reason why You-Kaveh fourth-order PDE can leave the processed images with isolated black and white speckles is analyzed, and a new fourth-order PDE based on the changes of Laplacian (LC fourth-order PDE) is proposed and tested. The new fourth-order PDE preserves the advantage of You-Kaveh fourth-order PDE and avoids leaving isolated black and white speckles. Moreover, the new fourth-order PDE keeps the boundary from being blurred and preserves the nuance in the processed images, so, the processed images look very natural.
Task-Oriented Contact Optimization for Pushing Manipulation with Mobile Robots

This work addresses the problem of transporting an object along a desired planar trajectory by pushing with mobile robots. More specifically, we concentrate on establishing optimal contacts between the object and the robots to execute the given task with minimum effort. We present a task-oriented contact placement optimization strategy for object pushing that allows calculating optimal contact points minimizing the amplitude of forces required to execute the task. Exploiting the optimized contact configuration, a motion controller uses the computed contact forces in feed-forward and position error feedback terms to realize the desired trajectory tracking task. Simulations and real experiments results confirm the validity of our approach.
Self-morphing Soft Parallel-and-coplanar Electroadhesive Grippers Based on Laser-scribed Graphene Oxide Electrodes

Electroadhesion is a versatile and controllable adhesion mechanism that has been used extensively in robotics. Soft electroadhesion embodies electrostatic adhesion in soft materials and is required for shape-adaptive and safe grasping of curved objects and delicate materials. In this work, we present a soft electroadhesive fabrication method based on laser scribing graphene oxide on a silicone film, which is cost-effective, facile and green. The method can be used to generate complex electroadhesive patterns without molds or stencils. We then present a 2D finite element model to demonstrate the shape-changing behavior and electric field distributions of a dual-mode parallel dielectric elastomer actuation and coplanar electroadhesion structure. The soft electroadhesive fabrication method based on laser-scribed graphene oxide electrodes and its experimental characterization results, together with its shape-morphing simulation model are expected to enable the wider adoption of soft electroadhesion in future robotics.
VGPN: 6-DoF Grasp Pose Detection Network Based on Hough Voting

In this paper, we propose a novel Voting based Grasp Pose Network (VGPN) to detect 6-DoF grasps in cluttered scenes. The motivation of this paper is that local object geometry can provide useful clues about where the object can be grasped. Generated by the sampled seed points from raw point cloud, the votes allow seed points in different object regions to contribute to locations where the object can be grasped. Geometric features from various local regions are aggregated to generate grasps in a more confident and dense space, which enables grasp prediction utilizing more global context features. The search space of grasp pose detection is also greatly reduced. Experimental results on both simulation and real-world environments show that our proposed method outperforms state-of-the-art approaches in terms of both success rate and coverage of the ground truth grasps. The objects can be grasped with fewer attempts which is critical in real-world applications.