Miracle-dz/UniFAR-FTUs huggingface dataset:
LLAMA: Design and Control of an Omnidirectional Human Mission Scale Quadrupedal Robot

This paper describes the design, control and initial experimental results of the quadruped robot LLAMA. Designed to operate in a human-scale world, this 67kg-class, all-electric robot is capable of rapid motion over a variety of terrains. Thanks to a unique leg configuration and custom high-torque, low gear-ratio motors, it can move omnidirectionally at speeds over 1 m/s. A hierarchical reactive control scheme allows for robust and efficient motion even under variable payloads. This paper describes the structure of the controller and outlines simulation results that probe the performance envelope of the robot suggesting payload capacities up to one third of its body weight. Initial testing shows robust motion over loose debris and a variety of ground slopes. Videos of the robot may be seen at https://tinyurl.com/llama-robot.
Weighted Scan Context: Global Descriptor with Sparse Height Feature for Loop Closure Detection

In simultaneous localization and mapping (SLAM), the loop detection module has an important impact on the global consistency of trajectory and mapping. Compared to vision-based methods, LIDAR-based methods are gaining attention because they can acquire a complete 360° horizontal field of view and are not affected by changes in illumination. However, pure height features extracted from LIDAR point clouds are subject to feature degradation in specific environments. To reduce the adverse effects of height feature degradation, we propose a global descriptor, Weighted Scan Context, that uses the intensity information of the points to sparse the geometric features in the height direction. To reduce the sensitivity of cosine distance to viewpoint translation motion, a hybrid distance metric that integrates cosine distance and Euclidean distance is used to measure the similarity of two scenes. Through experiments on the KITTI dataset, the proposed method shows better performance compared to existing methods.
A Bio–inspired Quadruped Robot Exploiting Flexible Shoulder for Stable and Efficient Walking

While most modern-day quadruped robots crouch their limbs during the stance phase to stabilize the trunk, mammals exploit the inverted-pendulum motions of their limbs and realize both efficient and stable walking. Although the flexibility of the shoulder region of mammals is expected to contribute to reconciling the discrepancy between the forelimbs and hindlimbs for natural walking, the complex body structure makes it difficult to understand the functionality of animal morphology. In this study, we developed a simple robot model that mimics the flexibility of shoulder region in the sagittal plane, and we conducted a two-dimensional simulation. The results suggest that the flexibility of the shoulder contributes to absorbing the different motions between the forelimbs and hindlimbs.
A Model-Free Solution for Stable Balancing and Locomotion of Floating-base Legged Systems

This paper presents novel control techniques for passivation and stabilisation of floating-base systems with contacts, whose dynamical models comprise both joint-space, and Cartesian floating-base coordinates. The aforementioned results are achieved using both minimally model-based, and completely model-free controllers that employ power-shaping signals. Model-free control is permitted through usage of a decoupled dynamical model, procured via coordinate transformation operations. It is demonstrated that even though passive closed-loop systems are attainable without utilisation of exteroceptive feedback, global stabilisation of a floating-base robot necessitates direct usage of either measured or estimated external forces. The presented asymptotical stabilisation results pertain to both the set-point regulation, and trajectory-tracking cases, thereby ensuring suitability for static balancing, and dynamical locomotion tasks. To ensure practicability and production of feasible input signals, a variable impedance control, power-shaping term is appended to the original design, wherein it circumstantially serves as either a power-dissipating, or power-injecting element. This enhancement provably preserves closed-loop stability, by appositely shaping the system’s power. Experiments involving a metamorphic, quadrupedal walking robot, corroborate the theoretical analysis, as they attest to the system’s ability to stably execute locomotory tasks using a single, unified, model-free control scheme.
Qurra : an Offline AI-based Mobile Doctor

The recent global health pandemic has shifted the way healthcare is provided. Healthcare providers are overwhelmed with hospitals' beds being occupied and concerned about well being of visiting patients in need of regular checkups. Hence, innovative mobile-based healthcare solutions are needed. This work named Qurra or in Arabic presents a real-time solution that uses pre-trained built-in machine learning (ML)-models on mobile devices for convenient health checkups. The Qurra application operates by sampling data from various mobile sensors and uses the sampled data as input to different machine learning modules to produce a meaningful health diagnosis. The developed modules are the heart rate and cough detection. These modules are the focus of this paper. The approach used in this work relies on externally ML- based trained models that are ported to the application. Then, sensory inputs are tested against these pre-trained models, locally computed and analyzed on the mobile phone. The results are displayed to the user in real-time. This approach of having the models embedded to the mobile phone eliminates the need for internet connectivity. Moreover, the developed system is compared with a third party application in addition to its native model on a desktop computer. Also, the app has fast overall processing time of approximately 2-3.5 seconds.
Rotating membrane filtration and rotating reverse osmosis

Rotating membrane filtration is a means of dynamic filtration that incorporates both high shear and flow instabilities to reduce membrane fouling. The device consists of a cylindrical membrane filter rotating within an outer cylindrical shell. The superior performance of rotating membrane filtration appears to be a consequence of the high shear in the annulus, which is enhanced by the redistribution of azimuthal momentum by the appearance of Taylor vortices in the annulus. The anti-fouling characteristics of rotating filtration can be utilized for reverse osmosis in addition to microfiltration. In this paper, we review the physics underlying rotating filtration, examine the anti-fouling mechanisms in rotating filtration, and describe recent efforts to apply rotating filtration to reverse osmosis.
Ospheel: Design of an Omnidirectional Spherical-sectioned Wheel

The holonomic and omnidirectional capabilities imparted to the mobile base platform depends mainly on two factors, i.e., the wheel design and its various arrangements in the platform chassis. This paper reports on the development of a novel omnidirectional spherical sectioned wheel named Ospheel. It is modular, and the spherical sectioned geometry of the wheel is driven using two actuators placed inside the housing above the wheel that rotates it independently about two perpendicular axes. The mechanical drive system for Ospheel consists of two gear trains, namely, internal spur gear and crown gear spatially assembled in orthogonal planes and are driven by two driving pinions. The kinematics of a single Ospheel is described, followed by the kinematic equation of a robot equipped with two Ospheels. Forward and inverse kinematic equations are derived explicitly. Experiments were carried out with the two Ospheels at a fixed inclination assembled with the base to illustrate the holonomic motion. The robustness of the wheel design is experimented with different trajectories and on different terrains.
A low-cost method for accurate localization of traffic participants

Accurate localization of traffic participants is very important and useful in Intelligent Transportation System(ITS). The most frequent discussed method for accurate vehicle localization in literature is based on Global Navigation Satellite System(GNSS) or Radio Frequency Identification(RFID). However, GNSS-based methods are vulnerable to signal loss when obscured and RFID-based methods have requirements for road construction. In this paper, a low-cost method which only uses the computer vision of a roadside camera to locate traffic participants including car, bus, pedestrian and cyclists precisely is proposed. The final localization result is represented by a center point for vehicle and a quadrilateral region for all targets, which also contain the information of object size. The proposal is consist of three main part: traffic participant detection and classification, contact point determination for vehicle and location area calculation for each kind of traffic participants. Experiment shows the average localization accuracy for car is 0.107 meter.
IoT-Sphere: A Framework To Secure IoT Devices From Becoming Attack Target And Attack Source

In this research we propose a framework that will strengthen the IoT devices security from dual perspectives; avoid devices to become attack target as well as a source of an attack. Unlike traditional devices, IoT devices are equipped with insufficient host-based defense system and a continuous internet connection. All time internet enabled devices with insufficient security allures the attackers to use such devices and carry out their attacks on rest of internet. When plethora of vulnerable devices become source of an attack, intensity of such attacks increases exponentially. Mirai was one of the first well-known attack that exploited large number of vulnerable IoT devices, that bring down a large part of Internet. To strengthen the IoT devices from dual security perspective, we propose a two step framework. Firstly, confine the communication boundary of IoT devices; IoT-Sphere. A sphere of IPs that are allowed to communicate with a device. Any communication that violates the sphere will be blocked at the gateway level. Secondly, only allowed communication will be evaluated for potential attacks and anomalies using advance detection engines. To show the effectiveness of our proposed framework, we perform couple of attacks on IoT devices; camera and google home and show the feasibility of IoT-Sphere.
THE EFFECT OF FLOODING ON NITROUS OXIDE EMISSIONS FROM AN ORGANIC SOIL

To determine the effect of flooding of Pahokee muck (a drained, cultivated Histosol) on nitrous oxide emissions from the soil, we measured field N2O fluxes and N2O produced in soil samples incubated under controlled laboratory conditions. During the first 5 days of flooding of a field that had previously been cropped to sweet corn (Zea mays L. rugosa), the NO3--N levels declined from 68 to <0.1 microgram nitrogen per cubic centimeter. Similarly, the N2O flux decreased from 174 g N/ha/d prior to flooding to approximately 0 after flooding. Comparing the flux values and the N2O concentrations in the flood water suggested that the flooded field may have acted as a sink for atmospheric N2O. In laboratory-incubated samples of the preflood and flooded soils, the potential denitrification rates increased from 16.2 μg N/cm3/day prior to flooding to 31.2 μg N/cm3/d after flooding. Whereas N2O was the major product of denitrification in soil samples collected prior to flooding, very little N2O accumulated in samples collected from the flooded field. Nitrous oxide was readily reduced in the flooded soil in the presence of NO3--N and NO2--N concentrations of 100 and 3 mg/liter, respectively.
Mechanical Design and Preliminary Performance Evaluation of a Passive Arm-support Exoskeleton

In this study, a passive arm-support exoskeleton was designed to provide assistive aid for manufacturing workers. The exoskeleton has two operating states which can be altered using an unique ratchet bar mechanism with two blocks fixed on the ratchet bar. When the upper arm is elevated to the highest poiont, the pawl module will touch the lower block to allow the pawl separated, so that the arm can move freely without any resistance. When the upper arm is depressed to the lowest point, the pawl module will touch the upper block to make the pawl re-engaged, so that the upper arm can be locked at any vertical position. For purpose to improve the ergonomical property, the structural parameters of the exoskeleton were determined by particle swarm optimization. The designed exoskeleton was simulated in the Adams model to investigate its actual performance. A preliminary experimental study was conducted to evaluate the effectiveness of the designed exoskeleton on alleviating users’ physical loads in holding heavy tools; the muscular activities on the shoulder muscle groups involved in the weights bearing, elicited by the surface electromyography (EMG) over the shoulder, were significantly reduced from three healthy subjects who carried hand-held tools. The simulation and experiment results show that the designed exoskeleton could effectively relieve the shoulder burden by transferring the bearing load to the waist, where the motion of the arm was not obstructed.
Joint Pairing and Resource Allocation for V2X Communications

The necessity of mobility brings the urgency of communications between vehicles and infrastructures for exchanging information. This network should attain strict requirements such as more reliable channels for vehicle to vehicle (V2V) links while preserve the quality of service (QoS) and performance of cellular users. Thus far, it has been considered, V2V receiver had decided to communicate with a transmitter arbitrarily or with the nearest transmitter. However in this paper attempts to review this assumption and add more potential options to it. For this purpose a joint pairing, resource allocation and power control problem in the context of V2V users is formulated and tried to investigate the best solutions for it. In the following the optimal and sub-optimal solutions are considered based on binary power allocation (BPC) criteria and finally, a joint pairing and resource allocation algorithm based on continuous power assignment is proposed for better performance. Simulation results demonstrate and compare the efficiency of our proposed algorithms.
Identification of Dynamic Parameters for Rigid Robots based on Polynomial Approximation

In this paper an approach for the identification of the dynamic parameters, i.e. base parameters, of rigid robots is presented. By using the polynomial approximation operator, an equation is obtained for the identification of the parameters which solely depends on measurable signals and thereby contains no equation error. The resulting expressions can be evaluated online or offline by filtering the measurable signals with FIR filters. In order to identify the parameters on the basis of measurements, an algorithm is presented to calculate the parameters numerically stable, even if the data is obtained sequentially, without a singular value decomposition. The parameters can be determined meaningfully by considering box constraints in order to ensure physical feasibility. The presented methods are finally used to identify the dynamic parameters of a delta robot and compared to the standard approach.
Netspot: a simple Intrusion Detection System with statistical learning

Machine learning is nowadays increasingly used in cyber-security. While intrusion detection was mainly based on human expertise in the 1990s, learning models to predict attacks are now built from data. However, a large part of the developed learning algorithms hitherto has missed real-world issues, making them unpractical. Indeed, many supervised algorithms described in the literature have been trained and tuned only on the KDD99 dataset. Besides, these algorithms are often static and are unable to automatically adapt for detecting attacks depending on the network traffic. Consequently, we are far from detecting zero-day or more general Advanced Persistent Threats (APT) since only pre-registered and well-characterized attacks can be catched. Some recent systems use unsupervised ML algorithms, but the resulting tools are overly complex: many ML components are stacked with various tuning parameters, usually making the results hard to interpret. And finally, a strong ML/DM expertise is required to set up these systems on real networks. We present netspot, a very simple network intrusion detection system (NIDS) powered by SPOT, a recent streaming statistical anomaly detector. This statistical test uses Extreme Value Theory, which is a powerful method for detecting anomalies. Unlike all the previous works, it is not an end-to-end solution aimed to detect all cyber-attacks with packet resolution. It is rather a module providing a behavioral information which can be integrated in a more general monitoring system. netspot is simple: it has few (simple) parameters, it adapts along time to the monitored network and it is as fast as current rule-based methods. But most importantly, it is able to detect realworld cyber-attacks, making it a credible practical anomaly-based NIDS.
A Review of Cybersecurity Guidelines for Manufacturing Factories in Industry 4.0

Industry 4.0 is a revolution in manufacturing by introducing disruptive technologies such as Internet of Things (IoT) and cloud-computing into the heart of the factory. The resulting increased automation and the improved production synergy between stocks, supply chains and customer demands, come along with the threats and attacks from the Internet. Despite extensive literature on the cybersecurity topic, many actors in manufacturing factories are just realizing the impact of cybersecurity in the preservation of their business. This paper introduces step-by-step the concepts and practical aspects of an Industry 4.0 manufacturing factory that are related to cybersecurity. Based on a subdivision of a typical factory into several generic perimeters, we present the vulnerabilities and threats regarding the network and devices usually found in each perimeter. Therefore, it is more efficient to present the recent proposals of the literature regarding cybersecurity guidelines and solutions in Industry 4.0. Instead of spreading a lot of references regarding every aspect of cybersecurity, we focused on a limited number of papers among the recent references. However, for each paper, we provide the details about the purpose of the proposal, the methodology adopted, the technical solution developed and its evaluation by the authors. These solutions range from classical cybersecurity countermeasures to innovative ones, such as those based on honeypots and digital twins. In order to deliver a review also useful to non scientists, we present our guidelines along with those of some organizations involved in cybersecurity harmonization and standardization in the world.
Efficient Top-k Temporal Closeness Calculation in Temporal Networks

We consider the problem of efficiently computing the top-$k$ temporal closeness values and the corresponding vertex sets in a given temporal network. The closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. Temporal distances in networks that change over time take the dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-$k$ temporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive a modification leading to a heuristic that often performs well on the majority of real-world data sets and drastically reduces the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-$k$ algorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-$k$ temporal and static closeness vertex sets differ quite largely in real-world networks.
Utility-Driven k-Anonymization of Public Transport User Data

In this article, we propose a $k$ -anonymity approach that prioritizes the generalization of attributes based on their utility. We focus on transport data, which we consider a special case in which many or all attributes are quasi-identifiers (e.g., origin, destination, ride start time), as they allow correlation with easily observable auxiliary data. The novelty in our approach lies in introducing normalization techniques as well as distance and utility metrics that allow the consideration of not only numerical attributes but also categorical attributes by representing them in tree or graph form. The prioritization of the attributes in the generalization process is based on the attributes’ utility and can further be influenced by either automatically or manually assigned attribute weights. We evaluate and compare different options for all components of our mechanism as well as present an extensive performance evaluation of our approach using real-world data. Lastly, we show in which cases suppression of records can counter-intuitively lead to higher data utility.
Human-Like Lane-Change Decision Making for Automated Driving with a Game Theoretic Approach

With the consideration of personalized driving for automated vehicles (AVs), this paper presents a human-like decision making framework for AVs. In the modelling process, the driver model is combined with the vehicle model, which yields the integrated model for the decision-making algorithm design. Three different driving styles, i.e., aggressive, normal, and conservative, are defined for human-like driving modelling. Additionally, motion prediction algorithm is designed with model predictive control (MPC) to advance the effectiveness of the decision-making approach. Furthermore, the decision-making cost function is constructed considering drive safety, ride comfort and travel efficiency, which reflect different driving styles. Based on the decision-making cost function, a noncooperative game theoretic approach is applied to solving the decision-making issue. Finally, the proposed human-like decision making algorithm is evaluated with an overtaking scenario. Testing results indicate different driving styles cause different decision-making results, and the designed algorithm can always make safe and reasonable decisions for AVs.
A Soft, Modular, and Bi-stable Dome Actuator for Programmable Multi-Modal Locomotion

Movement in bio-inspired robots typically relies on the use of a series of actuators and transmissions with one or more degrees of freedom (DOF), allowing asymmetrical ellipsoidal gaits for use in walking, running, swimming, and crawling. In an effort to simplify these multi-component systems, we present a novel, modular, soft, bi-stable, one DOF dome actuator platform that is capable of complex gaits through mechanical programming, driven by simple periodic fluid input. With a modular, reconfigurable design, the end effectors of these bi-stable dome actuators can be quickly modified for use on a variety of surfaces for specific applications. In the present study, we describe the finite element modeling, manufacturing, and characterization of different end effectors and outline a workflow for the implementation of these soft bi-stable dome actuators for the production of functional robotic prototypes.