Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechatronic Design and Control of a Light Weight Manipulator Arm for Mobile Platforms

Mobile robotic platforms equipped with manipulation capabilities can fulfill the requirements of a wide range of applications requiring functionality that is not only limited to the monitoring of the environment but also permits to interact and execute physical operations within the application space. This paper presents the IIT-INAIL arm, a light weight robotic manipulator designed to be mounted on the quadrupedal robot HyQReal, resulting in a highly capable platform for teleoperation and manipulation tasks in challenging real-world environments. The arm incorporates a number of key design features and components, such as high performance elastic actuators, robust and light-weight design principles, a dexterous end-effector, and kinematics tailored to the mobile application. The control system of the robot arm allows the fully transparent integration with a mobile platform to execute tele-manipulation in remote environments through a rich set of control modes from classical position to torque and high-fidelity impedance control. The performance of the developed manipulator is demonstrated by a set of experiments through heavy payload manipulation and impedance control tasks.
Optimum Averaging of Superimposed Training Schemes in OFDM Under Realistic Time-Variant Channels

The current global bandwidth shortage in orthogonal frequency division multiplexing (OFDM)-based systems motivates the use of more spectrally efficient techniques. Superimposed training (ST) is a candidate in this regard because it exhibits no information rate loss. Additionally, it is very flexible to deploy and it requires low computational cost. However, data symbols sent together with training sequences cause an intrinsic interference. Previous studies, based on an oversimplified channel (a quasi-static channel model) have solved this interference by averaging the received signal over the coherence time. In this paper, the mean square error (MSE) of the channel estimation is minimized in a realistic time-variant scenario. The optimization problem is stated and theoretical derivations are presented to attain the optimum amount of OFDM symbols to be averaged. The derived optimal value for averaging is dependent on the signal-to-noise ratio (SNR) and it provides a better MSE, of up to two orders of magnitude, than the amount given by the coherence time. Moreover, in most cases, the optimal number of OFDM symbols for averaging is much shorter, about 90% reduction of the coherence time, thus it provides a decrease of the system delay. Therefore, these results match the goal of improving performance in terms of channel estimation error while getting even better energy efficiency, and reducing delays.
FAST: Flexible and Low-Latency State Transfer in Mobile Edge Computing

Mobile Edge Computing (MEC) brings the benefits of cloud computing, such as computation, networking, and storage resources, close to end users, thus reducing end-to-end latency and enabling various novel use cases, such as vehicle platooning, autonomous driving, and the tactile internet. However, frequent user mobility makes it challenging for the MEC to guarantee the close proximity to the users. To tackle this challenge, the underlying network has to be capable of seamlessly migrating applications between multiple MEC sites. This application migration requires the quick and flexible migration of the application states without service interruption, while minimizing the state transfer cost. In this article, we first study the state transfer optimization problem in the MEC. To solve this problem, we propose a metaheuristic algorithm based on Tabu search. We then propose Flexible and Low-Latency State Transfer in Mobile Edge Computing (FAST), the first programmable state forwarding framework. FAST flexibly and directly forwards states between source instance and destination instance based on Software-Defined Networking (SDN). Both simulation results and practical testbed results demonstrate the favorable performance of the proposed Tabu search algorithm and the FAST framework compared to the state-of-the-art schemes.
Detecting Compensatory Motions and Providing Informative Feedback During a Tangible Robot Assisted Game for Post-Stroke Rehabilitation

Gamified rehabilitation tackles the problem of keeping patients engaged in, and motivated to do physical rehabilitation to improve its efficacy. However, with respect to standard rehabilitation, patients are freer to move about and may compensate their motion difficulties with parasite movements, which would greatly reduce the efficacy of the rehabilitation. To identify and characterize compensatory motions, we collected and analyzed video data of people playing the "tangible Pacman" game (an upper-limb rehabilitation game in which a patient moves a semi-passive robot, the "Pacman", on a map to collect 6 apples, while being chased by one or two autonomous robots, the "ghosts"). Participants include 10 healthy elderly adults and 10 chronic stroke patients, who played multiple runs of the game, with different sized maps and various game configurations. By analyzing the video recordings we successfully identified higher shoulder and torso lateral tilt compensation in stroke patients and developed a proof-of-concept compensatory motion detection system which relies on a wearable Inertial Measurement Unit and ROS to provide in-game, real-time visual feedback on compensation.
Cyber Attacks Detection Using Open Source ELK Stack

Nowadays, when computer technology plays an important role in our lives, attackers develop new techniques and tools that target these systems. Over time, there were hundreds of papers written with security methods that help to detect attackers inside corporate environments. Unfortunately, these days many of them seem to be obsolete or at least not so efficient because of few simple reasons: their focus was on either network or endpoint security, not both, they are very simplistic and easy to be by passed. In response to these deficiencies, a combined implementation using a mix of proactive techniques and threat anticipation mechanisms would bring a fast detection and a rapid response, by leveraging automation technologies. This new approach is applicable to small and mid-size enterprises and includes the ELK stack integrated with a robust security technology stack, as well as different open-source Threat Intelligence platforms. Thus, we put together an exhaustive system for cyber-attacks detection and analysis both at the network and endpoint level. In this paper, we achieved a series of attacks specific to an Advanced Persistent Threat (APT), using techniques from the MITRE ATT&CK matrix, which were detected using the ELK stack. We have integrated Elasticsearch with VirusTotal for automatically querying the hashes of some malicious files. Besides, we have enriched the network logs with geolocation information through the GeoIP processor in Logstash. Moreover, the integration of the ELK stack with the malware information sharing platform (MISP) allowed us to perform real-time searches and enrichments for indicators of compromise (IOCs). Lastly, with the help of Machine Learning algorithms in Elasticsearch, we were able to identify anomalies in the network traffic (DNS/HTTP data exfiltration).
Use of Forecasting in Energy Storage Applications: A Review

During the last decade there has been a major shift towards renewable energy sources to fulfill the increasing demand for energy in a sustainable manner. However, a major challenge with renewable energy generation is its dependency on weather conditions. Energy storage is deemed instrumental to harness renewable energy by providing a means to overcome stochasticity in renewable generation. Nonetheless, the operation of energy storage is not trivial due to its energy limitation and degradation behavior. Many works in literature consider forecasts as a cornerstone for effective management of energy storage for various grid applications. However, little work has been devoted to studying the actual value of forecast for energy storage management, which is highly dependent on the use case. This paper presents a review of the state of the art in the use of forecasts for energy storage management, identifying the estimated value of forecast with respect to baseline management approaches that do not rely on forecasts. The paper also discusses research pathways that would focus on improving forecast only on the energy storage applications that can benefit from it.
Fault Detection and Classification Scheme for PV System Using Array Power and Cross-Strings Differential Currents

Accurate and fast fault monitoring system is important for photovoltaic (PV) systems to reduce the damage and energy loss associated with faults. This paper presents a fast fault detection and classification scheme for PV systems. The rate of change of the measured power at the array level is firstly used to detect and differentiate between shading and PV system short-circuit faults. Then, the measured cross-strings differential currents classify the fault type in the case of short-circuit faults. The proposed method accounts for intra-string, string-to-negative terminal, cross-string (string to string), and pole-to-pole short-circuit faults, as well as open circuit faults. Two current transducers per two strings are only required to apply the method leading to a significant reduction in the number of transducers/sensors compared to the literature, making it a cost-effective solution. The fault is detected and classified in 1 ms. A 400 kW PV system consisting of 4 arrays simulated using MATLAB/Simulink package is used for the evaluation process. Short-circuit faults are imposed at different conditions of mismatch levels and fault resistance values. Also, operation at reduced irradiance level is considered, and shading faults are simulated in different levels. The simulation results prove the ability of the proposed scheme to detect and differentiate between shading and short-circuit faults. The proposed method can correctly classify different fault types in almost all cases. The low implementation cost due to the lower number of required sensors and the fast-operating time (1 ms) boost the feasibility of the proposed method.
Human-Robot Collaboration: Optimizing Stress and Productivity Based on Game Theory

In human-robot collaborative frameworks the maximization of productivity is of paramount importance. However, it is also crucial to mitigate the cognitive workload induced on the operator during cooperation. Indeed, a high level of stress can negatively affect the human capabilities, thus compromising the performance of the working dyad. In this work, we propose a novel paradigm where the robot is enabled to adapt its behavior online to simultaneously optimize in real-time the human physiological stress and productivity. The proposed control strategy exploits a game theoretic approach to model and locally estimate the state of collaboration in terms of human productivity and stress. Based on this estimate, a learning automaton suitably adjusts the production pace of the robot, thus influencing the dynamics of the cooperation. The proposed method was tested on a realistic collaborative assembly task. The results demonstrated that the novel control strategy effectively enhances the productivity of the human-robot team, while significantly mitigating the stress induced in the operator.
Effects of Nonlinear Nerve Dynamics on Selective Neural Activation for Designing Field Descriptor based on Activation Function

For analyzing the electrical stimulations to achieve selective neural activation, it is computationally time-consuming to solve the electric fields coupled with nonlinear nerve models. The electric-field-based descriptor considering nerve dynamics is an effective tool for predicting neural firings and optimizing stimulation configurations. Based on the mathematical analysis of nonlinear nerve models, this paper explains the mechanism behind temporal interference stimulation and investigates the effects of field descriptors on the predictions of neural firing. Two field descriptors (electric and activation) derived from the 1st and 2nd order spatial derivatives of the electric potential for predicting neural activations are compared. The prediction results of both the electric and activation field descriptors are validated by published numerical and experimental results. While both predict the activation of different nerve fibers, the activation descriptor that requires a larger finite-element mesh-number than the electric field descriptor for the same computational accuracy offers additional information on action potential initiation of different nodes within a fiber.
A QR Code-based High-Precision Docking System for Mobile Robots Exhibiting Submillimeter Accuracy

This paper presents a high-precision docking system enabling controlled motion of mobile robots and automatic guided vehicles in micrometer range. The developed system employs a low-cost, vehicle installed camera and a docking base with Quick Response (QR) codes in the form of a two-dimensional matrix. The docking base is free of active components, eliminating the need for maintenance, and minimizing manufacturing or installation costs. A robust localization algorithm is developed that provides real-time position feedback at 60 Hz with submillimeter accuracy. Redundancy is established by calculating the vehicle’s pose using multiple QR codes; thus, if one or more QR codes are damaged, the feedback stays unaffected. By design, the system is insusceptible to light variance. Using a prototype robotic platform, excellent mean absolute errors of $39 \mu \mathrm{m}$ in position, and 8.8 millidegrees in orientation are achieved, providing experimental validation of the docking system.
Would you trust a robot with your mental health? The interaction of emotion and logic in persuasive backfiring

Building trust in robots through social interactions has a major impact on user experience and adoption of robot technologies. The role of trust in such interactions is associated with the persuasive influence a robot has on humans. A persuasive attempt may decrease trusting attitudes towards robots if it leads to persuasive backfiring, which refers to the creation of an attitude change in a direction opposite to the one intended by the intervention. In order to explore persuasive backfiring in the context of Human-Robot Interaction, this research study tests the interaction between emotion and logic as elements present both in the attitudes to be influenced, and in the persuasive appeal delivered by a robot. Results indicate a significant backfiring effect when emotions are used to influence attitudes that are based on logic. This observation has practical design implications for persuasive robots, especially in high-stakes fields such as Psychotherapy and Urban Search and Rescue.
Intent-Aware Predictive Haptic Guidance and its Application to Shared Control Teleoperation

This paper presents a haptic shared control paradigm that modulates the level of robotic guidance, based on predictions of human motion intentions. The proposed method incorporates robot trajectories learned from human demonstrations and dynamically adjusts the level of robotic assistance based on how closely the detected intentions match these trajectories. An experimental study is conducted to demonstrate the paradigm on a teleoperated pick-and-place task using a Franka Emika Panda robot arm, controlled via a 3D Systems Touch X haptic interface. In the experiment, the human operator teleoperates a remote robot arm while observing the environment on a 2D screen. While the human teleoperates the robot arm, the objects are tracked, and the human’s motion intentions (e.g., which object will be picked or which bin will be approached) are predicted using a Deep Q-Network (DQN). The predictions are made considering the current robot state and baseline robot trajectories that are learned from human demonstrations using Probabilistic Movement Primitives (ProMPs). The detected intentions are then used to condition the ProMP trajectories to modulate the movement and accommodate changing object configurations. Consequently, the system generates adaptive force guidance as weighted virtual fixtures that are rendered on the haptic device. The outcomes of the user study, conducted with 12 participants, indicate that the proposed paradigm can successfully guide users to robust grasping configurations and brings better performance by reducing the number of grasp attempts and improving trajectory smoothness and length.
Towards Real-World Force-Sensitive Robotic Assembly through Deep Reinforcement Learning in Simulations

In manufacturing industries, vast potential exists in regards to the adaptability of automated systems through efficient robotic skill acquisition. This paper examines the use of deep reinforcement learning to automate the process of contact rich compliant assembly. Thereby, we consider an exemplary real-world use case in car assembly. To obtain training data, we use a simulated representation of the production system comprising a robotic arm which is controlled through a deep reinforcement learning agent trained with proximal policy optimization. Furthermore, we conduct a basic system analysis to improve the similarity between our physical and simulated environments. After iteratively training and evaluating different models, which distinguish through the reward design and the grade of environment variation, we validate the results on the physical hardware. We successfully obtain agents that generate expedient trajectories, which can generalize to changing environments. Success rates clearly above 90% can be achieved in simulation even with high grades of variation of the target position and the parts’ surface friction. For the transfer to the physical assembly system, we conclude that further optimization is necessary to obtain truly compliant behavior.
Development of an Internet-of-Healthcare System Using Blockchain

The Internet-of-Healthcare Systems is a highly distributed special emulation of the Internet of Things technology. Patient medical data sourced from the participating hospitals are integrated with a decentralized storage system using blockchain technology to provide the highest level of storage and access security possible, overcoming the security and data administration problems that may occur at the local hospital level where the patient data is stored within the hospital’s central server, especially if that data is subjected to external threats. The Internet-of-Healthcare Systems, currently implemented in over 350 hospitals, utilises software agents, accessed using the Message Queueing Telemetry Transport protocol, which efficiently handles potentially thousands of participating local systems without loss of network timeliness. Also, the system works with the wide variety of health information systems used in the participating hospitals. This makes the system a worthwhile candidate for a nationwide integrated health records system, and is an exemplar for many different types of secure networks that could be generically called an Internet of Special Things; a network of software agents programmed for a special purpose. Mobile device apps enable secure direct access to patient data in a central blockchain with download capability to a mobile device, under strict and fully managed access control using Amazon Web Services, with permissions controlled by the Key Management System. Feedback from participating medical staff indicates a high level of satisfaction with all aspects of the system: ease of use, ease of installation, maintenance and update, and, importantly, the security of the system.
CardioVinci: building blocks for virtual cardiac cells using deep learning

Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue ‘The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease’.
Characterization of a vaccine-elicited human antibody with sequence homology to VRC01-class antibodies that binds the C1C2 gp120 domain

Broadly HIV-1 neutralizing VRC01-class antibodies bind the CD4-binding site of the HIV-1 envelope (Env) and contain VH1-2*02-derived heavy chains paired with light chains expressing five amino acid long CDRL3s. Their unmutated forms do not recognize Env or neutralize HIV-1. The lack of elicitation of VRC01-class antibodies in human clinical trials could potentially be due to the absence of activation of the corresponding naïve B cells by the vaccine Env immunogens. To address this point directly, we examined Env-specific BCR sequences from participants in the HVTN 100 clinical trial. Of all the sequences analyzed only one displayed sequence homology to VRC01-class antibodies, but the corresponding antibody (FH1) recognized the C1C2 gp120 domain. For FH1 to switch epitope recognition to the CD4-binding site, alterations in both the CDRH3 and CDRL3 were necessary. Our findings support the use of specifically designed immunogens to activate VRC01-class B cells in future human vaccine trials.
Shared responsibility decreases the sense of agency in the human brain

Sharing responsibility in social decision-making helps individuals use the flexibility of the collective context to benefit themselves by claiming credit for good outcomes or avoiding the blame for bad outcomes. Using magnetoencephalography (MEG), we examined the neuronal basis of the impact that social context has on this flexible sense of responsibility. Participants performed a gambling task in various social contexts and reported feeling less responsibility when playing as a member of a team. A reduced MEG outcome processing effect was observed as a function of decreasing responsibility at 200 ms post outcome onset and was centred over parietal and pre-central brain regions. Prior to outcome revelation in socially made decisions, an attenuated motor preparation signature at 500 ms after stimulus onset was found. A boost in reported responsibility for positive outcomes in social contexts was associated with increased activity in regions related to social and reward processing. Together, these results show that sharing responsibility with others reduces agency, influencing pre-outcome motor preparation and post-outcome processing, and provides opportunities to flexibly claim credit for positive outcomes.