Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of a ‘Misconceiving’ Robot to Improve Computational Thinking in a Collaborative Problem Solving Activity: A Pilot Study

Robot-mediated learning activities are often designed as collaborative exercises where children work together to achieve the activity objectives. Although miscommunications and misunderstandings occur frequently, humans, unlike robots, are very good at overcoming them and converging to a shared solution. With the aim of equipping a robot with these abilities and exploring its effects, in this article we investigate how a humanoid robot can collaborate with a human learner to construct a shared solution to a problem via suggesting actions and (dis)agreeing with each other. Concretely, we designed a learning activity aiming to improve the computational thinking skills of children, in which the robot makes suggestions on what to do, that may be in line with what the human thinks or not. Furthermore, the robot may suggest wrong actions that could essentially prevent them from finding a correct solution. Via a pilot study conducted remotely with 9 school children, we investigate whether the interaction results in positive learning outcomes, how the collaboration evolves, and how these relate to each other. The results show positive learning outcomes for the participants in terms of finding better solutions, suggesting that the collaboration with the robot might have helped trigger the learning mechanisms.
Exploring the Application of Machine Learning Algorithms to Water Quality Analysis

In this experimental study, we use different Machine Learning algorithms to decide the quality of the water within the San Antonio River and its tributaries using datasets of previously collected water data from the San Antonio River Authority along with the Kaggle water potability dataset. For each algorithm to work, we used a set of parameters by which to measure the quality of the river water. Out of all the algorithms utilized, we found that random forest and K-Nearest Neighbor (KNN) were the best at achieving accurate results, with accuracy ratings of 0.6520 and 0.6469, respectively. Using K-means, we were able to find four distinct clusters in the San Antonio River data. The separation of these clusters was low for the parameters used resulting in a silhouette score of 0.229. From this data, we may be able to determine which sections of the main San Antonio River, as well as which tributaries, are acceptable (or healthy enough) for primary contact recreation use.
Structural optimization of variable stiffness mechanism with particle jamming and core-frame

As a collaboration between humans and robots becomes critical, stiffness control of the robot is essential for stability and efficiency of work. Therefore, research on the variable stiffness mechanism is being actively conducted in service robots, soft robots, and exoskeletons. The main types of variable stiffness mechanisms are jamming effect (particle jamming and layer jamming), shape memory polymer (SMP), and low melting point alloy (LMPA). The case of the jamming effect uses negative pneumatic pressure to change the stiffness. Because of that, it is possible to change the stiffness quickly, and it is easy to manufacture. However, both SMP and LMPA use thermal energy to increase the material’s stiffness. There is a risk of damage to humans or robots, and it takes much time to change the stiffness. Therefore, this study introduces a variable stiffness mechanism that combines particle jamming and core-frame. In addition, optimization studies are being conducted to use the jamming effect in industries. However, due to the randomness of particle jamming, the existing studies assumed that the variable stiffness mechanism was a simple beam or modeled it using hook’s law, so the accuracy was low. Therefore, in this study, five design variables are selected for particle and core-frame, the main elements constituting the variable stiffness mechanism. In addition, design variables are optimized through various FEM simulations. Furthermore, the simulation is proved by establishing a theoretical model for variable stiffness structure when the jamming effect occurs. Finally, the optimization of five design variables is proved through experiments.
Can Robots Help in the Evaluation of Mental Wellbeing in Children? An Empirical Study

Socially Assistive Robots (SARs) show promise in helping children during therapeutic and clinical interventions. However, using SARs for the evaluation of mental wellbeing of children has not yet been explored. Thus, this paper presents an empirical study with 28 children 8-13 years old interacting with a Nao robot in a 45-minute session where the robot administered (robotised) the Short Mood and Feelings Questionnaire (SMFQ) and the Revised Child Anxiety and Depression Scale (RCADS). Prior to the experimental session, we also evaluated children’s wellbeing using established standardised approaches via online RCADS questionnaires filled by the children (self-report) and their parents (parent-report). We clustered the participants into three groups (lower, medium, and higher tertile) based on their SMFQ scores. Further, we analysed the questionnaire responses across the three clusters and across the different modes of administration (self-report, parent-report, and robotised). Our results show that the robotised evaluation seems to be the most suitable mode in identifying wellbeing related anomalies in children across the three clusters of participants as compared with the self-report and the parent-report modes. Further, children with decreasing levels of wellbeing (lower, medium and higher tertiles) exhibit different response patterns: children of higher tertile are more negative in their responses to the robot while the ones of lower tertile are more positive in their responses to the robot. Findings from this work show that SARs can be a promising tool to potentially evaluate mental wellbeing related concerns in children.
Shock Trap: An active defense architecture based on trap vulnerabilities

Unlike traditional defense concepts, active defense is an asymmetric defense concept. It can not only identify potential threats in advance and nip them in the bud but also increase the attack cost of unknown threats by using change, interference, deception, or other means. Although active defense can reverse the asymmetric situation between attacks and defenses, current active defense technologies have two shortcomings: (i) they mainly aim at detecting attacks and increasing the cost of attacks without addressing the underlying problem; and (ii) they have problems such as high deployment costs and compromised system operational efficiency. This paper proposes an active defense architecture based on trap vulnerability with vulnerability as the core and introduces its design concept and specific implementation scheme. We deploy “traps” in the system to lure and find attackers while combining built-in detection, rejection, and traceback mechanisms to protect the system and trace the source of the attack.
Fiber Optic Sensors for Strain Measurement in Asphalt Pavements containing wireless pads

Inductive Power Transfer (IPT) is a safe, convenient, and reliable technology for wireless charging of Electric Vehicles (EVs). However, there are many pavement infrastructure issues that require consideration before a working system can be appropriately installed into a pavement system. To achieve this objective requires new pavement instrumentation methodologies to obtain the strain response of asphalt pavements with embedded IPT pads under cyclic traffic loading. In pavement strain data is required to understand the damage mechanisms of the IPT pavement system and predict its combined system life. Fiber Bragg Grating (FBG) sensors have advantages over other sensing technologies that enable them to measure pavement strain within the intense magnetic field of a high-frequency wireless power transfer system. The main objective of this study was to verify in the laboratory the accuracy of the proposed FBG sensor by conducting dynamic flexural tests using a four-point bending apparatus. This sensing technology is intended to be used in an Accelerated Pavement Testing (APT) program to study the mechanical performance of a combined IPT pad-asphalt pavement system. The results of this study show the current FBG sensor is sufficiently accurate and reliable to be used for strain measurement of asphalt pavements containing IPT pads under APT loading.
Evidence of the inhomogeneity of the stellar population in the differentially reddened globular cluster NGC 3201

We report on evidence of the inhomogeneity (multiplicity) of the stellar population in the Galactic globular cluster (GC) NGC 3201, which is irregularly reddened across its face. We carried out a more detailed and careful analysis of our recently published new multi-color photometry in a wide field of the cluster with particular emphasis on the U band. Using the photometric data corrected for differential reddening, we found for the first time two key signs of the inhomogeneity in the cluster's stellar population and of its radial variation in the GC. These are (1) an obvious trend in the color-position diagram, based on the (U-B) color-index, of red giant branch (RGB) stars, which shows that the farther from the cluster's center, the bluer on average the (U-B) color of the stars is; and (2) the dependence of the radial distribution of sub-giant branch (SGB) stars in the cluster on their U magnitude, where brighter stars are less centrally concentrated than their fainter counterparts at a confidence level varying between 99.2% and 99.9% depending on the color-index used to select the stars. The same effects were recently found by us in the GC NGC 1261. However, contrary to NGC 1261, we are not able to unambiguously suggest which of the sub-populations of SGB/RGB stars can be the progenitor of blue and red horizontal branch stars of the cluster. Apart from M4, NGC 3201 is another GC very probably with an inhomogeneous stellar population, which has essentially lower mass than the most massive Galactic GCs where multiple stellar populations were unambiguously detected for the first time
An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management

Economic dispatch in a multi-microgrid (MMG) system involves an increasing number of states from distributed energy resources (DERs) compared to a single microgrid. In these cases, traditional reinforcement learning (RL) approaches may become computationally expensive or less effective in finding the least-cost solution. This paper presents a novel RL approach that employs local learning agents to interact with individual microgrid environments in a distributed manner and a global agent to search for actions to minimize system cost at the MMG system level. The proposed distributed RL framework is more efficient in learning the dispatch policy compared to conventional approaches. Case studies are performed on a 3-microgrid system with different types of DERs. Results substantiate the effectiveness of the proposed approach in comparison with conventional methods in terms of operation costs, computation time, and peak-to-average ratio.
Wireless Power Transfer System for Mobile Robots via Magnetic Resonant Coupling with Automated Impedance Matching

Wireless power transfer via magnetic resonant coupling can be used to supply power to a mobile robot within a few meters of a transmitter coil. However, when the robot moves or its power consumption fluctuates, its input impedance varies and causes power reflection. Therefore, we propose the use of multiple identical driver coils on the transmitter side in order to match the input impedance. The input impedance is matched and power reflection is eliminated by switching driver coils, i.e., regulating the coupling coefficient between the driver and the transmitter. During experiments, the transmitting efficiency showed good agreement with the calculated value, and the input impedance was well-matched under assumed range of varying distances and load resistances. Therefore, the proposed system was expected to solve the power reflection issues in mobile robots automatically.
Experimental Analysis of a Dual-Evaporator Thermosyphon Cooling System for CPUs and GPUs

Thermosyphon cooling systems have great potential to be widely adopted for electronics cooling applications. They combine highly effective heat transfer with passive circulation triggered by the density difference between the liquid and vapour phases. It is not uncommon to have several processing units on a single motherboard, such as the Central Processing Units (CPUs) and Graphics Processing Units (GPUs) in electronic devices, such us desktop computers, servers, telecommunications equipment, etc. Thus, designing a thermosyphon cooling system with multiple evaporators and a single condenser is critical for practical implementations and broad market adoption. In this study, a thermosyphon-based system composed of two evaporators and a single air-cooled condenser is designed and tested for the layout of a desktop computer, removing heat from a CPU and a GPU. Specifically, the vertical CPU evaporator occupies the highest position, while the GPU evaporator is horizontal and located at the bottom of the loop. Both evaporators are in a parallel scheme in the proposed thermosyphon design. The total power dissipation of the thermosyphon-based system is 650 W and is split nonuniformly between the two evaporators (CPU power of 350 W and GPU power of 300 W). The results show that the thermosyphon can effectively cool both processing units without instabilities, with an overall thermal resistance varying between 0.055 and 0.061 K/W in steady-state operating conditions. Overall, the results demonstrate a thermosyphon system’s feasibility, reliability, and high efficiency in a dual-evaporator configuration as a starting point for future and more complex hardware arrangements.
ACFNet: An Adaptive Context Fusion Network for Skin Lesion Segmentation

Skin lesion segmentation is a key step in computer-aided diagnosis. Fully convolution network-based approaches have achieved great results. However, inadequate feature extraction has lost many important features as well as rough fusion has introduced many adverse noises. All of these limit the accuracy of skin lesion segmentation. In this paper, we propose a new and effective adaptive context fusion network for skin lesion segmentation. The proposed network is based on the U-Net architecture. An adaptive context fusion module (ACF) is used to extract more adequate feature. Combining the advantages of atrous convolution and attention mechanism, it can not only reduce the loss of spatial information, but also further refine the extracted feature. The gated residual fusion module (GRF) is added to skip architecture to make the fusion process more refined. It can suppress invalid information in the fusion process with less spatial information loss. We evaluate the proposed method on two benchmark datasets: ISIC 2016 and ISIC 2017, and the experimental results show that the proposed method achieves significant accuracy improvement compared with the existing several networks. The ablation experiments also prove the effectiveness of the proposed two modules.
Fuzzing DBMS via NNLM

Vulnerabilities in database management system (DBMS) can cause serious security problems affecting hundreds of millions of software systems. Fuzzing is an effective vulnerability mining technology, but few studies utilize neural network language model (NNLM) to fuzz DBMS. In this paper, we explore this technical roadmap and use the sequence model to automatically generate test cases to fuzz DBMS. One of the advantages of the method is that it can effectively test the DBMS in a black-box situation. We implemented our tool NNFuzz and evaluated it on SQLite. Experimental results show that NNFuzz can generate valid test cases and achieve higher code coverage than the initial training set.
SMIL-DeiT:Multiple Instance Learning and Self-supervised Vision Transformer network for Early Alzheimer's disease classification

Early diagnosis of Alzheimer's disease(AD) is becoming increasingly important in preventing and treating the disease as the world's population ages. We proposed a SMIL-DeiT network for AD classification tasks amongst three groups: Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Normal Cognitive (NC) in this study. Vision Transformer is the fundamental structure of our work. The data pre-training is performed utilizing DINO, a self-supervised technique, whereas the downstream classification task is done with Multiple Instance Learning. Our proposed technique works on the ADNI dataset. We used four performance metrics accuracy rates, precision, recall, and Fl-score in the evaluation, the most important of which was accuracy. The accuracy obtained by our method is higher than the transformer's 90.1% and CNN's 90.8%, reaching 93.2%.
HAPP: a Haptic Portable Pad for Hand Disease Manual Treatment

Nowadays, especially with the Covid-19 pandemic, researchers are focusing their attention on the remote delivery of devices designed for rehabilitation purposes, allowing people to recover without the physical presence of a doctor. Manual therapy is a physical treatment that is used by therapists for the treatment of musculoskeletal pain and/or disabilities. The aim of this work is to present HAPP, a new haptic portable device, designed to help patients suffering of different patholohgies, as for instance the Complex Regional Pain Syndrome type-I disease, and more in general to investigate the effects of manual therapy for diseases of the carpus and metacarpus, by mimicking traditional mechanical and rhythmic stimuli characteristics of manual treatments. Its structure consists of a plate oriented by revolute-prismatic-spherical joints, with a rack-pinion mechanism that actuates the end-effector, stimulating the user’s hand palm. We provide details about the device, such as the mechanical design, the mathematical model and a graphical user interface. Preliminary studies in order to evaluate the device force exerted at the user’s palm were carried out.
A Modular Interface for Controlling Interactive Behaviors of a Humanoid Robot for Socio-Emotional Skills Training

The usage of social robots in psychotherapy has gained interest in various applications. In the context of therapy for children with socio-emotional impairments, for example autism spectrum conditions, the first approaches have already been successfully evaluated in research. In this context, the robot can be seen as a tool for therapists to foster interaction with the children. To ensure a successful integration of social robots into therapy sessions, an intuitive and comprehensive interface for the therapist is needed to guarantee save and appropriate human-robot interaction. This publication addresses the development of a graphical user interface for robot-assisted therapy to train socio-emotional skills in children on the autism spectrum. The software follows a generic and modular approach. Furthermore, a robotic middleware is used to control the robot and the user interface is based on a local web application. During therapy sessions, the therapist interface is used to control the robot’s reactions and provides additional information from emotion and arousal recognition software. The approach is implemented with the humanoid robot Pepper (Softbank Robotics). A pilot study is carried out with four experts from a child and youth psychiatry to evaluate the feasibility and user experience of the therapist interface. In sum, the user experience and usefulness can be rated positively.
Immittance Network-based High-Frequency Paralleled Inverters for Capacitive Wireless Charging Systems

This paper proposes a high-frequency high-power capacitive WPT system using immittance network-based paralleled inverters. The immittance network-based paralleling technique is easily scalable, allowing to parallel any number of inverters based on the system output power requirement. A comprehensive design methodology of the immittance network-based paralleled inverter capacitive WPT system is presented. The effectiveness of the immittance networks in maintaining a constant output power and suppressing the differential currents between the paralleled inverters in the presence of mismatches is also shown and is validated using simulations. Furthermore, the performance of different immittance network topologies for paralleling inverters in a capacitive WPT system is compared. Finally, the findings of this study are validated using two prototype 6.78-MHz 12-cm air-gap capacitive WPT systems that transfer 1100 W power.
A novel hybrid CNN-Transformer model for EEG Motor Imagery classification

Motor imagery is one of the most popular brain-computer interface (BCI) paradigms with good potential to help disabled people. However, EEG decoding is still challenging because of its low signal-to-noise ratio, with which valuable features are difficult to perceive. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) with the Transformer for decoding motor imagery EEG signals. The CNN is used to extract local features, while the Transformer is utilized to perceive global dependencies. Moreover, we exploit spatial-spectral-temporal features to improve classification performance. To validate the effectiveness and superiority of the proposed method, we conduct experiments on the BCICIV dataset 2a and compare it with other efficient approaches. The results show that our algorithm outperforms the state-of-the-art methods, indicating that the CNN-Transformer model is a competing strategy.
Fuzzy Based Control of a Flexible Bevel-Tip Needle for Percutaneous Interventions

In Minimal Invasive Surgical procedures, flexible bevel tip needles are widely used for percutaneous interventions due to the advantage of enhancing the target reaching accuracy. Here, the target reaching accuracy suffers due to tissue in-homogeneity, deformation in tissue domain and improper steering techniques. The main objective of the percutaneous interventional procedures is ensuring patient safety and reaching desired target position accurately. Several researchers have al-ready developed many approaches to control the needle steering for precise target reaching. To overcome complex approaches in existing controllers, we have proposed a fuzzy based controller to regulate the needle in a specified plane. Our designed method involves the needle non-holonomic constraints based kinematics inside tissue domain and Lyapunov analysis based fuzzy rule base for fuzzy inference system which ensures the closed loop stability of needling system for percutaneous interventional procedures. We have also validated our designed control scheme through extensive simulations and experimentation in biological tissue.
Transparent and Conductive Graphene Oxide/Poly(ethylene glycol) diacrylate Coatings Obtained by Photopolymerization

Water-dispersed graphene oxide sheets were used to prepare graphene/poly(ethylene glycol) diacrylate resin composites by photopolymerization. It was found that graphene sheets undergo excellent morphological distribution within the resin system, giving rise to transparent composites with unaltered thermal properties with respect to the neat resin, that are electrically conductive at loading ratios as low as 0.02 wt. -% of graphene oxide. The proposed strategy based on photopolymerization provides an easy, energy-saving and environmental friendly technique that can find a wide application in coating technology, mainly for electromagnetic shielding and antistatic coatings.