Miracle-dz/UniFAR-FTUs huggingface dataset:
Would you help a sad robot? Influence of robots’ emotional expressions on human-multi-robot collaboration

With recent advancements in robotics and artificial intelligence, human-robot collaboration has drawn growing interests. In human collaboration, emotion can serve as an evaluation of events and as a communicative cue for people to express and perceive each other’s internal states. Thus, we are motivated to investigate the influence of robots’ emotional expressions on human-robot collaboration. In particular, we conducted experiments in which a participant interacted with two Cozmo robots in a collaborative game. We found that when the robots exhibited emotional expressions, participants were more likely to collaborate with them and achieved task success in shorter time. Moreover, participants perceived emotional robots more positively and reported to have a more enjoyable experience interacting with them. Our study provides insights on the benefit of incorporating artificial emotions in robots on human-robot collaboration and interaction.
A High Efficient Wide Range Cross Coupled Rectifier for RFID Applications

In this paper, a cross coupled rectifier is proposed and studied for Radio Frequency (RF) energy harvesting with wide frequency range. Threshold voltage of the MOS transistor is one of the major barriers of RF to DC converter. In this work the usage of an auxiliary MOSFET mechanism and storage capacitors to eliminate threshold voltage effect in MOS transistor is recommended, which drastically increases the DC extraction capability of charge pump. The proposed rectifier is designed and simulated in 90 nm CMOS process and simulation results exhibit a high power conversion efficiency (PCE) of 78.5% for $10 k \Omega$ load. The proposed circuit also gives a same output voltage from 915 MHz to 2.45 GHz. The results also show that the rectifier is insensitive to temperature and process corners. The rectifier can be used in numerous RF energy harvesting system, including RFID applications and other wireless applications.
On the Applicability of Secret Share Algorithms for Osmotic Computing

Osmotic Computing (OC) is an innovative computation paradigm that runs services on Cloud, Edge, and Internet of Things (IoT) resources based on their workload. Services are encapsulated in container images stored into a central repository on the Cloud. OC suffers from privacy and security issues, for example, hackers could attack the repository and download all container images. Furthermore, network latency could delay the deployment of services in Edge nodes. A possible solution to solve such problems is to employ Secret Share techniques to split the images of services into chunks and distribute them among Edge devices. This work aims at assessing the applicability of these techniques for OC employing the Redundant Residue Number System (RRNS) to split and store Micro-Elements (MELs). We made our analyses for different OC scenarios composed of 10, 100 and 1000 nodes running 1000 MELs each. Furthermore, we considered several degrees of redundancy from 0 to 7. From experimental analyses, we found that the reliability of the system increase with the increasing of the redundancy but the security decreases.
MCEA: A Resource-Aware Multicore CGRA Architecture for the Edge

Modern IoT edge devices must address the unpredictability of applications with strict power and temperature constraints. In this scenario, heterogeneous multicore architectures have been driving many solutions due to their high energy efficiency and ability to exploit Task-Level Parallelism. However, while their performance is highly dependent on the quality of the scheduling, their adaptability and generality get restricted when they use fixed-size hardware accelerators. Considering that, this work proposes MCEA, a transparent and power-adaptive multicore reconfigurable architecture. MCEA dynamically adapts the hardware to the workload rather than migrating applications; and predicatively sizes its reconfigurable accelerators without prior knowledge of the applications' behaviors. For that, MCEA uses a synergistic and online profiling system with power gating, achieving performance levels near of homogeneous architectures with fixed and oversized reconfigurable fabric (within 99% on average) while presenting energy efficiency levels similar to heterogeneous architectures statically tuned to a specific workload (within 99% on average). Therefore, MCEA improves Energy-Delay Product in 1.55x and 1.21x when compared to their heterogeneous and homogeneous counterparts, and in 4.72x when compared to a multicore with OoO processors only. We also show that MCEA outperforms a state-of-the-art reconfigurable architecture for the edge under the same power envelope.
Taxonomic and functional approaches of trophic interactions between large herbivores and plant communities in a mountain ecosystem

Given the key role of large herbivores on species and functional plant diversity, we aimed at better understanding the relationship between herbivory and plant communities mainly at a fine-scale, in order to reconcile objectives of population management and plant conservation. For this purpose, we used both taxonomic and functional approaches, and studied interactions at the inter- and intra-specific levels. We combined information coming from three databases: (1) diet data from DNA-metabarcoding applied on chamois (Rupicapra rupicapra), roe deer (Capreolus capreolus) and mouflon (Ovis gmelini musimon) faeces from the Bauges Massif, (2) characteristics of plant communities (plant composition, biomass, phenology), (3) plant functional traits. Analyses of intra-specific variability of the three large herbivores allowed us to upscale the niche variation hypothesis (NVH) of Van Valen from the intra- to the inter-specific level, i.e. we observed a positive relationship between the species niche breadth and among-individual variation. Then, based on two chamois subpopulations living in pastures, one living in sympatry with the mouflon and the other living in allopatry, we revealed the absence of negative effects of the introduced mouflon population on native chamois population diet, both for the taxonomic and functional dietary niche. Analyses of diet selection criteria allowed us to highlight differences in choice criteria between chamois and mouflon in some seasons, which helped to explain the taxonomic and functional niche partitioning of the two species. Furthermore, the proposed scenario of the evolution of diet selection over the year for both species were consistent with ungulate-specific morpho-physiological features. Finally, contrary to the literature where no studies could discriminate the direct and indirect effects of functional traits on diet selection because of correlations, we used path analyses, which allowed us to show that in most cases, biomechanical traits had a direct effect on diet choices, whereas chemical traits had an indirect effect. Furthermore, from a methodological point of view, we advised to use nitrogen fecal indices only to study the evolution of species-specific and location-specific population long-term diet quality, but not to compare diet quality between species, nor to study slight fluctuations at the intra-seasonal level. The complementarity of the approaches allowed us to better account for the structuration of herbivore communities, which should help to better assess the actual state and the evolution of relationships among individuals, species and their environment.Key-words: ungulates, intra- and inter-specific interactions, taxonomic and functional approach, DNA metabarcoding, NIRS, Bauges Massif, diet selection
What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

The present contribution investigates the effects of spoken language varieties, in particular non-standard / regional language compared to standard language (in our study: High German), in social robotics. Based on (media) psychological and sociolinguistic research, we assumed that a robot speaking in regional language (i.e., dialect and regional accent) would be considered less competent compared to the same robot speaking in standard language (H1). Contrarily, we assumed that regional language might enhance perceived social skills and likability of a robot, at least so when taking into account whether and how much the human observers making the evaluations talk in regional language themselves. More precisely, it was assumed that the more the study participants spoke in regional language, the better their ratings of the dialect-speaking robot on social skills and likeability would be (H2). We also investigated whether the robot’s gender (male vs. female voice) would have an effect on the ratings (RQ). H1 received full, H2 limited empirical support by the data, while the robot’s gender (RQ) turned out to be a mostly negligible factor. Based on our results, practical implications for robots speaking in regional language varieties are suggested.
Demagnetization Analysis of a V-shape PM Salient Pole Wind Generator under Sudden Short-Circuits after Rated Load Condition

To improve the output parameters of PM salient pole wind generator, the V-shape permanent magnet salient pole wind generator (VPMSPWG) was previously proposed. Its special structure can not only protect PMs well, but also increase the generator’s output power. In this paper, its PM demagnetization performance under three-phase symmetrical short-circuit is investigated. The maximum sudden short-circuit current is obtained based on a calculation formula which is deduced in this paper. In order to estimate the most serious demagnetization condition, the maximum sudden short-circuit is used as the demagnetization current and the PM demagnetization with different structure parameters are studied by finite element method (FEM). The investigation results show that the operating temperature determines whether there is irreversible demagnetization during sudden short circuit and the rotor structure have evident effect on demagnetization performance. After comparison and analysis, the appropriate working temperature is suggested. The PM angles and non-magnetic material shapes with minimum demagnetization are selected out to enhance the anti-demagnetization ability of VPMSPWG.
Performance Enhancement of Ship Hybrid Power System Using Photovoltaic Arrays

Ship electric power system utilizes a standalone micro-grid during sailing and when in harbor, it uses the ground grid. To increase the sailing period without berthing on ports, more fuel should be stored onboard the ship which consumes part of the allowable onboard carrying mass and hence reduces the permissible payload especially in the commercial ships or reduces the allowable amount of weapon and ammunition in the naval ships. The above arises the need for shipbuilders to look for a solution to increase the sailing period. All-electric ship technique and renewable energy is a perfect solution to solve these problems and on the other hand to reduce the environmental restriction due to use the fossil fuel. In this paper, a pre-designed electric power system with fuel cell and storage devices is used in the emergency aircraft landing system. Ships have wide areas exposed to the sun. Photo-voltaic arrays were added to the system to adapt the fuel cell dynamic beside the storage energy system to enhance the electric power system performance and reduce the fuel consumption which in turn leads to minimize the cost for the commercial ship's applications and allow for more reliable long cursing for both commercial and military ships.
Event-Triggered Synchronization for Memristor-Based Neural Networks

This paper studies the synchronization problem of memristor-based neural networks with networked environment, in which the network bandwidth and computational resources are limited. An event-triggered state feedback control strategy is proposed. By taking the proposed triggering mechanism into account, some criteria are developed such that memristor-based neural networks achieve synchronization with limited network bandwidth and computational resource. Different from the previous related works, continuous event detectors are adopted to determine when to broadcast synchronized error information to control input, and the Zeno phenomena are considered. Finally, a numerical simulation example is given to verify the effectiveness of the proposed event-triggered control strategy.
Maximizing Flexibility in a Power System with Reserve, Renewables, Active Demand and Storage

In a power system with intermittent sources of energy, operating conditions constantly vary. This puts the system in an always-changing situation that can go from stable to unstable at any time. Even if the system remains stable, the endless change with different characteristics could endanger the operation. In order to manage the impact of variabilities and reduce the risk of entering an unstable condition, it is vital to investigate the flexibility of the system. This paper proposes an approach to maximize flexibility in a power system by considering various sources of flexibility. The five main sources of flexibility considered here are reserve from conventional generators, renewable sources such as wind, energy storage such as battery, flexibility embedded in additional capacity of transmission lines and active demand. The results demonstrate how system operation is affected by these flexibility sources.
Detection of Mathematical Fluency Effects on Working Memory using Near Infrared Spectroscopy

The prefrontal cortex (PFC) not only plays a less mathematics-specific role in number processing and calculation but it is also responsible for working memory demands coping. Through functional near-infrared spectroscopy imaging, this study explored how differently the PFC of 31 subjects with contrasting mathematical fluency would respond to increasing working memory demands of mental arithmetic task. Area under the curve analyses of the oxygenated hemoglobin signals, hereinafter representing brain activation, were compared across levels and groups. Depending on task performance, the subjects were allocated to either normal performers (NP) or high performers (HP) group. Both groups showed sensitivity with regards to task performance (i.e., the number of problems attempted and accuracy) towards increasing working memory demands (level 1 to 3). With increasing task level, NP’s PFC activation increased while HP’s remained unchanged. NP also showed consistent greater PFC activation than HP in all three levels. These findings implied that the PFC plays a more auxiliary role to cope with working memory demands imposed by mental arithmetic on individuals who are less fluent in mathematics. This can potentially serve as a framework to monitor workload hemodynamics continuously and unobtrusively.
Real-Time Automatic Air Pollution Services from IOT Data Network

In recent years, there is an increasing attention on Air Quality and derived services. In most cases, the main objective consists in providing services independently on the number of sensors, and on any position of the users. On the other hand, sensors are collected in limited positions, then a dense grid is computed to satisfy the needs of services such as: conditional routing, alerting on data values for personal usage, general heatmap production for Dashboards in control room of the operators, and for web and mobile applications for the city users. The paper formalizes the development process and describes how it is possible to automatically integrate Data Analytics in data flow real time processes, called in the paper IoT Applications. Two interpolation methods have been compared and validated in order to assess the accuracy. Interpolation errors trends have been used to detected devices' dysfunctions on sensors. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City (https://www.snap4city.org) has been developed as a part Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.
An Adaptable High-Throughput FPGA Merge Sorter for Accelerating Database Analytics

This work improves on the latest research about sorting acceleration on FPGAs. An efficient design is introduced for sorting data that fit on-chip, with the additional functionality to merge sorted sublists recursively, for an input of arbitrary length. While many-leaf mergers are conventionally single-rate, a novel technique in our approach is to use a parallel merge tree only for the latest stages of the merge tree, to enable bandwidth-adapted multi-rate many-leaf merge. Our open-source RTL generator produces sorting peripherals with customisable parallelism and data format. We evaluate our FPGA design as an 128-bit wide peripheral on an MPSoC platform, with a speedup of up to 49 times over the A53 core for sorting, and up to 27 times speedup for our specialized database analytics application.
HS-AUTOFIT: a highly scalable AUTOFIT application for Cloud and HPC environments

The technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is an increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order to improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud and High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. Compared to the old program version, the new one is capable of scaling on multiple computing nodes, thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments show that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.
Optimal Location of DGs Into the Power Distribution Grid for Voltage and Power Improvement

The integration of renewable energy distributed generation (REDG) into the existing distribution networks (DNs) has over the years become popular. This practice has brought significant and numerous advantages in the technical, economic, and environmental sectors. However, locating REDGs in a random location may result in poor voltage profile and an increase in the active power losses. It is therefore imperative that the location of REDGs be optimized to obtain satisfactory results. In this research work, we propose an analysis-based method for REDGs location using a sensitive index algorithm. The method is carried out in order to improve the voltage profile of the power grid system and minimize the active power losses in the system. The validation of the proposed method is tested using an IEEE-24 bus system node arrangement. The results presented show that optimizing the locality of the REDGs improves the voltage and minimizes the power losses significantly.
A bistable soft gripper with mechanically embedded sensing and actuation for fast grasping

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.
A Numerical Method for Calculating the Power Loss of Device in Renewable Energy System Considering the Impact of Junction Temperature

With the rapid development of renewable energy, power devices have been widely used. However, the reliability of power devices is becoming a thorny problem due to the fluctuation of junction temperature. Since the junction temperature is closely related to the power loss, it is important to accurately obtain the power loss of the device. In this paper, a numerical method for calculating the power loss of device is proposed, which can consider the impact of junction temperature. Simulations are conducted to verify the correctness of proposed method. Results indicate that the junction temperature should be taken into account so that the accuracy of power loss calculation can be improved.
Are grouse populations unstable at the southern end of their range?

The long-term temporal dynamics of four grouse species in the Italian Alps have been investigated in the attempt to reveal if the populations exhibit the tendency to cycle at the southern edge of their European range. Hunting statistics or count data were collected as total number of individuals shot or counted each year from five provinces. For the province of Trento data were available at the level of mountain groups and a more detailed investigation was carried out. The results from Trento were compared with the more general findings from the other four Italian provinces. Time series analysis was performed to investigate the pattern of cycle and autoregressive models were used to describe the density dependence structure. In general, the populations showed a weak or no tendency to regular fluctuations: rock ptarmigan Lagopus mutus and black grouse Tetrao tetrix populations exhibited the highest tendency to cycle with periods of 5–9 years while capercaillie Tetrao urogallus never showed regular fluctuations. Hazel grouse Bonasa bonasia cyclic dynamics were restricted to a few populations of Trento. When time series from Trento were corrected for hunting effort the detection of cycle among populations sharply increased but no differences in the strength of second order density dependence or in the period length was observed. A linear first order autoregressive model explained better the intrinsic structure of the majority of populations. We compare the findings with studies conducted on populations of northern Europe and suggest possible reasons for the reduced tendency to cycle in the grouse populations of the Italian Alps.
Human activity and transportation mode recognition using smartphone sensors

During the day we use various means of transportation without perceiving the impact of our choices to the environment. Our aim is to create a mobile application to raise environmental awareness and guide people to follow a more sustainable way of life by monitoring and finally reducing their carbon footprint. The challenge that we addressed was to create a mobile application that would automatically and seamlessly identify the user’s mean of transportation exploiting various smartphone sensors. This paper focuses on the human activity and transportation mode recognition process of our mobile application. We present a machine-learning model that supports real-time transportation mode recognition following a multimodal recognition and exploiting the combination of accelerometer, gyroscope and magnetometer signals. Our model achieves results comparable to similar offline solutions or solutions that use battery consuming sensors.