Miracle-dz/UniFAR-FTUs huggingface dataset:
Mysticete migration revisited: are Mediterranean fin whales an anomaly?

The generally accepted model of Mysticete whale migration is that most individuals undertake seasonal migrations between high and low latitudes. Although numerous exceptions have been described in the literature, the traditional model remains unexamined. This paper represents the first ‘official’ challenge to the traditional model of Mysticete migration. We set out to review the current state of knowledge on the Mediterranean fin whale migratory pattern, and to examine whether the small, resident population in the Mediterranean is an anomaly within the framework of the traditional model of Mysticete migration. We investigated the prevalence of alternative migratory strategies among the Mysticete whales, reviewing the migratory habits of the Mediterranean fin whales and those of other fin whale and baleen whale populations. We reach three main conclusions. First, the seasonal behaviour of the resident Mediterranean fin whales is highly dynamic, and a decade of research has not clarified prevailing uncertainties regarding migration patterns. Second, migration strategies similar to those observed in the Mediterranean fin whales are commonly seen in other populations of baleen whales. Third, the traditional model of whale migration is too simplified to describe the repertoire of Mysticete migratory behaviours accurately. We argue that the paradigm of baleen whale migration warrants further scrutiny to account for more complex movement strategies. We suggest that Mysticete migration should be thought of as a continuum of different strategies that have evolved in the face of different selective pressures. Instead of representing an exception to the rule, the resident Mediterranean fin whales may in fact fall towards one end of a larger spectrum of migratory behaviours. A greater knowledge of ecological factors, reproductive patterns, and local adaptations will be needed to understand the evolutionary mechanisms behind the diversity of migratory habits.
Multi-Scale Feature Enhanced Domain Adaptive Object Detection For Power Transmission Line Inspection

Domain adaptive object detection aims to build an object detector for the unlabeled target domain by transferring knowledge from a well-labeled source domain, which can alleviate the problem of cumbersome labeling of object detection in cross-scene power transmission line inspection. Remarkable advances are made recently by mitigating distributional shifts via hierarchical domain feature alignment training of detection networks. However, domain adaptive object detection is still limited in learning the invariance representation of multi-scale features. Specifically, the scale of objects varies in the scenes of aerial inspection, which hinders the knowledge transfer from the labeled source domain. In this paper, we propose a multi-scale feature enhanced domain adaptation method for cross-domain object detection of power transmission lines inspection. The proposed method consists of two components: 1) Multi-Scale Fusion Feature Alignment module (MSFA) to strengthen similar representation characteristics of different scales object in domain adaptive by utilizing context information conveyed from other levels; 2) Multi-Scale Consistency Regularization module (MSCR) to jointly optimize the multi-scale feature learning of each level, which promotes domain invariant feature learning at each level. Experimental results demonstrate that our method significantly increases the performance of the object detector in several cross-scene transmission line inspection tasks.
Online Screening of X-System Music Playlists Using an Integrative Wellbeing Model Informed by the Theory of Autopoiesis

Research suggests that music has a powerful effect on the human mind and body. This article explores the impact of music as an intervention. For this purpose, the X-System technology is used to curate relaxing and enlivening music playlists designed to positively impact wellbeing and emotional state during the COVID-19 pandemic. A wellbeing model grounded in autopoietic theory of self-organisation in living systems is developed to inform the evaluation of the impact of the intervention and ensure the reliability of the data. More specifically, data quality is enhanced by focusing the participants’ awareness on their immediate embodied experience of physical, emotional and relational wellbeing and sense of pleasure/displeasure prior to and after listening to a preferred playlist. The statistical analysis shows significant positive changes in emotional wellbeing, valence and sense of meaning ( $p< 0.001$ ) with a medium effect size. It also reveals a statistically significant change for physical wellbeing ( $p=0.009$ ) with a small effect size. With the relaxing playlists leading to decrease in arousal levels and the enlivening playlists to an increase in activation, it is also concluded that appropriately curated playlists may be able to lead the listener to positive relaxation or activation states or indeed to positive mood change that may have health benefits.
Computational Ghost Imaging in Scattering Media Using Simulation-Based Deep Learning

Deep learning has been proven to provide solutions for computational ghost imaging (CGI). However, in current CGI techniques, the quality of the reconstructed image is adversely affected by the position and intensity of the scattering medium. In this study, the feasibility of using a deep neural network (DNN) by using the hybrid simulated data to facilitate CGI through a scattering medium is demonstrated, particularly when the scattering medium is in front of an object. Under a specific order of the measurement matrix, the CGI measurement equation is introduced along with a disturbance factor of the scattering effect to generate simulation data, thereby representing imaging situations with the scattering medium at different positions. The selection of disturbance parameters is determined by the correlation between the simulation signal and the experimental signal. Then an end-to-end DNN is trained using experimentally obtained light intensity signals, and it shows that the quality of the reconstruction results is improved when there is a scattering medium in the emission path. The reported technique effectively solves the problem of CGI reconstruction under different scattering paths with a common DNN, and may have broad applications in fast adaptive CGI for new and uncertain scenarios.
Towards Plastic Optical Fiber Magnetic Field Sensors exploiting Magnetic Fluids and Multimode SPR-POF platforms

We have studied how the optical property of a multimode plastic optical fiber (POF) can be used to monitor a magnetic field, exploiting a Surface Plasmon Resonance (SPR) POF sensor. To realize the sensor system, we have deposited a magnetic fluid on the cladding of a multimode POF connected in input to an SPR D-shaped POF sensor platform. The target magnetic field exerts a bending force on the POF covered with magnetic fluid, thus producing a change of light in input to the SPR POF sensor and modifying the SPR phenomenon. This work presents preliminary experimental results to demonstrate that this novel principle of sensing can be used for highly sensitive measuring of the magnetic field. Preliminary results have demonstrated the viability of this approach showing the possibility to obtain higher sensitivities, compared with the state of the art on SPR optical fiber magnetic field sensors exploiting magnetic fluids.
FPGA-Based 2-D FIR Frost Beamformers with Digital Mutual Coupling Compensation

This paper describes a technique for de-embedding electromagnetic mutual coupling effects between nearest neighbors of a ULA receiver. Measured S-parameters across a range of frequencies of interest are used in a closed-form mathematical model that relates measured S-parameters, measured LNA reflection coefficients, and transmission line parameters to digital beamformer design equations, such that the beam shape distortions arising from mutual coupling can be compensated in the DSP algorithm. A 5.8 GHz 32-element receiver array with custom receivers and 32-channel Xilinx Virtex-6 Sx35 FPGA based DSP system implementing a real-time complex-valued Frost beamformer operating at IF is proposed to show the benefits of de-embedding mutual coupling for far-field side-lobe suppression. The worst-case side-lobe level is reduced by 11.2 dB, and the average side-lobe level is reduced by 5.2 dB, for a 20 MHz IF signal.
On MAC Protocols Performance for M2M Communications

The Internet of Things (IoT) aims to interconnect most of the devices and equipment that surround us. One of the basic components of this new paradigm is machine-to-machine (M2M) communications, since they guarantee the interaction between the devices. One characteristic of M2M scenarios is the presence of many devices communicating with each other. This, along with the fact that communication is carried out through shared channels, causes the need to use medium access control (MAC) protocols. These guarantee the orderly and efficient use of the channel. In this paper, the performance of three well known MAC protocols; Slotted Aloha, FASA, and DPCF are evaluated through simulations. Results indicate that DPCF outperforms the other two protocols in terms of goodput and energy consumption. These advantages are achieved at the expense of increasing the complexity of the channel access mechanism compared to the complexity of Slotted Aloha and FASA.
A Layered Control Approach to Human-Aware Task and Motion Planning for Human-Robot Collaboration

Combining task and motion planning efficiently in human-robot collaboration (HRC) entails several challenges because of the uncertainty conveyed by the human behavior. Tasks plan execution should be continuously monitored and updated based on the actual behavior of the human and the robot to maintain productivity and safety. We propose control-based approach based on two layers, i.e., task planning and action planning. Each layer reasons at a different level of abstraction: task planning considers high-level operations without taking into account their motion properties; action planning optimizes the execution of high-level operations based on current human state and geometric reasoning. The result is a hierarchical framework where the bottom layer gives feedback to top layer about the feasibility of each task, and the top layer uses this feedback to (re)optimize the process plan. The method is applied to an industrial case study in which a robot and a human worker cooperate to assemble a mosaic.
Ecosystem services returned through seagrass restoration

Ecosystem restoration is often costly, but can be effective at increasing biodiversity and ecosystem services. We used a case study—reseeding seagrass to a coastal lagoon—to demonstrate the value of enhanced ecosystem services as a result of restoration. We modeled the recovery of areal plant coverage in a system where seagrasses were lost due to disease and disturbance, and estimated the value of the returned functions of nitrogen removal and carbon sequestration. We estimated, as of 2010, that this restoration removes 170 ton of nitrogen per year via denitrificiation and sequesters carbon at a rate of 630 tons carbon per year in the sediment. Further, we estimated that natural recovery would take more than 100 years to reach the areal coverage achieved by restoration using seeds in just 10 years. Restoration enhanced this recovery, and the earlier establishment of plants results in a net gain of at least 4,100 ton of nitrogen removed from the system via denitrification and 15,000 ton of carbon sequestered in the sediment. These services have significant ecological and societal value.
Towards Personalized Interaction and Corrective Feedback of a Socially Assistive Robot for Post-Stroke Rehabilitation Therapy

A robotic exercise coaching system requires the capability of automatically assessing a patient’s exercise to in-teract with a patient and generate corrective feedback. However, even if patients have various physical conditions, most prior work on robotic exercise coaching systems has utilized generic, pre-defined feedback.This paper presents an interactive approach that combines machine learning and rule-based models to automatically assess a patient’s rehabilitation exercise and tunes with patient’s data to generate personalized corrective feedback. To generate feedback when an erroneous motion occurs, our approach applies an ensemble voting method that leverages predictions from multiple frames for frame-level assessment. According to the evaluation with the dataset of three stroke rehabilitation exercises from 15 post-stroke subjects, our interactive approach with an ensemble voting method supports more accurate frame-level assessment (p < 0.01), but also can be tuned with held-out user’s unaffected motions to significantly improve the performance of assessment from 0.7447 to 0.8235 average F1-scores over all exercises (p < 0.01). This paper discusses the value of an interactive approach with an ensemble voting method for personalized interaction of a robotic exercise coaching system.
Attention-Based Event Characterization for Scarce Vehicular Sensing Data

Characterizing risky driving behavior is crucial in a connected vehicle environment, particularly to improve driving experience through enhanced safety features. Artificial intelligence-backed solutions are vital components of the modern transportation. However, such systems require significant volume of driving event data for an acceptable level of performance. To address the issue, this study proposes a novel framework for precise risky driving behavior detection that takes advantage of an attention-based neural network model. The proposed framework aims to recognize five driving events including harsh brake, aggressive acceleration, harsh left turn and harsh right turn alongside the normal driving behavior. Through numerical results, it is shown that the proposed model outperforms the state-of-the-art solutions by reaching an average accuracy of 0.96 and F1-score of 0.92 for all classes of driving events. Thus, it reduces the false positive instances compared to the previous models. Furthermore, through extensive experiments, structural details of the attention-based neural network is investigated to provide the most viable configuration for the analysis of the vehicular sensory data.
On the Effectiveness of Near-Field Feedback for Digital Pre-Distortion of Millimeter-Wave RF Beamforming Arrays

This paper investigates theoretically and experimentally the effectiveness of linearizing millimeter-wave analog beamforming arrays using a single digital pre-distortion (DPD) function trained utilizing a near-field feedback signal. First, it is shown that if the DPD is trained to minimize the error between the signal acquired in the near-field and the input to the DPD, the far-field received signal exhibits significant residual nonlinearity effects. It is then shown that a DPD function trained to minimize the difference between a properly synthesised nearfield target signal and the acquired near-field signal, results in the cancellation of nonlinearity effects in the far-field.
Explainable Temperament Estimation of Toddlers by a Childcare Robot

Personality estimation of others is a critical ability to communicate with each other. It enables robots to interact with humans and provides the former the ability to predict the intentions of the latter. Many researchers have developed personality estimation mechanisms. However, the estimation method for toddlers’ personality, such as the dominance of their innate temperament, has not been proposed yet. In this paper, we proposed an estimation model of toddlers’ temperament based on interaction data with a teleoperated childcare robot, ChiCaRo. The proposed method utilized the feature selection algorithm to increase estimation accuracy. Additionally, we employed an explainable AI model called Shapley additive explanations (SHAP) to understand which features from the interaction were important in terms of temperament estimation. The proposed estimation model demonstrated over 85% estimation accuracy for the average of all temperament factors. The experimental results of SHAP provided an understandable relation between the features and temperament factors and indicated that similar feature values from interaction videos used in child personality estimation could also be used for the temperament estimation of toddlers.
The dynamics of changes in selected parameters in relation to different air temperature in the farrowing house for sows

The purpose of this study was to analyse the influence of different external air temperature on changes in the air temperature of housing environment in the farrowing house for sows and consequent changes in selected parameters of performance in purebred Landrace sows and gilts. Raw data were analysed for 236 litters. The litters were born from October 2004 to March 2006. Air temperature in the farrowing house was monitored in the sow's living zone and was closely related to changes in external air temperature ( P < 0.01). At the optimal internal temperatures for lactating sows (16-22°C) during mild winter the lowest incidence of stillbirths (9.92%) was detected, whereas at high internal temperatures (above 28°C) the incidence of stillbirths was 11.32% (P < 0.01). The lowest average daily weight gain was recorded during mild winter ( P < 0.05).
Studies on the Phenolic Acid Compounds from Lonicera confusa DC.

AIM:To investigate the active constituents in Lonicera confusa DC. (Jinyinhua,in Chinese),to estimate the quality of Flos Lonicerae more roundly.METHOD: Some kinds of column chromatography were used to isolate the compounds from the EtOH extract of Lonicera confusa DC.The chemical constituents of the plant were identified by means of IR,MS, 1 H NMR and 3 C NMR ,respectively,in some cases by direct comparison with authentic samples.RESULT: Four phenolic acid compounds were isolated from L.confusa DC.and identified as:caffeic acid(Ⅰ),chlorogenic acid(Ⅱ),methyl chlorogenate(Ⅲ) and 5- O -caffeoyl quinic acid butyl ester(Ⅳ). CONCLUSION:compound Ⅳ is firstly isolated from this genus,and others from this plant.
B-VNF: Blockchain-enhanced Architecture for VNF Orchestration in MEC-5G Networks

The roll-out of 5G technology will nurture the realization of broadband, ultra-reliable, and zero latency services. Network Function Virtualization (NFV) and Multi-Access Edge Computing (MEC) are among the key enablers for 5G. The synergy between NFV and MEC allows migration of Virtual Network Functions (VNF) from cloud to the edge of the network thereby adding agility to the softwarized 5G networks. The overall orchestration of VNF includes, but is not limited to, processing VNF requests, selecting appropriate VNF, migrating VNF from cloud to MEC, instantiating migrated VNF at MEC, settling payment according to a VNF’s usage, maintaining VNF’s reputation, etc. The orchestration is not foolproof and raises doubts about its trustworthiness. To address all the existing issues in a unified manner, we leverage Blockchain technology as yet another enabling technology for MEC-enabled 5G. Thus, we propose a Blockchain-enhanced architecture for secure VNF orchestration such that issues like authenticity, integrity, confidentiality, reputation, payment transfer, and many more are resolved. To furnish a Proof-of-Concept (PoC), we develop a prototypical DApp (Decentralized Application) using Ethereum Blockchain and Suricata as an exemplar VNF. Further, we discuss the strong resiliency of the proposed architecture against numerous well-known attacks.
Electrical consumption in the Higher Education sector, during the COVID-19 shutdown

Excessive electrical consumption within buildings is a common issue across all sectors. Each sector responds to this differently, and the Higher Education (HE) sector is no exception, where energy-intensive laboratories are prevalent in most universities. The COVID-19 crisis has meant that these laboratories have either closed or reduced their opening hours; hereby, this offers an excellent opportunity to assess how shutdown periods impact on electrical consumption. This research assesses unregulated electrical consumption within a single laboratory building, which functions as a research engineering building. An Energy-Management System (EMS) was used to collect the room-level data, primarily electrical consumption. This study found that unregulated electricity consumption typically reduced rapidly during the shutdown period with a percentage reduction of 46.61% between the week before lockdown and the week during the lockdown.
Relevant Perception Modalities for Flexible Human-Robot Teams

Robust and reliable perception plays an important role when humans engage into cooperation with robots in industrial or household settings. Various explicit and implicit communication modalities and perception methods can be used to recognize expressed intentions. Depending on the modality, different sensors, areas of observation, and perception methods need to be utilized. More modalities increase the complexity and costs of the setup. We consider the scenario of a cooperative task in a potentially noisy environment, where verbal communication is hardly feasible. Our goal is to investigate the importance of different, non-verbal communication modalities for intention recognition. To this end, we build upon an established benchmark study for human cooperation and investigate which input modalities contribute most towards recognizing the expressed intention. To measure the detection rate, we conducted a second study. Participants had to predict actions based on a stream of symbolic input data. Findings confirm the existence of a common gesture dictionary and the importance of hand tracking for action prediction when the number of feasible actions increases. The contribution of this work is a usage ranking of gestures and a comparison of input modalities to improve prediction capabilities in human-robot cooperation.
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets

How different cultures react and respond given a crisis is predominant in a society’s norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation’s will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation’s support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people’s sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.