Miracle-dz/UniFAR-FTUs huggingface dataset:
Summary-Oriented Question Generation for Informational Queries

Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles – with further confirmation via a human evaluation.
A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.
Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.
Asbestos Detection with Fluorescence Microscopy Images and Deep Learning

Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0–50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 (mAP@0.5) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.
High Accuracy Ultrasound Micro-Distance Measurements with PMUTs under Liquid Operation

Ultrasonic systems driven by multi-frequency continuous waves (MFCW) have been used for range distance measurement, offering high accuracy in long and medium range distance estimation. However, the use of continuous waves in very short-distance measurements causes large errors due to multipath reflections. This paper presents a new strategy to estimate very short relative distances with high accuracy based on the use of multi-frequency pulsed waves (MFPW). The proposed strategy allows to avoid the multipath reflections that appear when continuous waves are used, and it improves the achieved accuracy compared to the original MFCW method. To validate it, an 80 µm square AlScN piezoelectric micromachined ultrasonic transducer (PMUT) was chosen as a transmitter while a hydrophone was utilized as a target and receiver, immersed in fluorinert (FC-70) as a propagation medium. Three independent and consecutive tone-burst signals were transmitted successively. The selected frequencies are f1 = 2.3962 MHz, f2 = 2.327 MHz and f3 = 2.1195 MHz, giving first and second-order resolutions of 6.88 and 0.79 µm/°, respectively. Experimental results show a ±6.2 μm measured range error in a range of 3.5 mm, and therefore it represents a good candidate for ultrasound micro-profilometer applications under liquid operation.
Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.
Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells

In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.
FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest

This paper proposed a liquid level measurement and classification system based on a fiber Bragg grating (FBG) temperature sensor array. For the oil classification, the fluids were dichotomized into oil and nonoil, i.e., water and emulsion. Due to the low variability of the classes, the random forest (RF) algorithm was chosen for the classification. Three different fluids, namely water, mineral oil, and silicone oil (Kryo 51), were identified by three FBGs located at 21.5 cm, 10.5 cm, and 3 cm from the bottom. The fluids were heated by a Peltier device placed at the bottom of the beaker and maintained at a temperature of 318.15 K during the entire experiment. The fluid identification by the RF algorithm achieved an accuracy of 100%. An average root mean squared error (RMSE) of 0.2603 cm, with a maximum RMSE lower than 0.4 cm, was obtained in the fluid level measurement also using the RF algorithm. Thus, the proposed method is a feasible tool for fluid identification and level estimation under temperature variation conditions and provides important benefits in practical applications due to its easy assembly and straightforward operation.
SleepPos App: An Automated Smartphone Application for Angle Based High Resolution Sleep Position Monitoring and Treatment

Poor sleep quality or disturbed sleep is associated with multiple health conditions. Sleep position affects the severity and occurrence of these complications, and positional therapy is one of the less invasive treatments to deal with them. Sleep positions can be self-reported, which is unreliable, or determined by using specific devices, such as polysomnography, polygraphy or cameras, that can be expensive and difficult to employ at home. The aim of this study is to determine how smartphones could be used to monitor and treat sleep position at home. We divided our research into three tasks: (1) develop an Android smartphone application (‘SleepPos’ app) which monitors angle-based high-resolution sleep position and allows to simultaneously apply positional treatment; (2) test the smartphone application at home coupled with a pulse oximeter; and (3) explore the potential of this tool to detect the positional occurrence of desaturation events. The results show how the ‘SleepPos’ app successfully determined the sleep position and revealed positional patterns of occurrence of desaturation events. The ‘SleepPos’ app also succeeded in applying positional therapy and preventing the subjects from sleeping in the supine sleep position. This study demonstrates how smartphones are capable of reliably monitoring high-resolution sleep position and provide useful clinical information about the positional occurrence of desaturation events.
Probing the Nature of Chemical Bonds by Atomic Force Microscopy

The nature of the chemical bond is important in all natural sciences, ranging from biology to chemistry, physics and materials science. The atomic force microscope (AFM) allows to put a single chemical bond on the test bench, probing its strength and angular dependence. We review experimental AFM data, covering precise studies of van-der-Waals-, covalent-, ionic-, metallic- and hydrogen bonds as well as bonds between artificial and natural atoms. Further, we discuss some of the density functional theory calculations that are related to the experimental studies of the chemical bonds. A description of frequency modulation AFM, the most precise AFM method, discusses some of the experimental challenges in measuring bonding forces. In frequency modulation AFM, forces between the tip of an oscillating cantilever change its frequency. Initially, cantilevers were made mainly from silicon. Most of the high precision measurements of bonding strengths by AFM became possible with a technology transfer from the quartz watch technology to AFM by using quartz-based cantilevers (“qPlus force sensors”), briefly described here.
Colorimetric Sensing of Amoxicillin Facilitated by Molecularly Imprinted Polymers

The scope of the presented research orientates itself towards the development of a Molecularly Imprinted Polymer (MIP)-based dye displacement assay for the colorimetric detection of the antibiotic amoxicillin in aqueous medium. With this in mind, the initial development of an MIP capable of such a task sets focus on monolithic bulk polymerization to assess monomer/crosslinker combinations that have potential towards the binding of amoxicillin. The best performing composition (based on specificity and binding capacity) is utilized in the synthesis of MIP particles by emulsion polymerization, yielding particles that prove to be more homogenous in size and morphology compared to that of the crushed monolithic MIP, which is an essential trait when it comes to the accuracy of the resulting assay. The specificity and selectivity of the emulsion MIP proceeds to be highlighted, demonstrating a higher affinity towards amoxicillin compared to other compounds of the aminopenicillin class (ampicillin and cloxacillin). Conversion of the polymeric receptor is then undertaken, identifying a suitable dye for the displacement assay by means of binding experiments with malachite green, crystal violet, and mordant orange. Once identified, the optimal dye is then loaded onto the synthetic receptor, and the displaceability of the dye deduced by means of a dose response experiment. Alongside the sensitivity, the selectivity of the assay is scrutinized against cloxacillin and ampicillin. Yielding a dye displacement assay that can be used (semi-)quantitatively in a rapid manner.
PSON: A Serialization Format for IoT Sensor Networks

In many Internet of Things (IoT) environments, the lifetime of a sensor is linked to its power supply. Sensor devices capture external information and transmit it. They also receive messages with control commands, which means that one of the largest computational overheads of sensor devices is spent on data serialization and deserialization tasks, as well as data transmission. The simpler the serialization/deserialization and the smaller the size of the information to be transmitted, the longer the lifetime of the sensor device and, consequently, the longer the service life. This paper presents a new serialization format (PSON) for these environments, which simplifies the serialization/deserialization tasks and minimizes the messages to be sent/received. The paper presents evaluation results with the most popular serialization formats, demonstrating the improvement obtained with the new PSON format.
The Analysis of Emotion Authenticity Based on Facial Micromovements

People tend to display fake expressions to conceal their true feelings. False expressions are observable by facial micromovements that occur for less than a second. Systems designed to recognize facial expressions (e.g., social robots, recognition systems for the blind, monitoring systems for drivers) may better understand the user’s intent by identifying the authenticity of the expression. The present study investigated the characteristics of real and fake facial expressions of representative emotions (happiness, contentment, anger, and sadness) in a two-dimensional emotion model. Participants viewed a series of visual stimuli designed to induce real or fake emotions and were signaled to produce a facial expression at a set time. From the participant’s expression data, feature variables (i.e., the degree and variance of movement, and vibration level) involving the facial micromovements at the onset of the expression were analyzed. The results indicated significant differences in the feature variables between the real and fake expression conditions. The differences varied according to facial regions as a function of emotions. This study provides appraisal criteria for identifying the authenticity of facial expressions that are applicable to future research and the design of emotion recognition systems.
Virtual Electric Dipole Field Applied to Autonomous Formation Flight Control of Unmanned Aerial Vehicles

The following paper presents a method for the use of a virtual electric dipole potential field to control a leader-follower formation of autonomous Unmanned Aerial Vehicles (UAVs). The proposed control algorithm uses a virtual electric dipole potential field to determine the desired heading for a UAV follower. This method’s greatest advantage is the ability to rapidly change the potential field function depending on the position of the independent leader. Another advantage is that it ensures formation flight safety regardless of the positions of the initial leader or follower. Moreover, it is also possible to generate additional potential fields which guarantee obstacle and vehicle collision avoidance. The considered control system can easily be adapted to vehicles with different dynamics without the need to retune heading control channel gains and parameters. The paper closely describes and presents in detail the synthesis of the control algorithm based on vector fields obtained using scalar virtual electric dipole potential fields. The proposed control system was tested and its operation was verified through simulations. Generated potential fields as well as leader-follower flight parameters have been presented and thoroughly discussed within the paper. The obtained research results validate the effectiveness of this formation flight control method as well as prove that the described algorithm improves flight formation organization and helps ensure collision-free conditions.
Resonant Subwavelength and Nano-Scale Grating Structures for Biosensing Application: A Comparative Study

Resonant-based sensors are attractive optical structures due to the easy detection of shifts in the resonance location in response to variations in the analyte refractive index (RI) in comparison to non-resonant-based sensors. In particular, due to the rapid progress of nanostructures fabrication methods, the manufacturing of subwavelength and nano-scale gratings in a large area and at a low cost has become possible. A comparative study is presented involving analysis and experimental work on several subwavelength and nanograting structures, highlighting their nano-scale features’ high potential in biosensing applications, namely: (i) Thin dielectric grating on top of thin metal film (TDGTMF), which can support the excitation of extended surface plasmons (ESPs), guided mode resonance, or leaky mode; (ii) reflecting grating for conventional ESP resonance (ESPR) and cavity modes (CMs) excitation; (iii) thick dielectric resonant subwavelength grating exhibiting guided mode resonance (GMR) without a waveguide layer. Among the unique features, we highlight the following: (a) Self-referenced operation obtained using the TDGTMF geometry; (b) multimodal operation, including ESPR, CMs, and surface-enhanced spectroscopy using reflecting nanograting; (c) phase detection as a more sensitive approach in all cases, except the case of reflecting grating where phase detection is less sensitive than intensity or wavelength detection. Additionally, intensity and phase detection modes were experimentally demonstrated using off-the-shelf grating-based optical compact discs as a low-cost sensors available for use in a large area. Several flexible designs are proposed for sensing in the visible and infrared spectral ranges based on the mentioned geometries. In addition, enhanced penetration depth is also proposed for sensing large entities such as cells and bacteria using the TDGTMF geometry.
Cattle and sheep differentially alter floral resources and the native bee communities in working landscapes.

Within agricultural landscapes, native bees often rely on limited natural and semi-natural lands to provide the majority of the food and nesting resources that sustain them. To better understand how management can affect pollinators in these semi-natural areas, we compared how sheep or cattle herbivory influenced floral resources and bee communities in low-diversity, former Conservation Reserve Program (CRP) pastures managed with patch-burn grazing. We sampled bee communities and floral resources three times per season in 2017, 2018, and 2019. We used plant-pollinator line transect sampling and collected bees and counted all flowering stems within 1 m. Across all years, we found that floral abundance, floral richness, floral diversity (Simpson's) and bee richness and abundance were significantly higher in cattle pastures compared to sheep. In cattle pastures, 46 native bee species plus honey bees interacted with 25 of 68 available flowering forbs. In sheep pastures, we recorded 14 native bee species and honey bees interacted with 10 of 34 flowering species. Native bee abundance and native bee richness were best explained by models that included an interaction of floral richness and year. Overall, our results suggest that season-long sheep grazing in low-diversity grasslands greatly reduces available floral resources and correlates with much lower bee abundance and native bee diversity. Given the importance of pollinators to natural and agricultural systems, it is imperative that we take proactive actions to increase forb richness and native flower abundance in semi-natural lands to maintain a more diverse and resilient bee community that can continue to support pollination services and global food security.