Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on fNIRS-Based Working Memory Load Assessment and Potential Issues with Extracerebral Artifacts

Functional near-infrared spectroscopy (fNIRS) has gained interest in the development of brain-computer interface (BCI) for working memory (WM) training. Amplitude averaging of oxygenated hemoglobin (oxy-Hb) signal over the predefined region of interest (ROI) is typically used to compute WM load. It is unclear to what extent extracerebral artifacts can affect WM assessment. To examine this, a technique adopting multi-distance probe configuration and independent component analysis (MD-ICA) was applied to split the original fNIRS signals into hemodynamic signals originating from the deep (cerebral) and shallow (extracerebral) tissue layers. Twenty-five healthy participants performed letter 1- and 2-back tasks, symbolizing low and high WM load respectively. In the bilateral dorsolateral prefrontal cortex (DLPFC), increasing WM load evoked significant changes in both original and deep oxy-Hb activation, but not in the shallow oxy-Hb. Under low WM load, the bilateral DLFPC activation was significantly higher than that of the middle prefrontal cortex (mPFC), only seen through the deep signal. Conversely, under high WM load, the significant difference in brain activation between the bilateral DLPFC and mPFC were observed in both original and deep oxy-Hb. This highlights extracerebral artifacts correction might be useful when searching for activation regions. However, when activation areas are known, the signal intensities in original oxy-Hb are sufficiently profound to quantify and differentiate WM load.
Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children

Objective: Lung auscultation plays an important role in the diagnosis of pulmonary diseases in children. The objective of this study was to evaluate the use of an artificial intelligence (AI) algorithm for the detection of breath sounds in a real clinical environment among children with pulmonary diseases. Method: The auscultations of breath sounds were collected in the respiratory department of Shanghai Children's Medical Center (SCMC) by using an electronic stethoscope. The discrimination results for all chest locations with respect to a gold standard (GS) established by 2 experienced pediatric pulmonologists from SCMC and 6 general pediatricians were recorded. The accuracy, sensitivity, specificity, precision, and F1-score of the AI algorithm and general pediatricians with respect to the GS were evaluated. Meanwhile, the performance of the AI algorithm for different patient ages and recording locations was evaluated. Result: A total of 112 hospitalized children with pulmonary diseases were recruited for the study from May to December 2019. A total of 672 breath sounds were collected, and 627 (93.3%) breath sounds, including 159 crackles (23.1%), 264 wheeze (38.4%), and 264 normal breath sounds (38.4%), were fully analyzed by the AI algorithm. The accuracy of the detection of adventitious breath sounds by the AI algorithm and general pediatricians with respect to the GS were 77.7% and 59.9% (p < 0.001), respectively. The sensitivity, specificity, and F1-score in the detection of crackles and wheeze from the AI algorithm were higher than those from the general pediatricians (crackles 81.1 vs. 47.8%, 94.1 vs. 77.1%, and 80.9 vs. 42.74%, respectively; wheeze 86.4 vs. 82.2%, 83.0 vs. 72.1%, and 80.9 vs. 72.5%, respectively; p < 0.001). Performance varied according to the age of the patient, with patients younger than 12 months yielding the highest accuracy (81.3%, p < 0.001) among the age groups. Conclusion: In a real clinical environment, children's breath sounds were collected and transmitted remotely by an electronic stethoscope; these breath sounds could be recognized by both pediatricians and an AI algorithm. The ability of the AI algorithm to analyze adventitious breath sounds was better than that of the general pediatricians.
Making Kids Learning Joyful using Artistic Style Transferred YouTube VCs

The rise of E-learning systems offers a large number of open online educational videos through portals like YouTube, MOOCs, which result in an enormous volume of data for every inquisitive e-learner. This work proposes a novel approach for recommending educational video clips (VCs) for children’s joyful learning. It caters to the need for hard-pressed parents, who are unable to tutor their children efficaciously. As children are unable to choose the recommended VCs on their own, the proposed system offers a precise summary of the recommended VCs for parents to effectively select the learning content for their children. For a joyful learning experience, the selected VCs are then style transferred using our deep style transfer network. The extensive evaluation methods demonstrate the effectiveness and practicability of the proposed approach.
Acclimation of Photosynthesis to Low Leaf Water Potentials

Photosynthesis is reduced at low leaf water potentials (Ψl) but repeated water deficits can decrease this reduction, resulting in photosynthetic acclimation. The contribution of the stomata and the chloroplasts to this acclimation is unknown. We evaluated stomatal and chloroplast contributions when soil-grown sunflower (Helianthus annuus L.) plants were subjected to water deficit pretreatments for 2 weeks. The relationship between photosynthesis and Ψl, determined from gas-exchange and isopiestic thermocouple psychometry, was shifted 3 to 4 bars towards lower Ψl, in pretreated plants. Leaf diffusive resistance was similarly affected. Chloroplast activity, demonstrated in situ with measurements of quantum yield and the capacity to fix CO2 at all partial pressures of CO2, and in vitro by photosystem II activity of isolated organelles, was inhibited at low Ψl but less in pretreated plants than in control plants. The magnitude of this inhibition indicated that decreases in chloroplast activity contributed more than closure of stomata both to losses in photosynthesis and to the acclimation of photosynthesis to low Ψl.
Federated Reinforcement Learning for Automatic Control in SDN-based IoT Environments

Recently, reinforcement learning has been applied to various fields and shows better performance than humans. In particular, it is attracting attention in the fields of smart factories and robotics that require automatic control without human intervention. In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own IoT devices of the same type. There is no guarantee that the reinforcement learning agent that has learned the optimal control policy using one IoT device will perform optimal control of other IoT devices. Therefore, since reinforcement learning must be performed individually for each IoT device, it takes a lot of time and cost. To solve this problem, we propose a new method of federated reinforcement learning. In the proposed federated reinforcement learning, multiple agents have independent IoT devices, perform learning at the same time, and federate with each other to improve learning performance. Therefore, we apply a new gradient sharing method and transfer learning to reinforcement learning. In addition, Actor-Critic PPO, which shows good performance in reinforcement learning algorithms, is used. And, for smooth learning in the IoT environment where numerous devices exist, we propose an architecture based on Software-Defined Networking. Using multiple rotary inverted pendulum devices interconnected via a SDN, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple IoT devices.
An Imbalanced R-STDP Learning Rule in Spiking Neural Networks for Medical Image Classification

Spiking neural networks (SNNs) have the advantages of inherent power-efficiency, biological plausibility and good image recognition performance. They are good candidates for medical image classification especially when the labeled training data are limited. In medical image classification, one of the major challenges is the highly class imbalanced problem which causes deep learning networks to bias towards the majority class and poorly recognizes the minority class. Despite that there are some methods for addressing this problem, very few algorithm-level methods exist for SNNs. In this work, we propose an imbalanced reward-modulation spike-timing-dependent plasticity (R-STDP) learning rule for SNNs to solve the medical image class imbalanced problem. We introduce an imbalanced reward coefficient for the R-STDP learning rule to set the reward from the minority class to be higher than that of the majority class, and this reward coefficient can help to set the class-dependent rewards according to the data statistic of the training dataset. Experiment results on three benchmark datasets with imbalanced splits show that our method significantly improves the performance than that of the baseline SNNs and outperforms the compared state-of-the-art methods addressing medical image class imbalanced problem including data-level and algorithm-level methods. Moreover, our method achieves excellent classification performance on the imbalanced medical dataset ISIC-2018. The results show that the proposed method can well help SNNs in classifying imbalanced medical image datasets. Besides, our proposed method can obtain high sensitivity to disease class by adjusting the reward coefficient, which is very useful for identifying disease samples in medical diagnostic tasks.
3D Air-Ground Channel Model Reconstruction Based on the Experimental Data and Q-D Method

This paper proposes to use the Q-D (Quasi-Deterministic) method for reconstructing the angular and frequency characteristics of the 3D Air-to-Ground (A-G) channel from the available experimental data. This method allows to expand the applicability of tapped delay line (TDL) channel models for performance investigation of the aviation radio systems with directional antennas and multi-element antenna arrays. The developed 3D A-G channel models also give opportunity to take into account Doppler spread impact on the communication system performance.
Robot-Assisted Joint Attention: A Comparative Study Between Children With Autism Spectrum Disorder and Typically Developing Children in Interaction With NAO

Robots have been used in joint attention (JA) tasks with children with autism spectrum disorder (ASD). However, very few studies compared JA performance of children with ASD with typically developing children (TD) when interacting with a robotic partner and a traditional human partner. This study aims to: (a) to explore whether there are differences in response to and initiation of JA between ASD and TD children with two interactive partners: an adult and a social robot (NAO); and (b) to explore which characteristics of ASD children predicting their performance in robot-assisted JA tasks. Twenty-seven ASD and forty TD children were involved in this study in which they were exposed to diffident JA tasks. Mixed results were found per type of JA behavior over groups and conditions. Our results show that both ASD and TD children performed better with the human partner than with the robot in response to JA tasks. Among the characteristics of ASD children, ADOS total score is associated with response to JA performance. No significant result related to initiation of JA was found.
Mobile Convolutional Neural Networks for Facial Expression Recognition

We propose CNN models for facial expression recognition that work well in mobile and embedded devices. Previous studies introduced CNN models for image classification by stacking wider filters in depth to increase accuracy. The deep CNN models improve classification accuracy, but it is difficult to use in mobile devices because of its large parameter size and low responsiveness. We first analyzed the MobileNetV2 for facial expression recognition in mobile devices. After that, we designed CNN models with less than 1 million parameters by adjusting the width and depth of the bottlenecks. We trained the proposed CNN models and other mobile CNN models under the same experimental conditions and reviewed the results. The proposed CNN models have been carefully fine-tuned to use less than 0.5 million parameters. The fine-tuned CNN models achieved an accuracy of 90.3% for 5 classes and 86.8% for 7 classes in the RAF database.
Runtime Verification on FPGAs with LTLf Specifications

Runtime verification is a technique that evaluates a system's execution trace at runtime against a formal specification. This approach is particularly useful for safety-critical and autonomous systems to verify system functionality and allow for graceful recovery or intervention in the case of system faults. Specifications are often provided in a high-level form using some type of temporal logic, which can then be compiled into an automaton to be used as a monitor for the system. Existing work has mainly focused on implementing such monitors in software. In recent years there has been extensive research, however, in hardware acceleration of automata applications, which can potentially be extended to runtime monitoring. In this paper, we introduce an open-source framework for translating formulas in Linear Temporal Logic over finite traces (LTLf) into automata implementations on FPGAs for high-efficiency and high-performance runtime monitoring. By using the spatial dimension of FPGAs, we run many of these automata in parallel, significantly reducing the latency between violation and monitor report and achieving significant throughput. We compare the performance of four different architectures corresponding to the combinations of deterministic or nondeterministic automata with an explicit or symbolic representation, and determine the design parameters that result in efficient hardware utilization and higher clock frequencies. We found that explicit automata tend to use more hardware resources, in particular Lookup Tables (LUTs), than symbolic automata. An exception to this is in the case of Flip-Flop (FF) usage, where symbolic DFAs tend to use more FF resources than explicit NFAs for smaller designs. We also found that explicit NFAs can run at higher clock frequencies, except for very large automata with high edge densities. Symbolic NFAs use fewer Look-Up Table resources and run at higher clock frequencies than symbolic DFAs, whereas symbolic DFAs required fewer Flip-Flop resources, except in the case of very simple small automata with lower edge densities. Finally, we found that explicit automata hardware utilization significantly increases with input signal widths, motivating the use of symbolic automata for wide input signals.
The accident rate of cyclists in the Slovak Republic in bike-sharing system

The issue of bike-sharing is very timely since several public bicycle systems in Slovakia have been launched in recent years. The aim of the contribution is a comprehensive analysis of the current state and the development of accident accidents in Slovakia, focusing more closely on the monitoring and analysis of road traffic accidents for cyclists over the period 2014 to 2019. Also, the study focuses on the system of shared bicycles (bike-sharing) in terms of the accident.
A Study on the Digital Forensic Investigation Method of Clever Malware in IoT Devices

As IoT devices are always connected to mobile devices or other computing devices via the Internet, clever malwares targeting IoT devices or other computing devices connected to IoT devices are emerging. Therefore, effective IoT security research is needed to respond to hacking attacks by these kinds of malware. This paper studied the method of identifying and analyzing malware combined with social engineering from the perspective of digital forensics. The paper classified and analyzed intelligent malware characteristics and proposed a method of quickly identifying and analyzing the malware that secretly intruded into the devices installed with Android, Linux OS, using digital forensics techniques. Moreover, this paper proved its effectiveness by applying this investigation method to two actual malware cases. The research outcomes will be useful in responding to increasingly clever malware attacking IoT devices.
ADSORPTION KINETICS AND EQUILIBRIA OF BASIC DYES ONTO ZEOLITE IN SINGLE AND BINARY COMPONENT SYSTEMS

Adsorptive removal of Basic Blue 3 (BB 3) and Basic Red 18 (BR 18) by a clinoptilolite-type natural zeolite from their single-component solutions has been studied in the temperature range of 298–328 K. Experimental equilibrium results are well described by the Freundlich and the Langmuir isotherm models. The model parameters obtained for single-solute systems at 298 K have been used for the prediction of adsorption isotherms in binary dye solutions using multicomponent isotherm models. Competitive adsorption results between BB 3 and BR 18 in binary solutions satisfactorily fit the extended Freundlich, extended Langmuir, and modified Langmuir models. A site distribution function that gives information about affinity of adsorption sites for competing species in binary systems has been mathematically calculated using the Freundlich parameters. Time-dependent results for single and binary dye solutions have been analyzed according to a pseudo-second-order kinetic model based on chemisorption and a McKay model assuming two resistance diffusion processes, respectively. The diffuse reflectance FT-IR spectra indicate that the dyes are adsorbed via electrostatic interactions in external rings of clinoptilolite by replacing the zeolitic water in a single system, and they diffuse through the liquid associated with zeolitic water in TO4 tetrahedra (T: Al, Si) in a mixed solution.
Invited Commentary on Dr. Isaac Kaplan's Paper "Age Dating of Environmental Organic Residues"

I wish to thank both the Editors of Environmental Forensics and my longtime colleague and friend, Dr. Isaac Kaplan, for their invitation to contribute to this forum on age dating by commenting on Kaplan’s paper entitled “Age Dating of Environmental Organic Residues” (2003). This review summarizes the methods available to date organics of biologic and relatively recent anthropogenic origin; the former requiring chronometers with time scales of ∼103–105 years, while the latter focuses on events that transpired within the last ∼50–60 years. Kaplan (2003) provides a fine summary of both venues, especially for those individuals unfamiliar with available techniques and associated technical language, thereby adding, with this publication, to the body of work concerning age dating of organic remains. In this commentary, I will clarify and expand upon points pertinent to the use of lead isotopes and nC17/Pr (nheptadecane/pristine) ratios to estimate the age of hydrocarbon releases into the environment, which were not addressed in detail by Kaplan (2003). To accomplish this, I will summarize Anthropogenic Lead ArchaeoStratigraphy (ALAS model; Hurst, 2000, 2002a, 2002b) in order to provide a foundation for those unfamiliar with the model. The points in Kaplan (2003) that will be addressed include: (1) why anthropogenic lead (Pb) isotope ratios preserved in offshore California marine sediments were valid ALAS Model calibration points; (2) the relationship between U.S. aerosol lead isotope ratios and those of the ALAS Model; (3) a discussion addressing questions raised by Kaplan regarding the manufacturing and distribution of alkyllead; and (4) new information pertinent to the use of nC17/Pr ratios to estimate the age of refined petroleum product releases (Christensen and Larsen, 1993; Hurst and Schmidt, in preparation).
Time Course of Heart Rate Variability Decline Following Particulate Matter Exposures in an Occupational Cohort

Although research suggests that particles influence cardiac autonomic response as evidenced by decreases in heart rate variability (HRV), the time course of the response remains unclear. Using a crossover panel study, we monitored 36 male boilermaker welders, occupationally exposed to metal-rich particulate matter (PM) to investigate the temporal trend of hourly HRV subsequent to PM exposure. Ambulatory electrocardiograms were collected over work (exposure) and non-work (control) periods and the mean of the standard deviations of all normal-to-normal intervals for all 5-min segments (SDNNi) was calculated hourly for up to 14-hrs post-work. The exposure-response relationship was examined with linear mixed effects regression models to account for participants monitored over multiple occasions. Models were adjusted for non-work HRV to control for diurnal fluctuations and individual predictors of HRV. The mean (SD) work PM2. 5 concentration was 1.12 (0.76) mg/m3. Hourly SDNNi was consistently lower post-work as compared to the same time period on a non-work day. HRV was inversely associated with work PM2. 5 exposures in each of the 14-hrs post-work. The hourly associations suggested an early and later phase response, with the largest regression coefficients observed 2–3 hrs (β = −6.86 (95% CI: −11.91, −1.81) msec/1 mg/m3 at 3-hrs), and then 9–13 hrs (β = −8.60 (95% CI: −17.45, 0.24) msec/1 mg/m3 at 11-hrs), after adjusting for non-work HRV, smoking status, and age. This investigation demonstrates declines in HRV for up to 14 hours following PM exposure and a multiphase cardiovascular autonomic response with immediate (2 hrs) and delayed (9–13 hrs) responses.
Hydrogen gas sensors based on nanocrystalline TiO2 thin films

Titanium dioxide thin films are extensively studied for applications in solid state gas sensor devices. Their gas sensing properties are strongly dependent on deposition technique, annealing temperature, film thickness and consequent properties like crystalline structure, grain size or amount of defects and impurities. In this work we report the gas sensing properties of TiO2 thin films prepared by reactive magnetron sputtering technique and subsequently annealed at temperatures 600°C and 900°C. The films were exposed to different concentrations of H2 gas up to 10 000 ppm. Their sensitivity to gas at various operating temperatures, ranging from 250°C to 450°C, was obtained by measuring their resistance.
Erythrocyte Deformability and its Hemorheological Consideration

The suspension of hardened erythrocytes (red blood cells) differs from the suspension of normal erythrocytes with respect to their rheological behavior. The deformability of normal and hardened erythrocytes (obtained by heating blood at 49°C or by incubating erythrocytes in a solution of hydrogen peroxide) was measured with a slit diffractometer and erythrocyte suspension viscosity was measured with a rotational viscometer. We found that when erythrocytes were heated at 49°C, with much less deformability than normal erythrocytes, their suspension viscosity was almost the same as the normal blood viscosity at high shear rates, whereas when erythrocytes were incubated in a solution of hydrogen peroxide, with an intermediate decrease of deformability, their suspension viscosity was greatly increased. The present study concluded that not all rigid cells cause an increase in blood viscosity at high shear rates, and therefore that decreased cell deformability is not always a good indicator of high-shear blood viscosity.
Characterization of the Frequency Dependence of the AC Resistors Used in the Quadrature Bridge of the BIPM

At the Bureau International des Poids et Mesures (BIPM), Sèvres, France, the capacitance unit is linked to a dc quantized Hall resistance (QHR) by means of impedance bridges and stable transfer standards, establishing traceability to the defining fundamental constants of the International System of Units (SI). In the ohm–farad linking chain, a key step is the determination of the frequency dependence of two ac resistors. In this article, it is described how this determination was recently made using two different impedance bridges and how the results are validated using regression analysis.