Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Study of Distributed Big Data Analysis in YARN Cluster

In the 4-th Industrial Revolution era, various intelligent solutions and services have been emerging recently. To provide high quality service in those intelligent applications, the big data should be collected without any loss and comprehensively analyzed. Especially, when using machine and deep learning techniques, the big data processing delays should be minimized in order to guarantee the freshness of models. In this paper, we evaluate the performance of Apache Spark which is one of the most popular big data processing and analysis frameworks. Beyond the performance analysis of Spark in distributed cluster environment, we evaluate the performance of TensorFlowOnSpark which is the promising distributed deep learning framework designed to handle big data efficiently. From the experimental results, we can conclude that Spark on YARN is a solid underlying framework that guarantees the performance and scalability of distributed machine and deep learning by efficiently processing its data and algorithms in a parallel and distributed manner.
Supporting the Evaluation of Fog-based IoT Applications During the Design Phase

Fog application design is complex as it comprises not only the application architecture, but also the runtime infrastructure, and the deployment mapping from application modules to infrastructure machines. For each of these aspects, there is a variety of design options that all affect quality of service and cost of the resulting application. In this paper, we propose an approach for evaluating fog-based IoT applications already during an early design phase. Our approach relies on modeling and simulation to provide estimates for quality of service and cost so that developers can interactively choose a good application design.
Comparative pharmacokinetics of an adalimumab biosimilar SB5 administered via autoinjector or prefilled syringe in healthy subjects

Purpose The objective of this study was to demonstrate comparable pharmacokinetic (PK), safety, and tolerability parameters of the adalimumab biosimilar SB5 administered via autoinjector (AI) pen or prefilled syringe (PFS). Patients and methods In this phase 1, randomized, open-label, single-dose, parallel-group study, healthy subjects aged 18–55 years were randomized 1:1 to a single dose of 40 mg SB5 delivered subcutaneously via AI or PFS. PK parameters, safety, and tolerability were assessed for 57 days post-dose. The primary endpoint was area under the curve (AUC) of the concentration-time curve from zero to infinity (AUCinf) and from zero to last quantifiable concentration (AUClast) and maximum serum concentration (Cmax). Equivalence was determined using predefined margins of 0.80–1.25 for the 90% CI for the ratio of SB5 AI to SB5 PFS. Results Ninety-five subjects were randomized to each group. Mean serum concentration-time profiles were superimposable between groups. Mean values for AUCinf, AUClast, and Cmax were similar between the SB5 AI and SB5 PFS groups. For the primary endpoints, the 90% CIs for the ratio of geometric least squares means for SB5 AI to SB5 PFS ranged between 0.9503 and 1.2240, which were all within the equivalence margin of 0.80–1.25. Incidence of treatment-emergent adverse events and injection site reactions was similar between groups. Conclusion In healthy subjects receiving a single dose of SB5 via AI or PFS, PK parameters and corresponding 90% CIs were within the predefined margins, showing bioequivalence between the two delivery methods. Safety and tolerability assessments were also similar between groups. ClinicalTrials.gov identifier NCT02326233. EudraCT number 2014-005178-12.
Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.
R-TSCH: Proactive Jamming Attack Protection for IEEE 802.15.4-TSCH Networks

Time Slotted Channel Hopping (TSCH) has been proposed in various wireless protocols as a solution to combat external interference, path-loss fading and static jamming attacks. However, since TSCH algorithms generate a deterministic and periodic pattern of channel hops, they are still subject to jamming attacks. Proactive randomization of the channel generation process could provide a good solution against jamming attacks, however due to the strict time constraints of the timeslots, practical solutions should be very efficient. In this paper, we propose R-TSCH, a randomized radio channel generation algorithm that can be used to proactively protect wireless nodes from jamming attacks. Based on a cryptographic hash function and a secret key, R-TSCH produces a new pseudo-random channel sequence, which looks as truly random to anyone who has no access to the key. Our simulation results show that the attacked links of the TSCH network enhanced with the proposed mechanism can achieve an over 90% Packet Reception Rate (PRR) in presence of multiple jammers.
A Digital Respiratory Stethoscope Method using Skin Image

We propose a new digital respiratory stethoscope using skin images. Unlike the traditional digital stethoscope, the proposed stethoscope allows us to intuitively judge a user’s respiratory state through the five-senses: visual, auditory, tactile, etc. The proposed method involves extracting a respiratory wave signal, adding respiratory sounds, and creating vibration according to the degree of respiration.First, for the extraction and output of the respiratory wave signal, skin detection is conducted in the image and the skin color data are filtered through frequency analysis, then the respiratory waveform is output. Next, different vibration patterns are applied to the inspiration and expiration of the respiratory waveform and respiratory sounds are added to the respiratory wave signal. Upon completion of this process, user can feel vibration and respiratory sounds according to the degree of respiration. We used MOS evaluation methods for the evaluation of 10 people who scored an average of 3.9 points awarded out of a possible 5. This confirms that the proposed method adequately express the intensity and frequency of respiration through the five senses.
Staphylococcus aureus coagulases are exploitable yet stable public goods in clinically relevant conditions

Significance Clotting of blood is not exclusive to host physiology; pathogens are also able to generate clots as part of their life cycle. Here, we show that coagulases, enzymes responsible for bacteria-mediated clotting, can act as public goods in clinical conditions. Coagulases, secreted by producers, generate protective layers of fibrin around the bacteria, shielding them from antimicrobials and host immune factors. Remarkably, we find that this protection is also conferred onto strains that do not produce coagulases but still benefit from those made by others. Although this is a social trait, overexploitation of coagulases does not occur due to spatial segregation and population viscosity. Our study provides a social evolution perspective on the critical role of coagulases. Coagulation is an innate defense mechanism intended to limit blood loss and trap invading pathogens during infection. However, Staphylococcus aureus has the ability to hijack the coagulation cascade and generate clots via secretion of coagulases. Although many S. aureus have this characteristic, some do not. The population dynamics regarding this defining trait have yet to be explored. We report here that coagulases are public goods that confer protection against antimicrobials and immune factors within a local population or community, thus promoting growth and virulence. By utilizing variants of a methicillin-resistant S. aureus we infer that the secretion of coagulases is a cooperative trait, which is subject to exploitation by invading mutants that do not produce the public goods themselves. However, overexploitation, “tragedy of the commons,” does not occur at clinically relevant conditions. Our micrographs indicate this is due to spatial segregation and population viscosity. These findings emphasize the critical role of coagulases in a social evolution context and provide a possible explanation as to why the secretion of these public goods is maintained in mixed S. aureus communities.
Cross-Ratio Based Gaze Estimation using Polarization Camera System

Eye-based interaction is one of the solutions for achieving intuitive interfaces on surfaces such as a large display, and thus, various eye-tracking methods have been studied. Cross-ratio based gaze estimation, which determines the point-of-gaze on a screen, has been studied actively as a novel eye-tracking method because the method does not require a hardware calibration defining the relationship between a camera and monitor. We expect that the cross-ratio method will be a breakthrough for eye-based interaction under various circumstances such as tabletop devices and digital whiteboards. In eye-tracking, near-infrared light is often emitted, and at least four LEDs are located on display corners for detecting the screen plane in the cross-ratio based method. However, long-time radiation of near-infrared light can make a user fatigued. Therefore, in this study, we attempted to extract the screen area correctly without near-infrared radiation emission. A polarizing filter is included in the display, and thus, visibility of the screen can be controlled by the light's polarization direction of the external polarized light filter. We propose gaze estimation based on the cross-ratio method using a developed polarization camera system, which can capture two polarized images of different angles simultaneously. Further, we confirmed that the point-of-gaze could be estimated using the screen reflection detected by computing the differences between two images without near-infrared emission.
Leukemia development initiated by deletion of RBP-J: mouse strain, deletion efficiency and cell of origin

ABSTRACT Conditional deletion of RBP-J, the major transcriptional effector of Notch signaling, specifically within renin-expressing cells leads to the development of B-cell leukemia. However, the influence of contributing factors such as mouse strain, cell of origin and Cre recombinase copy number are unknown. In this study, we compared RBP-J deletion efficiency using one versus two copies of Cre recombinase. Further, we compared the incidence and timing of leukemia development in two unique strains of mice, C57BL/6 and 129/SV, as well as at different B-cell developmental stages. We found that animals expressing two copies of Cre recombinase developed B-cell leukemia at an earlier age and with more fulminant disease, compared with control animals and animals expressing one copy of Cre recombinase. In addition, we found a difference in leukemia incidence between C57BL/6 and 129/SV mouse strains. Whereas deletion of RBP-J in renin-expressing cells of C57BL/6 mice leads to the development of B-cell leukemia, 129/SV mice develop dermatitis with a reactive, myeloproliferative phenotype. The difference in phenotypes is explained, in part, by the differential expression of extra-renal renin; C57BL/6 mice have more renin-expressing cells within hematopoietic tissues. Finally, we found that deletion of RBP-J in Mb1- or CD19-expressing B lymphocytes does not result in leukemia development. Together, these studies establish that renin progenitors are vulnerable cells for neoplastic transformation and emphasize the importance of genetic background on the development of inflammatory and malignant conditions. This article has an associated First Person interview with the first author of the paper. Summary: Using a mouse model of precursor B-cell leukemia, we have investigated influencing factors on leukemia genesis, including genetic background, cell of origin and deletion efficiency.
Energy Efficient MIMO-NOMA HCN with IoT for Wireless Communication Systems

In this paper, the energy efficiency maximization problem is tackled in a multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) heterogeneous cellular network (HCN) with Internet of Things (IoT) for wireless communication systems. A fractional non-convex optimization problem is formulated to maximizes the energy efficiency (EE) subject to the transmit power constraints and the minimum rate requirement for the cell edge (CE) users present in both macro base station (MBS) and pico base station (PBS). The above problem is hard to solve due to its nonlinear fractional objective function. Fractional programming properties is firstly employed to convert the non-convex problem into its parametric form. In addition, an efficient iterative algorithm is proposed established on the branch and reduced bound (BRB) approach that achieves convergence to the above problem, mitigates the inter tier interference and also improves the fairness between the users. Comprehensive numerical results emphasize that the proposed scheme achieves higher energy efficiency as compared with the existing NOMA scheme and the conventional orthogonal multiple access (OMA) scheme.
Novel mechanism responsible for high-level macrolide resistance in Moraxella catarrhalis

Background High-level macrolide-resistant Moraxella catarrhalis strains have been isolated; however, the underlying mechanism has not been well elucidated. We investigated the role of mutations in the 23S rRNA gene and the L4 and L22 ribosomal proteins using spontaneous erythromycin-resistant mutants and transformants. Materials and methods The erythromycin-susceptible M. catarrhalis ATCC25238 and clinical isolate Mc19 were used as parental strains. To obtain spontaneous erythromycin-resistant mutants, in vitro stepwise selection was performed using brain–heart infusion agar plates containing various concentrations of erythromycin. The role of the mutations identified in the spontaneous mutants was validated using transformation experiments. Results We obtained two spontaneous mutants with high-level resistance to erythromycin, S25-32-af10 and S19-256-af10, from ATCC25238 and Mc19, respectively. S25-32-af10 exhibited mutations of Q61R in L4 and Insertion98SRADRIS in L22. S19-256-af10 exhibited three C2611T-mutated alleles in the 23S rRNA gene and G65A in L4. Transformants with single mutations identified in S25-32-af10 or S19-256-af10 showed higher erythromycin and azithromycin minimum inhibitory concentrations (MICs) than those of each parental strain. However, transformants with multiple mutations identified in S25-32-af10 or S19-256-af10 showed macrolide MICs similar to those of each parental strain. Conclusion Our results provide the first evidence suggesting that Q61R in L4 and Insertion98SRADRIS in L22 are involved in the synergistic acquisition of high-level resistance to both 14- and 15-member macrolides, and that C2611T in the 23S rRNA gene and G65A in L4 also synergistically contribute toward conferring high-level 14-member macrolide resistance to M. catarrhalis.
Functional Gains in Children With Spastic Hemiplegia Following a Tendon Achilles Lengthening Using Computerized Adaptive Testing—A Pilot Study

Purpose: This pilot study evaluated the outcomes of tendon Achilles lengthening in 12 children (mean age: 11.2 years) with spastic hemiplegia. Methods: Cerebral Palsy Computer Adaptive Tests, the timed up-and-go, the Gross Motor Function Measure, the Gillette Functional Assessment Questionnaire, and the Pediatric Outcomes Data Collection Instrument were administered at baseline and at 6, 12, and 24 months postsurgery. Results: Significant improvement at the latest follow-up (12-24 months following surgery) was seen in all domains of the Cerebral Palsy Computer Adaptive Test: activity (P = .017), lower extremity (P = .005), global (P = .005), pain (P = .005), and fatigue (P = .028), as well as in the Gross Motor Function Measure-standing domain (P = .02) and the mobility domain of the Pediatric Outcomes Data Collection Instrument (P = .04). Conclusion: These findings indicate that the tendon Achilles lengthening improved functional outcome in these children as measured by tests of physical function, walking speed, and activity performance.
A Mobility Prediction Based Beacon Rate Adaptation Scheme in VANETs

In vehicular ad hoc networks, it is common for vehicles to inform their current moving states which include a vehicle’s position, velocity and moving direction to their neighbors by using beacon messages periodically. However, high frequency of beacon broadcasting may lead to heavy channel load and congestion in certain scenarios. In this paper, a mobility prediction based beacon rate adaptation (MPBR) scheme is proposed to decrease beacon frequency and alleviate bandwidth consumption. By tracking their neighbors using the predicted moving states instead of using periodic beacon broadcasting, vehicles decrease the beacon rate significantly. Whenthe prediction error is higher than the threshold due to accumulation of tiny errors, a beacon broadcasting will be triggered. In order to further reduce the channel load and congestion,we classify the road traffic status into three categories to determine the value of error threshold. The simulation results clearly demonstrate the efficiency of our proposed approach.
Sex and Physiological Cycles Affect the Automatic Perception of Attractive Opposite-Sex Faces: A Visual Mismatch Negativity Study

Facial attractiveness plays important roles in social interaction. Electrophysiological and neuroimaging studies found several brain areas to be differentially responsive to attractive relative to unattractive faces. However, little is known about the time course of the information processing, especially under the unattended condition. Based on a “cross-modal delayed response” paradigm, the present study aimed to explore the automatic mechanism of facial attractiveness processing of females with different physiological cycles and males, respectively, through recording the event-related potentials in response to (un)attractive opposite-sex faces by two experiments. The attractiveness-related visual mismatch negativity (attractiveness vMMN) in posterior scalp distribution was recorded in both the experiments, which indicated that attractive faces could be processed automatically. And high-attractive opposite-sex faces can elicit larger vMMN in males than females in menstrual period in Study 1, but similar as females in ovulatory period in Study 2. Furthermore, by comparison, the latency of attractiveness vMMN in females with the ovulatory period was the longest. These results indicated as follows: (1) Males were more sensitive to attractive female faces, (2) females in ovulatory period were also attracted by the attractive male faces, (3) the long vMMN latency in females during ovulatory period suggested a special reproductive motivation to avoid being tainted by genes, which takes priority over the breeding motivation.
Policy Optimization with Model-based Explorations

Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from high variances and high sample complexity. On the other hand, model-based reinforcement learning methods that learn the transition dynamics are more sample efficient, but they often suffer from the bias of the transition estimation. How to make use of both model-based and model-free learning is a central problem in reinforcement learning.In this paper, we present a new technique to address the tradeoff between exploration and exploitation, which regards the difference between model-free and model-based estimations as a measure of exploration value. We apply this new technique to the PPO algorithm and arrive at a new policy optimization method, named Policy Optimization with Modelbased Explorations (POME). POME uses two components to predict the actions’ target values: a model-free one estimated by Monte-Carlo sampling and a model-based one which learns a transition model and predicts the value of the next state. POME adds the error of these two target estimations as the additional exploration value for each state-action pair, i.e, encourages the algorithm to explore the states with larger target errors which are hard to estimate. We compare POME with PPO on Atari 2600 games, and it shows that POME outperforms PPO on 33 games out of 49 games.
Convolutional ensembles for Arabic Handwritten Character and Digit Recognition

A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of adaptive gradient descent on the first epochs and regular stochastic gradient descent in the last epochs, to facilitate convergence. Different validation strategies are tested, namely Monte Carlo Cross-Validation and K-fold Cross Validation. Hyper-parameter tuning was done by using the MADbase digits dataset. State of the art validation and testing classification accuracies were achieved, with average values of 99.74% and 99.47% respectively. The same algorithm was then trained and tested with the AHCD character dataset, also yielding state of the art validation and testing classification accuracies: 98.60% and 98.42% respectively.
Ionic Liquid-Promoted Three-Component Domino Reaction of Propargyl Alcohols, Carbon Dioxide and 2-Aminoethanols: A Thermodynamically Favorable Synthesis of 2-Oxazolidinones

To circumvent the thermodynamic limitation of the synthesis of oxazolidinones starting from 2-aminoethanols and CO2 and realize incorporation CO2 under atmospheric pressure, a protic ionic liquid-facilitated three-component reaction of propargyl alcohols, CO2 and 2-aminoethanols was developed to produce 2-oxazolidinones along with equal amount of α-hydroxyl ketones. The ionic liquid structure, reaction temperature and reaction time were in detail investigated. And 15 mol% 1,5,7-triazabicylo[4.4. 0]dec-5-ene ([TBDH][TFE]) trifluoroethanol was found to be able to synergistically activate the substrate and CO2, thus catalyzing this cascade reaction under atmospheric CO2 pressure. By employing this task-specific ionic liquid as sustainable catalyst, 2-aminoethanols with different substituents were successfully transformed to 2-oxazolidinones with moderate to excellent yield after 12 h at 80 °C.