Miracle-dz/UniFAR-FTUs huggingface dataset:
Similarity Join and Similarity Self-Join Size Estimation in a Streaming Environment

We study the problem of similarity self-join and similarity join size estimation in a streaming setting where the goal is to estimate, in one scan of the input and with sublinear space in the input size, the number of record pairs that have a similarity within a given threshold. The problem has many applications in data cleaning and query plan generation, where the cost of a similarity join may be estimated before actually doing the join. On unary input where two records either match or don’t match, the problem becomes join and self-join size estimation for which one-pass algorithms are readily available. Our work addresses the problem for <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="rafiei-ieq1-2893175.gif"/></alternatives></inline-formula>-ary input, for <inline-formula><tex-math notation="LaTeX">$d \geq 1$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>d</mml:mi><mml:mo>≥</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="rafiei-ieq2-2893175.gif"/></alternatives></inline-formula>, where the degree of similarity can vary from 1 to <inline-formula><tex-math notation="LaTeX">$d$</tex-math><alternatives><mml:math><mml:mi>d</mml:mi></mml:math><inline-graphic xlink:href="rafiei-ieq3-2893175.gif"/></alternatives></inline-formula>. We show that our proposed algorithm gives an accurate estimate and scales well with the input size. We provide error bounds and time and space costs, and conduct an extensive experimental evaluation of our algorithm, comparing its estimation accuracy to a few competitors, including some multi-pass algorithms. Our results show that given the same space, the proposed algorithm has an order of magnitude less error for a large range of similarity thresholds.
Via-Hole Arrangement for Bandwidth Extension of Planar Microstrip-to-Waveguide Transition

A novel design technique for bandwidth extension of microstrip-to-waveguide transition with a simple via-hole arrangement surrounding waveguide is proposed. The proposed transition can be operated over a quite broad frequency bandwidth of $\mathbf{10, GHz}, (\pmb{S}_{11} < -15, \mathbf{dB})$ due to its double resonance.
Pattern recall, decision making and talent identification in rugby union

Abstract The ability to make fast, accurate decisions is an essential skill for all who play sport. However, measuring this ability has proved difficult for coaches and talent identification practitioners. Pattern recognition (a key factor in decision making) has commonly been measured using pattern recall tasks. This study aimed to understand whether accuracy when recalling rugby union patterns is a valid measure of on-field decision making performance. In Study 1, professional players recalled structured patterns of players from still images (N = 20) viewed for 5 s. On-field decision-making markers, including coaches’ rankings of decision-making ability, playing position, number of years playing professionally and total number of years playing rugby union, were used as predictor variables of recall accuracy. Results showed that only total number of years playing rugby union was correlated with recall accuracy, suggesting that caution is necessary when adopting these tasks for talent identification purposes. The structured stimuli used in Study 1 were not representative of a true rugby union game, so Study 2 tested novice and expert players on a pattern recall task that included structured, semi-structured and unstructured rugby union patterns. Experts were significantly more accurate than novices when recalling structured and semi-structured patterns; however, there were no differences when recalling unstructured patterns. It was concluded that structured and semi-structured patterns should be used in future studies to test whether pattern recall tasks can be used for talent identification in rugby union.
A generic solution for weaving business code into executable models

The separation of concerns is a fundamental principle that allows to build a software with separate parts, thereby improving their maintainability and evolutivity. Executable models are good potential representatives of this principle since they capture the behavior of a software-intensive system, that is, when, why and how calling business operations, while the latter are specified apart. EMF is the de facto framework used to create an executable DSL (xDSL) but a solution to weave business operations into it is still missing. This is compounded by the fact that such business operations can be tied to specific technological platforms that stand outside the EMF world (e.g. Android SDK). To that purpose, in this paper we describe a solution for managing business operations both at design-time (creation of executable models with EMF) and at run-time (operation calls from the deployed execution engine). This solution is generic enough to be integrated into any Java-based environment and for any xDSL.
A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale

Influenza is one of the most common causes of human illness and death; thus, accurate and timely predictions for influenza trends are critical tasks for public health. Many studies have attempted to conduct influenza prediction at or beyond the city scale; however, larger spatial scales are too coarse to help analyze influenza epidemics or allow offering precise interventions inside a city. Moreover, the existing prediction models often ignore the spatial correlations of influenza activity between neighbouring regions although such correlations are potentially helpful in influenza prediction. To address the above issues, this study proposes an influenza prediction model based on a deep residual network that predicts influenza trends by integrating the spatial-temporal properties of influenza at an intra-urban scale. Using a real dataset of influenza in Shenzhen City, China, we tested our prediction model on 10 districts within the city. Our results show that our proposed deep residual model outperforms four baseline models, including linear regression (LR), artificial neural network (ANN), long short-term memory (LSTM) and spatiotemporal LSTM (ST-LSTM) models, thus demonstrating the effectiveness of the proposed prediction model. To our best knowledge, although deep-learning-based approaches have been shown to be useful in many fields in recent years, there has been no attempt to apply such approaches to influenza prediction. Therefore, this study is an initial attempt to introduce a deep learning model into influenza prediction. The proposed deep residual network is able to incorporate the spatial correlations of influenza, and it has obvious potential for making influenza predictions at finer spatial scales within a city, which can offer critical support for preciser public health interventions.
Vehicle-Based Relay Assistance for Opportunistic Crowdsensing Over Narrowband IoT (NB-IoT)

The Internet of Things (IoT) undergoes a fundamental transformation by augmenting its conventional sensor network deployments with more advanced and mobile devices, such as connected and self-driving cars. This fusion of embedded and automotive domains promises to deliver unprecedented mutual benefits, where vehicles will receive timely updates from their proximate sensors while assisting them in delivering their sensory data to the remote network infrastructure. In this paper, we put forward the vision of opportunistic crowdsensing applications, in which the ubiquitous deployments of low-cost and battery-constrained IoT sensors take advantage of more capable and energy-abundant vehicle-mounted mobile relays. In particular, we consider the use of the emerging narrowband IoT radio technology recently ratified by 3GPP and offering efficient means for underlying wireless connectivity. Our rigorous mathematical analysis supported with comprehensive system-level evaluations reveals the effects of vehicle-based relays on the important metrics of interest, such as connection reliability, transmission latency, and communication energy efficiency. These systematic findings advocate for an extensive utilization of vehicular relays as part of the next-generation IoT ecosystem.
Fuzzy Logic Based Data Provisioning using Cloud Computing

The emergence of cloud computing which gives a revolutionary innovation to the management of the data resources. For data storage, the servers store a huge amount of shared data, it could be accessed by authorized users. The possibilistic C means module (PCM) is widely used for processing of Image and its analysis. Knowledge extraction in case of the large heterogeneous data is not possible by PCM. To solve the issue, we proposed fuzzy logic based data provisioning using cloud computing.
Learning based biometric key generation method using CNN and RNN

Studies on the biometric authentication and biometric key generation have been underway for a long time. Fundamentally, there is a disadvantage that it is difficult to obtain uniform biometrics due to noise, and issues of security and privacy are still mentioned, but high user convenience is an advantage that cannot be ignored. Recently, the results of existing researches on biometric key generation show very good results. However, since the algorithms presented in many studies are suited to the specific dataset, applying these algorithms to different datasets makes it difficult to achieve the good results mentioned in the paper. The reason is probably because most datasets are collected in one place with one camera. We wanted to present a key generation method that is not limited to datasets, and we came up with a training-based method for this. In this paper, we propose a method with the convolutional neural network (CNN) and the recurrent neural network (RNN) for cryptographic key generation from face biometrics. CNN is used to extract the feature vector from the face image, and RNN generates the key from the feature vector. In the registration process, the RNN is iteratively trained. Experimental results on the databases show that the proposed approach is effective in the biometric key generation. The results for mixed database also show good performance.
Aerosol Jet Printed Strain Sensor: Simulation Studies Analyzing the Effect of Dimension and Design on Performance (September 2018)

The 3-D printing has been gaining widespread attention for electronic applications. Aerosol jet printing has emerged as a powerful technique to print new-age sensors and devices on various substrates. In this paper, we use simulation studies to optimize the dimensions and design of aerosol jet printed strain sensor for a good performance. Physical dimensions such as the end loop length, number of grids, grid line width, gauge length, and five different sensor designs are simulated to study their effect on the performance of the strain sensor. This paper helps to identify the factors that affect the sensitivity and gauge factor of the flexible strain sensor fabricated using aerosol jet printing technology.
Intratympanic Methylprednisolone versus Dexamethasone for the Primary Treatment of Idiopathic Sudden Sensorineural Hearing Loss.

OBJECTIVES The aim of the present study was to compare the therapeutic effectiveness of intratympanic (IT) methylprednisolone and dexamethasone in the initial treatment of patients with idiopathic sudden sensorineural hearing loss (ISSHL). MATERIALS AND METHODS A total of 46 patients with ISSHL who had been treated with IT methylprednisolone or dexamethasone were included in the present study. Dexamethasone (4 mg/mL) and methylprednisolone (20 mg/mL) were given transtympanically to 22 and 24 patients, respectively, one dosage per day for 5 consecutive days. Audiologic evaluations were performed pretreatment, daily in inpatient clinics, and in the first week and second month after discharge, using four-frequency pure-tone average (PTA) and speech discrimination score (SDS). Audiologic improvement was classified according to the Furuhashi criteria. RESULTS According to the Furuhashi criteria, the therapeutic success rate was 62.5% (complete improvement 16.7% and marked improvement 45.8%) in the methylprednisolone group, whereas it was 54.6% (complete improvement 27.3% and marked improvement 27.3%) in the dexamethasone group. Therapeutic success was higher in the methylprednisolone group; however, it was not statistically significant. When the audiologic improvement was accepted as >10 dB in PTA, the therapeutic success rates were 83.3% in the methylprednisolone group and 72.8% in the dexamethasone group. The mean (±SD) improvement of PTA before and after treatment was 30.8±21.4 in the methylprednisolone group and 24.7±2.5 in the dexamethasone group. The mean improvement in SDS was 32.6±25 in the methylprednisolone group and 23.7±26.9 in the dexamethasone group. CONCLUSION IT steroids are safe, effective, and well-tolerated agents in the initial treatment of patients with ISSHL. Despite having different pharmacokinetic characteristics, IT methylprednisolone and dexamethasone have no superiorities over each other in the primary treatment in patients with ISSHL.
Adverse Drug Event Rate in Israeli Hospitals: Validation of an International Trigger Tool and an International Comparison Study.

BACKGROUND Adverse drug events (ADEs) are a major cause of morbidity and mortality worldwide. Hence, identifying and monitoring ADEs is of utmost importance. The Trigger Tool introduced by the Institute of Healthcare Improvement in the United States has been used in various countries worldwide, but has yet to be validated in Israel. OBJECTIVES To validate the international Trigger Tool in Israel and to compare the results with those generated in various countries. METHODS A retrospective descriptive correlative analysis surveying four general hospitals in Israel from different geographical regions was conducted. Patient medical charts (n=960) were screened for 17 established triggers and confirmed for the presence of an ADE. Trigger incidence was compared to the actual ADE rate. Further comparison among countries was conducted using published literature describing Trigger Tool validation in various countries. RESULTS A total of 421 triggers in 279 hospitalizations were identified, of which 75 ADEs in 72 hospitalizations (7.5%) were confirmed. In addition, two ADEs were identified by chart review only. Mean positive predictive value was 17.81% and overall sensitivity was 97%. We found 1.54 ADEs for every 100 hospitalization days, 7.8 ADEs per 100 admissions, and 1.81 ADEs for every 1000 doses of medication. Of the 77 ADEs identified, 22.7% were defined as preventable. CONCLUSIONS Our results support the Trigger Tool validity in Israel as a standardized method. Further studies should evaluate between hospital and region differences in ADE rate, in particular for the preventable events.
Tailoring structural and magnetic properties of Mn3−xFexGa alloys towards multifunctional applications

The crystal structures and magnetic properties of Mn3−xFexGa alloys (x = 0, 0.2, 0.4, 0.6, 0.8, 1) can be extensively tailored by different heat-treatment conditions. This investigation sheds light on tuning the crystal structures of these materials via heat treatment towards wide functionalities for technological applications.
Machine learning of brain gray matter differentiates sex in a large forensic sample

Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n = 1,300) using machine learning. We apply source‐based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex. Models using components of brain gray matter volume and concentration were able to differentiate between males and females with greater than 93% generalizable accuracy. Highly differentiated components include orbitofrontal and frontopolar regions, proportionally larger in females, and anterior medial temporal regions proportionally larger in males. We also provide a complimentary analysis of a nonforensic healthy control sample and replicate our 93% sex discrimination. These findings demonstrate that the brains of males and females are highly distinguishable. Understanding sex differences in the brain has implications for elucidating variability in the incidence and progression of disease, psychopathology, and differences in psychological traits and behavior. The reliability of these differences confirms the importance of sex as a moderator of individual differences in brain structure and suggests future research should consider sex specific models.
DESIRe: Discontinuous energy seam carving for image retargeting via structural and textural energy functionals

This paper proposes DESIRe (Discontinuous Energy Seam-carving Image Retargeting), an improved seam carving approach for content-aware image retargeting. The proposed algorithm introduces a novel discontinuous seam carving optimization process that incorporates not only a structural energy cost functional, but also a texture energy cost functional to handle scenes with complex structural and textural characteristics. Experimental results using a variety of scenes with dense image detail characteristics show that the proposed DESIRe seam carving approach can provide improved visual quality when compared to a number of existing seam carving methods. These results illustrate the potential of the proposed DESIRe approach for improving content-aware image retargeting that reduces the loss of important image information without causing significant visual distortions or artifacts.
Outer electronic shell visualization by NMR chemical shift laplacian of a helium probe

A new method of visualization of lone pairs is proposed using fluorine atoms in fluoroacetylene, trifluoroethylene, and fluoroform (sp, sp2, and sp3 hybridized carbons) as an example. The space around fluorines was probed by a helium atom and at each point 3He NMR chemical shift δHe and its Laplacian ∇2δHe were calculated. The ∇2δHe isosurfaces have symmetrical toroidal shapes along CF axes; the maximum values of ∇2δHe are reached at about 1.5 Å from fluorine atom center in the direction roughly coinciding with the lone pair localization. The absolute values of the ∇2δHe could be used to quantify the electron‐rich regions of lone pairs. The results are compared with more common visualization methods—3D maps of the molecular electrostatic potential and the electron localization function. We show that a helium atom could be used for evaluation of fine features of molecular electronic shell, since 3He NMR spectral characteristics remain sensitive at relatively large distances. © 2018 Wiley Periodicals, Inc.
Saliency Detection With Features From Compressed HEVC

In this paper, a saliency detection algorithm with features from compressed high-efficiency video coding (HEVC) is proposed. The proposed algorithm consists of three parts: static saliency detection, dynamic saliency detection, and competitive fusion. Static features are generated by downsampling and discrete cosine transform, and dynamic features are extracted from compressed HEVC, specifically motion vector. A Gaussian kernel is used to extract the data structure in static feature maps. For dynamic feature map, a coding unit depth and bits combined mask is designed to filter out the dynamic background. Finally, competitive fusion is designed to adaptively fuse the static and dynamic saliency maps. Experimental results show that the proposed method is superior to classic methods by up to 0.1223 area under curve gaining and 0.8362 Kullback–Leibler divergence decreasing on average. The average detection speed is 2.3 s per frame.
On executable models that are integrated with program code

Software development is usually supported by designmodels. These models and the program code represent overlapping information redundantly. When the implementation is changed, the models are often not changed along with the code, which introduces inconsistencies between these artifacts. Such inconsistencies imply a risk of misunderstandings and errors during the development, maintenance, and evolution. In this paper we present a method for creating executable models, that are integrated with the program code. The integration allows for an interplay between the model and the program code at run-time, and leaves no room for inconsistencies between the artifacts.
A Robust Double Closed-Loop Control Scheme for PMLSM Drives

In this paper, a novel velocity and current double closed-loop control scheme applied to the permanent magnet linear synchronous motor (PMLSM) drives is proposed, and to suppress various uncertainties and disturbances, the advanced observation technique is introduced. First, as the inner loop, the current loop adopts the predictive current control method, which can increase the current control bandwidth and tracking precision. To improve the dynamic response and robustness of the velocity loop, a super-twisting sliding-mode control strategy is presented. Second, to overcome the parameter mismatch issue in the current loop and suppress different force disturbances in the velocity loop, their respective super-twisting sliding-mode observers are structured, and the corresponding estimated values are injected into the double closed loops by the feedforward method. At last, based on the precise PMLSM testing platform, the experimental studies are carried out to prove the effectiveness and correctness of the proposed scheme.
First Demonstration of Ultra-Low-Noise Long-Haul 110-km MDM+WDM Transmission of Orbital Angular Momentum (OAM) Modes with 1st / 2nd-Order Distributed Raman Amplifier

We experimentally demonstrate the first record long-single-span MDM+WDM transmission of 3 orbital angular momentum (OAM) modes and 22 wavelength channels in 110-km large-core fiber without using MIMO-DSP assisted by homemade all-fiber OAM (de)multiplexer and 1st-/2nd-order distributed Raman amplification.
A Bitstream Reverse Engineering Tool for FPGA Hardware Trojan Detection

Since FPGAs are field-programmable and reconfigurable integrated circuits, there are many security concerns that malicious functions like hardware Trojans can be infiltrated into circuits not only in development stages but also in deployment stages -- malicious fabrication and modification are possible even after deployment. To detect hardware Trojans effectively, we must be able to deal with the netlists available at development stages and the bitstreams available at deployment stages -- it is highly desired to reverse-engineer the bitstreams to the netlists, but unfortunately greatly challenging. In this poster, we introduce our project aiming at hardware Trojans detection at both stages in FPGAs, and present our bitstream reverse engineering tool called BRET, recently developed for Xilinx Virtex-5 bitstreams. We also discuss the prospective results and directions.