Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Dictionary and n-gram Alignment to Improve Fine-grained Cross-Language Plagiarism Detection

The Web offers fast and easy access to a wide range of documents in various languages, and translation and editing tools provide the means to create derivative documents fairly easily. This leads to the need to develop effective tools for detecting cross-language plagiarism. Given a suspicious document, cross-language plagiarism detection comprises two main subtasks: retrieving documents that are candidate sources for that document and analyzing those candidates one by one to determine their similarity to the suspicious document. In this paper we focus on the second subtask and introduce a novel approach for assessing cross-language similarity between texts for detecting plagiarized cases. Our proposed approach has two main steps: a vector-based retrieval framework that focuses on high recall, followed by a more precise similarity analysis based on dynamic text alignment. Experiments show that our method outperforms the methods of the best results in PAN-2012 and PAN-2014 in terms of plagdet score. We also show that aligning n-gram units, instead of aligning complete sentences, improves the accuracy of detecting plagiarism.
Knowledge Integration and Dynamic Organizational Adaptation in Family Firms

The speed of change in competitive environments has prompted firms to develop processes directed at enabling organizational adaptation. This is captured by the concept of dynamic capabilities. We focus on a particular form of business organization, that is, the family firm. Specifically, we argue that knowledge integration—a dynamic capability through which family members' specialized knowledge is recombined—guides the evolution of capabilities. We present a general framework illustrating factors that affect knowledge integration in family firms. We conclude that only those family firms that are able to effectively integrate individual family members' specialized knowledge will be successful in dynamic markets by changing their capabilities over time.
Classifying flexible pavement defects using hybrid machine learning approach

The transportation infrastructure sector significantly impacts a country’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.
MOSAIC: unified declarative platform for dynamic overlay composition

Overlay networks create new networking services across nodes that communicate using pre-existing networks. MOSAIC is a unified declarative platform for constructing new overlay networks from multiple existing overlays, each possessing a subset of the desired new network's characteristics. MOSAIC overlays are specified using Mozlog, a new declarative language for expressing overlay properties independently from their particular implementation or underlying network. This paper focuses on the runtime aspects of MOSAIC: composition and deployment of control and/or data plane functions of different overlay networks, dynamic compositions of overlay networks to meet changing application needs and network conditions, and seamless support for legacy applications. MOSAIC is validated experimentally using compositions specified in Mozlog: we combine an indirection overlay that supports mobility (i3), a resilient overlay (RON), and scalable lookups (Chord), to provide new overlay networks with new functions. MOSAIC uses runtime composition to simultaneously deliver application-aware mobility, NAT traversal and reliability. We further demonstrate MOSAIC'S dynamic composition capabilities by Chord switching its underlay from IP to RON at runtime. These benefits are obtained at a low performance cost, as demonstrated by measurements on both a local cluster and PlanetLab.
Feasibility of Deep Learning in Shear Wave Splitting analysis using Synthetic-Data Training and Waveform Deconvolution

Teleseismic shear-wave splitting analyses are often performed by reversing the splitting process through the application of frequency- or time-domain operations aimed at minimizing the transverse-component energy of waveforms. These operations yield two splitting parameters, ɸ (fast-axis orientation) and δt (delay time). In this study, we investigate the applicability of a baseline recurrent neural network, SWSNet, for determining the splitting parameters from pre-selected waveform windows. Due to the scarcity of sufficiently labelled real waveform data, we generate our own synthetic dataset to train the model. The model is capable of determining ɸ and δt with a root mean squared error (RMSE) of 9.7° and 0.14 s on a noisy synthetic test data. The application to real data involves a deconvolution step to homogenize the waveforms. When applied to data from the USArray dataset, the results exhibit similar patterns to those found in previous studies with mean absolute differences of 9.6° and 0.16 s in the calculation of ɸ and δt respectively.
Modeling collective cell migration in geometric confinement

Monolayer expansion has generated great interest as a model system to study collective cell migration. During such an expansion the culture front often develops ‘fingers’, which we have recently modeled using a proposed feedback between the curvature of the monolayer’s leading edge and the outward motility of the edge cells. We show that this model is able to explain the puzzling observed increase of collective cellular migration speed of a monolayer expanding into thin stripes, as well as describe the behavior within different confining geometries that were recently observed in experiments. These comparisons give support to the model and emphasize the role played by the edge cells and the edge shape during collective cell motion.
Monosaccharide-NAIM Derivatives for D-, L-Configurational Analysis

The D-, L-enantiomeric pairs of common monosaccharides (xylose, ribose, rhamnose, arabinose, fucose, glucose, mannose, galactose, N-acetylgalactosamine, glucuronic acid and galacturonic acid) were derivatized with 2,3-naphthalenediamine to form the corresponding D-, L-aldo-NAIM derivatives. A simple and facile capillary electrophoretic method was established for sugar composition analysis by simultaneously determining the migration times of these aldo-NAIMs using borate buffer at high pH (100 mM, pH 9.0). The methodology is also applicable to sialic acid (ketose monosaccharides). The quantitation level of the proposed method was in the 10~500 ppm range and the LOD was 1 ppm. The enantioseparation of D, L pairs of aldo-NAIMs were also achieved by using modified sulfated−α−cyclodextrin as the chiral selector in phosphate buffer (300 mM, pH 3.0). In addition, the combination by reductive amination of amino-aldo-NAIM agent and D-, L-enantiomeric pairs of monosaccharides formed a diastereomeric pair for saccharide configuration analysis. Aldo-NAIM derivatives are thus shown to be rapid and efficient agents for analyzing saccharide compositions and configurations with good linearity and short analysis times via capillary electrophoresis.
THE RELATION BETWEEN ALPHA BAND POWER, HEART RATE AND FMRI

The brain's resting state is studied with the analysis of co-registered fMRI/EEG/ECG. In particular, it was investigated whether spontaneous heart rate fluctuations are correlated to fMRI-signals. This might be the case because of a direct hemodynamic coupling, or due to an indirect coupling between heart rate and alpha band power, which are both hall marks of the resting state. It was found that (1) higher alpha power coincides with a higher heart rate, (2) heart rate regressors are in many subjects strongly coupled to fMRI, and (3) inclusion of heart rate confounders hardly affects earlier reported correlations between alpha band and fMRI.
Subject-adaptive steady-state visual evoked potential detection for brain-computer interface

We report on the development of a four command Brain-Computer Interface (BCI) based on steady-state visual evoked potential (SSVEP) responses detected from human electroencephalograms (EEGs). The proposed system combines spatial filtering, feature extraction and selection, and a classifier. Two types of classifiers were compared: one based on equal treatment of all harmonics in all EEG channels and the second based on preliminary training resulting in a weighted treatment of the harmonics. Results from six healthy subjects are evaluated.
Deep Active Contours

We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.
Rates and predictors of prenatal depression in women living with and without HIV

Depression is a significant mental health and public health concern, and women living with HIV are at increased risk for depression. This risk may be especially elevated during pregnancy; however, few studies have attempted to identify rates and predictors of depression in pregnant, HIV-infected women. The purpose of the present study was to investigate rates and predictors of prenatal depression, such as history of depression, childhood sexual abuse (CSA), and social support among HIV-infected and HIV-uninfected women in Philadelphia, Pennsylvania. It was hypothesized that pregnant women with HIV will have higher rates of depressive symptoms and will exhibit a greater number of mood disorder diagnoses as compared to HIV-uninfected pregnant women. It was also hypothesized that HIV status, history of depression, CSA, and inadequate social support will emerge as predictors of depressive symptoms. A sample of 163 women, 31% (n=50) of whom were HIV-infected and 69% (n=113) of whom were HIV-uninfected, were recruited from an obstetrics/gynecology clinic affiliated with an urban university hospital. The Center for Epidemiological Studies-Depression Scale (CES-D) was used to identify depressive symptoms, and Modules A and D of the Structured Clinical Interview for DSM-IV (SCID) confirmed the presence of a mood disorder. Findings demonstrated that rates of depressive symptoms and mood disorder diagnoses during pregnancy did not differ according to HIV serostatus. History depression, CSA, and inadequate social support predicted depressive symptoms during pregnancy in this sample. Due to their association with depressive symptoms, history of depression, CSA, and inadequate social support may be important to identify during pregnancy.
ADSandbox: sandboxing JavaScript to fight malicious websites

We present ADSandbox, an analysis system for malicious websites that focusses on detecting attacks through JavaScript. Since, in contrast to Java, JavaScript does not have any built-in sandbox concept, the idea is to execute any embedded JavaScript within an isolated environment and log every critical action. Using heuristics on these logs, ADSandbox decides whether the site is malicious or not. In contrast to previous work, this approach combines generality with usability, since the system is executed directly on the client running the web browser before the web page is displayed. We show that we can achieve false positive rates close to 0% and false negative rates below 15% with a performance overhead of only a few seconds, what is a bit high for real time application, but supposes a great potential for future versions of our tool.
A Quality Improvement Project to Reduce the ‘No Show’ rate in a Paediatric Neurology Clinic

This quality improvement project aimed to reduce the 'no show' rate in a paediatric neurology clinic in Qatar. No show, in outpatient clinics, is defined as patients who fail to attend their scheduled clinic appointments. It is one of the targets for improving quality of care. It leads to longer waiting times for patients to be seen in outpatient clinics, and the result is patients missing their important appointments. It also results in a waste of the clinic resources, and physician and other healthcare practitioners' time. This study was undertaken as part of the CCITP (clinical care improvement training programme). A project team was assembled with coaching support. The department chairman and the appointment system personnel were involved. Baseline and ongoing measures were collected and charted. The baseline no-show rate was identified as 49%. Following three intervention PDSAs, mainly addressing communication and appointment flexibility, the post intervention no-show rate dropped to 18% and was sustained below the target of 25% for two years. Better communication and appointment flexibility can significantly reduce the no-show rate in outpatient clinics.
An estimator model for distributed estimation in heterogenous wireless sensor networks

In this paper, we deal with distributed estimation using consensus algorithms for heterogenous wireless sensor networks (WSNs). To accommodate with the heterogeneity, we introduce a novel distributed estimator to track the weighted average of the input signals. Different from existing models, we consider a more practical scenario to take account of hierarchical processing abilities of different sensors: type-I sensors with high processing ability and type-II senors with low processing ability for distributed sensor fusion in WSNs. We investigate the properties of our model and illustrate the feasibility of the proposed estimator via a case study where we use the estimator to track the weighted average of a noisy time-varying signal based on the sensors' noisy and distorted measurements. Convergence analysis in this scenario is given as well as the effect of network topology and estimator parameters are also studied. Simulation results are provided to demonstrate the performance and effectiveness of the proposed estimator.
Joint acoustic factor learning for robust deep neural network based automatic speech recognition

Deep neural networks (DNNs) for acoustic modeling have been shown to provide impressive results on many state-of-the-art automatic speech recognition (ASR) applications. However, DNN performance degrades due to mismatches in training and testing conditions and thus adaptation is necessary. In this paper, we explore the use of discriminative auxiliary input features obtained using joint acoustic factor learning for DNN adaptation. These features are derived from a bottleneck (BN) layer of a DNN and are referred to as BN vectors. To derive these BN vectors, we explore the use of two types of joint acoustic factor learning which capture speaker and auxiliary information such as noise, phone and articulatory information of speech. In this paper, we show that these BN vectors can be used for adaptation and thereby improve the performance of an ASR system. We also show that the performance can be further improved on augmenting these BN vectors to conventional i-vectors. In this paper, experiments are performed on Aurora-4, REVERB challenge and AMI databases.
BFGS Algorithm in Electrical Resistance Tomography for the Measurement of Two-phase Flow

Electrical resistance tomography (ERT) is one of new methods for parameters measurement of two-phase flow. The image reconstruction algorithm is one of key parts of ERT system. Based on the optimization principle of BFGS algorithm (one kind of quasi-Newton methods), a new image reconstruction algorithm of ERT for two-phase flow is proposed. To enable the acquirement of on-line measurement of two-phase flow, BFGS algorithm is modified to reduce computation and raise image reconstruction speed. The details are that the iteration direction is changed and the Hessian matrix of the objection function is replaced by the identity matrix. Experimental results show that the modified BFGS algorithm is stable and effective to reconstruct high quality image at fast speed
Effect of statin use on pain relief by transforaminal epidural steroid injection

Aim To investigate the impact of statin use on response to fluoroscopy-assisted transforaminal anterior epidural steroid injection (TAESI). Methods Patients undergoing TAESI for low back pain were recruited and stratified according to statin use. Pain was evaluated with a visual analogue scale (VAS) before and at 1, 3, and 6 months after TAESI. Health-related quality-of-life was evaluated using the Short Form 36 (SF-36) questionnaire 6 months after TAESI. Results There were no significant differences in VAS scores after TAESI between statin users (n = 40) and statin nonusers (n = 253). The SF-36 subgroup: role limitations due to emotional problems score was significantly lower in statin users than statin nonusers. There were no significant between-group differences in any other SF-36 parameter. Conclusion Statin use had no effect on pain scores after TAESI.
Hemodynamics and coil distribution with changing coil stiffness and length in intracranial aneurysms

Purpose The purpose of this study was to investigate hemodynamics and coil distribution with changing coil stiffness and length using the finite element method (FEM) and computational fluid dynamics (CFD) analysis. Methods Basic side-wall and bifurcation type aneurysm models were used. Six types of coil models were generated by changing the coil stiffness and length, based on commercially available embolic coils. Coil embolization was simulated using FEM. CFD was performed to characterize the hemodynamics in the aneurysms after embolization. Coil distribution and velocity reduction in the aneurysms were evaluated. Results The median value of radial coil distribution was shifted from the center to the outer side of the aneurysmal dome by changing coil stiffness: harder coils entered the outer side of the aneurysmal dome more easily. Short coils were more distributed at the neck region, since their small size made it easy for them to enter the tighter area. CFD results also indicated that velocity in the aneurysm was effectively reduced when the coils were more distributed at the neck region and the outer side of the aneurysmal dome because of the disturbance in blood inflow. Conclusions It is easier for coils to enter the outer side of the aneurysmal sphere when they are harder. If coils are short, they can enter tighter areas more easily. In addition, high coil density at the outer side of the aneurysmal dome and at the neck region is important to achieve effective velocity reduction.
PCA-Based Estimation of Velocity Feature for Visual Tracking

The performance of visual tracking mainly depends on observation models and search methods. To stabilize the tracker, an algorithm for estimating velocity feature based on principal component analysis (PCA) is proposed. The proposed algorithm calculates the velocity feature using PCA by the states of the object in the previous k frames. In addition, integrating color and motion cues for visual tracking is implemented with a layered particle filter which consists of two stages of importance sampling. At the first stage, the state of object is crudely estimated with color kernel histogram in RGB color space. The velocity features are used to precisely calculate the state of object in the second stage. The experimental results show that the proposed algorithm has stronger ability to resist to interference of noise, and significantly improves the tracking accuracy in comparison with tracker based on the standard particle filter with a single color cue.
Learning in the Model Space for Fault Diagnosis

AbstractThe emergence of large scaled sensor networks facilitates the collec-tion of large amounts of real-time data to monitor and control complexengineering systems. However, in many cases the collected data may be in-complete or inconsistent, while the underlying environment may be time-varying or un-formulated. In this paper, we have developed an innova-tive cognitive fault diagnosis framework that tackles the above challenges.This framework investigates fault diagnosis in the model space insteadof in the signal space. Learning in the model space is implemented byﬁtting a series of models using a series of signal segments selected witha rolling window. By investigating the learning techniques in the ﬁttedmodel space, faulty models can be discriminated from healthy models us-ing one-class learning algorithm. The framework enables us to constructfault library when unknown faults occur, which can be regarded as cog-nitive fault isolation. This paper also theoretically investigates how tomeasure the pairwise distance between two models in the model spaceand incorporates the model distance into the learning algorithm in themodel space. The results on three benchmark applications and one simu-lated model for the Barcelona water distribution network have conﬁrmedthe eﬀectiveness of the proposed framework.
The impact of country of birth and time in Sweden on overweight and obesity: A population-based study

Aims: A study was undertaken to investigate the relationship between country of birth, time in Sweden, and overweight and obesity. Methods: Approximately 4,000 people aged 20—80 participated in a cross-sectional survey in 1994. The ethnic differences in overweight and obesity were analysed in a multivariate logistic regression analysis adjusting for age and education. The effect of number of years spent in Sweden on overweight and obesity was assessed for some of the ethnic groups compared with the Swedish-born group. Results: Men from Yugoslavia and Arabic-speaking countries were overweight/obese (BMI 25.0—) and obese (BMI 30.0—) to a significantly higher extent than men born in Sweden. Women born in Poland, Arabic-speaking countries, and all other countries were overweight/obese and obese to a significantly higher extent than women born in Sweden. Both Arabic men and women who had immigrated to Sweden in 1989 or earlier had an increased risk of overweight/obesity and obesity compared with the participants born in Sweden, while no increased risk was observed for the Arabic group that immigrated after 1989. Conclusions: There were significant differences in overweight and obesity between the country of birth groups. The findings follow the patterns of low leisure time physical activity among certain ethnic groups reported in a previous study, which has implications for public health measures directed to decrease differences in overweight and obesity by country of birth.