Miracle-dz/UniFAR-FTUs huggingface dataset:
MultiGED-2023 shared task at NLP4CALL: Multilingual Grammatical Error Detection

This paper reports on the NLP4CALL shared task on Multilingual Grammatical Error Detection (MultiGED-2023), which included five languages: Czech, English, German, Italian and Swedish. It is the first shared task organized by the Computational SLA 1 working group, whose aim is to promote less represented languages in the fields of Grammatical Error Detection and Correction, and other related fields. The MultiGED datasets have been produced based on second language (L2) learner corpora for each particular language. In this paper we introduce the task as a whole, elaborate on the dataset generation process and the design choices made to obtain MultiGED datasets, provide details of the evaluation metrics and CodaLab setup. We further briefly describe the systems used by participants and report the results.
Sensitivity Analysis and Computations of the Time Relaxation Model

This paper presents a numerical study of the sensitivity of a fluid model known as time relaxation model with respect to variations of the time relaxation coefficient χ . The sensitivity analysis of this model is utilized by the sensitivity equation method and uses the finite element method along with Crank Nicolson method in the fully discretization of the partial differential equations. We present a test case in support of the sensitivity convergence and also provide a numerical comparison between two different strategies of computing the sensitivity, sensitivity equation method and forward finite differences.
Multi-model analysis of the Adriatic dense-water dynamics

Abstract. This study aims to enhance our understanding of the bora-driven dense-water dynamics in the Adriatic Sea using different state-of-the-art modelling approaches during the 2014–2015 period. Practically, we analyse and compare the results of the following four different simulations: the latest reanalysis product for the Mediterranean Sea, a recently evaluated fine-resolution atmosphere–ocean Adriatic Sea climate model, and a long-time-running Adriatic Sea atmosphere–ocean forecast model used in both hindcast and data assimilation (with 4 d cycles) modes. As a first step, we evaluate the resolved physics in each simulation by focusing on the performance of the models. Then, we derive the general conditions in the ocean and the atmosphere during the investigated period. Finally, we analyse in detail the numerical reproduction of the dense-water dynamics as seen by the four simulations. The likely prerequisites for proper modelling of the ocean circulation in the Adriatic basin, including a kilometre-scale atmosphere–ocean approach, non-hydrostatic atmospheric models, fine vertical resolutions in both atmosphere and ocean, and the location and forcing of the open boundary conditions, are thus discussed in the context of the different simulations. In conclusion, a 31-year-long run of the fine-resolution Adriatic Sea climate model is found to be able to outperform most aspects of the reanalysis product, the short-term hindcast, and the data-assimilated simulation in reproducing the dense-water dynamics in the Adriatic Sea.
The approximated variance of the output of nonlinear stochastic systems

The equivalent linearization technique is applied to approximate and analyze the input-output relationships of nonlinear stochastic systems in the frequency domain. When it is applied, the original coefficients of the system are approximated to give the structure of the equivalent system. These new coefficients depend on the variance of the output, our aim is to approximate it. The spectral representation of the involved processes allows us to express the variance of the output as the solution of an integral equation depending on itself. We apply the technique to combined Duffing-van der Pol systems and we deal with the use of numerical methods to accelerate the convergence of the iterative procedure used to approximate the variance. The procedure depends on the nature of the input, therefore the results are presented for several inputs.
Mathematical hydraulics of surface irrigation

The general hydrodynamic equations for a spatially varied unsteady flow in a prismatic open channel having an arbitrary eros ssectional shape can be derived from the equations of continuity and momentum. The assumptions based on the general concept of hydrodynamic s and the theory of shallow water are intr oduced. The mathematical models in the surface irrigation can be formulated by these equations of motion with the appropriate initial and boundary conditions prescribed at the singularity point (the origin in the x, t-plane) and at x = 0. Therefore, the flow in the surface irrigation must be described by solving the boundary-value problem for the velocity and the depth of flow. In the two-dimensional flow, the discontinuity at the origin in the x, t-plane is overcome by imposing a critical velocity and correspondingly, a critical depth at the initial state. Without considering all the channel slope, friction, and infiltration terms, the mathematical model becomes the model for a "centered simple wave, " in which an exact solution, the Ritter solution, in the darn-breaking problem is already well-known. The Ritter solution satisfies the boundary condition at x = 0, where the discharge is always constant. However, even though an additional term, the channel slope, is considered, the modified Ritter solution no longer satisfies the same boundary condition. No exact solution seems possible with the present knowledge of mathematical techniques. i The theory of characteristics is presented and the method of finite-difference based on this basic theory with the fixed-time interval is introduced for the basic equations of motion in the surface irrigation. The trajectory of the wavefront is usually defined as a locus of zero celerity (c = 0) and forming an envelope of two different families of characteristics. Such an extremely complicated situation at the wavefront results in the possible failure of the present technique by simply using the finite-difference method unless some additional judicious as sumptions at the wavefront, some type of the boundary-layer technique must be developed. This is the present status of the finitedifference method in the surface ir rigation. The more s irnplified approximation by the kinematic -wa ve method is presented, based on an additional as sumption analogous to the Dupit-Forchheimer theory in the groundwater flow. The method possesses a potential applicability in the surface irrigation. Inasmuch as no results are yet available, this study will be limited to a description of what is hoped to be accomplished and how it will bed 0 n e .
Investigating the Effect of Cigarette Smoking on Serum Uric Acid Levels in Multiple Sclerosis Patients: A Cross Sectional Study

Objectives: The present study is aimed at determining the effect of cigarette smoking (CS) on serum uric acid (UA) levels quantitatively before and after smoking cessation among people with MS (pwMS). Additionally, a possible correlation between UA levels and both disability progression and disease severity was also investigated. A retrospective cross-sectional study was conducted using the Nottingham University Hospitals MS Clinics database. It involves 127 people with definite MS recorded when reporting the latest smoking status and the clinical diagnosis. All necessary demographics and clinical characteristics were collected. We found that smoker pwMS had significantly lower serum UA levels than non-smoker pwMS (p-value = 0.0475), and this reduction was recovered after smoking cessation (p-value = 0.0216). However, the levels of disability or disease severity were not correlated with the levels of serum UA in current smoker pwMS, measured by the expanded disability status scale (EDSS; r = −0.24; p-value = 0.38), multiple sclerosis impact scale 29 (MSIS-29; r = 0.01; p-value = 0.97) and MS severity score (MSSS; r = −0.16; p-value = 0.58), respectively. Our result suggests that the reduction in UA levels is more likely a consequence of oxidative stress triggered by many risk factors, including CS, and could be considered a potential indicator of smoking cessation. In addition, the absence of a correlation between UA levels and disease severity and disability suggests that UA is not an optimal biomarker for disease severity and disability prediction among current smoker, ex-smoker or non-smoker pwMS.
Asymptotic covariances for the parameters of biadditive models

The statistical model for the two-way table with expectation $Y = \textit{m}11^{\mathit{T}}+a1^{\mathit{T}}+1b^{\mathit{T}}+\sum_{u=1}^{\textit{r}}c_{u}d_{u}^{\mathit{T}}$ is increasingly being used to represent the interaction between genotype and environment in plant-breeding experiments. Proper interpretation of analyses based on this model requires estimates of the standard errors of estimated parameter values. This paper provides the asymptotic variances and covariances of the parameters for this model and for similar models in which one or both of the additive parameters are excluded. It is especially concerned with the technical details of their derivations. The results are given for two equivalent ways of parameterizing the models.
Labeled Multi-Bernoulli Filter Based Multiple Resolvable Group Targets Tracking With Leader–Follower Model

This article addresses the multiple resolvable group targets tracking (MRGTT) problem, wherein the leader–follower (LF) model is adopted to evolve the group structure. Since there is an interaction between the destination and each leader, such that an attraction force of the destination information on the leader needs to be considered. To cope with the time-varying number of targets/groups and target-measurement association uncertainty, the so-called labeled multi-Bernoulli (LMB) filter is regarded as the filtering tool and combined with the LF model, named LF-LMB. Totally speaking, the implementation of the proposed LF-LMB filter consists of three steps. First, the stochastic differential equation (SDE) of the state is used to, respectively, characterize the motion models of the leader and the follower. Then, the state transition equations (STEs) for the leader and the follower are derived. Lastly, the derived STEs are used to modify the prediction process of the standard LMB filter. Simulation experiments are carried out to demonstrate the superiority (e.g., optimal subpattern assignment (OSPA) and OSPA$^{(2)}$ errors are reduced by at least 15$\%$ compared to the standard LMB filter) and robustness of the proposed approach.
Analysis of Factors That Affect E-Commerce Technology Adoption for Msmes in Indonesia

MSMEs (Micro, Small, and Medium Enterprises) play a significant role in Indonesia's economy. E-commerce has many advantages for MSMEs which includes competitive advantage, greater market share and better customer service, among others. Indonesian users are already proficient with e-commerce but unfortunately only 19% of Indonesian MSMEs have adopted e-commerce. This research investigates the variables that influence e-commerce technology adoption in Indonesian MSMEs using Technology Acceptance Model, Technology Organization Environment (TOE) framework and Diffusion of Innovation (DOI) Theory. A survey questionnaire was conducted to collect data from 260 MSME owners in the food and beverage industry who have or have not adopted e-commerce technology for their business. The result was calculated using logistic regression and Relative Importance Index (RII) to get which variables are relevant in e-commerce adoption in Indonesia MSMEs. From the research, it is revealed that relative advantage, perceived usefulness, security, employee’s IT knowledge and government’s support have a crucial influence in e-commerce adoption for MSME owners. An e-commerce adoption strategy was recommended based on the study’s results. It is focused on the most significant factors of e-commerce adoption for MSMEs to increase the chances of adoption.
The Influence of Various E-Learning Techniques Upon Technology Acceptance and Student Engagement in Differing Classroom Environments

The impact of technology is becoming increasingly relevant in its everyday use amongst a variety of different industries and practices. This most prominently includes educational systems and services. As a direct result of the on-going COVID-19 pandemic, the majority of students and educators have had to relocate unto online platforms. Therefore the implementation and acceptance of such technological resources has become widespread in its outreach. Through the understanding and usage of predictive theories presented in Technology Acceptance Models (Davis, 1989), research review processes have suggested that student acceptance, engagement and retention of such essential technological tools varies based upon factors adjacent to motivation. The influence of motivation and interest to partake in the technology presented to them as well as balance in activities and opportunities made available to them can directly impact a student’s perception of their capabilities and subsequently their performance within a learning environment. By understanding the factors which directly impact and influence a student’s motivation and perception towards education. This can further allow educators and creators to structure educational technologies and tools for students of all educational levels, backgrounds and capabilities in the future.
Cognitive Demands And the EFL Writer: Observations on Kind, Degree, and Agency

Research into understanding the process through which EFL writers revise texts have centered on matters of degree: of cognitive capacity and complexity or volume of tasks. This essay explores some of the junctures between dual process theories and EFL composition, and argues that questions of kind can be as pertinent. It aims to show that the kind of cognitive processes involved in revision often define what information the writers process, how they do so, and how they feel about their decisions.
Toward prediction of hyperspectral target detection performance after lossy image compression

Hyperspectral imagery (HSI) oﬀers numerous advantages over traditional sensing modalities with its high spectral content that allows for classiﬁcation, anomaly detection, target discrimination, and change detection. However, this imaging modality produces a huge amount of data, which requires transmission, processing, and storage resources; hyperspectral compression is a viable solution to these challenges. It is well known that lossy compression of hyperspectral imagery can impact hyperspectral target detection. Here we examine lossy compressed hyperspectral imagery from data-centric and target-centric perspectives. The compression ratio (CR), root mean square error (RMSE), the signal to noise ratio (SNR), and the correlation coeﬃcient are computed directly from the imagery and provide insight to how the imagery has been aﬀected by the lossy compression process. With targets present in the imagery, we perform target detection with the spectral angle mapper (SAM) and adaptive coherence estimator (ACE) and evaluate the change in target detection performance by examining receiver operating characteristic (ROC) curves and the target signal-to-clutter ratio (SCR). Finally, we observe relationships between the data- and target-centric metrics for selected visible/near-infrared to shortwave infrared (VNIR/SWIR) HSI data, targets, and backgrounds that motivate potential prediction of change in target detection performance as a function of compression ratio.
Low-Sample Image Classification Based on Intrinsic Consistency Loss and Uncertainty Weighting Method

As is well known, the classification performance of large deep neural networks is closely related to the amount of annotated data. However, in practical applications, the quantity of annotated data is minimal for many computer vision tasks, which poses a considerable challenge for deep convolutional neural networks that aim to achieve ideal classification performance. This paper proposes a new, fully supervised low-sample image classification model to alleviate the problem of limited marked sample quantity in real life. Specifically, this paper presents a new sample intrinsic consistency loss, which can more effectively update model parameters from a “fundamental” perspective by exploring the difference between intrinsic sample features and semantic information contained in sample labels. Secondly, a new uncertainty weighting method is proposed to weigh the original supervised loss. It can more effectively learn sample features by weighting sample losses one by one based on their classification status and help the model autonomously understand the importance of different local information. Finally, a sample generation model generates some artificial samples to supplement the limited quantity of actual training samples. The model adjusts parameters through the combined effect of sample intrinsic consistency loss and weighted supervised loss. This paper uses <inline-formula> <tex-math notation="LaTeX">$25 \%$ </tex-math></inline-formula> of the SVHN dataset and <inline-formula> <tex-math notation="LaTeX">$30 \%$ </tex-math></inline-formula> of the CIFAR-10 dataset as training samples to simulate scenarios with limited sample quantities in real life, achieving accuracies of <inline-formula> <tex-math notation="LaTeX">$94.59 \%$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$91.27 \%$ </tex-math></inline-formula> respectively, demonstrating the effectiveness of our method on small real datasets.
A New Flexible Four Parameter Bathtub Curve Failure Rate Model, and Its Application to Right-Censored Data

This article introduces a new flexible four parameter distribution by convolution of the exponential and Weibull distribution using the odd function transformation, which offers greater flexibility in terms of fit, its called the modified exponential-Weibull (MEW). The MEW model is designed to provide a more accurate description of failure time data resulting from a system with one or more failure modes and is characterized by a hazard rate (HR) that takes the shape of a bathtub due to its complexity. The moments properties, quantile function, and residual life are derived and discussed. We discussed the HR function and several distributional properties of the MEW model, and applied maximum likelihood and Bayesian techniques to estimate its unknown parameters. The Hamiltonian Monte Carlo (HMC) algorithm is employed to simulate the posterior distributions and verify the MEW Bayes estimators. We examined the behavior of the MEW model on two data sets with bathtub-shaped HR and compare it with five other popular bathtub-shaped methodologies. The results indicate that the MEW model provided the best description of the two failure time data sets, suggesting that the proposed model could be a viable candidate for solving various real-life problems.
Switch-Multiplexed Quasi-Two-Stage Isolated Bidirectional Buck-DAB Converter With Full Load ZVS Range

In this letter, a switch-multiplexed quasi-two-stage buck-dual-active-bridge (DAB) (SMBDAB) and its modulation strategy are proposed. Under the quasi-two-stage buck-DAB architecture, one bridge of the primary side of the proposed converter is multiplexed as both the bridge of the buck circuit and the DAB circuit to reduce the power switches. In addition, with the voltage-matching modulation, multiplexing still ensures that the voltages on both sides of the leakage inductor of the DAB circuit are always matched when the voltage gain of the converter changes, and thus, the zero-voltage switching (ZVS) of all the switches can be achieved. Furthermore, the partial power transfer characteristics of the buck circuit still maintains, which could reduce the power rating of the inductor for the buck circuit. The working principle, power transfer characteristics, and full range ZVS design are analyzed. Finally, the effectiveness of the SMBDAB converter is verified through experimental results from a 1kW prototype.
clustertools: A Python Package for Analyzing Star Cluster Simulations

$clustertools$ is a Python package for analyzing star cluster simulations. The package is built around the $StarCluster$ class, which stores all data read in from the snapshot of a given model star cluster. The package contains functions for loading data from commonly used $N$-body codes, generic snapshots, and software for generating initial conditions. All operations and functions within $clustertools$ are then designed to act on a $StarCluster$. $clustertools$ can be used for unit and coordinate transformations, the calculation of key structural and kinematic parameters, analysis of the cluster's orbit and tidal tails, and measuring common cluster properties like its mass function, density profile, and velocity dispersion profile (among others). While originally designed with star clusters in mind, $clustertools$ can be used to study other types of $N$-body systems, including stellar streams and dark matter sub-halos.
AN EFFECTIVE MANAGEMENT OF SUICIDAL IDEATION AND SELF-INJURIOUS BEHAVIORS IN YOUTH

Depression is the most common yet ignored public health issues in Pakistan. The prevalence is uncommon among children but vastly evident during adolescence. Unhealthy parenting practices and childhood trauma directly contributes towards depressive disorders and suicidal ideation later in life if left unresolved. A similar case of 17 years old was referred with her presenting complaints of: sadness, irritability, crying spells, suicidal ideation, self-harm, negative thinking, loss of interest in pleasurable activities, sleeplessness, psychomotor agitation, fatigue, lack of concentration, feelings of rejection and worthlessness for a year. The formal assessment was done by using Beck Depression Inventory (BDI). Based upon the assessment, the client was provisionally diagnosed with Major Depressive Disorder 296.23. A management plan includes ABC model, Cognitive distortions, Double column technique, Triple column technique and Relapse prevention. For the management of the suicidal thoughts/ ideation, strengthening of familial support, creating supportive environment, ensuring protective environment, reducing access to objects, substance or any means that could be lethal danger to life, promoting connectedness. A total number of sessions conducted with the client were 12 and the change was 35%. Keywords: Suicidal Ideation, Self-Injurious Behavior, Adolescence, Cognitive Behavior Therapy, Efficacy
An Empirical Study on Android malware behavior signature extraction

Malicious applications, especially in mobile devices, constitute a serious threats to user data. Dueto the openness, Android have become the most popular smart phone operating system in mobilemarket. Although fast and straightforward, Static analysis approach has many difficulties to detectstealthy malware. In this paper, we propose a behavior signature-based to classify whether malwareor not. To achieve this, we first hook a number of sensitive APIs to collect all possible invocationsof the app. We then extract the behaviors of malicious applications by comparing their flows and thevalue of parameters and results of each called APIs. In our study, we extracted behavior signaturesfrom malware in each family. Our works help to improve the quality of analysis compared with staticanalysis-only approaches.