Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel multi-objective learning-to-rank method for software defect prediction

Search-Based Software Engineering (SBSE) is one of the techniques used for software defect prediction (SDP), in which search-based optimization algorithms are used to identify the optimal solution to construct a prediction model. As we know, the ranking methods of SBSE are used to solve insufficient sample problems, and the feature selection approaches of SBSE are employed to enhance the prediction model? s performance with curse-of-dimensionality or class imbalance problems. However, it is ignored that there may be a complex problem in the process of building prediction models consisting of the above problems. To address the complex problem, two multi-objective learning-to-rank methods are proposed, which are used to search for the optimal linear classifier model and reduce redundant and irrelevant features. To evaluate the performance of the proposed methods, excessive experiments have been conducted on 11 software programs selected from the NASA repository and AEEEM repository. Friedman? s rank test results show that the proposed method using NSGA-II outperforms other state-of-the-art single objective methods for software defect prediction.
Measuring the suboptimality of dividend controls in a Brownian risk model

Abstract We consider an insurance company modelling its surplus process by a Brownian motion with drift. Our target is to maximise the expected exponential utility of discounted dividend payments, given that the dividend rates are bounded by some constant. The utility function destroys the linearity and the time-homogeneity of the problem considered. The value function depends not only on the surplus, but also on time. Numerical considerations suggest that the optimal strategy, if it exists, is of a barrier type with a nonlinear barrier. In the related article of Grandits et al. (Scand. Actuarial J. 2, 2007), it has been observed that standard numerical methods break down in certain parameter cases, and no closed-form solution has been found. For these reasons, we offer a new method allowing one to estimate the distance from an arbitrary smooth-enough function to the value function. Applying this method, we investigate the goodness of the most obvious suboptimal strategies—payout on the maximal rate, and constant barrier strategies—by measuring the distance from their performance functions to the value function.
H2O ALGORITHM FOR JATROPHA CURCAS DISEASE IDENTIFICATION WITH FEATURE SELECTION USING GENETIC ALGORITHM

Jatropha curcas is a plant that can be used as a substitute for diesel fuel. Lack of knowledge of farmers and the limited number of experts and extension agents into the problem of dealing with the disease Jatropha curcas plant which resulted in lower quality of Jatropha curcas. H2O Algorithm can be used for Jatropha Curcas disease identification. Based on previous research, H2O Algorithm gave 96.066%. In this research, we used Genetic Algorithm to do feature selection. H2O algorithm with feature selection gave average accuracy 97.03%, that means were better than without feature selection. The parameters that we got are number of populations 600, crossover rate 0.8 and mutation rate 0.2, and number of iterations 400. However, the time spent using feature selection is so longer than without feature selection.
Phoenix dactylifera (Ajwa Dates) Alleviate LPS-Induced Sickness Behaviour in Rats by Attenuating Proinflammatory Cytokines and Oxidative Stress in the Brain

Traditional medicine claims that various components of the Phoenix dactylifera (date plant) can be used to treat memory loss, fever, inflammation, loss of consciousness, and nerve disorders. The present study aims to evaluate the effectiveness of Phoenix dactylifera fruit extracts (PDF) against rat sickness behaviour caused by lipopolysaccharide (LPS) by assessing behavioural and biochemical parameters. PDF was prepared by extracting dry fruits of P. dactylifera with a methanol:water (4:1, v/v) mixture. The PDF was evaluated for phenolic and flavonoid content and HPLC analysis of quercetin estimation. Adult Wistar rats were treated with LPS, PDF + LPS and dexamethasone + LPS. Water and food intake, behavioural tests such as locomotor activity, tail suspension and forced swim tests were conducted. Furthermore, alanine transaminase (ALT) and aspartate transaminase (AST) were estimated in plasma and malondialdehyde (MDA), reduced glutathione (GSH), nitrite, tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), were estimated in the brain. PDF ameliorated LPS-induced sickness behaviour by reducing MDA, nitrite, IL-6, and TNF-α levels and improving GSH, behavioural alteration, water and food intake in the treated rats. In the plasma of the treated rats, PDF also decreased the levels of ALT and AST. The outcomes demonstrated the efficacy of PDF in reducing the sickness behaviour caused by LPS in rats. The authors believe that this study will provide the groundwork for future research to better understand the underlying mechanisms of action and therapeutic efficacy.
Extending the Privacy Calculus to the mHealth Domain: Survey Study on the Intention to Use mHealth Apps in Germany

Background With the increasing digitalization of the health sector, more and more mobile health (mHealth) apps are coming to the market to continuously collect and process sensitive health data for the benefit of patients and providers. These technologies open up new opportunities to make the health care system more efficient and save costs but also pose potential threats such as loss of data or finances. Objective This study aims to present an empirical review and adaptation of the extended privacy calculus model to the mHealth domain and to understand what factors influence the intended usage of mHealth technologies. Methods A survey study was conducted to empirically validate our model, using a case vignette as cover story. Data were collected from 250 German participants and analyzed using a covariance-based structural equation model. Results The model explains R2=79.3% of the variance in intention to use. The 3 main factors (social norms, attitude to privacy, and perceived control over personal data) influenced the intention to use mHealth apps, albeit partially indirectly. The intention to use mHealth apps is driven by the perceived benefits of the technology, trust in the provider, and social norms. Privacy concerns have no bearing on the intention to use. The attitude to privacy has a large inhibiting effect on perceived benefits, as well as on trust in the provider. Perceived control over personal data clearly dispels privacy concerns and supports the relationship of trust between the user and the provider. Conclusions Based on the privacy calculus, our domain-specific model explains the intention to use mHealth apps better than previous, more general models. The findings allow health care providers to improve their products and to increase usage by targeting specific user groups.
Comparing Multilevel and Classical Confirmatory Factor Analysis Parameterizations of Multirater Data: A Monte Carlo Simulation Study

This simulation study assesses the statistical performance of two mathematically equivalent parameterizations for multitrait–multimethod data with interchangeable raters—a multilevel confirmatory factor analysis (CFA) and a classical CFA parameterization. The sample sizes of targets and raters, the factorial structure of the trait factors, and rater missingness are varied. The classical CFA approach yields a high proportion of improper solutions under conditions with small sample sizes and indicator-specific trait factors. In general, trait factor related parameters are more sensitive to bias than other types of parameters. For multilevel CFAs, there is a drastic bias in fit statistics under conditions with unidimensional trait factors on the between level, where root mean square error of approximation (RMSEA) and χ2 distributions reveal a downward bias, whereas the between standardized root mean square residual is biased upwards. In contrast, RMSEA and χ2 for classical CFA models are severely upwardly biased in conditions with a high number of raters and a small number of targets.
Nonparametric estimation in hidden Markov models

This paper outlines a new procedure to perform nonparametric estimation in hidden Markov models. It is assumed that a Markov chain (Xk) is observed only through a process (Yk), where Yk is a noisy observation of f(Xk). We propose a maximum likelihood based procedure to estimate the function f using a block of observations. This paper shows the identifiability of the model under several assumptions on the Markov chain and on the function f. We also provide a proof of the consistency of the estimator of f as the number of observations grows to infinity. This consistency result relies on the Hellinger consistency of an estimator of the likelihood of the observations. Finally, we provide numerical experiments to highlight the performance of the estimator.
Identifying ecosystem patterns from time series of anchovy (Engraulis ringens) and sardine (Sardinops sagax) landings in northern Chile

ABSTRACT Useful knowledge acquisition from known and systematized information (data) is a big challenge for researchers, users and finally, decision makers. In this sense, knowledge discovery from data (KDD) process represents a valuable tool for information analysis. Moreover, this work presents an approach through KDD in time series pattern identification for anchovy and sardine fisheries and environmental data, in northern Chile. Time series, multivariate analysis and data mining techniques, along with technical literature review for results validation. The KDD approach and the data mining techniques implemented achieved an integration between these variables, identifying relevant patterns associated with fisheries abundance fluctuations and strong association with environmental changes such as El Niño and long-term cold–warm regimes between them, establishing anchovy and sardine pre-dominant time-periods, associated with environmental conditions are identified. The latter establishes groundwork for studying underlying functional relationships that could reduce gaps in the national fisheries management policies for those fisheries.
A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots’ Mental States from Imbalanced Physiological Data

This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential.
Not every Gibbs sampler is a special case of the Metropolis–Hastings algorithm

ABSTRACT It is commonly asserted that the Gibbs sampler is a special case of the Metropolis–Hastings (MH) algorithm. While this statement is true for certain Gibbs samplers, it is not true in general for the version that is taught and used most often, namely, the deterministic scan Gibbs sampler. In this note, I prove that that there exist deterministic scan Gibbs samplers that do not exhibit detailed balance and hence cannot be considered MH samplers. The nuances of various Gibbs sampling schemes are discussed.
A Test Case Selection Framework and Technique: Weighted Average Scoring Method

Test case selection techniques identify and eliminate the modification revealing test cases and try to reduce the test suite size for optimization of regression testing. The objective of this experiment is to assess the effectiveness of weighted average scoring method of test case selection against single objective test case selection techniques. The multiobjective test case selection with the weighted average scoring framework and technique are proposed in this study to select the test cases. This method is trying to solve conflicting test case selection objectives with six selection scenarios. The method used test data of cost, coverage, fault detection ability and code change information, convert them into the weighted average score as scalar function and presented this score to 100-index slabs related to low to high scores, then select the test cases. The results for these selection scenarios are computed and evaluated using size reduction of the test suite, inclusiveness, and precision. The results showed that all scenarios performed acceptable level within conditions applied from 17% minimum to 86% maximum in size reduction metrics. The inclusiveness showed 17% to 88% and 33% to 85% for precision metric.
Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor Decomposition

The goal of crime prediction is to forecast the number of crime incidents at each region of a city based on the historical crime data. It has attracted a great deal of attention from both academic and industrial communities due to its considerable significance in improving urban safety and reducing financial losses. Although much progress has been made in this field, most of the existing approaches assume that the historical crime data are complete, which does not hold in many real-world scenarios. Meanwhile, crime incidents are affected by multiple factors and have intricate spatial, temporal, and categorical correlations, which are not fully utilized by the current methods. In this article, we propose a novel tensor decomposition based framework, named TD-Crime, to conduct prediction directly on the incomplete crime data. Specifically, we first organize the crime data as a tensor and then apply the nonnegative CP decomposition to it, which not only provides a natural solution to the missing data problem but also captures the spatial, temporal, and categorical correlations implicitly. Moreover, we attempt to exploit the spatial and temporal correlations explicitly by directly learning from the crime data to further improve the forecasting performance. Finally, we obtain a joint optimization problem and present an efficient alternating optimization scheme to find a satisfactory solution. Extensive experiments on the real-world crime datasets show that TD-Crime can address the crime prediction task effectively under different missing data scenarios.
An observation‐based climatology and forecasts of winter fog in Ghaziabad, India

Long‐term ground observations of visibility and other meteorological parameters (for the period 1971–2010) have been analysed over Ghaziabad city, National Capital Region (NCR) to understand the formation of fog and its relevance during the winter months. The study reveals that the maximum frequency of fog occurs during December (~23 days), followed by January (~21 days), November (~20 days), February (~14 days) and October (~11 days), respectively. In the last four decades, an increasing trend in fog frequency has been observed during the months of October to February over the study area. During the period of study, the frequency of fog has increased by 205.5% in the month of October and 50.2% in the month of November. Similar increasing trends have also been observed during the other winter months. A statistically significant increasing trend in fog occurrence has been observed between October and February during the study period at the 95% confidence level. The magnitude of the trend for each month is 0.50, 0.47, 0.30, 0.39 and 0.37 between October and February. Furthermore, the magnitude of the trend is greatest in the month of October, whereas the average monthly fog frequency is highest during the month of December. An attempt has also been made to forecast fog events using the ARIMA model over the study region. The forecast values obtained for five years into the future indicate that a further increase in the number of fog days is expected during the months of October to February. The study, combined with knowledge of meteorology and topography, suggests that, due to an increase in the availability of fine particulates and rapid urbanisation, fog formation may further increase in future and thus drastically disturb the social and economic activities across Ghaziabad, National Capital Region of India.
Studying the spatio-temporal changes of urban green space: A case study of Pokhara Metropolitan City

Green space refers to land areas covered primarily with vegetation, including parks, gardens, nature reserves, and other similar areas. These spaces offer various benefits such as supporting biodiversity, enhancing air and water quality, promoting mental and physical well-being, providing opportunities for recreation and social interaction, and improving the overall beauty of the environment. To assess the dynamics of green space in Pokhara Metropolitan City (PMC), maximum likelihood classification and normalized difference vegetation index (NDVI) methods were utilized to analyze satellite imagery. The study showed two distinct patterns of urban green space, with the green space in the central city gradually decreasing, while the nearby suburban and remote areas of PMC experienced an increase as agricultural land was transformed into grassland due to population migration. Consequently, the area of moderately healthy vegetation increased, while healthy vegetation decreased from 4926 hectares (10.61% of total area) to 2535 hectares (5.46% of total area). Additionally, the area of no vegetation such as urban areas increased from 1.8% to 2.17%. An accuracy assessment was used to validate the analyzed data, resulting in an overall accuracy of 81.91% and a Kappa coefficient of 0.63. The primary factors contributing to the loss of green spaces were urban growth and population growth within the inner metropolis. Therefore, it is crucial to maintain PMC's green areas and give more attention to this issue while developing municipal plans and policies in the future.
Discontinuous Galerkin finite element modelling of geophysical and environmental flows

Thesis committee: Prof. Eric Deleersnijder, Université catholique de Louvain, Belgium (Supervisor) Prof. Vincent Legat, Université catholique de Louvain, Belgium (Supervisor) Prof. Jean-François Remacle, Université catholique de Louvain, Belgium (Supervisor) Prof. Emmanuel Hanert, Université catholique de Louvain, Belgium Dr. Jonathan Lambrechts, Université catholique de Louvain, Belgium Prof. Wim Thiery, ETH Zürich, Switzerland & Vrije Universiteit Brussel, Belgium Prof. Eric Wolanski, James Cook University, Australia Prof. Renaud Ronsse, Université catholique de Louvain, Belgium (President)
New Sensing Technologies for Monitoring Machinery, Structures, and Manufacturing Processes

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery, structures and manufacturing processes. In this paper, we briefly discuss the general idea and advances of various new sensing technologies, including multi-physics sensing, smart materials and metamaterials sensing, microwave sensing, fibre optic sensors, and terahertz sensing, for measuring vibration, deformation, strain, acoustics, temperature, spectroscopic, etc. Based on the observations from the state of the art, we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer the future development.
ADEROS: Artificial Intelligence-Based Detection System of Critical Events for Road Security

The deployment of artificial intelligence (AI) in Intelligent Transportation Systems (ITS), especially in the field of Intelligent Transportation Cyber-Physical Systems (ITCPS) has a strong potential to achieve higher efficiency, reliability, and increased safety in both transportation and traffic. This work focuses on the real-world implementation of ITCPS, in which structure and elements in combination with advanced image processing methods increase safety and fluidity of road traffic at crossroads and railway crossings. In this work, we present a novel system called Artificial Intelligence-based Detection System for Road Security (ADEROS), which combines elements of CPS systems, object detection, and classification, computer vision (CV) which analyzes vehicle trajectory tracking, vehicle and pedestrian presence, light signaling systems, railway barriers at railway crossings, and railway warnings. The presented system is based on a camera module that is suitably positioned to capture the entire scene. The module uses graphics processing units (GPU) for accelerated image processing techniques and the YOLOv4 deep neural network model to detect traffic participants and then dangerous situations in various crossroads and railway crossings. Our improved unique detector can distinguish between individual types of road users and the status of several safety devices at crossroads and railway crossings (for example, the state of traffic lights (TL) or rail barriers). Furthermore, we present experimental implementation details of the ADEROS system, which includes a central server web interface for live traffic situation monitoring, various communication channels for the camera module, and a central server based on.NET core, Cassandra DB, and different security protocols. All data from risky situations are evaluated and transferred to the central server securely without human intervention. The central server aggregates and archives all risky situational data from connected cameras. Finally, we present our experimental results from a real-world pilot project that consists of a camera module prototype deployed in a real crossroad and an operational central web server.