Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Module Clustering Using Grid-Based Many-Objective Particle Swarm Optimization

The poor performance of traditional multi-objective optimization algorithms over the many-objective optimization problems has led to the development of a variety of many-objective optimization algorithms. Recently, several many-objective optimization algorithms have been proposed to address the different class of many-objective optimization problems. Most of the existing many-objective optimization algorithms were designed from the perspective of synthetic many-objective optimization problems. Despite the tremendous work made in the development of the many-objective optimization algorithms for solving the synthetic many-objective optimization problems, still real-world many-objective optimization problems gained little attention. In this work, we propose a grid-based many-objective particle swarm optimization (GrMaPSO) for the many-objective software optimization problem. In this contribution, the grid-based selection strategies along with other supportive strategies such as two-archive storing and crowding distance have been exploited in the framework of particle swarm optimization. The performance of the proposed approach is evaluated and compared to three existing approaches over five problem instances. The results demonstrate that the proposed approach is more effective and has significant advantages over existing many-objective approaches designed for the software module clustering problems.
Bayesian meta-elliptical multivariate regression models with fixed marginals on unit intervals

Abstract In this paper, we make use of meta-elliptical copula functions to build a new multivariate distribution with fixed marginal distributions and dependence structure to analyze bounded data. Specifically, we present a flexible p-elliptical multivariate probability distribution in the hypercube p with fixed marginal GF-quantile distributions. We then present some illustrative examples and a meta-elliptical multivariate regression model as a flexible alternative to the multivariate normal regression model on unit intervals. A simulation study and real-life data analysis using a Bayesian framework with the extreme-value quantile functions show the flexibility of the proposed meta-multivariate normal regression model for modeling the observed proportion response variables.
Success Factors Affecting the Intention to Use Business Analytics: An Empirical Study

ABSTRACT In the era of knowledge-based economy, the firm’s ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilising BA which is designed to help business executives and policy makers make well-thought and informed decisions. To have a clear picture of what will lead to the serious consideration of BA as a business intelligence tool, this paper identifies contextual variables that include privacy, security, risk concerns, information technology (IT) capability and perceived value that may significantly influence the firm’s intention to use BA. This paper conducted confirmatory factor analyses and used the structural equation model to determine what either motivate or inhibit the BA adoption Through a series of hypothesis testing, we discovered that higher security and risk concerns along with a lack of IT capability became important deterrents to BA adoption. Also, we found that firms which recognised the value of BA were more inclined to adopt BA than the others. This paper is one of the first attempts to develop practical guidelines for the potential adopters of BA based on the empirical study of BA practices among the Korean firms.
How Productive Are the Microfinance Institutionsin Bangladesh? An Application of Malmquist Productivity Index

The poverty reduction and financial inclusion of Sustainable Development Goals (SDG) by 2030 can be significantly facilitated by the microfinance industry. However, it is pertinent to assess the sustainability of microfinance institutions (MFIs) in serving this purpose. The estimation of productivity of MFIs in Bangladesh gives a glimpse of their ability to fulfil the dual objectives of financial sustainability and social outreach. Hence, this study aims to measure the productivity of MFIs in Bangladesh using secondary data obtained from the Microfinance Information Exchange (MIX) market. The study employs Malmquist Productivity Index (MPI), which is an extension of the Data Envelopment Analysis (DEA) to estimate the overall, social and financial productivities of 26 MFIs in Bangladesh during the period from 2009 to 2018. In general, this study revealed that majority of the MFIs’ overall productivity score varies between 0.9 and 1.20. Moreover, we observed that the social and financial productivities of MFIs in Bangladesh progressed during the entire study period, except for the years 2011 and 2017. This development may be attributed to the average growth in catch-up and technological effect witnessed during the study period. The study has also applied sensitivity analysis by changing the output to evaluate the robustness of the overall productivity results; consequently, the new estimates followed a similar pattern (mostly) and further corroborate the outcomes of this study. JEL Classifications: C14, O43, G21
Using Dynamic Graphics to Teach the Sampling Distribution with Active Learning

The sampling distribution and the Central Limit Theorem (CLT) are the basis for many statistical procedures and inferences. Despite their ubiquitous nature in statistics, these concepts are some of the most abstract and diﬃcult for students to understand. To foster a deeper understanding of these concepts, a web-based application was created that uses dynamic graphics to illustrate the concepts and engage students with active learning. We provide an outline of three in-class activities using the web application to promote the learning of population distributions, simple random sampling, sampling variability, the idea of a statistic, the sampling distribution, the Law of Large Numbers, and the CLT. These in-class activities tie the concepts together and place emphasis on their role as building blocks of statistical inference. By linking abstract theoretical concepts together before introducing statistical inference, the web application facilitates statistical thinking that students can utilize both inside and outside the classroom.
Finite element estimation of the effective mechanical properties of coir fiber-reinforced high-density polyethylene

Abstract Natural fibers (NFs) have gained attention as replacement of synthetic fibers due to their advantages such as eco-friendliness, nontoxicity, and they are available in nature ensuring a continuous supply of the fibers. The aim of this study is to evaluate the elastic properties of NFs-reinforced polymer composites at different volume fractions of fiber using the finite element method. A micromechanical three-dimensional representative volume element with a square packing geometry was developed for the analysis. Results from the finite element analysis were validated with selected analytical models and were found to be in agreement.
Dynamically Adjusted Genetic and Fireworks Algorithm for Deployment Optimization of Indoor Positioning Signal Sources

The performance of indoor positioning systems is affected by spatial deployment of signal sources, thus we presented a combined method of adjusted Genetic algorithm (AGA) and Fireworks algorithm (FWA) to provide the optimal deployment solution. The threshold value of fitness function is dynamically adjusted in GA, which reduces the probability of falling into local optimum and decreases the number of trivial solutions. With fireworks of fine chromosomes, the number of explosion sparks and radius of all explosion sparks are set as explosion factors. Through explosion and random generation, supplemented individuals are produced to increase population diversity and guarantee individual quality. Population evolves to the next generation by crossover and mutation, and the optimal result from evolutions is one solution of deployment with a number of signal sources and their locations. Based on a set of experiments, the AGA&FWA algorithm has been proved with good performance of convergence ability and computing expense. AGA&FWA has the best searching ability compared with the existing optimization methods, considering only the positioning error, or together with the cost of iBeacons. Our proposed AGA&FWA approach thus can provide the optimal signal sources' deployment for indoor positioning.
Matrix Gaussian Mechanisms for Differentially-Private Learning

The wide deployment of machine learning algorithms has become a severe threat to user data privacy. As the learning data is of high dimensionality and high orders, preserving its privacy is intrinsically hard. Conventional differential privacy mechanisms often incur significant utility decline as they are designed for scalar values from the start. We recognize that it is because conventional approaches do not take the data structural information into account, and fail to provide sufficient privacy or utility. As the main novelty of this work, we propose <italic>Matrix Gaussian Mechanism</italic> (MGM), a new <inline-formula><tex-math notation="LaTeX">$ (\epsilon,\delta)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>ε</mml:mi><mml:mo>,</mml:mo><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-3093316.gif"/></alternatives></inline-formula>-differential privacy mechanism for preserving learning data privacy. By imposing the unimodal distributions on the noise, we introduce two mechanisms based on MGM with an improved utility. We further show that with the utility space available, the proposed mechanisms can be instantiated with optimized utility, and has a closed-form solution scalable to large-scale problems. We experimentally show that our mechanisms, applied to privacy-preserving federated learning, are superior than the state-of-the-art differential privacy mechanisms in utility.
Effects of Ferroelectric Fillers on Composite Dielectric Elastomer Actuator

Integrating nano- to micro-sized dielectric fillers to elastomer matrices to form dielectric composites is one of the commonly utilized methods to improve the performance of dielectric elastomer actuators (DEAs). Barium titanate (BaTiO3) is among the widely used ferroelectric fillers for this purpose; however, calcium copper titanate CaCu3Ti4O12 (CCTO) has the potential to outperform such conventional fillers. Despite their promising performance, CCTO-based dielectric composites for DEA application are studied to a relatively lower degree. Particularly, the composites are characterized for a comparably small particle loading range, while critical DEA properties such as breakdown strength and nonlinear elasticity are barely addressed in the literature. Thus, in this study, CCTO was paired with polydimethylsiloxane (CH3)3SiO[Si(CH3)2O]nSi(CH3)3 (PDMS), Sylgard 184, to gain a comprehensive understanding of the effects of particle loading and size on the dielectric composite properties important for DEA applications. The dielectric composites’ performance was described through the figures of merit (FOMs) that consider materials’ Young’s modulus, dielectric permittivity, and breakdown strength. The optimum amounts of the ferroelectric filler were determined through the FOMs to maximize composite DEA performance. Lastly, electromechanical testing of the pre-stretched CCTO-composite DEA validated the improved performance over the plain elastomer DEA, with deviations from prediction attributed to the studied composites’ nonlinearity.
A Novel Approach for Developing Paraphrase Detection System using Machine Learning

Plagiarism detection is difficult since there can be changes made to a sentence at several levels, namely, lexical, semantic, and syntactic level, to construct a paraphrased or plagiarized sentence posing as original. To identify cases of plagiarism and hence discourage the same, this paper presents a novel Supervised Machine Learning based Paraphrase Detection System developed by conducting experiments using Microsoft Research Paraphrase (MSRP) Corpus and assessed on the same. The proposed paraphrase detection system has achieved comparable performance with existing paraphrase detection systems. The major contributions of this paper are the utilization of a unique combination of lexical, semantic, and syntactic features, utilization of Shapley Additive Explanations (SHAP) Feature Importance Plots in XGBoost, and application of a soft voting classifier comprising of the top 3 performing standalone machine learning classifiers on the training dataset of MSRP Corpus. Another major contribution of the paper is the finding that applying data augmentation techniques degrades performance of machine learning classifiers.
Conversations through Web 2.0 tools: Nurturing 21st century Values in the ESL Classroom

21st century skills framework proposed by the World Economic Forum (2015) suggests that the development of foundational literacies, competencies and character qualities in students can help them function as responsible and productive global citizens. However, most educational systems focus only on developing foundational literacies like reading and writing, numeracy, scientific literacy, etc. This paper describes an exploratory research conducted to investigate how the 21st century ESL teacher can nurture the development of competencies and character qualities through language development tasks delivered using digital tools. The paper is based on the premise that since character qualities are both social and individualistic in nature, they are ideally delivered through collaborative events and acquired best through self-reflection. Reporting from an ESL teacher’s perspective, the paper elucidates how participating in the communication, collaboration, critical thinking and creativity activities mediated through web 2.0 tools can facilitate the acquisition of character qualities like curiosity, persistence, adaptability, social and cultural awareness, etc. It was found that certain features of web 2.0 tools like participatory environments, asynchronicity and ease of use for creating content offered students multiple opportunities for meaningful, sustained and reflective conversations. Using observational data, the researcher identifies the acquisition of character qualities and the development of competencies in students through these conversations.
Bankruptcy Regulation and Self-Employment Entry: The Moderating Roles of Income Share, Parenthood, and Hybrid Entrepreneurship

We investigate how individual factors moderate the impact of bankruptcy exemption levels—that is, the amount of wealth individuals can keep in case of bankruptcy—on entry into self-employment. Conceptually, we combine Prospect Theory’s axiom of diminishing sensitivity with insights from research on entrepreneurial failure. We hypothesize that individuals who face higher financial, social, or psychological costs because of bankruptcy will be less sensitive to higher exemption levels than will those who face lower costs across these dimensions. Our empirical results, which are based on a quasi-natural experiment in the United States, support our theoretical predictions.
Computational Chemistry-Guided Syntheses and Crystal Structures of the Heavier Lanthanide Hydride Oxides DyHO, ErHO, and LuHO

Heteroanionic hydrides offer great possibilities in the design of functional materials. For ternary rare earth hydride oxide REHO, several modifications were reported with indications for a significant phase width with respect to H and O of the cubic representatives. We obtained DyHO and ErHO as well as the thus far elusive LuHO from solid-state reactions of RE2O3 and REH3 or LuH3 with CaO and investigated their crystal structures by neutron and X-ray powder diffraction. While DyHO, ErHO, and LuHO adopted the cubic anion-ordered half-Heusler LiAlSi structure type (F4¯3m, a(DyHO) = 5.30945(10) Å, a(ErHO) = 5.24615(7) Å, a(LuHO) = 5.171591(13) Å), LuHO additionally formed the orthorhombic anti-LiMgN structure type (Pnma; LuHO: a = 7.3493(7) Å, b = 3.6747(4) Å, c = 5.1985(3) Å; LuDO: a = 7.3116(16) Å, b = 3.6492(8) Å, c = 5.2021(7) Å). A comparison of the cubic compounds’ lattice parameters enabled a significant distinction between REHO and REH1+2xO1−x (x < 0 or x > 0). Furthermore, a computational chemistry study revealed the formation of REHO compounds of the smallest rare earth elements to be disfavored in comparison to the sesquioxides, which is why they may only be obtained by mild synthesis conditions.
1-D velocity structure modelling of the Earth's Crust in the NW Dinarides

Abstract. The investigated area of the NW Dinarides is located at the NE corner of the Adriatic microplate and is bordered by the Adriatic foreland, the Southern Alps, and the Pannonian basin. Its complex crustal structure is the result of interactions among different tectonic units, mainly the Eurasian plate and the Adriatic microplate. Despite numerous seismic studies in this tectonically complex area, there is still a need for a detailed, small scale study focusing mainly on the upper, brittle part of the crust. We investigated the crustal velocity structure with 1-D simultaneous hypocenter-velocity inversion using routinely picked P wave arrival times. Most of the computed models converged to a stable solution in the depth range between 0 and 26 km. We further evaluated the inversion results with hypocenter shift tests, high and low velocity tests, and relocations. This helped us to select two best performing velocity models for the whole study area. Based on these results and the seismicity distribution, we further divided the study area into three parts, redefined the earthquake-station geometry, and performed inversion for each part separately to gain better insight into the crustal structure of each subregion. Median velocities in the upper 20 km of the crust in the eastern subregion are lower compared to the regional median and the median of the other two subregions. The northwestern and southwestern subregions are very similar in terms of crustal structure between about 8 and 23 km depth. The largest difference between them is observed in the upper 8 km, with higher median velocities in the southwestern subregion. Compared to the model currently used at Slovenian Environment Agency to locate earthquakes, the velocity models obtained show higher velocities in the upper 30 km depth and agree very well with some of the previous studies. In addition to general structural implications and a potential for improving seismic tomography results, the new 1-D velocity models can also be used for fast routine earthquake location and for detecting systematic travel time errors in seismological bulletins.
Changes in Interaction Between Accessory Protein 8 and IL-17RA in UK Isolates Caused by Mutations in the SARS-CoV-2 Open Reading Frame 8

SARS-CoV-2 is the infectous agent of Covid-19, one of the most important health problems of the twenty-first century. IL-17RA is an crucial receptor in the generation of the host immune response. ORF8 is the viral accessory protein of SARS-CoV-2 that suppresses the host immune response. Mutations can alter the viral properties and clinical course of SARS-CoV-2. In this study, we investigated the changes that SARS-CoV-2 ORF8 mutations may cause in the interaction of IL-17RA with ORF8. The study was carried out using 825 complete genome sequences from UK isolates. Mutation analyzes were performed using RDP4 and MEGAX. The protein model was created using the Swiss Model. Protein protein interaction was analyzed by Haddock ver 2.4. Analysis of changes in protein stability was performed using SDM2, mCSM stability and DUET tools. The change in ORF8 - IL-17RA binding affinity before and after the mutation was evaluated using mCSM-PPI2. We detected P30S, R52I, Y73C and L118V mutations in SARS-CoV-2 ORF8. Mutations have been shown to reduce protein stability and affinity. After the mutation, the binding dynamics of ORF8 to IL-17RA were changed. Molecular attachment scores were -78.0±3.4 kcal.mol-1 and -76.3±11.9 kcal.mol-1, for wild type and mutant, respectively. After the mutations, the hydrogen bond number and position between ORF8 and IL-17RA changed. While establishing ten hydrogen bonds between the wild type and IL-17RA, four hydrogen bonds were established between the mutant ORF8 and IL-17RA. The decreased affinity between ORF8 and IL-17RA can be seen as a stronger immune response and a milder clinical course. Although our results contain important data for understanding ORF8, which is an important drug target, it needs to be repeated with in-vivo and crystallgraphy studies.
Outbreak of epidemic diseases and stock returns: an event study of emerging economy

Abstract The massive contagion diseases such as COVID-19 amongst others, has affected nearly all the economies and business concerns that leads to substantial decline in the cashflows and returns dynamics. Considering the same, this paper intends to investigate the impact of outbreak of epidemic diseases on the stock return (observed through cumulative average abnormal return (CAAR)) for the listed banks in Pakistan from 2011 to 2020. Event study method was employed and five days pre and ten days post event of each disease were observed as an event window. The results confirm that none of the epidemic disease outbreak significantly determines the CAAR for all listed banks, except COVID-19 and Dengue Fever during the event day. There is a negative and significant impact of COVID-19 on the stock returns for all the banks in Pakistan from the event day to day eight except day seven. More specifically, COVID-19 is found to be a significant indicator for the stock returns of private banks. However, in case of public listed banks, only the outbreak of HIV cases possesses significant and positive impact on CAAR at the day of event. These findings would guide all stakeholders such as investors, financial analysts, regulators, and chief risk officers specifically in banks to make strategic decisions while analyzing the relationship between epidemic outbreaks and stock returns.
Assessment of the Direct Economic Losses of Flood Disasters Based on the Spatial Valuation of Land Use and Quantification of Vulnerabilities: A Case Study of the 2014 Flood in Lishui city, China

Abstract. The refined assessment of the direct economic losses of flood disasters is important for emergency dispatch and risk management in small- and medium-sized cities. There are still great challenges in the accuracy and timeliness of the previous research methods. In this study, a single flood disaster in Lishui city in 2014 was taken as an example to study and verify a method for the rapid and refined assessment of direct economic loss. First, based on a field investigation, the inundation range and submerged depth simulated by the flooding model were verified. Next, the urban land use status map and high-precision remote sensing classification data were fused and combined with expert questionnaire surveys, thereby providing the types and values of disaster-bearing bodies. Then, the existing vulnerability curve database was summarized, and the curves were calibrated by disaster loss reporting. Finally, the spatial distributions of the flood disaster loss ratio and loss value were estimated by spatial analysis. It is found that the constructed land use map has detailed types and value attributes as well as high-precision spatial information. Secondly, the vulnerability curves after function fitting and calibration effectively reflect the change characteristics of land use loss ratio in this area. Finally, the estimated loss ratio and loss value distributions can accurately reflect the spatial pattern of flood disaster loss, which is useful for the government to formulate effective disaster reduction and relief measures.