Miracle-dz/UniFAR-FTUs huggingface dataset:
Phonological and phonetic contributions to Thai-naïve Mandarin and Vietnamese speakers’ imitation of Thai lexical tones: Effects of memory load and stimulus variability

The present study examined how native phonological and phonetic factors in non-native speech perception (Perceptual Assimilation Model [PAM]: Best, 1995) affect non-native imitation of Thai tones by Thai-naïve Mandarin and Vietnamese participants, and how memory load and stimulus variability shift the balance between phonological versus phonetic modes (Automatic Selective Perception model [ASP]: Strange, 2011) in imitation. Although overall imitation was quite good, native phonological influences, as reflected in Categorised versus Uncategorised assimilation types in a prior perception study with the same participants (Chen, Antoniou, & Best, 2023), constrained non-native tone imitations. Residual perceptual sensitivity to phonetic differences between the target stimuli and native tones, as reflected in percent choice and goodness ratings in assimilation, also affected imitation. Effects of stimulus variability and memory load were restricted and interacted with specific tones for each participant group. Imitations were generally more accurate under low memory load, constant talker and vowel conditions, consistent with a more phonetic mode of perception. Imitation accuracy decreased under high memory load, variable talker and vowel conditions, consistent with a more phonological mode of perception. Results support the PAM and ASP accounts of native phonological and phonetic effects in non-native perception and extend them to non-native tone imitation.
Indeterminacy in Stochastic Overlapping Generations Models: Real Eects in the Long Run *

Indeterminate equilibria are known to exist for overlapping generations models , though recent research has been limited to deterministic settings in which all equilibria converge to a steady state in the long run. This paper analyzes stochas-tic overlapping generations models with 3-period lived representative consumers and adopts a novel computational algorithm to numerically approximate the entire set of competitive equilibria. In a stochastic setting with incomplete markets, indeterminacy has real eects in the long run. Our numerical simulations reveal that indeterminacy is an order of magnitude more important than endowment shocks in explaining long-run consumption and asset price volatility. * This paper was previously circulated under the title "Indeterminacy and Asset Price Volatility in Stochastic Overlapping Generations Models" by Zhigang Feng and Matthew Hoelle and under the title Numerical Simulation of the Overlapping Generations Models with Indeterminacy by Zhigang Feng. We are grateful to Felix Kubler, Udara Peiris, conference participants at Arizona State, and seminar participants at ICEF (Moscow) and the University of Miami for comments and feedback.
Comprehensive Analysis of Android Malware detection through Semi-supervised Autoencoder models

Recent years have seen a rapid proliferation of malicious software on mobile devices, notably repackaged Android malware. Grasping Android malware detection via vibrant analysis will provide a thorough read, but we must also establish a connection between the app's features and the elements required to ful�ll the category's functionality. In this work, we propose a novel method of detecting malwares. This model is a part of semi-supervised learning. We employed autoencoder training features from noisy input. They generate clusters of malicious predictions utilizing the reconstruction error and helped in dimensionality reduction of the large feature set. The newly reconstructed input features are then fed to the machine learning model and Deep learning models for supervised prediction. Later, we contrast their performance based on performance metrics and accuracy. Three datasets were chosen for this experiment to assess the execution and operation of the proposed model. There were two labels in the ‘class’ attribute such as ‘0’ as Benign and ‘1’ as Malicious. The proposed model achieved superior accuracy and can deliver promising results
Design and Development of Artificial Intelligence based Model for Securing Digital Content

: In today’s technical oriented world, digital communication has grown proficiently. Owing to the proliferation of World Wide Web and galloping expansion of high-speed Internet, a huge amount of digital contents such as images, videos, audios and texts are easily available and accessible to almost any layman across the globe. With the wide spread use of the Internet in our society, it is greatly facilitated that each and every person can communicate by distributing and exchanging the multimedia content. So, in techno-oriented world modification, manipulation, replication of digital content has become ubiquitous and becoming a serious issue in the present scenario. In addition to this, illegitimate copies of digital data can be handily produced using the advancements in the technology today. As a result, any malicious or unauthorized user can access the original documents namely movies, songs, photographs or texts, henceforth misuse them; leading to somber threats to the rights of content owners. To deal with these problems, digital watermarking with Artificial Intelligence has become advantageous solution for providing ownership of digital content. In this paper, we represent digital watermarking using fusion of Fuzzy Logic and Neural Networks. Moreover, this paper also demonstrates the working of this hybrid technique with various inputs and obtained results .
A Pseudoproxy Evaluation of the Cca and Regem Methods for Reconstructing Climate Fields of the Last Millennium Center for Atmospheric Research Climate System Model 1.4. a Parsimonious Method for Selecting

14 Canonical correlation analysis (CCA) is evaluated for paleoclimate field reconstructions in the context 15 of pseudoproxy experiments assembled from the millennial integration (850-1999 C. 17 the order of the CCA model is presented. Results suggest that the method is capable of resolving 18 approximately 3-18 climatic patterns given the estimated proxy observational network and the amount 19 of observational uncertainty. CCA reconstructions are compared to those derived from the regularized 20 expectation maximization method using ridge regression regularization (RegEM-Ridge). CCA and 21 RegEM-Ridge yield similar skill patterns that are characterized by high correlation regions collocated 22 with dense pseudoproxy sampling areas in North America and Europe. Both methods also produce 23 reconstructions characterized by spatially variable warm biases and variance losses, particularly at high 24 pseudoproxy noise levels. RegEM-Ridge in particular is subject to significantly larger variance losses 25 than CCA, even though the spatial correlation patterns of the two methods are comparable. Results 26 collectively indicate the importance of evaluating the field performance of methods that target spatial 27 climate patterns during the last several millennia, and indicate that the results of currently available 28 climate field reconstructions should be interpreted carefully.
Edinburgh Research Explorer Exploring Nurses’ Online Perspectives and Social Networks During a Global Pandemic COVID-19

Objectives: Examinetheonlineinteractions,socialnetworks,andperspectivesofnurs-ing actors on COVID-19 from conversations on Twitter to understand how the profession responded to this global pandemic. Design: Mixed methods. Sample: Ten-thousand five-hundred and seventy-four tweets by 2790 individuals and organizations. Measurements: NodeXL software was used for social network analysis to produce a network visualization. The betweenness centrality algorithm identified key users who wereinfluentialinCOVID-19relatedconversationsonTwitter.Inductivecontentanal-ysis enabled exploration of tweet content. A communicative figurations framework guided the study. Results: Nursing actors formed different social groupings, and communicated with one another across groups. Tweets covered four themes; (1) outbreak and clinical management of the infectious disease, (2) education and information sharing, (3) social, economic, and political context, and (4) working together and supporting each other. Conclusion: In addition to spreading knowledge, nurses tried to reach out through social media to political and healthcare leaders to advocate for improvements needed to address COVID-19. However, they primarily conversed within their own professional community. Action is needed to better understand how social media is and can be used by nurses for health communication, and to improve their preparedness to be influential on social media beyond the nursing community.
Harvard Tops in Scientific Impact

Harvard University wins bragging rights in the latest ranking of U.S. research universities, according to the September/October ScienceWatch. It not only churned out more papers than any other university between 1993 and 1997, but the work was rated as having higher scientific impact across the board.
Learning from Citation: Interpretable Patent Valuation via Conditional Variational Autoencoder

Abstract Patent value prediction is one of the most fundamental components in many applications for knowledge value retrieval. It is important in medical and high-tech ﬁelds and crucial in commercial legal pro-ceedings. Since the patent citation relationship has been proven to be one of the most critical factors aﬀecting the value of a patent. Hence, extracting such a citation relationship becomes an essential process. However, extracting the citation relationship between patent documents is still challenging because patent relationship updates frequently. This paper embeds the citation relationship from the patent’s Cross Relationship Network (CRN) via the Conditional Variational Autoencoder (CVAE) into prediction tasks. This is the ﬁrst attempt to leverage the deep generative model to extract a patent’s citation relationship into a patent valuation prediction task. Moreover, we also
breed-Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/ dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis -9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01 × 10 −3 and 4.17 × 10 −3 for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model.
Antibacterial, antioxidant potency and chemical composition of essential oils from dried powdered leaves and �owers of Hypericum revolutum subsp. keniense

Hypericum revolutum subsp. keniense is a plant mainly used to treat diarrhoea, rheumatism, nervous disorders and wounds in African traditional medicine. The objective of the current work was to establish antibacterial, antioxidant potency and chemical composition of essential oil from the leaves and �owers of Hypericum revolutum subsp. keniense. The oils were isolated by steam distillation. Antibacterial activity against methicillin-resistant Staphylococcus aureus, Staphylococcus aureus (ATCC 12393), Escherichia coli (ATCC 25922), Acinetobacter baumannii (ATTC 19606), Salmonella Enteritidis (NCTC12023), Salmonella Typhimurium (ATCC 14028), Pseudomonas aeruginosa (ATCC 15442), and Haemophilus in�uenzae (ATCC 49766) were done by Agar Disk Diffusion and Microtiter Broth Dilution methods. Antioxidant activities of the essentail oils were examined by different methods, DPPH, FRAP and H 2 O 2 assays. Chemical characterization was done using Gas Chromatography interfaced with Mass Spectrometry, Fourier-Transform Infra-red (FTIR) spectroscopy and the quanti�cation of phenolics and �avonoids by Folin-Ciocalteu reagent and Aluminium nitrate respectively. The oils showed potential antibacterial activity with mean zone of inhibition ranging from 20.67 ± 0.33 to 32.00 ± 1.00 mm at 100 % oil concentration against the tested bacteria. Furthermore, the minimum inhibitory concentrations (MICs) in all the tested microorganisms were in the range of 250 to 15.6250 µg/ml. The essential oils derived from th leaves revealed varied antioxidant activity levels with the different methods of assay. The IC 50 of values obtained from the three methods, DPPH, FRAP and H 2 O 2 were (cid:0)1000 µg/ml, 0.31 µg/ml and 12.33 µg/ml respectively. Caryophyllene (22.1 %) and 2,3,4-trimethylhexane were the major chemical components of the essential oils derived from the leaves and �owers respectively. FTIR spectroscopy of the essential oils from the leaves and �owers showed similarity at peaks for hydroxyl, unsaturated ole�nic and amine functional groups. Further �ndings indicated that the total phenolic and �avonoid contents essential oils derived from leaves were 130.4 6 ± 10.5 mg GAE/g dry weight and 0.911 ± 0.04 mg CE/g dry weight respectively. It was therefore concluded that essential oils from the leaves and �owers of H. revolutum subsp. keniense have compounds that have antibacterial and antioxidant potency.
Biochar's Contribution for Enhancing Soil Properties and Crop Productivity by Alleviating Soil Acidity Stress: A Comprehensive Review

: Soil acidity stress is a significant challenge in modern agriculture, impacting crop productivity and overall soil health. One promising solution to mitigate this stress is the use of biochar, a carbon-rich material derived from the pyrolysis of organic matter. This review aims to provide an in-depth analysis of the role of biochar in reducing soil acidity stress. It covers various aspects including the physicochemical properties of biochar, its interactions with soil components and the mechanisms through which it can effectively counteract soil acidity. The review also discusses the potential benefits and limitations of biochar application in diverse agricultural systems and highlights key considerations for its successful integration into soil management practices. Through a comprehensive examination of current research and findings, this review offers insights into the promising potential of biochar as a sustainable amendment for enhancing soil quality and crop production while addressing soil acidity stress.
Concentration level and seasonal variation of organic carbon and elemental carbon in Beijing aerosols

The organic carbon (OC) and elemental carbon (EC) have been determined for 110 atmospheric aerosol samples at two monitories sites (the Ming Tomb and Temple Heaven) in Beijing The OC and EC data showed obvious seasonal variations The annual average of OC concentration of the Ming Tomb site was 22 0μg/m 3, and EC was 3 6μg/m 3; while that of the Temple Heaven site was 41 5μg/m 3for OC, and 7 8μg/m 3for EC The percentage content of OC was the highest in autumn, as the consequence of the active atmospheric reactions and severe air pollution The lower percentage value in spring was due to the windy weather The widespread coal burning caused the high EC values in urban Beijing in wintertime The two case studies indicate that air pollution episodes are attributable to the steady meteorological conditions and the specific geographic features of Beijing The air pollution episode in autumn 1998 in Beijing is featured by organic pollution, as indicated by high OC values
The 4DEnVar-based land coupled data assimilation 1 system for E3SM version 2 2

2 Abstract. A new land coupled data assimilation (LCDA) system based on the four-dimensional ensemble 14 variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth 15 System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational 16 (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the 17 adjoint technique. Monthly mean soil moisture and temperature analyses from a global land reanalysis 18 product are assimilated into the land component of E3SMv2 with a one-month assimilation window 19 along the coupled model trajectory from 1980 to 2016. The coupled assimilation experiment is evaluated 20 using multiple metrics, including the cost function, assimilation efficiency index, correlation, root mean 21 square error and bias, and compared with a control simulation without land data assimilation. The LCDA 22 system yields improved simulation of soil moisture and temperature compared with the control 23 simulation, with improvements found throughout the soil layers and in many regions of the global land. 24 Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 U.S. 25 Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The LCDA system is 26 intended to be a foundational resource to investigate land-derived climate predictability for future 27 prediction research by the E3SM community. 28
A LION OPTIMIZATION BASED K-PROTOTYPE CLUSTERING ALGORITHM FOR MIXED DATA

Data Mining is used to gather information from huge set of data. Clustering is a grouping task for a set of objects. Clustering algorithms are divided by several types including hierarchical clustering algorithms,partitioning clustering and density based. The partitioning clustering includes K-Means clustering, K-Modes Clustering and CLARA algorithm. The K-Means clustering is only used for numeric data which has original optima. The K-Modes extends to the K-Means when the sphere is categorical. One of the most important algorithms for clustering heterogeneous type of data is the K-Prototype algorithm. This algorithm is veritably salutary for clustering large data sets. One of the simple optimization methods is Lion Optimization, that could be applied effectively for enhancing clustering results. It's useful for handling mixed data set. This leads a good optimization to calculate the centroid with K-Prototype clustering method. To overcome the problem in this clustering, Lion optimization Algorithm can be used. The proposed algorithm is enforced on standard standard dataset taken from UCI Machine Learning Repository. The Lion Optimization grounded K-Prototype clustering algorithm yields a better result when compared with the K-Prototype clustering.
Finance and Income Inequality Test of Alternative Theories

Although theoretical models make distinct predictions (1993). Moreover, consistent with the insight of Kuznets, about the relationship between financial sector the relation between the Gini coefficient and financial development and income inequality, little empirical intermediary development appears to depend on the research has been conducted to compare their relative sectoral structure of the economy' a larger modern sector explanatory power. Clarke, Xu, and Zou examine the is associated with a smaller drop in the Gini coefficient relation between financial intermediary development and for the same level of financial intermediary development. income inequality in a panel data set of 91 countries for But there is no evidence of an inverted-U-shaped relation the period 1960-95. Their results provide evidence that between financial sector development and income inequality decreases as economies develop their financial inequality, as suggested by Greenwood and Jovanovic intermediaries, consistent with the theoretical models in (1990). The results are robust to controlling for biases Galor and Zeira (1993) and Banerjee and Newman introduced by simultaneity. This paper-a product of Investment Climate, Development Research Group-is part of a larger effort in the group to understand the link between economic development and financial sector performance
Artificial Intelligence-Enabled Multi-Function Activity Monitoring and Reporting System

- There are critical times in life when human activities require close monitoring, these crucial moments (an elderly-aged at home where wrong body posture or positioning may affect the person’s health status, and an ill-health patient in a hospital bed on account of orthopedical admissions) prohibit humans from changing its position on the bed without the caregivers’ approval. The traditional ways of monitoring the aforementioned persons by fellow human or camera recording prove ineffective in that it lacks intelligent real-time capturing and reporting systems. Considering the activities’ critical nature, a Raspberry Pi microcontroller and a mobile app for real-time tracking and reporting of stated human activities were adopted. Aged-elderly and ill-health persons were monitored with a camera mounted to cross-communicate the change in positions on the bed through a wireless network for prompt rescue service. The video recorders push notifications to the mobile application. The system's intelligence was powered by cutting-edge computer vision and artificial intelligence algorithms, which make it possible to detect and recognize human posture with high accuracy. Convolutional Neural Networks were used to train the system on the Common Objects in the Context dataset to extract hidden information from photos. This system deploys Single Shot Detection, mobileNet, and Posenet to estimate postures and track human positions in real time. The result shows that the system detects sitting, standing, lying down, and motion activities via the camera feed using posture recognition. This study highlights the potential solution for advancing human activity recognition and provides the foundation for further research.
On Micro b -open Sets

: In this paper, a new kind of set called a Micro b -open set in micro topological space is introduced. Basic properties of Micro b -open sets are analyzed and also this set used to introduce and study the new types of functions called Micro b -irresolute, Micro b -continuous and Micro Wb -continuous functions.