Miracle-dz/UniFAR-FTUs huggingface dataset:
Variations in Plumage Wear in Three Closely Related Tidal Marsh Sparrow Species

Tidal marsh sparrow species like Saltmarsh Sparrows (Ammospiza caudacuta), Nelson’s Sparrows (Ammospiza nelsoni) and Seaside Sparrows (Ammospiza maritima) are particularly vulnerable to the environmental stressors related to climate change and human activity like sea-level rise, warming temperatures, and increased coastal development, as they nest in the grasses of tidal marsh ecosystems where the principal mode of nest mortality is flooding. With increased sea-level rise, these species may not be equipped to adapt to changing tidal cycles, and thus have reduced fitness and population sizes. Saltmarsh Sparrows are experiencing sharp declines in population, so it is more vital than ever to investigate patterns in breeding behaviors, plumage wear, and latitudinal differences to develop feasible conservation strategies. My study investigates the differences in plumage wear and severity across conspecifics in Saltmarsh, Nelson’s, and Seaside Sparrows and identifies significant relationships between the date of capture, latitude, and severity of feather wear observed. I observed a decrease in plumage wear and broken feather percentage with latitude but an increase in these metrics in relation to date. Conversely, fault bars and severity displayed an increase with latitude but a decrease with date. Lastly, my findings demonstrate high amounts of feather wear in Seaside Sparrows compared to Saltmarsh and Nelson’s Sparrows.
The Innovation of Virtual Tour of Malang State University Learning Museum as an Alternative for Educational Tourism in the Disruptive Era

Advances in technology and information have become a stimulus for life changes, including digitalization and the internet of things. These two things linearly then motivate researchers to present an innovation virtual museum tour. The aim of the researcher is to present this innovation to assist in the process of developing educational tourism in Indonesia, especially for the UM Learning Museum. In addition, this innovation is also carried out with the aim of bringing the museum closer as one of the media for inheriting culture to the community through the help of advances in technology and information. The virtual tour innovation is also carried out with the aim of stimulating the development of innovation through enriching essence and behavioral intention. In order to facilitate efforts to achieve these goals, the research and development method from Borg and Gall has been modified. Through this method, it was found that the virtual tour innovation of the UM Learning Museum has many benefits for visitors, such as the experience of exploring independently and interactively. Other benefits obtained from the virtual tour of the UM Learning Museum include getting new information and knowledge and developing behavioral intentions to support the existence of the UM Learning Museum for the community.
Bio-optical signatures of insitu photosymbionts predict bleaching severity prior to thermal stress in the Caribbean coral species Acropora palmata

The identification of bleaching tolerant traits among individual corals is a major focus for many restoration and conservation initiatives but often relies on large scale or high-throughput experimental manipulations which may not be accessible to many front-line restoration practitioners. Here we evaluate a machine learning technique to generate a predictive model which estimates bleaching severity using non-destructive chlorophyll-a fluorescence photophysiological metrics measured with a low-cost and open access bio-optical tool. First, a four-week long thermal bleaching experiment was performed on 156 genotypes of Acropora palmata at a land-based restoration facility. Resulting bleaching responses (percent change in Fv/Fm or Absorbance) significantly differed across the four distinct phenotypes generated via a photophysiology-based dendrogram, indicating strong concordance between fluorescence-based photophysiological metrics and future bleaching severity. Next, these correlations were used to train and then test a Random Forest algorithm-based model using a bootstrap resampling technique. Correlation between predicted and actual bleaching responses in test corals was significant (p < 0.0001) and increased with the number of corals used in model training (Peak average R2 values of 0.42 and 0.33 for Fv/Fm and absorbance, respectively). Strong concordance between photophysiology-based phenotypes and future bleaching severity may provide a highly scalable means for assessing reef corals.
The Role of Transformational Leadership in the Relationship between Proactive Personality and Employee Voice among Male and Female Nurses

Transformational leadership is seen as a mediator of employee voice behavior. This study attempts to investigate the influence of leadership style as an organizational variable that can motivate employee voice behavior and expand understanding of the relationship between personal characteristics and employee voice. The research was conducted among public sector employees online. A total of 410 questionnaires were collected. After the elimination of invalid questionnaires, 356 questionnaires were received. For data processing, we used the six-item Employee Voice Questionnaire, the 17-item Proactive Personality Scale (PPS), and the Multifactor Leadership Questionnaire (MLQ- 5S). The result shows that leadership style as an important organizational variable helps employees with a proactive personality to show more positive behavior. Transformational leadership style positively and significantly moderates the relationship between proactive personality and employee voice.
Functions in TEX and Elsewhere

After a short survey about the notion of function in Mathematics and Computer Science, we look into the relationships between (LA)TEX’s commands and features associated with functions: processing arguments, possible side eﬀects, . Then we explore the relationships between the notion of types and the diﬀerent kinds of objects handled by (LA)TEX’s commands
MANM-CS : Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data

In recent years, the growing interest in methods of causal structure learning (CSL) has been confronted with a lack of access to a well-deﬁned ground truth within real-world scenarios to evaluate these methods. Commonly used synthetic benchmarks are limited in their scope as they are either restricted to a “static” low-dimensional data set or do not allow examining mixed discrete-continuous or nonlinear data. This work introduces the mixed additive noise model that provides a ground truth framework for generating observational data following various distribution models. Moreover, we present our reference implementation MANM-CS that provides easy access and demonstrate how our framework supports researchers and practitioners. Further, we propose future research directions and possible extensions.
Nonlinear observer based sliding mode control and oxygen fraction estimation for diesel engine

In this paper, a nonlinear observer based sliding mode control (NOSMC) approach for air-path and a model-based observer for oxygen concentration in the diesel engine equipped with a variable geometry turbocharger and exhaust gas recirculation is introduced. We propose a less conservative observer design technique for Lipschitz nonlinear systems using Ricatti equations. The observer gains are obtained by solving the linear matrix inequality (LMI). Then a robust nonlinear control method, sliding mode control is applied for the states of intake and exhaust manifold pressure and compressor mass flow rate for the sake of the minimization of emissions. The proposed NOSMC controller is applied on a mean value model of turbocharged diesel engine. Besides this, a model-based observer is developed to estimate the oxygen concentration in the intake and exhaust manifolds owing to its significance in reducing emissions of diesel engines. The validation and efficiency of the proposed method are demonstrated by AMESim and Matlab/Simulink co-simulation results.
A Comparative Study Among Selected Fruit Peel Extracts And Their Poly Herbal Mixture's Effects On CCL4-Induced Liver Toxicity

Context: One of the leading causes of death in India is severe hepatic dysfunction. The primary goal of this study is to determine whether the polyherbal combination of chosen fruit peel extracts and hepatoprotective efficacy. Environment and Design: 1. With CCl4, induce hepatotoxicity 2. Investigation of the PHM and hepatoprotective properties of certain peel extracts. 3 Calculating different enzymes to determine liver function. Materials and Procedures After inducing hepatotoxicity, male Wistar albino rats (180-200 g) were separated into 12 groups and treated with PHM and specific extracts. Blood and liver samples were then taken and analysed on the 14th day. The experimental findings were reported as the mean of the statistical analysis (SEM). ANOVA was applied to the data where appropriate.Results and Discussion: All the selected plant extracts and PHM shown to prevent the leakage of SGOT, SGPT, and ALP in CCl4 induced hepatotoxicity. The order of potency was PHM>APMP>APCM>APMI>APAC. The APMP was the better hepatoprotective among the selected plant extracts. The activities might be due to the conditioning of hepatocytes by protecting the integrity of the membrane from CCl 4 induced leakage of serum markers into circulation. All the selected plant extracts and PHM shown to revert back the SOD, CAT and GSH to the normal values in CCl 4 induced rats in a dose dependent manner in liver homogenates
Growth Patterns and Reproduction of Hampala Barb ( Hampala macrolepidota ) Kuhl and Van Hasselt 1823 in Jatigede Reservoir, West Java Indonesia

The indigenous species of the Cimanuk River that can adapt to the stagnant waters of the Jatigede Reservoir is the hampala barb. This fish grows and breeds well; it has an ecological function as a predatory fish from the cyprinidae family. Its economic function is that it can be traded in the Jatigede Reservoir. However, its growth pattern, condition, and reproduction factors have yet to be widely known. The research was conducted for six months (September - December 2017 and January - February 2018). The research aimed to find the growth pattern, condition factors, and reproductive characteristics of the hampala barb. Field survey research method, composite fish sampling from five research stations, data calculated using fisheries biology methods. The results showed that the growth was negative allometric. b value of male fish was 2.429; female fish was 2.717; good condition factor K value of male fish was 1.17 to 1.47, and female fish was 1.16 to 1.38. Reproduction is a partially spawn.
Diving into Robocall Content with SnorCall

Unsolicited bulk telephone calls — termed "robocalls" — nearly outnumber legitimate calls, overwhelming telephone users. While the vast majority of these calls are illegal, they are also ephemeral. Although telephone service providers, regulators, and researchers have ready access to call metadata, they do not have tools to investigate call content at the vast scale required. This paper presents SnorCall, a framework that scalably and efficiently extracts content from robocalls. SnorCall leverages the Snorkel framework that allows a domain expert to write simple labeling functions to classify text with high accuracy. We apply SnorCall to a corpus of transcripts covering 232,723 robocalls collected over a 23-month period. Among many other findings, SnorCall enables us to obtain first estimates on how prevalent different scam and legitimate robocall topics are, determine which organizations are referenced in these calls, estimate the average amounts solicited in scam calls, identify shared infrastructure between campaigns, and monitor the rise and fall of election-related political calls. As a result, we demonstrate how regulators, carriers, anti-robocall product vendors, and researchers can use SnorCall to obtain powerful and accurate analyses of robocall content and trends that can lead to better defenses.
DSSE: a drone swarm search environment

The Drone Swarm Search project is an environment, based on PettingZoo, that is to be used in conjunction with multi-agent (or single-agent) reinforcement learning algorithms. It is an environment in which the agents (drones), have to find the targets (shipwrecked people). The agents do not know the position of the target and do not receive rewards related to their own distance to the target(s). However, the agents receive the probabilities of the target(s) being in a certain cell of the map. The aim of this project is to aid in the study of reinforcement learning algorithms that require dynamic probabilities as inputs.
Analytic approximation of the transition density function under a multi-scale volatility model

The transition density function plays an important role in understanding and explaining the dynamics of the stochastic process. We propose an approach which can be used for the analytic approximation of the transition density related to a multi-scale stochastic volatility model. Using perturbation theory, we compute the leading-order term and the first-order correction terms. A numerical test also confirms the effectiveness of the model.
Multi-Class Product Counting And Recognition for Automated Retail Checkout: A survey paper of The 6th AI City Challenge Track4

Track 4 of of the 6th AI City Challenge specifically focuses on implementing accurate and automatic check-out systems in retail stores. The challenge includes identifying and counting products as they move along a retail checkout conveyor belt, despite obstacles such as occlusion, movement, and similarity between items. I was on the evaluation team for this track where I evaluated the methods of the top-performing teams on hidden Testset B along with my professor, David C. Anastasiu, who is on the organizing team of the challenge. Teams were provided with a combination of real-world and synthetic data for training and were evaluated on their ability to accurately recognize products in both closed and open-world scenarios, as well as the efficiency of their program. The team with the highest combined score for effectiveness and efficiency was declared the winner. The goal of this track is to accurately identify and count products as they move along a retail checkout lane, even if the items are similar or obscured by hands. Distinguishing this track from others, only synthetic data was provided for training models. The synthetic data included a variety of environmental conditions to train models on, while real-world validation and test data will be used to evaluate the performance of models in realistic scenarios.
Ergolide mediates anti-cancer effects on metastatic uveal melanoma cells and modulates their cellular and extracellular vesicle proteomes

Background Uveal melanoma is a poor prognosis cancer. Ergolide, a sesquiterpene lactone isolated from Inula Brittanica, exerts anti-cancer properties. The objective of this study was to 1) evaluate whether ergolide reduced metastatic uveal melanoma (MUM) cell survival/viability in vitro and in vivo; and 2) to understand the molecular mechanism of ergolide action. Methods Ergolide bioactivity was screened via long-term proliferation assay in UM/MUM cells and in zebrafish MUM xenograft models. Mass spectrometry profiled proteins modulated by ergolide within whole cell or extracellular vesicle (EVs) lysates of the OMM2.5 MUM cell line. Protein expression was analyzed by immunoblots and correlation analyses to UM patient survival used The Cancer Genome Atlas (TCGA) data. Results Ergolide treatment resulted in significant, dose-dependent reductions (48.5 to 99.9%; p<0.0001) in OMM2.5 cell survival in vitro and of normalized primary zebrafish xenograft fluorescence (56%; p<0.0001) in vivo, compared to vehicle controls. Proteome-profiling of ergolide-treated OMM2.5 cells, identified 5023 proteins, with 52 and 55 proteins significantly altered at 4 and 24 hours, respectively ( p<0.05; fold-change >1.2). Immunoblotting of heme oxygenase 1 (HMOX1) and growth/differentiation factor 15 (GDF15) corroborated the proteomic data. Additional proteomics of EVs isolated from OMM2.5 cells treated with ergolide, detected 2931 proteins. There was a large overlap with EV proteins annotated within the Vesiclepedia compendium. Within the differentially expressed proteins, the proteasomal pathway was primarily altered. Interestingly, BRCA2 and CDKN1A Interacting Protein (BCCIP) and Chitinase Domain Containing 1 (CHID1), were the only proteins significantly differentially expressed by ergolide in both the OMM2.5 cellular and EV isolates and they displayed inverse differential expression in the cells versus the EVs. Conclusions Ergolide is a novel, promising anti-proliferative agent for UM/MUM. Proteomic profiling of OMM2.5 cellular/EV lysates identified candidate pathways elucidating the action of ergolide and putative biomarkers of UM, that require further examination.
Fruit Image Classification Using Deep Learning Algorithm: Systematic Literature Review (SLR)

Systematic literature review (SLR) research studies various classification models with deep learning algorithms on fruit with digital images. In recent years, computer vision and processing techniques are increasingly useful in the fruit industry, especially for quality and color inspection, sizing, and shape sorting applications. Research in this area demonstrates the feasibility of using a machine computer vision system to improve product quality. Utilizing deep learning in the field of image processing or digital image processing, Image Processing is used to assist humans in recognizing and/or classifying objects quickly, and precisely, and can process large amounts of data simultaneously. Classifying fruit through a computerized system using deep learning algorithms with CNN, MASK-RCNN, FASTER-RCNN, and SSD models. Developed on the multilayer perceptron (MLP) layer, the algorithm is processed into two-dimensional data, to the image and is capable of classifying images with larger classes.
Stellar Spirals in Triaxial Dark Matter Halos

Abstract Two-armed grand-design spirals may form if the shape of its dark matter halo changes abruptly enough. The feasibility of such a mechanism is tested in realistic simulations. The interplay of such externally-driven spirals and self-induced transient spirals is then studied. Subhaloes are also found to lead to transient grand-design spiral structures when they impact the disk.
Semi-Implicit Variational Inference via Score Matching

Semi-implicit variational inference (SIVI) greatly enriches the expressiveness of variational families by considering implicit variational distributions defined in a hierarchical manner. However, due to the intractable densities of variational distributions, current SIVI approaches often use surrogate evidence lower bounds (ELBOs) or employ expensive inner-loop MCMC runs for unbiased ELBOs for training. In this paper, we propose SIVI-SM, a new method for SIVI based on an alternative training objective via score matching. Leveraging the hierarchical structure of semi-implicit variational families, the score matching objective allows a minimax formulation where the intractable variational densities can be naturally handled with denoising score matching. We show that SIVI-SM closely matches the accuracy of MCMC and outperforms ELBO-based SIVI methods in a variety of Bayesian inference tasks.
Automatic Error Correction of Machine Translation Output

We present MLFix, an automatic statistical post-editing system, which is a spiritual successor of the rule-based system, Depfix. The aim of this thesis was to investigate the possible approaches to automatic identification of the most common morphological errors produced by the state-of-the-art machine translation systems and to train sufficient statistical models built on the acquired knowledge. We performed both automatic and manual evaluation of the system and compared the results with Depfix. The system was mainly developed on the English-to-Czech machine translation output, however, the aim was to generalize the post-editing process so it can be applied to other language pairs. We modified the original pipeline to post-edit English-German machine translation output and performed additional evaluation of this modification.
Development of future typical meteorological year (TMY) for major cities in Indonesia (cid:29)(cid:29)(cid:3) Identification of suitable GCM

Today, with the rapid process of urbanization, the proportion of building energy consumption will continue to increase and speed up the emission of greenhouse gases which can intensify the process of global warming. Thus, building energy conservation has become one of the essential aspects of a sustainable development strategy. A typical meteorological year (TMY) is frequently used in building energy simulation to assess the expected heating and cooling costs in the design of the building. Therefore, by considering the future alternations in climate, it is important to develop future TMY data. To generate the TMY for future climate, the projected weather dataset obtained from GCMs from the IPCC coupled inter comparison project phase 6 (CMIP6) can be helpful. However, a key issue with the use of GCM data is the low resolution and bias of the data. Thus, it is important to identify best suitable GCM for a particular region. Therefore, present study aims to evaluate the performance of 6 global GCMs from the CMIP6 for simulating the surface air temperature over the 29 major cities in Indonesia during 1980-2014. Here, dataset (MERRA-2) was utilized to compare the simulations of GCMs. Further three statistical metrics viz. correlation coefficient, standard deviation and centered root mean square error were computed to check the performance of each GCM against the reanalysis data. For most cities, the correlation coefficient values between the results of GCMs, and the reanalysis dataset ranges from 0.3 to 0.7 whereas the value of standard deviation varies from 0.3 to 1. The result revelled that among all the GCMs MPI-HR is one of the most appropriate choices to simulate the surface air temperature over 8 different cities. However, Nor-MM shows the worse performance over the cities located in Indonesia. For the future period, the input dataset from the best identified GCMs will be downscaled for the generation of TMY for future climate.