Miracle-dz/UniFAR-FTUs huggingface dataset:
Attitudes of English medium school teachers in Bangladesh towards inclusive education: Challenges and possible solutions

While practicing inclusive education (IE), teachers are critical stakeholders in ensuring the active participation of all learners. Thus, this study explored primary level (grade 1-5) English-medium schoolteachers’ attitudes towards IE in Bangladesh. This research was conducted in two phases. In phase-1, the Sentiments, Attitudes, Concerns regarding Inclusive Education-Revised (SACIE-R) scale was used, including some demographic questions. 120 randomly selected participants were taken as samples. The overall means of the SACIE-R score indicated that primary English-medium schoolteachers have a moderately positive attitude towards IE. It was also found that several demographic variables such as age, gender, confidence in teaching students with disabilities, and training on teaching students with disabilities had a significant impact on participants' attitudes toward IE. Two findings – regarding gender and length of training - from phase-1 were contradictory to the global trend. Moreover, teachers' concern level was found to be higher than their attitude and sentiment about IE. Interviews were conducted to understand further these uncommon results from the survey and their challenges in implementing IE and possible solutions. This study will guide educationists and policymakers to take the required initiatives to improve teachers' attitudes and empower them to implement IE in primary-level English-medium schools in Bangladesh.
SMART PARKING SYSTEM USING MACHINE LEARNING MODELS

Smart Parking and Parking Guidance and Information systems are needed in today’s big cities. It will reduce traffic, air pollution, and drivers stress and wasted time in finding a parking space. This report outline the design and implementation of a CNN model trained on the PKLot dataset to detect vacant parking spaces in parking lot images. The goal of this work is to build a robust model with increased generalization capabilities for it to be trained on PKLot but used on new parking lot camera feeds. This work achieved 0.9976 an accuracy of the model when trained on multiple parking lots. The model also achieved 0.9772 accuracy in average when trained on a single parking and testes on the other two parking lot camera feeds. The previous results show that this work could achieve robust generalization capabilities. These generalization capabilities allow the model to be deployed for commercial use in real-life parking lot management systems. It can also be a basis for future research covering such as transfer learning to new parking lots data or addition of more layers that will fine-tune the model and improve it.
e An Efficient Protocol for Isolation, Purification, and Characterization of Human Erythroid Progenitor Cells from Peripheral Blood Samples of Healthy Adult Volunteers

Erythroid progenitor cells (EPCs) can differentiate into erythroid precursors and eventually in to mature red blood cells. Cluster of differentiation (CD)34 is a known surface marker for identification of primitive human erythroid progenitor cells including EPCs. CD34 + cells can be isolated from peripheral blood samples using magnetic-assisted cell sorting (MACS) or by flowcytometry-mediated cell sorting. There are no reported studies from South Asia in which CD34 + -derived EPCs have been isolated and purified from the peripheral blood samples obtained from healthy adult humans. The aim of the present study was to isolate CD34 + cells, differentiate CD34 + cells in to EPCs, purify and characterize CD34 + -derived EPCs from peripheral blood samples from healthy adult human subjects. Blood samples were obtained from apparently healthy Pakistani adult volunteer human subjects. Human peripheral blood mononuclear cells (PBMNCs) were separated from the blood sample by density gradient centrifugation. CD34 + cells were isolated from PBMNCs by MACS using CD34 + selection kit. CD34 + cells were differentiated in to EPCs by using erythroid expansion supplement. Purity of the CD34 + -derived EPCs was assessed through immune fluorescence microscopy using monoclonal antibodies against erythroid specific surface antigens CD34, CD235a and CD71. The purity of CD34 + -derived EPCs was determined to be 95%-98%. Cord blood samples contain immature cells, which can result in contamination of PBMNCs layer. Thus, obtaining EPCs from CD34 + peripheral blood cells of healthy adult humans is superior as the risk of contamination is minimal. The method of isolation and purification of CD34 + -derived EPCs used in this study is simpler, user friendly and is more efficient as compared to other techniques. Human EPCs differentiated by this protocol can then be used to study normal and abnormal erythropoiesis.
Robustness of Controlling and Observing Edge Dynamics in Complex Networks

The dynamic processes on the edges of complex networks are closely related to various real-world situations. In recent years, more and more scholars have devoted themselves to the study of the edge dynamics in complex networks and achieved fruitful results. The robustness of controlling the edge dynamics in complex networks has been extensively studied. However, there is no relevant work to study the robustness of observability. In this paper, we develop a framework based on the edge classification to study the robustness of controlling and observing the edge dynamics in complex networks. We apply the framework to model networks and give a theoretical formulation of the edge classification. By comparing with the robustness that only considers the structural controllability, we find that this framework enables us to have a more comprehensive analysis method for the edge failure problem in the edge dynamics of complex networks.
Global Warming and Topography Impact the Amplitude of the Synoptic-Scale Surface Temperature Variability across the US

In recent decades, the United States has experienced changing patterns of extreme temperature. Although much progress has been made, delineating the change in synoptic surface temperature variability (SSTV) and understanding its potential causes remain to be pursued. In this study, we seek to provide a quantitative description of the change in SSTV in the past seven decades across the US and its potential relevant physical factors. To achieve this goal, we develop a spatiotemporally local analysis method based on the ensemble empirical mode decomposition that bypasses the stationary assumption and makes it possible to continuously track the change in SSTV in the spatiotemporal domain. We have found that the change in SSTV across the US is spatially inhomogeneous and temporally non-uniform. The change in the SSTV amplitude ranges from −36% to 39% across the continental United States, Northern Mexico, and surrounding oceans. Higher altitudes and surrounding regions generally see an increase in variability, while elsewhere over land, a reduction is observed, creating a three-band zonal structure across the continental United States. Generally, increases in variability are observed in the subtropics. The shape of the spatiotemporal evolution of SSTV implies that the topography of the United States may play an important role in altering synoptic-scale variability.
A robust algorithm for estimating regression and dispersion parameters in non-stationary longitudinally correlated Com-Poisson data

In recent years, Com–Poisson has emerged as one of the most popular discrete models in the analysis of count data owing to its ﬂexibility in handling diﬀerent types of dispersion. However, in a stationary longitudinal Com–Poisson count data set-up where the covariates are time independent, estimation of regression and dispersion parameters based on a generalized quasi-likelihood (GQL) approach involves some major computational diﬃculties particularly in the inversion of the joint covariance matrix. On the other hand, in practical real-life longitudinal studies, time-dependent covariates leading to non-stationary responses are more frequently encountered. This implies that further computational problems will now arise when estimating parameters under non-stationary set-ups. This paper overcomes this problem by approximating the inverse of the ill-conditioned covariance matrix in the GQL approach through a multidimensional conjugate gradient method. The performance of this novel version of the GQL approach is then assessed on simulations of AR(1) stationary and AR(1) non-stationary longitudinal Com–Poisson counts and on real-life epileptic seizure counts. However, there is not yet an algorithm to generate non-stationary longitudinal Com–Poisson counts nor a GQL algorithm to estimate the parameters under non-stationary set-ups. Thus, the paper also provides a framework to generate non-stationary AR(1) Com–Poisson counts along with the construction of a GQL equation under non-stationary set-ups.
Identiﬁcation of Robot Dynamics from Motor Currents/Torques with Unknown Signs

—Robot dynamic identiﬁcation techniques rely on the quality and completeness of the signals available as inputs, typically joint positions and motor currents or joint torques. These signals are often noisy and ﬁltering operations are required before using them for identiﬁcation. Moreover, some robot control units (e.g., in the KUKA KR5 Sixx) return the user only the absolute values of the motor currents (or of the torques), thus preventing a correct dynamic estimation. We present a method for the identiﬁcation of the robot dynamic model when the motor torques/currents have unknown signs. The method consists in solving a sequence of constrained optimization problems, exploiting physical feasibility constraints. A tree of solutions is built, and the branches leading to unfeasible solutions are pruned. As output, the torque signs are estimated together with the resulting robot dynamic model.
Analysis of the Impact of Money Supply on Balance Of Payments in Nigeria

The question of whether the supply of money (M) is fixed exogenously by the monetary authorities, or should be regarded as endogenous to the system which determines the level of income, rate of interest etc., has been the subject of most recent debate. In macroeconomic theory M has generally been assumed to be exogenous. In practice monetary authorities do to some extent adjust the money supply passively in response to changes, explaining the money supply within our models. However, the comparative static analysis of the models to be developed is not substantially affected by the assumption that money supply is exogenous and constant unless the monetary authority makes a deliberate change. This study therefore sought to analyse the impact of money supply on BOP in Nigeria. This study used secondary data sourced from Central Bank of Nigeria Statistical Bulletin (1986-2021) in its analysis. The study employed Auto-Regressive Distributed Lag (ARDL) to estimate the short run and long run impact of the money supply on BOP in Nigeria. The ARDL bound co-integration showed a long run negative and insignificant relationship between BOP and money supply in the country. The study recommended that the monetary authorities and the financial institutions be made to be effective in handling the economy’s financial flowsthat has led to the disequilibrium on Balance of payment (BOP) deficit.
Reinforcement Learning-Based Transportation and Sway Suppression Methods for Gantry Cranes in Simulated Environment

To improve the productivity and safety of cranes, deep reinforcement learning (DRL) has received widespread attention as a framework for developing automated control methods. However, the major challenge of DRL is sample efficiency, which is further exacerbated by the operational and kinematic characteristics of the crane. Our study proposes an approach to improve the sample efficiency in training control policies for two subtasks: horizontal transportation and sway suppression. To do this, we built a simulation environment and defined the state of the environment and the reward. Then, we performed experiments to find out whether three DRL techniques (reward shaping, curriculum learning, and generative adversarial imitation learning) can mitigate the sample efficiency degradation caused by operational and kinematic characteristics. The results show that the techniques used in our experiment are effective in the improvement of the sample efficiency and learning performance of the DRL model for crane operation.
Meta-learning for Robust Anomaly Detection

We propose a meta-learning method to improve the anomaly detection performance on unseen target tasks that have only unlabeled data. Existing meta-learning methods for anomaly detection have shown remarkable performance but require labeled data in target tasks. Although they can treat unlabeled data as normal assuming anomalies in the unlabeled data are negligible, this assumption is often violated in practice. As a result, the methods have low performance. Our method meta-learns with related tasks that have labeled and unlabeled data such that the expected test anomaly detection performance is directly improved when the anomaly detector is adapted to given unlabeled data. Our method is based on autoencoders (AEs), which are widely used neural network-based anomaly detectors. We model anomalous attributes for each unlabeled instance in the reconstruction loss of the AE, which are used to prevent the anomalies from being re-constructed; they can remove the effect of the anomalies. We formulate adaptation to the unlabeled data as a learning problem of the last layer of the AE and the anomalous attributes. This formulation enables the optimum solution to be obtained with a closed-form alternate update formula, which is preferable to efﬁciently maximize the expected test anomaly detection performance. The effectiveness of our method is experimentally shown with four real-world datasets.
Reinforcement Learning-based Traffic Signal Control Using Delayed Observations for V2X

Vehicle-to-everything (V2X) is communication between a vehicle and any entity that may affect, or may be affected by the vehicle. It is useful for road safety, traffic efficiency, energy savings and mass surveillance, etc. Intelligent traffic signal control (TSC) is a critical building block for V2X. In contrast to most existing TSC designs that assume real-time traffic flow information is available, this work proposes a reinforcement learning (RL)-based TSC system by explicitly taking into account the delay in the TSC input. To cope with the input delay, the proposed RL-based system extracts features to characterize the traffic flow by exploiting multiple delayed historical observations using either the convolutional neural networks (CNN) or the recurrent neural networks (RNN). As a result, the proposed TSC system can make more appropriate actions on choosing traffic signal plans that better match with the traffic flow. Extensive computer experiments performed on the Simulation of Urban MObility (SUMO) platform confirm that the proposed RL-based TSC system can achieve up to 76.0% reduction in the average vehicle waiting time as compared to the conventional RL-based TSC system for observations with large feedback delays.
Controllable Synthesis and Charge Density Wave Phase Transitions of Two-Dimensional 1T-TaS2 Crystals

1T-TaS2 has attracted much attention recently due to its abundant charge density wave phases. In this work, high-quality two-dimensional 1T-TaS2 crystals were successfully synthesized by a chemical vapor deposition method with controllable layer numbers, confirmed by the structural characterization. Based on the as-grown samples, their thickness-dependency nearly commensurate charge density wave/commensurate charge density wave phase transitions was revealed by the combination of the temperature-dependent resistance measurements and Raman spectra. The phase transition temperature increased with increasing thickness, but no apparent phase transition was found on the 2~3 nm thick crystals from temperature-dependent Raman spectra. The transition hysteresis loops due to temperature-dependent resistance changes of 1T-TaS2 can be used for memory devices and oscillators, making 1T-TaS2 a promising material for various electronic applications.
How are gameful experience dimensions associated with intrinsic and extrinsic motivation? Results from an online vignette study

Gamification is one of the leading behavioral change strategies whose central premise is to afford experiences that resemble those present in games to provide intrinsic motivation. At the same time, gamification has an underlying utilitarian purpose and therefore its use is additionally driven by expected benefits. However, while gamification designs can afford a wide range of experiences, the corpus needs studies that examine which of these experiences induce intrinsic motivation, and which are associated with extrinsic motivation related to the utilitarian outcomes. Therefore, this study deploys a vignette-based online study (n=937) to examine the relationships between gameful experience dimensions (i.e., accomplishment, challenge, social experience, immersion, competition, guided, playfulness), and intrinsic and extrinsic motivation in a case study related to social distancing during the COVID-19 pandemic. Based on the results, accomplishment, social experience, competition, and playfulness are positively associated with intrinsic motivation. Moreover, accomplishment, social experience, guided, and playfulness are positively associated with extrinsic motivation.
Balanced Spatial-Temporal Graph Structure Learning for Multivariate Time Series Forecasting: A Trade-off between Efficiency and Flexibility

Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science. Fully mining the correlation and causation between the variables in a multivariate time series exhibits noticeable results in improving the performance of a time series model. Recently, some models have explored the dependencies between variables through end-to-end graph structure learning without the need for predefined graphs. However, current models do not incorporate the trade-off be-tween efficiency and flexibility and make insufficient use of the information contained in time series in the design of graph structure learning algorithms. This paper alleviates the above issues by proposing Balanced Graph Structure Learning for Forecasting (BGSLF), a novel and effective deep learning model that joins graph structure learning and forecasting. Technically, BGSLF leverages the spatial information into convolutional operations and extracts temporal dynamics using the diffusion convolutional recurrent network. The proposed framework emphasizes the trade-off between efficiency and flexibility by introducing Multi-Graph Generation Network (MGN) and Graph Selection Module. In addition, a method named Smooth Sparse Unit (SSU) is designed to sparse the learned graph structures, which conforms to the sparse spatial correlations in the real world. Extensive experiments on four real-world datasets demonstrate that our model achieves state-of-the-art performances with minor trainable parameters. Our code is publicly available at https://github.com/onceCWJ
W EAKLY -S UPERVISED L EARNING OF D ISENTANGLED AND I NTERPRETABLE S KILLS FOR H IERARCHICAL R EINFORCEMENT L EARNING

Hierarchical reinforcement learning (RL) usually requires skills that can be applicable to various downstream tasks. While many recent works have been proposed to learn such skills in supervised or unsupervised manners, the learned skills are often entangled, which makes it difficult to interpret those skills. To alleviate this, we propose a novel WEakly-supervised learning approach for learning Disentangled and Interpretable Skills (WEDIS) from the continuous latent representations of trajectories. We accomplish this by extending a trajectory variational autoencoder (VAE) to impose an inductive bias with weak labels, which explicitly enforces the trajectory representations to be disentangled into factors of interest that we intend the model to learn. Given the latent representations as skills, a skill-based policy network is trained to generate similar trajectories to the learned decoder of the trajectory VAE. Additionally, we propose to train a policy network with single-step transitions and perform the trajectory-level behaviors at test time with the knowledge on the skills, which simplifies the training procedure for the policy. With a sample-efficient planning strategy based on the skills, we show that our method is effective in solving the hierarchical RL problems in experiments on several challenging navigation tasks with a long horizon and sparse rewards.
Ammonia Emissions from Cattle Manure under Variable Moisture Exchange between the Manure and the Environment

When reducing ammonia emissions from cowsheds, it is recommended to reduce the ventilation intensity, air temperature in the barn, manure moisture by using bedding and manure-contaminated surfaces, and to prevent urine from accumulating in the airways. Using the mass flow method in the wind tunnel, after research on seven types of cattle manure with different moisture contents, it was found that ammonia evaporates up to 3.9 times more intensively from liquid manure than from solid manure. There is a strong correlation between ammonia and water evaporation from manure. Ammonia emission from liquid manure decrease by 2.0–2.3 times, emissions from solid manure decrease by 1.9–2.1 times. Different cowsheds have different opportunities to reduce air pollution and conditions for manure to dry and crusts to form on the surface. The best results will be achieved by applying complex measures to reduce air pollution.
Assessing the Current State of Louisiana's Crawfish Fishery: Trends and Challenges in Wild Capture and Aquaculture

Crawfish are found throughout the southern U.S.; however, Louisiana is by far the largest producer. The state’s industry includes harvesting wild crawfish from natural habitats such as bayous, swamps, and marshes, and crawfish reared in outdoor ponds. Although commercial harvest records are available, characterization of Louisiana’s wild capture crawfish fishery in recent years is undocumented, and analysis of environmental variables that could be correlated with annual harvest totals have not yet been explored. Using trip ticket data from the Louisiana Department of Wildlife and Fisheries, I characterized monthly and annual trends within Louisiana basins to assess changes in crawfish harvest over the past 20 years. To understand the wild harvest fishery through changes in hydrological conditions within the Atchafalaya Basin, where more than 80% of wild harvest occurs, I used a linear regression to look at the effects of Atchafalaya River discharge and monthly precipitation accumulation on monthly crawfish harvest from 1999–2020 during months of peak harvest. Louisiana’s crawfish industry is comprised of two unique yet similar components: wild harvest and pond production. Research on crawfish biology and improved methods for pond production has been ongoing since the early 1960’s. Challenges within the crawfish aquaculture industry are documented but of most recent concern is the emergence of white spot syndrome virus (WSSV). WSSV possesses the ability to create mass mortality outbreaks with little notice to farmers. Outbreaks often present in multiple conditions and can be hard to quantify. While white spot syndrome virus was first documented in Louisiana crawfish ponds in 2007, recorded laboratory testing that provides spatial and temporal outbreak data are available beginning in 2020. To better understand WSSV and the challenges it presents within Louisiana’s crawfish industry, I used data from the Louisiana Animal Disease Diagnostic Lab on positive testing