Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Teacher Intrinsic and Extrinsic Motivation on Teacher Self-Efficacy

Teacher recruitment and retention is a growing concern for educational organizations. This is in large part due to the increasing work demands and underfunding of public funding. The purpose of this study was to investigate the relationship between teacher motivation and the adaptive factor of self-efficacy. The theoretical frameworks of Bandura’s social learning theory, Deci and Ryan’s self-determination theory, and Rotter’s locus of control theory were used to guide this study. The researcher used a quantitative correlation study to examine the relationship between self-efficacy and intrinsic motivation, extrinsic motivation, and overall motivation. The two surveys were given to 130 elementary school teachers from two school districts in a southeastern state. The Teacher Sense of Efficacy Scale (TSES) and Work Tasks Motivation Scale for Teachers (WTMST) surveys were used to measure perceived teacher self-efficacy and motivational beliefs. Pearson product-moment correlations were used to measure the relationship between the dependent variable (teacher self-efficacy) and the independent variables (intrinsic motivation and extrinsic motivation). Data analysis revealed a positive correlation between selfefficacy and intrinsic motivation; however, no relationship was found between self-efficacy and extrinsic motivation nor overall motivation. Recommendations for future research include replicating this study using both elementary and secondary teachers as well as utilizing demographics to determine generalizations about teacher self-efficacy and motivation.
Preliminary results of cetacean sightings in the eastern Mediterranean Sea of Turkey

Lack of baseline knowledge is often the principal conservation barrier, especially if the species of concern shows steady decline. The primary goal of the current project is to fill the knowledge gaps in understudied cetacean populations through dedicated visual and acoustic surveys in the Eastern Mediterranean Sea of Turkey. The surveys were conducted over six seasons, following 4384.76 km in 49 days between April 2018 and July 2019. Cetaceans were detected on 39 days (146 sightings), with the highest sightings belonging to delphinids (bottlenose, common and unidentified dolphins) followed by sperm whales (Physeter macrocephalus) and a single acoustic detection of a beaked whale. Fethiye Canyon held the highest cetacean concentration and thus is identified as an “important habitat”. Reported encounter rates were higher than previous studies, with 2.78 and 0.55 groups/100 km for delphinids and sperm whales, respectively. Despite the lack of significant variation in cetacean presence due to season and diurnal effects, there was a noticeable increase in summer sightings for sperm whales, where they were encountered as solitary individuals and social units with calves. The current research presented the first seasonal survey results of the Turkish Mediterranean and underlines the importance of local research efforts for valid conclusions in an understudied environment that holds not only unrecognized cetacean habitats but also a wide range of human pressures.
SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda Using Sentence-Level Emotional Salience Features

This paper describes a system developed for detecting propaganda techniques from news articles. We focus on examining how emotional salience features extracted from a news segment can help to characterize and predict the presence of propaganda techniques. Correlation analyses surfaced interesting patterns that, for instance, the “loaded language” and “slogan” techniques are negatively associated with valence and joy intensity but are positively associated with anger, fear and sadness intensity. In contrast, “flag waving” and “appeal to fear-prejudice” have the exact opposite pattern. Through predictive experiments, results further indicate that whereas BERT-only features obtained F1-score of 0.548, emotion intensity features and BERT hybrid features were able to obtain F1-score of 0.570, when a simple feedforward network was used as the classifier in both settings. On gold test data, our system obtained micro-averaged F1-score of 0.558 on overall detection efficacy over fourteen propaganda techniques. It performed relatively well in detecting “loaded language” (F1 = 0.772), “name calling and labeling” (F1 = 0.673), “doubt” (F1 = 0.604) and “flag waving” (F1 = 0.543).
Discovering Bilingual Lexicons in Polyglot Word Embeddings

Bilingual lexicons and phrase tables are critical resources for modern Machine Translation systems. Although recent results show that without any seed lexicon or parallel data, highly accurate bilingual lexicons can be learned using unsupervised methods, such methods rely on the existence of large, clean monolingual corpora. In this work, we utilize a single Skip-gram model trained on a multilingual corpus yielding polyglot word embeddings, and present a novel finding that a surprisingly simple constrained nearest-neighbor sampling technique in this embedding space can retrieve bilingual lexicons, even in harsh social media data sets predominantly written in English and Romanized Hindi and often exhibiting code switching. Our method does not require monolingual corpora, seed lexicons, or any other such resources. Additionally, across three European language pairs, we observe that polyglot word embeddings indeed learn a rich semantic representation of words and substantial bilingual lexicons can be retrieved using our constrained nearest neighbor sampling. We investigate potential reasons and downstream applications in settings spanning both clean texts and noisy social media data sets, and in both resource-rich and under-resourced language pairs.
Persistence homology of entangled rings

Topological constraints (TCs) between polymers determine the behaviour of complex fluids such as creams, oils and plastics. Most of the polymer solutions used every day life employ linear chains; their behaviour is accurately captured by the reptation and tube theories which connect microscopic TCs to macroscopic viscoelasticity. On the other hand, polymers with non-trivial topology, such as rings, hold great promise for new technology but pose a challenging problem as they do not obey standard theories; additionally, topological invariance -- i.e. the fact that rings must remain unknotted and unlinked if prepared so -- precludes any serious analytical treatment. Here we propose an unambiguous, parameter-free algorithm to characterise TCs in polymeric solutions and show its power in characterising TCs of entnagled rings. We analyse large-scale molecular dynamics (MD) simulations via persistent homology, a key mathematical tool to extract robust topological information from large datasets. This method allows us to identify ring-specific TCs which we call "homological threadings" (H-threadings) and to connect them to the polymers' behaviour. It also allows us to identify, in a physically appealing and unambiguous way, scale-dependent loops which have eluded precise quantification so far. We discover that while threaded neighbours slowly grow with the rings' length, the ensuing TCs are extensive also in the asymptotic limit. Our proposed method is not restricted to ring polymers and can find broader applications for the study of TCs in generic polymeric materials.
A Survey of Requirement Engineering Process in Android Application Development

Mobile application development is the most rapidly growing industry in the world. Nowadays, people totally depend on smart phones for performing daily routine tasks which results in tremendous rises in the expectation of human being from IT industry which increase the requirements of human being. In order to tackle the uncontrolled changes in the requirements, IT experts performed some proper requirement engineering process (REP). Therefore, in this paper we are performing industry survey by asking them several questions related to the REP from android developer in order to understand the REP used in the IT industry. Results we extract from this study is satisfactory used in order to make REP more effective.
Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks

We study symmetric spiked matrix models with respect to a general class of noise distributions. Given a rank-1 deformation of a random noise matrix, whose entries are independently distributed with zero mean and unit variance, the goal is to estimate the rank-1 part. For the case of Gaussian noise, the top eigenvector of the given matrix is a widely-studied estimator known to achieve optimal statistical guarantees, e.g., in the sense of the celebrated BBP phase transition. However, this estimator can fail completely for heavy-tailed noise. In this work, we exhibit an estimator that works for heavy-tailed noise up to the BBP threshold that is optimal even for Gaussian noise. We give a non-asymptotic analysis of our estimator which relies only on the variance of each entry remaining constant as the size of the matrix grows: higher moments may grow arbitrarily fast or even fail to exist. Previously, it was only known how to achieve these guarantees if higher-order moments of the noises are bounded by a constant independent of the size of the matrix. Our estimator can be evaluated in polynomial time by counting self-avoiding walks via a color -coding technique. Moreover, we extend our estimator to spiked tensor models and establish analogous results.
An Experience of Introducing Primary School Children to Programming using Ozobots (Practical Report)

Algorithmic thinking is a central concept in the context of computational thinking, and it is commonly taught by computer programming. A recent trend is to introduce basic programming concepts already very early on at primary school level. There are, however, several challenges in teaching programming at this level: Schools and teachers are often neither equipped nor trained appropriately, and the best way to move from initial “unplugged” activities to creating programs on a computer is still a matter of open debate. In this paper, we describe our experience of a project aiming at supporting local primary schools in introducing children to programming concepts using Ozobot robots. These robots have two distinct advantages: First, they can be programmed with and without computers, thus helping the transition from unplugged programming to programming with a computer. Second, they are small and easy to transport, even when used together with tablet computers. Although we learned in our outreach events that the use of Ozobots is not without challenges, our overall experience is positive and can hopefully support others in setting up first encounters with programming at primary schools.
Loss convergence in a causal Bayesian neural network of retail firm performance

We extend the empirical results from the structural equation model (SEM) published in the paper Assortment Planning for Retail Buying, Retail Store Operations, and Firm Performance [1] by implementing the directed acyclic graph as a causal Bayesian neural network. Neural network convergence is shown to improve with the removal of the node with the weakest SEM path when variational inference is provided by perturbing weights with Flipout layers, while results from perturbing weights at the output with the Vadam optimizer are inconclusive.
Enhanced Time-Frequency Representation and Mode Decomposition

Time-frequency representation (TFR) plays a significant role in interpreting and analyzing nonstationary multi-mode signals. However, currently, it is still challenging to handle multi-mode signals with closely-spaced or spectrally-overlapped instantaneous frequencies (IFs), especially under low signal-to-noise ratio (SNR) conditions. To address this issue, we propose an enhanced TFR and mode decomposition (ETFR-MD) method, which is especially suitable to represent and decompose multi-mode signals with such complex IFs. The proposed ETFR-MD method exploits both the IFs and instantaneous amplitudes (IAs) of the signal under investigation. First, we design an initial IF estimation method to deal with overlapped IFs. Then, a low-complexity mode enhancement scheme is proposed so that the enhanced IFs could better fit the underlying IF laws. After that, three alternatives, i.e., short-time Fourier transform (STFT) coefficients, de-chirping, and intrinsic chirp component decomposition (ICCD), are considered for IA recovery. Finally, the extracted IAs are combined with the enhanced IFs to reconstruct each signal mode, respectively. To gain more insights, we quantitatively analyze the interference during mode decomposition and derive the optimal window length for mode separation. Experimental results implemented on both simulated and real-world data confirm the superior performance of the ETFR-MD compared with the state-of-the-art solutions.
SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engines, wherein all the document vectors are pre-computed, and near-neighbor search (NNS) is performed with the query vector to find relevant documents. However, the bottleneck of indexing a large number of dense vectors and performing an NNS hurts the query time and accuracy of these models. In this paper, we argue that high-dimensional and ultra-sparse embedding is a significantly superior alternative to dense low-dimensional embedding for both query efficiency and accuracy. Extreme sparsity eliminates the need for NNS by replacing them with simple lookups, while its high dimensionality ensures that the embeddings are informative even when sparse. However, learning extremely high dimensional embeddings leads to blow up in the model size. To make the training feasible, we propose a partitioning algorithm that learns such high dimensional embeddings across multiple GPUs without any communication. This is facilitated by our novel asymmetric mixture of Sparse, Orthogonal, Learned and Random (SOLAR) Embeddings. The label vectors are random, sparse, and near-orthogonal by design, while the query vectors are learned and sparse. We theoretically prove that our way of one-sided learning is equivalent to learning both query and label embeddings. With these unique properties, we can successfully train 500K dimensional SOLAR embeddings for the tasks of searching through 1.6M books and multi-label classification on the three largest public datasets. We achieve superior precision and recall compared to the respective state-of-the-art baselines for each of the tasks with up to 10 times faster speed.
GKM-theory for torus actions on cyclic quiver Grassmannians

We define and investigate algebraic torus actions on quiver Grassmannians for nilpotent representations of the equioriented cycle. Examples of such varieties are type $\tt A$ flag varieties, their linear degenerations, finite dimensional approximations of the $GL_n$-affine flag variety and affine Grassmannian. We show that these quiver Grassmannians, equipped with our torus action, are GKM-varieties and that their moment graph admits a combinatorial description in terms of coefficients quiver of the underlying quiver representations. By adapting to our setting results by Gonzales, we are able to prove that moment graph techniques can be applied to construct module bases for the equivariant cohomology of the above quiver Grassmannians.
Atomic and molecular gas properties during cloud formation

Molecular clouds, which harbor the birthplaces of stars, form out of the atomic phase of the interstellar medium (ISM). We aim to characterize the atomic and molecular phases of the ISM and set their physical properties into the context of cloud formation processes. We studied the cold neutral medium (CNM) by means of $\rm HI$ self-absorption (HISA) toward the giant molecular filament GMF20.0-17.9 and compared our results with molecular gas traced by $^{13}\rm CO$ emission. We fitted baselines of HISA features to $\rm HI$ emission spectra using first and second order polynomial functions. The CNM identified by this method spatially correlates with the morphology of the molecular gas toward the western region. However, no spatial correlation between HISA and $^{13}\rm CO$ is evident toward the eastern part of the filament. The distribution of HISA peak velocities and line widths agrees well with $^{13}\rm CO$ within the whole filament. The column density probability density functions (N-PDFs) of HISA (CNM) and $\rm HI$ emission (tracing both the CNM and the warm neutral medium, WNM) have a log-normal shape for all parts of the filament, indicative of turbulent motions as the main driver for these structures. The $\rm H_2$ N-PDFs show a broad log-normal distribution with a power-law tail suggesting the onset of gravitational contraction. The saturation of $\rm HI$ column density is observed at $\sim$25$\rm\,M_{\odot}\,pc^{-2}$. We conjecture that different evolutionary stages are evident within the filament. In the eastern region, we witness the onset of molecular cloud formation out of the atomic gas reservoir while the western part is more evolved, as it reveals pronounced $\rm H_2$ column density peaks and signs of active star formation.
Predicting Game Difficulty and Churn Without Players

We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribution is to combine AI gameplay using Deep Reinforcement Learning (DRL) with a simulation of how the player population evolves over the levels. The AI players predict level difficulty, which is used to drive a player population model with simulated skill, persistence, and boredom. This allows us to model, e.g., how less persistent and skilled players are more sensitive to high difficulty, and how such players churn early, which makes the player population and the relation between difficulty and churn evolve level by level. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences, without requiring costly retraining of agents or collecting new DRL gameplay data for each simulated player.
Quantitative inference of the H2 column densities from 3 mm molecular emission: case study towards Orion B

Context. Based on the finding that molecular hydrogen is unobservable in cold molecular clouds, the column density measurements of molecular gas currently rely either on dust emission observation in the far-infrared, which requires space telescopes, or on star counting, which is limited in angular resolution by the stellar density. The (sub)millimeter observations of numerous trace molecules can be effective using ground-based telescopes, but the relationship between the emission of one molecular line and the H2 column density is non-linear and sensitive to excitation conditions, optical depths, and abundance variations due to the underlying physico- chemistry. Aims. We aim to use multi-molecule line emission to infer the H2 molecular column density from radio observations. Methods. We propose a data-driven approach to determine the H2 gas column densities from radio molecular line observations. We use supervised machine-learning methods (random forest) on wide-field hyperspectral IRAM-30m observations of the Orion B molecular cloud to train a predictor of the H2 column density, using a limited set of molecular lines between 72 and 116 GHz as input, and the Herschel-based dust-derived column densities as “ground truth” output. Results. For conditions similar to those of the Orion B molecular cloud, we obtained predictions of the H2 column density within a typical factor of 1.2 from the Herschel-based column density estimates. A global analysis of the contributions of the different lines to the predictions show that the most important lines are 13CO(1–0), 12CO(1–0), C18O(1–0), and HCO+(1–0). A detailed analysis distinguishing between diffuse, translucent, filamentary, and dense core conditions show that the importance of these four lines depends on the regime, and that it is recommended that the N2H+(1–0) and CH3OH(20–10) lines be added for the prediction of the H2 column density in dense core conditions. Conclusions. This article opens a promising avenue for advancing direct inferencing of important physical parameters from the molecular line emission in the millimeter domain. The next step will be to attempt to infer several parameters simultaneously (e.g., the column density and far-UV illumination field) to further test the method.
A bridge between elliptic and parabolic Harnack inequalities

The notion of conformal walk dimension serves as a bridge between elliptic and parabolic Harnack inequalities. The importance of this notion is due to the fact that the finiteness of the conformal walk dimension characterizes the elliptic Harnack inequality. Roughly speaking, the conformal walk dimension is the infimum of all possible values of the walk dimension that can be attained by a time-change of the process and by a quasisymmetric change of the metric. We show that the conformal walk dimension of any space satisfying the elliptic Harnack inequality is two. We also provide examples that show that the infimum in the definition of conformal walk dimension may or may not be attained.
Compact sources and cosmological horizons in lower dimensional bootstrapped Newtonian gravity

We study the bootstrapped Newtonian potential generated by a localised source in one and two spatial dimensions, and show that both cases naturally lead to finite spatial extensions of the outer vacuum. We speculate that this implies the necessary existence of a cosmological (particle) horizon associated with compact sources. In view of the possible dimensional reduction occurring in ultra-high energy processes—like scatterings at Planckian energies, the gravitational collapse of compact objects or the end-point of black hole evaporation—one can consider such lower-dimensional ‘bubbles’ immersed in our Universe as describing (typically Planckian size) baby Universes relevant to those dynamics.
Ecological functioning in arid Australia and research to assist conservation of biodiversity

This discussion paper outlines what we perceive to be current conservation problems in arid Australia. We call for better land-use planning for conservation, for the integration of conservation and other types of land use, and for an agenda for research that is required to assist this planning and integration. We identify four key themes on which we believe research is imperative to assist the conservation of biodiversity. The research themes are: (1) identification of spatial and temporal patterns of distribution of native biota; (2) quantification of the impact of pastoralism on native biota; (3) identification of, and control of, potential non-native pest species; and (4) development of methods and technologies to allow regional conservation planning. For each of these themes we examine current knowledge and on-going research and provide a framework in which these research areas could be addressed. We offer this discussion to help refine and target research expenditure.