Miracle-dz/UniFAR-FTUs huggingface dataset:
KOMPETENSI TEKNOLOGI INFORMASI DAN KOMUNIKASI GURU DALAM PENINGKATAN KUALITAS PEMBELAJARAN

Abstrak:Tulisan ini bertujuan untuk mengkaji berbagai solusi untuk peningkatan kompetensi TIK guru sehingga dapat merancang dan memanfaatkan TIK secara terpadu di dalam kegiatan pembelajaran yang pada akhirnya bermuara pada peningkatan proses dan hasil pembelajaran. Masalah yang akan jadi fokus pembahasan adalah berbagai upaya yang kemungkinan dapat meningkatkan kompetensi TIK guru. Hasil kajian mengungkapkan bahwa upaya peningkatan kompetensi TIK guru menuntut dukungan: kebijakan pemerintah pusat dan daerah di bidang pemanfaatan TIK dan pelatihan guru di bidang pemanfaatan TIK, kesadaran guru untuk meningkatkan potensi diri mereka di bidang pemanfaatan TIK,organisasi profesi guru mewadahi penyelenggaraan seminar atau lokakarya, dan menerbitkan jurnal ilmiah, swasta/dunia usaha diharapkan optimal di bidang pemanfaatan TIK untuk pembelajaran melalui tanggung jawab sosial di bidang pendidikan (corporate social responsibility atau csr),kebijakan kepala sekolah untuk penugasan guru mengikuti pelatihan pemanfaatan TIK untuk pembelajaran, dan kepedulian orang tua/masyarakat untuk peningkatan kemampuan TIK guru dapat berupa bantuan peralatan TIK atau motivasi kepada siswa, guru dan sekolah.Kata Kunci: Teknologi Informasi dan Komunikasi Guru, Kompetensi Guru, Teknologi Informasi dan KomunikasiÂ Abstract:Along with the development of information communication and technology (ICT) and also its potential to support the activities of learning process, the writer had been inspired to conduct a study about teacherâ€™s ICT competency. To do so, the writer had studied various facts and documents about the teacherâ€™s ICT competency and the factors that contribute to it. The focused problems on this writing is variety of efforts to improve teacherâ€™s ICT competency. The findings of the study revealed that those variety of efforts to improve the teacherâ€™s ICT competency demanding support as in the following: (1) the policy of the central and local government and conducting teacherâ€™s training in ICT utilization, (2) awareness of teachers to improve their capacity building in ICT utilization, (3) teacherâ€™s professional organization encouraging for active involvement in seminars or workshops and publish scientific journal), (4) private businesses and their support in the utilization of ICT in learning process through corporate social responsibility (CSR), (5)The head masterâ€™s policies to ask teachers following the training in ICT utilization, and (6) Concern from parents / the community to increase teacherâ€™s ICT by procuring the ICT facilities or creating motivation to students, teachers and schools.Key words: Information and Communication Technology (ICT), Teacher, Competency
Running Head: Perceptions of Emotional Abuse in the coach-athlete in The influence of outcome

Emotional abuse has been highlighted as a key issue within the youth sport context. The present 2 study investigated how perceptions of emotional abuse are influenced by situational factors. Two 3 hundred and eight participants (107 athletes and 101 coaches) were shown a series of vignettes 4 depicting emotionally abusive behaviour by a coach towards a 14 year old athlete. Differences in 5 perceptions were explored in relation to the level of competition (elite, county and club) and 6 performance outcome (successful/unsuccessful) depicted in the vignette. Participants rated each 7 vignette on a 5 point scale in terms of the extent to which the coach’s behaviour had an impact 8 on the athlete’s performance and wellbeing as well as the perceived commonality and 9 acceptability of the behaviour. Two-way ANOVAs revealed that competitive level and 10 performance outcome, both as main effects and as an interaction, significantly influenced 11 perceptions. These findings can inform policy and practice to change attitudes and behaviours 12 which support and justify emotionally abusive behaviours in youth sport contexts.
University of Birmingham Other people ’ s money :

Many important decisions are taken not by the person who will ultimately gain or lose from the outcome, but on their behalf by somebody else. We examined economic decision-making about risk and time in situations in which deciders chose for others who also chose for them. We propose that this unique setting, which has not been studied before, elicits perception of reciprocity that prompts a unique bias in preferences. We found that decision-makers are less patient (more discounting), and also more risk-averse for losses than gains, with other peoples’ money, especially when their choices for others are more uncertain. Those results were derived by exploiting a computational modelling framework that has been shown to account for the underlying psychological and neural decision processes. We propose a novel theoretical mechanism – precautionary preferences under social uncertainty, which explains the findings. Implications for future research and alternative models are also discussed.
m-STONE LATTICES

A Stone lattice is a distributive, pseudo-complemented lattice L such that a* V a** = 1, for all a in L; or equivalently, a bounded distributive lattice L in which, for all a in L, the annihilator a = {ô Ç L|a A 6 = 0} is a principal ideal generated by an element of the centre of L, namely a*. Thus it is natural to define an m-Stone lattice to be a bounded distributive lattice L in which, for each subset A of cardinality less than or equal to m, the annihilator A= {b Ç L\a A b = 0, for all a Ç A} is a principal ideal generated by an element of the centre of L. In this paper we characterize m-Stone lattices, and show, by considering the lattice of global sections of an appropriate sheaf, that any bounded distributive lattice can be embedded in an m-Stone lattice, the embedding being a left adjoint to the forgetful functor.
Precision Grazing Management- Understanding Farmer Uptake Of Grazing Software

Increased precision in grazing management decisions requires regular pasture measurement and effective use of the data for enhanced decision-making. Technology adoption theory can help to provide insights on the incentives and barriers to uptake and on-farm adaptation of precision farming tools. In this study, we used an inductive workshop process with experts in grazing management software design and marketing to explore software adoption by farmers. Important factors included alignment of data for monitoring key performance indicators, using data for benchmarking and reporting, enabling farm team communication. Discontinued use by farmers was seen to be influenced by; technology features being misaligned with farmer practice, users not having the skills required to interpret data, practice change inertia, and farmers lacking the time to invest in learning. Future research aimed at understanding those farmers who apply ‘best practice’ in respect to grazing management, either with or without the use of precision farming tools, would guide better product development.
AND CONTRACTILE STRESSES IN AN ACTIVE NEMATIC AND ACTIVE

We use a continuum model to examine the eﬀect of activity on a phase separating mixture of an extensile active nematic and a passive ﬂuid. We highlight the distinct role of previously considered interfacial active stresses and bulk active stresses that couple to liquid crystalline degrees of freedom. Interfacial active stresses can arrest phase separation, as previously demonstrated. Bulk extensile active stresses can additionally strongly suppress phase separation by sustained self-stirring of the ﬂuid, substantially reducing the size of the coexistence region in the temperature/concentration plane relative to that of the passive system. The phase separated state is a dynamical emulsion-like steady state of continuously splitting and merging droplets, as suggested by recent experiments. Using scaling analysis and simulations, we identify various regimes for the dependence of droplet size on activity. These results can provide a criterion for identifying the mechanisms responsible for arresting phase separation in experiments. EXTENSILE MODEL H This section includes a short summary of extensile and contractile stresses for both models for the reader’s convenience. The main text focuses mainly on the bulk active stress created by the liquid-crystalline degrees of freedom, although we also brieﬂy mention and discuss a purely interfacial stress. The bulk active stress of an active nematic is written as σ Q = αQ ij , where positive and negative values of α describe contractile and extensile stresses respectively. If the constituent particles making the active nematic are uniaxial rods, these stresses are of the form sketched in Figure S1 (a) and (b). We have additionally focused only on the extensile case, as uniform states are generally unstable to these stresses [1] and develop chaotic ﬂows that provide the self-stirring force that stabilizes the uniform state. The second form of activity is that of active model H, produced by allowing the stress of interface gradients to have non equilibrium values. In the main text we include this activity through the parameter σ a = κ a σ/κ , which has its equilibrium value at κ a = κ . Notice that the form of the stress, σ φ = − κ a ( ∂ i φ∂ j φ − δ ij / 2( ∇ φ ) 2 ), resembles the bulk stress for a nematic director in the direction of ∂ i φ . Physically, we can then interpret negative values of κ a as contractile stresses in the direction of ∂ i φ , or extensile in a direction tangential to the interface, and viceversa for positive values of κ a . This is sketched in Figure S1 (c) and (d). Notice that active model H also generally includes other terms that function as time reversal symmetry breaking terms in the chemical potential of φ [2]. We have ignored these terms as they do not have an impact in the ﬂows created by interfacial stresses nor in the typical growth laws observed [2, 3].
Scalp-Mounted Electrical Impedance Tomography of Cerebral Hemodynamics

An Electrical Impedance Tomography (EIT) system has been developed for dynamic three-dimensional imaging of changes in conductivity distribution in the human head, using scalp-mounted electrodes. We attribute these images to changes in cerebral perfusion. At 100 frames per second (fps), voltage measurement is achieved with full-scale signal-to-noise ratio of 105 dB and common-mode rejection ratio > 90 dB. A novel nonlinear method is presented for 3-D imaging of the difference in conductivity distribution in the head, relative to a reference time. The method achieves much reduced modelling error. It successfully localizes conductivity inclusions in experimental and simulation tests, where previous methods fail. For > 50 human volunteers, the rheoencephalography (REG) waveform is observed in EIT voltage measurements for every volunteer, with peak-to-peak amplitudes up to approx. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$50 ~\mu \text{V}_{{\mathrm {rms}}}$ \end{document}. Images are presented of the change in conductivity distribution during the REG/cardiac cycle, at 50 fps, showing maximum local conductivity change of approx. 1% in grey/white matter. A total of 17 tests were performed during short (typically 5s) carotid artery occlusions on 5 volunteers, monitored by Transcranial Doppler ultrasound. From EIT measurements averaged over complete REG/cardiac cycles, 13 occlusion tests showed consistently decreased conductivity of cerebral regions on the occluded side, and increased conductivity on the opposite side. The maximum local conductivity change during occlusion was approx. 20%. The simplicity of the carotid artery intervention provides a striking validation of the scalp-mounted measurement system in imaging cerebral hemodynamics, and the REG images indicate its unique combination of sensitivity and temporal resolution.
A Cross-Sectional Assessment of Dietary Patterns and Their Relationship to Hypertension and Obesity in Indonesia

ABSTRACT Background There is a marked increase in the intake of foods associated with higher risks for hypertension and obesity in Indonesia. However, studies assessing the relationship between dietary patterns and health outcomes are few. Objective The purpose of this study was to characterize dietary patterns and investigate their relationship with hypertension and obesity in Indonesia. Methods Exploratory factor analysis was used to derive dietary patterns from a brief food scanner filled by 31,160 respondents aged 15 y and older in the Indonesian Family Life Survey wave 5 (IFLS 5). Age- and gender-specific quintiles of consumption were created for each pattern and the association between quintiles of each dietary pattern and the odds for hypertension and obesity were assessed using multivariate logistic regression analyses. Results Two dietary patterns were identified: a modern dietary pattern characterized by fast foods, soft drinks, sweet snacks, and salty snacks and a traditional pattern characterized by fish, vegetables, and fruits. Younger age and being male were significantly correlated with higher consumption of the modern pattern (P < 0.0001 and P = 0.03, respectively). Analyses showed no association between hypertension and the modern pattern. However, the traditional pattern revealed lower odds for hypertension among those in the highest quintile compared with the lowest quintile (OR: 0.84; 95% CI: 0.74, 0.95; P-trend < 0.05). Individuals in the highest quintile of each dietary pattern had higher odds of obesity compared with those in the lowest quintile (modern pattern—OR: 1.31; 95% CI: 1.15, 1.49; P-trend < 0.00; traditional pattern—OR: 1.25; 95% CI: 1.10, 1.42; P-trend < 0.01). Conclusions More studies using gold-standard measures of dietary intake are needed to better understand the relationship between the modern dietary pattern and hypertension in Indonesia. Also, both modern and traditional dietary patterns in Indonesia may be energy dense, leading to higher risk for obesity.
ScholarWorks @ UTRGV ScholarWorks @ UTRGV Capital and Labor Mobility and their Impacts on Mexico’s Regional Capital and Labor Mobility and their Impacts on Mexico’s Regional Labor Markets Labor Markets

: This paper examines Mexico’s real wages across its 32 states for the period 1997-2006. Employing dynamic panel data methods, our estimates for the effects of Mexico’s “second wave” of market liberalization on real wages provide interesting results. First, we find strong positive effects on real wages from foreign direct investment (FDI) and also positive but smaller effects from migration. Second, slightly larger wage effects are observed from domestic than from foreign migration. Third, alternative partitions indicate that real wages are more sensitive to FDI-related fluctuations across states with relatively lower wages and migration levels. Other robustness exercises are provided. Abstract: This paper examines Mexico’s real wages across its 32 states for the period 1997-2006. Employing dynamic panel data methods, our estimates for the effects of Mexico’s “second wave” of market liberalization on real wages provide interesting results. First, we find strong positive effects on real wages from foreign direct investment (FDI) and also positive but smaller effects from migration. Second, slightly larger wage effects are observed from domestic than from foreign migration. Third, alternative partitions indicate that real wages are more sensitive to FDI-related fluctuations across states with relatively lower wages and migration levels. Other robustness exercises are provided.
Chemical Diversity in Protoplanetary Disks and Its Impact on the Formation History of Giant Planets

Giant planets can interact with multiple and chemically diverse environments in protoplanetary disks while they form and migrate to their final orbits. The way this interaction affects the accretion of gas and solids shapes the chemical composition of the planets and of their atmospheres. Here we investigate the effects of different chemical structures of the host protoplanetary disk on the planetary composition. We consider both scenarios of molecular (inheritance from the prestellar cloud) and atomic (complete chemical reset) initial abundances in the disk. We focus on four elemental tracers of different volatility: C, O, N, and S. We explore the entire extension of possible formation regions suggested by observations by coupling the disk chemical scenarios with N-body simulations of forming and migrating giant planets. The planet formation process produces giant planets with chemical compositions significantly deviating from that of the host disk. We find that the C/N, N/O, and S/N ratios follow monotonic trends with the extent of migration. The C/O ratio shows a more complex behavior, dependent on the planet accretion history and on the chemical structure of the formation environment. The comparison between S/N* and C/N* (where * indicates normalization to the stellar value), constrains the relative contribution of gas and solids to the total metallicity. Giant planets whose metallicity is dominated by the contribution of the gas are characterized by N/O* > C/O* > C/N* and allow to constrain the disk chemical scenario. When the planetary metallicity is instead dominated by the contribution of the solids we find that C/N* > C/O* > N/O*.
Data augmentation for learning predictive models on EEG: a systematic comparison

Objective. The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis. Approach. We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments. Main results. We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task. Significance. Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain–computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.
On-device synaptic memory consolidation using Fowler-Nordheim quantum-tunneling

Introduction For artificial synapses whose strengths are assumed to be bounded and can only be updated with finite precision, achieving optimal memory consolidation using primitives from classical physics leads to synaptic models that are too complex to be scaled in-silico. Here we report that a relatively simple differential device that operates using the physics of Fowler-Nordheim (FN) quantum-mechanical tunneling can achieve tunable memory consolidation characteristics with different plasticity-stability trade-offs. Methods A prototype FN-synapse array was fabricated in a standard silicon process and was used to verify the optimal memory consolidation characteristics and used for estimating the parameters of an FN-synapse analytical model. The analytical model was then used for large-scale memory consolidation and continual learning experiments. Results We show that compared to other physical implementations of synapses for memory consolidation, the operation of the FN-synapse is near-optimal in terms of the synaptic lifetime and the consolidation properties. We also demonstrate that a network comprising FN-synapses outperforms a comparable elastic weight consolidation (EWC) network for some benchmark continual learning tasks. Discussions With an energy footprint of femtojoules per synaptic update, we believe that the proposed FN-synapse provides an ultra-energy-efficient approach for implementing both synaptic memory consolidation and continual learning on a physical device.
Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding

End-to-end spoken language understanding (SLU) systems benefit from pretraining on large corpora, followed by fine-tuning on application-specific data. The resulting models are too large for on-edge applications. For instance, BERT-based systems contain over 110M parameters. Observing the model is overparameterized, we propose lean transformer structure where the dimension of the attention mechanism is automatically reduced using group sparsity. We propose a variant where the learned attention subspace is transferred to an attention bottleneck layer. In a low-resource setting and without pre-training, the resulting compact SLU model achieves accuracies competitive with pre-trained large models.
TweetNLP: Cutting-Edge Natural Language Processing for Social Media

In this paper we present TweetNLP, an integrated platform for Natural Language Processing (NLP) in social media. TweetNLP supports a diverse set of NLP tasks, including generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. Task-specific systems are powered by reasonably-sized Transformer-based language models specialized on social media text (in particular, Twitter) which can be run without the need for dedicated hardware or cloud services. The main contributions of TweetNLP are: (1) an integrated Python library for a modern toolkit supporting social media analysis using our various task-specific models adapted to the social domain; (2) an interactive online demo for codeless experimentation using our models; and (3) a tutorial covering a wide variety of typical social media applications.
Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme

Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. Regression adjustment is highly encouraged in the analysis stage to adjust for the remaining covariate imbalances. This study proposes methods that combine blocking, rerandomization, and regression adjustment techniques in randomized experiments with high-dimensional covariates and a general blocking scheme. In the design stage, we suggest the implementation of blocking and rerandomization to balance the fixed number of covariates most relevant to the outcomes. For the analysis stage, we propose a regression adjustment method based on least absolute shrinkage and selection operator (Lasso) to adjust for the remaining imbalances in additional highdimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions under which this estimator is more efficient than the unadjusted one. Moreover, we provide a conservative variance estimator to facilitate valid inferences. Our design-based analysis allows for model misspecification and is applicable to heterogeneous block sizes, propensity scores, and treatment effects. Simulation studies and two real-data analyses demonstrate the advantages of the proposed method.