Miracle-dz/UniFAR-FTUs huggingface dataset:
Representing reality: Investigating the perception-action couplings of expert soccer Goalkeepers under representative constraints

Representative Design has become a frequently used term across sport science (Pinder et al. 2011). Brunswiks original assertion that there “is little technical basis for telling whether a given experiment is an ecological normal, located in the midst of a crowd of natural instances, or whether it is more like a bearded lady at the fringes of reality” (Brunswik, 1955, p.204) led towards a key concept, representation design. Brunswik (1955, p.198) proposed that researchers “must resist the temptation {. } to interfere” with the environment and “instead strive to retain its natural causal texture in the stimuli presented to participants” (Dhami et al, 2004 p.962). In football goalkeeping, early work on expertise used a penalty kick task in in-situ and digital environments (See Savelsbergh et al. 2002). Whilst the findings are interesting, it is worth proceeding with caution and attention should be turned towards creating representative experiments that are reflective of the environment the individual inhabits. This study attempted to show differences in perceptual behaviours between a traditional task, and a representative task. In a professional football case study, 4 goalkeepers took part in both conditions over a season. Using the Quiet Eye(QE) (Vickers, 1996) as the perceptual mechanism for analysis, eye behaviours were recorded via an SMI-ETG mobile device, and coded with a Vision-In-Action system. QE duration was significantly longer (t2=2.66,P≤0.05) in Penalty trial (PT) than in the Representative trial (RT) (50.75%±2.84% v 45.57%±0.93%). QE onset occurred significantly (t2=4.75,P<0.05) earlier in PT than in RT (21.13%±4.21% v 36.38%±4.30%). Significant differences were observed (t2=3.36,P≤0.05) in QE offset, with PT occurring earlier than in RT (73.48%±1.58% v 82.40%±3.79%). In PT, a significantly greater (t2=3.27,P≤ 0.05) number of fixations occurred at the ball (6.75f±2.22) than the visual pivot (2.25f±1.26). However, in RT, there was no significant differences between mean number of fixations at the ball (7.75f±2.22) and visual pivot (6.25f±3.5). Our findings here illustrate the task specificity of expertise. To try and provide an account of what expert performance looks like, coaches, practitioners and talent developers must be certain they are utilising environments that are reflective of where expert behaviours exist.
Small infrared target detection by data-driven proposal and deep learning-based classification

In this paper, we propose a data-driven proposal and deep-learning based classification scheme for small target detection. Previous studies have shown feasible performance using conventional computer vision techniques such as spatial and temporal filters. However, those are handcrafted approaches and are not optimized due to the nature of the application fields. Recently, deep learning has shown excellent performance for many computer vision problems, which motivates the deep learning-based small target detection. The proposed data-driven proposal and convolutional neural network (DDP-CNN) can generate possible target locations through the data-driven proposal and final targets are recognized through the classification network. According to the experimental results using drone database, the DDP-CNN shows 91% of train accuracy and 0.85 of average precision (AP) of the target detection.
Subsequential minimality in Gowers and Maurey spaces

We define block sequences (xn) in every block subspace of a variant of the space of Gowers and Maurey, so that the map x2n−1↦ x2n extends to an isomorphism. This implies the existence of a subsequentially minimal HI space, which solves a question in V. Ferenczi and C. Rosendal (‘Banach spaces without minimal subspaces’, J. Funct. Anal. 257 (2009) 149–193).
Why Story Matters: A Review of Narrative in Serious Games

Literature has shown that immersive learning environments such as digital educational games and simulations often incorporate storytelling elements in their designs as narrative can be an effective way of making learning more meaningful to students. The purpose of this study is to review the literature on the role narrative can play in the experience of a learner engaging in learning games and to synthesize research on features of story that have demonstrated success in these learning environments. The findings have shown that distributed narrative, intrinsically integrated fantasies, empathetic characters and virtual agents, and adaptiveness or responsivity are four characteristics of game narratives found to be effective. Several learning game analyses were performed to illustrate how these games used narrative to foster greater immersion, engagement, motivation, and learning. Finally, a narrative design strategy for serious games is suggested which integrates the effective narrative features as shown in the example games, along with two analysis frameworks, Game Discourse Analysis and Narrative Centered Informant Design. The findings of this study should provide much-needed insights to designers and researchers who are involved in creating immersive learning environments.
A Usage of McCall's Software Quality Analysis on the Bonus System of PT Surya Pratama Alam

This research is to analyse the software quality of the Bonus System of PT Surya Pratama Alam Yogyakarta. The goal of this study is to determine the level of users’ satisfaction in the use of the system. Method of analysis used is McCall method which is based on operation products with usability factors. The metrics used are Operability metrics and Training metrics. In the operability metrics, the researchers refer to the J.R. Lewis Questionnaires. Based on the conducted research, researchers can state that the results of software quality measurements of the Bonus System of PT Surya Pratama Alam Yogyakarta is 74.72 % with standard deviation ±14.39 % for Operability metrics and 4.41 seconds with standard deviation ± 1.36 seconds for Training metrics. Based on these two metrics, it can be concluded that the Bonus System of PT Surya Pratama Alam Yogyakarta already has good usability value.
Buffer overflow asymptotics for a buffer handling many traffic sources

As a model for an ATM switch we consider the overflow frequency of a queue that is served at a constant rate and in which the arrival process is the superposition of N traffic streams. We consider an asymptotic as N → ∞ in which the service rate Nc and buffer size Nb also increase linearly in N. In this regime, the frequency of buffer overflow is approximately exp(–NI(c, b)), where I(c, b) is given by the solution to an optimization problem posed in terms of time-dependent logarithmic moment generating functions. Experimental results for Gaussian and Markov modulated fluid source models show that this asymptotic provides a better estimate of the frequency of buffer overflow than ones based on large buffer asymptotics.
Bayesian closed-loop robust process design considering model uncertainty and data quality

Abstract Response-surface-based design optimization has been commonly used in Robust Process Design (RPD) to seek optimal process settings for minimizing the output variability around a target value. Recently, the online RPD strategy has attracted increasing research attention, as it is expected to provide a better performance than offline RPD by utilizing online process feedback to continuously adjust process settings during process operation. However, the lack of knowledge about process model parameter uncertainty and data quality in the online RPD decisions means that this superiority cannot be guaranteed. This motivates this article to present a Bayesian approach for online RPD, which can provide systematic decisions of when and how to update the process model parameters for online process design optimization by considering data quality. The effectiveness of the proposed approach is illustrated with both simulation studies and a case study on a micro-milling process. The comparison results demonstrate that the proposed approach can achieve a better process performance than two conventional design approaches that do not consider the data quality and model parameter uncertainty.
CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain)

Abstract. The NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona.
On a Notion of Exactness for Reduced Free Products of C*-Algebras

We will study some modifications to the notion of an exact C*-algebra by replacing the minimal tensor product with the reduced free product. First we will demonstrate how the reduced free product of a short exact sequence of C*-algebras with another C*-algebra may be taken. It will then be demonstrated that this operation preserves exact sequences. We will also establish that adjoining arbitrary k-tuples of operators in a free way behaves well with respect to taking ultrapowers.
Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms

Nowadays, the application of data mining is widely prevalent in the education system. The ability of data mining to obtain meaningful information from meaningless data makes it very useful to predict students’ achievement, university’s performance, and many more. According to the Department of Statistics Malaysia, the numbers of student who do not manage to graduate on time rise dramatically every year. This challenging scenario worries many parties, especially university management teams. They have to timely devise strategies in order to enhance the students’ academic achievement and discover the main factors contributing to the timely graduation of undergraduate students. This paper discussed the factors utilized by other researchers from previous studies to predict students’ graduation time and to study the impact of different types of factors with different prediction methods. Taken together, findings of this research confirmed the usefulness of Neural Network and Support Vector Machine as the most competitive classifiers compared with Naïve Bayes and Decision Tree. Furthermore, our findings also indicate that the academic assessment was a prominent factor when predicting students’ graduation time.
Reaction–diffusion on the fully-connected lattice: A+A→A

Diffusion-coagulation can be simply described by a dynamic where particles perform a random walk on a lattice and coalesce with probability unity when meeting on the same site. Such processes display non-equilibrium properties with strong fluctuations in low dimensions. In this work we study this problem on the fully-connected lattice, an infinite-dimensional system in the thermodynamic limit, for which mean-field behaviour is expected. Exact expressions for the particle density distribution at a given time and survival time distribution for a given number of particles are obtained. In particular, we show that the time needed to reach a finite number of surviving particles (vanishing density in the scaling limit) displays strong fluctuations and extreme value statistics, characterized by a universal class of non-Gaussian distributions with singular behaviour.
Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis

This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results. The task is a followup to Task 4 of DCASE 2018, and involves training systems for large-scale detection of sound events using a combination of weakly labeled data, i.e. training labels without time boundaries, and strongly-labeled synthesized data. The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that we’ll describe more in detail. We also report the performance of the submitted systems on the official evaluation (test) and development sets as well as several additional datasets. The best systems from this year outperform last year’s winning system by about 10% points in terms of F-measure.
Regionally refined capability in E3SM Atmosphere Model Version 1 (EAMv1) and applications for high-resolution modelling

Abstract. Climate simulation with more accurate process-level representation at finer resolutions is a pressing need in order to provide actionable information to policy-makers regarding extreme events in a changing climate. Computational limitation is a major obstacle for building, and running high-resolution (HR, here 0.25° average grid spacing at the equator) models (HRM). A more affordable path to HRM is to use a global regionally refined model (RRM), which only simulates a portion of the globe at HR while the remaining is at low-resolution (LR, 1°). In this study, we compare the Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) RRM with the HR mesh over the contiguous United States (CONUS) to its corresponding globally uniform LR and HR configurations, as well as to observations and reanalysis data. The RRM has a significantly reduced computational cost (roughly proportional to the HR mesh size) relative to the globally uniform HRM. Over the CONUS, we evaluate the simulation of important dynamical and physical quantities as well as various precipitation measures. Differences between the RRM and HRM over the HR region are predominantly small, demonstrating that the RRM reproduces both well- and poorly simulated behaviours of the HRM over the CONUS. Further analysis based on RRM simulations with the LR vs. HR model parameters reveals that RRM performance is greatly influenced by the different parameter choices used in the LR and HR EAMv1. This is a result of the poor scale-aware behaviour of physical parameterizations, especially for variables influencing sub-grid scale physical processes. RRM can serve as a useful framework to test physics schemes across a range of scales, leading to improved consistency in future E3SM versions. Applying nudging-to-observations techniques within the RRM framework also demonstrates significant advantages over a free-running configuration for use as a testbed, and as such represents an efficient and more robust physics testbed capability. Our results provide additional confirmatory evidence that the RRM is an efficient and effective approach for HRM development and hydrologic research.
Moose (Alces alces) predation by eastern coyotes (Canis latrans) and eastern coyote × eastern wolf (Canis latrans × Canis lycaon) hybrids

It has been widely assumed that coyotes (Canis latrans Say, 1823) are incapable of killing adult moose (Alces alces (L., 1758)) and previous studies of coyote predation support this assumption. However, eastern coyotes and eastern coyote × eastern wolf (Canis lycaon Schreber, 1775) are larger than western coyotes and appear to rely on larger prey in some areas. We used a combination of GPS telemetry, genetic analysis, and field investigation to test the hypothesis that eastern coyotes and coyote × wolf hybrids are capable of preying on adult moose in central Ontario. Our hypothesis was supported, as we documented four definitive cases of eastern coyotes and (or) eastern coyote × eastern wolf hybrids killing moose ≥1.5 years old. Predation by coyotes and coyote × wolf hybrids probably does not represent a threat to moose population viability in central Ontario, but our results suggest that researchers and managers in other areas with declining moose populations that are sympatric with eastern coyotes and (or) coyote × wolf hybrids should consider coyote predation as a potential source of mortality.
Effect of ocean acidification and elevated temperature on growth of calcifying tubeworm shells (Spirorbis spirorbis): An in-situ benthocosm approach

The calcareous tubeworm Spirorbis spirorbis is a wide-spread serpulid species in the Baltic Sea, where it commonly grows as an epibiont on brown macroalgae (genus Fucus). It lives within a Mg-calcite shell and could be affected by ocean acidification and temperature rise induced by the predicted future atmospheric CO2 increase. However, Spirorbis tubes grow in a chemically modified boundary layer around the algae, which may mitigate acidification. In order to investigate how increasing temperature and rising pCO2 may influence S. spirorbis shell growth we carried out four seasonal experiments in the 'Kiel Outdoor Benthocosms' at elevated pCO2 and temperature conditions. Compared to laboratory batch culture experiments the benthocosm approach provides a better representation of natural conditions for physical and biological ecosystem parameters, including seasonal variations. We find that growth rates of S. spirorbis are significantly controlled by ontogenetic and seasonal effects. The length of the newly grown tube is inversely related to the initial diameter of the shell. Our study showed no significant difference of the growth rates between ambient atmospheric and elevated (1100 ppm) pCO2 conditions. No influence of daily average CaCO3 saturation state on the growth rates of S. spirorbiswas observed. We found, however, net growth of the shells even in temporarily undersaturated bulk solutions, under conditions that concurrently favored selective shell surface dissolution. The results suggest an overall resistance of S. spirorbis growth to acidification levels predicted for the year 2100 in the Baltic Sea. In contrast, S. spirorbis did not survive at mean seasonal temperatures exceeding 24 °C during the summer experiments. In the autumn experiments at ambient pCO2, the growth rates of juvenile S. spirorbis were higher under elevated temperature conditions. The results reveal that S. spirorbis may prefer moderately warmer conditions during their early life stages but will suffer from an excessive temperature increase and from increasing shell corrosion as a consequence of progressing ocean acidification.
Effects of Caffeine on Renal Toxicity Induced by Diethylnitrosamine

1. Resident of Medical Pharmacology, Faculty of Pharmacy, Jundishapur University of Medical Sciences, Ahvaz, Iran 2. Department of Pathobiology, Faculty of Veterinary Medicine, Shahid Chamran University of Ahvaz, Ahvaz, Iran 3. Ph.D Student of Biochemistry, International Campus, Shahid Soudoghi University of Medical Sciences, Yazd, Iran 4. Department of Food Hygiene, Faculty of Veterinary Medicine, Shahid Chamran University of Ahvaz, Ahvaz, Iran 5. Department of Basic Sciences, Faculty of Veterinary Medicine, Shahid Chamran University of Ahvaz, Ahvaz, Iran
A green proactive orchestration architecture for cloud resources

ABSTRACT Intrinsically, cloud computing is the paradigm that is been envisioned to dynamically furnish endless computing services to pervasive worldwide clients. However, the massive energy consumption resulted from the underpinning data centers would drastically hinder the full potential from the dreamed paradigm. Accordingly, this work opts towards devising green proactive cloud resources orchestration architecture. Specifically, this work focuses on cloud-based web applications which considered one of the most paramount cloud service categories. The proposed framework hence geared for leveraging cloud system resources utilization, ensuring explicit guarantees on negotiated web services’ obligations, meanwhile achieving power consumption minimization objectives. The proposed framework consolidates heuristic methodologies along with control theory approaches in a green resources orchestration hierarchical framework. Firstly, an energy-aware global controller is presented. The proposed global controller employs an online graph coloring-based approach for mapping the submitted virtual machines (VMs) to the appropriate cloud resources. The proposed global controller seeks energy-efficient configurations for the submitted VMs in a constrained environment without obligating the negotiated services SLAs. Secondly, a proactive fuzzy-logic-based local controller is proposed. The proposed local controller aimed at in confronting workloads’ sustainable fluctuations via proactive amendment for the placement and provisioning schedules. Additionally, the proposed local controller oriented towards maintaining active power management policy, especially during transient peak of usage, thereby mitigating overall costs, and extending resources capacity and performance capabilities. Conducted simulations and comparisons highly demonstrate the effectiveness of the proposed architecture at outperforming counterpart approaches specifically in terms of scalability, total energy consumption, and maintaining the web applications SLAs objectives in spite of workload fluctuations.
Measuring Enterprise Resource Planning (ERP) Systems Effectiveness in Indonesia

Refining DeLone and McLean’s (D&M) information system model and technology-organisation-environment (TOE) framework, this research identifies the prominent factors that determine ERP system success. Hypotheses are also drawn based on supporting theories to evaluate the causal relationship between the success determinants. The level of achievement is measured by system quality, information quality, service quality, external quality and top management support, which intermediated by perceived usefulness and user satisfaction towards business benefits. To provide empirical evidence, 86 valid samples out of 156 were collected using a web survey that targeted ERP users in Indonesia. Furthermore, Partial Least Squares–Structural Equation Modelling (PLS-SEM) algorithm were applied to check the proposed hypotheses. The results suggest system quality, information quality and service quality significantly affect user satisfaction, whereas they moderately impact on perceived usefulness. Interestingly, external pressures were reported as being the biggest influence on user satisfaction and positively impacted on perceived usefulness. Despite being fairly predictive to perceived usefulness, top management support along with general perceptual factors ultimately promote system success by elevating business benefits.