Miracle-dz/UniFAR-FTUs huggingface dataset:
Indonesian Journal of Electrical Engineering and Computer Science

Received May 11, 2021 Revised Oct 23, 2021 Accepted Oct 27, 2021 Gamification is described as the art of changing human activity to a gamelike environment to gain user engagement. Rimigs is a developed gamification system discussed in this research and was built based on Octalysis framework. This research aims to design and identify the impact of using a gamification system on students’ motivation and performance, especially in programming classes. Through Rimigs, students’ engagement and performance can be enhanced by freely choosing tasks or quests according to the rank points, completing these tasks or quests, getting experience points, getting rewards, avoiding punishment at certain times, and competing with others friends. The proposed research was done by using the iterative methodologies and consists of four phases, starting from i) literature review, which identifies the Octalysis’ core, ii) design, which identifies the game elements used in the system and designing the unified modeling language (UML) diagram of the system, iii) testing, which involve the user in the using of the system, and iv) implementation, which is the analysis step for the impact of Rimigs in students’ performance and engagement. This research finds that Rimigs as a gamification system can positively impact both students’ motivation and performance.
Malaria Consortium ’ s seasonal malaria chemoprevention program : Philanthropy report 2020

lower monthly counts of both 0.82, 95 percent and confirmed malaria cases (RR: 0.81, 95 CI: 0.71 a reduction in malaria incidence of around 20 percent. Sensitivity analyses showed effect sizes of up to 28 percent after modifying model assumptions. The findings may not be comparable with other studies and may over- or underestimate impact of SMC, as not all malaria cases present at health facilities, not all suspected cases are tested, and not all facilities report cases consistently. This study ’ s approach presents a solution for employing readily available routine data to evaluate the impact of health interventions at scale without extensive covariate data. However, further efforts are needed to improve the quality of routine data in Chad and elsewhere. Study findings were presented at logistic regression models for binary outcomes to identify factors associated with caregiver adherence.
Coronary Artery and Myocardium Injuries Induced by a High Cholesterol and Fructose Diet in Growing Male and Female Wistar Rats

Cardiovascular disease (CVD) are mainly consequent of atherosclerosis. Men develop CVD at a young age, this risk increases in women at an older age. Several studies have been carried out on male rats, but experiments on growing rats especially female are rare. The objective of this study was to investigate the effect of a high cholesterol and high fructose diet on the coronary artery and myocardium in growing male and female rats Young Wistar rats were divided into control groups fed a standard diet, cholesterol groups supplemented with 3% cholesterol (ChD), and cholesterol-fructose groups supplemented with 3% cholesterol and 15% fructose (ChFrD) for 14 weeks. Each group consists of male (n=6) and female (n=6) rats. We found, in comparison with corresponding controls rats, that both ChD and ChFrD diets caused a significant hyperglycemia and dyslipidaemia. In hearts supernatants, we highlighted increases of total lipids, malondialdehyde and Catalase assays. The histopathological examination showed a disorganization of the myocardial structure, arterial walls damage and endothelium injuries. Our study showed that ChD and ChFrD diets, caused weight, biochemical, oxidative and tissue disturbances that could lead to CVD in both young male and female Wistar rats even during
Adaption of an organizational innovation, Total Quality Control in industrial firms

SUMMARY This survey of the adoption of total quality control (TQC) in 98 companies in the Netherlands shows that few firms do set goals with respect to quality and quality control and that even fewer firms evaluate their own quality performance. The adoption of total quality control is a very time-consuming process, which may take about ten years. Furthermore the adoption process is often dominated by solving the quality problems by experts instead of improving the problem-solving capability of the organization. The adoption process of TQC and the actual application of TQC are related to each other and to company characteristics. In particular the size of the company is related to the formalization of the quality system.
Comparative Evaluation of Shear Bond Strength of Bioactive Restorative Material, Zirconia Reinforced Glass Ionomer Cement and Conventional Glass Ionomer Cement to the Dentinal Surface of Primary Molars: an in vitro Study

Statement of the Problem: The success of dental restorations depends mainly on its ability to bond to dental structures and resist the multitude of forces acting on it within the oral cavity. Purpose: Therefore, the aim of this study was to evaluate the shear bond strength (SBS) of three different glass ionomer based restorative materials. Materials and Method: In this in vitro analytical study, 30 intact primary molars were sectioned buccolingually to obtain 60 sections. These sections were embedded in auto polymerizing acrylic resin and polished to obtain a flat dentin surface. Restoration cylinders were built on the dentin surface with the help of a Teflon template called bonding jig. Each group (n= 20) was restored as group A with conventional glass ionomer cement (GIC) (GC Fuji Gold Label Type 9), group B with Bioactive restorative material (ACTIVATM KIDS BioACTIVE Restorative material), and group C with Zirconia reinforced glass ionomer cement (Zirconomer). Following restoration, SBS testing was performed using Universal Testing Machine. The data obtained were statistically analyzed using One way ANOVA test and post hoc Tukey test (p= 0.05). Results: The SBS values were significantly greater in the ACTIVA KIDS group as compared to the other two groups (p< 0.05). There was no significant difference in the SBS values between group B and group C (p> 0.05). Conclusion: The SBS of the ACTIVA KIDS to primary teeth dentin was the highest as compared to Zirconomer and conventional GIC. Therefore ACTIVA KIDS may protect primary teeth against recurrent caries and failure of the restoration.
Experimental Study of Magnetorheological Dampers and Application to Cable Vibration Control

As primary members of cable-stayed bridges, cables are susceptible to vibrations because of their low intrinsic damping. Mechanical dampers have been used to improve cable damping. Magnetorheological (MR) dampers have been proven efficient for seismic applications because of their large output damping forces, stable performance, low power requirement, and quick response from both laboratory research and field practice. In this paper, experimental work was carried out to demonstrate that MR dampers are also suitable for cable vibration control. First, a MR damper was tested with various test parameters to obtain the performance curves of the MR damper under different loading conditions, including different electric currents, loading frequencies, loading wave types, and working temperatures. The MR damper was then installed on a cable to reduce the cable vibration. A 7.16 m long stay cable with a prototype-to-model scale factor of 8 was established for this study. The frequencies of the stay cable under different tension forces were measured and compared with those obtained through theoretical calculations. Then, a free vibration control test was carried out with the MR damper being installed at the 1/4 point of the cable. In the forced vibration test, a shaker was installed at 0.18 m from the lower end of the cable. The measured data show that the damper is efficient for cable vibration control within its working current range (zero to maximum) although there is a saturation effect. It was also observed that the damper could reduce cable vibration under a variety of excitation frequencies, especially for resonant vibrations.
The Effect of Exogenous Surfactant on Moderate and Severe Stages of COVID-19 Induced ARDS: the Pilot Study of a Clinical Trial

COVID-19 pandemic has created a global health challenge. Many pharmaceuticals have been repurposed as potential treatments, though many have not been promising. Due to the inflammatory and destructive effects of the virus on alveolar cells, the effect of exogenous surfactant was assessed as a potential treatment of lung dysfunction in COVID-19 patients. In this pilot study of the clinical trial, 49 patients aged 35-80 years with COVID-19 admitted in ICU entered the study (22 patients intubated and 23 had face masks; 4 patients in the control arm). The treatment arm patients received two consecutive doses of surfactant. P/F ratio (based on serial blood gas analyses before and 12 hours after 2 doses of surfactant) and also, clinical outcomes were assessed.in COVID-19 adult patients, surfactant significantly improved pulmonary P/F ratio both in intubated and face mask COVID-19 patients (increasing from 119.2 ± 51.7 to 179.4 ± 115.5). The rate of extubation was much better than similar country-wide studies. Surfactant significantly alleviates the respiratory status in moderate to severe COVID-19 ARDS with two consecutive 100 mg doses of surfactant (with 6 hours’ interval) though previous studies have been controversial, regarding the effect of surfactant in general forms of ARDS. Higher doses might have better effects, mandating more trials.
Technical Appraisal of Grain Storage Systems in the Nigerian Sudan Savanna

A technical survey of the village level grain storage structures existing in the Sudan Savanna climatic zone of Nigeria was undertaken. Preliminary investigation result shows that the common grain storage structures existing in this zone are the mud rhombus, thatched rhombus, underground pit, and earthen pot and warehouse storage. The grain usually stored in unthreshed forms includes millet, sorghum, maize and cowpea. Most of these structures are not moisture proof, rodent proof and are not airtight. Structural defects occur mainly in the roof, walls and columns of the storage structures. The common types of physical defects are cracks, leakage, termite infestation and structural failure of parts. The causes of defects among others include poor strength of material, inadequate columns, low elevation and pest infestation. The result however, shows that it might be more profitable for subsistent farmers to continue the storage of unthreshed cereal grain and possible unshelled pulsed in rhombus and underground pits since these have relatively low storage cost per kilogram of grain. However there is need for some technical improvement on the construction materials, nature of columns, elevation and the loading/unloading facilities. The farmers’ shows willingness in adopting new storage techniques provided such structures are cheap and affordable.
Scheduling Servers with Stochastic Bilinear Rewards

In this paper we study a multi-class, multi-server queueing system with stochastic rewards of job-server assignments following a bilinear model in feature vectors representing jobs and servers. Our goal is regret minimization against an oracle policy that has a complete information about system parameters. We propose a scheduling algorithm that uses a linear bandit algorithm along with dynamic allocation of jobs to servers. For the baseline setting, in which mean job service times are identical for all jobs, we show that our algorithm has a sub-linear regret, as well as a sub-linear bound on the mean queue length, in the horizon time. We further show that similar bounds hold under more general assumptions, allowing for non-identical mean job service times for different job classes and a time-varying set of server classes. We also show that better regret and mean queue length bounds can be guaranteed by an algorithm having access to traffic intensities of job classes. We present results of numerical experiments demonstrating how regret and mean queue length of our algorithms depend on various system parameters and compare their performance against a previously proposed algorithm using synthetic randomly generated data and a real-world cluster computing data trace.
On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters

Bloom Filters are a fundamental and pervasive data structure. Within the growing area of Learned Data Structures, several Learned versions of Bloom Filters have been considered, yielding advantages over classic Filters. Each of them uses a classifier, which is the Learned part of the data structure. Although it has a central role in those new filters, and its space footprint as well as classification time may affect the performance of the Learned Filter, no systematic study of which specific classifier to use in which circumstances is available. We report progress in this area here, providing also initial guidelines on which classifier to choose among five classic classification paradigms.
Aging and memory of transitional turbulence

The recent classification of the onset of turbulence as a directed percolation (DP) phase transition has been applied to all major shear flows including pipe, channel, Couette and boundary layer flows. A cornerstone of the DP analogy is the memoryless (Markov) property of turbulent sites. We here show that for the classic case of channel flow, the growth of turbulent stripes is deterministic and that memorylessness breaks down. Consequently turbulence ages and the one to one mapping between turbulent patches and active DP-sites is not fulfilled. In addition, the interpretation of turbulence as a chaotic saddle with supertransient properties, the basis of recent theoretical progress, does not apply. The discrepancy between channel flow and the established transition model illustrates that seemingly minor geometrical differences between flows can give rise to instabilities and growth mechanisms that fundamentally alter the nature of the transition to turbulence.
WALLABY Pre-pilot Survey: The Effects of Tidal Interaction on Radial Distribution of Color in Galaxies of the Eridanus Supergroup

We study the tidal interaction of galaxies in the Eridanus supergroup, using H i data from the pre-pilot survey of the Widefield ASKAP L-band Legacy All-sky Blind surveY. We obtain optical photometric measurements and quantify the strength of tidal perturbation using a tidal parameter S sum. For low-mass galaxies of M * ≲ 109 M ⊙, we find a dependence of decreasing H i to optical disk size ratio with increasing S sum, but no dependence of H i spectral line asymmetry with S sum. This is consistent with the behavior expected under tidal stripping. We confirm that the color profile shape and color gradient depend on the stellar mass, but there is an additional correlation of low-mass galaxies having their color gradients within 2R 50 increasing with higher S sum. For these low-mass galaxies, the dependence of color gradients on S sum is driven by the color becoming progressively redder in the inner disk when tidal perturbations are stronger. For high-mass galaxies, there is no dependence of color gradients on S sum, and we find a marginal reddening throughout the disks with increasing S sum. Our result highlights tidal interaction as an important environmental effect in producing the faint end of the star formation suppressed sequence in galaxy groups.
Mesoscopic Structure of the Stock Market and Portfolio Optimization

The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic ‘equally-weighted’ portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the beneﬁts, in terms of predicted and realized risk proﬁles, of constructing portfolios by ﬁltering out both random and systemic co-movements from the correlation matrix. Second, we redeﬁne the portfolio optimization problem in terms of stock clusters that emerge after ﬁltering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across diﬀerent time spans, cross-sectional dimensions and set of constraints deﬁning the optimization problem
MPLR: a novel model for multi-target learning of logical rules for knowledge graph reasoning

Large-scale knowledge graphs (KGs) provide structured representations of human knowledge. However, as it is impossible to contain all knowledge, KGs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logic rules for reasoning on knowledge graphs for completing missing factual triplets. Learninglogicrulesequipsamodelwithstronginterpretabilityaswellastheabilitytogeneralizetosimilartasks.WeproposeamodelcalledMPLRthatimprovestheexistingmodelstofullyuse training data and multi-target scenarios are considered. In addition, considering the deﬁciency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our MPLR model outperforms state-of-the-art methods on ﬁve benchmark datasets. The results also prove the eﬀectiveness of the indicators.
Communication-Efficient Federated Learning for Neural Machine Translation

Training neural machine translation (NMT) models in federated learning (FL) settings could be inefficient both computationally and communication-wise, due to the large size of translation engines as well as the multiple rounds of updates required to train clients and a central server. In this paper, we explore how to efficiently build NMT models in an FL setup by proposing a novel solution. In order to reduce the communication overhead, out of all neural layers we only exchange what we term"Controller"layers. Controllers are a small number of additional neural components connected to our pre-trained architectures. These new components are placed in between original layers. They act as liaisons to communicate with the central server and learn minimal information that is sufficient enough to update clients. We evaluated the performance of our models on five datasets from different domains to translate from German into English. We noted that the models equipped with Controllers preform on par with those trained in a central and non-FL setting. In addition, we observed a substantial reduction in the communication traffic of the FL pipeline, which is a direct consequence of using Controllers. Based on our experiments, Controller-based models are ~6 times less expensive than their other peers. This reduction is significantly important when we consider the number of parameters in large models and it becomes even more critical when such parameters need to be exchanged for multiple rounds in FL settings.
Quantifying and Understanding Adversarial Examples in Discrete Input Spaces

Modern classification algorithms are susceptible to adversarial examples--perturbations to inputs that cause the algorithm to produce undesirable behavior. In this work, we seek to understand and extend adversarial examples across domains in which inputs are discrete, particularly across new domains, such as computational biology. As a step towards this goal, we formalize a notion of synonymous adversarial examples that applies in any discrete setting and describe a simple domain-agnostic algorithm to construct such examples. We apply this algorithm across multiple domains--including sentiment analysis and DNA sequence classification--and find that it consistently uncovers adversarial examples. We seek to understand their prevalence theoretically and we attribute their existence to spurious token correlations, a statistical phenomenon that is specific to discrete spaces. Our work is a step towards a domain-agnostic treatment of discrete adversarial examples analogous to that of continuous inputs.
Formalising the Foundations of Discrete Reinforcement Learning in Isabelle/HOL

We present a formalisation of finite Markov decision processes with rewards in the Isabelle theorem prover. We focus on the foundations required for dynamic programming and the use of reinforcement learning agents over such processes. In particular, we derive the Bellman equation from first principles (in both scalar and vector form), derive a vector calculation that produces the expected value of any policy p, and go on to prove the existence of a universally optimal policy where there is a discounting factor less than one. Lastly, we prove that the value iteration and the policy iteration algorithms work in finite time, producing an epsilon-optimal and a fully optimal policy respectively.
Demystifying Developers' Issues in Distributed Training of Deep Learning Software

Deep learning (DL) has been pervasive in a wide spectrum of nowadays software systems and applications. The rich features of these DL based software applications (i.e., DL software) usually rely on powerful DL models. To train powerful DL models with large datasets efficiently, it has been a common practice for developers to parallelize and distribute the computation and memory over multiple devices in the training process, which is known as distributed training. However, existing efforts in the software engineering (SE) research community mainly focus on issues in the general process of training DL models. In contrast, to the best of our knowledge, issues that developers encounter in distributed training have never been well studied. Given the surging importance of distributed training in the current practice of developing DL software, this paper fills in the knowledge gap and presents the first comprehensive study on developers’ issues in distributed training. To this end, we extract and analyze 1,054 real-world developers’ issues in distributed training from Stack Overflow and GitHub, two commonly used data sources for studying software issues. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. Based on the results, we suggest actionable implications and research avenues that can potentially facilitate the future development of distributed training.
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings

Learning from set-structured data is an essential problem with many applications in machine learning and computer vision. This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (ANN) solutions, particularly locality-sensitive hashing. We consider the problem of set retrieval from an input set query. Such retrieval problem requires: 1) an efficient mechanism to calculate the distances/dissimilarities between sets, and 2) an appropriate data structure for fast nearest neighbor search. To that end, we propose Sliced-Wasserstein set embedding as a computationally efficient"set-2-vector"mechanism that enables downstream ANN, with theoretical guarantees. The set elements are treated as samples from an unknown underlying distribution, and the Sliced-Wasserstein distance is used to compare sets. We demonstrate the effectiveness of our algorithm, denoted as Set-LOcality Sensitive Hashing (SLOSH), on various set retrieval datasets and compare our proposed embedding with standard set embedding approaches, including Generalized Mean (GeM) embedding/pooling, Featurewise Sort Pooling (FSPool), and Covariance Pooling and show consistent improvement in retrieval results. The code for replicating our results is available here: \href{https://github.com/mint-vu/SLOSH}{https://github.com/mint-vu/SLOSH}.