Miracle-dz/UniFAR-FTUs huggingface dataset:
Load balancing clustering on moodle LMS to overcome performance issue of e-learning system

In dealing with the rapid growth of digitalization, the e-learning system has become a mandatory component of any Higher Education (HE) to serve academic processes requests. Along with the increasing number of users, the need for service availability and capabilities of eLearning are increasing day by day. The organization should always look for strategies to keep the eLearning always able to meet these demands. This report presents the implementation of Load Balancing Clustering (LBC) mechanism applied to Moodle LMS in an HE Institution to deal with the poor performance issues. By utilizing existing tools such as HAProxy and keepalived, the implemented LBC configuration delivers a qualified e-learning system performance. Both qualitative and quantitative parameters convince better performance than before. In four months of the operation there is no user complaint received. Meanwhile, in the current semester has been running for two months, the up-time is 99.8 % of 52.685 minutes operational time.
Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning

Path planning is an essential task for the mobile robot navigation. However, such a task is difficult to solve, due to the optimal path needs to be rerouted in real-time when a new obstacle appears. It produces a sub-optimal path and the robot can be trapped in local minima. To overcome the problem the Ant Colony Optimization (ACO) is combined with Fuzzy Logic Approach to make a globally optimal path. The Fuzzy-ACO algorithm is selected because the fuzzy logic has good performance in imprecision and uncertain environment and the ACO produce simple optimization with an ability to find the globally optimal path. Moreover, many optimization algorithms addressed only at the simulation level. In this research, the real experiment is conducted with the low-cost Explorer-Follower robot. The results show that the proposed algorithm, enables them to successfully identify the shortest path without collision and stack in â€œlocal minimaâ€.
Efficacy and perceptions of assessment digitization within a large‐enrollment mechanical and aerospace engineering course

Engineering's design‐oriented and problem‐solving emphases pose considerable challenges to the use of multiple choice assessments, and thus the use of digitized forms of assessment within Engineering curricula warrants a substantive evaluation of their validity. To address this research need, novel computer‐based assessment infrastructures and delivery protocols were launched via a crossover study as defined below. The objective of the study was to investigate the impact of proctored digitized examination that was delivered using a secure or “lockdown” internet browser interface. Measures tracked in the study included test score validity, learning achievement, and student perceptions within a large‐size undergraduate Mechanical and Aerospace Engineering course. Using a crossover‐based study design, the class was divided into two cohorts. Half of each cohort's formative assessments were delivered in a computer‐based testing center via and the other half of the assessments were delivered on paper. Within each cohort, the intervention of computerized delivery was interchanged with paper‐based delivery during successive topic modules on a mutually‐exclusive basis. An identical paper‐based final exam was delivered to all students. The data collected was analyzed and de‐correlated to investigate the topic‐specific learning outcomes of digitized assessment. Test score validity and learning outcomes were studied using independent sample t‐test statistical analysis. Results indicate that well‐formed digitized exams can determine scores differing as little as 0.6% from paper‐based assessment. Moreover, when the labor hours of the grader which were freed up were reallocated to tutoring and remediation of formative assessments, the as much as 16.9% higher learning outcomes were obtained during summative assessment as compared to paper‐based delivery.
A Visual Programming Tool to Design Mixed and Virtual Reality Interactions

Mixed and Virtual Reality (MVR) devices are now more accessible. However, developing MVR applications is still complex for the majority of developers, because it requires specific expertise. For the past few years, several packaged solutions offered to assist developers who are non-MVR experts. These solutions rarely offer full freedom to create specific interactions adapted to the context. We therefore propose a new MVR tool named MIREDGE (MIxed and virtual REality Development tool for Game Engine). Its interface allows visual programming of MVR interactions. This solution aims at allowing developers to capitalize, re- use, share and associate interaction algorithms. It also takes into account software and hardware compatibility in order to compose new algorithms. The specific architecture of MIREDGE provides opportunities for MVR and non-MVR developers to collaborate to meet a common need: writing efficient MVR interaction algorithms. MIREDGE Editor was evaluated by 31 MVR and non-MVR developers. Results shows that MIREDGE Editor seems effective and efficient particularly for non-MVR developers.
Improved label propagation algorithms with node attribute and link strength for community detection

Community detection has a wide variety of applications in different fields such as data mining, social network analysis and so on. Label Propagation Algorithm (LPA) is a simple and fast community detection algorithm, but it has low accuracy. There have been presented some advanced versions of LPA in recent years such as CenLP and WILPAS. In this paper, we present improved versions of CenLP and WILPAS methods called CenLP+ and WILPAS+ respectively. Experiments and benchmarks demonstrate that while CenLP+ is as fast as CenLP, it outperforms CenLP on both synthetic and real-world networks. Moreover, while accuracy of WILPAS+ on synthetic networks comparable with that of WILPAS, on real-world networks, WILPAS+ excels WILPAS. In addition, whereas both presented methods CenLP+ and WILPAS+ show high accuracy on synthetic networks, on real-world networks they outperform remarkably all other tested label propagation based algorithms for community detection. Therefore, since CenLP+ and WILPAS+ are both fast and accurate, specially on real-world networks, they can efficiently reveal community structures of mega-scale social networks.
A novel approach based on sequence prediction for webpage access

Predicting the next item of a sequence over a finite alphabet is highly important in Web Mining. This paper presents a solution to improve the performance of sequence prediction; first and foremost, predicting what is the next Web page that will be visited by that user for prefetching the Web page. The proposed approach is how to decrease the complexity of the prediction space. Experimental results on a few real-life datasets show that the time execution of this novel approach is better than that of traditional approaches.
Using K-Fold Cross Validation Proposed Models for Spikeprop Learning Enhancements

Spiking Neural Network (SNN) uses individual spikes in time field to perform as well as to communicate computation in such a way as the actual neurons act. SNN was not studied earlier as it was considered too complicated and too hard to examine. Several limitations concerning the characteristics of SNN which were not researched earlier are now resolved since the introduction of SpikeProp in 2000 by Sander Bothe as a supervised SNN learning model. This paper defines the research developments of the enhancement Spikeprop learning using K-fold cross validation for datasets classification. Hence, this paper introduces acceleration factors of SpikeProp using Radius Initial Weight and Differential Evolution (DE) Initialization weights as proposed methods. In addition, training and testing using K-fold cross validation properties of the new proposed method were investigated using datasets obtained from Machine Learning Benchmark Repository as an improved Bohte’s algorithm. A comparison of the performance was made between the proposed method and Backpropagation (BP) together with the Standard SpikeProp. The findings also reveal that the proposed method has better performance when compared to Standard SpikeProp as well as the BP for all datasets performed by K-fold cross validation for classification datasets.
Evaluation of Non-Parametric Selection Mechanisms in Evolutionary Computation: A Case Study for the Machine Scheduling Problem

Evolutionary Algorithms have been extensively used for solving stochastic, robust, and dynamic optimization problems of a high complexity. Selection mechanisms play a very important role in design of Evolutionary Algorithms, as they allow identifying the parent chromosomes, that will be used for producing the offspring, and the offspring chromosomes, that will survive in the given generation and move on to the next generation. Selection mechanisms, reported in the literature, can be classified in two groups: (1) parametric selection mechanisms, and (2) non-parametric selection mechanisms. Unlike parametric selection mechanisms, non-parametric selection mechanisms do not have any parameters that have to be set, which significantly facilitates the Evolutionary Algorithm parameter tuning analysis. This study presents a comprehensive analysis of the commonly used non-parametric selection mechanisms. Comparison of the selection mechanisms is performed for the machine scheduling problem. The objective of the presented mathematical model is to determine the assignment of the arriving jobs among the available machines, and the processing order of jobs on each machine, aiming to minimize the total job processing cost. Different categories of Evolutionary Algorithms, which deploy various non-parametric selection mechanisms, are evaluated in terms of the objective function value at termination, computational time, and changes in the population diversity. Findings indicate that the Roulette Wheel Selection and Uniform Sampling selection mechanisms generally yield higher population diversity, while the Stochastic Universal Sampling selection mechanism outperforms the other non-parametric selection mechanisms in terms of the solution quality.
Logic Synthesis of Pass-Gate Logic Circuits With Emerging Ambipolar Technologies

Emerging devices and new ultrascaled silicon transistors have shown disruptive electrical and functional properties that might bring digital hardware to the next level. The key issue today concerns their integration. Even though the classical complementary logic style is the most intuitive option, other strategies such as pass-transistors that were discarded in the past because they did not fit silicon MOSFETs logic should be reconsidered. Obviously, the assessment of such alternatives requires customized CAD tools and optimization engines. The objective of this paper is to introduce a synthesis and optimization flow for pass-gate logic circuits mapped onto emerging ambipolar technologies. As main contributions we propose: 1) a novel EXNOR-based decomposition technique that fully exploits do not care conditions to generate compact logic function representations and 2) a dedicated one-pass synthesis flow where optimization and technology mapping are concurrently run on a common data structure, the reduced ordered pass-diagram. Experimental results demonstrate that the proposed flow outperforms existing synthesis tools by achieving more compact circuit representations with 8.5 $\times$ less devices and about 8 $\times$ shallower structures (on average), while still yielding lower CPU times.
Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease

It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms.
A Novel Measurement-Based Dynamic Equivalent Model of Grid-Connected Microgrids

For transient stability studies of power systems with high penetration of microgrids, models of the connected microgrids must be used. In this paper, a novel dynamic equivalent model is proposed for grid-connected microgrids using measurement data of point of common coupling (PCC). The proposed equivalent model includes electrical components, such as synchronous generator (SG), voltage source converter (VSC), and active and reactive loads. The proposed equivalent model contains different controllers, such as excitation system, governor, and VSC's controllers. All SG-based distributed generation (DG) units of the microgrid are represented by one equivalent SG, while an equivalent VSC represents converters-based DGs. Similarly, a parallel active and reactive static load is considered in the equivalent model to represent the static loads of the microgrid. In order to make the equivalent model more efficient, accurate, and valid at different operating points, a series resistance and reactance are utilized to connect the equivalent generator to the PCC. The final goal is to identify the parameters of the equivalent components such that the equivalent model behaves as like as the detailed microgrid facing different disturbances. The identification procedure is carried out by means of a genetic algorithm using measurement data at the PCC. To show the effectiveness and accuracy of the proposed model, the identified equivalent model is studied at different operating points and the results are compared with the original detailed model.
International Conference on Information Systems ( ICIS ) December 1998 Supporting Cooperation in a Virtual Organization

This paper describes the design of groupware for virtual organizations that share certain characteristics. First the concept of virtual organizations is introduced. Then our application partner, the “virtual” service company Sigma, and our empirical work at Sigma are described. The identified problems are categorized and discussed. Finally, we present the derived aspects for the design of groupware in the context of virtual organizations.
Evaluation of AR embedded physical puzzle game on students’ learning achievement and motivation on elementary natural science

ABSTRACT Emerging technologies have been widely used in applications designed to increase potential for learning enhancement. This study develops a mobile application to integrate augmented reality (AR) functionality into physical puzzle-type games. In the application, learners see visualizations of three-dimensional (3D) dinosaurs and interact with relevant multimedia learning content. To evaluate the effectiveness of the proposed platform, experiments were conducted to evaluate impact on learner achievement and motivation. Experimental (AR-based) and control (paper-based) groups included a total of 53 fifth-grade students. Pre- and post-activity questionnaires (adapted from the Attention, Relevance, Confidence, Satisfaction (ARCS) Model of Motivational Design), and pre- and post-learning quizzes were used for assessment. Results showed that the AR-based group scored significantly higher (p < 0.05) than the Paper-based group, raising implications for a new approach to teaching natural science at the elementary school level. However, the AR-based group also showed a relative drop in confidence, suggesting that further investigation is needed to understand the impact of AR technology on learner motivation. This study provides a case for the integration of AR technology into typical physical games. The results raise important concerns for further research in technology enhancement in education. Future research should include a wider range of content topics and target groups.
Title Dietary vitamin D 2-a potentially underestimated contributor to vitamin D nutritional status of adults ?

It has been suggested that vitamin D2 is not very prevalent in the human food chain. However, data from a number of recent intervention studies suggest that the majority of subjects had measurable serum 25-hydroxyvitamin D2 (25(OH)D2) concentrations. Serum 25(OH)D2, unlike 25(OH)D3, is not directly influenced by exposure of skin to sun and thus has dietary origins; however, quantifying dietary vitamin D2 is difficult due to the limitations of food composition data. Therefore, the present study aimed to characterise serum 25(OH)D2 concentrations in the participants of the National Adult Nutrition Survey (NANS) in Ireland, and to use these serum concentrations to estimate the intake of vitamin D2 using a mathematical modelling approach. Serum 25(OH)D2 concentration was measured by a liquid chromatography–tandem MS method, and information on diet as well as subject characteristics was obtained from the NANS. Of these participants, 78·7 % (n 884) had serum 25(OH)D2 concentrations above the limit of quantification, and the mean, maximum, 10th, 50th (median) and 90th percentile values of serum 25(OH)D2 concentrations were 3·69, 27·6, 1·71, 2·96 and 6·36 nmol/l, respectively. To approximate the intake of vitamin D2 from these serum 25(OH)D2 concentrations, we used recently published data on the relationship between vitamin D intake and the responses of serum 25(OH)D concentrations. The projected 5th to 95th percentile intakes of vitamin D2 for adults were in the range of 0·9–1·2 and 5–6mg/d, respectively, and the median intake ranged from 1·7 to 2·3mg/d. In conclusion, the present data demonstrate that 25(OH)D2 concentrations are present in the sera of adults from this nationally representative sample. Vitamin D2 may have an impact on nutritional adequacy at a population level and thus warrants further investigation.
Title Differential arousal regulation by prokineticin 2 signaling in the nocturnal mouse and the diurnal monkey Permalink

The temporal organization of activity/rest or sleep/wake rhythms for mammals is regulated by the interaction of light/dark cycle and circadian clocks. The neural and molecular mechanisms that confine the active phase to either day or night period for the diurnal and the nocturnal mammals are unclear. Here we report that prokineticin 2, previously shown as a circadian clock output molecule, is expressed in the intrinsically photosensitive retinal ganglion cells, and the expression of prokineticin 2 in the intrinsically photosensitive retinal ganglion cells is oscillatory in a clock-dependent manner. We further show that the prokineticin 2 signaling is required for the activity and arousal suppression by light in the mouse. Between the nocturnal mouse and the diurnal monkey, a signaling receptor for prokineticin 2 is differentially expressed in the retinorecipient suprachiasmatic nucleus and the superior colliculus, brain projection targets of the intrinsically photosensitive retinal ganglion cells. Blockade with a selective antagonist reveals the respectively inhibitory and stimulatory effect of prokineticin 2 signaling on the arousal levels for the nocturnal mouse and the diurnal monkey. Thus, the mammalian diurnality or nocturnality is likely determined by the differential signaling of prokineticin 2 from the intrinsically photosensitive retinal ganglion cells onto their retinorecipient brain targets.
EFFECTS OF ACTINOBACILLUS PLEUROPNEUMONIAE CYTOTOXINS ON SIZE , GRANULARITY AND VIABILITY OF PORCINE NEUTROPHILS

TARIGAN, S. 1998 . Effects of Actinobacillus pleuropneumoniae cytotoxins on size, granularity and viability of porcine neutrophils . Jurnal Ilmu Ternak dan Veteriner 3 (3): 188-196. Cytotoxins produced by Actinobacillus pleuropneumoniae are supposed to play major roles in bacterial pathogenicity and virulence . To gain better understanding in the mechanism of the pathogenicity, cytotoxic activities of the toxins on porcine neutrophils were investigated in vitro. Changes in cell size, granularity and viability were examined with a flow cytometer. Cell size and granularity correlate with forward light scatter and right angle light scatter, respectively; whereas, cell viability corresponds with fluorescent intensity of cells stained with propidium iodide . At low concentrations (dilutions between 1/10 and 1/100 of bacterial culture supernatants), the cytotoxins induced severe swelling and degranulation of neutrophils ; whereas, at higher concentrations (dilutions of 51/10 bacterial culture supernatants), the cytotoxins caused rapid cell death. There was no significant difference in cytotoxic activities of Cyooxins derived from various serotypes (serotypes 1, 2, 3, 5 and 7 ) of A. pleuropneumoniae . Morphologically, the cytotoxin-treated neutrophils stained with Giemsa showed profound changes. Neutrophils treated with low dosages of Cyooxins became swollen with spherical nuclei . Higher concentration of cytotoxins study indicates strongly that important mechanism in the caused vactiolation of cytoplasts, enlargement or disintegration of nuclei . This in vitro intoxication of neutrophils by cytotoxins produced by A. pleuropneumoniae comprises an pathogenicity of the bacteria.
13 ‐ 01 Whither China ? Reform and Economic Integration among

This paper investigates the changing nature of economic integration in China. Specifically, we consider business-cycle synchronization (correlation of demand and supply shocks) among Chinese provinces during the period 1955-2007. We find that the symmetry of supply shocks has declined after the liberalization initiated in 1978. In contrast, the correlation of demand shocks has increased during the same period. We then seek to explain these correlations by relating them to factors that proxy for interprovincial trade and vulnerability of regions to idiosyncratic shocks. Interprovincial trade and similarity in factor endowments tend to make shocks more symmetric. Surprisingly, foreign trade and inward FDI have little effect on the symmetry of shocks. JEL Codes: E32, F15, H77.