Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGARUH PEMBELAJARAN BERBASIS MASALAH DAN PEMBELAJARAN KONTEKSTUAL TERHADAP BERPIKIR KRITIS SISWA SMP

Tujuan penelitian ini adalah untuk menganalisis perbedaan peningkatan kemampuan berpikir kritis siswa yang belajar melalui pembelajaran berbasis masalah dan pembelajaran kontekstual yang dilakukan di sekolah SMPN 2 Simanindo di Simarmata. Penelitian ini menggunakan metode eksperimen semu dengan desain penelitian The Matching Pretest-Posttest Control Group. Siswa kelas VII (n = 45) terlibat sebagai subjek penelitian yang diambil dengan teknik cluster random sampling dan penelitian ini terdiri dari 2 kelas. Teknik pengumpulan data menggunakan dengan 15 butir soal pilihan ganda beralasan. Teknik pengolahan data melalui uji normalitas, uji homogenitas dan N-gain serta uji- t dua pihak dengan bantuan program IBM SPSS Statistic 22 dan Microsoft excel. Hasil analisis data menunjukkan tidak terdapat perbedaan yang signifikan antara siswa yang belajar melalui pembelajaran berbasis masalah dan pembelajaran kontekstual, dengan nilai signifikansi 0.411 > 0.05, yang berarti diterima dan ditolak. Hasil penelitian juga menunjukkan bahwa siswa yang belajar melalui pembelajaran berbasis masalah memiliki nilai rata – rata N-gain 0.41, sedangkan siswa yang belajar melalui pembelajaran kontekstual memiliki nilai rata –rata N-gain 0.31. Sehingga berdasarkan data penelitian tersebut, pembelajaran berbasis masalah dan pembelajaran kontekstual dapat membantu meningkatkan berpikir kritis siswa
Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities

An analytics-empowered enterprise system looks to many organizations to be a far-fetched target, owing to the vast amounts of factors that need to be controlled across the implementation lifecycle activities, especially during usage and maintenance phases. On the other hand, advanced analytics techniques such as machine learning and data mining have been strongly present in academic as well as industrial arenas through robust classification and prediction. Correspondingly, this paper is set out to address a methodological approach that works on tackling post-live implementation activities, focusing on employing advanced analytics techniques to detect (business process) problems, find and recommend a solution to them, and confirm the solution. The objective is to make enterprise systems self-moderated by reducing the reliance on vendor support. The paper will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach. Employing an advanced analytics engine has the potential to support post-implementation activities. Our research is innovative in two ways: (1) it enables enterprise systems to become self-moderated and increase their availability; and (2) the IT artifact i.e., the analytics engine, has the potential to solve other problems and be used by other systems, e.g., HRIS. This paper is beneficial to businesses implementing enterprise systems. It highlights how enterprise systems could be safeguarded from retirement caused by post-implementation problems.
The use of Web 2.0 tools by students in learning and leisure contexts: a study in a Portuguese institution of higher education

This study analyses and compares the use of Web 2.0 tools by students in both learning and leisure contexts. Data were collected based on a questionnaire applied to 234 students from the University of Aveiro (Portugal) and the results were analysed by using descriptive analysis, paired samples t-tests, cluster analyses and Kruskal–Wallis tests. The results show that the tools most used by students in a learning context are video sharing, social network sites and wikis, and that in a leisure context students use social network sites and video sharing more. The profiles of the groups resulting from the cluster analyses reveal that 42% of the students do not use Web 2.0 tools intensively in either one of the contexts. However, findings from comparing the clusters of both contexts show that students who more frequently use the tools in the leisure context are occasional users in the learning context and vice versa. This study contributes to a better knowledge of the student profiles concerning the use of Web 2.0 tools in learning and leisure contexts which can help teachers direct their strategies to the use of the most appropriate tools, thus improving the success of the teaching-learning process.
What is that thing called internal quality assurance system

Purpose: The main purpose of this paper is to explore and to propose a comprehensive definition and conceptualization of an Internal Quality Assurance System (IQAS) for Higher Education Institution (HEI) by using Work Systems Theory (WST) as a theoretical lens. Design/methodology/approach: The paper follows a multiple method research approach by grounding on a literature review and validating against a published study case. Findings: After analysing a number of IQAS definitions, the paper shows how these systems can be defined as a specific case of a “work system”, the main unit of analysis and conceptualization provided by WST. To further demonstrate the academic relevance and practical applicability of the proposed conceptualization, we provide a concrete instantiation as a “work system” of the IQAS implemented in a real HEI case. Research limitations: Additional instantiations of IQAS implemented in other different real contexts should be conducted to foster the generalizability of the proposed framework. Practical implications: Using the same theoretical lens as a reference point, the paper provides insights and directions for further research as well as potential future uses and practical applications of the derived conceptualization. Originality/value: To our best knowledge, this is the first comprehensive formal IQAS definition and first WST-based conceptualizing an IQAS for HEIs.
Pre-Select Static Caching and Neighborhood Ordering for BFS-like Algorithms on Disk-based Graph Engines

Many important graph algorithms are based on the breadth first search (BFS) approach, which builds itself on recursive vertex traversal. We classify algorithms that share this characteristic into what we call a BFS-like algorithm. In this work, we first analyze and study the I/O request patterns of BFS-like algorithms executed on disk-based graph engines. Our analysis exposes two shortcomings in executing BFS-like algorithms. First, we find that the use of the cache is ineffective. To make use of the cache more effectively, we propose an inmemory static cache, which we call BFS-Aware Static Cache or Basc, for short. Basc is static as its contents, which are edge lists of vertices that are pre-selected before algorithm execution, do not change throughout the execution of the algorithm. Second, we find that the state-of-the-art ordering method for graphs on disks is ineffective with BFS-like algorithms. Thus, based on an I/O cost model that estimates the performance based on the ordering of graphs, we develop an efficient graph ordering called Neighborhood Ordering or Norder. We provide extensive evaluations of Basc and Norder on two well-known graph engines using five real-world graphs including Twitter that has 1.9 billion edges. Our experimental results show that Basc and Norder, collectively have substantial performance impact.
Overcoming data shortages in robotic applications

The abundance of data has played a major role in the recent machine learning boom. The availability of large datasets however, while useful, hasn't directly translated to successful applications in robotics. This thesis attempts to present ways to overcome the lack of data in robotics applications through domain adaptation methods. We propose and study the performance in data-poor scenarios of algorithms designed for two Robotics applications: visual tracking and learning from demonstrations. We first present a robust multi-robot convoying approach that relies on visual detection of the leading agent. Our method is based on the idea of tracking-by-detection, which interleaves efficient object detection with temporal filtering of image-based bounding box estimation. Using a bounding box annotated dataset of images extracted from footage of an underwater robot in ocean settings, we compare multiple tracker variants, including several convolutional neural networks with and without recurrent connections and frequency-based model-free trackers. We investigate the domain adaptation ability of our most applicable architecture through training on synthetic data, generated from a realistic game engine. To demonstrate the practicality of this tracking- by-detection strategy in real-world scenarios, we successfully control a 5-DOF legged underwater robot to follow another robot's independent motion. We then focus on the impact of data shortages when learning from demonstration. Extending the options framework with the notion of reward options, we develop a method for learning joint reward-policy options in the context of generative adversarial inverse RL and show that methods in this context suffer in demonstration data-poor scenarios. We then study the one shot domain adaptation abilities of our approach. That is, given expert demonstrations from a mixture of environments with different dynamics, can the agent learn to properly complete a task in a previously unseen environment with different dynamics. Our results show that our method is able to successfully learn a task in these domain adaptation scenarios, and significantly outperforms inverse RL without options. %%%%L'abondance de donnees a joue un role majeur dans le recent boom de l'apprentissage automatique. Mais, la disponibilite de ces larges ensembles de donnees, bien qu'utile, ne s'est pas directement traduite a des applications reussies en robotique. Cette these presente des moyens de pallier le manque de donnees dans les applications robotiques grace a des methodes d'adaptation de domaine. Nous proposons et etudions la performance d'algorithmes concus pour deux applications en robotiques dans des situations de manque de donnees: le suivi visuel et l'apprentissage a partir de demonstrations. Nous presentons d'abord une approche robuste de convoi a robots multiples qui repose sur la detection visuelle de l'agent principal. Notre methode est basee sur l'idee de suivi par detection, qui couple la detection d'objet avec le filtrage temporel de…
Can Nudges Be Transparent and Yet Effective?

Nudges receive growing attention as an effective concept to alter people's decisions without significantly changing economic incentives or limiting options. However, being often very subtle and covert, nudges are also criticized as unethical.By not being transparent about the intention to influence individual choice they might be perceived as limiting freedom of autonomous actions and decisions. So far, empirical research on this issue is scarce. In this study, we investigate whether nudges can be made transparent without limiting their effectiveness. For this purpose we conduct a laboratory experiment where we nudge contributions to carbon emission reduction by introducing a default value. We test how different types of transparency (i.e. knowledge of the potential influence of the default, its purpose, or both) influence the effect of the default. Our findings demonstrate that the default increases contributions, and information on the potential influence, its purpose, or both combined do not significantly influence the default effect. Furthermore, we do not find evidence that psychological reactance interacts with the influence of transparency. Findings support the policy-relevant claim that nudges (in the form of defaults) can be transparent and yet effective.
What Types of Codes are Mixed in Indonesia?: An Investigation of Code Mixing in a Magazine

Mass media have been regarded as playing crucial roles in introducing and promoting foreign terms into Indonesian. It can be seen that many magazines have been mostly influenced by English through code mixing to make the writing become more prestigious and cool. This present study aims at investigating the types of code mixing used in a magazine. It employs the three types of code mixing, proposed by Musyken’s (2000), which are: insertion, alternation, and congruent lexicalization. The data were taken from Aplaus magazine in four editions from April to July 2016 on the Journey Section. In collecting the data, the occurrence of English code mixing in one of the sections in the magazine was noted, and the data were then classified and tabulated. The results of this research show that not all of three types of code mixing by Musyken (2000) were applicable in the Aplaus magazine. There are 36 insertion types found or about 76.6%, 11 or about 23.4% for alternation, and no case of congruent lexicalization appeared. Thus, the dominant type of code mixing used in the magazine was insertion.
Delta-Bar-Delta Neural-Network-Based Control Approach for Power Quality Improvement of Solar-PV-Interfaced Distribution System

A serious concern regarding deterioration in power quality has emerged with the increasing integration of solar photovoltaic (PV) energy sources to the utility primarily in the scenario of a weak distribution grid. Therefore, power quality improvement of the grid-tied solar energy conversion system is paramount by implementation of a robust control technique. This paper deals with a delta-bar-delta neural network (NN) control for operating optimally by feeding active power to the loads and remaining power to the grid as a function of distribution static compensator capabilities, such as mitigating harmonics, balancing of load, and improving power factor. The control algorithm provides the ability to adjust weights adaptively in an independent manner, and hence, it offers alleviation in model complexity predominant during abnormal grid conditions along with reduction in computational time. Moreover, the NN-based control technique offers enhanced accuracy due to the combinational neural structure in the estimation process. In addition, the system performance according to the IEEE-519 standard has been verified; hence, it is proficient in maintaining the power quality. The solar-PV-array-efficient utilization is accomplished through an incremental-conductance-based maximum power point tracking technique. For validating the behavior of the proposed system, its performance is studied using simulation results. Moreover, a prototype is developed for validation, and experimental results corroborate reliable operation under nonideal grid conditions comprising of a wide range of load variations, voltage sag, and varying solar insolation conditions.
Sparse representations & compressed sensing with application to the problem of direction-of-arrival estimation

The significance of sparse representations has been highlighted in numerous signal processing applications ranging from denoising to source separation and the emerging field of compressed sensing has provided new theoretical insights into the problem of inverse systems with sparsity constraints. In this thesis, these advances are exploited in order to tackle the problem of directionof-arrival (DOA) estimation in sensor arrays. Assuming spatial sparsity e.g. few sources impinging on the array, the problem of DOA estimation is formulated as a sparse representation problem in an overcomplete basis. The resulting inverse problem can be solved using typical sparse recovery methods based on convex optimization i.e. `1 minimization. However, in this work a suite of novel sparse recovery algorithms is initially developed, which reduce the computational cost and yield approximate solutions. Moreover, the proposed algorithms of Polytope Faces Pursuits (PFP) allow for the induction of structured sparsity models on the signal of interest, which can be quite beneficial when dealing with multi-channel data acquired by sensor arrays, as it further reduces the complexity and provides performance gain under certain conditions. Regarding the DOA estimation problem, experimental results demonstrate that the proposed methods outperform popular subspace based methods such as the multiple signal classification (MUSIC) algorithm in the case of rank-deficient data (e.g. presence of highly correlated sources or limited amount of data) for both narrowband and wideband sources. In the wideband scenario, they can also suppress the undesirable effects of spatial aliasing. However, DOA estimation with sparsity constraints has its limitations. The compressed sensing requirement of incoherent dictionaries for robust recovery sets limits to the resolution capabilities of the proposed method. On the other hand, the unknown parameters are continuous and therefore if the true DOAs do not belong to the predefined discrete set of potential locations the algorithms’ performance will degrade due to errors caused by mismatches. To overcome this limitation, an iterative alternating descent algorithm for the problem of off-grid DOA estimation is proposed that alternates between sparse recovery and dictionary update estimates. Simulations clearly illustrate the performance gain of the algorithm over the conventional sparsity approach and other existing off-grid DOA estimation algorithms.
MEDICAL STUDENT SYNDROME; THE AFFLICTION IN MEDICAL STUDENTS

Objective: The aim of the study is to find out the prevalence of Medical Student Syndrome (MSS) in Pakistan and its association with age, maturity level and the degree of medical knowledge.Study Design: Descriptive cross-sectional study.Place and Duration of Study: The survey was completed in one year, from Jul 2016 to Jul 2017Material and Methods: A self-designed, self-explanatory questionnaire was used. The link of the questionnaire was disseminated electronically. The data were analyzed by using Statistical Package for Social Sciences (SPSS-22)Results: Total 129 responses were received from the students of different medical colleges/universities ofPakistan. Mean age of the participants was 21.94 years. Among the respondents 43% were males while 57% were females. Maximum participants were from final year. Majority of the respondents that is about 81% reported they had symptoms of the diseases they study, yet 44% pursued and moved forward. Among them 28% of the respondents said that they investigated their felt symptoms further, of which maximum were from 3rd year. Only 43% of the students knew about the Medical Student Syndrome. Some of the respondents, about 22.8% admitted the fact that they used psychiatric medicines either for relaxing or sleep.Conclusion: It can be concluded that medical students are more vulnerable to conditions like Medical studentsyndrome so there is a need to counsel medical students about the symptoms of MSS as well as to support the students by discussing different techniques to subsist the stress level.
Soccer heading evaluation during learning process using an accelerometer

Heading is one of the basic game activities in football but it is still discussed in term of risk of health problems, especially cumulative head trauma. This study aims to find out how to improve the supervision over heading performance during learning process in youth footballers using an accelerometer. The research sample consisted of 63 footballers from age groups U11 – U17. Each person executed headings of a ball dropped from 0.5 m, 1 m, and 1.5 m. A triaxial accelerometer fixed to the back of the head was used in the measurements. For individual groups of participants, an approximate linear increase in peak head acceleration was observed for an increase in ball drop height (R2 = ± .99). Furthermore, strong correlations in the maximum head acceleration parameter amax were found between the attempts from all three heights. A linear decrease in peak head accelerations were observed with increasing age of the participants. The core importance lies in between relations of measured variables that show changes during acquisition of skills in heading in young footballers. Regular control accelerometer measurements can help determine the level of acquired skill and serve as an appropriate tool for preventing overloads or injuries of individuals.
Collocation-Based Output-Error Method for Aircraft System Identification

The output-error method is a mainstay of aircraft system identification from flight-test data. It is the method of choice for a wide range of applications, from the estimation of stability and control derivatives for aerodynamic database generation to sensor bias estimation in flight-path reconstruction. However, notable limitations of the output-error method are that it requires ad hoc modifications for applications to unstable systems and it is an iterative method which is particularly sensitive to the initial guess. In this paper, we show how to reformulate the estimation as a collocation problem, an approach common in other disciplines but seldomly used in flight vehicle system identification. Both formulations are equivalent in terms of having the same solution, but the collocation-based can be applied without modifications or loss of efficiency to unstable systems. Examples with simulated and real-world flight-test data also show that convergence to the optimum is obtained even with poor initial guesses.
Learning from nature: image segmentation based on local symmetry detection

Symmetry has become synonymous with abstract shape representation in human vision. However, recordings from neurons in low-level visual brain areas in monkeys revealed neural sensitivity to medialness. The same neurons also detect edges and oriented lines. We propose a symmetry computation scheme in noisy images, and re-interpret the role of symmetry in vision to understand why it is important at early stages in the hierarchy of the primate visual system. The new symmetries correspond closely to that observed in human subjects. Noise sensitivity can be controlled, overcoming severe restrictions on traditional models. Boundary reconstruction leads to meaningful segmented image regions as illustrated with synthetic images, and are useful in tasks like detecting boundaries in textures. Model performance and problems encountered with natural images are demonstrated. Extension to 3D does not introduce additional complications.