Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the effectiveness of wiki-based learning in higher education: the case of an introductory ICT course

In this paper, the effectiveness of a framed wiki-based learning activity is investigated. A one-group pretest–posttest design was adopted to examine the impact of the activity. The study involved 139 first year university students of a Greek Education Department using wikis to learn general information about Web 2.0 and its applications, in the context of an introductory ICT course. Study results showed significant improvement in learning outcomes, both for students with low and high initial performance. In addition, it was found that students benefited from the activity regardless of their role in the project. Furthermore, results showed that students with a higher number of logged wiki edits had a significantly higher learning gain compared to those that were less active. These findings suggest that properly designed, framed wiki-based activities have great potential in higher education.
Co-simulation of Electricity Distribution Networks and Blockchain Energy Trading Platforms

There has been significant recent interest in local electricity trading platforms, and particularly in the application of blockchain technology for distributed, or peer-to-peer energy trading in local energy communities. Several projects worldwide have demonstrated this concept on a small scale in Low Voltage (LV) distribution networks and microgrids. However, previous work in this area has not sufficiently addressed the potential impacts of peer-to-peer energy trading and other local electricity trading mechanisms on the control, operation and planning of the electricity distribution networks. Accordingly, this paper presents a methodology for the co-simulation of power distribution networks and local peer-to-peer energy trading platforms. The co-simulation approach uses the open-source distribution system simulator, OpenDSS for modelling the electricity distribution networks. The distribution system simulator is interfaced with a peer-to-peer energy trading simulator, which employs a blockchain-based distributed double auction trading mechanism. The presented co-simulation approach is demonstrated using a case study of typical European suburban distribution network.
Using HPMIXED with Other SAS ® 9.2 Procedures to Efficiently Analyze Large Dimension Registry Data

HPMIXED is an experimental procedure introduced in SAS/STAT ® 9.2 software. Using sparse matrix techniques, PROC HPMIXED can process models with a very large number of fixed or random effects much more efficiently than the MIXED or GLIMMIX procedures. However, this initial release lacks some tools which are often needed in analyses. With Registry data (1994-2007) from the Cystic Fibrosis Foundation, we estimate FEV 1% predicted (a standard measure of lung function) slope using a random intercept and slopes mixed model and compare parameter estimates and run-times of PROC HPMIXED with PROC MIXED and PROC GLIMMIX. We then use the SGPLOT and UNIVARIATE procedures to produce residual, normal and quantile-quantile plots from the PROC HPMIXED output. Using the covariance parameter estimates from PROC HPMIXED, we show how PROC GLIMMIX and PROC MIXED can be used to “re-run” the models (now more quickly) to produce output not yet available in PROC HPMIXED (i.e. residual diagnostics, influence diagnostics, LS-means comparisons, etc.). Finally, we use a simulated registry data set to further demonstrate diagnostic tools after obtaining covariance parameter estimates from PROC HPMIXED. PROC HPMIXED is a powerful, consistent tool for fitting large dimension data – especially mixed modeling. Such models might not be possible using PROC MIXED or PROC GLIMMIX. The applications described in this paper can be used by someone with an intermediate knowledge of mixed models and SAS procedures, using computing resources with at least 3 GB of RAM.
The Moderating Role of Gender in the Relationship among Organizational Justice, Commitment, Job Satisfaction and Organizational Citizenship Behaviors of Secondary School Teacher

This study intends to search the structural relations among organizationaljustice, job satisfaction, supervisor support, organizational commitment,and organizational citizenship behaviors (OCBs) of a sample of 500secondary school teachers (SSTs) of district Sargodha using a surveyresearch design. The analyses revealed that all demographic variableshad insignificant effects on most of the organizational citizenshipbehaviors of SSTs while only gender had significant effects as amoderator variable. Regression analysis revealed affective commitmentand operational justice were significantly correlated to all dimensions ofcitizenship behaviors, whereas only sportsmanship behavior of teacherswas related to normative commitment and interactional justice. Teachers’compliance behavior and civic virtue were correlated with continuancecommitment. Gender turned out to be a significant moderator betweenthe relationships of the pairs of variables such as; continuancecommitment and civic virtue, affective commitment and compliance,interactional justice and helping, operational justice and civic virtue, jobsatisfaction and sportsmanship, continuance commitment andsportsmanship.
Neural networks for cost estimation: Simulations and pilot application

Neural networks in a multilayer perceptron architecture are able to classify data and approximate functions based on a set of sample data (curve fitting). These properties are used to investigate experimentally the applicability of neural networks for cost estimation in early phases of product design. Experiments are based on pilot cost data from a manufacturing company. In addition, artificially created simulative data are used for benchmarking. The cost estimation performance is compared to conventional methods, i.e. linear and non-linear parametric regression. Neural networks achieve lower deviations in their cost estimations. Beyond the use of standard neural architectures, simple modifications for a performance improvement are suggested and tested. Finally, a profile for situations where neural networks are appropriate is derived from the results.
Fast bug finding in lock-free data structures with CB-DPOR

This thesis describes CB-DPOR, an algorithm for quickly finding bugs in lock-free data structures. CB-DPOR is a combination of the CHESS and DPOR model checking algorithms. CB-DPOR performs similar to the concurrently developed preemption-bounded BPOR algorithm. CODEX is a tool for finding bugs in lock-free data structures. CODEX implements CBDPOR and this thesis demonstrates how to use CODEX to find bugs. This thesis describes new bugs in open-source lock-free data structures, and compares the performance of CBDPOR with the earlier model checking algorithms CHESS, DPOR, and PCT. CB-DPOR find bugs one to two orders of magnitude faster than earlier algorithms. Thesis Supervisor: M. Frans Kaashoek Title: Professor Thesis Supervisor: Nickolai Zeldovich Title: Associate Professor
PENILAIAN ESAI JAWABAN BAHASA INDONESIA MENGGUNAKAN METODE SVM - LSA DENGAN FITUR GENERIK

Paper ini mengkaji sebuah solusi untuk permasalahan penilaian jawaban esai secara otomatis dengan menggabungkan support vector machine (SVM) sebagai teknik klasifikasi teks otomatis dengan LSA sebagai usaha untuk menangani sinonim dan polisemi antar index term. Berbeda dengan sistem penilaian esai yang biasa yakni fitur yang digunakan berupa index term, fitur yang digunakan proses penilaian jawaban esai adalah berupa fitur generic yang memungkinkan pengujian model penilaian esai untuk berbagai pertanyaan yang berbeda. Dengan menggunakan fitur generic ini, seseorang tidak perlu melakukan pelatihan ulang jika orang tersebut akan melakukan penilaian esai jawaban untuk beberapa pertanyaan. Fitur yang dimaksud meliputi persentase kemunculan kata kunci, similarity jawaban esai dengan jawaban referensi, persentase kemunculan gagasan kunci, persentase kemunculan gagasan salah, serta persentase kemunculan sinonim kata kunci. Hasil pengujian juga memperlihatkan bahwa metode yang diusulkan mempunyai tingkat akurasi penilaian yang lebih tinggi jika dibandingkan dengan metode lain seperti SVM atau LSA menggunakan index term sebagai fitur pembelajaran mesin. This paper examines a solution for problems of assessment an essay answers automatically by combining support vector machine (SVM) as automatic text classification techniques and LSA as an attempt to deal with synonyms and the polysemy between index terms. Unlike the usual essay scoring system that used index terms features, the feature used for the essay answers assessment process is a generic feature which allows testing of valuation models essays for a variety of different questions. By using these generic features, one does not need to re training if the person will conduct an assessment essay answers to some questions. The features include percentage of keywords, similarity essay answers with the answer reference, percentage of key ideas, percentage of wrong answer, and percentage of keyword synonyms. The test results also show that the proposed method has a higher valuation accuracy rate compared to other methods such as SVM or LSA, use term index as features in machine learning.
Analysis of Performance Metric of Finfet Based SRAM Cell

For molecules to be used as components in molecular machines, methods that couple individual molecules to external energy sources and that selectively excite motion in a given direction are required. Significant progress has been made in the construction of molecular motors powered by light and by chemical reactions, but electrically driven motors have not yet been built, despite several theoretical proposals for such motors. Here we report that a butyl methyl supplied molecule adsorbed on a copper surface can be operated as a single-molecule electric motor. Electrons from a scanning tunneling microscope are used to drive the directional motion of the molecule in a two-terminal setup. Moreover, the temperature and electron flux can be adjusted to allow each rotational event to be monitored at the molecular scale in real time. The direction and rate of the rotation are related to the chiralities of both the molecule and the tip of the microscope (which serves as the electrode), illustrating the importance of the symmetry of the metal contacts in atomic-scale electrical devices.
Algorithms for Multi-target Learning

In this work we will investigate multi-target Learning, also called multi-task learning, a branch of Machine Learning. A multi-target learner builds a model which, given some input, returns/predicts multiple variables simultaneously. This stand in contrast with the usual Single-Target model which predicts only one variable. We will see that MT-models have numerous advantages and that many real world applications can benefit from using a multi-target approach. We observe that when multiple targets are predicted simultaneously by a MTmodel, the predictive accuracy might increase in comparison to the single-target model. This is a result of inductive transfer which we discuss in depth in chapter 3. An algorithm is proposed which tries to maximally exploit this effect in order to increase the predictive accuracy of the model for one specific target (the main target). Next, we focus our attention to a different problem: Collaborative Filtering, a type of recommender system. Such a system tries to predict user preferences, for items such as books or movies, based on already known preferences of the users. We demonstrate how this problem fits the multi-target setting and propose a graph based algorithm to alleviate two common problems inherent to the Collaborative Filtering setting: the Cold Start and the Sparsity problem. Finally, we propose a new learning setting called Two-Way learning. A specific type of multi-target learning where we try to predict an attribute of a relation between two types of objects. We propose data transposition as an effective method to solve a Two-Way learning problem. This method is applied and studied in depth on the problem of processor performance prediction.
A new pharmacological preconditioning-based target: from drosophila to kidney transplantation

One of the biggest challenges in medicine is to dampen the pathophysiological stress induced by an episode of ischemia. Such stress, due to various pathological or clinical situations, follows a restriction in blood and oxygen supply to tissue, causing a shortage of oxygen and nutrients that are required for cellular metabolism. Ischemia can cause irreversible damage to target tissue leading to a poor physiological recovery outcome for the patient. Contrariwise, preconditioning by brief periods of ischemia has been shown in multiple organs to confer tolerance against subsequent normally lethal ischemia. By definition, preconditioning of organs must be applied preemptively. This limits the applicability of preconditioning in clinical situations, which arise unpredictably, such as myocardial infarction and stroke. There are, however, clinical situations that arise as a result of ischemia-reperfusion injury, which can be anticipated, and are therefore adequate candidates for preconditioning. Organ and more particularly kidney transplantation, the optimal treatment for suitable patients with end stage renal disease (ESRD), is a predictable surgery that permits the use of preconditioning protocols to prepare the organ for subsequent ischemic/reperfusion stress. It therefore seems crucial to develop appropriate preconditioning protocols against ischemia that will occur under transplantation conditions, which up to now mainly referred to mechanical ischemic preconditioning that triggers innate responses. It is not known if preconditioning has to be applied to the donor, the recipient, or both. No drug/target pair has been envisioned and validated in the clinic. Options for identifying new target/drug pairs involve the use of model animals, such as drosophila, in which some physiological pathways, such as the management of oxygen, are highly conserved across evolution. Oxygen is the universal element of life existence on earth. In this review we focus on a very specific pathway of pharmacological preconditioning identified in drosophila that was successfully transferred to mammalian models that has potential application in human health. Very few mechanisms identified in these model animals have been translated to an upper evolutionary level. This review highlights the commonality between oxygen regulation between diverse animals.
Scale Model Study for a Ventilation System Optimization inside a Sludge Dewatering Area

The paper focus on the air quality inside the final sludge dewatering area where the working environment is very unsuitable for human exploitation mainly due to ammonia. The existing ventilation system is not facing this situation and a new ventilation system is required. The solution obtained by means of numerical simulation need to be validated by experimental approach. We have built a reduced scaled physical model in order to simulate the flow pattern inside the sludge dewatering area and the main equipment inside the building has been reproduced using a geometrical scale ratio. The flow pattern was considered isothermal and incompressible. The similarity criteria used was the Reynolds number to characterize the flow pattern inside the enclosure. From here there were many imposed constraints related to the velocity scale in order to avoid high velocities which could lead to a compressible flow pattern which is no more characterized by Re. The opportunity to use a different fluid for the reduced scaled model because of the different viscosities values which could help to obtain more convenient values for the geometrical and velocity scales from the Re similarity criteria but finally the same fluid – air on the model and nature was used.
Recognizing Cities from Street View Images

In this research, we explore the ability of a computer to determine the location of a photo from its pixel data. GPS generally enables the geotagging of most images; however, for photos without GPS data, it can be difficult to estimate location. This problem can be difficult, as many cities have similar visual characteristics and locations, and photos can be arbitrarily uninformative. Despite these challenges, can a computer correlate the potentially numerous visual cues in an image with that image’s city of origin? We train a convolutional neural network, called LittlePlaNet, to answer this question. We show that our approach achieves superhuman accuracy on a dataset of photos take from Google Street View in 10 different cities. Furthermore, we also explore the limitations of our dataset by testing our model on photos from Flickr, which contains geo-tagged images of everything from landmarks to people.
List of academic databases and search engines

This page contains a representative list of major databases and search engines useful in an academic setting for finding and accessing articles in academic journals, or in repositories, archives, or other collections of scientific and other articles. As the distinction between adatabase and a search engine is unclear for these complex document retrieval systems, see:  the general list of search engines for all-purpose search engines that can be used for academic purposes  bibliographic databases for information about databases giving bibliographic information about finding books and journal articles. Note that "free" or "subscription" can refer both to the availability of the database or of the journal articles included. This has been indicated as precisely as possible in the lists below. Academic Journals Database Multidisciplinary Universal index of periodical literature covering basic research from all fields of knowledge. It contains complete bibliographic citations, precise indexing, and informative abstracts for papers from a wide range of periodicals. Full-text from most of the articles is available. It contains about 700,000 articles.
Experimental methodology for benchmarking ICP-like algorithms

Scan matching techniques have been widely used to compute the displacement of robots. This estimate is part of many algorithms addressing navigation and mapping, being ICP the most used to solve this problem. This paper addresses the methodological aspects for evaluating ICP-like algorithms. In this sense, several tools are provided to select scenarios for validating results and to validate point to facet distance, correspondence and pose estimation individually. In addition to this, the paper provides tools for analyzing robustness, precision, computational time and robustness sensibility to parameter variation. At hte end of the paper, we also present some map building results.
A REVIEW ON BUG TRACKING SYSTEM USING NAÏVE BYES IN DATA MINING

For many years, bug-tracking mechanism is employed only in some of the large software development houses. Our aim is to distinguish the very fast and the very slow bugs in order to prioritize which bugs to start with and which to exclude at the mean time respectively. We used naive Bayes classifier to compute our prediction model of the data of four systems taken from three large open source projects Mozilla, Eclipse and Gnome. Conventional procedure of simply relying on shared lists and email to monitor the status of defects is error-prone and tends to cause those bugs judged least significant by developers to be dropped or ignored. Bug(Defect) Tracking System allows individual or groups of developers to keep track of outstanding bugs in product, solution or an application effectively.
A TECHNIQUE FOR QUICK ESTIMATION OF SEISMIC GROUND MOTION DISTRIBUTION FROM OBSERVED SEISMIC INTENSITIES ASSISTED BY PREVIOUSLY CONDUCTED MINUTE SIMULATIONS

First, we make strict simulation of seismic ground motion distribution for possible source faults and store the result. This process takes much computation time especially we introduce three dimensional wave propagation. However, fortunately we have enough time before the severe earthquake hits the target area. Secondly, we obtain observed strong motion data, i.e. maximum values or seismic intensities, just after the earthquake. Next, system searches most approved simulation result from the previously conducted stocks. Then, we obtain differences between the observed and the simulated values at sites, and make a mathematical model of spatial distribution from the discrepancies. We use Spline functions here. Finally, we modify the previously simulated distribution of ground motion adding the correction values derived from the discrepancy model. The estimation is done within several seconds after receiving observed data. The stocked simulations are also useful for disaster prevention planning as an earthquake scenario. And once severe earthquake hits the area, the scenario is immediately revised based on observed data by the technique described above.
Machine Vision Processing System based on Embedded Linux

Machine vision is widely used in automatic equipments, but the vision system based on embedded Linux is seldom at present. The key points and difficulties are the image processing and its realtime on embedded system. This paper introduces the vision processing system based on ARM and Linux, mainly introduces the USB camera driver, image matching, camera calibration and object localization. Comparing the conventional method using PC with universal software, vision processing system based on ARM and Linux greatly reduces the system size and cost. Experimental results show that this system can accurately recognize and locate the target object. The efficiency can meet the requirement basically, and it still has room to improve. Combined with industrial robot, this vision system can be used to automatic carrying, assembly, etc. KEYWORD: machine vision; template match; camera calibration International Conference on Industrial Technology and Management Science (ITMS 2015) © 2015. The authors Published by Atlantis Press 1277 2) Configure and make install on embedded linux system; 3) Copy the generated libs to ARM controller (eg: libusb-1.0.so, libusb-1.0.so.0, libusb-1.0.so.0.1.0) 4) Use the API functions to driver camera (eg: libusb_bulk_transfer / libusb_control_transfer to transfer image data or control commands) 2.2 Camera image processing In industrial application, the camera is used to distinguish and locate the objects. Some image processing methods should be used to reduce noise or interference, like the gray conversion, binarization, smooth filtering, etc. After preprocessing, the key step is the template matching and object localization. The match method usually includes: 1) Least square error (LSE):
THE RELATIONSHIPS AMONG GRATITUDE, APPRECIATION, PSYCHOLOGICAL WELL-BEING AND RELIGIOUS ORIENTATION: AN EMPIRICAL STUDY WITH TURKISH SAMPLE

Recent researches in psychology have increasingly focused on exploring the psychology of positive emotions. Particularly, gratitude and appreciation have become an important area for researchers. This article aims to investigate the links between gratitude, appreciation, psychological well-being and religious orientation on Turkish sample. Moreover, it is reveals the role of gratitude and appreciation on psychological well-being. Thus, Gratitude Questionnaire (GQ6), The Appreciation Scale (App-57), Intrinsic Religious Motivation Scale (IRM-10), and Psychological Well-Being Scale (PWB-18) were applied to consist of 611 adults aged between 17 to 60 years. The findings reveal that there are positive correlations among gratitude, appreciation, psychological well-being and religiosity. According to the regression analyses, gratitude is found to be more effective factor than appreciation on the psychological wellbeing. Moreover the findings indicate that “Gratitude to Parents”, “Gratitude for the Close Relationships” and “Gratitude as Life Satisfaction” are significant predictors of psychological well-being.