Astronomical bounds on future big freeze singularity

Recently it was found that dark energy in the form of phantom generalized Chaplygin gas may lead to a new form of the cosmic doomsday, the big freeze singularity. Like the big rip singularity, the big freeze singularity would also take place at a finite future cosmic time, but unlike the big rip singularity it happens for a finite scale factor.Our goal is to test if a universe filled with phantom generalized Chaplygin gas can conform to the data of astronomical observations. We shall see that if the universe is only filled with generalized phantom Chaplygin gas with equation of state $p=-c^2s^2/\rho^{\alpha}$ with $\alpha<-1$, then such a model cannot be matched to the data of astronomical observations. To construct matched models one actually need to add dark matter. This procedure results in cosmological scenarios which do not contradict the data of astronomical observations and allows one to estimate how long we are now from the future big freeze doomsday.Comment: 8 page

Hadoop Performance Analysis Model with Deep Data Locality

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS

THE INFLUENCE OF PARENTS ATTENTION, ATTITUDE DISCIPLINE LEARN AND STUDENT CREATIVITY WITH STUDENT ACHIEVEMENT GRADE XII ELECTRONICS ENGINEERING SKILL PROGRAM AT SMKN 3 YOGYAKARTA ACADEMIC YEAR 2012/2013

This study aimed to determine: (1) the influence of parents attention, attitude discipline learn, student creativity with student achievement grade XII electronics engineering skill program at smkn 3 yogyakarta academic year 2012/ 2013. (2) factor influentialer between parents attention, attitude discipline learn, student creativity with student achievement grade XII electronics engineering skill program at smkn 3 yogyakarta academic year 2012/ 2013. This research is a study of ex-post facto and descriptive korelasional with approach quantitative. Subject in this research is student of grade xii electronics engineering skill program at smkn 3 yogyakarta academic year 2012/ 2013 amount of 69 students. Data taking method uses documentation method and kuesioner. Instrument validity is done to pass expert judgment and grain analysis that counted with correlation formula product moment. reliabilitas instrument is counted by using formula alpha cronbrach. Analysis rules test covers normality test, linearity test and multikolinearity test. Data analysis technique that worn to test hypothesis with double regression analysis technique 3 predictor in standard significance 5 % and look for big effective contribution or relative from each variable. The result shows that: (1) found positive influence and siginificant between parents attention, attitude discipline learn and student creativity according to together with student achievement grade XII electronics engineering skill program at smkn 3 yogyakarta academic year 2012/ 2013. This matter is showed with coefficient r = 0,506, r count bigger than r table (0,506 > 0,235). (2) factor influential dominant towards student achievement was student creativity. This matter is based on relative contribution (sr) that is got from parents attention as big as 44,5%, attitude discipline learn as big as 5,1% and student creativity as big as 50,9%. while effective contribution magnitude (se) 25,6% with parents attention details 11,592%, attitude discipline learn 1,3056% and student creativity 12,902%. Keywords: parents attention, attitude discipline student, student creativity

Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three gaps stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind ( http://www.dataonthemind.org ), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally occurring datasets are most powerfully used to supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets

Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems