label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs

Label switching is a well-known and fundamental problem in Bayesian estimation of mixture or hidden Markov models. In case that the prior distribution of the model parameters is the same for all states, then both the likelihood and posterior distribution are invariant to permutations of the parameters. This property makes Markov chain Monte Carlo (MCMC) samples simulated from the posterior distribution non-identifiable. In this paper, the \pkg{label.switching} package is introduced. It contains one probabilistic and seven deterministic relabelling algorithms in order to post-process a given MCMC sample, provided by the user. Each method returns a set of permutations that can be used to reorder the MCMC output. Then, any parametric function of interest can be inferred using the reordered MCMC sample. A set of user-defined permutations is also accepted, allowing the researcher to benchmark new relabelling methods against the available onesComment: Accepted to Journal of Statistical Softwar

Sensorless Solar Tracker Based on Sun Position for Maximum Energy Conversion

The performance of solar panels is dependent on sunlight it receives. Therefore, it is necessary to design a device that can set the direction of the solar panel always follows the sun position based solar tracker. The two-axis sensorless trackers have used in this research to produce maximum energy conversion. Position of solar panel move based on sun position using sunrise and sunset database. By using linear interpolation the sun position in latitute and longitude for other time can be obtained during a day. Based on these value the solar panel set its position using two servo motor which drived by Arduino. This technique independent from weather conditions, although cloudy, panel position remains consistent with the maximum illumination when the weather is sunny back later. By this way, the solar panel absorbs maximum sunlight and generate maximum electricity

Benchmarking database systems for Genomic Selection implementation

Motivation: With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results: We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix

Semantic Support for Log Analysis of Safety-Critical Embedded Systems

Testing is a relevant activity for the development life-cycle of Safety Critical Embedded systems. In particular, much effort is spent for analysis and classification of test logs from SCADA subsystems, especially when failures occur. The human expertise is needful to understand the reasons of failures, for tracing back the errors, as well as to understand which requirements are affected by errors and which ones will be affected by eventual changes in the system design. Semantic techniques and full text search are used to support human experts for the analysis and classification of test logs, in order to speedup and improve the diagnosis phase. Moreover, retrieval of tests and requirements, which can be related to the current failure, is supported in order to allow the discovery of available alternatives and solutions for a better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic discovery, ontology, big dat

Collective Sensemaking About the Implementation of Two Multi-Tiered Systems of Support: A Comparative Case Study of Two Selected Elementary School Teams

Positive Behavior Interventions and Supports (PBIS) and Response to Intervention (RTI) provide two examples of multi-tiered systems of support (MTSS). Over several decades, MTSS developed as policy-based initiatives intended to increase equity, access, and quality of education. These initiatives integrate school and classroom practices for improving academic and social/behavioral development for all students. However, studies indicate continued implementation problems within each system across all levels of intervention. Such results signal concerns about implementation capacity for the intent of both MTSS policies\u27 regarding educational access, equity and quality. Literature indicates that policy intent is converted at the micro, or school, level into models and practices. As school teams are charged with implementing RTI and PBIS, questions arise concerning how these teams make sense of the two initiatives. This study utilized a multiple case study method to examine the collective sensemaking of RTI and PBIS team members within two selected elementary schools. Both schools\u27 faculty defined RTI and PBIS only through Tiers 1 and 2, likely as a result of district structures and resources. The two cases provide similar interpretations of multiple, disparate teams for addressing academic versus behavioral needs. Thus, each school implemented two separate teams. One school\u27s RTI and PBIS teams employed frequent opportunities for distributed cognition and leadership through communities of practice, further supporting a continuum of student needs in Tiers 1 and 2. This school\u27s teams supported a databased decision-making approach, but only one of the other school\u27s teams espoused and demonstrated data literacy for making decisions

PRODUCTION DATA BASED FINANCIAL LEVEL ANALYSIS THROUGH RECURRENT NEURAL BASED MULTIVARIATE DATA SELECTION ALGORITHM USING DATA MINING APPLICATION

The Financial data series, to mimic the excessive fluctuation, is near a hard type of statistics on some imitation of prediction. To all about the rumors, through the additional elements of the next within the range of available communication network, various forms of data, the fluctuations, production companies are effectively related inventory, equipment, and the need to use the personnel to increase the kind of financial information delay, to enhance its product. Businesses use the ratio of currency that is similarly based on the evaluation of the business. While maintaining the data level business's effectiveness, these ratios have been implemented based on a study to determine to integrate into an industrial process properly. Previous algorithm for Long Short Term Memory (LSTM), then Gated Recurrent Unit (GRU); in particular, will concentrate on certain types of twin networks. The former is the case of many, series is predicted, the second is the difference between excellent large newborns and offer original. To analyze the application of special neural networks, especially Recurrent Neural Networks (RNNs). In the forecasts gathered in the causal era, the structure of high-risk economic variables creates the motivation behind using multivariate relevant data. Previous algorithm supported GRU after, empirical results are particularly suitable for use because mimicking the performance of coach era. The LSTM reproduces them with the same accuracy. Since the clinical data set of the real world has not yet been shown to the synthetic data set, but the reliability is high, also, due to the use of the absence of a value of between series analysis, the data collection of artistic expression even in a state, the classification task of periodic sequence to provide useful insights experiments

Dokumen Prosiding Internasional dan Nasional

Dokumen Prosiding Internasional dan Nasiona