A Review of Unsupervised Keyphrase Extraction Methods Using Within-Collection Resources

An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions.

Information growth for sequential monitoring of clinical trials with a stepped wedge cluster randomized design and unknown intracluster correlation

Background/aims In a stepped wedge study design, study clusters usually start with the baseline treatment and then cross over to the intervention at randomly determined times. Such designs are useful when the intervention must be delivered at the cluster level and are becoming increasingly common in practice. In these trials, if the outcome is death or serious morbidity, one may have an ethical imperative to monitor the trial and stop before maximum enrollment if the new therapy is proven to be beneficial. In addition, because formal monitoring allows for the stoppage of trials when a significant benefit for new therapy has been ruled out, their use can make a research program more efficient. However, use of the stepped wedge cluster randomized study design complicates the implementation of standard group sequential monitoring methods. Both the correlation of observations introduced by the clustered randomization and the timing of crossover from one treatment to the other impact the rate of information growth, an important component of an interim analysis. Methods We simulated cross-sectional stepped wedge study data in order to evaluate the impact of sequential monitoring on the Type I error and power when the true intracluster correlation is unknown. We studied the impact of varying intracluster correlations, treatment effects, methods of estimating the information growth, and boundary shapes. Results While misspecified information growth can impact both the Type I error and power of a study in some settings, we observed little inflation of the Type I error and only moderate reductions in power across a range of misspecified information growth patterns in our simulations. Conclusion Taking the study design into account and using either an estimate of the intracluster correlation from the ongoing study or other data in the same clusters should allow for easy implementation of group sequential methods in future stepped wedge designs.

METODE DAN MATERI DAKWAH UNTUK WANITA BERMASALAH

<p><em>Today, along with the pace of information growth, technology raises increasingly complex social problems. The complexity of the problems faced by humans increases and cones like a mountain. For those who are able to overcome social problems will not create new problems. Conversely, for people who are not able to adapt to the development of the times will have new problems. In today's modern era many women have problems that make it one of the "victims" of the progress of the age. This short article elaborates on the methods and material of Dakwah in dealing with problems for women involved in life issues. Analysis of this article states that returning to Dakwah methods and material adapted to al quran and hadith is a panacea that can solve their problems</em><em></em></p><p align="center">****.</p><p> </p><p>Dewasa ini, seiring dengan laju pertumbuhan informasi, teknologi memunculkan persoalan sosial yang semakin komplek. Kompleksitas persoalan yang dihadapi manusia bertambah dan mengerucut seperti gunung. Bagi mereka yang mampu mengatasi masalah sosial tidak akan memunculkan persoalan baru. Sebaliknya, bagi masyarakat yang tidak mampu adaptasi dengan perkembangan zaman akan mempunyai persoalan baru. Di era modern sekarang ini wanita banyak mempunyai persoalan yang menjadikannya sebagai salah satu “korban” atas kemajuan zaman. Artikel singkat ini mengelaborasikan tentang metode dan materi dakwah dalam menghadapi persoalan bagi wanita yang terlibat dalam persolan kehidupan. Analisa artikel ini menyatakan bahwa kembali kepada metode dan materi dakwah yang disesuaikan dengan al qur’an dan hadits merupakan obat mujarab yang dapat menyelesaikan persoalan mereka.</p>

Implementation of STEM at the District, School, and Classroom Level

At the district level of a school system, there are immense pressures to add value to the community as a whole. This is achieved through high test scores, graduation rates, and the production of a vital workforce that will propel the community financially. This is a daunting task when the rate of information growth is increasing exponentially faster than educators can adapt. Offering STEM education is an effective means of meeting this demand. This chapter will outline steps that will facilitate the implementation of STEM throughout a school district, school, and classrooms.

Discovering Knowledge Hidden in Big Data From Machine-Learning Techniques

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.