Lower bounds for uniform read-once threshold formulae in the randomized decision tree model

We investigate the randomized decision tree complexity of a specific class of read-once threshold functions. A read-once threshold formula can be defined by a rooted tree, every internal node of which is labeled by a threshold function $T_k^n$ (with output 1 only when at least $k$ out of $n$ input bits are 1) and each leaf by a distinct variable. Such a tree defines a Boolean function in a natural way. We focus on the randomized decision tree complexity of such functions, when the underlying tree is a uniform tree with all its internal nodes labeled by the same threshold function. We prove lower bounds of the form $c(k,n)^d$, where $d$ is the depth of the tree. We also treat trees with alternating levels of AND and OR gates separately and show asymptotically optimal bounds, extending the known bounds for the binary case

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

THE DEVELOPMENT OF ANTI-CORRUPTION EDUCATION (AN EVALUATION STUDY ON THE EFFECTIVENESS OF LITERATURE STUDY OF ANTI-CORRUPTION EDUCATION)

The purpose of this research is to develop learning tools as well as test the effectiveness of the implementation of anti-corruption education. The research method refers to the development of procedural models, which is descriptive, that shows the steps to produce a product that is effectively used at schools, not to test theories. The research procedures of every stage of development were done through expert assessment, individual assessment, group assessment, and field assessment. The model system approach, which was done to the formative evaluation measures, was developed by Dick & Carey. The trials included learning experts assessment, content experts assessment, learning media experts assessment, individual assessment, group assessment, and field assessment. The results of the assessment trials were used as an input to improve product development which was conducted using the t test (Paired Samples Test) to determine the effectiveness of the teaching materials. Descriptive quantitative analysis techniques were used to compare the competence of students before and after the use of teaching materials through the pretest and posttest which showed significant results, namely the difference in the value of pretest and posttest. It means anti-corruption education teaching materials are very effectively implemented to the students