Juvenile Perceptions of Probation Officers In Relation to the Use of Strength-Based Programs

Research on the influence of strength-based programs on recidivism with juvenile offenders in probation is minimal. This study will seek to analyze the perceptions of juvenile delinquents on their interactions with probation officers. Using quota sampling, based on levels of recidivism rates in the state of Ohio, we will interview a select number of juvenile offenders between the ages of 12-16. We will be conducting general one-on-one interviews with juvenile offenders from our selected sample. We will also review multiple sources of data such as case notes, policies, and agency process and programs to determine the use of strength-based programs and identify recidivism rates. Our data will be transcribed using outside researchers to transcribe verbatim and documented through audio-recording. Our data will be coded using first-level data to identify information categories and second-level coding to identify relationships and themes within the information categories. We expect to find an increase in juvenile offenders’ positive perceptions of probation officers when a strengths-based program is implemented within probation

Building Reusable Software Component For Optimization Check in ABAP Coding

Software component reuse is the software engineering practice of developing new software products from existing components. A reuse library or component reuse repository organizes stores and manages reusable components. This paper describes how a reusable component is created, how it reuses the function and checking if optimized code is being used in building programs and applications. Finally providing coding guidelines, standards and best practices used for creating reusable components and guidelines and best practices for making configurable and easy to use.Comment: 9 pages, 6 figure

The use of blocking sets in Galois geometries and in related research areas

Blocking sets play a central role in Galois geometries. Besides their intrinsic geometrical importance, the importance of blocking sets also arises from the use of blocking sets for the solution of many other geometrical problems, and problems in related research areas. This article focusses on these applications to motivate researchers to investigate blocking sets, and to motivate researchers to investigate the problems that can be solved by using blocking sets. By showing the many applications on blocking sets, we also wish to prove that researchers who improve results on blocking sets in fact open the door to improvements on the solution of many other problems

The Lost Melody Phenomenon

A typical phenomenon for machine models of transfinite computations is the existence of so-called lost melodies, i.e. real numbers $x$ such that the characteristic function of the set $\{x\}$ is computable while $x$ itself is not (a real having the first property is called recognizable). This was first observed by J. D. Hamkins and A. Lewis for infinite time Turing machine, then demonstrated by P. Koepke and the author for $ITRM$s. We prove that, for unresetting infinite time register machines introduced by P. Koepke, recognizability equals computability, i.e. the lost melody phenomenon does not occur. Then, we give an overview on our results on the behaviour of recognizable reals for $ITRM$s. We show that there are no lost melodies for ordinal Turing machines or ordinal register machines without parameters and that this is, under the assumption that $0^{\sharp}$ exists, independent of $ZFC$. Then, we introduce the notions of resetting and unresetting $\alpha$-register machines and give some information on the question for which of these machines there are lost melodies

Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity

The relationship between the Bayesian approach and the minimum description length approach is established. We sharpen and clarify the general modeling principles MDL and MML, abstracted as the ideal MDL principle and defined from Bayes's rule by means of Kolmogorov complexity. The basic condition under which the ideal principle should be applied is encapsulated as the Fundamental Inequality, which in broad terms states that the principle is valid when the data are random, relative to every contemplated hypothesis and also these hypotheses are random relative to the (universal) prior. Basically, the ideal principle states that the prior probability associated with the hypothesis should be given by the algorithmic universal probability, and the sum of the log universal probability of the model plus the log of the probability of the data given the model should be minimized. If we restrict the model class to the finite sets then application of the ideal principle turns into Kolmogorov's minimal sufficient statistic. In general we show that data compression is almost always the best strategy, both in hypothesis identification and prediction.Comment: 35 pages, Latex. Submitted IEEE Trans. Inform. Theor